diff --git a/demo/bootstrap_particle_filter.ipynb b/demo/bootstrap_particle_filter.ipynb
index d3a25804..e15e9268 100644
--- a/demo/bootstrap_particle_filter.ipynb
+++ b/demo/bootstrap_particle_filter.ipynb
@@ -11,16 +11,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Precompiling Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n",
-      "└ @ Base loading.jl:1273\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "using ForneyLab, LinearAlgebra, Plots;"
    ]
@@ -39,425 +30,7 @@
    "outputs": [
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-       "  585.747,1148.83 586.443,1150.39 587.139,1151.95 587.834,1153.5 588.53,1155.04 589.226,1156.59 589.921,1158.12 590.617,1159.66 591.313,1161.19 592.008,1162.72 \n",
-       "  592.704,1164.24 593.4,1165.76 594.095,1167.27 594.791,1168.78 595.487,1170.29 596.182,1171.79 596.878,1173.29 597.574,1174.79 598.269,1176.28 598.965,1177.77 \n",
-       "  599.661,1179.25 600.356,1180.73 601.052,1182.21 601.748,1183.68 602.443,1185.14 603.139,1186.61 603.835,1188.07 604.53,1189.52 605.226,1190.97 605.922,1192.42 \n",
-       "  606.617,1193.86 607.313,1195.3 608.009,1196.74 608.705,1198.17 609.4,1199.59 610.096,1201.02 610.792,1202.43 611.487,1203.85 612.183,1205.26 612.879,1206.66 \n",
-       "  613.574,1208.07 614.27,1209.46 614.966,1210.86 615.661,1212.25 616.357,1213.63 617.053,1215.02 617.748,1216.39 618.444,1217.77 619.14,1219.14 619.835,1220.5 \n",
-       "  620.531,1221.86 621.227,1223.22 621.922,1224.57 622.618,1225.92 623.314,1227.26 624.009,1228.6 624.705,1229.94 625.401,1231.27 626.096,1232.6 626.792,1233.92 \n",
-       "  627.488,1235.24 628.183,1236.56 628.879,1237.87 629.575,1239.17 630.27,1240.48 630.966,1241.78 631.662,1243.07 632.357,1244.36 633.053,1245.65 633.749,1246.93 \n",
-       "  634.444,1248.2 635.14,1249.48 635.836,1250.75 636.531,1252.01 637.227,1253.27 637.923,1254.53 638.618,1255.78 639.314,1257.03 640.01,1258.27 640.705,1259.51 \n",
-       "  641.401,1260.74 642.097,1261.98 642.792,1263.2 643.488,1264.42 644.184,1265.64 644.879,1266.86 645.575,1268.07 646.271,1269.27 646.967,1270.47 647.662,1271.67 \n",
-       "  648.358,1272.86 649.054,1274.05 649.749,1275.23 650.445,1276.41 651.141,1277.59 651.836,1278.76 652.532,1279.93 653.228,1281.09 653.923,1282.25 654.619,1283.4 \n",
-       "  655.315,1284.55 656.01,1285.7 656.706,1286.84 657.402,1287.98 658.097,1289.11 658.793,1290.24 659.489,1291.36 660.184,1292.48 660.88,1293.6 661.576,1294.71 \n",
-       "  662.271,1295.82 662.967,1296.92 663.663,1298.02 664.358,1299.11 665.054,1300.2 665.75,1301.29 666.445,1302.37 667.141,1303.45 667.837,1304.52 668.532,1305.59 \n",
-       "  669.228,1306.65 669.924,1307.71 670.619,1308.77 671.315,1309.82 672.011,1310.87 672.706,1311.91 673.402,1312.95 674.098,1313.99 674.793,1315.02 675.489,1316.04 \n",
-       "  676.185,1317.06 676.88,1318.08 677.576,1319.09 678.272,1320.1 678.967,1321.11 679.663,1322.11 680.359,1323.1 681.054,1324.09 681.75,1325.08 682.446,1326.06 \n",
-       "  683.141,1327.04 683.837,1328.02 684.533,1328.99 685.229,1329.95 685.924,1330.91 686.62,1331.87 687.316,1332.82 688.011,1333.77 688.707,1334.72 689.403,1335.66 \n",
-       "  690.098,1336.59 690.794,1337.52 691.49,1338.45 692.185,1339.38 692.881,1340.29 693.577,1341.21 694.272,1342.12 694.968,1343.03 695.664,1343.93 696.359,1344.82 \n",
-       "  697.055,1345.72 697.751,1346.61 698.446,1347.49 699.142,1348.37 699.838,1349.25 700.533,1350.12 701.229,1350.99 701.925,1351.85 702.62,1352.71 703.316,1353.57 \n",
-       "  704.012,1354.42 704.707,1355.26 705.403,1356.11 706.099,1356.94 706.794,1357.78 707.49,1358.61 708.186,1359.43 708.881,1360.25 709.577,1361.07 710.273,1361.88 \n",
-       "  710.968,1362.69 711.664,1363.5 712.36,1364.3 713.055,1365.09 713.751,1365.89 714.447,1366.67 715.142,1367.46 715.838,1368.24 716.534,1369.01 717.229,1369.78 \n",
-       "  717.925,1370.55 718.621,1371.31 719.316,1372.07 720.012,1372.82 720.708,1373.57 721.403,1374.32 722.099,1375.06 722.795,1375.8 723.491,1376.53 724.186,1377.26 \n",
-       "  724.882,1377.98 725.578,1378.71 726.273,1379.42 726.969,1380.13 727.665,1380.84 728.36,1381.55 729.056,1382.25 729.752,1382.94 730.447,1383.64 731.143,1384.32 \n",
-       "  731.839,1385.01 732.534,1385.69 733.23,1386.36 733.926,1387.03 734.621,1387.7 735.317,1388.36 736.013,1389.02 736.708,1389.68 737.404,1390.33 738.1,1390.97 \n",
-       "  738.795,1391.62 739.491,1392.26 740.187,1392.89 740.882,1393.52 741.578,1394.15 742.274,1394.77 742.969,1395.39 743.665,1396 744.361,1396.61 745.056,1397.22 \n",
-       "  745.752,1397.82 746.448,1398.42 747.143,1399.01 747.839,1399.6 748.535,1400.19 749.23,1400.77 749.926,1401.35 750.622,1401.92 751.317,1402.49 752.013,1403.06 \n",
-       "  752.709,1403.62 753.404,1404.18 754.1,1404.73 754.796,1405.28 755.491,1405.83 756.187,1406.37 756.883,1406.91 757.578,1407.45 758.274,1407.98 758.97,1408.5 \n",
-       "  759.665,1409.03 760.361,1409.54 761.057,1410.06 761.753,1410.57 762.448,1411.08 763.144,1411.58 763.84,1412.08 764.535,1412.57 765.231,1413.07 765.927,1413.55 \n",
-       "  766.622,1414.04 767.318,1414.52 768.014,1414.99 768.709,1415.46 769.405,1415.93 770.101,1416.4 770.796,1416.86 771.492,1417.31 772.188,1417.77 772.883,1418.22 \n",
-       "  773.579,1418.66 774.275,1419.1 774.97,1419.54 775.666,1419.97 776.362,1420.4 777.057,1420.83 777.753,1421.25 778.449,1421.67 779.144,1422.09 779.84,1422.5 \n",
-       "  780.536,1422.9 781.231,1423.31 781.927,1423.71 782.623,1424.1 783.318,1424.5 784.014,1424.89 784.71,1425.27 785.405,1425.65 786.101,1426.03 786.797,1426.4 \n",
-       "  787.492,1426.77 788.188,1427.14 788.884,1427.5 789.579,1427.86 790.275,1428.22 790.971,1428.57 791.666,1428.92 792.362,1429.26 793.058,1429.6 793.753,1429.94 \n",
-       "  794.449,1430.27 795.145,1430.6 795.84,1430.93 796.536,1431.25 797.232,1431.57 797.927,1431.89 798.623,1432.2 799.319,1432.51 800.014,1432.81 800.71,1433.12 \n",
-       "  801.406,1433.41 802.102,1433.71 802.797,1434 803.493,1434.29 804.189,1434.57 804.884,1434.85 805.58,1435.13 806.276,1435.4 806.971,1435.67 807.667,1435.94 \n",
-       "  808.363,1436.2 809.058,1436.46 809.754,1436.72 810.45,1436.97 811.145,1437.22 811.841,1437.46 812.537,1437.7 813.232,1437.94 813.928,1438.18 814.624,1438.41 \n",
-       "  815.319,1438.64 816.015,1438.86 816.711,1439.09 817.406,1439.3 818.102,1439.52 818.798,1439.73 819.493,1439.94 820.189,1440.15 820.885,1440.35 821.58,1440.55 \n",
-       "  822.276,1440.74 822.972,1440.93 823.667,1441.12 824.363,1441.31 825.059,1441.49 825.754,1441.67 826.45,1441.84 827.146,1442.01 827.841,1442.18 828.537,1442.35 \n",
-       "  829.233,1442.51 829.928,1442.67 830.624,1442.83 831.32,1442.98 832.015,1443.13 832.711,1443.27 833.407,1443.42 834.102,1443.56 834.798,1443.69 835.494,1443.83 \n",
-       "  836.189,1443.96 836.885,1444.09 837.581,1444.21 838.276,1444.33 838.972,1444.45 839.668,1444.56 840.364,1444.68 841.059,1444.78 841.755,1444.89 842.451,1444.99 \n",
-       "  843.146,1445.09 843.842,1445.19 844.538,1445.28 845.233,1445.37 845.929,1445.46 846.625,1445.54 847.32,1445.62 848.016,1445.7 848.712,1445.78 849.407,1445.85 \n",
-       "  850.103,1445.92 850.799,1445.99 851.494,1446.05 852.19,1446.11 852.886,1446.17 853.581,1446.22 854.277,1446.27 854.973,1446.32 855.668,1446.37 856.364,1446.41 \n",
-       "  857.06,1446.45 857.755,1446.49 858.451,1446.52 859.147,1446.55 859.842,1446.58 860.538,1446.61 861.234,1446.63 861.929,1446.65 862.625,1446.67 863.321,1446.68 \n",
-       "  864.016,1446.69 864.712,1446.7 865.408,1446.71 866.103,1446.71 866.799,1446.71 867.495,1446.71 868.19,1446.71 868.886,1446.7 869.582,1446.69 870.277,1446.68 \n",
-       "  870.973,1446.66 871.669,1446.64 872.364,1446.62 873.06,1446.6 873.756,1446.57 874.451,1446.54 875.147,1446.51 875.843,1446.47 876.538,1446.44 877.234,1446.4 \n",
-       "  877.93,1446.36 878.626,1446.31 879.321,1446.26 880.017,1446.21 880.713,1446.16 881.408,1446.11 882.104,1446.05 882.8,1445.99 883.495,1445.93 884.191,1445.86 \n",
-       "  884.887,1445.79 885.582,1445.72 886.278,1445.65 886.974,1445.57 887.669,1445.5 888.365,1445.42 889.061,1445.33 889.756,1445.25 890.452,1445.16 891.148,1445.07 \n",
-       "  891.843,1444.98 892.539,1444.88 893.235,1444.79 893.93,1444.69 894.626,1444.58 895.322,1444.48 896.017,1444.37 896.713,1444.26 897.409,1444.15 898.104,1444.04 \n",
-       "  898.8,1443.92 899.496,1443.81 900.191,1443.68 900.887,1443.56 901.583,1443.44 902.278,1443.31 902.974,1443.18 903.67,1443.05 904.365,1442.91 905.061,1442.78 \n",
-       "  905.757,1442.64 906.452,1442.5 907.148,1442.35 907.844,1442.21 908.539,1442.06 909.235,1441.91 909.931,1441.76 910.626,1441.61 911.322,1441.45 912.018,1441.29 \n",
-       "  912.713,1441.13 913.409,1440.97 914.105,1440.8 914.8,1440.64 915.496,1440.47 916.192,1440.3 916.888,1440.13 917.583,1439.95 918.279,1439.77 918.975,1439.59 \n",
-       "  919.67,1439.41 920.366,1439.23 921.062,1439.05 921.757,1438.86 922.453,1438.67 923.149,1438.48 923.844,1438.29 924.54,1438.09 925.236,1437.89 925.931,1437.7 \n",
-       "  926.627,1437.49 927.323,1437.29 928.018,1437.09 928.714,1436.88 929.41,1436.67 930.105,1436.46 930.801,1436.25 931.497,1436.04 932.192,1435.82 932.888,1435.6 \n",
-       "  933.584,1435.38 934.279,1435.16 934.975,1434.94 935.671,1434.72 936.366,1434.49 937.062,1434.26 937.758,1434.03 938.453,1433.8 939.149,1433.56 939.845,1433.33 \n",
-       "  940.54,1433.09 941.236,1432.85 941.932,1432.61 942.627,1432.37 943.323,1432.13 944.019,1431.88 944.714,1431.63 945.41,1431.39 946.106,1431.13 946.801,1430.88 \n",
-       "  947.497,1430.63 948.193,1430.37 948.888,1430.12 949.584,1429.86 950.28,1429.6 950.975,1429.34 951.671,1429.07 952.367,1428.81 953.062,1428.54 953.758,1428.27 \n",
-       "  954.454,1428 955.15,1427.73 955.845,1427.46 956.541,1427.19 957.237,1426.91 957.932,1426.63 958.628,1426.36 959.324,1426.08 960.019,1425.79 960.715,1425.51 \n",
-       "  961.411,1425.23 962.106,1424.94 962.802,1424.65 963.498,1424.37 964.193,1424.08 964.889,1423.78 965.585,1423.49 966.28,1423.2 966.976,1422.9 967.672,1422.61 \n",
-       "  968.367,1422.31 969.063,1422.01 969.759,1421.71 970.454,1421.41 971.15,1421.1 971.846,1420.8 972.541,1420.49 973.237,1420.19 973.933,1419.88 974.628,1419.57 \n",
-       "  975.324,1419.26 976.02,1418.95 976.715,1418.63 977.411,1418.32 978.107,1418 978.802,1417.69 979.498,1417.37 980.194,1417.05 980.889,1416.73 981.585,1416.41 \n",
-       "  982.281,1416.08 982.976,1415.76 983.672,1415.44 984.368,1415.11 985.063,1414.78 985.759,1414.46 986.455,1414.13 987.15,1413.8 987.846,1413.46 988.542,1413.13 \n",
-       "  989.237,1412.8 989.933,1412.46 990.629,1412.13 991.324,1411.79 992.02,1411.46 992.716,1411.12 993.412,1410.78 994.107,1410.44 994.803,1410.1 995.499,1409.75 \n",
-       "  996.194,1409.41 996.89,1409.07 997.586,1408.72 998.281,1408.38 998.977,1408.03 999.673,1407.68 1000.37,1407.33 1001.06,1406.98 1001.76,1406.63 1002.46,1406.28 \n",
-       "  1003.15,1405.93 1003.85,1405.58 1004.54,1405.22 1005.24,1404.87 1005.93,1404.51 1006.63,1404.16 1007.32,1403.8 1008.02,1403.44 1008.72,1403.08 1009.41,1402.72 \n",
-       "  1010.11,1402.36 1010.8,1402 1011.5,1401.64 1012.19,1401.28 1012.89,1400.91 1013.59,1400.55 1014.28,1400.19 1014.98,1399.82 1015.67,1399.45 1016.37,1399.09 \n",
-       "  1017.06,1398.72 1017.76,1398.35 1018.46,1397.98 1019.15,1397.62 1019.85,1397.25 1020.54,1396.88 1021.24,1396.5 1021.93,1396.13 1022.63,1395.76 1023.33,1395.39 \n",
-       "  1024.02,1395.01 1024.72,1394.64 1025.41,1394.27 1026.11,1393.89 1026.8,1393.51 1027.5,1393.14 1028.2,1392.76 1028.89,1392.38 1029.59,1392.01 1030.28,1391.63 \n",
-       "  1030.98,1391.25 1031.67,1390.87 1032.37,1390.49 1033.06,1390.11 1033.76,1389.73 1034.46,1389.35 1035.15,1388.97 1035.85,1388.59 1036.54,1388.2 1037.24,1387.82 \n",
-       "  1037.93,1387.44 1038.63,1387.05 1039.33,1386.67 1040.02,1386.29 1040.72,1385.9 1041.41,1385.52 1042.11,1385.13 1042.8,1384.74 1043.5,1384.36 1044.2,1383.97 \n",
-       "  1044.89,1383.59 1045.59,1383.2 1046.28,1382.81 1046.98,1382.42 1047.67,1382.04 1048.37,1381.65 1049.07,1381.26 1049.76,1380.87 1050.46,1380.48 1051.15,1380.09 \n",
-       "  1051.85,1379.7 1052.54,1379.31 1053.24,1378.92 1053.94,1378.53 1054.63,1378.14 1055.33,1377.75 1056.02,1377.36 1056.72,1376.97 1057.41,1376.58 1058.11,1376.19 \n",
-       "  1058.8,1375.8 1059.5,1375.41 1060.2,1375.01 1060.89,1374.62 1061.59,1374.23 1062.28,1373.84 1062.98,1373.45 1063.67,1373.05 1064.37,1372.66 1065.07,1372.27 \n",
-       "  1065.76,1371.88 1066.46,1371.48 1067.15,1371.09 1067.85,1370.7 1068.54,1370.31 1069.24,1369.91 1069.94,1369.52 1070.63,1369.13 1071.33,1368.73 1072.02,1368.34 \n",
-       "  1072.72,1367.95 1073.41,1367.56 1074.11,1367.16 1074.81,1366.77 1075.5,1366.38 1076.2,1365.98 1076.89,1365.59 1077.59,1365.2 1078.28,1364.81 1078.98,1364.41 \n",
-       "  1079.67,1364.02 1080.37,1363.63 1081.07,1363.24 1081.76,1362.84 1082.46,1362.45 1083.15,1362.06 1083.85,1361.67 1084.54,1361.28 1085.24,1360.89 1085.94,1360.49 \n",
-       "  1086.63,1360.1 1087.33,1359.71 1088.02,1359.32 1088.72,1358.93 1089.41,1358.54 1090.11,1358.15 1090.81,1357.76 1091.5,1357.37 1092.2,1356.98 1092.89,1356.59 \n",
-       "  1093.59,1356.2 1094.28,1355.81 1094.98,1355.42 1095.68,1355.03 1096.37,1354.64 1097.07,1354.25 1097.76,1353.87 1098.46,1353.48 1099.15,1353.09 1099.85,1352.7 \n",
-       "  1100.55,1352.32 1101.24,1351.93 1101.94,1351.54 1102.63,1351.16 1103.33,1350.77 1104.02,1350.39 1104.72,1350 1105.41,1349.62 1106.11,1349.23 1106.81,1348.85 \n",
-       "  1107.5,1348.46 1108.2,1348.08 1108.89,1347.7 1109.59,1347.31 1110.28,1346.93 1110.98,1346.55 1111.68,1346.17 1112.37,1345.79 1113.07,1345.41 1113.76,1345.03 \n",
-       "  1114.46,1344.65 1115.15,1344.27 1115.85,1343.89 1116.55,1343.51 1117.24,1343.13 1117.94,1342.75 1118.63,1342.38 1119.33,1342 1120.02,1341.62 1120.72,1341.25 \n",
-       "  1121.42,1340.87 1122.11,1340.5 1122.81,1340.12 1123.5,1339.75 1124.2,1339.37 1124.89,1339 1125.59,1338.63 1126.28,1338.25 1126.98,1337.88 1127.68,1337.51 \n",
-       "  1128.37,1337.14 1129.07,1336.77 1129.76,1336.4 1130.46,1336.03 1131.15,1335.66 1131.85,1335.3 1132.55,1334.93 1133.24,1334.56 1133.94,1334.2 1134.63,1333.83 \n",
-       "  1135.33,1333.47 1136.02,1333.1 1136.72,1332.74 1137.42,1332.37 1138.11,1332.01 1138.81,1331.65 1139.5,1331.29 1140.2,1330.93 1140.89,1330.57 1141.59,1330.21 \n",
-       "  1142.29,1329.85 1142.98,1329.49 1143.68,1329.13 1144.37,1328.77 1145.07,1328.42 1145.76,1328.06 1146.46,1327.71 1147.16,1327.35 1147.85,1327 1148.55,1326.65 \n",
-       "  1149.24,1326.29 1149.94,1325.94 1150.63,1325.59 1151.33,1325.24 1152.02,1324.89 1152.72,1324.54 1153.42,1324.19 1154.11,1323.85 1154.81,1323.5 1155.5,1323.15 \n",
-       "  1156.2,1322.81 1156.89,1322.46 1157.59,1322.12 1158.29,1321.78 1158.98,1321.43 1159.68,1321.09 1160.37,1320.75 1161.07,1320.41 1161.76,1320.07 1162.46,1319.73 \n",
-       "  1163.16,1319.39 1163.85,1319.06 1164.55,1318.72 1165.24,1318.39 1165.94,1318.05 1166.63,1317.72 1167.33,1317.38 1168.03,1317.05 1168.72,1316.72 1169.42,1316.39 \n",
-       "  1170.11,1316.06 1170.81,1315.73 1171.5,1315.4 1172.2,1315.07 1172.9,1314.75 1173.59,1314.42 1174.29,1314.09 1174.98,1313.77 1175.68,1313.45 1176.37,1313.12 \n",
-       "  1177.07,1312.8 1177.76,1312.48 1178.46,1312.16 1179.16,1311.84 1179.85,1311.52 1180.55,1311.21 1181.24,1310.89 1181.94,1310.57 1182.63,1310.26 1183.33,1309.94 \n",
-       "  1184.03,1309.63 1184.72,1309.32 1185.42,1309.01 1186.11,1308.7 1186.81,1308.39 1187.5,1308.08 1188.2,1307.77 1188.9,1307.46 1189.59,1307.16 1190.29,1306.85 \n",
-       "  1190.98,1306.55 1191.68,1306.25 1192.37,1305.94 1193.07,1305.64 1193.77,1305.34 1194.46,1305.04 1195.16,1304.74 1195.85,1304.44 1196.55,1304.15 1197.24,1303.85 \n",
-       "  1197.94,1303.56 1198.63,1303.26 1199.33,1302.97 1200.03,1302.68 1200.72,1302.39 1201.42,1302.1 1202.11,1301.81 1202.81,1301.52 1203.5,1301.23 1204.2,1300.94 \n",
-       "  1204.9,1300.66 1205.59,1300.37 1206.29,1300.09 1206.98,1299.81 1207.68,1299.53 1208.37,1299.24 1209.07,1298.96 1209.77,1298.69 1210.46,1298.41 1211.16,1298.13 \n",
-       "  1211.85,1297.86 1212.55,1297.58 1213.24,1297.31 1213.94,1297.03 1214.64,1296.76 1215.33,1296.49 1216.03,1296.22 1216.72,1295.95 1217.42,1295.68 1218.11,1295.42 \n",
-       "  1218.81,1295.15 1219.51,1294.89 1220.2,1294.62 1220.9,1294.36 1221.59,1294.1 1222.29,1293.84 1222.98,1293.58 1223.68,1293.32 1224.37,1293.06 1225.07,1292.8 \n",
-       "  1225.77,1292.55 1226.46,1292.29 1227.16,1292.04 1227.85,1291.79 1228.55,1291.53 1229.24,1291.28 1229.94,1291.03 1230.64,1290.78 1231.33,1290.54 1232.03,1290.29 \n",
-       "  1232.72,1290.04 1233.42,1289.8 1234.11,1289.56 1234.81,1289.31 1235.51,1289.07 1236.2,1288.83 1236.9,1288.59 1237.59,1288.35 1238.29,1288.12 1238.98,1287.88 \n",
-       "  1239.68,1287.64 1240.38,1287.41 1241.07,1287.18 1241.77,1286.95 1242.46,1286.71 1243.16,1286.48 1243.85,1286.25 1244.55,1286.03 1245.25,1285.8 1245.94,1285.57 \n",
-       "  1246.64,1285.35 1247.33,1285.13 1248.03,1284.9 1248.72,1284.68 1249.42,1284.46 1250.11,1284.24 1250.81,1284.02 1251.51,1283.8 1252.2,1283.59 1252.9,1283.37 \n",
-       "  1253.59,1283.16 1254.29,1282.94 1254.98,1282.73 1255.68,1282.52 1256.38,1282.31 1257.07,1282.1 1257.77,1281.89 1258.46,1281.69 1259.16,1281.48 1259.85,1281.28 \n",
-       "  1260.55,1281.07 1261.25,1280.87 1261.94,1280.67 1262.64,1280.47 1263.33,1280.27 1264.03,1280.07 1264.72,1279.87 1265.42,1279.67 1266.12,1279.48 1266.81,1279.28 \n",
-       "  1267.51,1279.09 1268.2,1278.9 1268.9,1278.71 1269.59,1278.52 1270.29,1278.33 1270.98,1278.14 1271.68,1277.95 1272.38,1277.77 1273.07,1277.58 1273.77,1277.4 \n",
-       "  1274.46,1277.21 1275.16,1277.03 1275.85,1276.85 1276.55,1276.67 1277.25,1276.49 1277.94,1276.31 1278.64,1276.14 1279.33,1275.96 1280.03,1275.79 1280.72,1275.61 \n",
-       "  1281.42,1275.44 1282.12,1275.27 1282.81,1275.1 1283.51,1274.93 1284.2,1274.76 1284.9,1274.59 1285.59,1274.43 1286.29,1274.26 1286.99,1274.1 1287.68,1273.94 \n",
-       "  1288.38,1273.77 1289.07,1273.61 1289.77,1273.45 1290.46,1273.29 1291.16,1273.14 1291.86,1272.98 1292.55,1272.82 1293.25,1272.67 1293.94,1272.51 1294.64,1272.36 \n",
-       "  1295.33,1272.21 1296.03,1272.06 1296.72,1271.91 1297.42,1271.76 1298.12,1271.61 1298.81,1271.46 1299.51,1271.32 1300.2,1271.17 1300.9,1271.03 1301.59,1270.89 \n",
-       "  1302.29,1270.74 1302.99,1270.6 1303.68,1270.46 1304.38,1270.32 1305.07,1270.19 1305.77,1270.05 1306.46,1269.91 1307.16,1269.78 1307.86,1269.64 1308.55,1269.51 \n",
-       "  1309.25,1269.38 1309.94,1269.25 1310.64,1269.12 1311.33,1268.99 1312.03,1268.86 1312.73,1268.73 1313.42,1268.61 1314.12,1268.48 1314.81,1268.36 1315.51,1268.23 \n",
-       "  1316.2,1268.11 1316.9,1267.99 1317.59,1267.87 1318.29,1267.75 1318.99,1267.63 1319.68,1267.52 1320.38,1267.4 1321.07,1267.28 1321.77,1267.17 1322.46,1267.05 \n",
-       "  1323.16,1266.94 1323.86,1266.83 1324.55,1266.72 1325.25,1266.61 1325.94,1266.5 1326.64,1266.39 1327.33,1266.28 1328.03,1266.18 1328.73,1266.07 1329.42,1265.97 \n",
-       "  1330.12,1265.87 1330.81,1265.76 1331.51,1265.66 1332.2,1265.56 1332.9,1265.46 1333.6,1265.36 1334.29,1265.26 1334.99,1265.17 1335.68,1265.07 1336.38,1264.97 \n",
-       "  1337.07,1264.88 1337.77,1264.79 1338.47,1264.69 1339.16,1264.6 1339.86,1264.51 1340.55,1264.42 1341.25,1264.33 1341.94,1264.24 1342.64,1264.16 1343.33,1264.07 \n",
-       "  1344.03,1263.98 1344.73,1263.9 1345.42,1263.81 1346.12,1263.73 1346.81,1263.65 1347.51,1263.57 1348.2,1263.49 1348.9,1263.41 1349.6,1263.33 1350.29,1263.25 \n",
-       "  1350.99,1263.17 1351.68,1263.09 1352.38,1263.02 1353.07,1262.94 1353.77,1262.87 1354.47,1262.8 1355.16,1262.72 1355.86,1262.65 1356.55,1262.58 1357.25,1262.51 \n",
-       "  1357.94,1262.44 1358.64,1262.37 1359.34,1262.31 1360.03,1262.24 1360.73,1262.17 1361.42,1262.11 1362.12,1262.04 1362.81,1261.98 1363.51,1261.92 1364.21,1261.85 \n",
-       "  1364.9,1261.79 1365.6,1261.73 1366.29,1261.67 1366.99,1261.61 1367.68,1261.55 1368.38,1261.49 1369.07,1261.44 1369.77,1261.38 1370.47,1261.33 1371.16,1261.27 \n",
-       "  1371.86,1261.22 1372.55,1261.16 1373.25,1261.11 1373.94,1261.06 1374.64,1261.01 1375.34,1260.96 1376.03,1260.91 1376.73,1260.86 1377.42,1260.81 1378.12,1260.76 \n",
-       "  1378.81,1260.71 1379.51,1260.67 1380.21,1260.62 1380.9,1260.58 1381.6,1260.53 1382.29,1260.49 1382.99,1260.44 1383.68,1260.4 1384.38,1260.36 1385.08,1260.32 \n",
-       "  1385.77,1260.28 1386.47,1260.24 1387.16,1260.2 1387.86,1260.16 1388.55,1260.12 1389.25,1260.08 1389.94,1260.05 1390.64,1260.01 1391.34,1259.97 1392.03,1259.94 \n",
-       "  1392.73,1259.91 1393.42,1259.87 1394.12,1259.84 1394.81,1259.81 1395.51,1259.77 1396.21,1259.74 1396.9,1259.71 1397.6,1259.68 1398.29,1259.65 1398.99,1259.62 \n",
-       "  1399.68,1259.59 1400.38,1259.56 1401.08,1259.54 1401.77,1259.51 1402.47,1259.48 1403.16,1259.46 1403.86,1259.43 1404.55,1259.41 1405.25,1259.38 1405.95,1259.36 \n",
-       "  1406.64,1259.33 1407.34,1259.31 1408.03,1259.29 1408.73,1259.27 1409.42,1259.25 1410.12,1259.22 1410.82,1259.2 1411.51,1259.18 1412.21,1259.16 1412.9,1259.15 \n",
-       "  1413.6,1259.13 1414.29,1259.11 1414.99,1259.09 1415.68,1259.07 1416.38,1259.06 1417.08,1259.04 1417.77,1259.02 1418.47,1259.01 1419.16,1258.99 1419.86,1258.98 \n",
-       "  1420.55,1258.96 1421.25,1258.95 1421.95,1258.94 1422.64,1258.92 1423.34,1258.91 1424.03,1258.9 1424.73,1258.89 1425.42,1258.88 1426.12,1258.86 1426.82,1258.85 \n",
-       "  1427.51,1258.84 1428.21,1258.83 1428.9,1258.82 1429.6,1258.81 1430.29,1258.81 1430.99,1258.8 1431.69,1258.79 1432.38,1258.78 1433.08,1258.77 1433.77,1258.77 \n",
-       "  1434.47,1258.76 1435.16,1258.75 1435.86,1258.74 1436.55,1258.74 1437.25,1258.73 1437.95,1258.73 1438.64,1258.72 1439.34,1258.72 1440.03,1258.71 1440.73,1258.71 \n",
-       "  1441.42,1258.7 1442.12,1258.7 1442.82,1258.7 1443.51,1258.69 1444.21,1258.69 1444.9,1258.69 1445.6,1258.68 1446.29,1258.68 1446.99,1258.68 1447.69,1258.68 \n",
-       "  1448.38,1258.67 1449.08,1258.67 1449.77,1258.67 1450.47,1258.67 1451.16,1258.67 1451.86,1258.67 1452.56,1258.67 1453.25,1258.67 1453.95,1258.67 1454.64,1258.67 \n",
-       "  1455.34,1258.67 1456.03,1258.67 1456.73,1258.67 1457.43,1258.67 1458.12,1258.67 1458.82,1258.67 1459.51,1258.67 1460.21,1258.67 1460.9,1258.67 1461.6,1258.67 \n",
-       "  1462.29,1258.67 1462.99,1258.67 1463.69,1258.68 1464.38,1258.68 1465.08,1258.68 1465.77,1258.68 1466.47,1258.68 1467.16,1258.68 1467.86,1258.69 1468.56,1258.69 \n",
-       "  1469.25,1258.69 1469.95,1258.69 1470.64,1258.69 1471.34,1258.7 1472.03,1258.7 1472.73,1258.7 1473.43,1258.7 1474.12,1258.7 1474.82,1258.71 1475.51,1258.71 \n",
-       "  1476.21,1258.71 1476.9,1258.71 1477.6,1258.71 1478.3,1258.72 1478.99,1258.72 1479.69,1258.72 1480.38,1258.72 1481.08,1258.72 1481.77,1258.73 1482.47,1258.73 \n",
-       "  1483.17,1258.73 1483.86,1258.73 1484.56,1258.73 1485.25,1258.74 1485.95,1258.74 1486.64,1258.74 1487.34,1258.74 1488.03,1258.74 1488.73,1258.74 1489.43,1258.74 \n",
-       "  1490.12,1258.74 1490.82,1258.75 1491.51,1258.75 1492.21,1258.75 1492.9,1258.75 1493.6,1258.75 1494.3,1258.75 1494.99,1258.75 1495.69,1258.75 1496.38,1258.75 \n",
-       "  1497.08,1258.75 1497.77,1258.75 1498.47,1258.75 1499.17,1258.74 1499.86,1258.74 1500.56,1258.74 1501.25,1258.74 1501.95,1258.74 1502.64,1258.74 1503.34,1258.73 \n",
-       "  1504.04,1258.73 1504.73,1258.73 1505.43,1258.73 1506.12,1258.72 1506.82,1258.72 1507.51,1258.72 1508.21,1258.71 1508.9,1258.71 1509.6,1258.7 1510.3,1258.7 \n",
-       "  1510.99,1258.69 1511.69,1258.69 1512.38,1258.68 1513.08,1258.68 1513.77,1258.67 1514.47,1258.67 1515.17,1258.66 1515.86,1258.65 1516.56,1258.64 1517.25,1258.64 \n",
-       "  1517.95,1258.63 1518.64,1258.62 1519.34,1258.61 1520.04,1258.6 1520.73,1258.59 1521.43,1258.58 1522.12,1258.57 1522.82,1258.56 1523.51,1258.55 1524.21,1258.54 \n",
-       "  1524.91,1258.53 1525.6,1258.52 1526.3,1258.51 1526.99,1258.49 1527.69,1258.48 1528.38,1258.47 1529.08,1258.45 1529.78,1258.44 1530.47,1258.42 1531.17,1258.41 \n",
-       "  1531.86,1258.39 1532.56,1258.38 1533.25,1258.36 1533.95,1258.34 1534.64,1258.33 1535.34,1258.31 1536.04,1258.29 1536.73,1258.27 1537.43,1258.25 1538.12,1258.23 \n",
-       "  1538.82,1258.21 1539.51,1258.19 1540.21,1258.17 1540.91,1258.15 1541.6,1258.12 1542.3,1258.1 1542.99,1258.08 1543.69,1258.05 1544.38,1258.03 1545.08,1258.01 \n",
-       "  1545.78,1257.98 1546.47,1257.95 1547.17,1257.93 1547.86,1257.9 1548.56,1257.87 1549.25,1257.84 1549.95,1257.82 1550.65,1257.79 1551.34,1257.76 1552.04,1257.73 \n",
-       "  1552.73,1257.69 1553.43,1257.66 1554.12,1257.63 1554.82,1257.6 1555.52,1257.56 1556.21,1257.53 1556.91,1257.49 1557.6,1257.46 1558.3,1257.42 1558.99,1257.39 \n",
-       "  1559.69,1257.35 1560.38,1257.31 1561.08,1257.27 1561.78,1257.23 1562.47,1257.19 1563.17,1257.15 1563.86,1257.11 1564.56,1257.07 1565.25,1257.03 1565.95,1256.98 \n",
-       "  1566.65,1256.94 1567.34,1256.9 1568.04,1256.85 1568.73,1256.8 1569.43,1256.76 1570.12,1256.71 1570.82,1256.66 1571.52,1256.61 1572.21,1256.56 1572.91,1256.51 \n",
-       "  1573.6,1256.46 1574.3,1256.41 1574.99,1256.36 1575.69,1256.3 1576.39,1256.25 1577.08,1256.2 1577.78,1256.14 1578.47,1256.08 1579.17,1256.03 1579.86,1255.97 \n",
-       "  1580.56,1255.91 1581.25,1255.85 1581.95,1255.79 1582.65,1255.73 1583.34,1255.67 1584.04,1255.61 1584.73,1255.54 1585.43,1255.48 1586.12,1255.41 1586.82,1255.35 \n",
-       "  1587.52,1255.28 1588.21,1255.21 1588.91,1255.14 1589.6,1255.07 1590.3,1255 1590.99,1254.93 1591.69,1254.86 1592.39,1254.79 1593.08,1254.72 1593.78,1254.64 \n",
-       "  1594.47,1254.57 1595.17,1254.49 1595.86,1254.41 1596.56,1254.34 1597.26,1254.26 1597.95,1254.18 1598.65,1254.1 1599.34,1254.01 1600.04,1253.93 1600.73,1253.85 \n",
-       "  1601.43,1253.77 1602.13,1253.68 1602.82,1253.59 1603.52,1253.51 1604.21,1253.42 1604.91,1253.33 1605.6,1253.24 1606.3,1253.15 1606.99,1253.06 1607.69,1252.97 \n",
-       "  1608.39,1252.87 1609.08,1252.78 1609.78,1252.68 1610.47,1252.59 1611.17,1252.49 1611.86,1252.39 1612.56,1252.29 1613.26,1252.19 1613.95,1252.09 1614.65,1251.99 \n",
-       "  1615.34,1251.89 1616.04,1251.78 1616.73,1251.68 1617.43,1251.57 1618.13,1251.46 1618.82,1251.36 1619.52,1251.25 1620.21,1251.14 1620.91,1251.02 1621.6,1250.91 \n",
-       "  1622.3,1250.8 1623,1250.69 1623.69,1250.57 1624.39,1250.45 1625.08,1250.34 1625.78,1250.22 1626.47,1250.1 1627.17,1249.98 1627.86,1249.86 1628.56,1249.73 \n",
-       "  1629.26,1249.61 1629.95,1249.49 1630.65,1249.36 1631.34,1249.23 1632.04,1249.11 1632.73,1248.98 1633.43,1248.85 1634.13,1248.71 1634.82,1248.58 1635.52,1248.45 \n",
-       "  1636.21,1248.32 1636.91,1248.18 1637.6,1248.04 1638.3,1247.91 1639,1247.77 1639.69,1247.63 1640.39,1247.49 1641.08,1247.34 1641.78,1247.2 1642.47,1247.06 \n",
-       "  1643.17,1246.91 1643.87,1246.76 1644.56,1246.62 1645.26,1246.47 1645.95,1246.32 1646.65,1246.17 1647.34,1246.01 1648.04,1245.86 1648.74,1245.71 1649.43,1245.55 \n",
-       "  1650.13,1245.39 1650.82,1245.24 1651.52,1245.08 1652.21,1244.92 1652.91,1244.76 1653.6,1244.59 1654.3,1244.43 1655,1244.26 1655.69,1244.1 1656.39,1243.93 \n",
-       "  1657.08,1243.76 1657.78,1243.59 1658.47,1243.42 1659.17,1243.25 1659.87,1243.08 1660.56,1242.9 1661.26,1242.73 1661.95,1242.55 1662.65,1242.37 1663.34,1242.19 \n",
-       "  1664.04,1242.01 1664.74,1241.83 1665.43,1241.65 1666.13,1241.47 1666.82,1241.28 1667.52,1241.09 1668.21,1240.91 1668.91,1240.72 1669.61,1240.53 1670.3,1240.34 \n",
-       "  1671,1240.14 1671.69,1239.95 1672.39,1239.76 1673.08,1239.56 1673.78,1239.36 1674.48,1239.17 1675.17,1238.97 1675.87,1238.76 1676.56,1238.56 1677.26,1238.36 \n",
-       "  1677.95,1238.15 1678.65,1237.95 1679.34,1237.74 1680.04,1237.53 1680.74,1237.32 1681.43,1237.11 1682.13,1236.9 1682.82,1236.69 1683.52,1236.47 1684.21,1236.26 \n",
-       "  1684.91,1236.04 1685.61,1235.82 1686.3,1235.6 1687,1235.38 1687.69,1235.16 1688.39,1234.94 1689.08,1234.71 1689.78,1234.49 1690.48,1234.26 1691.17,1234.03 \n",
-       "  1691.87,1233.8 1692.56,1233.57 1693.26,1233.34 1693.95,1233.11 1694.65,1232.87 1695.35,1232.64 1696.04,1232.4 1696.74,1232.16 1697.43,1231.92 1698.13,1231.68 \n",
-       "  1698.82,1231.44 1699.52,1231.19 1700.21,1230.95 1700.91,1230.7 1701.61,1230.46 1702.3,1230.21 1703,1229.96 1703.69,1229.71 1704.39,1229.45 1705.08,1229.2 \n",
-       "  1705.78,1228.95 1706.48,1228.69 1707.17,1228.43 1707.87,1228.17 1708.56,1227.91 1709.26,1227.65 1709.95,1227.39 1710.65,1227.12 1711.35,1226.86 1712.04,1226.59 \n",
-       "  1712.74,1226.33 1713.43,1226.06 1714.13,1225.79 1714.82,1225.51 1715.52,1225.24 1716.22,1224.97 1716.91,1224.69 1717.61,1224.42 1718.3,1224.14 1719,1223.86 \n",
-       "  1719.69,1223.58 1720.39,1223.3 1721.09,1223.01 1721.78,1222.73 1722.48,1222.44 1723.17,1222.16 1723.87,1221.87 1724.56,1221.58 1725.26,1221.29 1725.95,1220.99 \n",
-       "  1726.65,1220.7 1727.35,1220.41 1728.04,1220.11 1728.74,1219.81 1729.43,1219.52 1730.13,1219.22 1730.82,1218.91 1731.52,1218.61 1732.22,1218.31 1732.91,1218 \n",
-       "  1733.61,1217.7 1734.3,1217.39 1735,1217.08 1735.69,1216.77 1736.39,1216.46 1737.09,1216.15 1737.78,1215.83 1738.48,1215.52 1739.17,1215.2 1739.87,1214.88 \n",
-       "  1740.56,1214.57 1741.26,1214.25 1741.96,1213.92 1742.65,1213.6 1743.35,1213.28 1744.04,1212.95 1744.74,1212.63 1745.43,1212.3 1746.13,1211.97 1746.83,1211.64 \n",
-       "  1747.52,1211.31 1748.22,1210.97 1748.91,1210.64 1749.61,1210.3 1750.3,1209.97 1751,1209.63 1751.69,1209.29 1752.39,1208.95 1753.09,1208.61 1753.78,1208.27 \n",
-       "  1754.48,1207.92 1755.17,1207.58 1755.87,1207.23 1756.56,1206.88 1757.26,1206.53 1757.96,1206.18 1758.65,1205.83 1759.35,1205.48 1760.04,1205.13 1760.74,1204.77 \n",
-       "  1761.43,1204.41 1762.13,1204.06 1762.83,1203.7 1763.52,1203.34 1764.22,1202.98 1764.91,1202.61 1765.61,1202.25 1766.3,1201.89 1767,1201.52 1767.7,1201.15 \n",
-       "  1768.39,1200.78 1769.09,1200.41 1769.78,1200.04 1770.48,1199.67 1771.17,1199.3 1771.87,1198.92 1772.56,1198.55 1773.26,1198.17 1773.96,1197.79 1774.65,1197.41 \n",
-       "  1775.35,1197.03 1776.04,1196.65 1776.74,1196.27 1777.43,1195.88 1778.13,1195.5 1778.83,1195.11 1779.52,1194.72 1780.22,1194.34 1780.91,1193.95 1781.61,1193.56 \n",
-       "  1782.3,1193.16 1783,1192.77 1783.7,1192.38 1784.39,1191.98 1785.09,1191.58 1785.78,1191.19 1786.48,1190.79 1787.17,1190.39 1787.87,1189.98 1788.57,1189.58 \n",
-       "  1789.26,1189.18 1789.96,1188.77 1790.65,1188.37 1791.35,1187.96 1792.04,1187.55 1792.74,1187.14 1793.44,1186.73 1794.13,1186.32 1794.83,1185.91 1795.52,1185.5 \n",
-       "  1796.22,1185.08 1796.91,1184.67 1797.61,1184.25 1798.3,1183.83 1799,1183.41 1799.7,1182.99 1800.39,1182.57 1801.09,1182.15 1801.78,1181.73 1802.48,1181.3 \n",
-       "  1803.17,1180.88 1803.87,1180.45 1804.57,1180.02 1805.26,1179.59 1805.96,1179.16 1806.65,1178.73 1807.35,1178.3 1808.04,1177.87 1808.74,1177.43 1809.44,1177 \n",
-       "  1810.13,1176.56 1810.83,1176.13 1811.52,1175.69 1812.22,1175.25 1812.91,1174.81 1813.61,1174.37 1814.31,1173.93 1815,1173.49 1815.7,1173.04 1816.39,1172.6 \n",
-       "  1817.09,1172.15 1817.78,1171.7 1818.48,1171.26 1819.17,1170.81 1819.87,1170.36 1820.57,1169.91 1821.26,1169.46 1821.96,1169 1822.65,1168.55 1823.35,1168.1 \n",
-       "  1824.04,1167.64 1824.74,1167.18 1825.44,1166.73 1826.13,1166.27 1826.83,1165.81 1827.52,1165.35 1828.22,1164.89 1828.91,1164.43 1829.61,1163.97 1830.31,1163.5 \n",
-       "  1831,1163.04 1831.7,1162.57 1832.39,1162.11 1833.09,1161.64 1833.78,1161.17 1834.48,1160.7 1835.18,1160.23 1835.87,1159.76 1836.57,1159.29 1837.26,1158.82 \n",
-       "  1837.96,1158.35 1838.65,1157.87 1839.35,1157.4 1840.05,1156.92 1840.74,1156.45 1841.44,1155.97 1842.13,1155.49 1842.83,1155.01 1843.52,1154.54 1844.22,1154.06 \n",
-       "  1844.91,1153.57 1845.61,1153.09 1846.31,1152.61 1847,1152.13 1847.7,1151.64 1848.39,1151.16 1849.09,1150.67 1849.78,1150.19 1850.48,1149.7 1851.18,1149.21 \n",
-       "  1851.87,1148.72 1852.57,1148.24 1853.26,1147.75 1853.96,1147.26 1854.65,1146.76 1855.35,1146.27 1856.05,1145.78 1856.74,1145.29 1857.44,1144.79 1858.13,1144.3 \n",
-       "  1858.83,1143.8 1859.52,1143.31 1860.22,1142.81 1860.92,1142.31 1861.61,1141.82 1862.31,1141.32 1863,1140.82 1863.7,1140.32 1864.39,1139.82 1865.09,1139.32 \n",
-       "  1865.79,1138.82 1866.48,1138.32 1867.18,1137.81 1867.87,1137.31 1868.57,1136.81 1869.26,1136.3 1869.96,1135.8 1870.65,1135.29 1871.35,1134.79 1872.05,1134.28 \n",
-       "  1872.74,1133.77 1873.44,1133.27 1874.13,1132.76 1874.83,1132.25 1875.52,1131.74 1876.22,1131.23 1876.92,1130.72 1877.61,1130.21 1878.31,1129.7 1879,1129.19 \n",
-       "  1879.7,1128.68 1880.39,1128.16 1881.09,1127.65 1881.79,1127.14 1882.48,1126.63 1883.18,1126.11 1883.87,1125.6 1884.57,1125.08 1885.26,1124.57 1885.96,1124.05 \n",
-       "  1886.66,1123.54 1887.35,1123.02 1888.05,1122.5 1888.74,1121.99 1889.44,1121.47 1890.13,1120.95 1890.83,1120.43 1891.52,1119.91 1892.22,1119.39 1892.92,1118.88 \n",
-       "  1893.61,1118.36 1894.31,1117.84 1895,1117.32 1895.7,1116.8 1896.39,1116.28 1897.09,1115.76 1897.79,1115.23 1898.48,1114.71 1899.18,1114.19 1899.87,1113.67 \n",
-       "  1900.57,1113.15 1901.26,1112.63 1901.96,1112.1 1902.66,1111.58 1903.35,1111.06 1904.05,1110.53 1904.74,1110.01 1905.44,1109.49 1906.13,1108.96 1906.83,1108.44 \n",
-       "  1907.53,1107.92 1908.22,1107.39 1908.92,1106.87 1909.61,1106.34 1910.31,1105.82 1911,1105.29 1911.7,1104.77 1912.4,1104.25 1913.09,1103.72 1913.79,1103.2 \n",
-       "  1914.48,1102.67 1915.18,1102.15 1915.87,1101.62 1916.57,1101.1 1917.26,1100.57 1917.96,1100.05 1918.66,1099.52 1919.35,1099 1920.05,1098.47 1920.74,1097.94 \n",
-       "  1921.44,1097.42 1922.13,1096.89 1922.83,1096.37 1923.53,1095.84 1924.22,1095.32 1924.92,1094.79 1925.61,1094.27 1926.31,1093.75 1927,1093.22 1927.7,1092.7 \n",
-       "  1928.4,1092.17 1929.09,1091.65 1929.79,1091.12 1930.48,1090.6 1931.18,1090.07 1931.87,1089.55 1932.57,1089.03 1933.27,1088.5 1933.96,1087.98 1934.66,1087.46 \n",
-       "  1935.35,1086.93 1936.05,1086.41 1936.74,1085.89 1937.44,1085.37 1938.13,1084.85 1938.83,1084.32 1939.53,1083.8 1940.22,1083.28 1940.92,1082.76 1941.61,1082.24 \n",
-       "  1942.31,1081.72 1943,1081.2 1943.7,1080.68 1944.4,1080.16 1945.09,1079.64 1945.79,1079.12 1946.48,1078.6 1947.18,1078.09 1947.87,1077.57 1948.57,1077.05 \n",
-       "  1949.27,1076.53 1949.96,1076.02 1950.66,1075.5 1951.35,1074.98 1952.05,1074.47 1952.74,1073.95 1953.44,1073.44 1954.14,1072.93 1954.83,1072.41 1955.53,1071.9 \n",
-       "  1956.22,1071.39 1956.92,1070.87 1957.61,1070.36 1958.31,1069.85 1959.01,1069.34 1959.7,1068.83 1960.4,1068.32 1961.09,1067.81 1961.79,1067.3 1962.48,1066.8 \n",
-       "  1963.18,1066.29 1963.87,1065.78 1964.57,1065.28 1965.27,1064.77 1965.96,1064.27 1966.66,1063.76 1967.35,1063.26 1968.05,1062.76 1968.74,1062.25 1969.44,1061.75 \n",
-       "  1970.14,1061.25 1970.83,1060.75 1971.53,1060.25 1972.22,1059.75 1972.92,1059.25 1973.61,1058.76 1974.31,1058.26 1975.01,1057.76 1975.7,1057.27 1976.4,1056.78 \n",
-       "  1977.09,1056.28 1977.79,1055.79 1978.48,1055.3 1979.18,1054.81 1979.88,1054.32 1980.57,1053.83 1981.27,1053.34 1981.96,1052.85 1982.66,1052.36 1983.35,1051.88 \n",
-       "  1984.05,1051.39 1984.75,1050.91 1985.44,1050.43 1986.14,1049.94 1986.83,1049.46 1987.53,1048.98 1988.22,1048.5 1988.92,1048.02 1989.61,1047.54 1990.31,1047.07 \n",
-       "  1991.01,1046.59 1991.7,1046.12 1992.4,1045.64 1993.09,1045.17 1993.79,1044.7 1994.48,1044.23 1995.18,1043.76 1995.88,1043.29 1996.57,1042.82 1997.27,1042.36 \n",
-       "  1997.96,1041.89 1998.66,1041.43 1999.35,1040.97 2000.05,1040.5 2000.75,1040.04 2001.44,1039.58 2002.14,1039.13 2002.83,1038.67 2003.53,1038.21 2004.22,1037.76 \n",
-       "  2004.92,1037.3 2005.62,1036.85 2006.31,1036.4 2007.01,1035.95 2007.7,1035.5 2008.4,1035.06 2009.09,1034.61 2009.79,1034.16 2010.48,1033.72 2011.18,1033.28 \n",
-       "  2011.88,1032.84 2012.57,1032.4 2013.27,1031.96 2013.96,1031.52 2014.66,1031.09 2015.35,1030.65 2016.05,1030.22 2016.75,1029.79 2017.44,1029.36 2018.14,1028.93 \n",
-       "  2018.83,1028.5 2019.53,1028.08 2020.22,1027.65 2020.92,1027.23 2021.62,1026.81 2022.31,1026.39 2023.01,1025.97 2023.7,1025.56 2024.4,1025.14 2025.09,1024.73 \n",
-       "  2025.79,1024.31 2026.49,1023.9 2027.18,1023.49 2027.88,1023.09 2028.57,1022.68 2029.27,1022.28 2029.96,1021.87 2030.66,1021.47 2031.36,1021.07 2032.05,1020.68 \n",
-       "  2032.75,1020.28 2033.44,1019.88 2034.14,1019.49 2034.83,1019.1 2035.53,1018.71 2036.22,1018.32 2036.92,1017.94 2037.62,1017.55 2038.31,1017.17 2039.01,1016.79 \n",
-       "  2039.7,1016.41 2040.4,1016.03 2041.09,1015.66 2041.79,1015.28 2042.49,1014.91 2043.18,1014.54 2043.88,1014.17 2044.57,1013.81 2045.27,1013.44 2045.96,1013.08 \n",
-       "  2046.66,1012.72 2047.36,1012.36 2048.05,1012 2048.75,1011.65 2049.44,1011.29 2050.14,1010.94 2050.83,1010.59 2051.53,1010.25 2052.23,1009.9 2052.92,1009.56 \n",
-       "  2053.62,1009.21 2054.31,1008.88 2055.01,1008.54 2055.7,1008.2 2056.4,1007.87 2057.1,1007.54 2057.79,1007.21 2058.49,1006.88 2059.18,1006.56 2059.88,1006.23 \n",
-       "  2060.57,1005.91 2061.27,1005.59 2061.96,1005.28 2062.66,1004.96 2063.36,1004.65 2064.05,1004.34 2064.75,1004.03 2065.44,1003.72 2066.14,1003.42 2066.83,1003.12 \n",
-       "  2067.53,1002.82 2068.23,1002.52 2068.92,1002.23 2069.62,1001.93 2070.31,1001.64 2071.01,1001.36 2071.7,1001.07 2072.4,1000.79 2073.1,1000.51 2073.79,1000.23 \n",
-       "  2074.49,999.95 2075.18,999.675 2075.88,999.403 2076.57,999.134 2077.27,998.867 2077.97,998.602 2078.66,998.339 2079.36,998.079 2080.05,997.822 2080.75,997.567 \n",
-       "  2081.44,997.314 2082.14,997.064 2082.83,996.816 2083.53,996.571 2084.23,996.328 2084.92,996.087 2085.62,995.849 2086.31,995.614 2087.01,995.381 2087.7,995.151 \n",
-       "  2088.4,994.923 2089.1,994.698 2089.79,994.476 2090.49,994.256 2091.18,994.038 2091.88,993.824 2092.57,993.611 2093.27,993.402 2093.97,993.195 2094.66,992.991 \n",
-       "  2095.36,992.789 2096.05,992.59 2096.75,992.394 2097.44,992.201 2098.14,992.01 2098.84,991.822 2099.53,991.636 2100.23,991.454 2100.92,991.274 2101.62,991.097 \n",
-       "  2102.31,990.923 2103.01,990.751 2103.71,990.582 2104.4,990.417 2105.1,990.253 2105.79,990.093 2106.49,989.936 2107.18,989.781 2107.88,989.63 2108.57,989.481 \n",
-       "  2109.27,989.335 2109.97,989.192 2110.66,989.052 2111.36,988.914 2112.05,988.78 2112.75,988.649 2113.44,988.52 2114.14,988.395 2114.84,988.273 2115.53,988.153 \n",
-       "  2116.23,988.037 2116.92,987.923 2117.62,987.813 2118.31,987.705 2119.01,987.601 2119.71,987.5 2120.4,987.401 2121.1,987.306 2121.79,987.214 2122.49,987.125 \n",
-       "  2123.18,987.039 2123.88,986.956 2124.58,986.877 2125.27,986.8 2125.97,986.727 2126.66,986.657 2127.36,986.59 2128.05,986.526 2128.75,986.465 2129.44,986.407 \n",
-       "  2130.14,986.353 2130.84,986.302 2131.53,986.254 2132.23,986.21 2132.92,986.168 2133.62,986.13 2134.31,986.095 2135.01,986.064 2135.71,986.036 2136.4,986.011 \n",
-       "  2137.1,985.989 2137.79,985.971 2138.49,985.956 2139.18,985.944 2139.88,985.936 2140.58,985.931 2141.27,985.93 2141.97,985.932 2142.66,985.937 2143.36,985.946 \n",
-       "  2144.05,985.958 2144.75,985.974 2145.45,985.993 2146.14,986.015 2146.84,986.041 2147.53,986.07 2148.23,986.103 2148.92,986.139 2149.62,986.179 2150.32,986.223 \n",
-       "  2151.01,986.27 2151.71,986.32 2152.4,986.374 2153.1,986.432 2153.79,986.493 2154.49,986.557 2155.18,986.625 2155.88,986.697 2156.58,986.773 2157.27,986.852 \n",
-       "  2157.97,986.934 2158.66,987.021 2159.36,987.111 2160.05,987.204 2160.75,987.301 2161.45,987.402 2162.14,987.507 2162.84,987.615 2163.53,987.727 2164.23,987.843 \n",
-       "  2164.92,987.962 2165.62,988.085 2166.32,988.212 2167.01,988.343 2167.71,988.477 2168.4,988.615 2169.1,988.757 2169.79,988.903 2170.49,989.053 2171.19,989.206 \n",
-       "  2171.88,989.363 2172.58,989.524 2173.27,989.689 2173.97,989.857 2174.66,990.03 2175.36,990.206 2176.06,990.386 2176.75,990.57 2177.45,990.758 2178.14,990.95 \n",
-       "  2178.84,991.146 2179.53,991.346 2180.23,991.549 2180.92,991.757 2181.62,991.969 2182.32,992.184 2183.01,992.403 2183.71,992.627 2184.4,992.854 2185.1,993.085 \n",
-       "  2185.79,993.321 2186.49,993.56 2187.19,993.804 2187.88,994.051 2188.58,994.302 2189.27,994.558 2189.97,994.817 2190.66,995.081 2191.36,995.349 2192.06,995.62 \n",
-       "  2192.75,995.896 2193.45,996.176 2194.14,996.46 2194.84,996.748 2195.53,997.041 2196.23,997.337 2196.93,997.637 2197.62,997.942 2198.32,998.251 2199.01,998.564 \n",
-       "  2199.71,998.881 2200.4,999.202 2201.1,999.528 2201.79,999.857 2202.49,1000.19 2203.19,1000.53 2203.88,1000.87 2204.58,1001.22 2205.27,1001.57 2205.97,1001.92 \n",
-       "  2206.66,1002.28 2207.36,1002.65 2208.06,1003.01 2208.75,1003.39 2209.45,1003.76 2210.14,1004.14 2210.84,1004.53 2211.53,1004.92 2212.23,1005.31 2212.93,1005.71 \n",
-       "  2213.62,1006.11 2214.32,1006.52 2215.01,1006.93 2215.71,1007.34 2216.4,1007.76 2217.1,1008.19 2217.8,1008.62 2218.49,1009.05 2219.19,1009.49 2219.88,1009.93 \n",
-       "  2220.58,1010.37 2221.27,1010.82 2221.97,1011.28 2222.67,1011.74 2223.36,1012.2 2224.06,1012.67 2224.75,1013.14 2225.45,1013.62 2226.14,1014.1 2226.84,1014.59 \n",
-       "  2227.53,1015.08 2228.23,1015.57 2228.93,1016.07 2229.62,1016.58 2230.32,1017.08 2231.01,1017.6 2231.71,1018.11 2232.4,1018.64 2233.1,1019.16 2233.8,1019.69 \n",
-       "  2234.49,1020.23 2235.19,1020.77 2235.88,1021.31 2236.58,1021.86 2237.27,1022.42 2237.97,1022.98 2238.67,1023.54 2239.36,1024.11 2240.06,1024.68 2240.75,1025.26 \n",
-       "  2241.45,1025.84 2242.14,1026.42 2242.84,1027.01 2243.54,1027.61 2244.23,1028.21 2244.93,1028.81 2245.62,1029.42 2246.32,1030.04 2247.01,1030.65 2247.71,1031.28 \n",
-       "  2248.41,1031.91 2249.1,1032.54 2249.8,1033.17 2250.49,1033.82 2251.19,1034.46 2251.88,1035.11 2252.58,1035.77 2253.27,1036.43 2253.97,1037.1 2254.67,1037.77 \n",
-       "  2255.36,1038.44 2256.06,1039.12 2256.75,1039.81 2257.45,1040.49 2258.14,1041.19 2258.84,1041.89 2259.54,1042.59 2260.23,1043.3 2260.93,1044.01 2261.62,1044.73 \n",
-       "  2262.32,1045.45 2263.01,1046.18 2263.71,1046.91 2264.41,1047.65 2265.1,1048.39 2265.8,1049.13 2266.49,1049.89 2267.19,1050.64 2267.88,1051.4 2268.58,1052.17 \n",
-       "  2269.28,1052.94 2269.97,1053.71 2270.67,1054.49 2271.36,1055.28 2272.06,1056.07 2272.75,1056.86 2273.45,1057.66 2274.14,1058.47 2274.84,1059.28 2275.54,1060.09 \n",
-       "  2276.23,1060.91 2276.93,1061.73 2277.62,1062.56 2278.32,1063.39 2279.01,1064.23 2279.71,1065.08 2280.41,1065.92 2281.1,1066.78 2281.8,1067.63 2282.49,1068.5 \n",
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-       "  2290.15,1078.31 \n",
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311.671,365.397 312.368,367.437 313.064,369.48 313.761,371.525 314.458,373.573 315.155,375.623 315.852,377.676 316.549,379.731 317.246,381.789 \n  317.942,383.849 318.639,385.911 319.336,387.976 320.033,390.043 320.73,392.112 321.427,394.184 322.124,396.258 322.82,398.334 323.517,400.412 324.214,402.493 \n  324.911,404.575 325.608,406.66 326.305,408.747 327.002,410.836 327.698,412.927 328.395,415.02 329.092,417.116 329.789,419.213 330.486,421.312 331.183,423.413 \n  331.879,425.516 332.576,427.621 333.273,429.728 333.97,431.836 334.667,433.947 335.364,436.059 336.061,438.173 336.757,440.289 337.454,442.406 338.151,444.526 \n  338.848,446.647 339.545,448.769 340.242,450.893 340.939,453.019 341.635,455.147 342.332,457.276 343.029,459.406 343.726,461.539 344.423,463.672 345.12,465.807 \n  345.817,467.944 346.513,470.082 347.21,472.221 347.907,474.362 348.604,476.504 349.301,478.648 349.998,480.793 350.695,482.939 351.391,485.086 352.088,487.235 \n  352.785,489.385 353.482,491.536 354.179,493.688 354.876,495.841 355.573,497.996 356.269,500.152 356.966,502.308 357.663,504.466 358.36,506.625 359.057,508.785 \n  359.754,510.946 360.45,513.108 361.147,515.271 361.844,517.434 362.541,519.599 363.238,521.765 363.935,523.931 364.632,526.098 365.328,528.267 366.025,530.435 \n  366.722,532.605 367.419,534.776 368.116,536.947 368.813,539.119 369.51,541.291 370.206,543.464 370.903,545.638 371.6,547.813 372.297,549.988 372.994,552.164 \n  373.691,554.34 374.388,556.517 375.084,558.694 375.781,560.872 376.478,563.05 377.175,565.229 377.872,567.408 378.569,569.588 379.266,571.768 379.962,573.948 \n  380.659,576.129 381.356,578.31 382.053,580.491 382.75,582.672 383.447,584.854 384.144,587.036 384.84,589.218 385.537,591.401 386.234,593.584 386.931,595.766 \n  387.628,597.949 388.325,600.132 389.022,602.315 389.718,604.498 390.415,606.682 391.112,608.865 391.809,611.048 392.506,613.231 393.203,615.415 393.899,617.598 \n  394.596,619.781 395.293,621.964 395.99,624.146 396.687,626.329 397.384,628.512 398.081,630.694 398.777,632.876 399.474,635.058 400.171,637.24 400.868,639.421 \n  401.565,641.602 402.262,643.783 402.959,645.964 403.655,648.144 404.352,650.323 405.049,652.503 405.746,654.682 406.443,656.86 407.14,659.039 407.837,661.216 \n  408.533,663.393 409.23,665.57 409.927,667.746 410.624,669.922 411.321,672.097 412.018,674.271 412.715,676.445 413.411,678.618 414.108,680.791 414.805,682.963 \n  415.502,685.134 416.199,687.305 416.896,689.474 417.593,691.643 418.289,693.812 418.986,695.979 419.683,698.146 420.38,700.312 421.077,702.477 421.774,704.641 \n  422.47,706.805 423.167,708.967 423.864,711.129 424.561,713.289 425.258,715.449 425.955,717.608 426.652,719.765 427.348,721.922 428.045,724.078 428.742,726.232 \n  429.439,728.386 430.136,730.538 430.833,732.69 431.53,734.84 432.226,736.989 432.923,739.137 433.62,741.284 434.317,743.43 435.014,745.574 435.711,747.717 \n  436.408,749.859 437.104,752 437.801,754.139 438.498,756.278 439.195,758.414 439.892,760.55 440.589,762.684 441.286,764.817 441.982,766.948 442.679,769.078 \n  443.376,771.207 444.073,773.334 444.77,775.459 445.467,777.583 446.164,779.706 446.86,781.827 447.557,783.947 448.254,786.065 448.951,788.182 449.648,790.296 \n  450.345,792.41 451.042,794.522 451.738,796.632 452.435,798.74 453.132,800.847 453.829,802.952 454.526,805.055 455.223,807.157 455.919,809.257 456.616,811.355 \n  457.313,813.452 458.01,815.547 458.707,817.639 459.404,819.73 460.101,821.82 460.797,823.907 461.494,825.993 462.191,828.076 462.888,830.158 463.585,832.238 \n  464.282,834.316 464.979,836.392 465.675,838.466 466.372,840.538 467.069,842.608 467.766,844.676 468.463,846.742 469.16,848.806 469.857,850.868 470.553,852.928 \n  471.25,854.985 471.947,857.041 472.644,859.095 473.341,861.146 474.038,863.195 474.735,865.242 475.431,867.287 476.128,869.33 476.825,871.371 477.522,873.409 \n  478.219,875.445 478.916,877.479 479.613,879.51 480.309,881.54 481.006,883.567 481.703,885.591 482.4,887.614 483.097,889.634 483.794,891.651 484.49,893.666 \n  485.187,895.679 485.884,897.69 486.581,899.698 487.278,901.703 487.975,903.707 488.672,905.707 489.368,907.705 490.065,909.701 490.762,911.694 491.459,913.685 \n  492.156,915.673 492.853,917.659 493.55,919.642 494.246,921.623 494.943,923.601 495.64,925.576 496.337,927.549 497.034,929.519 497.731,931.486 498.428,933.451 \n  499.124,935.413 499.821,937.373 500.518,939.33 501.215,941.284 501.912,943.235 502.609,945.184 503.306,947.129 504.002,949.073 504.699,951.013 505.396,952.95 \n  506.093,954.885 506.79,956.817 507.487,958.746 508.184,960.673 508.88,962.596 509.577,964.517 510.274,966.434 510.971,968.349 511.668,970.261 512.365,972.17 \n  513.062,974.076 513.758,975.979 514.455,977.88 515.152,979.777 515.849,981.671 516.546,983.562 517.243,985.451 517.939,987.336 518.636,989.218 519.333,991.097 \n  520.03,992.974 520.727,994.847 521.424,996.717 522.121,998.584 522.817,1000.45 523.514,1002.31 524.211,1004.17 524.908,1006.02 525.605,1007.87 526.302,1009.72 \n  526.999,1011.57 527.695,1013.41 528.392,1015.25 529.089,1017.08 529.786,1018.91 530.483,1020.74 531.18,1022.57 531.877,1024.39 532.573,1026.21 533.27,1028.03 \n  533.967,1029.84 534.664,1031.65 535.361,1033.46 536.058,1035.26 536.755,1037.06 537.451,1038.86 538.148,1040.65 538.845,1042.44 539.542,1044.22 540.239,1046.01 \n  540.936,1047.79 541.633,1049.56 542.329,1051.34 543.026,1053.11 543.723,1054.87 544.42,1056.63 545.117,1058.39 545.814,1060.15 546.51,1061.9 547.207,1063.65 \n  547.904,1065.4 548.601,1067.14 549.298,1068.88 549.995,1070.61 550.692,1072.34 551.388,1074.07 552.085,1075.8 552.782,1077.52 553.479,1079.23 554.176,1080.95 \n  554.873,1082.66 555.57,1084.36 556.266,1086.07 556.963,1087.77 557.66,1089.46 558.357,1091.15 559.054,1092.84 559.751,1094.53 560.448,1096.21 561.144,1097.89 \n  561.841,1099.56 562.538,1101.23 563.235,1102.9 563.932,1104.56 564.629,1106.22 565.326,1107.88 566.022,1109.53 566.719,1111.18 567.416,1112.82 568.113,1114.46 \n  568.81,1116.1 569.507,1117.73 570.204,1119.36 570.9,1120.99 571.597,1122.61 572.294,1124.23 572.991,1125.84 573.688,1127.45 574.385,1129.06 575.082,1130.66 \n  575.778,1132.26 576.475,1133.86 577.172,1135.45 577.869,1137.04 578.566,1138.62 579.263,1140.2 579.959,1141.78 580.656,1143.35 581.353,1144.92 582.05,1146.49 \n  582.747,1148.05 583.444,1149.6 584.141,1151.16 584.837,1152.71 585.534,1154.25 586.231,1155.79 586.928,1157.33 587.625,1158.87 588.322,1160.4 589.019,1161.92 \n  589.715,1163.44 590.412,1164.96 591.109,1166.47 591.806,1167.98 592.503,1169.49 593.2,1170.99 593.897,1172.49 594.593,1173.99 595.29,1175.48 595.987,1176.96 \n  596.684,1178.44 597.381,1179.92 598.078,1181.4 598.775,1182.87 599.471,1184.33 600.168,1185.79 600.865,1187.25 601.562,1188.71 602.259,1190.16 602.956,1191.6 \n  603.653,1193.04 604.349,1194.48 605.046,1195.92 605.743,1197.35 606.44,1198.77 607.137,1200.19 607.834,1201.61 608.53,1203.02 609.227,1204.43 609.924,1205.84 \n  610.621,1207.24 611.318,1208.64 612.015,1210.03 612.712,1211.42 613.408,1212.8 614.105,1214.18 614.802,1215.56 615.499,1216.93 616.196,1218.3 616.893,1219.66 \n  617.59,1221.02 618.286,1222.38 618.983,1223.73 619.68,1225.08 620.377,1226.42 621.074,1227.76 621.771,1229.1 622.468,1230.43 623.164,1231.75 623.861,1233.08 \n  624.558,1234.39 625.255,1235.71 625.952,1237.02 626.649,1238.32 627.346,1239.63 628.042,1240.92 628.739,1242.22 629.436,1243.51 630.133,1244.79 630.83,1246.07 \n  631.527,1247.35 632.224,1248.62 632.92,1249.89 633.617,1251.15 634.314,1252.41 635.011,1253.67 635.708,1254.92 636.405,1256.16 637.102,1257.41 637.798,1258.65 \n  638.495,1259.88 639.192,1261.11 639.889,1262.34 640.586,1263.56 641.283,1264.77 641.979,1265.99 642.676,1267.2 643.373,1268.4 644.07,1269.6 644.767,1270.8 \n  645.464,1271.99 646.161,1273.18 646.857,1274.36 647.554,1275.54 648.251,1276.71 648.948,1277.88 649.645,1279.05 650.342,1280.21 651.039,1281.37 651.735,1282.52 \n  652.432,1283.67 653.129,1284.82 653.826,1285.96 654.523,1287.09 655.22,1288.23 655.917,1289.35 656.613,1290.48 657.31,1291.6 658.007,1292.71 658.704,1293.82 \n  659.401,1294.93 660.098,1296.03 660.795,1297.13 661.491,1298.22 662.188,1299.31 662.885,1300.4 663.582,1301.48 664.279,1302.55 664.976,1303.63 665.673,1304.69 \n  666.369,1305.76 667.066,1306.82 667.763,1307.87 668.46,1308.92 669.157,1309.97 669.854,1311.01 670.55,1312.05 671.247,1313.08 671.944,1314.11 672.641,1315.14 \n  673.338,1316.16 674.035,1317.17 674.732,1318.19 675.428,1319.19 676.125,1320.2 676.822,1321.2 677.519,1322.19 678.216,1323.18 678.913,1324.17 679.61,1325.15 \n  680.306,1326.13 681.003,1327.1 681.7,1328.07 682.397,1329.04 683.094,1330 683.791,1330.96 684.488,1331.91 685.184,1332.86 685.881,1333.8 686.578,1334.74 \n  687.275,1335.67 687.972,1336.61 688.669,1337.53 689.366,1338.45 690.062,1339.37 690.759,1340.29 691.456,1341.2 692.153,1342.1 692.85,1343 693.547,1343.9 \n  694.244,1344.79 694.94,1345.68 695.637,1346.56 696.334,1347.44 697.031,1348.32 697.728,1349.19 698.425,1350.06 699.122,1350.92 699.818,1351.78 700.515,1352.64 \n  701.212,1353.49 701.909,1354.33 702.606,1355.17 703.303,1356.01 703.999,1356.84 704.696,1357.67 705.393,1358.5 706.09,1359.32 706.787,1360.14 707.484,1360.95 \n  708.181,1361.76 708.877,1362.56 709.574,1363.36 710.271,1364.15 710.968,1364.95 711.665,1365.73 712.362,1366.52 713.059,1367.29 713.755,1368.07 714.452,1368.84 \n  715.149,1369.6 715.846,1370.37 716.543,1371.12 717.24,1371.88 717.937,1372.63 718.633,1373.37 719.33,1374.11 720.027,1374.85 720.724,1375.58 721.421,1376.31 \n  722.118,1377.04 722.815,1377.76 723.511,1378.47 724.208,1379.18 724.905,1379.89 725.602,1380.6 726.299,1381.3 726.996,1381.99 727.693,1382.68 728.389,1383.37 \n  729.086,1384.05 729.783,1384.73 730.48,1385.41 731.177,1386.08 731.874,1386.74 732.57,1387.41 733.267,1388.07 733.964,1388.72 734.661,1389.37 735.358,1390.02 \n  736.055,1390.66 736.752,1391.3 737.448,1391.93 738.145,1392.56 738.842,1393.19 739.539,1393.81 740.236,1394.43 740.933,1395.04 741.63,1395.65 742.326,1396.26 \n  743.023,1396.86 743.72,1397.46 744.417,1398.05 745.114,1398.64 745.811,1399.23 746.508,1399.81 747.204,1400.38 747.901,1400.96 748.598,1401.53 749.295,1402.09 \n  749.992,1402.66 750.689,1403.21 751.386,1403.77 752.082,1404.32 752.779,1404.86 753.476,1405.4 754.173,1405.94 754.87,1406.48 755.567,1407.01 756.264,1407.53 \n  756.96,1408.05 757.657,1408.57 758.354,1409.09 759.051,1409.6 759.748,1410.1 760.445,1410.61 761.142,1411.11 761.838,1411.6 762.535,1412.09 763.232,1412.58 \n  763.929,1413.06 764.626,1413.54 765.323,1414.02 766.019,1414.49 766.716,1414.96 767.413,1415.42 768.11,1415.88 768.807,1416.34 769.504,1416.79 770.201,1417.24 \n  770.897,1417.68 771.594,1418.12 772.291,1418.56 772.988,1419 773.685,1419.42 774.382,1419.85 775.079,1420.27 775.775,1420.69 776.472,1421.11 777.169,1421.52 \n  777.866,1421.92 778.563,1422.33 779.26,1422.73 779.957,1423.12 780.653,1423.51 781.35,1423.9 782.047,1424.29 782.744,1424.67 783.441,1425.05 784.138,1425.42 \n  784.835,1425.79 785.531,1426.16 786.228,1426.52 786.925,1426.88 787.622,1427.23 788.319,1427.58 789.016,1427.93 789.713,1428.28 790.409,1428.62 791.106,1428.95 \n  791.803,1429.29 792.5,1429.62 793.197,1429.94 793.894,1430.27 794.59,1430.59 795.287,1430.9 795.984,1431.21 796.681,1431.52 797.378,1431.83 798.075,1432.13 \n  798.772,1432.43 799.468,1432.72 800.165,1433.01 800.862,1433.3 801.559,1433.58 802.256,1433.86 802.953,1434.14 803.65,1434.41 804.346,1434.68 805.043,1434.95 \n  805.74,1435.21 806.437,1435.47 807.134,1435.72 807.831,1435.98 808.528,1436.23 809.224,1436.47 809.921,1436.71 810.618,1436.95 811.315,1437.19 812.012,1437.42 \n  812.709,1437.65 813.406,1437.87 814.102,1438.09 814.799,1438.31 815.496,1438.53 816.193,1438.74 816.89,1438.95 817.587,1439.15 818.284,1439.35 818.98,1439.55 \n  819.677,1439.75 820.374,1439.94 821.071,1440.13 821.768,1440.31 822.465,1440.49 823.162,1440.67 823.858,1440.85 824.555,1441.02 825.252,1441.19 825.949,1441.35 \n  826.646,1441.52 827.343,1441.67 828.039,1441.83 828.736,1441.98 829.433,1442.13 830.13,1442.28 830.827,1442.42 831.524,1442.56 832.221,1442.7 832.917,1442.83 \n  833.614,1442.96 834.311,1443.09 835.008,1443.21 835.705,1443.33 836.402,1443.45 837.099,1443.57 837.795,1443.68 838.492,1443.79 839.189,1443.89 839.886,1444 \n  840.583,1444.09 841.28,1444.19 841.977,1444.28 842.673,1444.37 843.37,1444.46 844.067,1444.55 844.764,1444.63 845.461,1444.71 846.158,1444.78 846.855,1444.85 \n  847.551,1444.92 848.248,1444.99 848.945,1445.05 849.642,1445.11 850.339,1445.17 851.036,1445.22 851.733,1445.28 852.429,1445.32 853.126,1445.37 853.823,1445.41 \n  854.52,1445.45 855.217,1445.49 855.914,1445.52 856.61,1445.56 857.307,1445.58 858.004,1445.61 858.701,1445.63 859.398,1445.65 860.095,1445.67 860.792,1445.69 \n  861.488,1445.7 862.185,1445.71 862.882,1445.71 863.579,1445.72 864.276,1445.72 864.973,1445.71 865.67,1445.71 866.366,1445.7 867.063,1445.69 867.76,1445.68 \n  868.457,1445.66 869.154,1445.64 869.851,1445.62 870.548,1445.6 871.244,1445.57 871.941,1445.54 872.638,1445.51 873.335,1445.48 874.032,1445.44 874.729,1445.4 \n  875.426,1445.36 876.122,1445.31 876.819,1445.27 877.516,1445.22 878.213,1445.16 878.91,1445.11 879.607,1445.05 880.304,1444.99 881,1444.93 881.697,1444.86 \n  882.394,1444.79 883.091,1444.72 883.788,1444.65 884.485,1444.58 885.182,1444.5 885.878,1444.42 886.575,1444.34 887.272,1444.25 887.969,1444.16 888.666,1444.07 \n  889.363,1443.98 890.059,1443.89 890.756,1443.79 891.453,1443.69 892.15,1443.59 892.847,1443.48 893.544,1443.38 894.241,1443.27 894.937,1443.16 895.634,1443.04 \n  896.331,1442.93 897.028,1442.81 897.725,1442.69 898.422,1442.57 899.119,1442.44 899.815,1442.31 900.512,1442.18 901.209,1442.05 901.906,1441.92 902.603,1441.78 \n  903.3,1441.64 903.997,1441.5 904.693,1441.36 905.39,1441.21 906.087,1441.07 906.784,1440.92 907.481,1440.77 908.178,1440.61 908.875,1440.46 909.571,1440.3 \n  910.268,1440.14 910.965,1439.98 911.662,1439.81 912.359,1439.64 913.056,1439.48 913.753,1439.31 914.449,1439.13 915.146,1438.96 915.843,1438.78 916.54,1438.6 \n  917.237,1438.42 917.934,1438.24 918.63,1438.05 919.327,1437.87 920.024,1437.68 920.721,1437.49 921.418,1437.29 922.115,1437.1 922.812,1436.9 923.508,1436.7 \n  924.205,1436.5 924.902,1436.3 925.599,1436.1 926.296,1435.89 926.993,1435.68 927.69,1435.47 928.386,1435.26 929.083,1435.05 929.78,1434.83 930.477,1434.61 \n  931.174,1434.39 931.871,1434.17 932.568,1433.95 933.264,1433.73 933.961,1433.5 934.658,1433.27 935.355,1433.04 936.052,1432.81 936.749,1432.58 937.446,1432.34 \n  938.142,1432.1 938.839,1431.87 939.536,1431.63 940.233,1431.38 940.93,1431.14 941.627,1430.89 942.324,1430.65 943.02,1430.4 943.717,1430.15 944.414,1429.9 \n  945.111,1429.64 945.808,1429.39 946.505,1429.13 947.202,1428.87 947.898,1428.61 948.595,1428.35 949.292,1428.09 949.989,1427.82 950.686,1427.56 951.383,1427.29 \n  952.079,1427.02 952.776,1426.75 953.473,1426.48 954.17,1426.2 954.867,1425.93 955.564,1425.65 956.261,1425.37 956.957,1425.09 957.654,1424.81 958.351,1424.53 \n  959.048,1424.24 959.745,1423.96 960.442,1423.67 961.139,1423.38 961.835,1423.09 962.532,1422.8 963.229,1422.51 963.926,1422.22 964.623,1421.92 965.32,1421.63 \n  966.017,1421.33 966.713,1421.03 967.41,1420.73 968.107,1420.43 968.804,1420.12 969.501,1419.82 970.198,1419.51 970.895,1419.21 971.591,1418.9 972.288,1418.59 \n  972.985,1418.28 973.682,1417.97 974.379,1417.65 975.076,1417.34 975.773,1417.03 976.469,1416.71 977.166,1416.39 977.863,1416.07 978.56,1415.75 979.257,1415.43 \n  979.954,1415.11 980.65,1414.79 981.347,1414.46 982.044,1414.14 982.741,1413.81 983.438,1413.48 984.135,1413.15 984.832,1412.82 985.528,1412.49 986.225,1412.16 \n  986.922,1411.83 987.619,1411.49 988.316,1411.16 989.013,1410.82 989.71,1410.48 990.406,1410.14 991.103,1409.81 991.8,1409.47 992.497,1409.12 993.194,1408.78 \n  993.891,1408.44 994.588,1408.1 995.284,1407.75 995.981,1407.41 996.678,1407.06 997.375,1406.71 998.072,1406.36 998.769,1406.01 999.466,1405.66 1000.16,1405.31 \n  1000.86,1404.96 1001.56,1404.61 1002.25,1404.25 1002.95,1403.9 1003.65,1403.54 1004.34,1403.19 1005.04,1402.83 1005.74,1402.47 1006.43,1402.12 1007.13,1401.76 \n  1007.83,1401.4 1008.52,1401.04 1009.22,1400.67 1009.92,1400.31 1010.62,1399.95 1011.31,1399.59 1012.01,1399.22 1012.71,1398.86 1013.4,1398.49 1014.1,1398.12 \n  1014.8,1397.76 1015.49,1397.39 1016.19,1397.02 1016.89,1396.65 1017.58,1396.28 1018.28,1395.91 1018.98,1395.54 1019.67,1395.17 1020.37,1394.8 1021.07,1394.43 \n  1021.76,1394.05 1022.46,1393.68 1023.16,1393.3 1023.86,1392.93 1024.55,1392.55 1025.25,1392.18 1025.95,1391.8 1026.64,1391.43 1027.34,1391.05 1028.04,1390.67 \n  1028.73,1390.29 1029.43,1389.91 1030.13,1389.53 1030.82,1389.15 1031.52,1388.77 1032.22,1388.39 1032.91,1388.01 1033.61,1387.63 1034.31,1387.25 1035.01,1386.86 \n  1035.7,1386.48 1036.4,1386.1 1037.1,1385.71 1037.79,1385.33 1038.49,1384.95 1039.19,1384.56 1039.88,1384.18 1040.58,1383.79 1041.28,1383.41 1041.97,1383.02 \n  1042.67,1382.63 1043.37,1382.25 1044.06,1381.86 1044.76,1381.47 1045.46,1381.08 1046.15,1380.7 1046.85,1380.31 1047.55,1379.92 1048.25,1379.53 1048.94,1379.14 \n  1049.64,1378.75 1050.34,1378.36 1051.03,1377.97 1051.73,1377.58 1052.43,1377.19 1053.12,1376.8 1053.82,1376.41 1054.52,1376.02 1055.21,1375.63 1055.91,1375.24 \n  1056.61,1374.85 1057.3,1374.46 1058,1374.07 1058.7,1373.68 1059.39,1373.28 1060.09,1372.89 1060.79,1372.5 1061.49,1372.11 1062.18,1371.72 1062.88,1371.32 \n  1063.58,1370.93 1064.27,1370.54 1064.97,1370.15 1065.67,1369.75 1066.36,1369.36 1067.06,1368.97 1067.76,1368.58 1068.45,1368.18 1069.15,1367.79 1069.85,1367.4 \n  1070.54,1367.01 1071.24,1366.61 1071.94,1366.22 1072.64,1365.83 1073.33,1365.44 1074.03,1365.04 1074.73,1364.65 1075.42,1364.26 1076.12,1363.87 1076.82,1363.47 \n  1077.51,1363.08 1078.21,1362.69 1078.91,1362.3 1079.6,1361.91 1080.3,1361.51 1081,1361.12 1081.69,1360.73 1082.39,1360.34 1083.09,1359.95 1083.78,1359.56 \n  1084.48,1359.17 1085.18,1358.78 1085.88,1358.38 1086.57,1357.99 1087.27,1357.6 1087.97,1357.21 1088.66,1356.82 1089.36,1356.43 1090.06,1356.04 1090.75,1355.65 \n  1091.45,1355.27 1092.15,1354.88 1092.84,1354.49 1093.54,1354.1 1094.24,1353.71 1094.93,1353.32 1095.63,1352.94 1096.33,1352.55 1097.03,1352.16 1097.72,1351.77 \n  1098.42,1351.39 1099.12,1351 1099.81,1350.61 1100.51,1350.23 1101.21,1349.84 1101.9,1349.46 1102.6,1349.07 1103.3,1348.69 1103.99,1348.3 1104.69,1347.92 \n  1105.39,1347.54 1106.08,1347.15 1106.78,1346.77 1107.48,1346.39 1108.17,1346.01 1108.87,1345.62 1109.57,1345.24 1110.27,1344.86 1110.96,1344.48 1111.66,1344.1 \n  1112.36,1343.72 1113.05,1343.34 1113.75,1342.96 1114.45,1342.58 1115.14,1342.21 1115.84,1341.83 1116.54,1341.45 1117.23,1341.08 1117.93,1340.7 1118.63,1340.32 \n  1119.32,1339.95 1120.02,1339.57 1120.72,1339.2 1121.41,1338.83 1122.11,1338.45 1122.81,1338.08 1123.51,1337.71 1124.2,1337.33 1124.9,1336.96 1125.6,1336.59 \n  1126.29,1336.22 1126.99,1335.85 1127.69,1335.48 1128.38,1335.11 1129.08,1334.75 1129.78,1334.38 1130.47,1334.01 1131.17,1333.64 1131.87,1333.28 1132.56,1332.91 \n  1133.26,1332.55 1133.96,1332.18 1134.66,1331.82 1135.35,1331.46 1136.05,1331.1 1136.75,1330.73 1137.44,1330.37 1138.14,1330.01 1138.84,1329.65 1139.53,1329.29 \n  1140.23,1328.93 1140.93,1328.58 1141.62,1328.22 1142.32,1327.86 1143.02,1327.51 1143.71,1327.15 1144.41,1326.79 1145.11,1326.44 1145.8,1326.09 1146.5,1325.73 \n  1147.2,1325.38 1147.9,1325.03 1148.59,1324.68 1149.29,1324.33 1149.99,1323.98 1150.68,1323.63 1151.38,1323.28 1152.08,1322.94 1152.77,1322.59 1153.47,1322.24 \n  1154.17,1321.9 1154.86,1321.55 1155.56,1321.21 1156.26,1320.87 1156.95,1320.53 1157.65,1320.18 1158.35,1319.84 1159.05,1319.5 1159.74,1319.16 1160.44,1318.83 \n  1161.14,1318.49 1161.83,1318.15 1162.53,1317.81 1163.23,1317.48 1163.92,1317.14 1164.62,1316.81 1165.32,1316.48 1166.01,1316.15 1166.71,1315.81 1167.41,1315.48 \n  1168.1,1315.15 1168.8,1314.82 1169.5,1314.5 1170.19,1314.17 1170.89,1313.84 1171.59,1313.52 1172.29,1313.19 1172.98,1312.87 1173.68,1312.54 1174.38,1312.22 \n  1175.07,1311.9 1175.77,1311.58 1176.47,1311.26 1177.16,1310.94 1177.86,1310.62 1178.56,1310.31 1179.25,1309.99 1179.95,1309.67 1180.65,1309.36 1181.34,1309.04 \n  1182.04,1308.73 1182.74,1308.42 1183.43,1308.11 1184.13,1307.8 1184.83,1307.49 1185.53,1307.18 1186.22,1306.87 1186.92,1306.57 1187.62,1306.26 1188.31,1305.95 \n  1189.01,1305.65 1189.71,1305.35 1190.4,1305.05 1191.1,1304.74 1191.8,1304.44 1192.49,1304.14 1193.19,1303.85 1193.89,1303.55 1194.58,1303.25 1195.28,1302.96 \n  1195.98,1302.66 1196.68,1302.37 1197.37,1302.07 1198.07,1301.78 1198.77,1301.49 1199.46,1301.2 1200.16,1300.91 1200.86,1300.62 1201.55,1300.34 1202.25,1300.05 \n  1202.95,1299.76 1203.64,1299.48 1204.34,1299.2 1205.04,1298.91 1205.73,1298.63 1206.43,1298.35 1207.13,1298.07 1207.82,1297.79 1208.52,1297.52 1209.22,1297.24 \n  1209.92,1296.96 1210.61,1296.69 1211.31,1296.42 1212.01,1296.14 1212.7,1295.87 1213.4,1295.6 1214.1,1295.33 1214.79,1295.06 1215.49,1294.79 1216.19,1294.53 \n  1216.88,1294.26 1217.58,1294 1218.28,1293.73 1218.97,1293.47 1219.67,1293.21 1220.37,1292.95 1221.07,1292.69 1221.76,1292.43 1222.46,1292.17 1223.16,1291.91 \n  1223.85,1291.66 1224.55,1291.4 1225.25,1291.15 1225.94,1290.9 1226.64,1290.65 1227.34,1290.4 1228.03,1290.15 1228.73,1289.9 1229.43,1289.65 1230.12,1289.4 \n  1230.82,1289.16 1231.52,1288.91 1232.21,1288.67 1232.91,1288.43 1233.61,1288.19 1234.31,1287.95 1235,1287.71 1235.7,1287.47 1236.4,1287.23 1237.09,1287 \n  1237.79,1286.76 1238.49,1286.53 1239.18,1286.29 1239.88,1286.06 1240.58,1285.83 1241.27,1285.6 1241.97,1285.37 1242.67,1285.14 1243.36,1284.92 1244.06,1284.69 \n  1244.76,1284.47 1245.45,1284.24 1246.15,1284.02 1246.85,1283.8 1247.55,1283.58 1248.24,1283.36 1248.94,1283.14 1249.64,1282.92 1250.33,1282.71 1251.03,1282.49 \n  1251.73,1282.28 1252.42,1282.06 1253.12,1281.85 1253.82,1281.64 1254.51,1281.43 1255.21,1281.22 1255.91,1281.01 1256.6,1280.81 1257.3,1280.6 1258,1280.4 \n  1258.7,1280.19 1259.39,1279.99 1260.09,1279.79 1260.79,1279.59 1261.48,1279.39 1262.18,1279.19 1262.88,1278.99 1263.57,1278.8 1264.27,1278.6 1264.97,1278.41 \n  1265.66,1278.21 1266.36,1278.02 1267.06,1277.83 1267.75,1277.64 1268.45,1277.45 1269.15,1277.26 1269.84,1277.07 1270.54,1276.89 1271.24,1276.7 1271.94,1276.52 \n  1272.63,1276.34 1273.33,1276.16 1274.03,1275.97 1274.72,1275.79 1275.42,1275.62 1276.12,1275.44 1276.81,1275.26 1277.51,1275.09 1278.21,1274.91 1278.9,1274.74 \n  1279.6,1274.57 1280.3,1274.39 1280.99,1274.22 1281.69,1274.06 1282.39,1273.89 1283.09,1273.72 1283.78,1273.55 1284.48,1273.39 1285.18,1273.22 1285.87,1273.06 \n  1286.57,1272.9 1287.27,1272.74 1287.96,1272.58 1288.66,1272.42 1289.36,1272.26 1290.05,1272.1 1290.75,1271.95 1291.45,1271.79 1292.14,1271.64 1292.84,1271.49 \n  1293.54,1271.33 1294.23,1271.18 1294.93,1271.03 1295.63,1270.89 1296.33,1270.74 1297.02,1270.59 1297.72,1270.44 1298.42,1270.3 1299.11,1270.16 1299.81,1270.01 \n  1300.51,1269.87 1301.2,1269.73 1301.9,1269.59 1302.6,1269.45 1303.29,1269.31 1303.99,1269.18 1304.69,1269.04 1305.38,1268.91 1306.08,1268.77 1306.78,1268.64 \n  1307.47,1268.51 1308.17,1268.38 1308.87,1268.25 1309.57,1268.12 1310.26,1267.99 1310.96,1267.86 1311.66,1267.74 1312.35,1267.61 1313.05,1267.49 1313.75,1267.36 \n  1314.44,1267.24 1315.14,1267.12 1315.84,1267 1316.53,1266.88 1317.23,1266.76 1317.93,1266.65 1318.62,1266.53 1319.32,1266.41 1320.02,1266.3 1320.72,1266.19 \n  1321.41,1266.07 1322.11,1265.96 1322.81,1265.85 1323.5,1265.74 1324.2,1265.63 1324.9,1265.52 1325.59,1265.42 1326.29,1265.31 1326.99,1265.2 1327.68,1265.1 \n  1328.38,1265 1329.08,1264.89 1329.77,1264.79 1330.47,1264.69 1331.17,1264.59 1331.86,1264.49 1332.56,1264.39 1333.26,1264.3 1333.96,1264.2 1334.65,1264.11 \n  1335.35,1264.01 1336.05,1263.92 1336.74,1263.83 1337.44,1263.73 1338.14,1263.64 1338.83,1263.55 1339.53,1263.46 1340.23,1263.38 1340.92,1263.29 1341.62,1263.2 \n  1342.32,1263.12 1343.01,1263.03 1343.71,1262.95 1344.41,1262.86 1345.11,1262.78 1345.8,1262.7 1346.5,1262.62 1347.2,1262.54 1347.89,1262.46 1348.59,1262.38 \n  1349.29,1262.3 1349.98,1262.23 1350.68,1262.15 1351.38,1262.08 1352.07,1262 1352.77,1261.93 1353.47,1261.86 1354.16,1261.79 1354.86,1261.71 1355.56,1261.64 \n  1356.25,1261.58 1356.95,1261.51 1357.65,1261.44 1358.35,1261.37 1359.04,1261.31 1359.74,1261.24 1360.44,1261.18 1361.13,1261.11 1361.83,1261.05 1362.53,1260.99 \n  1363.22,1260.93 1363.92,1260.86 1364.62,1260.8 1365.31,1260.75 1366.01,1260.69 1366.71,1260.63 1367.4,1260.57 1368.1,1260.52 1368.8,1260.46 1369.49,1260.41 \n  1370.19,1260.35 1370.89,1260.3 1371.59,1260.24 1372.28,1260.19 1372.98,1260.14 1373.68,1260.09 1374.37,1260.04 1375.07,1259.99 1375.77,1259.94 1376.46,1259.9 \n  1377.16,1259.85 1377.86,1259.8 1378.55,1259.76 1379.25,1259.71 1379.95,1259.67 1380.64,1259.62 1381.34,1259.58 1382.04,1259.54 1382.74,1259.49 1383.43,1259.45 \n  1384.13,1259.41 1384.83,1259.37 1385.52,1259.33 1386.22,1259.29 1386.92,1259.26 1387.61,1259.22 1388.31,1259.18 1389.01,1259.15 1389.7,1259.11 1390.4,1259.08 \n  1391.1,1259.04 1391.79,1259.01 1392.49,1258.97 1393.19,1258.94 1393.88,1258.91 1394.58,1258.88 1395.28,1258.85 1395.98,1258.82 1396.67,1258.79 1397.37,1258.76 \n  1398.07,1258.73 1398.76,1258.7 1399.46,1258.67 1400.16,1258.65 1400.85,1258.62 1401.55,1258.59 1402.25,1258.57 1402.94,1258.54 1403.64,1258.52 1404.34,1258.49 \n  1405.03,1258.47 1405.73,1258.45 1406.43,1258.43 1407.13,1258.4 1407.82,1258.38 1408.52,1258.36 1409.22,1258.34 1409.91,1258.32 1410.61,1258.3 1411.31,1258.28 \n  1412,1258.26 1412.7,1258.24 1413.4,1258.23 1414.09,1258.21 1414.79,1258.19 1415.49,1258.18 1416.18,1258.16 1416.88,1258.14 1417.58,1258.13 1418.27,1258.12 \n  1418.97,1258.1 1419.67,1258.09 1420.37,1258.07 1421.06,1258.06 1421.76,1258.05 1422.46,1258.04 1423.15,1258.02 1423.85,1258.01 1424.55,1258 1425.24,1257.99 \n  1425.94,1257.98 1426.64,1257.97 1427.33,1257.96 1428.03,1257.95 1428.73,1257.94 1429.42,1257.93 1430.12,1257.92 1430.82,1257.92 1431.51,1257.91 1432.21,1257.9 \n  1432.91,1257.89 1433.61,1257.89 1434.3,1257.88 1435,1257.87 1435.7,1257.87 1436.39,1257.86 1437.09,1257.86 1437.79,1257.85 1438.48,1257.85 1439.18,1257.84 \n  1439.88,1257.84 1440.57,1257.84 1441.27,1257.83 1441.97,1257.83 1442.66,1257.83 1443.36,1257.82 1444.06,1257.82 1444.76,1257.82 1445.45,1257.81 1446.15,1257.81 \n  1446.85,1257.81 1447.54,1257.81 1448.24,1257.81 1448.94,1257.81 1449.63,1257.8 1450.33,1257.8 1451.03,1257.8 1451.72,1257.8 1452.42,1257.8 1453.12,1257.8 \n  1453.81,1257.8 1454.51,1257.8 1455.21,1257.8 1455.9,1257.8 1456.6,1257.8 1457.3,1257.8 1458,1257.8 1458.69,1257.81 1459.39,1257.81 1460.09,1257.81 \n  1460.78,1257.81 1461.48,1257.81 1462.18,1257.81 1462.87,1257.81 1463.57,1257.81 1464.27,1257.82 1464.96,1257.82 1465.66,1257.82 1466.36,1257.82 1467.05,1257.82 \n  1467.75,1257.83 1468.45,1257.83 1469.15,1257.83 1469.84,1257.83 1470.54,1257.83 1471.24,1257.84 1471.93,1257.84 1472.63,1257.84 1473.33,1257.84 1474.02,1257.84 \n  1474.72,1257.85 1475.42,1257.85 1476.11,1257.85 1476.81,1257.85 1477.51,1257.86 1478.2,1257.86 1478.9,1257.86 1479.6,1257.86 1480.29,1257.86 1480.99,1257.86 \n  1481.69,1257.87 1482.39,1257.87 1483.08,1257.87 1483.78,1257.87 1484.48,1257.87 1485.17,1257.87 1485.87,1257.88 1486.57,1257.88 1487.26,1257.88 1487.96,1257.88 \n  1488.66,1257.88 1489.35,1257.88 1490.05,1257.88 1490.75,1257.88 1491.44,1257.88 1492.14,1257.88 1492.84,1257.88 1493.53,1257.88 1494.23,1257.88 1494.93,1257.88 \n  1495.63,1257.88 1496.32,1257.88 1497.02,1257.88 1497.72,1257.88 1498.41,1257.88 1499.11,1257.88 1499.81,1257.88 1500.5,1257.87 1501.2,1257.87 1501.9,1257.87 \n  1502.59,1257.87 1503.29,1257.87 1503.99,1257.86 1504.68,1257.86 1505.38,1257.86 1506.08,1257.85 1506.78,1257.85 1507.47,1257.84 1508.17,1257.84 1508.87,1257.84 \n  1509.56,1257.83 1510.26,1257.83 1510.96,1257.82 1511.65,1257.81 1512.35,1257.81 1513.05,1257.8 1513.74,1257.8 1514.44,1257.79 1515.14,1257.78 1515.83,1257.77 \n  1516.53,1257.77 1517.23,1257.76 1517.92,1257.75 1518.62,1257.74 1519.32,1257.73 1520.02,1257.72 1520.71,1257.71 1521.41,1257.7 1522.11,1257.69 1522.8,1257.68 \n  1523.5,1257.67 1524.2,1257.65 1524.89,1257.64 1525.59,1257.63 1526.29,1257.62 1526.98,1257.6 1527.68,1257.59 1528.38,1257.57 1529.07,1257.56 1529.77,1257.54 \n  1530.47,1257.53 1531.17,1257.51 1531.86,1257.5 1532.56,1257.48 1533.26,1257.46 1533.95,1257.44 1534.65,1257.43 1535.35,1257.41 1536.04,1257.39 1536.74,1257.37 \n  1537.44,1257.35 1538.13,1257.33 1538.83,1257.31 1539.53,1257.28 1540.22,1257.26 1540.92,1257.24 1541.62,1257.21 1542.31,1257.19 1543.01,1257.17 1543.71,1257.14 \n  1544.41,1257.12 1545.1,1257.09 1545.8,1257.06 1546.5,1257.04 1547.19,1257.01 1547.89,1256.98 1548.59,1256.95 1549.28,1256.92 1549.98,1256.89 1550.68,1256.86 \n  1551.37,1256.83 1552.07,1256.8 1552.77,1256.77 1553.46,1256.73 1554.16,1256.7 1554.86,1256.67 1555.55,1256.63 1556.25,1256.6 1556.95,1256.56 1557.65,1256.52 \n  1558.34,1256.49 1559.04,1256.45 1559.74,1256.41 1560.43,1256.37 1561.13,1256.33 1561.83,1256.29 1562.52,1256.25 1563.22,1256.21 1563.92,1256.17 1564.61,1256.12 \n  1565.31,1256.08 1566.01,1256.03 1566.7,1255.99 1567.4,1255.94 1568.1,1255.9 1568.8,1255.85 1569.49,1255.8 1570.19,1255.75 1570.89,1255.7 1571.58,1255.65 \n  1572.28,1255.6 1572.98,1255.55 1573.67,1255.5 1574.37,1255.44 1575.07,1255.39 1575.76,1255.33 1576.46,1255.28 1577.16,1255.22 1577.85,1255.16 1578.55,1255.11 \n  1579.25,1255.05 1579.94,1254.99 1580.64,1254.93 1581.34,1254.87 1582.04,1254.81 1582.73,1254.74 1583.43,1254.68 1584.13,1254.62 1584.82,1254.55 1585.52,1254.49 \n  1586.22,1254.42 1586.91,1254.35 1587.61,1254.28 1588.31,1254.21 1589,1254.14 1589.7,1254.07 1590.4,1254 1591.09,1253.93 1591.79,1253.85 1592.49,1253.78 \n  1593.19,1253.71 1593.88,1253.63 1594.58,1253.55 1595.28,1253.47 1595.97,1253.4 1596.67,1253.32 1597.37,1253.24 1598.06,1253.15 1598.76,1253.07 1599.46,1252.99 \n  1600.15,1252.91 1600.85,1252.82 1601.55,1252.73 1602.24,1252.65 1602.94,1252.56 1603.64,1252.47 1604.33,1252.38 1605.03,1252.29 1605.73,1252.2 1606.43,1252.11 \n  1607.12,1252.01 1607.82,1251.92 1608.52,1251.82 1609.21,1251.73 1609.91,1251.63 1610.61,1251.53 1611.3,1251.43 1612,1251.33 1612.7,1251.23 1613.39,1251.13 \n  1614.09,1251.03 1614.79,1250.92 1615.48,1250.82 1616.18,1250.71 1616.88,1250.6 1617.57,1250.5 1618.27,1250.39 1618.97,1250.28 1619.67,1250.17 1620.36,1250.05 \n  1621.06,1249.94 1621.76,1249.83 1622.45,1249.71 1623.15,1249.6 1623.85,1249.48 1624.54,1249.36 1625.24,1249.24 1625.94,1249.12 1626.63,1249 1627.33,1248.88 \n  1628.03,1248.75 1628.72,1248.63 1629.42,1248.5 1630.12,1248.38 1630.82,1248.25 1631.51,1248.12 1632.21,1247.99 1632.91,1247.86 1633.6,1247.73 1634.3,1247.59 \n  1635,1247.46 1635.69,1247.32 1636.39,1247.19 1637.09,1247.05 1637.78,1246.91 1638.48,1246.77 1639.18,1246.63 1639.87,1246.49 1640.57,1246.34 1641.27,1246.2 \n  1641.96,1246.06 1642.66,1245.91 1643.36,1245.76 1644.06,1245.61 1644.75,1245.46 1645.45,1245.31 1646.15,1245.16 1646.84,1245.01 1647.54,1244.85 1648.24,1244.7 \n  1648.93,1244.54 1649.63,1244.38 1650.33,1244.22 1651.02,1244.06 1651.72,1243.9 1652.42,1243.74 1653.11,1243.58 1653.81,1243.41 1654.51,1243.24 1655.21,1243.08 \n  1655.9,1242.91 1656.6,1242.74 1657.3,1242.57 1657.99,1242.4 1658.69,1242.22 1659.39,1242.05 1660.08,1241.88 1660.78,1241.7 1661.48,1241.52 1662.17,1241.34 \n  1662.87,1241.16 1663.57,1240.98 1664.26,1240.8 1664.96,1240.61 1665.66,1240.43 1666.35,1240.24 1667.05,1240.06 1667.75,1239.87 1668.45,1239.68 1669.14,1239.49 \n  1669.84,1239.29 1670.54,1239.1 1671.23,1238.91 1671.93,1238.71 1672.63,1238.51 1673.32,1238.32 1674.02,1238.12 1674.72,1237.92 1675.41,1237.71 1676.11,1237.51 \n  1676.81,1237.31 1677.5,1237.1 1678.2,1236.89 1678.9,1236.68 1679.59,1236.48 1680.29,1236.26 1680.99,1236.05 1681.69,1235.84 1682.38,1235.63 1683.08,1235.41 \n  1683.78,1235.19 1684.47,1234.98 1685.17,1234.76 1685.87,1234.54 1686.56,1234.31 1687.26,1234.09 1687.96,1233.87 1688.65,1233.64 1689.35,1233.41 1690.05,1233.19 \n  1690.74,1232.96 1691.44,1232.73 1692.14,1232.49 1692.84,1232.26 1693.53,1232.03 1694.23,1231.79 1694.93,1231.55 1695.62,1231.32 1696.32,1231.08 1697.02,1230.84 \n  1697.71,1230.59 1698.41,1230.35 1699.11,1230.11 1699.8,1229.86 1700.5,1229.61 1701.2,1229.36 1701.89,1229.11 1702.59,1228.86 1703.29,1228.61 1703.98,1228.36 \n  1704.68,1228.1 1705.38,1227.85 1706.08,1227.59 1706.77,1227.33 1707.47,1227.07 1708.17,1226.81 1708.86,1226.55 1709.56,1226.28 1710.26,1226.02 1710.95,1225.75 \n  1711.65,1225.48 1712.35,1225.22 1713.04,1224.95 1713.74,1224.67 1714.44,1224.4 1715.13,1224.13 1715.83,1223.85 1716.53,1223.58 1717.23,1223.3 1717.92,1223.02 \n  1718.62,1222.74 1719.32,1222.46 1720.01,1222.17 1720.71,1221.89 1721.41,1221.6 1722.1,1221.32 1722.8,1221.03 1723.5,1220.74 1724.19,1220.45 1724.89,1220.16 \n  1725.59,1219.86 1726.28,1219.57 1726.98,1219.27 1727.68,1218.98 1728.37,1218.68 1729.07,1218.38 1729.77,1218.08 1730.47,1217.78 1731.16,1217.47 1731.86,1217.17 \n  1732.56,1216.86 1733.25,1216.56 1733.95,1216.25 1734.65,1215.94 1735.34,1215.63 1736.04,1215.31 1736.74,1215 1737.43,1214.69 1738.13,1214.37 1738.83,1214.05 \n  1739.52,1213.73 1740.22,1213.41 1740.92,1213.09 1741.62,1212.77 1742.31,1212.45 1743.01,1212.12 1743.71,1211.79 1744.4,1211.47 1745.1,1211.14 1745.8,1210.81 \n  1746.49,1210.48 1747.19,1210.14 1747.89,1209.81 1748.58,1209.48 1749.28,1209.14 1749.98,1208.8 1750.67,1208.46 1751.37,1208.12 1752.07,1207.78 1752.76,1207.44 \n  1753.46,1207.1 1754.16,1206.75 1754.86,1206.4 1755.55,1206.06 1756.25,1205.71 1756.95,1205.36 1757.64,1205.01 1758.34,1204.65 1759.04,1204.3 1759.73,1203.95 \n  1760.43,1203.59 1761.13,1203.23 1761.82,1202.87 1762.52,1202.51 1763.22,1202.15 1763.91,1201.79 1764.61,1201.43 1765.31,1201.06 1766,1200.7 1766.7,1200.33 \n  1767.4,1199.96 1768.1,1199.59 1768.79,1199.22 1769.49,1198.85 1770.19,1198.48 1770.88,1198.1 1771.58,1197.73 1772.28,1197.35 1772.97,1196.97 1773.67,1196.59 \n  1774.37,1196.21 1775.06,1195.83 1775.76,1195.45 1776.46,1195.06 1777.15,1194.68 1777.85,1194.29 1778.55,1193.91 1779.25,1193.52 1779.94,1193.13 1780.64,1192.74 \n  1781.34,1192.35 1782.03,1191.95 1782.73,1191.56 1783.43,1191.16 1784.12,1190.77 1784.82,1190.37 1785.52,1189.97 1786.21,1189.57 1786.91,1189.17 1787.61,1188.77 \n  1788.3,1188.36 1789,1187.96 1789.7,1187.55 1790.39,1187.15 1791.09,1186.74 1791.79,1186.33 1792.49,1185.92 1793.18,1185.51 1793.88,1185.1 1794.58,1184.68 \n  1795.27,1184.27 1795.97,1183.85 1796.67,1183.44 1797.36,1183.02 1798.06,1182.6 1798.76,1182.18 1799.45,1181.76 1800.15,1181.34 1800.85,1180.92 1801.54,1180.49 \n  1802.24,1180.07 1802.94,1179.64 1803.64,1179.21 1804.33,1178.79 1805.03,1178.36 1805.73,1177.93 1806.42,1177.49 1807.12,1177.06 1807.82,1176.63 1808.51,1176.19 \n  1809.21,1175.76 1809.91,1175.32 1810.6,1174.88 1811.3,1174.45 1812,1174.01 1812.69,1173.57 1813.39,1173.12 1814.09,1172.68 1814.78,1172.24 1815.48,1171.79 \n  1816.18,1171.35 1816.88,1170.9 1817.57,1170.46 1818.27,1170.01 1818.97,1169.56 1819.66,1169.11 1820.36,1168.66 1821.06,1168.2 1821.75,1167.75 1822.45,1167.3 \n  1823.15,1166.84 1823.84,1166.39 1824.54,1165.93 1825.24,1165.47 1825.93,1165.01 1826.63,1164.55 1827.33,1164.09 1828.02,1163.63 1828.72,1163.17 1829.42,1162.71 \n  1830.12,1162.24 1830.81,1161.78 1831.51,1161.31 1832.21,1160.85 1832.9,1160.38 1833.6,1159.91 1834.3,1159.44 1834.99,1158.97 1835.69,1158.5 1836.39,1158.03 \n  1837.08,1157.56 1837.78,1157.08 1838.48,1156.61 1839.17,1156.13 1839.87,1155.66 1840.57,1155.18 1841.27,1154.7 1841.96,1154.23 1842.66,1153.75 1843.36,1153.27 \n  1844.05,1152.79 1844.75,1152.3 1845.45,1151.82 1846.14,1151.34 1846.84,1150.86 1847.54,1150.37 1848.23,1149.89 1848.93,1149.4 1849.63,1148.91 1850.32,1148.43 \n  1851.02,1147.94 1851.72,1147.45 1852.41,1146.96 1853.11,1146.47 1853.81,1145.98 1854.51,1145.49 1855.2,1145 1855.9,1144.5 1856.6,1144.01 1857.29,1143.52 \n  1857.99,1143.02 1858.69,1142.53 1859.38,1142.03 1860.08,1141.53 1860.78,1141.04 1861.47,1140.54 1862.17,1140.04 1862.87,1139.54 1863.56,1139.04 1864.26,1138.54 \n  1864.96,1138.04 1865.66,1137.54 1866.35,1137.04 1867.05,1136.53 1867.75,1136.03 1868.44,1135.53 1869.14,1135.02 1869.84,1134.52 1870.53,1134.01 1871.23,1133.51 \n  1871.93,1133 1872.62,1132.49 1873.32,1131.98 1874.02,1131.48 1874.71,1130.97 1875.41,1130.46 1876.11,1129.95 1876.8,1129.44 1877.5,1128.93 1878.2,1128.42 \n  1878.9,1127.91 1879.59,1127.39 1880.29,1126.88 1880.99,1126.37 1881.68,1125.86 1882.38,1125.34 1883.08,1124.83 1883.77,1124.31 1884.47,1123.8 1885.17,1123.28 \n  1885.86,1122.77 1886.56,1122.25 1887.26,1121.74 1887.95,1121.22 1888.65,1120.7 1889.35,1120.18 1890.04,1119.67 1890.74,1119.15 1891.44,1118.63 1892.14,1118.11 \n  1892.83,1117.59 1893.53,1117.07 1894.23,1116.55 1894.92,1116.03 1895.62,1115.51 1896.32,1114.99 1897.01,1114.47 1897.71,1113.95 1898.41,1113.43 1899.1,1112.91 \n  1899.8,1112.39 1900.5,1111.87 1901.19,1111.34 1901.89,1110.82 1902.59,1110.3 1903.29,1109.78 1903.98,1109.25 1904.68,1108.73 1905.38,1108.21 1906.07,1107.68 \n  1906.77,1107.16 1907.47,1106.64 1908.16,1106.11 1908.86,1105.59 1909.56,1105.06 1910.25,1104.54 1910.95,1104.02 1911.65,1103.49 1912.34,1102.97 1913.04,1102.44 \n  1913.74,1101.92 1914.43,1101.39 1915.13,1100.87 1915.83,1100.34 1916.53,1099.82 1917.22,1099.29 1917.92,1098.77 1918.62,1098.25 1919.31,1097.72 1920.01,1097.2 \n  1920.71,1096.67 1921.4,1096.15 1922.1,1095.62 1922.8,1095.1 1923.49,1094.57 1924.19,1094.05 1924.89,1093.52 1925.58,1093 1926.28,1092.47 1926.98,1091.95 \n  1927.68,1091.43 1928.37,1090.9 1929.07,1090.38 1929.77,1089.85 1930.46,1089.33 1931.16,1088.81 1931.86,1088.28 1932.55,1087.76 1933.25,1087.24 1933.95,1086.72 \n  1934.64,1086.19 1935.34,1085.67 1936.04,1085.15 1936.73,1084.63 1937.43,1084.11 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ForneyLab.draw(g)"
+    "# ForneyLab.draw(g)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -725,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -797,15 +211,15 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Julia 1.3.0",
+   "display_name": "Julia 1.6.4",
    "language": "julia",
-   "name": "julia-1.3"
+   "name": "julia-1.6"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.3.0"
+   "version": "1.6.4"
   }
  },
  "nbformat": 4,
diff --git a/demo/nonlinear_kalman_filter.ipynb b/demo/nonlinear_kalman_filter.ipynb
index f5df6db2..4b836e5a 100644
--- a/demo/nonlinear_kalman_filter.ipynb
+++ b/demo/nonlinear_kalman_filter.ipynb
@@ -33,8 +33,7 @@
     "# Import libraries to julia workspace\n",
     "using ForneyLab\n",
     "using ProgressMeter\n",
-    "using Plots\n",
-    "pyplot();"
+    "using Plots"
    ]
   },
   {
@@ -95,7 +94,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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      },
      "execution_count": 3,
      "metadata": {},
@@ -201,7 +200,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32mAt time t: 100%|████████████████████████████████████████| Time: 0:00:05\u001b[39m\n"
+      "\u001b[32mAt time t: 100%|████████████████████████████████████████| Time: 0:00:10\u001b[39m\n"
      ]
     }
    ],
@@ -283,7 +282,7 @@
    "outputs": [
     {
      "data": {
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      "execution_count": 7,
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@@ -311,7 +310,7 @@
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      },
      "execution_count": 8,
      "metadata": {},
@@ -339,7 +338,7 @@
    "outputs": [
     {
      "data": {
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@@ -432,7 +431,7 @@
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@@ -648,7 +647,7 @@
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      },
      "execution_count": 16,
      "metadata": {},
@@ -676,7 +675,7 @@
    "outputs": [
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zh9FoBMuyiIyMdDkWHR0NlmXR1NTks1ze8DSHHR1rP4+dHfc//vEPrF69GgEBAVi0aBESEhKg1+vBMAw+/vhjFBcXO537z3/+E7///e/x97//3eH7FRgYiPvvvx+///3vHf4iXSU6OtpteVevZ3fntKGhAYQQlJeXd7pvb/e+JEke2+osnhSSqKgolx8xgP/muLGxEUOGDHF5ofrK6tWroVKp8NZbb+G9997DO++844ht98c//tFrPD+j0eh1LPZQNe6+mz11XXyRKScnB0ajEYGBgQCAe+65B5s2bcIXX3zhULA+//xzxzE73bkPPcnTE/jrGQYAGzdu7AkR+xVUwRoAzJ07F3PnzoXZbMbx48fx3Xff4Z133sEtt9yC8+fP94st0N988w1Onz6NBx980MXhcfPmzfjkk0861V5QUBAYhkFtbW235AoKCoIsy6ipqXF5kVVXV0OWZbcPjr6is+N+8cUXodVqcerUKYwePdrp2ObNm13qGwwGbNy4ERs3bkRhYSH27duHd999F3/+859hNpvx3nvvAYDjpdveGmbHm0J6tUN2V8flL+zXNjU1Fenp6b3at69UV1d7LHenTPhrjkNCQlBZWQlZlrusZK1YsQIrVqyA0WjE0aNH8dVXX+HDDz/EokWLkJOT49Gx2n5dqqqq3B63l/fmd7MrMk2ZMgVTpkzBt99+i+bmZgDAt99+i6SkJKcNI925Dz1d7/6KfaztFdGBCo3kPoDQ6XSYN28e3nzzTTzzzDMwm81OJliO4/z6y7oz5OfnAwBuu+02l2OHDh1yew7Lsh7lnTFjBurq6pCbm9stuexLTO7SQhw4cACAe9N3X9HZcefn52P8+PEuytXly5cd18QTI0aMwP33348DBw4gICAA3377reNYaGgoANdlYODKUkpn8Nf17CyBgYEYP348srKy/Lqs1x6O4wB03Xri7vuRnp4Os9ncqXuzs3M8ffp0WK1Wx/egO9iXn99//33cd999qK6uxvHjx73WHzlyJPLy8tzeY33x3fT2rCgvL0d+fj5GjhzpojTcfffdMJvN2LZtG7Zt2waz2eyyrNkb96Gd7t6P3WXGjBkAriwVDmSognWNc+jQIYfpuj32X1M6nc5RFhYWhtraWlgsll6Tz449FMDhw4edyg8cOOBi0bITFhaGsrIyt8cee+wxAMD999+Puro6l+OVlZXIysrqUK61a9cCADZs2OA0j0aj0WGqt9fpD3R23PHx8cjLy3P61W2xWPDII4+4WJ9qampw4sQJlzYbGhpgtVqd7qVp06YBAD799FPIsuwo//HHH7u0Fd5f17MrPPbYYzCZTPj5z3/udgmmsLAQRUVFXW4/LCwMADzeyx3x2WefITMz0/FZFEU888wzADp3b3Z2jn/5y18CAH796187lnXay+DJkmNnz549bp81dotc+/vJHWvXroUgCPjd734HQoij/MKFC/joo48QHByM5cuXe22jI+bNm+dz3r1ly5YhODgYH330kdP1IITgd7/7HQRBcJvL9a677gLLsvj888/x2WefOXyzrqan70M73b0fu8u6devA8zweffRRlJaWuhxvbGzs0o+0/ghdIuyn5OXleY3kbj/25ptvYteuXZg/fz5GjhwJrVaL06dPY8+ePRg1ahRuv/12xzkLFixAeno6li5dihtuuAFqtRqzZ8/G7Nmze3g0wNKlS5GQkIDXX38dFy5cwKRJk5CTk4Pvv/8ey5cvx7Zt21zOWbBgAbZu3Yo77rgDU6dOBcdxuOWWWzB58mQsXrwYzz//PF5++WWMGjUKixcvRnx8POrq6pCXl4dDhw7hlVdewfjx473KNWfOHDz66KP4y1/+gkmTJmHlypUghOCrr75CaWkpHnvsMcyZM6enpqXTdHbcjz76KB599FFMnToVd9xxB0RRxK5du0AIQVJSEs6ePetou7y8HDNmzMDEiRORkpKCIUOGoK6uDt988w0EQcBTTz3lqJuWlubw/7v++usxZ84cFBcX49tvv8XSpUvxr3/9q0fH5U8eeughHDt2DJ988gmOHDmCm266CXFxcaiqqkJ2djaOHz+OL7/8ssv5JseNG4e4uDhs3rwZer0eQ4cOBcMweOSRR3xKTXXTTTchLS0Na9asQVhYGP7973/jwoULWLRokcd0Wu7o7BwvWbIETzzxBDZt2oTRo0fj9ttvd/h97dmzB0888YRTTL2refzxx1FSUoJ58+YhISEBDMPg8OHDOHHiBGbOnIlZs2Z5lfepp57C9u3b8dlnnyErKws33nijI86eIAj49NNPu73EZP9x4C12lJ2goCB88MEH+OlPf4oZM2Zg9erViIyMxJ49e5Ceno7p06fjySefdDkvLi4OCxYswN69ewEAN954I+Li4lzq9fR9aKe792N3mTRpEt555x088sgjGDt2LJYsWYLExETHxoYDBw7gvvvuw7vvvtvjsvQ4fbmFkeKKL2Ea2l+2HTt2kHvvvZeMHTuWBAYGkoCAADJhwgTy3HPPucSaaW5uJj//+c9JbGwsYVnWaatuR2Ea3OEtXAHchFgoKCggK1euJJGRkUSv15PrrruObN682eO24YqKCrJq1SoSERHhkPfqSO67du0iS5cuJZGRkUSlUpGYmBhy/fXXk5dfftntdnRP/O1vfyPXXXcd0ev1Dtn+9re/dXrc7vC2LdpdSAM73kIh+DpuWZbJu+++SyZOnOiIav3AAw+Qqqoql2vb0NBAXnzxRUfUd7VaTeLi4sjixYvJf/7zHxcZampqyD333EPCwsKITqcjaWlp5D//+U+Hkdy94eu4ujpva9eudQl9YWfLli3kpptuIqGhoUSlUpEhQ4aQefPmkTfffNMRqbqj9j3JdezYMTJ37lwSGBjo+A53JpL7e++954jkPnToUPL000+7RCb39xzb2bZtG5k/fz4JDg4mGo2GJCQkkHvuuccpNpe7Odm8eTNZtWoVSUxMJHq9ngQHB5Pk5GTy+uuvu0T39vSdamlpIc8//zwZM2YMUavVJCQkhNx8881ut/h39rrIskzCw8NJQkKCU9iOjjh48CC5+eabSUhICFGr1WTMmDHk+eef9xixnBBCPvnkE8d1/+STT7y274/7sCO83Y8dRXK/mq4+306cOEHWrFlD4uLiiEqlIhERESQlJYU8/fTTJCsrq9Nj6o8whLSzvVIoFAqlX/Diiy9iw4YN2Ldvn88ZFyi+c+HCBUyePBlvv/021q1b19fiUAYg1AeLQqFQKIOOQ4cOITo6Gvfff39fi0IZoFAFi0KhUCiDjkceeQSVlZXQarV9LQplgEIVLAqFQqFQKBQ/Q32wKBQKhUKhUPwMtWBRKBQKhUKh+BmqYFEoFAqFQqH4mQGpYJlMJpw+fRomk6mvRaFQKBQKhTIIGZAKVnZ2NlJTU5Gdnd2l870lq6X0HfS69E/odem/0GvTP6HXpf/iz2szIBWs7tJXSTAp3qHXpX9Cr0v/hV6b/gm9Lv0Xf14bqmBRKBQKhUKh+JleU7Aee+wxR8LPCxcuOMrvv/9+jB07FsnJyZgzZw4yMjLcnl9UVASe55GcnOz4y8/P7y3xKRQKhUKhUHym4xTifuKOO+7AU089hdmzZzuVL1++HO+//z54nsf333+PVatW4dKlS27bCAkJ8aiAUSgUCoVCofQXek3BmjNnjtvy2267zfF/WloaiouLIcsyWLZnjGslJSWora31WqepqQnBwcE90v9gJyIiAsOHD+9rMSgUCoVC6VF6TcHyhT//+c9YsmSJR+XKaDTiuuuugyRJWL58OZ599llwHOdz+yUlJRg/fjwN39CH6PV6ZGVlUSWLQqFQKAOafqNgff7559i6dSsOHTrk9nhsbCzKysoQFRWF+vp6rF69Gm+++Saeeuopj23+6le/crJEpaamwmQy4fPPP8f48eP9PgaKd7KysnD33XcjPz8fAQEBnT6/oaGhB6SidBd6Xfov9Nr0T+h16b9059qEhYU5fe4XCtaWLVuwYcMG7NmzB1FRUW7raDQax7GwsDDcf//9+PLLL70qWP/7v/+LlJQUx+fTp09j48aNGD9+vFM5pXcJDg52uRF9pavnUXoWel36L/Ta9E/odem/+Ova9HmYhq1bt+K5557D7t27vS4bVVdXQxAEAIDVasVXX32FqVOn9paYFAqF4hUiiSCE9LUYFAqln9BrCtYvf/lLDB06FGVlZbjpppswatQoAMBdd90Fi8WCZcuWOcIv1NXVAQBeeOEFvPvuuwCAw4cPY+rUqUhKSkJKSgpiYmLw7LPP9pb4PcqLL74Im83W12JQKJQuQgiB+exhyK00QjeFQlHotSXCt99+G2+//bZLud0q5Y6XXnrJ8f+KFSuwYsWKHpGtr9mwYQOeeOIJqNVqp3JRFMHz/WIVl0KheIBIEiwXT0CoKgUfNQxcQEhfi0ShUPoB9O3dxzz88MMAgJkzZ4JlWcTFxWHUqFG4dOkSSktLkZmZCYZh0Nzc7HAMj4iIQHp6OhISEpCbm4vf/OY3qK6uhs1mw0MPPYR169b15ZAolEGDJSsd1qIsx2epqRYYmtiHElEolP7CoFawTCJBdmPPtT8uBNDzjNc67777Lt577z0cPXoUAQEBuO+++3D48GEcPHiww512kiThzjvvxGeffYZx48bBZDIhLS0NaWlp1ImfQulhrAWZTsoVAEjG+j6ShkKh9DcGtYKV3Qik/kvssfZP3c4jJaLz561atcqnMAY5OTnIzMzEmjVrHGXNzc24ePEiVbAolB7EVpYP66UzLuWysR5ElsCwvsfno1AoA5NBrWCNC1GUoJ5svytcrVxxHOeU4dtisQBQHGsjIiJo+iAKpRcRKothuXDM7Y5BIsuQmxvBBYf3gWQUCqU/MagVLD3PdMnC5G8CAwPR1NTk0WqVmJiI48eP4yc/+Qm++uortLa2AgDGjh0LvV6PTz/9FPfeey8AIC8vD2FhYTTGCmVQQCQRkCQwak2P92UrzYVYVwGxqhSEyB7rSU11VMGiUCh9HweLAjz++ONYsGABkpOTUV1d7XL8rbfewi9/+UvMmjULp0+fRni48vDmeR7fffcdtm7diilTpmDixIl48MEHYTabe3sIFEqvQ0QbTCd2Qagu6/G+bGV5sGQeh1BRDCJ7Vq4AQDLW9bg8FAql/zOoLVj9hfXr12P9+vUej998883Izc11fH7llVcc/48ePRrff/99j8pHofQ1RBJBJBGsWgsAkC2tMJ87CrGxFmxgqF927hFRgFhTDlVsgqNMrK+CUF4AoTzf5yCiUlM9iM0Ksb4KfGQcGI4+ZimUwQj95lMolH4JEW2w5p0HOB5yUx3Exlrop84BGAamk7sdliSpQbH6Si2NgCR5XZ6TjPXgglyXz4lgQ+uJXZBbGsGFxYBRayBUFCq+Vu38H31Bbm6E6dReiI21YDgO+ukLwYdEAgDExhpAEMBHxnWqTQqFcu1BFSwKhdLvkJobYErfC9licio3nd4PhuOclunkVqOyhNfmeK4ZORGqIYmwXjoDXfINjh19lksZsBVnI2DWrWD1V/wdZVMLTGcOOEIs2EpyIDXVQqy53CXZCZEhNtYq/0sSxIpi8CGREBtqYErfAz5ySIcKlmxqcZKRQqFce1AFi0Kh9CuIYIPp9AEX5QpQlvGI6Jz9gRAC8/kfHZ+tBZmwlVxS6p3/EbqJM2C+8COEimIAQOuJnVDFjQQfGgXWEIjWE7shm1uunJ93zq/jESqLoYpNgCl9j2MZUja3QraaHJYtx1hkCda8c5DNrdAnzfarHBQKpXehChaFQuk3EEJgPncEsqm5e+20KWHC5UJItRWQbRbHMdncCmv+eVi71YPvyBYTWk/sUnY8tsnWcmQ7+Mg4JwVLtlpgOrkLUnMjWF3PWK+IYAOjUndckUKhdJvBqWARWfHX6AVYfRAYlm7WpFB8wZpz2u+7AtsrV32FXblyfBasEKvLQCQJxGqC6cxBEJvFYbWTzS2QLSawWr379mxWgFdBaqgGHx7jkwxSUx1MZw7AMGMRWJ2hewOiUCgdMigVLMZqQsVzazqu6AdiX9lMk79SKB1AJBGWrHTYSnM7rjxAIKIAc8ZBSE21kK2uSqBQWbjCtQwAACAASURBVAIiWKEdnXTlHEmENf88bEVZABiAYRB44yqXH3FScyPkViOgVixhYm0FTGcOgIgCJGM9VbAolF5gUCpYFAql/yBbzTCd2gepafDFj/JmrbNmpwMcD83ISWA4DmJtBcyZx12WT6WmOvChV5YaxYZqmE7tAxFskOMnwmZphOX8j46NAXJzAxA9rGcGRKFQHNC1q2uMjIwMbN261aksOTnZr8FF3fXhK0VFRYiI6Afh8Sn9EtnqfJ/KNgtMJ3cPSuWqIwghIKIAobIYpoyDaD25261vmlSn+Ji1HvsPWo/vhOnELhDBphzLzYDl3FGnXZdSc8fuEYQQWPMvwLh7S5d3U1Iogx2qYF1juFN+MjIyoNPperQPCqW7WLLS0XLwGxBRefnLphaYju/06YU/mLGcP+rYAekOW3EOWvb/C2JDNcT6KidlikiiS4BUyVgP2dIK06l9ECpd2yU2K0zpe2C5dAZEsEFsqPLfYCiUQQRdImxDP+vWbuczIzYrTEc6H1X95MmT+H//7//BaDRClmU8++yzmDNnDu666y5UVFSAYRikpqbitddewwsvvACj0Yjk5GSkpaXh3XffBcMwaG5uRkBAABISEnDvvfdi9+7dKCsrw+9//3vU1dXhiy++QF1dHT788EPMmzcPoijilltuQV1dHcxmM5KTk/HBBx+gpaXFbR/uZFy5ciUA4O2338af/vQnxMbGYu7cud2aQ8rARKgogrUoS/m/vBAAlBf4VSEXKK50FEG+s078xNyClkPfgYiCYhHjeMitRjAcD6mhBmJDtZOlzB4fjEKhdA6qYLXBqDWONBxdxXuGMvc0NjbioYcewvbt2xEbG4va2lqkpqbi4YcfRkJCAnbu3AkAqK+vR1hYGF566SV8//33+Oc//+mxTbPZjKNHj+LkyZOYO3cuNm3ahBMnTmDr1q145plncPToUXAchy+//BLh4eEghGDdunV455138MQTT7j04UnGWbNmobq6Ghs3bsSZM2cQHR2NdevWdWXqKP0YIsvd2gkrNTc4xamyZKd3mM+P0nMQQoA2xVZqaYIpfa/X+nI7BUuoKgURrFAPHXWlPUkCJBFiQzX4yCF01zSF0gZVsPqYo0ePoqCgADfffLOjjBCCmTNn4q9//Ssef/xxzJ07F4sWLfK5zdWrVwMAUlJSYDabsWrVKgBAamoqCgoKHH386U9/wvbt2yGKIpqamjBnzpxOyZiTk4OzZ8/illtuQXR0NADgF7/4BV1evIYhkghiszp2mdnKC0AsrdAkTu5Q0SKyBLHmMlTtHKhlmwWmU/udwhRQ5eraQrZaIJtaYCu9BGtBJhiGBWsIAh8aBbGuUtmd2ObzpR4+BrqJM/pYYgqlf0AVrDaIzdolC9TVbXT6HEIwZcoUHDx40OVYRkYGdu/ejW3btuG5557DmTNnfGpTq1UscRzHuXwWReVF9+WXX+LAgQM4ePAgAgMD8T//8z9uZfBFRsq1iX3piWEYR5nlwjEQWYZ2fCrM536EVF8JRqMDFxYNa/Yp6JJmA7zKxdorNTfAfO4oJGM99EmzoYoboQQNzTjsFCWdcm3SemInZHMrACUVkOnkbvBRwyBWloCQK09Osbair0SkUPodVMFqoyu+U/5g5syZyM3Nxd69e7FgwQIAV5zWR4wYgVWrVmHx4sWIiopCS0sLgoKC0NTU1O1+GxoaEB4ejsDAQDQ3N+Pjjz/GyJEjAcClD08yTpgwAfPnz8frr7+O6upqREVF4cMPP+y2bJSeh0gSzGcPQxUzHKq4EZAtJljzzsF2WfGPkhqqHTv+iMUE08k9IJKI5gNfQz1sNHST0q60ZTKiNfu4w4phvngCXGgUrIWZEOvoC3cgYFeu7BBJglBR5FrP1AzZ3OqwgEqtRnCGoA7bF+urwIdF+0NUCqXfQBfL+5jQ0FB89913ePnll5GUlIQJEybg6aefxt69e5Gamork5GTMmjULb7zxBoKDg3HjjTeitbUVSUlJePjhh7vc77333ouWlhZMmDABK1aswA033OA4dnUfnmSUZRlTpkzBM888g5kzZ2L27NmIi/OexJbS9xBJgunMfghVJbBcOgPz2cNo3rfNKcjn1eEU2i/xCRVF7dK+2CBe+NGhXAFKOpaWw9/BVpzTswOh9EvEugpYcs+i9ccf0HLwG6/BY4kswZJ7FqYTuyFbWj3W6y9ITXV0iZviMwzpaIvKNcjp06eRmpqKU6dOISUlxaX89JEDiNz6+16RhUZyv4Kn6+Irdkd/SvcwXzjW7YjpqujhYA2BkBpr0Vxe5NcwIdciQnkBLDmnAADaCdOhionvY4kUzGZzr18bhuOdFHKG42CYeQu4gGCnesLlQlguZTiWkPVT5/SbeWuPbG6FJec0pLpKyDYLtKOToBk1pVtt0mdZ/8Wf14YuEVIogwhbeYFf0tEIVSV+kGZgIJTlw5J5zPHZknlC2U3HDc7Hq0veRUmC+cwBqIaMhHrERECWYbnwo2M52o7UWNtvFCwiSxDrKmEryIRY7xwHzJp3HnxMvIvCSKFczaB8AhCNHrGvbO6Vvlh9x/4HFEpPQ2xW2C4XwJp7tq9FGTCI9VWwXjyh5PxzOmBT0tdQnyIHUksTpBwlcKlUXwWxsda1zlXR/GVTMxitHgzL9ZaYAJQckJbsdBe/MzuEyLBeyoA+hcb8o3hnUCpYYFi6bEcZVJgvnnDrlEzpGmJtBcyn9wPEvT+O1FhLFSw3WAsyPR6TGmrQcmS7EhLEZoYl+xT0qQvAh8d4PEe2miFWl4GPHubY2SrWVsBakAn18NFeLWL2xNmQJPARcSCyBKE0F0JNeYfjEKpKIDbUOOWAvHKsFFxQWJcSasuWVoBhAYbpdlxGSt8zOBUsCmWQIJuaYc07T5UrPyLWV8F85oBH5QoAZDcWGop3CJEhGethOnPAUSbWVbhVsIgsQ6wugyXrJGSLCbhwDFxgKBitzpE7kdgsbhUsQghshZmwFeco5wKOLAOdwZpzGtz0hWBYFkSWITXVwpp3DmJtBbRjU6AZOdHlHNlqhmxsAGE1kIz1YPUBEKvLIdZVQjLWO6LmqyKHQD9tQYcyEEIg1VWAj6Cbi/ojg1LBysrq/JeJ0n3ovPcusrkVrSf3uE0QTOkaUkONYrmSJe/1GmtACHGKMUbpPGJtBTBmqlOZtSgb1twMlzRLUnMD0Nzg9NlWlg+xugxcSDg0IydBtpphPnsYYl1l92VrqEbrjz+AUakh1Vc5pTQSKosdChaRZUh1lRBqyiFWFIEIVogMjxZZAMMwblMhiXUVSlxFlRqysR5sYAiEsjxIDTUAw4IIVsgWE4jVBGKzImDOMrD6wLb+pF5ZVpUtrWC1nbfSDSYGlYIVEREBvV6Pu+++u69FGbTo9XpERET0tRgDHltxDixZ6U5BICldR2pugLVtJ9nVsIGh0Iyaoli12iCCFcTUDMaHGFAUz8jGerQc2Q7VkJFQxY2A5eLJTlljzeePAmhb0quvgtRQ49f8l57yNEpNdbBdLgDDqWC5eMJhKbNDTEZAp/OYZ5LIMqz55xWZjfVgeJVXuW3lBdCOToJQVQpL9ikEzL61y5ssiCxBqq8GGxTqtEwpm1pgK88HFxAMW3E2pKY6qBPGg0gidBOmd6mvgc6gUrCGDx+OrKws1NZ6N983NTUhOJjuEOkJIiIiMHz48L4WY0Aj1lfBknNq0ChXPW0pEusqYD59wK3Vio8eBu2U2QDDgFFrQdolXpYaa8BSBatbEEIgGeshNzfAmnu2W8qRfemwtzCfPdKt89svW3Y0bltxNoTLhQ5rtdRQ3ellQ6GyGLaSXMjmFsimZnCGIOhS5gKyDGvuWYi1l11igCmpkxio48f5FFB2sDGoFCxAUbI6esHTGCWUaxVr3jlY8855/GU8kBBrypWXrtUM9chJUMeP7VZ7RLBCaqgBGxgKVmcAEQXYCjJhK7oIuJlPLnIItFNmOfIzciGREKtLHcctF47BVpoHPjwaquHjwDAMbEVZkC2tUA0dRZ3gO0H7BNUUV4hgcwr2K9ZXuVWwiCzBVpwDoSwPsrkFqqGjoY4fC2v2KQjVZU51pVYjWg5913HfhMBy/ig0o6Zc875gRPK+9N9ZBp2CRaEMVGxlebAMkjAMttI8WC8ed3y25pwCHxHbJYsRIQRCaS6sl84AkggwDDRjU2ErzAS5KqK9HS48FrqkG5x8XbhQZwULAOSmWtiaamEruQRGpQFpC6op1pTDMPMWl51mRBJhyzsPsbEafORQqEdMoH5clE4j1laAJE6GbGqGJec0WH0QuJBw2AouKr5qbdiKsyGU5HT7B5nYUAP5wjEEzL29X92vRJYhm4yOqAFyazOE6lIwKg3k5gZoxkwFw3EQG2oglOaCCFZgRJLf+qcKFoUyAJBNLbBkpfe1GH7HvlwhlOUDAPjIIZAaayGUXnKuSAgsWSehik0AFxEHMAyIxQQ2MAQMw4LYrLDknILUUAM+aig0Y5LBsJyyoyzvHGwFF5zasmZ7nkvNuGlQDRvtsFzZ4YK9+BaKgvMyjyig9eDX0CbfAFW0YlGXLSZYzh2B1FANALA11oI1BEEVPcyHmeoY2dQMoS24p2rYaLCawR19fyAjNdWhedfmdorTZaDYfV1/Wbtlcyuk2grwkX1rxSKSBKE8H2JNOcSGGhDBCi4oDCDESbkEFB86IgoOXzo+ItavslAFi0IZAJgzj/nVebevkU3NsGSfgnRVTCKx0sNbAoBUV+nihM4GhkI7KQ2WzOOQ2x6iQnE2iKkFmnEpim+Jlzbbw8cmQDvpehfFytFXUBjAqzq1lGU5ewTMVB5ifSUEN6ECxMpitwoWEayQrRZIdRVgA0LAhUV7tRwIlwthuXgcaFsCkRproUud36+sDRT/0hduAta8s8q9yPlnFyMRBcW/sZ3DvlBZDDCs0/dCtpqVqPt1lSA2i0suVU+bEa6O0u9vqIJFoVzj2MrylO3sAwRbWR6sWekdhkLwBbm5AaYff3ApF2vKINaUuTnDFUZngGZsKviooV4VEobjoBmbAmvWScDXhMBEhvn0Po+HxcpiCDHDHVYuqakO1uxTkBprnOppJ18PVdxI5ZyGagjFOQAhkPVBsDIEtsKLTvWlugoQiwlMF4JhUiieEBtrYTqxE0zb7kMuNBLqhAkef5QQSVIUKJZV/mdZMAwD2WqBUJYHW3EWVLEjoBk7FWJVKWwllyDWV4FRqUFsKZBbjZAtJojVZS4pmvoDVMGiUK5hpFbjgFoaFKvLYM083nHF3oBhoBo+FprRST5veVcPHaUoQ4QALIuW/dscVqOuYsk4BEy6HlJDNYTyfLd1rPkXwGgNEKtKIZRcAnDFemFze4byq95TtHGxtgLW7HTIphawhiCoEyf1mzyBlP5N+zRIQnUZpMZaaCelOYV8IIRAKC+ANTcDkGWwQaGQmxvBhUWBUesglF5y7Fi0FWdDqCpxSl1EBBvMF67k/+yvUAWLQrlGse/eGShLg1JLI8znfNzazvHQpc5XcgG2NHW/c46HdspMiOUFEKvLwOgCoJ04w2uaFk8wKrXjf83oZFizTwFQlivVCeNhaYvN1BksF370epyYmmE+ubtzbWYchBAeA82oJHAhiv8YkWXY8s/B1i6ljdzSCMu5I+ACQ2nYCUqnEapK23Y1xkIzOhlySxOsuRmQmhsddeQ2C7xc4bpcTwgB8ZAXsr9DFSwK5RrFVpQFsaGm44rXALLFpERIv8rMz8eNBB81BAyvAqPRw5Z/HpAlqBMnK46r46bBfPYQINigGpIIPjYelswTjt167eFCo6AakgjhcqGyxGZfguTV0CXfAD48BqqoYW0RtFVgGPfLGp1BNXwsuNBoEJsZXHgMGIYFHxGHlv1fuU21ox4xAbKpGWJVqZvW/I9UVwlTXSVUw0YDhECsLlXGfzWEQKwug3rEBI9xx3orgjjl2oMINggVxRArSwdNfD6AKlgUyjWJ1NKomNcHAGJ9lWKJuyrateJUnub0MtclzXauEx6DgLm3A5IERq0BABhmLwVAQEQB1pzTkI314KOHQz1yEhiWhWrISOVXsakFsqUFbEAoWM2V5Qt7O/6AYRhwQaEAQp3a109fCPPp/SCCFYxaA/WIiVDFK7GyxIZq7woWx0M9bAyEyiKXOfMIy4GPHgaxosjtYaE0t8MmrJfOwFaSA2IxgY8bAe2E6RArSyDWVUA21kNuNYINDodmdHKXLH+Ugc9gUq4AqmBRKNccytLgj34PitcXCGX5yu62q3Y8sUHh0E5M82mXG8PxQDsfKbtDLaPmoJs80/05DAPGEAjWENgN6bsOFxIBw5zlioKl1TuNkw+Ngv76JTD9+G+X8/jYBGjGpoDV6MCGRMCScdBN4zy046eBUWlgzj0LTqOFZnQyGJXGo4LlK3aFTrxciJa2kA/tkZvqYE7fC920+eDD/bvlnUK51qAKFoVyjUAEGxiVGrbibCdH0msBQmSI1WWQ6irBBoRANWw0hNJLym7Bq2ANQdClzPPbVu/+CsPzYHj3j2AuKBSG+SthOvYfEHMLGI0O2olpTjGG+Kih4ONGQLxcCPBqJV/f0FFgtXowvEqpExgOne5KvCsuIg5SbccpYxitHurEyV3ccEAglOSCC46A1NwAzhDs1SooVJVCKMkBo9ZCMzrJkbS4v0BkGVJ9FaTGWkhNNWB4FTRjUjxuEKBQ7FAFi0K5BpBtFrQe/QG6SWmwXmPR2olgg/n0fqfQAtask27rcmHR0E6Z5bRkN1hh1VoYblgKubUZrCHQxSeMYRjoJs8EGX8dwPE+Wft0U2ZBuFwAtLVlzc1Q4nbxKrABweCCI8AFhYGPGgZwrOKg34Xt72J1KVr2X1Z2UHI8tBNnQBWb4FSHiAKsl844LU8SmwX6627qdH89AREFJT9f4UWQthx/dqTGOhiuv9mvy8mUgQdVsCiUawBr9inI5haY0vdcU3kGCZFhPnfEJW6TO1QJ45XUFTT4pQOGYcEFeE88b7dW+dSeSg11/DjHZ9XQRKXcg3M6Hx7rkv7HHWxwOOSmOudC+xK2JMJy7gjAclBFDwORZQglObDmXwBE5yASUn0VZKulTxVsudUIW2muoogK7oNcEEsrLJnHoJs6t5elo1xLUAWL0qtYi7KhHprYqZfCYEeoKYetvABA30Rn7g7WSxk+LUmpEydDM2pKL0hEaU9Hu/40Y5Ihm5ohm4xKiqFx00DMLbDmnIFkrAcXHgPt2FSwhkC0Hvnea8gMe1gIYrVAbmn0WE9qrAHbpoiByD7HIOsKih8jAcPxikUt/zyE4my3yb2vRqwug1hTDj5yyJX2BJsSaVwSwceNcIr95FUOQiA1VEM22lO5EHDhMeACQ72eZ+8THO8xmCel76AKFqXXkBprYc1OBzG3QDt+WrfaIqINlktnoR2TBIZXd3zCNQoRbLD0UUA9IssQLheAWExQxSZ4jYFEJBG2oixI9VXgImKhThgPsaLIbfoXJzwsH1H6B6whCPqZSxRFx66MaXTQz/iJS7gGLiKuw5hkV6cycof14glItZchVBQBDAPtuGlQDRnpUo/IEoTLhSAWE7jQKHDBEZBbGmEtuAC5qQ6MWgvV8NFQDxvjcq7calRSMdVVKmPT6JR4cp1cDrVkn4IhPAaQCYTyPNjyLygJgwEIFUXQz1jkUHyIzQpb6SXIrUZIYGDVGUBkCXJzA6TGWte+WQ76GT9RwpFASQcjty1VEqsFxNyi+DU21oBRa6FLmQcuOLxT8ncFxZ+yHLKxHlxoFBhepeT87EFF+FqFzgil17AWZyu/1Lz8evUVW2kebMXZUA8bBS5wYCpYQkURbCWXIPu6Fb+byDYLbHnnQUQbVMNGw5Z3HlK98kK05Z8How+EOn6ckjJGo3O8XElzA0yXTkFuNQJQlnnE8kLHy8ATbGAodEmzafDKfg7DMADjaum6eilXnTAeYnmBomAwLFRDEyE11Hi1VrmD2JQ0KXYsWSeVe65dAFepsVbJL+mlbWKzwHrxJBherZzP8ZCtZlgvnnRZ9iRX5a5rDxcZB1XsCPAxwyFeLnIK+kpMzWjZtRlgGBerl2ysh1hbDoZhIVaXQagscVoS9RRh/0oDEkw//gBGq+8wHAexWWA+dwSq2AQIFUVg1BpoJ0z3yQLm1A6RIdVWQGpuBKvWKvOm1kBqaYQt95ySu090lZxRa6GfsQisPqBT/Q10qIJF6RWIKDhi+7gNZNiG2FgDPiTSa1tSUx1sbZYRb21dywhVpTCfPdyjS4LEZlVyf/EqEFGA+cQuh5Lkbjs/MTXDmnUS1qyT4CLioJs6F1J9JaSMAy5BM+VWV0uGZuIMcAEhECoKwRqCoRqSOOB3Cg4mWI0OhjnLIDVUgwuOAKPWgAg2mE7uhtzc4FKf0RmgnTwTkGWY0/d4blgSIdZVQBUTr6TwyTnVqej9lnNHlHx3vBpEEnzLE8mwUA0fA9XQRHABIY5iPm4E2NJcyE1X7eL18D21nHETRqOT+BrrjJialUC8bf9bMo/DkLbYfV1CQGwWMAwLRq2BWHsZtoJMSA3VzhUzAajUHn3RHO3ZLLBkp0OfMs8nWfsDSkwuBgzDKD/86yshVBRCbjWBzF3qF19QqmBRegWhqtSRjJN4+bJaLp6EYfpNHpf9xNrLaD155WFMbBb/CtoPEKrLYD57qMeUK0IIbLlnFSWVyOCjh0M2tziUK1+Qai/DmnNacQT2IXigavhYqIeOAgBHWhbKwIPhVU4+SYxKDX3aYgilubBmXwnJwYXFQJc0W1HCZAmsIcjr/Wc5exjW3LMuu/l8hhDH0l1H8FFDoRkz1a1llWEYaMdPg+nYjq7J0REc36Vdm+6Qm+ogm1vBaNrCdDAMxOoy2AovQm6u9z0heQfKlR2pphymE7ugS53fL5YLZYsJYu1lkNZmsEGhYHgVhMoSyM0NkM0tgCiA0QWAj4iDVF955f47dxQtkhWBN/5Xt2Xo+1mgDAqEiitBCT096MS6SkhNdZBbjG5fwoTIsGSfdi7z0YIlNTeCCwzpuGIfI7UaYc441GNBRIksw5J5TImd1IZYVdKltoSSHJ/qcWEx0IxN6VIflGsfhmWhjh8LPiIWQkURWH0A+JiEKwFhWQ661PmwFecoyaw95Nb0WbliWJ+UfvBqqOISwEcNg9xqBMPx4EIjO4zDxQWHQzN+GqxZp9A+qTYAsEFhkI31vskJxYrHBUeAjx4GLjgcjNYAy7kjECtdc/JdEYAHo9KA1RlcLU5X0XrkOwCM35S2jpAaqmHLPw/NmKm90l97iCxBbjFCrC6FWF3m1mrqco65BULpJZdyS85pqmBRrg1kqwVS7RXnViIKbvOW2dr8LqSWJoeCJVtaYck8AfXwsZBL8iFd9aWRfbBgiXWVkBpr+rWCJVtMkBqqYc0757D0+RtCiPLw7qJC1RF8zHCwhmDHMgUAsMERiqWC7nAa9LCGII87RVldALTjUqEZkwypvgrEaukwwTU4HuqE8VCPnAgQAqmhpi3nYxzEikJYc057PjVyCLST0q7s8utkah91W45JoSwPYBjwkXHgAsPAqDWwXDzp8tJmA0OhihsBPnIIGI0O5qZ66MKi3C5DqRMnQ6wpc4S5YLQGJUZZaBTUw8c6BaeVbRaYjnzv+YemP3+o8SowHA8+Ig5c1BBwgWFK2JirFF9baR7UiZO7bcUisgypoQqQJHARsQDDQqq9rFjgrGaoR0yAeugoyKZmWPMvKM81Pz07tRNn+KUdqmBRehyhosglBxURbFdM11AsUfYXf3v/HVvJJQjVZUq+s1YToHUO7OfLEqG1MBOsrv86Xwo15UpeOl9N9l3EVnDBZ+WK0eigHjFBibhecgmyqQV89FCY0/e6rc8GhUM76XqA5ZSULDXl4MKioE4YP2gSAHPB4ZBbjcpuNEqXYFgOfIQSrV6sq3Cf2odhoRmdBFXCOKfgq3zEldQ8qvhxygvZWAdWo4NYVwnZWA/WEARVwnjF/6+bPjZcYAg4N7uhNeOngQsOV3Y4igJUw0a59Mfogzz2zwUEwzBrqeLLFhLh1aLGtjmXC+VK8FjZ3Oxkne4MjFoL1bDR4GPiwWoNsBVmKvHrOA585BCohoxy+aFkmHULzCf3OMe5E21o2b0FquFjweoM4COHekxJJZtaAI4Do9YqilqbT5hQXqDkvWynvHEhkc7BijOPdzHTgGfY4HDw8/4LgXOX+6U9qmBRehx3JlgiWIF2CpYl66RjWczuwEoEG4RSxapFJMltotCOlgilpjqINZehihraZfl7CtliQsvh77z6pPkDdxGzXeDVUA1NhDphPNh21wVQdofZ0V13k6Jktb8W+kDoUuY6frGq48dCHT/Wr2Po73BBYTBMuxHmc0cg1JT3tTgDAu3k6yFGD4f10hnHi5YLjYRm3DRH6AJPMAzjdA+qCQFkqVd8gxiGUdIWuQkt4SuszgBWN8K3uvpAaEYnAVBCOSiWHM+WK0ajgzpxChieV6LUSyJUMcOhHjHRKT6hvU1vMG2hJFqP/wfyVem77C4E1pwzUI9OgnrEBIdSKTZUw5abAamh4wDEdnwJVuxGQKdnFRsUpiQiZ3nY8s+1FbYpkMPHQDM6CbZRqZ3vxwNUwaL4hNTcAMvFk9BNneNz8DygbXnOzY6f9kqFUFEEW7tfXVJTLUzpeyHWV3boi3S1BUuoKAIfEQdGpQYhxJFWprdCHXQGS3Z6zytXkgjT8Z1utrMz0CbfoOxQIgR8XIJP15UPi1Z2ijXVgWFZMCoNrCqdi1I2WOAMQYpiOnQ0GLUGXHgMVbD8BMOwUEUPgyp6GOS2MAqMWtslyxPDME4JwQcqrEYH7aSZilJqtluHdGA4DozOAD48DqohIx2KlL/iz2lGJXnZCUpgy82AeLkAjD4QRLC6KGN+heXAR8RCFT8OXEgkk3+PWwAAIABJREFUGJYFEUWAZZys6erEScpOSl7t2M3s7ywSA/+Oo3QbIsswnzsKyVgPqbYCbJxvv6wAwFbs3hG6veXJelUwStlqgezjS6q9giXWXIYp4xBYnQHqhAmQGqsdLzvZ3OqzzL2BUFkMocKLI6sfkC0mmM8ccBsrSD0mCaroYV1ql9XqwWr1js+M2XMMoYEMZwiCfvpCp7ngw6L7UKKBy2BV4LuCKmY4VDHDFaWC43ol9RQfHgP16CTYvORJlVuNQCd2KndOABVUQ0dBHT9OUcKvWsp0l1SdYRgnN5UeEatHW6cMCGxFFyG17YyRWo3wNcmNbG6FWF3m9ph9J6HigN71XzNOilqbc7VsboXlqmTCRLCCSGL/2D5sVhz3exLJ2ADzqb1ufdT4uJFQJ0zo0f4HOqw+EPrpNzkpV4CyBMGo1D1umaRQOsKdUtGTaEZOAqsLUJYoWQ5EsEKqrehiawz4NrcOe1BYLiQSqmGjwEcPh1hVAtncCtYQBEajAxcU3i9j6vX924bSr5GM9bDmnXN8bh+rRja3gtUZPJ5rK8lx6zcFKEuERLDBfL6DnUIdQAQrCCEQKgohdrBlWbaYwLCcV5l7GiLLMGUc9Gn3Y5faJwS2wouw5Wa4Pa4ZPw2qYWNoQuVuoFiubgKrdb2PGIYBHxoFwcMPCwplIKOKTXBadhRrymHJUtKjXQ2j1kI9YiK4kHBlCVgSITXVObIA2AO8ylYLIIlgdAbHc0sV13X/tt6EKlgUr5jPHnbyg7IrWLayPIh1ldAnzXZ7HhEF2Lw4VRPBCltxthLwrRsQQiBWFLXFpPGOVFsBqbUJugnTu9Vnd7CV5HTLYtcRQmmue+WK5aCdfD1UMfE91vdAhwsIBhceA03iZK9LVlxYNFWwKBQAfOQQGMJjIdVXQSi9BLGmHAyvhip+HNTxYx2+YHbbkzvFidX47vPb36AKFsUjUmOti4O6bGoGkSVYL2WASKLHZTdbWb7XZRJis0Co7Tjxqy+Yzh72qZ4l9yy4gGC/9NkVxNrLDqd7f0MIgTX7lNvgn2xACHTJczxulaZ0DBccDsO0G8GoNR3W5UOjwGq0SoT85kZITbWOEBwMrwKj1naYp5FCGSgwLAs+IhZ8RKwSwoTjB40Fvdei/z322GNISEgAwzC4cOGCo7y6uhqLFy/G6NGjMWnSJBw+7Pll+f3332PcuHEYNWoUVq5ciZaW7lk/KN4R3EQTJqKgJCC2mpX/Cy+6jd8klOd7bVusLu+29aqzEMHqEqjUVpIDyyX3y2n+RGppgunUvh6JkUQIgTXrpFvlio+JVxyxqXLVJVitHpqE8TBMX+iTcgUoflj66QuhmzgDhrRFUMUqm0JYjRaG6QuhGTnR6/lcSAS0Y5K7LfvVMBwH7bhUp6TJFEpvwvCqQaNcAb2oYN1xxx04fPgw4uOdlyiefvpppKWlITc3Fx999BHuuusuiKJrNNaWlhY88MAD+Prrr5GXl4fY2Fhs3Lixt8QfVIh1lTCdPgChosjtcVv+FQXZknsWLQe/gfnCj7AWZIIINkitRodTvCdkL9nrexIiCkpwOyhr+5acMz1uTSCEwHLxRI8FEhUvF7iNcaUaPlaJoj6AX6isRuc3nzo+PEaxUrW9ALjgcBiuvxna8dOc4gN1BMOyTgmCVcNHg9XqoZ/+E3DB4VDFJoAPi4YqerjzeQwL7bhUGGb8BKqYBJ/68jWALqvRQn/dTdCMmAAuONznsVAolK7Ta0uEc+bMcVu+detWFBYqMZCuu+46REdH4/Dhw5g3b55TvR9++AHTpk3DuHHjAADr1q3DkiVL8Oqrr/ao3IMNYrPCdHq/V0vL1Q7asrkFtraAoLaii+CC+vcDXKwth1hboTjaiwKIH5U9saEGDM+D4VSQrWZwgSEwnzsKsc4/y6FXI7c2w5KV7lzIsNCMSVaiWQ9gWH0gDDMWwlacA2tBpk/nMLzK7b2tiomHLmkWGJaDKjYBbHCE4iPih1/bfEgkDNff7NhxyPAq6KcvhGysh1BVAobjoB4xEXxopCOKOWMIBPv/2bvz6DrrOn/g7++z3C37zb42bZM03duU0rJDQQYqlkod1J8LIJtIj6PDcURFxKOgM+AyiB1nDjoUZ2QcQUFRBnCgUIYiS+kiS+maNl2SZt/u9iy/P257m7vf3D3J+3UOh9xnu5/kSZNPvt/v8/k4imB63WHxKuW1sLYuhZAkSPZCjLz4eFDyLoSAtf0sKOXVcL/6HOTCQjg6Lg4konJpJbQEnu5Sa5thnb0A7j3bM/b9SzSd5XQNVl9fHwzDQGVlZWBbc3MzDh8Ob+dx+PDhoNGv5uZmHD16FIZhQMpAn7MDwyYGZ+CT1srRA7AOpzCNNe4GBjJTaNHtEbCZ8Y+Lx3zzDQjzzIUMuODqTfHCpgnhdcH6178AqhWmkCAP98JUrZBcGRohMwzYdv4f5JD+W+62czBW1QxkaZAwXfclFlMIfxsNw//AhVY5C945HTBHHZCkWtjH34FhtcO0FkAa6YMwTRi2AkjuM/XPtOrZ8MxZAfvOZyFNGLX0Ni+Br3EJ0A8AJlB3jr8CdN+p12lhB0ZDr+WExTkfWnkDjNJT9bMmfB/K1R0wVRvsO54FABgWG7xzz4Je1Qzo8P/nA6yWGij9x6AXVwKyAl/DfOiFdYAHGGm8AEXOcmDMAoz5ry2bFbCNA0ZBCeDzQAr5g8mUFXjaVkGvmg34AMVSD+s4E6x0ysa/GZo8fQRwjwo4YzcKSFjOF7mH/oVomtG/6yb71+TGjRtRUnJmUfM111yDDRs2xD1vf/cQVr9SCAMzZ674tM/270G5ltmeeMmzAEh/bB4xhk1dKTQJNU1cMfIa5rsj9QDLTDkGADhvbCdWjfcFbdttm4vn+5uA/mzew8zcFwA4plbCJVmx31KP/dZ6XDC6E27Jgq39y4E9AKBBMstwxXAj/qd4NQwho9Z3Ek3ebrzpmI8PjfwF89ydeKVwGd7qnw+8BwjzSlRqg3AYbqimhr39TcD20Pufra/f6XYkkb7//L31PjFQhqNqFV5zLILvhBp2bKu7GaV6Od50zIcpJGD/xOud/uP1zDlWoxRLXIvxlqMdcz1HccnoW+hVSqALGZ1qDQ5ZajF4sjhwTo3PiU8O+L8ePqFgVLKjTD+ToGpCxphkR6elBi2eLjiMzH3P55omZChmOhooZ+7fDCWv06LjqQMFeO/DAyi3Tj4DdoZkZjlNsMrL/VNJJ0+eDIxidXZ2oqmpKezYpqYmvPDCmUazhw4dQn19fczRq4ceeggdHR1Jxbb34+qMG8GSRvph3z6CLC7NmxS3xw1bRh7Z1XHjBQJIsimxcvR9WPd1IptfN2moG7Zd7wZtM+xFmLP8LNwqZ/f+ZeK+mIoKb8tKaNWhj22fF+FoBcBF+E7gdS1OJyfAhRDjw/isozjknKq0xZpx+uWAHGsNWPSaQENDQ0F/ZPopOJPYzQGMWbG/981KOP5PhVFUAfeCCwAI2Hc+D2lsEFp5Azzt5wKKf52f3NcF21+3xP+cphBTCGh1rdDL6qCXVMHSuQtq1/spXTNzP8soFXqZjG+0jKG1tiwt18v5CNbf/u3f4qc//SnuuecevPHGGzhx4gTOPz+8ttIVV1yB22+/He+//z7a29uxadMmfOITn8hYXHOKZ97olbv3EDyO+MflikuYsGeos0FRoRuSI3zBsGvXq4Akwb5odcTzDI8Loyd3wczi183UfBj7YFvw5JWQULj0PJQUZf+fdLrvixASHCsuhFJZl6Yr5q40R3ok/5BCv2TA6Yz3syze94wM3+rzoVQ1BFqQmFVroQ+e9DfOncAsq8VIpwLz1LS1kM98HI1QrbAvPBuu3a/G7T2aTZLNAcusdqh1s4Mq9pvVZ2HM6E2pnl0mf5ZR8pQiwFOYvrnbrP00vv322/HUU0/hxIkTuOyyy1BYWIh9+/bhH//xH/GZz3wGra2tsFgs+OUvfwnlVIn/u+++G3V1dfj85z+PoqIiPPzww1i/fj00TcPixYuxefPmbIU/7fmroR/KdRg5Y3hdkByF/if+3nkNpscNYS+A9+h+SI7wEgem5oWpaf5CrFlui+J+/02Y7uDeipbWJdPm6TDbgpVpTK4oHdSakCceZTksufJvV/zbZRlKZQPUmia433szYtFhIUlQ6+fC2roUktUO7+EPoPV3Z+xzSJRcVAbrvOX+pvERlqUIIWBfdA7GXv1jxCeDJavdXyNwEiVZhKJCLnZCqW6EkGToo4NR+7iGnqfWzgo8ZJRpQgiYpgmlvAZKVSO8h971t8fJg/uWj4QZa9HTFLV9+3asWLECb731VlJThP39/WFzqdOdPtSH0Vf/lOswYnK5XLBn6M8+R8fFUKsb4XrnL/Ae/iBon5AkFF3+/4J+2Lp2b4O3Kzs/1Cby9RyB++2Xg7bJZZWwr7wMQuRmajed98XasgS21qXxD6SE5OJnmeEeDx7xMU2Mv/UCtJPHAtuUskrYFp8LueDM1K1n3y64M1SINxqlrBJCtUIf7ve3bmlsgdrQGtYsOBL33p1BbcSEaoG1eT4sp568jJQgKeU1UMprMXjgPZTOXQDT54FS1Qi5tCLo54vp9WBkyxMwdR2SvQCm2wW1YQ7ksmoISYKwOSA5igNVzsdeexbaQI+/VVNtM3zHIq0HTYzkKIJaNxv6QE/g6VG5sMTfTLmhBYZ7HHLRmTIkpubF6EtPZaz9V7r5f06akAqKA4W0haxAqW6EdVY7hgwpbf9mcj5FSPkhkce2pzPT44LnwDthyRXg7x9ouschTj3mrg30xC2kmtbYDAPe/bvgO3YobOQKsgLbonNzllyli5AV2OafBUtja65DoRSFNsAWQsCxYg1MzzjG33zBX2Zi3nKIkHVfcnkNcCrBEkJAbWiB78ThQGP4SIRqgVJRC33gpL/cihCwNM2DZfZ8eI/sC0qAJlJKK2Bpnh/UN2+yrHMX+T8H04BcUQu5yBloOGxpaAkkWHJhCdT6OVAq6yEX+df2qGV1sMb4JS4s1lOlO6oCFdBj1WKzzl0MqbsTlsY2yCXlGB0ZDCuqHEqy2vylagwDQlFhbV3q//cnyf6RKsOA79gBCEWFUt10pj5cSF09oVhgW3wOvAffzauRLLmgGHJZFeSySmgnjwWSKKWyzv/Ut6z4vz8UCyx1s88UEu6PXcNxMphgEQB/G5eZTOs9Bu1k9PIShmvU/5ekrsG169WYT7umm3fvTngPvRtxn639rIhrx6YSyV4AR8fFkItn1qjxTCKEgLAVoOCctYEkJJRSVoXiy/8ffMcPQrIX+qcaTTPiSLEQAkplPWzzVwa+/02fF6buCzThtjbPh7fzfcjF5TC9LugjgxCSBGvrMlhmL0i5xpmQZFjnLoq4Ty52wr7wbAACamNrUu81cSQ3XqFbpbIuaFpdqayLmGAJ1Qrr3EWwNLRAqBZo/d3Quo/AMmdhWH9NIUmwNLQkFKta1eBP7F76Xc7W0QlFhVxSAclRCMvsBUGjo9E+D2vLkozGxASL/NXXB0/mOoyc8nUfibnfGB8FnNXwfLAjq33ktIGeqMmVUtkApX5qdJWPRnIUoWDlZVM+SaTEREuuJu6f+MtQqWkKS7Ass9pha10a1qFAqJagbUK1oPD8j0CyOWAaOryde/yjSFnqR2ppmpeV94lEqW6E98g+yEWlsLYsgenzwBgbgdrQEtQ8WXFWQ3FWp+U9Jasdat3stK4Hk+yFgK4Fph8lmwPWuYthGjrc770JISQIewEsDS1BzaPzBRMsgq+nK2NtXKYLwzUKfagvoYWn6WJqPrh3b4u4T1issC5cNaX6eillVbDMXgDXjpdhGgYUZ7W/CTUfV6colPIaOJZfBK3vOLTe47AtWAm1sj7h8wPV8yUZ1tkLMhVm3lFKK1F82bVZf19b+wqYHrd/WldSYIwPw3CNRT1eSBJgAqZpQKhWQPcFfjZYGluh1DRB7++GZ/9fYWlq8z/JKvvTFqWiDlJBcV7/DGSCRdC6wyvnUzBzfMTfT9DMTiJqGgZcb78EM1JDbEmCbdE5UyoxkQtL4FhxCYRqgd48H5K9AGpjW17/cKTcE5IMtaYJak2Tf61QBrp2UPoIxQLHiksCr03DgGfvDngPvRfSzkmC2jAX1pbFEIoKY3wUUkExjNEhf4JVdqa7i1JRF2ghNVG2RiJTwQRrhjK9HkC1ALpvxi9wT4Sv+3DW1hboo0P+5CpkKlJY7bDMXQy5tDLoKZ58J1ltgeQKAGzzkiv+SzMbk6upR0gSbPM6YGmaB62/+9SaOCescxYGlb85vf5yupSaOY0J1gxkGjrGt2+BpXk+DNdo3EKAhKwlV4bXDdf2LeEjV7ICx9kfiliTK58JRYWj45IpFzcRpY9kL4Clfg4sU3zN6GQxwZqBtBOHoQ30wDR0/0gW5QXDPY7xN/4ccVrQ2r4i75MUudgJ69xFcL/3Jgz3uL8g49LzIZdW5Do0IqKsY4I1A51+Kkcf6otzJGWLqfkiTgsCgHXBqoQfl043xVkNqajMP7RfWBIozBfKOnsBrG3LICQZcpETY68/B2vrUqhVDVmOmIgoPzDBmmGM8RHoeVQMLhmmYUDvOw5jdAjCaodS1QihTM1vZcPjgueDt6Gd6ARCnuQUtgI4Vl0eVrgxk5TSCkiFpfCd6PTXC2psAQwDktUOy+wF0PtPQOvvgWf/7sA5ckMrbO0rAq+lgiIUXrg+7iP5RETT2dT8rURJ83UfyWqRzEzwvP8mfBN6m8llVadaxUydJ9JMXYN33+6oNa6EvdC/5iqLyZVcVAZ7x8UQigWWWfPOFP6UESioqFTUQS6tgO/IXn+bj+omaE0Lw+NnckVEMxwTrBkmlyUZ9OEBaD1dkIvLIFfWJ5UQ6cP9QckVAOgDPTDHRyAmVO4N2j94EvrIIJSqxrwobWCaJty7XoXWE6W4qSTDvvT8jCZX4lSpB32oD1pPF6yzF0Ctnw2h+J/0i1VVXSgWFF6wDpAVCFmGO42tJYiIpgsmWDOI4XFBH+zNyXvrQ30Yf+N54NTTeGpjK6zzVwY3ODV06EN9EKo1Yo0Tc2wY42/9OeL1DdcYpAgJlu/4Ibh3/R8AwPPBDhReuM5f0C6HtGMHoyZXsrMG1nnLM9o2RsgyHMsvhlJZB7N2FtC2NJBYJXwNS26/hkRE+Y4J1gyi9R3PyfSgaehw/3VbILkCAN+RvZAKSmCZNe/UMQZc21+C3uevyWVdsMq//uf0NXxe6Lu2Rn8Pjyt8m6753/c0zQvf8U5YmtpS/ZSSZrjH4X7/zbDtwl4I+7ILMt6PTwgB++JzA33LhCQDEqfziIjSjQnWDKL3nsjJ+/qOHYQR4ekzredIIMHyHTsQSK4AwLt/F9SGuTA9Luh9x+HZuxPwRS8pYXjDEyzfkb1hC8eNOB3mM8k0dP9omuYL2q7Wz4W1/aysLNS3zF0MtbY54+9DRDTTMcGaQbT+HCVYXZGbf5oefwNP0zDgeff1kH0uePZsh+/wHiCBUbfT1wq81jR4D4YvIDe97rBt2eL5YAf0gZ6gbUptM2yLVmf8vSV7ob9sQt3sjL8XERExwZox9LHYTTcz9r6jQzCi1Ns6nexoPUciJlG+zvcjX1RIUKrqoXWfWcekD/Vi/I0/wxgbhmQvhD7cDxjh1ddz8TUAAG2gJ+zzETYHbPPPyvh7CyHBsewCFvwkIsoiJlgzhNbTlZP39R3dH3Wf6fPANA34Dn8wqWtaW5cCQgQlWMaExft6hPVYgeNGBmBqPghFndR7pkIfOAnX9i3BGyUZ9mUXZnzBvVJeC/vCVZAK8rsKPBHRdMPumTOEdvJY1t/TNAxoxw7GPMZ39EDYtFksSlUj1Ob5EFZ70nGNvvgEzBjrudLJ9Hnh2rk1bN2VtXVpRhubCiHBUj8HjhUXM7kiIsoBjmBNc4bHDega9IHsV2/Xeo/GXfPkeecvCV3LumAlfEKBrb4ZQoiUEiwYOrxd+2CZ1Q7t2CHoQ72QK+qgVDWkVKzU1DX4uvb5G0MbBgATet+JsCccZWc11KZ5ycefANvic2ZcY1UionzCBGsaMw0Drre3QBs4mZP393XuCXotlVbAGBsGfN5JXcfSsgSWxjboLheE8A+6CkvsgqHCXgjJXghL83y4tr8Ytl873gntxGEYw/4imb6ufbCvWAOlonZSsZ1mGrp/DVic/o6ys9pfLV3KzOCxUFR/ixsmV0REOcUEaxrTeo7kLLnSh/vDeh5aGlrgPfgujCgJllLdCMle5G8fIwSU6iaoTW1QyqrCjpWijGBJpRVwnH150EiUXFYVNg0ZqVyD78ShpBIsfbAX468/D5hG7ANlBbZFqyHkzPyzE4oKx8pLoZRWZuT6RESUOCZY05jWl76yDFp/N0z3GOTy2qjJzUTeQ+8FvRYWG5TaZviOHgDGhiOeY21dBqmgGJbZCwBF8RfBjEZRAVkBdC1os23h6rBpPqW6KaF1XqZ7PO4xobSeLrh2bE0oubIvuxCSvXDS75Eo+5LzmFwREeUJJljTmJ6mBMtz4K/w7t3pfyHJsLYuhaV5ftTjDdcotBOdQdvUpjYISY7aYkWpbgq0ukmkDYsQAmpt85kaW4oF9iXnRmyxoza2AKYBz57tMa9puiNUgz9VPiLS2izDNQrXrlfiJldSQTFsS87LaJV2y6x2qNWNGbs+ERFNDhOsacpwj0GPMlI0GfpwP7z7dk24sA7Pnu2QisogZAVSsTNsPZH30PvBda1kGZZGf3uaSGunhGqFtX3FpGOzzl8JuaQCpuaFUtscdWRNSDIszfOhnTwaNm05keE5M4JlmgY8e94+VWZCQK1uhLVteSD5M00T7ndeD2r/E0RWoNY2Q6luguysij0alyK5qAy2eR0Zuz4REU0eE6xpSutL/alB0zDg/utrEYuAut78XwCAVFTmX/N0qs2L6fXAdzS4crta3zJhVCp8JMjSugySzTHp+IQkQW2Ym/DxSs2smAkWNB98xw5CO3k0bATOd3S/fw3V/LPgPfguPB+8HXa67KyBbcm5CU2hpotQLXAsvwhCZj9BIqJ8wgRrmprY1y9Z3oPvxu3dZ4wMwHtkD6yzF/rP6doXPKojBCzN7YGXUlGEKbws9cZTG1oA04Q+1AulqgFKeR1G//fXQce4d78a9Xyt9xh83UciJlfCaod92QUQqiXtccdiX3wu61wREeUhJljTVKoL3PXBXnj374p/IADvBztgDA/A1H3QQwqaKjWzghZ2K1WN8Lz3VmDdkqVlaVaaHAP+dVSWpjYAbRMCVMOKgEZjjo/A/c5rka4M26Jzsp5ccd0VEVH+YoI1DeljwzCSeCJuIvd7byTUZPm00Cm1006vvTpNstrhWHU5fEf3QyoshdrYmlKcqZKsDhjaUOInRCgxYV2wMun6WcmSS8pha+e6KyKifMUEa5rxdu33J0cp0EcGAgU4T1Mb2+A7MrmegcJRBClCg2G5pDyjbWImQ1hswFh4giWsdijVTfAd3hPhrDPHOM65MqtrroBT9a6WXpDRhfNERJQaJljTjNZ9GGaCU14Rz+89DtdbLwRtE1Y7rO0roJ3smlStKLV+bkqtZ7JBOAqAkGVmatM8WNtXQAgBfbgvqJF0gGLJenIlWWwwvG7Y5q/kuisiojzHBGsaMQ0dWqyn5OLQBnrgevulsO1KbbP/ib2aZn+VdQCQZDjOuhSGaxTG2JC//x4Avb8bxtgwlMr6oMXt+UouckLDgTOvy6pgbe8IJIaSoyhigmWbf1Z2kyt7AQrOXQut+wgsk3hykoiIcoMJ1jSiD5xMevTKNA24d28DjPC6TmrdbACAZe4iQFZgjA1BbWiBXFYJuWxqVw5X6+dAO9kFve8EpJJy2JacF+h3CPgTrFByRR2ULD35CPgX59sXnwvJYoMlx2vWiIgoMUywphHt5NGkz9V7j8N0jYZtt8xdDLmoDMCpRsIti5N+j3wkFBX2FWsA04i4pun05x6gqLAtXJXVqU/L3MVQymuy9n5ERJQ6JljTiNbTlfS5vq79wRtkBYUXrk+obc1UJ4QAROQF43JFHaTichjDfYCQYFu4OqmiqMlSnNWwzp1eSS0R0UzABGua0EeHkm6NY3hc0E4GJ2cT28LMZEKS4Fh9OfTBXkg2R0abNQe9ryzDvuxCKJX1ef+gABERhWOCNU1o3YeTPtd39EBY78BsVVefCoSQoJRVZe/9JAn2JedBrWrI2nsSEVF6McGaBkzThDd0im8S5/obGp+hVM/KelVy8hNCwL78IiZXRERTHBOsaUDvOwFjfCSpc43RQZgh51oaWtIRFk2SEBKs85YzuSIimgaYYE0DvhSmB0MXxgt7QcTq65Q5QpJgbVkKtXZWxLIQREQ09TDBmga03uPJn9sTXNpBqWzgouosUqsaoDa1Qa2sz3UoRESURkywpjhjbCT56UHXmL/8wAQKp6eyRq1qgGPFJbkOg4iIMiBugjU2Nobu7m64XC6Ul5ejpoYFD/OBMTYCqaAIWt+xpK/hO3EoeINimfKV2acKyWqHbdHqXIdBREQZIkXauHPnTvz93/89Fi9ejJKSErS2tmLJkiWor6+H0+nEVVddhc2bN2N8PPHGv5Q+puaFa/erAACt98Skzzfc43C/9ya8H+wI2q5WN0asZk7pJYQE+9Lzs9rLkIiIsisowdq2bRsuuugiLF++HFu3bsWll16Khx9+GE899RSeffZZ/Nd//RfuvPNO2O12fPnLX0Z9fT3uvfdejI2N5Sr+Gcl7eC+0gR4Y7nFo/ZNLsAzXGMa3/Qm+w3vC9mWzv95MZluwkq1viIimuaApwquuugq33347Nm/ejObm5pgn+nw+PPPMM/jxj38MwzDwzW9+M5Nx0immYcB7KjnyHnwXps+b+LmmCdfOV2B6PWH7hNUO2Zm9YprpIBeWwHCNwtTDG1TXhlkyAAAgAElEQVTnK7VmFixNbbkOg4iIMiwowers7ERhYWKtQFRVxbp167Bu3TqOYGWR1n0Yhsv/9fYe+WBS5+q9x2EM9UbcZ5nVDiEizhjnJcVZjYJVl2P0laehjwzEPFYoKpSKOvhOdGYpusjk0grYF67KaQxERJQdQb9RE02uQhUUFKQlGIrPN6Fu1WRHbnxH9kbeoVigNramElZWCSFgbV0KAAk1XrbMaoecxVY3kcgFxShYeRn7OxIRzRBRhyzWrFmD999/P5uxUASmrgW91gdOJnUdwz0O7eTRsO3C5oB9yXkQiprUdbNNsthQcM6VUJzVAPyFUeMdb52zAHJBcTbCi0jICmxLzp0yX2MiIkpd1ARry5YtGB4ejrivs7MTX/ziFzMW1EyiD/VF3zc6CO+EUSfD44LhGk3qffwV2yc2dFZQeOm1KLhwPZTKuqSumQvWecshl5QHXscbwbLMXQyhWCAVZKdCuhAShBAQigrJXgDJ5oBj5WVQSln+gohoJkmq0Og777yDRx55BA8++GC645lRDK8bWn93UMIwkWfvrqAEItnRKwDQeo4EvVaqGnIyoiJkOebUplzshD7cH3GfVF4DtX5u8PWswQmWUlYJU/PBGB+BpbEN1uZ2/3H2wrjvnQ6W2fMhFBVCtUKta4ZpGJAstoy+JxER5Z+YCdYPfvADVFdXB20bHR3Fc889h4svvjiTcc0IxuhQoAq74R4PSqZM04Te3w1U1Aa26YPJJVimz+u/1gRKVWNS10qFEBIKzr4cvpNH4dm3K8J+Afui1RjfvgWG219jTbI5YF92IaBr0HSEtfGZ+DVTSitQsPoKAP6nLYUkBV1bKiiBXFQG79H9we8rSTANI6XPTbIXwNI0D5bm9qBaYmw6REQ0M8VMsLZu3Qqr9cyiXEmSUFlZiWuvvRZf//rXMx7cdGeMDsEYHYLp9UAf6gtKFozRQRheNySPO7BNH+hJ6n20k0cBc8L0oCRBmZC4ZYva2AK5tAKmoUdMsJSqBsgl5ZDLqmAcPwQAsDTPh3KqurzoDx/ZOr0GSwgBa/uKM9ul8Nlv2/yzACAowbI2z4ewF8L93htJf16SzYGCVX8DKc56MCIimjliJlhPPvkkzj777GzFMuPop0awtKFemL7g2lRan3/EyfS6/P/X9ahTZ/FoE548BAC5vDbr04NCCFia5wMAJGvkdVOn9ytlVfAdPwS52Bm3ZtTppFRtbIUS50lBxVkNU9cDU4WSowjWecuh9U2+Gv5E9kXnMLkiIqIgbPacQ8boEAz3OPTe42FPw50erTK9/hEsfag3qWksU9eh9Qb3K8xFQ2fZWRN4kk/YwlvEKM7qwJOBckUt7EvPg1o7O2xKMJSQFVhmzYNtXkdCcQhZhlLZALW2GXKxE0KSU0qOrLMXTKmHBIiIKDuYYOWIaRjQh/1PEPqOHYQaMlJzer2V6fX4j01ygbvefwIIKvUgcpJgWZrO1NkSsgKhWgJV6CWrHfal5wf2ywXFkyqrYF8wuVFW+9Lzg6YQJXv0+m/2BWdD2BwY374laLtSXgvLrHk5+VoSEVH+i5pgGSku+qXY9KHeQIJheN2A5gvsM1xjgUXepmnC9HmgDXRHvE48vmMHg17LZRVZf6pNsjnCFtVLVjv0U5+/de7ihAqGpkvo+iwhK5CsNhin1rspZVWQnVUQFjsss+aFtSMSqhX2JedmNWYiIppa8mIEa3BwMOipxPHxcRw4cAA9PT1wOp2B7Vu2bMHatWvR1nZmtGfbtm2w28OnnPKddjJ42s6ckGCFPi1ouscnPYJlmiY8770BLaQ9TC6eHlQbWsKTGqsDGB2CZHNAbWjJekyhhL0Q8LihlNfCseISCHnCk4CqBZLNEUh67YtWM7kiIqKYghKss846C9/4xjdw9dVXQ4rwFFaow4cP40c/+hEaGhpwxx13JB1EaWkpduzYEXj9wAMP4KWXXgpKrk5bsGAB3nzzzaTfK19ofceDXgcnWMH9ArXeY0H7E+E7sjdia5xsT2kJRYV1VnvYdslmP1WW4ZygZCZXJHsBBABHx0UR45EKS2C4x2FpaoNa05T9AImIaEoJSrCuv/56fOELX8Ctt96K9evX47zzzsPixYtRWVkJq9WKwcFBHDx4EG+99RaeeeYZbNu2DVdffTU2btyY1qD+/d//Hffee29ar5lPTMOAEdKgeOI0lB7SkNl3fHJNig2PG54P3g7bLpWUQ3Jkp6L5aZaGloj994TVDrVhbt4sEJdLKqC2nxX16Uq5sBSmexy2CaUgiIiIoglKsDZu3Igbb7wRjz32GB599FFs3rwZmhbcC880TdTW1uJjH/sY/uVf/gWLFi1Ka0Dbtm1DX18frrrqqoj79+zZg46ODsiyjBtuuAFf+MIX0vr+2WCMD4dVFDd1/wiVaYSXY9BDkrF4tO7DIQvbASgW2BaunnywKRCSBMvs+RH3SbYCyCFV2XPJ0jw/5hOLUrET9tpmCDkvZtWJiCjPhf22sNvt+NznPofPfe5zcLvd2LFjB44fPw632w2n04l58+ahubk5YwH94he/wGc/+1koSvgvso6ODnR1daGkpARdXV1Yu3YtKioqcO2110a81saNG1FSUhJ4fc0112DDhg1xYxgYmFxCM1lGTxc0lytomxCD8Pb3wxjugzaaXL/B0/Tu4LY4orQK8pLz4QWAkPfNJKl2NrRxDzDuCdtnWosgvDoQoXhoNJm+L7GYtmIIQ5pUvDNFLu8LxcZ7k594X/JXKvcmdFlTzD/HbTYbVq/O3qjH2NgYfv3rX+P111+PuL+4+Myj+w0NDfjkJz+JrVu3Rk2wHnroIXR0JFYfKVSk9V/pYLjH4ZVNeEIW5ksWBUVOJzzD3XCnsGjfNHSMhkwxWhtboGb5QQAhSShcujrtU5KZui+UGt6X/MV7k594X/JXuu5N/JXsWfSb3/wGS5YsQXt7+KJoADh+/HigfMTIyAiefvppLF++PJshpsTwuOHZvxvG6FDYvtOL2PWhvpTeQx/oCZselHPRFqd+TtbXexEREeWLvEqwfv7zn+PGG28M2nbTTTfh97//PQDgiSeewOLFi7F06VKsXr0aH/rQh3DDDTfkItSkGCMD8B09ELFps6nr/nY4IU8QTpbWG/x0olRSnpa6V0JWYF+4CnJhSfyDAVhmRV57RURENBPk1YrdrVu3hm17+OGHAx9v3Lgx7U8sZpM+OghT12CGLkA/xRgfgTE+ktJ7aCHrr5SK9DylZ21dCktTm7+1z+huSFYb1IZWePbvDjtWKauEXFSalvclIiKaivIqwZrujNHBmPtDa2NNhmma8LzzF5iu4AXy6Uiw5JJyWE7VslLKquABYJu/Ekp1E4zRIZi6D3KxE0plfaANDhER0UzGBCuL9JHYCZbem3yCpfefgO/o/uCNqgVSSWqL9YQkwb5wVaASu1xWAbmwBErNLAgh4Oi4KKXrExERTUdxE6xjx46hq6sLbrc7bN+FF16YkaCmI1PXYMRJsLSBnqSvr0UoRqrWzIIQqS2zs7YshVxSHngtFAtsi86JWTOKiIhopouaYB04cACf+cxn8NprrwHwT0EBgBACpmlCCAE9pFgmRaf1dEVde3XaZNvhBM4zDGg9XcEbFRXWttSesJQstoiFQpWyypSuS0RENN1FTbBuvvlmHD58GP/2b/+GBQsWwGLhuppU+I4dzNi19cGTMH3BxTwLzlkbte1LotS62RBS7vsEEhERTTVRE6zXX38dmzdvxjXXXJPNeKYl0+eF1nssY9cPHb2SisogOQpTuqZcVAq1sSWlaxAREc1UUROs+vp6yDJHL9LBd7IL5qkCqelmmia0npDSDFUNKV3TUj8H9iXnpXQNIiKimSzqCujvfve7+P73v49+9l5LmXbiSPyDkmSMDMJ0jQVtU6oak76eZLVnvSk0ERHRdBN1BGvz5s3o6upCc3Mzli1bhtLS4MKRQgg89dRTGQ9wqjMNI6PTg75jB4JeC3sBpBSKfKqNrRAcuSQiIkpJ1ARrdHQULS1n1uCMjKRWYXymMsaG4j49mCxT1+A7GpxgKdVNSZdQEJIES9O8dIRGREQ0o0VNsF588cVsxjFt6SMDGbu21n0E0LxB2ywNrUlfT6luhGRNvW8hERHRTJdQFUrTNDEyMhKohUWJM4YzmGCFPD0ol9dCKihK+nqWxrZUQyIiIiLESbBeeuklrFmzBna7HaWlpbDb7bj00ksjNmWmyPQMJVimoYf1LlRrZiV9PcleANlZnWpYREREhBhThM8//zzWrl2LtrY2fO1rX0NNTQ2OHz+Oxx9/HJdeein+9Kc/4bLLLstmrFOSkeYpQtM04d23E94D74TtkyuTb+ysprB2i4iIiIJFTbDuuusurF27Fk8++WTQL95vfetbWL9+Pe666y4mWHEYHjcMb3gPx1T4jnwQMbmSip2QrPakr6vUJj/6RURERMGiThHu3r0bt912W9iohhACt912G3bt2pXx4KY6Y3w4rdczDQOefZG/7kplfdLXlRxFkEsqkj6fiIiIgkVNsAoLC3H06NGI+7q6ulBYmForlpnAGEtvaQvtRCfg80bcl0qCpdbN5vQgERFRGkVNsNatW4c777wTzz77bND25557Dt/4xjdw9dVXZzy4qS6dI1imacJ78N2o+6ViZ1LXFULAUj8n2bCIiIgogqhrsO6//37s3r0bV155JYqLi1FdXY3u7m6MjIxg5cqVuP/++7MZ55SUzhEsve8EjNHBiPvUxtakR6CUynpIjuRLOxAREVG4qAlWWVkZtm3bhqeffhqvvPIKBgYG4HQ6cf755+PDH/4wJCmhElozmjGWvhEs34lDEbdLBSWwtCyZ9PUsdbMBIUFtSr4wKREREUUWNcECAEmSsG7dOqxbty5b8UwbpmnCGE9tBMtwj8MYG4ZcWgG9vydon3VeB5S62RCqddKjV5LNAdvCVRCKmlJ8REREFFlQgtXf34/S0lJIkoT+/v64Jzudya37mQlMz3hKPQi1gR64tm8BNF/E/XJ5DSRLcm1tLLPmMbkiIiLKoKAEq7KyEtu2bcPZZ5+NioqKuCMjuq5nNLipLJX1V6ahw717W9TkCqoFUmFpUtcWsgK1oSX+gURERJS0oATrF7/4BebOnRv4mI/uJy+V6UHfkb0wXaNR9ytl1ckvaq9uTHrki4iIiBITlGBdd911gY+vv/76bMcyrSSSYJ1unh2aLPmOHYx5nuysSjouSx1LMhAREWVazEXuoQ4dOoR9+/aho6OD66/iiDdFqA/2wrVzK0yPC0plA9SGuZDLqgFDhzEce/2bXF6TVEyS1Z70uURERJS4qAnWHXfcAV3X8eMf/xgA8Lvf/Q6f+MQn4PP5UFZWhueeew4rVqzIWqBTTbwRLPeet2C6xwEAWs8RaD1HIBWWwjJ7QczzhMUGqaAkqZiU6kYIltcgIiLKuKi/bX/3u9/hrLPOCrz++te/jrVr12LXrl04++yzcdddd2UlwKkoXokGU9dgDPaGbTdGB+HZuzPmteXymqTXX6nVjUmdR0RERJMTNcE6fvw4mpqaAAD79+/Hnj17cNddd2HRokX44he/iDfffDNrQU41pscVs0SDMToU/Vz3WMxrK0lO8QnVAtlZndS5RERENDlRE6ySkhL09PiLWz7//PNwOp2BKUGLxQKXy5WdCKegeBXc9ZGBpK8tO5NLsNSqRghJTvp9iYiIKHFR12BdeOGFuPvuu9Hd3Y0HHngA69evD+zbs2dPYHSLwsVbf2WMRO4pmAjJXpDUeWptc9LvSURERJMTdQTrRz/6EWpqanDnnXeiqakJ9957b2DfL3/5S1xwwQVZCXAqip9gxa+SH0nSTw/aHHx6kIiIKIuijmDV19fjhRdeiLjv2Wefhc3GYpXRxCrRYOoa9KHEEiypoDhoutHaumzSsVjqZsO26Bw+PUhERJRFk6qDdVpxcXG645hWYo1g6f3dgJFYiyHrwlUw3ePQB09CqWqEXFI+qTgkqx22+SshZK69IiIiyqagBGvdunX4wQ9+gNbWVqxbty7miUIIPPXUUxkNbqoyYrS50U4eTewikgS5uByirCrp9VNq3WwIizWpc4mIiCh5QQnWyMhIoIHz8PAwexEmwXCPw4zWpBmA1ns86LVcXgu973jYcXJJecojT0pVQ0rnExERUXKCEqwXX3wx8PGWLVuyHcu0EKtEg+n1hDVxVuuaIydYZcn3GwQAyWqDXFqZ0jWIiIgoOVz5nGaxEqyw+leS5O8/GEGqCZZS2cCF7URERDkS9TfwT37yE9x5550R991555346U9/mrGgcs1McBF6JNESLGNsGK43/zdom1RYCmGN/DRmqqNPnB4kIiLKnagJ1qZNmzB37tyI+9ra2rBp06aMBZVzMdrcxBMtwXL9dVvYNrnYGbW6ulDUpGMQigqloi7p84mIiCg1UROszs5OtLa2Rtw3Z84cHDp0KFMx5Z6e/AiWHiHBMrzuiM2dpeKyiNeQCpIvgyGEgHXOQpZmICIiyqGoCVZxcTEOHjwYcd+BAwfgcDgyFlTOGcmNYJmGDtMV3qzZGI7ce1Au9te1kkNGm6zzOpJ6fwBQm+bBOndx0ucTERFR6qImWJdffjm+/e1v48iRI0Hbu7q68J3vfAdXXnllxoPLFTPJESxjeACmaYRt14fDK7fLZdWQip0AAOvcxYF6VUrNrLCEazLYc5CIiCj3olZy//73v4/Vq1dj3rx5WLNmDerq6nDs2DG88MILqKysxPe+971sxpldSa7B0nqPRdwe2ntQWO2wn7UmUGdMLq1AwUUfhenzQYqy6D0Rks0BubQi6fOJiIgoPaKOYNXV1WHHjh348pe/jL6+PmzZsgV9fX2444478Pbbb6O+vj6bcWZX0glWeD0rANBDpggtLUvCSigISU4puQIAtWYWi8MSERHlgZi9CJ1OJ+69995sxZI/kijTYGpe6BEWshvjozBDehPKRZEXt6eK04NERET5IW4lyoGBAWzduhW/+tWvMDDgH4lxu90wjPC1RtNGgiNYE+tl6SODEddfad2HgzeoFkgZSLDkgmJODxIREeWJqAmWYRj4+te/jsbGRlx00UX4zGc+E3iq8JprrsF3vvOdrAWZbYkuctf7uwMfG6NDEY/xhSRYalVjRiqsq/WRa5YRERFR9kX9TX/33XfjoYcewv333493330XpmkG9q1btw5/+MMfshJgTpgGzARG6Hw9RwPHRSowarhGYQz1BW1TaprSE+MEQpahNrak/bpERESUnKhrsB555BHcd999uO2226CHjOjMnTsX+/fvz3hwOaVrgGSJfchQL0yvG8LmiJhgad3BJS6gWiA7a9IZpf+yNbMgWVJbIE9ERETpE3UEq6+vD/Pnz4+4zzAM+Hy+jAWVD8w467BMQ4cxMgDT4wIQeQTLd6Iz6HWmpgcVLm4nIiLKK1F/27e1teH555+PuO/FF1/EokWLMhZUPoiXYBkjgzB1HYZ73J9sjY8Gn+/1hE8PVmdgelC1QClP/6gYERERJS/qFOGXv/xl3HzzzVBVFR/72McA+Ku4b9u2DQ8++CAeeeSRbMWYG3ESrNPV2U3POIyx4bAnCMOqt0sy5PLqtIYIAGpVQ9SG0URERJQbUROs66+/Hv39/bjnnntw3333AQDWr18Ph8OB7373u7j22muzFmQuxHuSUB/xl6ww3C5goCd8f8jolVRUlpFEKBOjYkRERJSaiAmWaZro7+/HbbfdhltuuQWvvvoqent74XQ6ce6556K4uDjbcWafHnuN2ekGzqbHBS2kkKh/f3CCJZeUpy82ALa2ZfAceAdKCn0LiYiIKDMiJliapqGmpgZPPvkkPvzhD+Pyyy/Pdlw5F2sEyzTNMyNYnnEYI4Nhx4SOYMmnGjung1zshHXuYv+omMzpQSIionwTMcFSVRX19fVh5RlmEjPGCJbpGoWp+ffrg30wfZ6g/YbHHXi68DQpjSNY1tkLAPjXXxEREVH+ifoU4caNG/GjH/0IHo8n2iHTW5TkUus9jrE3/jfwOjS5AgBjNGRES5IhFRSlJSzJXgClZlZarkVERESZEXWRe2dnJ/bs2YOmpiZcfPHFqKqqghAisF8IgX/+539OSxDNzc2w2Wyw2fzFMr/2ta/h4x//eNhxP//5z/H9738fhmHg0ksvxaZNm6AoMftVJy1amQbf0f0wIqy5msgYC26bIxUUQ4j01L+yNLZlpJYWERERpU/U7OTpp5+G1WoFALz++uth+9OZYAHA448/HrO21sGDB/HNb34Tb7/9NqqqqnD11Vfj5z//OW699da0xRAkQoJlGgZ8J4/FPTW0L6FUWJK2sJRKLmonIiLKd1ETrNONnfPF448/jo9+9KOorvbXkvr85z+Pf/qnf8pYghVpBEsfPBlxSjBUphIsyeZI62J5IiIiyoy8mWv61Kc+hcWLF+Omm27CyZMnw/YfPnwYs2adWXvU3NyMw4cPZyweUwtPsEKrtUeTqQRLKa9Ny3WIiIgos2IuYOrt7cWPf/xjvPbaazh+/Dhqa2uxevVq/N3f/R0qKyvTFsTLL7+MpqYm+Hw+3HXXXbjuuuvwpz/9Key4iWvATNOMe92NGzeipORMcnPNNddgw4YNcc8bGRmBaXjh6g+uxq73dkN3uaKcdSourydslMun2KDFOS8aIUTgc/VZCsJimkkGBgZyHQJFwPuSv3hv8hPvS/5K5d44ncEzTFETrL/85S+44ooroOs61qxZg/POOw89PT148MEH8ZOf/ATPPfccVq1alXQgEzU1+auRq6qKL33pS2hra4t4zKFDhwKvOzs7A+dF89BDD6Gjo2PS8ehFRbAPn0RhyBfL3W2Bx26Pea7mGkZQKiVJsJeVJ7XIXSgq1Po58HbugRASiua0Q1isk77OdBL6DUz5gfclf/He5Cfel/yVrnsTNcG6/fbbsXDhQvzxj38MGgUaGhrClVdeiY0bN+KNN95IOYCxsTH4fD6UlpYCAB577DEsX7487LgNGzbg/PPPx913342qqir87Gc/wyc+8YmU3z8aY3wEpmkGjZoZXnf880KnBwtKkn6C0Dp7AaSCYng790AuLZ/xyRUREdFUETXBeuedd/Cb3/wmKLkCgJKSEtx5550Ryygko7u7Gxs2bICu6zBNE3PmzMGjjz4KALjpppuwbt06rFu3DnPmzMG3v/1tnHfeeTAMA2vWrMGNN96YlhgiMXUNpmsMwlF4Zpsn/jRfOtdfqXWzAxXllcr6pK9DRERE2RU1wWppacHgYHgLGMA/ijVnzpy0BDBnzhy8/fbbEfc9/PDDQa9vvvlm3HzzzWl530QY48OQHIUwxkchOQqTS7AKkkuw5MISSI4imIYOISQmWERERFNI1Lmr+++/H9/61rfw0ksvBW3fsmUL7rnnHjzwwAMZDy7X9NFhAICvpwuAvwVOPGFFRpMcwTqdUAlJhuysZnkGIiKiKSTqCNZXvvIVDA0NYc2aNSgpKUFlZSVOnjyJoaEhlJWV4atf/Sq++tWvAvA/6bZz586sBZ0txrg/wTKG+2C4xgDNG/t4rxtmyDotOcUECwCscxYkdQ0iIiLKjagJ1ooVK4IWeM9Exoh/itRwjUHrPRa3NETo9CCEBGEvjHxwDEJWIJedKYOhVLB6OxER0VQSNcF65JFHshhGfjqdMBmuMWh9xxM+/jSpoDjhvoGSvcA/SgZAcVZDSPIkoyUiIqJ8kTeV3POR4XXDcI/DdI9D7+uOf/xo8EMBUlFp3HPk0gpINkdQlXa5ghXbiYiIpjImWHH4pwaN5GpgxVl/JVntsM5ZBKW8FsLmCGxXmGARERFNaTFb5RCgnTyW8LGTTbDkknIolXUQkgzD7Z8elGwOyIXxR76IiIgof3EEKw6tN7EEy/C4w3oQygWxEyW5tAJCkv1J1qkRLDZ0JiIimvqYYMVhar6EjgtdfwVJhnAUxDxHLik/c/jpBIvTg0RERFMeE6w0ifgEYYwehEKWIZeeKcUgWf0Jllxek5kAiYiIKGuYYKVJ2BOEcdZfKeW1EIoaeC0sViilFZCs9ozER0RERNnDBCtNwhe4x15/pVQ1hG1T69PT35GIiIhyiwlWGpimCT0kwYrXIkepDK/OrtbOTmtcRERElBtMsNLA9LjC+hTGGsGSbA5ItvAF8EK1pD02IiIiyj4mWGmg94dUeZcVCHv0JwgnLm4nIiKi6YcJVhqE1spSnDUxG2XLpRWZDomIiIhyiAlWikzThB7SCDpeL0GOYBEREU1vbJWTImN0EKY3uIJ7tGKhQpZRtOZvg8ozEBER0fTDEawUGSMDQa+FzQHJURTxWLmojMkVERHRDMARrCQZYyPwdu2Fr/P9oO1SUVnUczg1SERENDMwwUqCqfkw/vqzYVODACAXRS/PwMXtREREMwOnCJOgnTgcMbkCYte/kss4gkVERDQTMMFKgtZ7NOq+aFOEckl5xOKiRERENP0wwUqC6XVH3iGkqAvc1ZpZGYyIiIiI8gkTrEkyTQP68EDEfVKJE0KK/CVlgkVERDRzMMGaJGNsBNC1iPsUZ3XE7XJJOSRHYSbDIiIiojzCBGuSjKG+qPtkZ03E7Wp1U6bCISIiojzEBGuS9FgJVpQyDEp1Q6bCISIiojzEBGuS9OHICZZSNxtCDi8rJtkckGOUbiAiIqLph4VGJ8E09PDWOI4iqLXNsMxqj3gOa18RERHNPEywJkHv7wYMI2hbweorIFRL1HPYHoeIiGjm4RRhgkzDgGfP20HbhKMoZnIFAEpZVSbDIiIiojzEBCtBWu9RGKODQdvU+rkxzxGyErP5MxEREU1PTLASpPceD3otFRTD0hx53dVpclFp1MKjRERENH3xt3+CtJAES6mfAyHJMc+Rip2ZDImIiIjyFBOsBBjjIzBdo0HblPLauOfJTLCIiFM5ym4AABOTSURBVIhmJCZYCdAHe4NeC4s1obVVTLCIiIhmJiZYCTBcY0GvpWInhBAxzxGSBKmIBUaJiIhmIiZYCTA940GvJVtB3HOkwtK4a7SIiIhoemKCFcK9dye015+F1tMF0zQBAIY7eARLWB1xryOXcHqQiIhopmIl9wm8nXvQ+y9fAwwDLgC25RdCrWqE6QoZwbInkGAVMcEiIiKaqTiCNYFn/+6gVjjasUMAAMMdnGAlMoLFEg1EREQzFxOsCYTFFvTacI3BtXsboHmDj7PFTrCEEJC5wJ2IiGjG4hThBHJZcGNmY7gPxnBf2HFSnARLKiiGUNS0xkZERERTB0ewJki0MXO85InTg0RERDMbE6wJ5AQSLKmwJP51mGARERHNaEywJpDsBRBxalzJZdVxr8MEi4iIaGZjghVCCVmHFUQIWOYuinsNOYE2OkRERDR9McEKEWua0HHuhyFZ7THPlxxFEBZrusMiIiKiKYQJVohYCZZkj98ihxXciYiIiAlWCLWmKeJ2YbFByPGrWsjF5ekOiYiIiKYYJlghHCsugXDWhG0XjsKEzpdLmGARERHNdEywQkiOIqhX3QS1aV7QdqWiPu65Qgg+QUhERESs5B6JUFRY2ldALi6D73gn5OIyWJrb454nFZZAqJYsREhERET5jAlWFEIIqPVzodbPTfgcpaIugxERERHRVMEpwjRSKuNPIxIREdH0xwQrTYSihjWLJiIiopmJCVaayCUVEJKc6zCIiIgoD+Q8wXK73Vi/fj3a2tqwbNkyXHHFFTh06FDYcVu2bIHD4cCyZcsC/7lcruwHHIVcyvIMRERE5JcXi9xvueUWXHnllRBC4KGHHsItt9yC5557Luy4BQsW4M0338xBhPGxwCgRERGdlvMRLJvNhrVr10IIAQBYvXo1Dhw4kOOoJo8jWERERHRazhOsUA8++CA+8pGPRNy3Z88edHR0YOXKldi0aVOWI4tOstoh2eL3KSQiIqKZIS+mCE+77777sHfvXvzsZz8L29fR0YGuri6UlJSgq6sLa9euRUVFBa699tqo19u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      },
      "execution_count": 17,
      "metadata": {},
@@ -708,7 +707,7 @@
    "outputs": [
     {
      "data": {
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      "execution_count": 18,
      "metadata": {},
@@ -728,7 +727,7 @@
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      },
      "execution_count": 19,
      "metadata": {},
@@ -755,15 +754,15 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Julia 1.3.1",
+   "display_name": "Julia 1.6.4",
    "language": "julia",
-   "name": "julia-1.3"
+   "name": "julia-1.6"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.3.1"
+   "version": "1.6.4"
   }
  },
  "nbformat": 4,
diff --git a/demo/nonlinear_online_estimation.ipynb b/demo/nonlinear_online_estimation.ipynb
index 1d3e0212..b6744c3f 100644
--- a/demo/nonlinear_online_estimation.ipynb
+++ b/demo/nonlinear_online_estimation.ipynb
@@ -13,16 +13,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Precompiling PyPlot [d330b81b-6aea-500a-939a-2ce795aea3ee]\n",
-      "└ @ Base loading.jl:1273\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "using ForneyLab\n",
     "using PyPlot"
@@ -68,7 +59,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "Figure(PyObject <Figure size 640x480 with 1 Axes>)"
       ]
@@ -140,64 +131,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "begin\n",
-      "\n",
-      "function init()\n",
-      "\n",
-      "messages = Array{Message}(undef, 6)\n",
-      "\n",
-      "messages[1] = Message(vague(GaussianMeanVariance))\n",
-      "messages[2] = Message(vague(GaussianMeanVariance))\n",
-      "\n",
-      "return messages\n",
-      "\n",
-      "end\n",
-      "\n",
-      "function step!(data::Dict, marginals::Dict=Dict(), messages::Vector{Message}=Array{Message}(undef, 6))\n",
-      "\n",
-      "messages[1] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=data[:m_a_t_min]), Message(Univariate, PointMass, m=data[:v_a_t_min]))\n",
-      "messages[2] = ruleSPGaussianMeanVarianceOutNPP(nothing, Message(Univariate, PointMass, m=data[:m_x_t_min]), Message(Univariate, PointMass, m=data[:v_x_t_min]))\n",
-      "messages[3] = ruleSPGaussianMeanVarianceMPNP(Message(Univariate, PointMass, m=data[:y_t]), nothing, Message(Univariate, PointMass, m=0.2))\n",
-      "messages[4] = ruleSPNonlinearUTInGX(g, 1, messages[3], messages[2], messages[1])\n",
-      "messages[5] = ruleSPNonlinearUTOutNGX(g, nothing, messages[2], messages[1])\n",
-      "messages[6] = ruleSPNonlinearUTInGX(g, 2, messages[3], messages[2], messages[1])\n",
-      "\n",
-      "marginals[:a] = messages[1].dist * messages[6].dist\n",
-      "marginals[:x_t] = messages[5].dist * messages[3].dist\n",
-      "marginals[:x_t_min] = messages[2].dist * messages[4].dist\n",
-      "marginals[:x_t_min_a] = ruleMNonlinearUTInGX(g, messages[3], messages[2], messages[1])\n",
-      "\n",
-      "return marginals\n",
-      "\n",
-      "end\n",
-      "\n",
-      "function freeEnergy(data::Dict, marginals::Dict)\n",
-      "\n",
-      "F = 0.0\n",
-      "\n",
-      "F += averageEnergy(GaussianMeanVariance, marginals[:x_t_min], ProbabilityDistribution(Univariate, PointMass, m=data[:m_x_t_min]), ProbabilityDistribution(Univariate, PointMass, m=data[:v_x_t_min]))\n",
-      "F += averageEnergy(GaussianMeanVariance, marginals[:a], ProbabilityDistribution(Univariate, PointMass, m=data[:m_a_t_min]), ProbabilityDistribution(Univariate, PointMass, m=data[:v_a_t_min]))\n",
-      "F += averageEnergy(GaussianMeanVariance, ProbabilityDistribution(Univariate, PointMass, m=data[:y_t]), marginals[:x_t], ProbabilityDistribution(Univariate, PointMass, m=0.2))\n",
-      "\n",
-      "F -= differentialEntropy(marginals[:x_t_min_a])\n",
-      "\n",
-      "return F\n",
-      "\n",
-      "end\n",
-      "\n",
-      "end # block\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "println(code)"
+    "# println(code)"
    ]
   },
   {
@@ -278,7 +216,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "Figure(PyObject <Figure size 640x480 with 3 Axes>)"
       ]
@@ -323,15 +261,15 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Julia 1.3.0",
+   "display_name": "Julia 1.6.4",
    "language": "julia",
-   "name": "julia-1.3"
+   "name": "julia-1.6"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.3.0"
+   "version": "1.6.4"
   }
  },
  "nbformat": 4,
diff --git a/demo/variational_laplace_and_sampling.ipynb b/demo/variational_laplace_and_sampling.ipynb
index 4dca8eb0..9196da77 100644
--- a/demo/variational_laplace_and_sampling.ipynb
+++ b/demo/variational_laplace_and_sampling.ipynb
@@ -27,16 +27,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "┌ Info: Precompiling ForneyLab [9fc3f58a-c2cc-5bff-9419-6a294fefdca9]\n",
-      "└ @ Base loading.jl:1273\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "using ForneyLab, LinearAlgebra\n",
     "\n",
@@ -206,7 +197,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The marginal for l is a ProbabilityDistribution{Univariate,SampleList} with mean 0.636 and variance 0.014\n"
+      "The marginal for l is a ProbabilityDistribution{Univariate, SampleList} with mean 0.611 and variance 0.014\n"
      ]
     }
    ],
@@ -320,7 +311,7 @@
     {
      "data": {
       "text/plain": [
-       "𝒩(xi=1.40, w=2.33)\n"
+       "𝒩(xi=1.40, w=2.32)\n"
       ]
      },
      "execution_count": 14,
@@ -341,7 +332,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Free energy per iteration: 2.037, 1.952, 1.957, 1.942, 1.943"
+      "Free energy per iteration: 2.041, 1.946, 1.938, 1.943, 1.939"
      ]
     }
    ],
@@ -437,7 +428,7 @@
     {
      "data": {
       "text/plain": [
-       "Dir(a=[2.35, 5.15, 3.20])\n"
+       "Dir(a=[2.34, 5.16, 3.20])\n"
       ]
      },
      "execution_count": 19,
@@ -458,10 +449,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The marginal for x is a ProbabilityDistribution{Univariate,SampleList} with mean vector entries\n",
-      "  [1]  =  0.227738\n",
-      "  [2]  =  0.772048\n",
-      "  [3]  =  0.000214287\n"
+      "The marginal for x is a ProbabilityDistribution{Univariate, SampleList} with mean vector entries\n",
+      "  [1]  =  0.225319\n",
+      "  [2]  =  0.774444\n",
+      "  [3]  =  0.000237294\n"
      ]
     }
    ],
@@ -480,7 +471,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Free energy per iteration: 2.463, 2.459, 2.468, 2.416, 2.449"
+      "Free energy per iteration: 2.504, 2.43, 2.44, 2.426, 2.492"
      ]
     }
    ],
@@ -553,7 +544,7 @@
     {
      "data": {
       "text/plain": [
-       "𝒩(xi=0.91, w=0.99)\n"
+       "𝒩(xi=0.77, w=0.85)\n"
       ]
      },
      "execution_count": 25,
@@ -573,7 +564,7 @@
     {
      "data": {
       "text/plain": [
-       "𝒩(xi=6.75, w=3.63)\n"
+       "𝒩(xi=6.10, w=3.29)\n"
       ]
      },
      "execution_count": 26,
@@ -594,7 +585,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The marginal for m is a ProbabilityDistribution{Univariate,SampleList} with mean 4.229 and variance 0.979\n"
+      "The marginal for m is a ProbabilityDistribution{Univariate, SampleList} with mean 4.175 and variance 0.902\n"
      ]
     }
    ],
@@ -617,15 +608,15 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Julia 1.3.0",
+   "display_name": "Julia 1.6.4",
    "language": "julia",
-   "name": "julia-1.3"
+   "name": "julia-1.6"
   },
   "language_info": {
    "file_extension": ".jl",
    "mimetype": "application/julia",
    "name": "julia",
-   "version": "1.3.0"
+   "version": "1.6.4"
   }
  },
  "nbformat": 4,
diff --git a/src/engines/julia/generators.jl b/src/engines/julia/generators.jl
index 038ef807..6e52e319 100644
--- a/src/engines/julia/generators.jl
+++ b/src/engines/julia/generators.jl
@@ -192,7 +192,7 @@ function vagueSourceCode(entry::ScheduleEntry)
     family_code = removePrefix(entry.family)
     dims = entry.dimensionality
     if dims == ()
-        vague_code = "vague($family_code)"
+        vague_code = "vague($family_code)" # Default
     else
         vague_code = "vague($family_code, $dims)"
     end
diff --git a/src/engines/julia/update_rules/gaussian_mean_precision.jl b/src/engines/julia/update_rules/gaussian_mean_precision.jl
index 046f693b..bf62caf1 100644
--- a/src/engines/julia/update_rules/gaussian_mean_precision.jl
+++ b/src/engines/julia/update_rules/gaussian_mean_precision.jl
@@ -54,7 +54,7 @@ function ruleVBGaussianMeanPrecisionW(  dist_out::ProbabilityDistribution{Multiv
     (m_mean, v_mean) = unsafeMeanCov(dist_mean)
     (m_out, v_out) = unsafeMeanCov(dist_out)
 
-    Message(MatrixVariate, Wishart, v=cholinv( v_mean + v_out + (m_mean - m_out)*(m_mean - m_out)' ), nu=dims(dist_out) + 2.0) 
+    Message(MatrixVariate, Wishart, v=cholinv( v_mean + v_out + (m_mean - m_out)*(m_mean - m_out)' ), nu=dims(dist_out)[1] + 2.0) 
 end
 
 ruleVBGaussianMeanPrecisionOut( dist_out::Any,
@@ -63,21 +63,21 @@ ruleVBGaussianMeanPrecisionOut( dist_out::Any,
     Message(V, GaussianMeanPrecision, m=unsafeMean(dist_mean), w=unsafeMean(dist_prec))
 
 ruleSVBGaussianMeanPrecisionOutVGD(dist_out::Any,
-                                   msg_mean::Message{F, V},
-                                   dist_prec::ProbabilityDistribution) where{F<:Gaussian, V<:VariateType} = 
+                                   msg_mean::Message{<:Gaussian, V},
+                                   dist_prec::ProbabilityDistribution) where V<:VariateType = 
     Message(V, GaussianMeanVariance, m=unsafeMean(msg_mean.dist), v=unsafeCov(msg_mean.dist) + cholinv(unsafeMean(dist_prec)))
 
 function ruleSVBGaussianMeanPrecisionW(
     dist_out_mean::ProbabilityDistribution{Multivariate, F},
     dist_prec::Any) where F<:Gaussian
 
-    joint_dims = dims(dist_out_mean)
+    joint_d = dims(dist_out_mean)[1]
     d_out_mean = convert(ProbabilityDistribution{Multivariate, GaussianMeanVariance}, dist_out_mean)
     (m, V) = unsafeMeanCov(d_out_mean)
-    if joint_dims == 2
+    if joint_d == 2
         return Message(Univariate, Gamma, a=1.5, b=0.5*(V[1,1] - V[1,2] - V[2,1] + V[2,2] + (m[1] - m[2])^2))
     else
-        d = Int64(joint_dims/2)
+        d = Int64(joint_d/2)
         return Message(MatrixVariate, Wishart, v=cholinv( V[1:d,1:d] - V[1:d,d+1:end] - V[d+1:end, 1:d] + V[d+1:end,d+1:end] + (m[1:d] - m[d+1:end])*(m[1:d] - m[d+1:end])' ), nu=d + 2.0) 
     end
 end
diff --git a/src/engines/julia/update_rules/gaussian_mixture.jl b/src/engines/julia/update_rules/gaussian_mixture.jl
index 8cc93e44..d1ee343a 100644
--- a/src/engines/julia/update_rules/gaussian_mixture.jl
+++ b/src/engines/julia/update_rules/gaussian_mixture.jl
@@ -55,7 +55,7 @@ function ruleVBGaussianMixtureW(dist_out::ProbabilityDistribution,
     (m_mean_k, v_mean_k) = unsafeMeanCov(dist_means[k])
     (m_out, v_out) = unsafeMeanCov(dist_out)
     z_bar = unsafeMeanVector(dist_switch)
-    d = dims(dist_means[1])
+    d = dims(dist_means[1])[1]
 
     return Message(MatrixVariate, Wishart,
         nu = 1.0 + z_bar[k] + d,
@@ -123,7 +123,7 @@ function ruleVBGaussianMixtureOut(  dist_out::Any,
     dist_means = collect(dist_factors[1:2:end])
     dist_precs = collect(dist_factors[2:2:end])
     z_bar = unsafeMeanVector(dist_switch)
-    d = dims(dist_means[1])
+    d = dims(dist_means[1])[1]
 
     w = Diagonal(zeros(d))
     xi = zeros(d)
diff --git a/src/engines/julia/update_rules/multiplication.jl b/src/engines/julia/update_rules/multiplication.jl
index e4f48983..d61f3e51 100644
--- a/src/engines/julia/update_rules/multiplication.jl
+++ b/src/engines/julia/update_rules/multiplication.jl
@@ -139,7 +139,6 @@ function ruleSPMultiplicationIn1GNP(msg_out::Message{F, Multivariate},
 
     dist_a_matr = convert(ProbabilityDistribution{MatrixVariate, PointMass}, msg_a.dist)
     msg_in1_mult = ruleSPMultiplicationIn1GNP(msg_out, nothing, Message(dist_a_matr))
-    (dims(msg_in1_mult.dist) == 1) || error("Implicit conversion to Univariate failed for $(msg_in1_mult.dist)")
 
     return Message(Univariate, GaussianWeightedMeanPrecision, xi=msg_in1_mult.dist.params[:xi][1], w=msg_in1_mult.dist.params[:w][1,1])
 end
diff --git a/src/engines/julia/update_rules/nonlinear_extended.jl b/src/engines/julia/update_rules/nonlinear_extended.jl
index b4686b72..9c2aeee0 100644
--- a/src/engines/julia/update_rules/nonlinear_extended.jl
+++ b/src/engines/julia/update_rules/nonlinear_extended.jl
@@ -6,10 +6,10 @@ ruleSPNonlinearEInGX,
 ruleMNonlinearEInGX
 
 """
-Concatenate a vector of vectors and return with original dimensions (for splitting)
+Concatenate a vector (of vectors and floats) and return with original dimensions (for splitting)
 """
-function concatenate(xs::Vector{Vector{Float64}})
-    ds = [length(x_k) for x_k in xs] # Extract dimensions
+function concatenate(xs::Vector)
+    ds = [size(x_k) for x_k in xs] # Extract dimensions
     x = vcat(xs...)
 
     return (x, ds)
@@ -17,22 +17,27 @@ end
 
 """
 Return local linearization of g around expansion point x_hat
+for Nonlinear node with single input interface
 """
-function localLinearization(V::Type{Univariate}, g::Function, x_hat::Float64)
+function localLinearizationSingleIn(g::Function, x_hat::Float64)
     a = ForwardDiff.derivative(g, x_hat)
     b = g(x_hat) - a*x_hat
 
     return (a, b)
 end
 
-function localLinearization(V::Type{Multivariate}, g::Function, x_hat::Vector{Float64})
+function localLinearizationSingleIn(g::Function, x_hat::Vector{Float64})
     A = ForwardDiff.jacobian(g, x_hat)
     b = g(x_hat) - A*x_hat
 
     return (A, b)
 end
 
-function localLinearization(V::Type{Univariate}, g::Function, x_hat::Vector{Float64})
+"""
+Return local linearization of g around expansion point x_hat
+for Nonlinear node with multiple input interfaces
+"""
+function localLinearizationMultiIn(g::Function, x_hat::Vector{Float64})
     g_unpacked(x::Vector) = g(x...)
     A = ForwardDiff.gradient(g_unpacked, x_hat)'
     b = g(x_hat...) - A*x_hat
@@ -40,7 +45,7 @@ function localLinearization(V::Type{Univariate}, g::Function, x_hat::Vector{Floa
     return (A, b)
 end
 
-function localLinearization(V::Type{Multivariate}, g::Function, x_hat::Vector{Vector{Float64}})
+function localLinearizationMultiIn(g::Function, x_hat::Vector{Vector{Float64}})
     (x_cat, ds) = concatenate(x_hat)
     g_unpacked(x::Vector) = g(split(x, ds)...)
     A = ForwardDiff.jacobian(g_unpacked, x_cat)
@@ -57,74 +62,82 @@ end
 # Forward rule
 function ruleSPNonlinearEOutNG(g::Function,
                                msg_out::Nothing,
-                               msg_in1::Message{<:Gaussian, V}) where V<:VariateType
+                               msg_in1::Message{<:Gaussian})
     
     (m_in1, V_in1) = unsafeMeanCov(msg_in1.dist)
-    (A, b) = localLinearization(V, g, m_in1)
+    (A, b) = localLinearizationSingleIn(g, m_in1)
+    m = A*m_in1 + b
+    V = A*V_in1*A'
 
-    return Message(GaussianMeanVariance, A*m_in1 + b, A*V_in1*A') # Automatically determine VariateType
+    return Message(variateType(m), GaussianMeanVariance, m=m, v=V)
 end
 
 # Multi-argument forward rule
 function ruleSPNonlinearEOutNGX(g::Function, # Needs to be in front of Vararg
                                 msg_out::Nothing,
-                                msgs_in::Vararg{Message{<:Gaussian, V}}) where V<:VariateType
+                                msgs_in::Vararg{Message{<:Gaussian}})
 
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
-    (A, b) = localLinearization(V, g, ms_fw_in)
+    (A, b) = localLinearizationMultiIn(g, ms_fw_in)
     (m_fw_in, V_fw_in, _) = concatenateGaussianMV(ms_fw_in, Vs_fw_in)
+    m = A*m_fw_in + b
+    V = A*V_fw_in*A'
 
-    return Message(GaussianMeanVariance, A*m_fw_in + b, A*V_fw_in*A') # Automatically determine VariateType
+    return Message(variateType(m), GaussianMeanVariance, m=m, v=V)
 end
 
 # Backward rule with given inverse
 function ruleSPNonlinearEIn1GG(g::Function,
                                g_inv::Function,
-                               msg_out::Message{<:Gaussian, V},
-                               msg_in1::Nothing) where V<:VariateType
+                               msg_out::Message{<:Gaussian},
+                               msg_in1::Nothing)
 
     (m_out, V_out) = unsafeMeanCov(msg_out.dist)
-    (A, b) = localLinearization(V, g_inv, m_out)
+    (A, b) = localLinearizationSingleIn(g_inv, m_out)
+    m = A*m_out + b
+    V = A*V_out*A'
 
-    return Message(GaussianMeanVariance, A*m_out + b, A*V_out*A') # Automatically determine VariateType
+    return Message(variateType(m), GaussianMeanVariance, m=m, v=V)
 end
 
 # Multi-argument backward rule with given inverse
 function ruleSPNonlinearEInGX(g::Function, # Needs to be in front of Vararg
                               g_inv::Function,
                               msg_out::Message{<:Gaussian},
-                              msgs_in::Vararg{Union{Message{<:Gaussian, V}, Nothing}}) where V<:VariateType
+                              msgs_in::Vararg{Union{Message{<:Gaussian}, Nothing}})
 
     (ms, Vs) = collectStatistics(msg_out, msgs_in...) # Returns arrays with individual means and covariances
-    (A, b) = localLinearization(V, g_inv, ms)
+    (A, b) = localLinearizationMultiIn(g_inv, ms)
     (mc, Vc) = concatenateGaussianMV(ms, Vs)
+    m = A*mc + b
+    V = A*Vc*A'
 
-    return Message(V, GaussianMeanVariance, m=A*mc, v=A*Vc*A')
+    return Message(variateType(m), GaussianMeanVariance, m=m, v=V)
 end
 
 # Backward rule with unknown inverse
 function ruleSPNonlinearEIn1GG(g::Function,
                                msg_out::Message{<:Gaussian},
-                               msg_in1::Message{<:Gaussian, V}) where V<:VariateType
+                               msg_in1::Message{<:Gaussian})
 
     m_in1 = unsafeMean(msg_in1.dist)
-    d_out = convert(ProbabilityDistribution{V, GaussianMeanPrecision}, msg_out.dist)
-    m_out = d_out.params[:m]
-    W_out = d_out.params[:w]
-    (A, b) = localLinearization(V, g, m_in1)
+    (m_out, W_out) = unsafeMeanPrecision(msg_out.dist)
+    (A, b) = localLinearizationSingleIn(g, m_in1)
+    xi = A'*W_out*(m_out - b)
+    W = A'*W_out*A
 
-    return Message(V, GaussianWeightedMeanPrecision, xi=A'*W_out*(m_out - b), w=A'*W_out*A)
+    return Message(variateType(xi), GaussianWeightedMeanPrecision, xi=xi, w=W)
 end
 
 # Multi-argument backward rule with unknown inverse
 function ruleSPNonlinearEInGX(g::Function,
                               inx::Int64, # Index of inbound interface inx
                               msg_out::Message{<:Gaussian},
-                              msgs_in::Vararg{Message{<:Gaussian, V}}) where V<:VariateType
+                              msgs_in::Vararg{Message{<:Gaussian}})
 
     # Approximate joint inbounds
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
-    (A, b) = localLinearization(V, g, ms_fw_in)
+    (A, b) = localLinearizationMultiIn(g, ms_fw_in)
     
     (m_fw_in, V_fw_in, ds) = concatenateGaussianMV(ms_fw_in, Vs_fw_in)
     m_fw_out = A*m_fw_in + b
@@ -136,27 +149,27 @@ function ruleSPNonlinearEInGX(g::Function,
     (m_in, V_in) = smoothRTS(m_fw_out, V_fw_out, C_fw, m_fw_in, V_fw_in, m_bw_out, V_bw_out)
 
     # Marginalize joint belief on in's
-    (m_inx, V_inx) = marginalizeGaussianMV(V, m_in, V_in, ds, inx) # Marginalization is overloaded on VariateType V
+    (m_inx, V_inx) = marginalizeGaussianMV(m_in, V_in, ds, inx)
     W_inx = cholinv(V_inx) # Convert to canonical statistics
     xi_inx = W_inx*m_inx
 
     # Divide marginal on inx by forward message
     (xi_fw_inx, W_fw_inx) = unsafeWeightedMeanPrecision(msgs_in[inx].dist)
     xi_bw_inx = xi_inx - xi_fw_inx
-    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef inconsistencies
+    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef violations
 
-    return Message(V, GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
+    return Message(variateType(xi_bw_inx), GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
 end
 
 function ruleMNonlinearEInGX(g::Function,
                              msg_out::Message{<:Gaussian},
-                             msgs_in::Vararg{Message{<:Gaussian, V}}) where V<:VariateType
+                             msgs_in::Vararg{Message{<:Gaussian}})
 
     # Approximate joint inbounds
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
-    (A, b) = localLinearization(V, g, ms_fw_in)
+    (A, b) = localLinearizationMultiIn(g, ms_fw_in)
     
-    (m_fw_in, V_fw_in, ds) = concatenateGaussianMV(ms_fw_in, Vs_fw_in)
+    (m_fw_in, V_fw_in, _) = concatenateGaussianMV(ms_fw_in, Vs_fw_in)
     m_fw_out = A*m_fw_in + b
     V_fw_out = A*V_fw_in*A'
     C_fw = V_fw_in*A'
diff --git a/src/engines/julia/update_rules/nonlinear_sampling.jl b/src/engines/julia/update_rules/nonlinear_sampling.jl
index 01f0ae97..7d89301c 100644
--- a/src/engines/julia/update_rules/nonlinear_sampling.jl
+++ b/src/engines/julia/update_rules/nonlinear_sampling.jl
@@ -3,9 +3,9 @@ ruleSPNonlinearSOutNM,
 ruleSPNonlinearSIn1MN,
 ruleSPNonlinearSOutNGX,
 ruleSPNonlinearSInGX,
-ruleSPNonlinearSOutNFactorX,
-ruleSPNonlinearSInFactorX,
-ruleMNonlinearSInGX,
+ruleSPNonlinearSOutNMX,
+ruleSPNonlinearSInMX,
+ruleMNonlinearSInMGX,
 prod!
 
 const default_n_samples = 1000 # Default value for the number of samples
@@ -15,78 +15,59 @@ const default_n_samples = 1000 # Default value for the number of samples
 # Sampling Update Rules
 #----------------------
 
-# Custom message constructors for undetermined VariateType
-Message(::Type{SampleList}, s::Vector{Float64}, w::Vector{Float64}) = Message(Univariate, SampleList, s=s, w=w)
-Message(::Type{SampleList}, s::Vector{<:AbstractVector}, w::Vector{Float64}) = Message(Multivariate, SampleList, s=s, w=w)
-Message(::Type{SampleList}, s::Vector{<:AbstractMatrix}, w::Vector{Float64}) = Message(MatrixVariate, SampleList, s=s, w=w)
-
 function ruleSPNonlinearSOutNM(g::Function,
                                msg_out::Nothing,
-                               msg_in1::Message{F, V};
-                               n_samples=default_n_samples,
-                               variate=V) where {F<:FactorFunction, V<:VariateType}
+                               msg_in1::Message; # Applies to any message except SampleList
+                               dims::Any=nothing,
+                               n_samples=default_n_samples)
 
     samples = g.(sample(msg_in1.dist, n_samples))
     weights = ones(n_samples)/n_samples
 
-    return Message(variate, SampleList, s=samples, w=weights)
-end
-
-function ruleSPNonlinearSIn1MN(g::Function,
-                               msg_out::Message{<:FactorFunction, V},
-                               msg_in1::Nothing;
-                               n_samples=default_n_samples,
-                               variate=V) where {F<:FactorFunction, V<:VariateType}
-
-    return Message(variate, Function, log_pdf = (z)->logPdf(msg_out.dist, g(z)))
+    return Message(variateType(dims), SampleList, s=samples, w=weights)
 end
 
 function ruleSPNonlinearSOutNM(g::Function,
                                msg_out::Nothing,
-                               msg_in1::Message{SampleList, V};
-                               n_samples=default_n_samples,
-                               variate=V) where {V<:VariateType}
+                               msg_in1::Message{SampleList}; # Special case for SampleList
+                               dims::Any=nothing,
+                               n_samples=default_n_samples)
 
     samples = g.(msg_in1.dist.params[:s])
     weights = msg_in1.dist.params[:w]
 
-    return Message(variate, SampleList, s=samples, w=weights)
+    return Message(variateType(dims), SampleList, s=samples, w=weights)
 end
 
-function msgSPNonlinearSOutNGX(g::Function,
-    msg_out::Nothing,
-    msgs_in::Vararg{Message{<:Gaussian, <:VariateType}};
-    n_samples=default_n_samples,
-    variate)
-
-    samples_in = [sample(msg_in.dist, n_samples) for msg_in in msgs_in]
-
-    samples = g.(samples_in...)
-    weights = ones(n_samples)/n_samples
+function ruleSPNonlinearSIn1MN(g::Function,
+                               msg_out::Message,
+                               msg_in1::Nothing;
+                               dims::Any=nothing,
+                               n_samples=default_n_samples)
 
-    return Message(variate, SampleList, s=samples, w=weights)
+    return Message(variateType(dims), Function, log_pdf = (z)->logPdf(msg_out.dist, g(z)))
 end
 
-function ruleSPNonlinearSOutNGX(g::Function, variate,
+function ruleSPNonlinearSOutNGX(g::Function,
                                 msg_out::Nothing,
-                                msgs_in::Vararg{Message{<:Gaussian, <:VariateType}};
+                                msgs_in::Vararg{Message{<:Gaussian}};
+                                dims::Any=nothing,
                                 n_samples=default_n_samples)
-    return msgSPNonlinearSOutNGX(g, msg_out, msgs_in..., n_samples=n_samples, variate=variate)
-end
 
-function ruleSPNonlinearSOutNGX(g::Function,
-                                msg_out::Nothing,
-                                msgs_in::Vararg{Message{<:Gaussian, V}};
-                                n_samples=default_n_samples) where V<:VariateType
-    return msgSPNonlinearSOutNGX(g, msg_out, msgs_in..., n_samples=n_samples, variate=V)
+    samples_in = [sample(msg_in.dist, n_samples) for msg_in in msgs_in]
+
+    samples = g.(samples_in...)
+    weights = ones(n_samples)/n_samples
+
+    return Message(variateType(dims), SampleList, s=samples, w=weights)
 end
 
-function msgSPNonlinearSInGX(g::Function,
-                             inx::Int64, # Index of inbound interface inx
-                             msg_out::Message{<:FactorFunction, <:VariateType},
-                             msgs_in::Vararg{Message{<:Gaussian, <:VariateType}};
-                             n_samples=default_n_samples,
-                             variate)
+function ruleSPNonlinearSInGX(g::Function,
+                              inx::Int64, # Index of inbound interface inx
+                              msg_out::Message,
+                              msgs_in::Vararg{Message{<:Gaussian}};
+                              dims::Any=nothing,
+                              n_samples=default_n_samples)
 
     # Extract joint statistics of inbound messages
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Return arrays with individual means and covariances
@@ -94,84 +75,51 @@ function msgSPNonlinearSInGX(g::Function,
     W_fw_in = cholinv(V_fw_in) # Convert to canonical statistics
 
     # Construct joint log-pdf function and gradient
-    (log_joint, d_log_joint) = logJointPdfs(variate, m_fw_in, W_fw_in, msg_out.dist, g, ds) # Overloaded on VariateType V
+    (log_joint, d_log_joint) = logJointPdfs(m_fw_in, W_fw_in, msg_out.dist, g, ds)
 
     # Compute joint belief on in's by gradient ascent
     m_in = gradientOptimization(log_joint, d_log_joint, m_fw_in, 0.01)
     V_in = cholinv(-ForwardDiff.jacobian(d_log_joint, m_in))
 
     # Marginalize joint belief on in's
-    (m_inx, V_inx) = marginalizeGaussianMV(variate, m_in, V_in, ds, inx) # Marginalization is overloaded on VariateType V
+    (m_inx, V_inx) = marginalizeGaussianMV(m_in, V_in, ds, inx)
     W_inx = cholinv(V_inx) # Convert to canonical statistics
     xi_inx = W_inx*m_inx
 
     # Divide marginal on inx by forward message
     (xi_fw_inx, W_fw_inx) = unsafeWeightedMeanPrecision(msgs_in[inx].dist)
     xi_bw_inx = xi_inx - xi_fw_inx
-    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef inconsistencies
+    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef violations
 
-    return Message(variate, GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
+    return Message(variateType(dims), GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
 end
 
-function ruleSPNonlinearSInGX(g::Function,
-                              inx::Int64, # Index of inbound interface inx
-                              msg_out::Message{<:FactorFunction, V},
-                              msgs_in::Vararg{Message{<:Gaussian, V}};
-                              n_samples=default_n_samples) where V<:VariateType
-
-    msgSPNonlinearSInGX(g, inx, msg_out, msgs_in..., n_samples=n_samples, variate=V)
-end
-
-function ruleSPNonlinearSInGX(g::Function, variate,
-                              inx::Int64, # Index of inbound interface inx
-                              msg_out::Message{<:FactorFunction, <:VariateType},
-                              msgs_in::Vararg{Message{<:Gaussian, <:VariateType}};
-                              n_samples=default_n_samples)
-    msgSPNonlinearSInGX(g, inx, msg_out, msgs_in..., n_samples=n_samples, variate=variate)
-end
-
-function msgSPNonlinearSOutNFactorX(g::Function,
-                                    msg_out::Nothing,
-                                    msgs_in::Vararg{Message{<:FactorNode}};
-                                    n_samples=default_n_samples,
-                                    variate)
+function ruleSPNonlinearSOutNMX(g::Function,
+                                msg_out::Nothing,
+                                msgs_in::Vararg{Message};
+                                dims::Any=nothing,
+                                n_samples=default_n_samples)
+                                     
     samples_in = [sample(msg_in.dist, n_samples) for msg_in in msgs_in]
-
     samples = g.(samples_in...)
     weights = ones(n_samples)/n_samples
 
-    return Message(variate, SampleList, s=samples, w=weights)
-end
-
-function ruleSPNonlinearSOutNFactorX(g::Function,
-                                     msg_out::Nothing,
-                                     msgs_in::Vararg{Message{<:FactorNode, V}};
-                                     n_samples=default_n_samples) where V<:VariateType
-    msgSPNonlinearSOutNFactorX(g, msg_out, msgs_in..., n_samples=n_samples, variate=V)
+    return Message(variateType(dims), SampleList, s=samples, w=weights)
 end
 
-function ruleSPNonlinearSOutNFactorX(g::Function, variate,
-                                     msg_out::Nothing,
-                                     msgs_in::Vararg{Message{<:FactorNode}};
-                                     n_samples=default_n_samples)
-    msgSPNonlinearSOutNFactorX(g, msg_out, msgs_in..., n_samples=n_samples, variate=variate)
-end
-
-
-function msgSPNonlinearSInFactorX(g::Function,
-                                  inx::Int64, # Index of inbound interface inx
-                                  msg_out::Message{<:FactorFunction},
-                                  msgs_in::Vararg{Message{<:FactorNode}};
-                                  n_samples=default_n_samples,
-                                  variate)
-
+function ruleSPNonlinearSInMX(g::Function,
+                              inx::Int64, # Index of inbound interface inx
+                              msg_out::Message,
+                              msgs_in::Vararg{Message};
+                              dims::Any=nothing,
+                              n_samples=default_n_samples)
     arg_sample = (z) -> begin
         samples_in = []
         for i=1:length(msgs_in)
             if i==inx
-                push!(samples_in,collect(Iterators.repeat([ z ], n_samples)))
+                push!(samples_in, collect(Iterators.repeat([z], n_samples)))
             else
-                push!(samples_in,sample(msgs_in[i].dist, n_samples))
+                push!(samples_in, sample(msgs_in[i].dist, n_samples))
             end
         end
 
@@ -180,31 +128,38 @@ function msgSPNonlinearSInFactorX(g::Function,
 
     approximate_pdf(z) = sum(exp.(logPdf.([msg_out.dist],g.(arg_sample(z)...))))/n_samples
 
-    return Message(variate, Function, log_pdf = (z)->log(approximate_pdf(z)))
+    return Message(variateType(dims), Function, log_pdf = (z)->log(approximate_pdf(z)))
 end
 
-function ruleSPNonlinearSInFactorX(g::Function,
-                                   inx::Int64, # Index of inbound interface inx
-                                   msg_out::Message{<:FactorFunction, V},
-                                   msgs_in::Vararg{Message{<:FactorNode, V}};
-                                   n_samples=default_n_samples) where V<:VariateType
+# Special case for two inputs with one PointMass (no inx required)
+function ruleSPNonlinearSInMX(g::Function,
+                              msg_out::Message,
+                              msg_in1::Message{PointMass},
+                              msg_in2::Nothing;
+                              dims::Any=nothing,
+                              n_samples=default_n_samples)
 
-    msgSPNonlinearSInFactorX(g, inx, msg_out, msgs_in..., n_samples=n_samples, variate=V)
+    m_in1 = msg_in1.dist.params[:m]
 
+    return Message(variateType(dims), Function, log_pdf = (z)->logPdf(msg_out.dist, g(m_in1, z)))                          
 end
 
-function ruleSPNonlinearSInFactorX(g::Function, variate,
-                                   inx::Int64, # Index of inbound interface inx
-                                   msg_out::Message{<:FactorFunction},
-                                   msgs_in::Vararg{Message{<:FactorNode}};
-                                   n_samples=default_n_samples)
+# Special case for two inputs with one PointMass (no inx required)
+function ruleSPNonlinearSInMX(g::Function,
+                              msg_out::Message,
+                              msg_in1::Nothing,
+                              msg_in2::Message{PointMass};
+                              dims::Any=nothing,
+                              n_samples=default_n_samples)
 
-    msgSPNonlinearSInFactorX(g, inx, msg_out, msgs_in..., n_samples=n_samples, variate=variate)
+    m_in2 = msg_in2.dist.params[:m]
+
+    return Message(variateType(dims), Function, log_pdf = (z)->logPdf(msg_out.dist, g(z, m_in2)))                          
 end
 
-function ruleMNonlinearSInGX(g::Function,
-                             msg_out::Message{<:FactorFunction, <:VariateType},
-                             msgs_in::Vararg{Message{<:Gaussian, <:VariateType}})
+function ruleMNonlinearSInMGX(g::Function,
+                              msg_out::Message,
+                              msgs_in::Vararg{Message{<:Gaussian}})
 
     # Extract joint statistics of inbound messages
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Return arrays with individual means and covariances
@@ -212,7 +167,7 @@ function ruleMNonlinearSInGX(g::Function,
     W_fw_in = cholinv(V_fw_in) # Convert to canonical statistics
 
     # Construct log-pdf function and gradient
-    (log_joint, d_log_joint) = logJointPdfs(Multivariate, m_fw_in, W_fw_in, msg_out.dist, g, ds) # Overloaded on VariateType V
+    (log_joint, d_log_joint) = logJointPdfs(m_fw_in, W_fw_in, msg_out.dist, g, ds)
 
     # Compute joint marginal belief on in's by gradient ascent
     m_in = gradientOptimization(log_joint, d_log_joint, m_fw_in, 0.01)
@@ -226,7 +181,7 @@ end
 # Custom inbounds collectors
 #---------------------------
 
-# Unscented transform and extended approximation
+# Sampling approximation
 function collectSumProductNodeInbounds(node::Nonlinear{Sampling}, entry::ScheduleEntry)
     inbounds = Any[]
 
@@ -238,16 +193,6 @@ function collectSumProductNodeInbounds(node::Nonlinear{Sampling}, entry::Schedul
     multi_in = isMultiIn(node) # Boolean to indicate a nonlinear node with multiple stochastic inbounds
     inx = findfirst(isequal(entry.interface), node.interfaces) - 1 # Find number of inbound interface; 0 for outbound
     
-    # Message on out interface
-    if (inx == 0) && (node.out_variate !== nothing)
-        push!(inbounds, Dict{Symbol, Any}(:variate => node.out_variate,
-                                        :keyword   => false))
-    end
-    # Message on in interface
-    if (inx > 0) && (node.in_variates !== nothing)
-        push!(inbounds, Dict{Symbol, Any}(:variate => node.in_variates[inx],
-                                        :keyword   => false))
-    end
     if (inx > 0) && multi_in # Multi-inbound backward rule
         push!(inbounds, Dict{Symbol, Any}(:inx => inx, # Push inbound identifier
                                           :keyword => false))
@@ -272,7 +217,11 @@ function collectSumProductNodeInbounds(node::Nonlinear{Sampling}, entry::Schedul
         end
     end
 
-    # Push custom arguments if manually defined
+    # Push custom arguments if defined
+    if (node.dims !== nothing)
+        push!(inbounds, Dict{Symbol, Any}(:dims => node.dims[inx + 1],
+                                          :keyword => true))
+    end
     if (node.n_samples !== nothing)
         push!(inbounds, Dict{Symbol, Any}(:n_samples => node.n_samples,
                                           :keyword   => true))
@@ -294,8 +243,8 @@ end
 
 @symmetrical function prod!(
     x::ProbabilityDistribution{Univariate}, # Includes function distributions
-    y::ProbabilityDistribution{Univariate, F},
-    z::ProbabilityDistribution{Univariate, GaussianMeanPrecision}=ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=1.0)) where {F<:Gaussian}
+    y::ProbabilityDistribution{Univariate, <:Gaussian},
+    z::ProbabilityDistribution{Univariate, GaussianMeanPrecision}=ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=1.0))
 
     # Optimize with gradient ascent
     log_joint(s) = logPdf(y,s) + logPdf(x,s)
@@ -313,8 +262,8 @@ end
 
 @symmetrical function prod!(
     x::ProbabilityDistribution{Multivariate}, # Includes function distributions
-    y::ProbabilityDistribution{Multivariate, F},
-    z::ProbabilityDistribution{Multivariate, GaussianMeanPrecision}=ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[0.0], w=mat(1.0))) where {F<:Gaussian}
+    y::ProbabilityDistribution{Multivariate, <:Gaussian},
+    z::ProbabilityDistribution{Multivariate, GaussianMeanPrecision}=ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[0.0], w=mat(1.0)))
 
     # Optimize with gradient ascent
     log_joint(s) = logPdf(y,s) + logPdf(x,s)
@@ -343,7 +292,7 @@ function gradientOptimization(log_joint::Function, d_log_joint::Function, m_init
     m_old = m_initial
     satisfied = false
     step_count = 0
-    m_latests = if dim_tot == 1 Queue{Float64}() else Queue{Vector}() end
+    m_latests = if (dim_tot == 1) Queue{Float64}() else Queue{Vector}() end
 
     while !satisfied
         m_new = m_old .+ step_size.*d_log_joint(m_old)
@@ -381,16 +330,9 @@ end
 # Helpers
 #--------
 
-function logJointPdfs(V::Type{Multivariate}, m_fw_in::Vector, W_fw_in::AbstractMatrix, dist_out::ProbabilityDistribution, g::Function, ds::Vector{Int64})
-    log_joint(x) = -0.5*sum(ds)*log(2pi) + 0.5*logdet(W_fw_in) - 0.5*(x - m_fw_in)'*W_fw_in*(x - m_fw_in) + logPdf(dist_out, g(split(x, ds)...))
+function logJointPdfs(m_fw_in::Vector, W_fw_in::AbstractMatrix, dist_out::ProbabilityDistribution, g::Function, ds::Vector)
+    log_joint(x) = -0.5*sum(intdim.(ds))*log(2pi) + 0.5*logdet(W_fw_in) - 0.5*(x - m_fw_in)'*W_fw_in*(x - m_fw_in) + logPdf(dist_out, g(split(x, ds)...))
     d_log_joint(x) = ForwardDiff.gradient(log_joint, x)
 
     return (log_joint, d_log_joint)
-end
-
-function logJointPdfs(V::Type{Univariate}, m_fw_in::Vector, W_fw_in::AbstractMatrix, dist_out::ProbabilityDistribution, g::Function, ds::Vector{Int64})
-    log_joint(x) = -0.5*sum(ds)*log(2pi) + 0.5*logdet(W_fw_in) - 0.5*(x - m_fw_in)'*W_fw_in*(x - m_fw_in) + logPdf(dist_out, g(x...))
-    d_log_joint(x) = ForwardDiff.gradient(log_joint, x)
-
-    return (log_joint, d_log_joint)
-end
+end
\ No newline at end of file
diff --git a/src/engines/julia/update_rules/nonlinear_unscented.jl b/src/engines/julia/update_rules/nonlinear_unscented.jl
index 1651f8ad..cd6fa7e1 100644
--- a/src/engines/julia/update_rules/nonlinear_unscented.jl
+++ b/src/engines/julia/update_rules/nonlinear_unscented.jl
@@ -64,6 +64,7 @@ end
 """
 Return the statistics for the unscented approximation to the forward joint
 """
+# Single univariate inbound
 function unscentedStatistics(m::Float64, V::Float64, g::Function; alpha=default_alpha, beta=default_beta, kappa=default_kappa)
     (sigma_points, weights_m, weights_c) = sigmaPointsAndWeights(m, V; alpha=alpha, beta=beta, kappa=kappa)
 
@@ -75,21 +76,6 @@ function unscentedStatistics(m::Float64, V::Float64, g::Function; alpha=default_
     return (m_tilde, V_tilde, C_tilde)
 end
 
-# Multiple univariate inbounds
-function unscentedStatistics(ms::Vector{Float64}, Vs::Vector{Float64}, g::Function; alpha=default_alpha, beta=default_beta, kappa=default_kappa)
-    (m, V, ds) = concatenateGaussianMV(ms, Vs)
-    (sigma_points, weights_m, weights_c) = sigmaPointsAndWeights(m, V; alpha=alpha, beta=beta, kappa=kappa)
-
-    g_sigma = [g(sp...) for sp in sigma_points] # Unpack each sigma point in g
-
-    d = sum(ds) # Dimensionality of joint
-    m_tilde = sum(weights_m.*g_sigma) # Scalar
-    V_tilde = sum(weights_c.*(g_sigma .- m_tilde).^2) # Scalar
-    C_tilde = sum([weights_c[k+1]*(sigma_points[k+1] - ms)*(g_sigma[k+1] - m_tilde) for k=0:2*d]) # Vector
-
-    return (m_tilde, V_tilde, C_tilde)
-end
-
 # Single multivariate inbound
 function unscentedStatistics(m::Vector{Float64}, V::AbstractMatrix, g::Function; alpha=default_alpha, beta=default_beta, kappa=default_kappa)
     (sigma_points, weights_m, weights_c) = sigmaPointsAndWeights(m, V; alpha=alpha, beta=beta, kappa=kappa)
@@ -103,14 +89,14 @@ function unscentedStatistics(m::Vector{Float64}, V::AbstractMatrix, g::Function;
     return (m_tilde, V_tilde, C_tilde)
 end
 
-# Multiple multivariate inbounds
-function unscentedStatistics(ms::Vector{Vector{Float64}}, Vs::Vector{<:AbstractMatrix}, g::Function; alpha=default_alpha, beta=default_beta, kappa=default_kappa)
+# Multiple inbounds of possibly mixed variate type
+function unscentedStatistics(ms::Vector, Vs::Vector, g::Function; alpha=default_alpha, beta=default_beta, kappa=default_kappa)
     (m, V, ds) = concatenateGaussianMV(ms, Vs)
     (sigma_points, weights_m, weights_c) = sigmaPointsAndWeights(m, V; alpha=alpha, beta=beta, kappa=kappa)
 
     g_sigma = [g(split(sp, ds)...) for sp in sigma_points] # Unpack each sigma point in g
 
-    d = sum(ds) # Dimensionality of joint
+    d = sum(intdim.(ds)) # Dimensionality of joint
     m_tilde = sum([weights_m[k+1]*g_sigma[k+1] for k=0:2*d]) # Vector
     V_tilde = sum([weights_c[k+1]*(g_sigma[k+1] - m_tilde)*(g_sigma[k+1] - m_tilde)' for k=0:2*d]) # Matrix
     C_tilde = sum([weights_c[k+1]*(sigma_points[k+1] - m)*(g_sigma[k+1] - m_tilde)' for k=0:2*d]) # Matrix
@@ -148,10 +134,6 @@ end
 # Unscented Update Rules
 #-----------------------
 
-# Custom message constructors for undetermined VariateType
-Message(::Type{GaussianMeanVariance}, m::Float64, v::Float64) = Message(Univariate, GaussianMeanVariance, m=m, v=v)
-Message(::Type{GaussianMeanVariance}, m::Vector{Float64}, v::AbstractMatrix) = Message(Multivariate, GaussianMeanVariance, m=m, v=v)
-
 # Forward rule (unscented transform)
 function ruleSPNonlinearUTOutNG(g::Function,
                                 msg_out::Nothing,
@@ -161,19 +143,19 @@ function ruleSPNonlinearUTOutNG(g::Function,
     (m_fw_in1, V_fw_in1) = unsafeMeanCov(msg_in1.dist)
     (m_tilde, V_tilde, _) = unscentedStatistics(m_fw_in1, V_fw_in1, g; alpha=alpha)
 
-    return Message(GaussianMeanVariance, m_tilde, V_tilde) # Automatically determine VariateType
+    return Message(variateType(m_tilde), GaussianMeanVariance, m=m_tilde, v=V_tilde)
 end
 
 # Multi-argument forward rule (unscented transform)
 function ruleSPNonlinearUTOutNGX(g::Function, # Needs to be in front of Vararg
                                  msg_out::Nothing,
-                                 msgs_in::Vararg{Message{<:Gaussian, V}}; # Inbound variate types must match
-                                 alpha::Float64=default_alpha) where V<:VariateType
+                                 msgs_in::Vararg{Message{<:Gaussian}};
+                                 alpha::Float64=default_alpha)
 
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
     (m_tilde, V_tilde, _) = unscentedStatistics(ms_fw_in, Vs_fw_in, g; alpha=alpha)
 
-    return Message(GaussianMeanVariance, m_tilde, V_tilde) # Automatically determine VariateType
+    return Message(variateType(m_tilde), GaussianMeanVariance, m=m_tilde, v=V_tilde)
 end
 
 # Backward rule with given inverse (unscented transform)
@@ -186,27 +168,27 @@ function ruleSPNonlinearUTIn1GG(g::Function,
     (m_bw_out, V_bw_out) = unsafeMeanCov(msg_out.dist)
     (m_tilde, V_tilde, _) = unscentedStatistics(m_bw_out, V_bw_out, g_inv; alpha=alpha)
 
-    return Message(GaussianMeanVariance, m_tilde, V_tilde) # Automatically determine VariateType
+    return Message(variateType(m_tilde), GaussianMeanVariance, m=m_tilde, v=V_tilde)
 end
 
 # Multi-argument backward rule with given inverse (unscented transform)
 function ruleSPNonlinearUTInGX(g::Function, # Needs to be in front of Vararg
                                g_inv::Function,
                                msg_out::Message{<:Gaussian},
-                               msgs_in::Vararg{Union{Message{<:Gaussian, V}, Nothing}}; # Inbound variate types must match
-                               alpha::Float64=default_alpha) where V<:VariateType
+                               msgs_in::Vararg{Union{Message{<:Gaussian}, Nothing}};
+                               alpha::Float64=default_alpha)
 
     (ms, Vs) = collectStatistics(msg_out, msgs_in...) # Returns arrays with individual means and covariances
     (m_tilde, V_tilde, _) = unscentedStatistics(ms, Vs, g_inv; alpha=alpha)
 
-    return Message(V, GaussianMeanVariance, m=m_tilde, v=V_tilde)
+    return Message(variateType(m_tilde), GaussianMeanVariance, m=m_tilde, v=V_tilde)
 end
 
 # Backward rule with unknown inverse (unscented transform)
 function ruleSPNonlinearUTIn1GG(g::Function,
                                 msg_out::Message{<:Gaussian},
-                                msg_in1::Message{<:Gaussian, V};
-                                alpha::Float64=default_alpha) where V<:VariateType
+                                msg_in1::Message{<:Gaussian};
+                                alpha::Float64=default_alpha)
 
     (m_fw_in1, V_fw_in1) = unsafeMeanCov(msg_in1.dist)
     (m_tilde, V_tilde, C_tilde) = unscentedStatistics(m_fw_in1, V_fw_in1, g; alpha=alpha)
@@ -215,15 +197,15 @@ function ruleSPNonlinearUTIn1GG(g::Function,
     (m_bw_out, V_bw_out) = unsafeMeanCov(msg_out.dist)
     (m_bw_in1, V_bw_in1) = smoothRTSMessage(m_tilde, V_tilde, C_tilde, m_fw_in1, V_fw_in1, m_bw_out, V_bw_out)
 
-    return Message(V, GaussianMeanVariance, m=m_bw_in1, v=V_bw_in1)
+    return Message(variateType(m_bw_in1), GaussianMeanVariance, m=m_bw_in1, v=V_bw_in1)
 end
 
 # Multi-argument backward rule with unknown inverse (unscented transform)
 function ruleSPNonlinearUTInGX(g::Function,
                                inx::Int64, # Index of inbound interface inx
                                msg_out::Message{<:Gaussian},
-                               msgs_in::Vararg{Message{<:Gaussian, V}};
-                               alpha::Float64=default_alpha) where V<:VariateType
+                               msgs_in::Vararg{Message{<:Gaussian}};
+                               alpha::Float64=default_alpha)
 
     # Approximate joint inbounds
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
@@ -235,22 +217,22 @@ function ruleSPNonlinearUTInGX(g::Function,
     (m_in, V_in) = smoothRTS(m_tilde, V_tilde, C_tilde, m_fw_in, V_fw_in, m_bw_out, V_bw_out)
 
     # Marginalize joint belief on in's
-    (m_inx, V_inx) = marginalizeGaussianMV(V, m_in, V_in, ds, inx) # Marginalization is overloaded on VariateType V
+    (m_inx, V_inx) = marginalizeGaussianMV(m_in, V_in, ds, inx) # Marginalization is overloaded on VariateType V
     W_inx = cholinv(V_inx) # Convert to canonical statistics
     xi_inx = W_inx*m_inx
 
     # Divide marginal on inx by forward message
     (xi_fw_inx, W_fw_inx) = unsafeWeightedMeanPrecision(msgs_in[inx].dist)
     xi_bw_inx = xi_inx - xi_fw_inx
-    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef inconsistencies
+    W_bw_inx = W_inx - W_fw_inx # Note: subtraction might lead to posdef violations
 
-    return Message(V, GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
+    return Message(variateType(xi_bw_inx), GaussianWeightedMeanPrecision, xi=xi_bw_inx, w=W_bw_inx)
 end
 
 function ruleMNonlinearUTInGX(g::Function,
                               msg_out::Message{<:Gaussian},
-                              msgs_in::Vararg{Message{<:Gaussian, V}};
-                              alpha::Float64=default_alpha) where V<:VariateType
+                              msgs_in::Vararg{Message{<:Gaussian}};
+                              alpha::Float64=default_alpha)
 
     # Approximate joint inbounds
     (ms_fw_in, Vs_fw_in) = collectStatistics(msgs_in...) # Returns arrays with individual means and covariances
@@ -281,7 +263,7 @@ function collectSumProductNodeInbounds(node::Nonlinear{T}, entry::ScheduleEntry)
 
     multi_in = isMultiIn(node) # Boolean to indicate a nonlinear node with multiple stochastic inbounds
     inx = findfirst(isequal(entry.interface), node.interfaces) - 1 # Find number of inbound interface; 0 for outbound
-    undefined_inverse = (node.g_inv == nothing) || (multi_in && (inx > 0) && (node.g_inv[inx] == nothing))
+    undefined_inverse = (node.g_inv === nothing) || (multi_in && (inx > 0) && (node.g_inv[inx] === nothing))
 
     if inx > 0 # A backward message is required
         if multi_in && undefined_inverse # Multi-inbound with undefined inverse
@@ -315,8 +297,8 @@ function collectSumProductNodeInbounds(node::Nonlinear{T}, entry::ScheduleEntry)
         end
     end
 
-    # Push custom arguments if manually defined
-    if (T == Unscented) && (node.alpha != nothing)
+    # Push custom arguments if defined
+    if (node.alpha !== nothing)
         push!(inbounds, Dict{Symbol, Any}(:alpha => node.alpha,
                                           :keyword => true))
     end
@@ -370,7 +352,7 @@ Collect the statistics of separate Gaussian messages
 function collectStatistics(msgs::Vararg{Union{Message{<:Gaussian}, Nothing}})
     stats = []
     for msg in msgs
-        (msg == nothing) && continue # Skip unreported messages
+        (msg === nothing) && continue # Skip unreported messages
         push!(stats, unsafeMeanCov(msg.dist))
     end
 
@@ -381,31 +363,36 @@ function collectStatistics(msgs::Vararg{Union{Message{<:Gaussian}, Nothing}})
 end
 
 """
-Return the marginalized statistics of the Gaussian corresponding to an inbound inx
+Return integer dimensionality
 """
-marginalizeGaussianMV(T::Type{<:Univariate}, m::Vector{Float64}, V::AbstractMatrix, ds::Vector{Int64}, inx::Int64) = (m[inx], V[inx, inx])
-
-function marginalizeGaussianMV(T::Type{<:Multivariate}, m::Vector{Float64}, V::AbstractMatrix, ds::Vector{Int64}, inx::Int64)
-    ds_start = cumsum([1; ds]) # Starting indices
-    d_start = ds_start[inx]
-    d_end = ds_start[inx + 1] - 1
-    mx = m[d_start:d_end] # Vector
-    Vx = V[d_start:d_end, d_start:d_end] # Matrix
+intdim(tup::Tuple) = prod(tup) # Returns 1 for ()
 
-    return (mx, Vx)
+"""
+Return the marginalized statistics of the Gaussian corresponding to an inbound inx
+"""
+function marginalizeGaussianMV(m::Vector{Float64}, V::AbstractMatrix, ds::Vector{<:Tuple}, inx::Int64)
+    if ds[inx] == () # Univariate original
+        return (m[inx], V[inx, inx]) # Return scalars
+    else # Multivariate original
+        dl = intdim.(ds)
+        dl_start = cumsum([1; dl]) # Starting indices
+        d_start = dl_start[inx]
+        d_end = dl_start[inx + 1] - 1
+        mx = m[d_start:d_end] # Vector
+        Vx = V[d_start:d_end, d_start:d_end] # Matrix
+        return (mx, Vx)
+    end
 end
 
 """
 Concatenate independent means and (co)variances of separate Gaussians in a unified mean and covariance.
 Additionally returns a vector with the original dimensionalities, so statistics can later be re-separated.
 """
-concatenateGaussianMV(ms::Vector{Float64}, Vs::Vector{Float64}) = (ms, Diagonal(Vs), ones(Int64, length(ms)))
-
-# Concatenate multiple multivariate statistics
-function concatenateGaussianMV(ms::Vector{Vector{Float64}}, Vs::Vector{<:AbstractMatrix})
+function concatenateGaussianMV(ms::Vector, Vs::Vector)
     # Extract dimensions
-    ds = [length(m_k) for m_k in ms]
-    d_in_tot = sum(ds)
+    ds = [size(m_k) for m_k in ms]
+    dl = intdim.(ds)
+    d_in_tot = sum(dl)
 
     # Initialize concatenated statistics
     m = zeros(d_in_tot)
@@ -414,35 +401,11 @@ function concatenateGaussianMV(ms::Vector{Vector{Float64}}, Vs::Vector{<:Abstrac
     # Construct concatenated statistics
     d_start = 1
     for k = 1:length(ms) # For each inbound statistic
-        d_end = d_start + ds[k] - 1
-
-        m[d_start:d_end] = ms[k]
-        V[d_start:d_end, d_start:d_end] = Vs[k]
-
-        d_start = d_end + 1
-    end
-
-    return (m, V, ds) # Return concatenated mean and covariance with original dimensions (for splitting)
-end
-
-# Concatenate multiple mixed statistics
-function concatenateGaussianMV(ms::Vector{Any}, Vs::Vector{Any})
-    # Extract dimensions
-    ds = [length(m_k) for m_k in ms]
-    d_in_tot = sum(ds)
-
-    # Initialize concatenated statistics
-    m = zeros(d_in_tot)
-    V = zeros(d_in_tot, d_in_tot)
-
-    # Construct concatenated statistics
-    d_start = 1
-    for k = 1:length(ms) # For each inbound statistic
-        d_end = d_start + ds[k] - 1
-        if ds[k] == 1
+        d_end = d_start + dl[k] - 1
+        if ds[k] == () # Univariate
             m[d_start] = ms[k]
             V[d_start, d_start] = Vs[k]
-        else
+        else # Multivariate
             m[d_start:d_end] = ms[k]
             V[d_start:d_end, d_start:d_end] = Vs[k]
         end
@@ -455,15 +418,19 @@ end
 """
 Split a vector in chunks of lengths specified by ds.
 """
-function split(vec::Vector, ds::Vector{Int64})
+function split(vec::Vector, ds::Vector{<:Tuple})
     N = length(ds)
-    res = Vector{Vector}(undef, N)
+    res = Vector{Any}(undef, N)
 
     d_start = 1
     for k = 1:N # For each original statistic
-        d_end = d_start + ds[k] - 1
+        d_end = d_start + intdim(ds[k]) - 1
 
-        res[k] = vec[d_start:d_end]
+        if ds[k] == () # Univariate
+            res[k] = vec[d_start] # Return scalar
+        else # Multivariate
+            res[k] = vec[d_start:d_end] # Return vector
+        end
 
         d_start = d_end + 1
     end
diff --git a/src/factor_nodes/bernoulli.jl b/src/factor_nodes/bernoulli.jl
index d182a162..58870513 100644
--- a/src/factor_nodes/bernoulli.jl
+++ b/src/factor_nodes/bernoulli.jl
@@ -42,7 +42,7 @@ format(dist::ProbabilityDistribution{Univariate, Bernoulli}) = "$(slug(Bernoulli
 ProbabilityDistribution(::Type{Univariate}, ::Type{Bernoulli}; p=0.5) = ProbabilityDistribution{Univariate, Bernoulli}(Dict(:p=>p))
 ProbabilityDistribution(::Type{Bernoulli}; p=0.5) = ProbabilityDistribution{Univariate, Bernoulli}(Dict(:p=>p))
 
-dims(dist::ProbabilityDistribution{Univariate, Bernoulli}) = 1
+dims(dist::ProbabilityDistribution{Univariate, Bernoulli}) = ()
 
 vague(::Type{Bernoulli}) = ProbabilityDistribution(Univariate, Bernoulli, p=0.5)
 
diff --git a/src/factor_nodes/beta.jl b/src/factor_nodes/beta.jl
index de5b5d4a..313fa71d 100644
--- a/src/factor_nodes/beta.jl
+++ b/src/factor_nodes/beta.jl
@@ -45,7 +45,7 @@ format(dist::ProbabilityDistribution{Univariate, Beta}) = "$(slug(Beta))(a=$(for
 ProbabilityDistribution(::Type{Univariate}, ::Type{Beta}; a=1.0, b=1.0) = ProbabilityDistribution{Univariate, Beta}(Dict(:a=>a, :b=>b))
 ProbabilityDistribution(::Type{Beta}; a=1.0, b=1.0) = ProbabilityDistribution{Univariate, Beta}(Dict(:a=>a, :b=>b))
 
-dims(dist::ProbabilityDistribution{Univariate, Beta}) = 1
+dims(dist::ProbabilityDistribution{Univariate, Beta}) = ()
 
 vague(::Type{Beta}) = ProbabilityDistribution(Univariate, Beta, a=1.0, b=1.0)
 
diff --git a/src/factor_nodes/categorical.jl b/src/factor_nodes/categorical.jl
index 97c4b22d..28a547d2 100644
--- a/src/factor_nodes/categorical.jl
+++ b/src/factor_nodes/categorical.jl
@@ -46,7 +46,7 @@ format(dist::ProbabilityDistribution{Univariate, Categorical}) = "$(slug(Categor
 ProbabilityDistribution(::Type{Univariate}, ::Type{Categorical}; p=[1/3, 1/3, 1/3]) = ProbabilityDistribution{Univariate, Categorical}(Dict(:p=>p))
 ProbabilityDistribution(::Type{Categorical}; p=[1/3, 1/3, 1/3]) = ProbabilityDistribution{Univariate, Categorical}(Dict(:p=>p))
 
-dims(dist::ProbabilityDistribution{Univariate, Categorical}) = 1
+dims(dist::ProbabilityDistribution{Univariate, Categorical}) = ()
 
 vague(::Type{Categorical}, n_factors::Int64=3) = ProbabilityDistribution(Univariate, Categorical, p=(1/n_factors)*ones(n_factors))
 vague(::Type{Categorical}, n_factors::Tuple) = vague(Categorical, n_factors[1])
diff --git a/src/factor_nodes/contingency.jl b/src/factor_nodes/contingency.jl
index bf2a9bf4..1c4e474a 100644
--- a/src/factor_nodes/contingency.jl
+++ b/src/factor_nodes/contingency.jl
@@ -55,7 +55,7 @@ format(dist::ProbabilityDistribution{Multivariate, Contingency}) = "$(slug(Conti
 ProbabilityDistribution(::Type{Multivariate}, ::Type{Contingency}; p=1/9*ones(3,3)) = ProbabilityDistribution{Multivariate, Contingency}(Dict(:p=>p))
 ProbabilityDistribution(::Type{Contingency}; p=1/9*ones(3,3)) = ProbabilityDistribution{Multivariate, Contingency}(Dict(:p=>p))
 
-dims(dist::ProbabilityDistribution{Multivariate, Contingency}) = length(size(dist.params[:p]))
+dims(dist::ProbabilityDistribution{Multivariate, Contingency}) = (length(size(dist.params[:p])),)
 
 vague(::Type{Contingency}, n_factors::Tuple{Int64, Int64}=(3,3)) = ProbabilityDistribution(Multivariate, Contingency, p=(1/prod(n_factors))*ones(n_factors))
 
diff --git a/src/factor_nodes/dirichlet.jl b/src/factor_nodes/dirichlet.jl
index a3e930e0..ff14d630 100644
--- a/src/factor_nodes/dirichlet.jl
+++ b/src/factor_nodes/dirichlet.jl
@@ -47,8 +47,7 @@ ProbabilityDistribution(::Type{MatrixVariate}, ::Type{Dirichlet}; a=ones(3,3)) =
 ProbabilityDistribution(::Type{Multivariate}, ::Type{Dirichlet}; a=ones(3)) = ProbabilityDistribution{Multivariate, Dirichlet}(Dict(:a=>a))
 ProbabilityDistribution(::Type{Dirichlet}; a=ones(3)) = ProbabilityDistribution{Multivariate, Dirichlet}(Dict(:a=>a))
 
-dims(dist::ProbabilityDistribution{Multivariate, Dirichlet}) = length(dist.params[:a])
-dims(dist::ProbabilityDistribution{MatrixVariate, Dirichlet}) = size(dist.params[:a])
+dims(dist::ProbabilityDistribution{<:VariateType, Dirichlet}) = size(dist.params[:a])
 
 vague(::Type{Dirichlet}, dims::Int64) = ProbabilityDistribution(Multivariate, Dirichlet, a=ones(dims))
 vague(::Type{Dirichlet}, dims::Tuple{Int64}) = ProbabilityDistribution(Multivariate, Dirichlet, a=ones(dims))
diff --git a/src/factor_nodes/gamma.jl b/src/factor_nodes/gamma.jl
index e77d36bd..3cb8dcc2 100644
--- a/src/factor_nodes/gamma.jl
+++ b/src/factor_nodes/gamma.jl
@@ -41,7 +41,7 @@ format(dist::ProbabilityDistribution{Univariate, Gamma}) = "$(slug(Gamma))(a=$(f
 ProbabilityDistribution(::Type{Univariate}, ::Type{Gamma}; a=1.0, b=1.0) = ProbabilityDistribution{Univariate, Gamma}(Dict(:a=>a, :b=>b))
 ProbabilityDistribution(::Type{Gamma}; a=1.0, b=1.0) = ProbabilityDistribution{Univariate, Gamma}(Dict(:a=>a, :b=>b))
 
-dims(dist::ProbabilityDistribution{Univariate, Gamma}) = 1
+dims(dist::ProbabilityDistribution{Univariate, Gamma}) = ()
 
 vague(::Type{Gamma}) = ProbabilityDistribution(Univariate, Gamma, a=1.0, b=tiny) # Flat prior leads to more stable behaviour than Jeffrey's prior
 
diff --git a/src/factor_nodes/gaussian.jl b/src/factor_nodes/gaussian.jl
index 6068212f..e0a52ecf 100644
--- a/src/factor_nodes/gaussian.jl
+++ b/src/factor_nodes/gaussian.jl
@@ -52,9 +52,9 @@ convert(::Type{ProbabilityDistribution{Multivariate, GaussianWeightedMeanPrecisi
     ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[dist.params[:xi]], w=mat(dist.params[:w]))
 
 function prod!(
-    x::ProbabilityDistribution{Univariate, F1},
-    y::ProbabilityDistribution{Univariate, F2},
-    z::ProbabilityDistribution{Univariate, GaussianWeightedMeanPrecision}=ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=1.0)) where {F1<:Gaussian, F2<:Gaussian}
+    x::ProbabilityDistribution{Univariate, <:Gaussian},
+    y::ProbabilityDistribution{Univariate, <:Gaussian},
+    z::ProbabilityDistribution{Univariate, GaussianWeightedMeanPrecision}=ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=1.0))
 
     z.params[:xi] = unsafeWeightedMean(x) + unsafeWeightedMean(y)
     z.params[:w] = unsafePrecision(x) + unsafePrecision(y)
@@ -63,18 +63,18 @@ function prod!(
 end
 
 @symmetrical function prod!(
-    x::ProbabilityDistribution{Univariate, F},
+    x::ProbabilityDistribution{Univariate, <:Gaussian},
     y::ProbabilityDistribution{Univariate, PointMass},
-    z::ProbabilityDistribution{Univariate, PointMass}=ProbabilityDistribution(Univariate, PointMass, m=0.0)) where F<:Gaussian
+    z::ProbabilityDistribution{Univariate, PointMass}=ProbabilityDistribution(Univariate, PointMass, m=0.0))
 
     z.params[:m] = y.params[:m]
     return z
 end
 
 function prod!(
-    x::ProbabilityDistribution{Multivariate, F1},
-    y::ProbabilityDistribution{Multivariate, F2},
-    z::ProbabilityDistribution{Multivariate, GaussianWeightedMeanPrecision}=ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[NaN], w=transpose([NaN]))) where {F1<:Gaussian, F2<:Gaussian}
+    x::ProbabilityDistribution{Multivariate, <:Gaussian},
+    y::ProbabilityDistribution{Multivariate, <:Gaussian},
+    z::ProbabilityDistribution{Multivariate, GaussianWeightedMeanPrecision}=ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[NaN], w=transpose([NaN])))
 
     z.params[:xi] = unsafeWeightedMean(x) + unsafeWeightedMean(y)
     z.params[:w] = unsafePrecision(x) + unsafePrecision(y)
@@ -83,35 +83,35 @@ function prod!(
 end
 
 @symmetrical function prod!(
-    x::ProbabilityDistribution{Multivariate, F},
+    x::ProbabilityDistribution{Multivariate, <:Gaussian},
     y::ProbabilityDistribution{Multivariate, PointMass},
-    z::ProbabilityDistribution{Multivariate, PointMass}=ProbabilityDistribution(Multivariate, PointMass, m=[NaN])) where F<:Gaussian
+    z::ProbabilityDistribution{Multivariate, PointMass}=ProbabilityDistribution(Multivariate, PointMass, m=[NaN]))
 
     z.params[:m] = deepcopy(y.params[:m])
 
     return z
 end
 
-function sample(dist::ProbabilityDistribution{Univariate, F}) where F<:Gaussian
+function sample(dist::ProbabilityDistribution{Univariate, <:Gaussian})
     isProper(dist) || error("Cannot sample from improper distribution")
     (m,v) = unsafeMeanCov(dist)
     return sqrt(v)*randn() + m
 end
 
-function sample(dist::ProbabilityDistribution{Univariate, F}, n_samples::Int64) where F<:Gaussian
+function sample(dist::ProbabilityDistribution{Univariate, <:Gaussian}, n_samples::Int64)
     isProper(dist) || error("Cannot sample from improper distribution")
     (m,v) = unsafeMeanCov(dist)
 
     return sqrt(v).*randn(n_samples) .+ m
 end
 
-function sample(dist::ProbabilityDistribution{Multivariate, F}) where F<:Gaussian
+function sample(dist::ProbabilityDistribution{Multivariate, <:Gaussian})
     isProper(dist) || error("Cannot sample from improper distribution")
     (m,V) = unsafeMeanCov(dist)
     return (cholesky(default_cholesky_mode, V)).U' *randn(dims(dist)) + m
 end
 
-function sample(dist::ProbabilityDistribution{Multivariate, F}, n_samples::Int64) where F<:Gaussian
+function sample(dist::ProbabilityDistribution{Multivariate, <:Gaussian}, n_samples::Int64)
     isProper(dist) || error("Cannot sample from improper distribution")
     (m,V) = unsafeMeanCov(dist)
     U = (cholesky(default_cholesky_mode, V)).U
@@ -121,14 +121,15 @@ function sample(dist::ProbabilityDistribution{Multivariate, F}, n_samples::Int64
 end
 
 # Entropy functional
-function differentialEntropy(dist::ProbabilityDistribution{Univariate, F}) where F<:Gaussian
+function differentialEntropy(dist::ProbabilityDistribution{Univariate, <:Gaussian})
     return  0.5*log(unsafeCov(dist)) +
             0.5*log(2*pi) +
             0.5
 end
 
-function differentialEntropy(dist::ProbabilityDistribution{Multivariate, F}) where F<:Gaussian
+function differentialEntropy(dist::ProbabilityDistribution{Multivariate, <:Gaussian})
+    d = dims(dist)[1]
     return  0.5*logdet(unsafeCov(dist)) +
-            (dims(dist)/2)*log(2*pi) +
-            (dims(dist)/2)
+            (d/2)*log(2*pi) +
+            d/2
 end
diff --git a/src/factor_nodes/gaussian_mean_precision.jl b/src/factor_nodes/gaussian_mean_precision.jl
index 1c709e28..8db2287b 100644
--- a/src/factor_nodes/gaussian_mean_precision.jl
+++ b/src/factor_nodes/gaussian_mean_precision.jl
@@ -42,10 +42,9 @@ ProbabilityDistribution(::Type{Univariate}, ::Type{GaussianMeanPrecision}; m=0.0
 ProbabilityDistribution(::Type{GaussianMeanPrecision}; m::Number=0.0, w::Number=1.0) = ProbabilityDistribution{Univariate, GaussianMeanPrecision}(Dict(:m=>m, :w=>w))
 ProbabilityDistribution(::Type{Multivariate}, ::Type{GaussianMeanPrecision}; m=[0.0], w=transpose([1.0])) = ProbabilityDistribution{Multivariate, GaussianMeanPrecision}(Dict(:m=>m, :w=>w))
 
-dims(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = length(dist.params[:m])
+dims(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = size(dist.params[:m])
 
 vague(::Type{GaussianMeanPrecision}) = ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=tiny)
-vague(::Type{GaussianMeanPrecision}, dims::Int64) = ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=zeros(dims), w=tiny*diageye(dims))
 vague(::Type{GaussianMeanPrecision}, dims::Tuple{Int64}) = ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=zeros(dims), w=tiny*diageye(dims[1]))
 
 unsafeMean(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = deepcopy(dist.params[:m]) # unsafe mean
@@ -63,10 +62,12 @@ unsafeWeightedMean(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) wher
 
 unsafePrecision(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = deepcopy(dist.params[:w]) # unsafe precision
 
+unsafeMeanPrecision(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = (deepcopy(dist.params[:m]), deepcopy(dist.params[:w]))
+
 unsafeWeightedMeanPrecision(dist::ProbabilityDistribution{V, GaussianMeanPrecision}) where V<:VariateType = (dist.params[:w]*dist.params[:m], deepcopy(dist.params[:w]))
 
 logPdf(dist::ProbabilityDistribution{Univariate, GaussianMeanPrecision}, x) = -0.5*(log(2pi) - log(dist.params[:w]) + (x-dist.params[:m])^2*dist.params[:w])
-logPdf(dist::ProbabilityDistribution{Multivariate, GaussianMeanPrecision}, x) = -0.5*(dims(dist)*log(2pi) - logdet(dist.params[:w]) + transpose(x-dist.params[:m])*dist.params[:w]*(x-dist.params[:m]))
+logPdf(dist::ProbabilityDistribution{Multivariate, GaussianMeanPrecision}, x) = -0.5*(dims(dist)[1]*log(2pi) - logdet(dist.params[:w]) + transpose(x-dist.params[:m])*dist.params[:w]*(x-dist.params[:m]))
 
 isProper(dist::ProbabilityDistribution{Univariate, GaussianMeanPrecision}) = (floatmin(Float64) < dist.params[:w] < floatmax(Float64))
 isProper(dist::ProbabilityDistribution{Multivariate, GaussianMeanPrecision}) = isRoundedPosDef(dist.params[:w])
@@ -95,12 +96,12 @@ function averageEnergy(::Type{GaussianMeanPrecision}, marg_out::ProbabilityDistr
     (m_mean, v_mean) = unsafeMeanCov(marg_mean)
     (m_out, v_out) = unsafeMeanCov(marg_out)
 
-    0.5*dims(marg_out)*log(2*pi) -
+    0.5*dims(marg_out)[1]*log(2*pi) -
     0.5*unsafeDetLogMean(marg_prec) +
     0.5*tr( unsafeMean(marg_prec)*(v_out + v_mean + (m_out - m_mean)*(m_out - m_mean)' ))
 end
 
-function averageEnergy(::Type{GaussianMeanPrecision}, marg_out_mean::ProbabilityDistribution{Multivariate, F}, marg_prec::ProbabilityDistribution{Univariate}) where F<:Gaussian
+function averageEnergy(::Type{GaussianMeanPrecision}, marg_out_mean::ProbabilityDistribution{Multivariate, <:Gaussian}, marg_prec::ProbabilityDistribution{Univariate})
     (m, V) = unsafeMeanCov(marg_out_mean)
 
     0.5*log(2*pi) -
@@ -108,9 +109,9 @@ function averageEnergy(::Type{GaussianMeanPrecision}, marg_out_mean::Probability
     0.5*unsafeMean(marg_prec)*( V[1,1] - V[1,2] - V[2,1] + V[2,2] + (m[1] - m[2])^2 )
 end
 
-function averageEnergy(::Type{GaussianMeanPrecision}, marg_out_mean::ProbabilityDistribution{Multivariate, F}, marg_prec::ProbabilityDistribution{MatrixVariate}) where F<:Gaussian
+function averageEnergy(::Type{GaussianMeanPrecision}, marg_out_mean::ProbabilityDistribution{Multivariate, <:Gaussian}, marg_prec::ProbabilityDistribution{MatrixVariate})
     (m, V) = unsafeMeanCov(marg_out_mean)
-    d = Int64(dims(marg_out_mean)/2)
+    d = Int64(dims(marg_out_mean)[1]/2)
 
     0.5*d*log(2*pi) -
     0.5*unsafeDetLogMean(marg_prec) +
diff --git a/src/factor_nodes/gaussian_mean_variance.jl b/src/factor_nodes/gaussian_mean_variance.jl
index 8a6133f9..6154dc1c 100644
--- a/src/factor_nodes/gaussian_mean_variance.jl
+++ b/src/factor_nodes/gaussian_mean_variance.jl
@@ -44,10 +44,9 @@ ProbabilityDistribution(::Type{Univariate}, ::Type{GaussianMeanVariance}; m=0.0,
 ProbabilityDistribution(::Type{GaussianMeanVariance}; m::Number=0.0, v::Number=1.0) = ProbabilityDistribution{Univariate, GaussianMeanVariance}(Dict(:m=>m, :v=>v))
 ProbabilityDistribution(::Type{Multivariate}, ::Type{GaussianMeanVariance}; m=[0.0], v=mat(1.0)) = ProbabilityDistribution{Multivariate, GaussianMeanVariance}(Dict(:m=>m, :v=>v))
 
-dims(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where V<:VariateType = length(dist.params[:m])
+dims(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where V<:VariateType = size(dist.params[:m])
 
 vague(::Type{GaussianMeanVariance}) = ProbabilityDistribution(Univariate, GaussianMeanVariance, m=0.0, v=huge)
-vague(::Type{GaussianMeanVariance}, dims::Int64) = ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=zeros(dims), v=huge*diageye(dims))
 vague(::Type{GaussianMeanVariance}, dims::Tuple{Int64}) = ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=zeros(dims), v=huge*diageye(dims[1]))
 
 unsafeMean(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where V<:VariateType = deepcopy(dist.params[:m]) # unsafe mean
@@ -65,8 +64,10 @@ unsafeWeightedMean(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where
 
 unsafePrecision(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where V<:VariateType = cholinv(dist.params[:v])
 
+unsafeMeanPrecision(dist::ProbabilityDistribution{V, GaussianMeanVariance}) where V<:VariateType = (deepcopy(dist.params[:m]), cholinv(dist.params[:v]))
+
 logPdf(dist::ProbabilityDistribution{Univariate, GaussianMeanVariance}, x) = -0.5*(log(2pi) + log(dist.params[:v]) + (x-dist.params[:m])^2/dist.params[:v])
-logPdf(dist::ProbabilityDistribution{Multivariate, GaussianMeanVariance}, x) = -0.5*(dims(dist)*log(2pi) + logdet(dist.params[:v]) + transpose(x-dist.params[:m])*cholinv(dist.params[:v])*(x-dist.params[:m]))
+logPdf(dist::ProbabilityDistribution{Multivariate, GaussianMeanVariance}, x) = -0.5*(dims(dist)[1]*log(2pi) + logdet(dist.params[:v]) + transpose(x-dist.params[:m])*cholinv(dist.params[:v])*(x-dist.params[:m]))
 
 # Converting from m,v to xi,w would require two separate inversions of the covariance matrix;
 # this function ensures only a single inversion is performed
@@ -102,12 +103,12 @@ function averageEnergy(::Type{GaussianMeanVariance}, marg_out::ProbabilityDistri
     (m_mean, v_mean) = unsafeMeanCov(marg_mean)
     (m_out, v_out) = unsafeMeanCov(marg_out)
 
-    0.5*dims(marg_out)*log(2*pi) +
+    0.5*dims(marg_out)[1]*log(2*pi) +
     0.5*unsafeDetLogMean(marg_var) +
     0.5*tr( unsafeInverseMean(marg_var)*(v_out + v_mean + (m_out - m_mean)*(m_out - m_mean)'))
 end
 
-function averageEnergy(::Type{GaussianMeanVariance}, marg_out_mean::ProbabilityDistribution{Multivariate, F}, marg_var::ProbabilityDistribution{Univariate}) where F<:Gaussian
+function averageEnergy(::Type{GaussianMeanVariance}, marg_out_mean::ProbabilityDistribution{Multivariate, <:Gaussian}, marg_var::ProbabilityDistribution{Univariate})
     (m, V) = unsafeMeanCov(marg_out_mean)
 
     0.5*log(2*pi) +
@@ -115,9 +116,9 @@ function averageEnergy(::Type{GaussianMeanVariance}, marg_out_mean::ProbabilityD
     0.5*unsafeInverseMean(marg_var)*( V[1,1] - V[1,2] - V[2,1] + V[2,2] + (m[1] - m[2])^2 )
 end
 
-function averageEnergy(::Type{GaussianMeanVariance}, marg_out_mean::ProbabilityDistribution{Multivariate, F}, marg_var::ProbabilityDistribution{MatrixVariate}) where F<:Gaussian
+function averageEnergy(::Type{GaussianMeanVariance}, marg_out_mean::ProbabilityDistribution{Multivariate, <:Gaussian}, marg_var::ProbabilityDistribution{MatrixVariate})
     (m, V) = unsafeMeanCov(marg_out_mean)
-    d = Int64(dims(marg_out_mean)/2)
+    d = Int64(dims(marg_out_mean)[1]/2)
 
     0.5*d*log(2*pi) +
     0.5*unsafeDetLogMean(marg_var) +
diff --git a/src/factor_nodes/gaussian_weighted_mean_precision.jl b/src/factor_nodes/gaussian_weighted_mean_precision.jl
index c3e93d4d..aa14dd07 100644
--- a/src/factor_nodes/gaussian_weighted_mean_precision.jl
+++ b/src/factor_nodes/gaussian_weighted_mean_precision.jl
@@ -42,10 +42,9 @@ ProbabilityDistribution(::Type{Univariate}, ::Type{GaussianWeightedMeanPrecision
 ProbabilityDistribution(::Type{GaussianWeightedMeanPrecision}; xi::Number=0.0, w::Number=1.0) = ProbabilityDistribution{Univariate, GaussianWeightedMeanPrecision}(Dict(:xi=>xi, :w=>w))
 ProbabilityDistribution(::Type{Multivariate}, ::Type{GaussianWeightedMeanPrecision}; xi=[0.0], w=transpose([1.0])) = ProbabilityDistribution{Multivariate, GaussianWeightedMeanPrecision}(Dict(:xi=>xi, :w=>w))
 
-dims(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType = length(dist.params[:xi])
+dims(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType = size(dist.params[:xi])
 
 vague(::Type{GaussianWeightedMeanPrecision}) = ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=tiny)
-vague(::Type{GaussianWeightedMeanPrecision}, dims::Int64) = ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=zeros(dims), w=tiny*diageye(dims))
 vague(::Type{GaussianWeightedMeanPrecision}, dims::Tuple{Int64}) = ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=zeros(dims), w=tiny*diageye(dims[1]))
 
 unsafeMean(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType = cholinv(dist.params[:w])*dist.params[:xi]
@@ -66,6 +65,11 @@ unsafeWeightedMean(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecisio
 
 unsafePrecision(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType = deepcopy(dist.params[:w]) # unsafe precision
 
+function unsafeMeanPrecision(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType
+    v = cholinv(dist.params[:w])
+    return (v*dist.params[:xi], dist.params[:w])
+end
+
 unsafeWeightedMeanPrecision(dist::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}) where V<:VariateType = (deepcopy(dist.params[:xi]), deepcopy(dist.params[:w]))
 
 function logPdf(dist::ProbabilityDistribution{Univariate, GaussianWeightedMeanPrecision}, x)
@@ -75,7 +79,7 @@ end
 
 function logPdf(dist::ProbabilityDistribution{Multivariate, GaussianWeightedMeanPrecision}, x) 
     m = cholinv(dist.params[:w])*dist.params[:xi]
-    return -0.5*(dims(dist)*log(2pi) - logdet(dist.params[:w]) + transpose(x-m)*dist.params[:w]*(x-m))
+    return -0.5*(dims(dist)[1]*log(2pi) - logdet(dist.params[:w]) + transpose(x-m)*dist.params[:w]*(x-m))
 end
 
 isProper(dist::ProbabilityDistribution{Univariate, GaussianWeightedMeanPrecision}) = (floatmin(Float64) < dist.params[:w] < floatmax(Float64))
@@ -86,7 +90,7 @@ function ==(t::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}, u::Pro
     isApproxEqual(t.params[:xi], u.params[:xi]) && isApproxEqual(t.params[:w], u.params[:w])
 end
 
-function ==(t::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}, u::ProbabilityDistribution{V, F}) where {V<:VariateType, F<:Gaussian}
+function ==(t::ProbabilityDistribution{V, GaussianWeightedMeanPrecision}, u::ProbabilityDistribution{V, <:Gaussian}) where V<:VariateType
     (t === u) && return true
     isApproxEqual(t.params[:xi], unsafeWeightedMean(u)) && isApproxEqual(t.params[:w], unsafePrecision(u))
 end
\ No newline at end of file
diff --git a/src/factor_nodes/log_normal.jl b/src/factor_nodes/log_normal.jl
index 24b9a172..41ea81d9 100644
--- a/src/factor_nodes/log_normal.jl
+++ b/src/factor_nodes/log_normal.jl
@@ -41,7 +41,7 @@ format(dist::ProbabilityDistribution{Univariate, LogNormal}) = "$(slug(LogNormal
 ProbabilityDistribution(::Type{Univariate}, ::Type{LogNormal}; m::Float64=1.0, s::Float64=1.0) = ProbabilityDistribution{Univariate, LogNormal}(Dict(:m=>m, :s=>s))
 ProbabilityDistribution(::Type{LogNormal}; m::Float64=1.0, s::Float64=1.0) = ProbabilityDistribution{Univariate, LogNormal}(Dict(:m=>m, :s=>s))
 
-dims(dist::ProbabilityDistribution{Univariate, LogNormal}) = 1
+dims(dist::ProbabilityDistribution{Univariate, LogNormal}) = ()
 
 vague(::Type{LogNormal}) = ProbabilityDistribution(Univariate, LogNormal, m=1.0, s=huge)
 
diff --git a/src/factor_nodes/nonlinear.jl b/src/factor_nodes/nonlinear.jl
index 193ea466..4cff4078 100644
--- a/src/factor_nodes/nonlinear.jl
+++ b/src/factor_nodes/nonlinear.jl
@@ -40,19 +40,15 @@ mutable struct Nonlinear{T<:ApproximationMethod} <: DeltaFactor
     g::Function # Vector function that expresses the output as a function of the inputs
     g_inv::Union{Function, Nothing, Vector} # Inverse of g with respect to individual inbounds (optional)
     alpha::Union{Float64, Nothing} # Spread parameter for unscented transform
-    dims::Union{Tuple, Vector} # Dimension of breaker message(s) on input interface(s)
+    dims::Union{Vector, Nothing} # Dimensions of messages on (all) interfaces
     n_samples::Union{Int64, Nothing} # Number of samples for sampling
-    in_variates::Union{Vector{DataType}, Nothing} # Variate types of messages on in interfaces
-    out_variate::Union{DataType, Nothing} # Variate types of message on out interface
 
     function Nonlinear{T}(id::Symbol,
                           g::Function,
                           g_inv::Union{Function, Nothing, Vector},
                           alpha::Union{Float64, Nothing},
-                          dims::Union{Tuple, Vector},
+                          dims::Union{Vector, Nothing},
                           n_samples::Union{Int64, Nothing},
-                          in_variates::Union{Vector{DataType}, Nothing},
-                          out_variate::Union{DataType, Nothing},
                           out::Variable,
                           args::Vararg) where T<:ApproximationMethod
         
@@ -60,7 +56,7 @@ mutable struct Nonlinear{T<:ApproximationMethod} <: DeltaFactor
         for i=1:n_args
             @ensureVariables(args[i])
         end
-        self = new(id, Vector{Interface}(undef, n_args+1), Dict{Symbol,Interface}(), g, g_inv, alpha, dims, n_samples, in_variates, out_variate)
+        self = new(id, Vector{Interface}(undef, n_args+1), Dict{Symbol,Interface}(), g, g_inv, alpha, dims, n_samples)
         ForneyLab.addNode!(currentGraph(), self)
         self.i[:out] = self.interfaces[1] = associate!(Interface(self), out)
         for k = 1:n_args
@@ -71,16 +67,16 @@ mutable struct Nonlinear{T<:ApproximationMethod} <: DeltaFactor
     end
 end
 
-function Nonlinear{Unscented}(out::Variable, args::Vararg; g::Function, g_inv=nothing, alpha=nothing, dims=(), id=ForneyLab.generateId(Nonlinear{Unscented}))
-    return Nonlinear{Unscented}(id, g, g_inv, alpha, dims, nothing, nothing, nothing, out, args...)
+function Nonlinear{Unscented}(out::Variable, args::Vararg; g::Function, g_inv=nothing, alpha=nothing, dims=nothing, id=ForneyLab.generateId(Nonlinear{Unscented}))
+    return Nonlinear{Unscented}(id, g, g_inv, alpha, dims, nothing, out, args...)
 end
 
-function Nonlinear{Sampling}(out::Variable, args::Vararg; g::Function, dims=(), n_samples=nothing, in_variates=nothing, out_variate=nothing, id=ForneyLab.generateId(Nonlinear{Sampling}))
-    return Nonlinear{Sampling}(id, g, nothing, nothing, dims, n_samples, in_variates, out_variate, out, args...)
+function Nonlinear{Sampling}(out::Variable, args::Vararg; g::Function, dims=nothing, n_samples=nothing, id=ForneyLab.generateId(Nonlinear{Sampling}))
+    return Nonlinear{Sampling}(id, g, nothing, nothing, dims, n_samples, out, args...)
 end
 
-function Nonlinear{Extended}(out::Variable, args::Vararg; g::Function, g_inv=nothing, dims=(), id=ForneyLab.generateId(Nonlinear{Extended}))
-    return Nonlinear{Extended}(id, g, g_inv, nothing, dims, nothing, nothing, nothing, out, args...)
+function Nonlinear{Extended}(out::Variable, args::Vararg; g::Function, g_inv=nothing, dims=nothing, id=ForneyLab.generateId(Nonlinear{Extended}))
+    return Nonlinear{Extended}(id, g, g_inv, nothing, dims, nothing, out, args...)
 end
 
 # A breaker message is required if interface is partnered with a Nonlinear{Sampling} inbound and there are multiple inbounds
@@ -91,12 +87,12 @@ function requiresBreaker(interface::Interface, partner_interface::Interface, par
     return backward && multi_in
 end
 
-# A breaker message is required if interface is partnered with a Nonlinear{Unscented} inbound and no inverse is available
+# A breaker message is required if interface is partnered with a Nonlinear{Unscented/Extended} inbound and no inverse is available
 function requiresBreaker(interface::Interface, partner_interface::Interface, partner_node::Nonlinear{T}) where T<:Union{Unscented, Extended}
     backward = (partner_interface != partner_node.i[:out]) # Interface is partnered with an inbound
     multi_in = isMultiIn(partner_node) # Boolean to indicate a nonlinear node with multiple stochastic inbounds
     inx = findfirst(isequal(partner_interface), partner_node.interfaces) - 1 # Find number of inbound interface; 0 for outbound
-    undefined_inverse = (partner_node.g_inv == nothing) || (multi_in && (inx > 0) && (partner_node.g_inv[inx] == nothing)) # (Inbound-specific) inverse is undefined
+    undefined_inverse = (partner_node.g_inv === nothing) || (multi_in && (inx > 0) && (partner_node.g_inv[inx] === nothing)) # (Inbound-specific) inverse is undefined
 
     return backward && undefined_inverse
 end
@@ -104,21 +100,14 @@ end
 function breakerParameters(interface::Interface, partner_interface::Interface, partner_node::Nonlinear)
     requiresBreaker(interface, partner_interface, partner_node) || error("Breaker dimensions requested for non-breaker interface: $(interface)")
 
-    if isa(partner_node.dims, Vector) # Varying inbound dimensionalities are defined
-        inx = findfirst(isequal(partner_interface), partner_node.interfaces) - 1 # Find number of inbound interface
-        dims = partner_node.dims[inx] # Extract dimensionality from vector
+    if partner_node.dims === nothing
+        dims = ()
     else
-        dims = partner_node.dims # All inbound dimensions are equal
+        inx = findfirst(isequal(partner_interface), partner_node.interfaces) # Find interface index
+        dims = partner_node.dims[inx] # Extract dimensionality from node.dims vector
     end
 
-    # Deterimine message variate type
-    if dims == ()
-        var_type = Univariate
-    else
-        var_type = Multivariate
-    end
-
-    return (Message{GaussianMeanVariance, var_type}, dims)
+    return (Message{GaussianMeanVariance, variateType(dims)}, dims)
 end
 
 slug(::Type{Nonlinear{T}}) where T<:ApproximationMethod = "g{$(removePrefix(T))}"
diff --git a/src/factor_nodes/poisson.jl b/src/factor_nodes/poisson.jl
index fb697dda..ecb90d00 100644
--- a/src/factor_nodes/poisson.jl
+++ b/src/factor_nodes/poisson.jl
@@ -41,7 +41,7 @@ format(dist::ProbabilityDistribution{Univariate, Poisson}) = "$(slug(Poisson))(l
 ProbabilityDistribution(::Type{Univariate}, ::Type{Poisson}; l=1.0) = ProbabilityDistribution{Univariate, Poisson}(Dict(:l=>l))
 ProbabilityDistribution(::Type{Poisson}; l=1.0) = ProbabilityDistribution{Univariate, Poisson}(Dict(:l=>l))
 
-dims(dist::ProbabilityDistribution{Univariate, Poisson}) = 1
+dims(dist::ProbabilityDistribution{Univariate, Poisson}) = ()
 
 vague(::Type{Poisson}) = ProbabilityDistribution(Univariate, Poisson, l=huge)
 
diff --git a/src/factor_nodes/sample_list.jl b/src/factor_nodes/sample_list.jl
index 4bf8653c..1f0f8195 100644
--- a/src/factor_nodes/sample_list.jl
+++ b/src/factor_nodes/sample_list.jl
@@ -25,9 +25,7 @@ ProbabilityDistribution(::Type{Univariate}, ::Type{SampleList}; s=[0.0], w=[1.0]
 ProbabilityDistribution(::Type{Multivariate}, ::Type{SampleList}; s=[[0.0]], w=[1.0]) = ProbabilityDistribution{Multivariate, SampleList}(Dict{Symbol, Any}(:s=>s, :w=>w))
 ProbabilityDistribution(::Type{MatrixVariate}, ::Type{SampleList};s=[mat(0.0)], w=[1.0]) = ProbabilityDistribution{MatrixVariate, SampleList}(Dict{Symbol,Any}(:s=>s,:w=>w))
 
-dims(dist::ProbabilityDistribution{Univariate, SampleList}) = 1
-dims(dist::ProbabilityDistribution{Multivariate, SampleList}) = length(dist.params[:s][1])
-dims(dist::ProbabilityDistribution{MatrixVariate, SampleList}) = size(dist.params[:s][1])
+dims(dist::ProbabilityDistribution{<:VariateType, SampleList}) = size(dist.params[:s][1])
 
 function vague(::Type{SampleList})
     n_samples = default_n_samples # Fixed number of samples
@@ -35,21 +33,10 @@ function vague(::Type{SampleList})
     return ProbabilityDistribution(Univariate, SampleList, s=rand(n_samples), w=ones(n_samples)/n_samples)
 end
 
-function vague(::Type{SampleList}, dims::Int64)
+function vague(::Type{SampleList}, dims::Tuple)
     n_samples = default_n_samples # Fixed number of samples
 
-    s_list = Vector{Vector{Number}}(undef, n_samples)
-    for n=1:n_samples
-        s_list[n] = rand(dims)
-    end
-
-    return ProbabilityDistribution(Multivariate, SampleList, s=s_list, w=ones(n_samples)/n_samples)
-end
-
-function vague(::Type{SampleList}, dims::Tuple{Int64,Int64})
-    n_samples = default_n_samples # Fixed number of samples
-
-    s_list = Vector{Matrix{Number}}(undef, n_samples)
+    s_list = Vector(undef, n_samples)
     for n=1:n_samples
         s_list[n] = randn(dims)
     end
@@ -88,7 +75,8 @@ function unsafeCov(dist::ProbabilityDistribution{Multivariate, SampleList})
 
     n_samples = length(samples)
     m = unsafeMean(dist)
-    tot = zeros(dims(dist), dims(dist))
+    d = dims(dist)[1]
+    tot = zeros(d, d)
     for i = 1:n_samples
         tot += (samples[i] .- m)*transpose(samples[i] .- m).*weights[i]
     end
@@ -102,8 +90,9 @@ function unsafeCov(dist::ProbabilityDistribution{MatrixVariate, SampleList})
 
     n_samples = length(samples)
     m = unsafeMean(dist)
-    cov1 = zeros(dims(dist)[1],dims(dist)[1])
-    cov2 = zeros(dims(dist)[2],dims(dist)[2])
+    (d1, d2) = dims(dist)
+    cov1 = zeros(d1, d1)
+    cov2 = zeros(d2, d2)
 
     for i = 1:n_samples
         cov1 += ((samples[i] .- m))*transpose((samples[i] .- m)).*weights[i]
@@ -115,7 +104,7 @@ function unsafeCov(dist::ProbabilityDistribution{MatrixVariate, SampleList})
     return kron(cov1, cov2)
 end
 
-unsafeMeanCov(dist::ProbabilityDistribution{V, SampleList}) where V<:VariateType = (unsafeMean(dist), unsafeCov(dist))
+unsafeMeanCov(dist::ProbabilityDistribution{<:VariateType, SampleList}) = (unsafeMean(dist), unsafeCov(dist))
 
 function unsafeMirroredLogMean(dist::ProbabilityDistribution{Univariate, SampleList})
     all(0 .<= dist.params[:s] .< 1) || error("unsafeMirroredLogMean does not apply to variables outside of the range [0, 1]")
@@ -123,9 +112,9 @@ function unsafeMirroredLogMean(dist::ProbabilityDistribution{Univariate, SampleL
     return sum(log.(1 .- dist.params[:s]) .* dist.params[:w])
 end
 
-unsafeMeanVector(dist::ProbabilityDistribution{V, SampleList}) where V<:VariateType = sum(dist.params[:s].*dist.params[:w])
+unsafeMeanVector(dist::ProbabilityDistribution{<:VariateType, SampleList}) = sum(dist.params[:s].*dist.params[:w])
 
-isProper(dist::ProbabilityDistribution{V, SampleList}) where V<:VariateType = abs(sum(dist.params[:w]) - 1) < 0.001
+isProper(dist::ProbabilityDistribution{<:VariateType, SampleList}) = abs(sum(dist.params[:w]) - 1) < 0.001
 
 # prod of a pdf (or distribution) message and a SampleList message
 # this function is capable to calculate entropy with SampleList messages in VMP setting
@@ -188,8 +177,8 @@ end
 # Disambiguate beteen SampleList product and nonlinear product of any distribution with a Gaussian
 # Following two definitions must be parameterized on separate Univariate and Multivariate types
 @symmetrical function prod!(
-    x::ProbabilityDistribution{Univariate, F},
-    y::ProbabilityDistribution{Univariate, SampleList}) where F<:Gaussian
+    x::ProbabilityDistribution{Univariate, <:Gaussian},
+    y::ProbabilityDistribution{Univariate, SampleList})
 
     z = ProbabilityDistribution(Univariate, SampleList, s=[0.0], w=[1.0])
 
@@ -197,15 +186,15 @@ end
 end
 
 @symmetrical function prod!(
-    x::ProbabilityDistribution{Multivariate, F},
-    y::ProbabilityDistribution{Multivariate, SampleList}) where F<:Gaussian
+    x::ProbabilityDistribution{Multivariate, <:Gaussian},
+    y::ProbabilityDistribution{Multivariate, SampleList})
 
     z = ProbabilityDistribution(Multivariate, SampleList, s=[[0.0]], w=[1.0])
 
     return prod!(x, y, z) # Return a SampleList
 end
 
-function sampleWeightsAndEntropy(x::ProbabilityDistribution{V,F}, y::ProbabilityDistribution) where {V<:VariateType, F<:Function}
+function sampleWeightsAndEntropy(x::ProbabilityDistribution{<:VariateType, <:Function}, y::ProbabilityDistribution)
     sampleWeightsAndEntropy(y, x)
 end
 
@@ -280,13 +269,12 @@ function bootstrap(dist_mean::ProbabilityDistribution{Univariate, SampleList}, d
 end
 
 function bootstrap(dist_mean::ProbabilityDistribution{Multivariate, SampleList}, dist_var::ProbabilityDistribution{MatrixVariate, PointMass})
-    d = dims(dist_mean)
     s_m = dist_mean.params[:s] # Samples representing the mean
     N = length(s_m)
     V = dist_var.params[:m] # Fixed variance
     U = (cholesky(default_cholesky_mode, V)).U # Precompute Cholesky
 
-    return [U' *randn(d) + s_m[i] for i in 1:N] # New samples
+    return [U' *randn(dims(dist_mean)) + s_m[i] for i in 1:N] # New samples
 end
 
 function bootstrap(dist_mean::ProbabilityDistribution{Univariate, <:Gaussian}, dist_var::ProbabilityDistribution{Univariate, SampleList})
@@ -299,23 +287,22 @@ function bootstrap(dist_mean::ProbabilityDistribution{Univariate, <:Gaussian}, d
 end
 
 function bootstrap(dist_mean::ProbabilityDistribution{Multivariate, <:Gaussian}, dist_var::ProbabilityDistribution{MatrixVariate, SampleList})
-    d = dims(dist_mean)
     s_V = dist_var.params[:s] # Samples representing the covariance
     N = length(s_V)
     (m, V) = unsafeMeanCov(dist_mean)
     s_U = [(cholesky(default_cholesky_mode, s_V[i] + V)).U for i in 1:N] # Precompute Cholesky for each covariance sample; this can be expensive
 
-    return [s_U[i]' *randn(d) + m for i in 1:N] # New samples
+    return [s_U[i]' *randn(dims(dist_mean)) + m for i in 1:N] # New samples
 end
 
 #Sampling from a distribution in ForneyLab returns equally weighted samples from the distribution
 #To retain the unified standard procedure, we allow sampling from SampleList not through directly returning
 #sample and weight parameters but drawing samples from the existing list of samples according to weights.
 # Inverse-transform sampling
-sample(dist::ProbabilityDistribution{V, SampleList}) where V<:VariateType = sample(dist.params[:s], Weights(dist.params[:w]))
+sample(dist::ProbabilityDistribution{<:VariateType, SampleList}) = sample(dist.params[:s], Weights(dist.params[:w]))
 
 # Differential entropy for SampleList
-function differentialEntropy(dist::ProbabilityDistribution{V, SampleList}) where V<:VariateType
+function differentialEntropy(dist::ProbabilityDistribution{<:VariateType, SampleList})
     haskey(dist.params, :entropy) || error("Missing entropy for SampleList; quantity is requested but not computed")
 
     return dist.params[:entropy] # Entropy is pre-computed during computation of the marginal
diff --git a/src/factor_nodes/wishart.jl b/src/factor_nodes/wishart.jl
index d09b53b1..dbf01205 100644
--- a/src/factor_nodes/wishart.jl
+++ b/src/factor_nodes/wishart.jl
@@ -43,10 +43,8 @@ ProbabilityDistribution(::Type{Wishart}; v=mat(1.0), nu=1.0) = ProbabilityDistri
 
 dims(dist::ProbabilityDistribution{MatrixVariate, Wishart}) = size(dist.params[:v])
 
-vague(::Type{Wishart}, dims::Int64) = ProbabilityDistribution(MatrixVariate, Wishart, v=huge*diageye(dims), nu=Float64(dims)) # Flat prior
 vague(::Type{Wishart}, dims::Tuple{Int64, Int64}) = ProbabilityDistribution(MatrixVariate, Wishart, v=huge*diageye(dims[1]), nu=Float64(dims[1]))
 
-vague(::Type{Union{Gamma, Wishart}}, dims::Int64) = vague(Wishart, dims)
 vague(::Type{Union{Gamma, Wishart}}, dims::Tuple{Int64, Int64}) = vague(Wishart, dims)
 vague(::Type{Union{Gamma, Wishart}}, dims::Tuple) = vague(Gamma) # Univariate fallback
 
diff --git a/src/helpers.jl b/src/helpers.jl
index e505b4b9..1b88eda2 100644
--- a/src/helpers.jl
+++ b/src/helpers.jl
@@ -39,7 +39,7 @@ cholinv(m::Number) = 1.0/m
 cholinv(D::Diagonal) = Diagonal(1 ./ D.diag)
 
 eye(n::Number) = Diagonal(I,n)
-diageye(dims::Int64) = Diagonal(ones(dims))
+diageye(d::Int64) = Diagonal(ones(d))
 
 # Symbol concatenation
 *(sym::Symbol, num::Number) = Symbol(string(sym, num))
diff --git a/src/probability_distribution.jl b/src/probability_distribution.jl
index 1bb3954b..7148e1a9 100644
--- a/src/probability_distribution.jl
+++ b/src/probability_distribution.jl
@@ -35,7 +35,13 @@ end
 sample(dist::ProbabilityDistribution, n_samples::Int64) = [sample(dist) for i in 1:n_samples] # TODO: individual samples can be optimized
 
 """Extract VariateType from dist"""
-variateType(dist::ProbabilityDistribution{V, F}) where {V<:VariateType, F<:FactorFunction} = V
+variateType(::ProbabilityDistribution{V, <:FactorFunction}) where V<:VariateType = V
+
+"""Extract VariateType from dims tuple"""
+variateType(::Nothing) = Univariate # Default
+variateType(::Tuple{}) = Univariate 
+variateType(::Tuple{Int}) = Multivariate 
+variateType(::Tuple{Int, Int}) = MatrixVariate
 
 show(io::IO, dist::ProbabilityDistribution) = println(io, format(dist))
 
@@ -59,9 +65,7 @@ slug(::Type{PointMass}) = "δ"
 
 format(dist::ProbabilityDistribution{V, PointMass}) where V<:VariateType = "$(slug(PointMass))(m=$(format(dist.params[:m])))"
 
-dims(dist::ProbabilityDistribution{Univariate, PointMass}) = 1
-dims(dist::ProbabilityDistribution{Multivariate, PointMass}) = length(dist.params[:m])
-dims(dist::ProbabilityDistribution{MatrixVariate, PointMass}) = size(dist.params[:m])
+dims(dist::ProbabilityDistribution{<:VariateType, PointMass}) = size(dist.params[:m])
 
 # PointMass distribution constructors
 ProbabilityDistribution(::Type{Univariate}, ::Type{PointMass}; m::Number=1.0) = ProbabilityDistribution{Univariate, PointMass}(Dict(:m=>m))
@@ -89,10 +93,10 @@ unsafeDetLogMean(dist::ProbabilityDistribution{MatrixVariate, PointMass}) = logd
 unsafeMirroredLogMean(dist::ProbabilityDistribution{Univariate, PointMass}) = log(1.0 - dist.params[:m])
 
 unsafeVar(dist::ProbabilityDistribution{Univariate, PointMass}) = 0.0
-unsafeVar(dist::ProbabilityDistribution{Multivariate, PointMass}) = zeros(dims(dist))
+unsafeVar(dist::ProbabilityDistribution{Multivariate, PointMass}) = zeros(dims(dist)) # Vector
 
 unsafeCov(dist::ProbabilityDistribution{Univariate, PointMass}) = 0.0
-unsafeCov(dist::ProbabilityDistribution{Multivariate, PointMass}) = zeros(dims(dist), dims(dist))
+unsafeCov(dist::ProbabilityDistribution{Multivariate, PointMass}) = zeros(dims(dist)[1], dims(dist)[1]) # Matrix
 
 unsafeMeanCov(dist::ProbabilityDistribution) = (unsafeMean(dist), unsafeCov(dist)) # Can be overloaded for efficiency
 
@@ -123,7 +127,7 @@ convert(::Type{ProbabilityDistribution{Multivariate, PointMass}}, dist::Probabil
 convert(::Type{ProbabilityDistribution{MatrixVariate, PointMass}}, dist::ProbabilityDistribution{Univariate, PointMass}) =
     ProbabilityDistribution(MatrixVariate, PointMass, m=mat(dist.params[:m]))
 convert(::Type{ProbabilityDistribution{MatrixVariate, PointMass}}, dist::ProbabilityDistribution{Multivariate, PointMass}) =
-    ProbabilityDistribution(MatrixVariate, PointMass, m=reshape(dist.params[:m], dims(dist), 1))
+    ProbabilityDistribution(MatrixVariate, PointMass, m=reshape(dist.params[:m], dims(dist)[1], 1))
 
 sample(dist::ProbabilityDistribution{T, PointMass}) where T<:VariateType = deepcopy(dist.params[:m])
 
diff --git a/src/update_rules/nonlinear_extended.jl b/src/update_rules/nonlinear_extended.jl
index fb4096b4..26bdbb27 100644
--- a/src/update_rules/nonlinear_extended.jl
+++ b/src/update_rules/nonlinear_extended.jl
@@ -39,7 +39,7 @@ function isApplicable(::Type{SPNonlinearEInGX}, input_types::Vector{<:Type})
         end
     end
 
-    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs-1)
+    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs - 1)
 end
 
 mutable struct MNonlinearEInGX <: MarginalRule{Nonlinear{Extended}} end
diff --git a/src/update_rules/nonlinear_sampling.jl b/src/update_rules/nonlinear_sampling.jl
index c42cfafb..de4594dd 100644
--- a/src/update_rules/nonlinear_sampling.jl
+++ b/src/update_rules/nonlinear_sampling.jl
@@ -27,14 +27,10 @@ outboundType(::Type{SPNonlinearSInGX}) = Message{GaussianWeightedMeanPrecision}
 function isApplicable(::Type{SPNonlinearSInGX}, input_types::Vector{<:Type})
     total_inputs = length(input_types)
     (total_inputs > 2) || return false
-    (input_types[1] != Nothing) || return false
+    (input_types[1] != Nothing) || return false # Require any message on out
 
     nothing_inputs = 0
-    factorfunction_input = false
     gaussian_inputs = 0
-    if matches(input_types[1], Message{FactorFunction})
-        factorfunction_input = true
-    end
     for input_type in input_types[2:end]
         if input_type == Nothing
             nothing_inputs += 1
@@ -43,70 +39,50 @@ function isApplicable(::Type{SPNonlinearSInGX}, input_types::Vector{<:Type})
         end
     end
 
-    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs-2) && factorfunction_input
+    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs - 2)
 end
 
-mutable struct SPNonlinearSOutNFactorX <: SumProductRule{Nonlinear{Sampling}} end
-outboundType(::Type{SPNonlinearSOutNFactorX}) = Message{SampleList}
-function isApplicable(::Type{SPNonlinearSOutNFactorX}, input_types::Vector{<:Type})
+mutable struct SPNonlinearSOutNMX <: SumProductRule{Nonlinear{Sampling}} end
+outboundType(::Type{SPNonlinearSOutNMX}) = Message{SampleList}
+function isApplicable(::Type{SPNonlinearSOutNMX}, input_types::Vector{<:Type})
     total_inputs = length(input_types)
     (total_inputs > 2) || return false
     (input_types[1] == Nothing) || return false
-
-    factorNode_input = false
+    
     gaussian_inputs = 0
     for input_type in input_types[2:end]
-        if matches(input_type, Message{SampleList})
-            return false
-        elseif matches(input_type, Message{FactorNode})
-            factorNode_input += 1
-            if matches(input_type, Message{Gaussian})
-                gaussian_inputs += 1
-            end
-        else
-            return false
+        matches(input_type, Message) || return false
+        if matches(input_type, Message{Gaussian})
+            gaussian_inputs += 1
         end
     end
+    (gaussian_inputs == total_inputs - 1) && return false # Rule does not apply if all inputs are Gaussian
 
-    return (gaussian_inputs < total_inputs-1) && (factorNode_input == total_inputs-1)
+    return true
 end
 
-mutable struct SPNonlinearSInFactorX <: SumProductRule{Nonlinear{Sampling}} end
-outboundType(::Type{SPNonlinearSInFactorX}) = Message{Function}
-function isApplicable(::Type{SPNonlinearSInFactorX}, input_types::Vector{<:Type})
+mutable struct SPNonlinearSInMX <: SumProductRule{Nonlinear{Sampling}} end
+outboundType(::Type{SPNonlinearSInMX}) = Message{Function}
+function isApplicable(::Type{SPNonlinearSInMX}, input_types::Vector{<:Type})
     total_inputs = length(input_types)
     (total_inputs > 2) || return false
     (input_types[1] != Nothing) || return false
 
     nothing_inputs = 0
-    factorfunction_input = false
-    factorNode_input = 0
     gaussian_inputs = 0
-    if matches(input_types[1], Message{FactorFunction})
-        factorfunction_input = true
-    end
     for input_type in input_types[2:end]
         if input_type == Nothing
             nothing_inputs += 1
-        elseif matches(input_type, Message{PointMass})
-            return false
-        elseif matches(input_type, Message{SampleList})
-            return false
-        elseif matches(input_type, Message{FactorNode})
-            factorNode_input += 1
-            if matches(input_type, Message{Gaussian})
-                gaussian_inputs += 1
-            end
-        else
-            return false
+        elseif matches(input_type, Message{Gaussian})
+            gaussian_inputs += 1
         end
     end
 
-    return (nothing_inputs == 1) && (gaussian_inputs < total_inputs-2) && (factorNode_input == total_inputs-2) && factorfunction_input
+    return (nothing_inputs == 1) && (gaussian_inputs != total_inputs - 2) # Rule does not apply if all inbounds are Gaussian
 end
 
-mutable struct MNonlinearSInGX <: MarginalRule{Nonlinear{Sampling}} end
-function isApplicable(::Type{MNonlinearSInGX}, input_types::Vector{<:Type})
+mutable struct MNonlinearSInMGX <: MarginalRule{Nonlinear{Sampling}} end
+function isApplicable(::Type{MNonlinearSInMGX}, input_types::Vector{<:Type})
     total_inputs = length(input_types)
     (total_inputs > 2) || return false
     (input_types[1] == Nothing) || return false # Indicates marginalization over outbound variable
diff --git a/src/update_rules/nonlinear_unscented.jl b/src/update_rules/nonlinear_unscented.jl
index d01a5eca..92634f0e 100644
--- a/src/update_rules/nonlinear_unscented.jl
+++ b/src/update_rules/nonlinear_unscented.jl
@@ -39,7 +39,7 @@ function isApplicable(::Type{SPNonlinearUTInGX}, input_types::Vector{<:Type})
         end
     end
 
-    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs-1)
+    return (nothing_inputs == 1) && (gaussian_inputs == total_inputs - 1)
 end
 
 mutable struct MNonlinearUTInGX <: MarginalRule{Nonlinear{Unscented}} end
diff --git a/test/factor_nodes/test_bernoulli.jl b/test/factor_nodes/test_bernoulli.jl
index b44770a6..d31c5875 100644
--- a/test/factor_nodes/test_bernoulli.jl
+++ b/test/factor_nodes/test_bernoulli.jl
@@ -16,7 +16,7 @@ using ForneyLab: SPBernoulliOutNP, SPBernoulliIn1PN, SPBernoulliOutNB, VBBernoul
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Bernoulli, p=0.5)) == 1
+    @test dims(ProbabilityDistribution(Bernoulli, p=0.5)) == ()
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_beta.jl b/test/factor_nodes/test_beta.jl
index 5014b016..9ef2a206 100644
--- a/test/factor_nodes/test_beta.jl
+++ b/test/factor_nodes/test_beta.jl
@@ -17,7 +17,7 @@ using SpecialFunctions: digamma
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Beta, a=2.0, b=2.0)) == 1
+    @test dims(ProbabilityDistribution(Beta, a=2.0, b=2.0)) == ()
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_categorical.jl b/test/factor_nodes/test_categorical.jl
index f82e0cad..7e9279a1 100644
--- a/test/factor_nodes/test_categorical.jl
+++ b/test/factor_nodes/test_categorical.jl
@@ -17,7 +17,7 @@ using SparseArrays: SparseVector, spzeros
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Categorical, p=[0.1, 0.8, 0.1])) == 1
+    @test dims(ProbabilityDistribution(Categorical, p=[0.1, 0.8, 0.1])) == ()
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_contingency.jl b/test/factor_nodes/test_contingency.jl
index 1188405c..0bce9171 100644
--- a/test/factor_nodes/test_contingency.jl
+++ b/test/factor_nodes/test_contingency.jl
@@ -12,7 +12,7 @@ using ForneyLab: vague, dims
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Contingency, p=[0.1 0.4; 0.3 0.2])) == 2
+    @test dims(ProbabilityDistribution(Contingency, p=[0.1 0.4; 0.3 0.2])) == (2,)
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_dirichlet.jl b/test/factor_nodes/test_dirichlet.jl
index 36a26b98..e3654f1d 100644
--- a/test/factor_nodes/test_dirichlet.jl
+++ b/test/factor_nodes/test_dirichlet.jl
@@ -19,12 +19,11 @@ using SpecialFunctions: digamma
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Multivariate, Dirichlet, a=[2.0, 2.0, 2.0])) == 3
+    @test dims(ProbabilityDistribution(Multivariate, Dirichlet, a=[2.0, 2.0, 2.0])) == (3,)
     @test dims(ProbabilityDistribution(MatrixVariate, Dirichlet, a=[2.0 2.0 2.0; 2.0 2.0 2.0])) == (2,3)
 end
 
 @testset "vague" begin
-    @test vague(Dirichlet, 3) == ProbabilityDistribution(Dirichlet, a=ones(3))
     @test vague(Dirichlet, (3,)) == ProbabilityDistribution(Dirichlet, a=ones(3))
     @test vague(Dirichlet, (2,3)) == ProbabilityDistribution(MatrixVariate, Dirichlet, a=ones(2,3))
 end
diff --git a/test/factor_nodes/test_gamma.jl b/test/factor_nodes/test_gamma.jl
index 07cf4d5d..a9166230 100644
--- a/test/factor_nodes/test_gamma.jl
+++ b/test/factor_nodes/test_gamma.jl
@@ -6,7 +6,7 @@ using ForneyLab: prod!, unsafeMean, unsafeVar, outboundType, isApplicable, dims
 using ForneyLab: SPGammaOutNPP, VBGammaOut, VBGammaA, VBGammaB
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, Gamma, a=1.0, b=1.0)) == 1
+    @test dims(ProbabilityDistribution(Univariate, Gamma, a=1.0, b=1.0)) == ()
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_gaussian_mean_precision.jl b/test/factor_nodes/test_gaussian_mean_precision.jl
index 91929506..83085afe 100644
--- a/test/factor_nodes/test_gaussian_mean_precision.jl
+++ b/test/factor_nodes/test_gaussian_mean_precision.jl
@@ -2,17 +2,16 @@ module GaussianMeanPrecisionTest
 
 using Test
 using ForneyLab
-using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
+using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeMeanPrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
 using ForneyLab: SPGaussianMeanPrecisionOutNPP, SPGaussianMeanPrecisionMPNP, SPGaussianMeanPrecisionOutNGP, SPGaussianMeanPrecisionMGNP, VBGaussianMeanPrecisionOut, VBGaussianMeanPrecisionM, VBGaussianMeanPrecisionW, SVBGaussianMeanPrecisionOutVGD, SVBGaussianMeanPrecisionMGVD, SVBGaussianMeanPrecisionW, MGaussianMeanPrecisionGGD, MGaussianMeanPrecisionGGN
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=1.0)) == 1
-    @test dims(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=ones(2), w=diageye(2))) == 2
+    @test dims(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=1.0)) == ()
+    @test dims(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=ones(2), w=diageye(2))) == (2,)
 end
 
 @testset "vague" begin
     @test vague(GaussianMeanPrecision) == ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=0.0, w=tiny)
-    @test vague(GaussianMeanPrecision, 2) == ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=zeros(2), w=tiny*eye(2))
     @test vague(GaussianMeanPrecision, (2,)) == ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=zeros(2), w=tiny*eye(2))
 end
 
@@ -45,6 +44,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == 0.25
     @test unsafeMeanCov(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == (2.0, 0.25)
     @test unsafePrecision(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == 4.0
+    @test unsafeMeanPrecision(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == (2.0, 4.0)
     @test unsafeWeightedMean(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == 8.0
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Univariate, GaussianMeanPrecision, m=2.0, w=4.0)) == (8.0, 4.0)
 
@@ -55,6 +55,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == mat(0.25)
     @test unsafeMeanCov(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == ([2.0], mat(0.25))
     @test unsafePrecision(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == mat(4.0)
+    @test unsafeMeanPrecision(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == ([2.0], mat(4.0))
     @test unsafeWeightedMean(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == [8.0]
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Multivariate, GaussianMeanPrecision, m=[2.0], w=mat(4.0))) == ([8.0], mat(4.0))
 end
diff --git a/test/factor_nodes/test_gaussian_mean_variance.jl b/test/factor_nodes/test_gaussian_mean_variance.jl
index 53512aa4..44604a07 100644
--- a/test/factor_nodes/test_gaussian_mean_variance.jl
+++ b/test/factor_nodes/test_gaussian_mean_variance.jl
@@ -2,17 +2,16 @@ module GaussianMeanVarianceTest
 
 using Test
 using ForneyLab
-using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
+using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeMeanPrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
 using ForneyLab: SPGaussianMeanVarianceOutNGS, SPGaussianMeanVarianceOutNPP,SPGaussianMeanVarianceMSNP, SPGaussianMeanVarianceMPNP, SPGaussianMeanVarianceOutNGP, SPGaussianMeanVarianceMGNP, SPGaussianMeanVarianceVGGN, SPGaussianMeanVarianceVPGN, SPGaussianMeanVarianceOutNSP, VBGaussianMeanVarianceM, VBGaussianMeanVarianceOut, bootstrap
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=0.0, v=1.0)) == 1
-    @test dims(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=ones(2), v=diageye(2))) == 2
+    @test dims(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=0.0, v=1.0)) == ()
+    @test dims(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=ones(2), v=diageye(2))) == (2,)
 end
 
 @testset "vague" begin
     @test vague(GaussianMeanVariance) == ProbabilityDistribution(Univariate, GaussianMeanVariance, m=0.0, v=huge)
-    @test vague(GaussianMeanVariance, 2) == ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=zeros(2), v=huge*eye(2))
     @test vague(GaussianMeanVariance, (2,)) == ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=zeros(2), v=huge*eye(2))
 end
 
@@ -45,6 +44,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == 4.0
     @test unsafeMeanCov(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == (2.0, 4.0)
     @test unsafePrecision(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == 0.25
+    @test unsafeMeanPrecision(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == (2.0, 0.25)
     @test unsafeWeightedMean(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == 0.5
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Univariate, GaussianMeanVariance, m=2.0, v=4.0)) == (0.5, 0.25)
 
@@ -55,6 +55,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == mat(4.0)
     @test unsafeMeanCov(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == ([2.0], mat(4.0))
     @test unsafePrecision(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == mat(0.25)
+    @test unsafeMeanPrecision(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == ([2.0], mat(0.25))
     @test unsafeWeightedMean(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == [0.5]
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(4.0))) == ([0.5], mat(0.25))
 end
diff --git a/test/factor_nodes/test_gaussian_weighted_mean_precision.jl b/test/factor_nodes/test_gaussian_weighted_mean_precision.jl
index 24163981..8a28e4f9 100644
--- a/test/factor_nodes/test_gaussian_weighted_mean_precision.jl
+++ b/test/factor_nodes/test_gaussian_weighted_mean_precision.jl
@@ -2,17 +2,16 @@ module GaussianWeightedMeanPrecisionTest
 
 using Test
 using ForneyLab
-using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
+using ForneyLab: outboundType, isApplicable, isProper, unsafeMean, unsafeMode, unsafeVar, unsafeCov, unsafeMeanCov, unsafePrecision, unsafeMeanPrecision, unsafeWeightedMean, unsafeWeightedMeanPrecision
 using ForneyLab: SPGaussianWeightedMeanPrecisionOutNPP, VBGaussianWeightedMeanPrecisionOut
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=1.0)) == 1
-    @test dims(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=ones(2), w=diageye(2))) == 2
+    @test dims(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=1.0)) == ()
+    @test dims(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=ones(2), w=diageye(2))) == (2,)
 end
 
 @testset "vague" begin
     @test vague(GaussianWeightedMeanPrecision) == ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=0.0, w=tiny)
-    @test vague(GaussianWeightedMeanPrecision, 2) == ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=zeros(2), w=tiny*eye(2))
     @test vague(GaussianWeightedMeanPrecision, (2,)) == ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=zeros(2), w=tiny*eye(2))
 end
 
@@ -45,6 +44,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == 0.25
     @test unsafeMeanCov(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == (0.5, 0.25)
     @test unsafePrecision(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == 4.0
+    @test unsafeMeanPrecision(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == (0.5, 4.0)
     @test unsafeWeightedMean(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == 2.0
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Univariate, GaussianWeightedMeanPrecision, xi=2.0, w=4.0)) == (2.0, 4.0)
 
@@ -55,6 +55,7 @@ end
     @test unsafeCov(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == mat(0.25)
     @test unsafeMeanCov(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == ([0.5], mat(0.25))
     @test unsafePrecision(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == mat(4.0)
+    @test unsafeMeanPrecision(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == ([0.5], mat(4.0))
     @test unsafeWeightedMean(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == [2.0]
     @test unsafeWeightedMeanPrecision(ProbabilityDistribution(Multivariate, GaussianWeightedMeanPrecision, xi=[2.0], w=mat(4.0))) == ([2.0], mat(4.0))
 end
diff --git a/test/factor_nodes/test_log_normal.jl b/test/factor_nodes/test_log_normal.jl
index 1aa48ef9..9c600527 100644
--- a/test/factor_nodes/test_log_normal.jl
+++ b/test/factor_nodes/test_log_normal.jl
@@ -6,7 +6,7 @@ using ForneyLab: prod!, unsafeMean, unsafeLogMean, unsafeVar, unsafeLogVar, unsa
 using ForneyLab: SPLogNormalOutNPP, VBLogNormalOut
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, LogNormal, m=1.0, s=1.0)) == 1
+    @test dims(ProbabilityDistribution(Univariate, LogNormal, m=1.0, s=1.0)) == ()
 end
 
 @testset "vague" begin
diff --git a/test/factor_nodes/test_nonlinear_extended.jl b/test/factor_nodes/test_nonlinear_extended.jl
index 14d233bd..65bf619f 100644
--- a/test/factor_nodes/test_nonlinear_extended.jl
+++ b/test/factor_nodes/test_nonlinear_extended.jl
@@ -5,7 +5,7 @@ using LinearAlgebra
 using ForneyLab
 using ForneyLab: outboundType, isApplicable, Extended, requiresBreaker, breakerParameters
 using ForneyLab: SPNonlinearEOutNG, SPNonlinearEIn1GG, SPNonlinearEOutNGX, SPNonlinearEInGX, MNonlinearEInGX
-using ForneyLab: concatenate, localLinearization, requiresBreaker, breakerParameters
+using ForneyLab: concatenate, localLinearizationSingleIn, localLinearizationMultiIn, requiresBreaker, breakerParameters
 
 f(x) = x
 
@@ -25,23 +25,24 @@ h_inv_x(z::Vector, y::Vector) = sqrt.(z .+ y)
 #--------
 
 @testset "concatenate" begin
-    @test concatenate([[1.0, 2.0], [3.0]]) == ([1.0, 2.0, 3.0], [2, 1])
+    @test concatenate([[1.0, 2.0], [3.0]]) == ([1.0, 2.0, 3.0], [(2,), (1,)])
+    @test concatenate([[1.0, 2.0], 3.0]) == ([1.0, 2.0, 3.0], [(2,), ()])
 end
 
 @testset "localLinearization" begin
-    (a, b) = localLinearization(Univariate, g, 1.0)
+    (a, b) = localLinearizationSingleIn(g, 1.0)
     @test a == 2.0
     @test b == -6.0
 
-    (A, b) = localLinearization(Multivariate, g, [1.0])
+    (A, b) = localLinearizationSingleIn(g, [1.0])
     @test A == mat(2.0)
     @test b == [-6.0]
 
-    (A, b) = localLinearization(Univariate, h, [1.0, 2.0])
+    (A, b) = localLinearizationMultiIn(h, [1.0, 2.0])
     @test A == [2.0, -1.0]'
     @test b == -1.0
 
-    (A, b) = localLinearization(Multivariate, h, [[1.0], [2.0]])
+    (A, b) = localLinearizationMultiIn(h, [[1.0], [2.0]])
     @test A == [2.0 -1.0]
     @test b == [-1.0]
 end
@@ -120,8 +121,8 @@ end
     @test ruleSPNonlinearEInGX(h, 1, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0)), Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(1.0)), Message(Multivariate, GaussianMeanVariance, m=[5.0], v=mat(1.0))) == Message(Multivariate, GaussianWeightedMeanPrecision, xi=[10.999999999999996], w=mat(3.9999999999999982))
 
     # With given inverse
-    @test ruleSPNonlinearEInGX(h, h_inv_x, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), nothing, Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianMeanVariance, m=1.3228756555322954, v=0.14285714285714282)
-    @test ruleSPNonlinearEInGX(h, h_inv_x, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0)), nothing, Message(Multivariate, GaussianMeanVariance, m=[5.0], v=mat(1.0))) == Message(Multivariate, GaussianMeanVariance, m=[1.3228756555322954], v=mat(0.14285714285714282))
+    @test ruleSPNonlinearEInGX(h, h_inv_x, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), nothing, Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianMeanVariance, m=2.6457513110645907, v=0.14285714285714282)
+    @test ruleSPNonlinearEInGX(h, h_inv_x, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0)), nothing, Message(Multivariate, GaussianMeanVariance, m=[5.0], v=mat(1.0))) == Message(Multivariate, GaussianMeanVariance, m=[2.6457513110645907], v=mat(0.14285714285714282))
 end
 
 @testset "MNonlinearEInGX" begin
diff --git a/test/factor_nodes/test_nonlinear_sampling.jl b/test/factor_nodes/test_nonlinear_sampling.jl
index 4c8f4a82..e11a76b7 100644
--- a/test/factor_nodes/test_nonlinear_sampling.jl
+++ b/test/factor_nodes/test_nonlinear_sampling.jl
@@ -5,7 +5,7 @@ using Random
 # using LinearAlgebra
 using ForneyLab
 using ForneyLab: outboundType, isApplicable, prod!, logPdf, unsafeMean, unsafeVar, Sampling, requiresBreaker, breakerParameters
-using ForneyLab: SPNonlinearSInGX, SPNonlinearSOutNGX, SPNonlinearSOutNM, SPNonlinearSIn1MN, SPNonlinearSOutNFactorX, SPNonlinearSInFactorX, MNonlinearSInGX, gradientOptimization
+using ForneyLab: SPNonlinearSInGX, SPNonlinearSOutNGX, SPNonlinearSOutNM, SPNonlinearSIn1MN, SPNonlinearSOutNMX, SPNonlinearSInMX, MNonlinearSInMGX, gradientOptimization
 using ForwardDiff
 
 Random.seed!(1234)
@@ -106,29 +106,29 @@ end
     @test d.params[:log_pdf](1) == 2
 end
 
-@testset "SPNonlinearSOutNFactorX" begin
-    @test SPNonlinearSOutNFactorX <: SumProductRule{Nonlinear{Sampling}}
-    @test outboundType(SPNonlinearSOutNFactorX) == Message{SampleList}
-    @test !isApplicable(SPNonlinearSOutNFactorX, [Nothing, Message{Gamma}])
-    @test !isApplicable(SPNonlinearSOutNFactorX, [Message{Gaussian}, Message{Gaussian}, Nothing])
-    @test !isApplicable(SPNonlinearSOutNFactorX, [Message{Gaussian}, Message{Gamma}, Nothing])
-    @test isApplicable(SPNonlinearSOutNFactorX, [Nothing, Message{Gaussian}, Message{Gamma}])
+@testset "SPNonlinearSOutNMX" begin
+    @test SPNonlinearSOutNMX <: SumProductRule{Nonlinear{Sampling}}
+    @test outboundType(SPNonlinearSOutNMX) == Message{SampleList}
+    @test !isApplicable(SPNonlinearSOutNMX, [Nothing, Message{Gamma}])
+    @test !isApplicable(SPNonlinearSOutNMX, [Message{Gaussian}, Message{Gaussian}, Nothing])
+    @test !isApplicable(SPNonlinearSOutNMX, [Message{Gaussian}, Message{Gamma}, Nothing])
+    @test isApplicable(SPNonlinearSOutNMX, [Nothing, Message{Gaussian}, Message{Gamma}])
 end
 
-@testset "SPNonlinearSInFactorX" begin
-    @test SPNonlinearSInFactorX <: SumProductRule{Nonlinear{Sampling}}
-    @test outboundType(SPNonlinearSInFactorX) == Message{Function}
-    @test !isApplicable(SPNonlinearSInFactorX, [Message{FactorFunction}, Message{Gamma}])
-    @test !isApplicable(SPNonlinearSInFactorX, [Message{Gaussian}, Message{Gaussian}, Nothing])
-    @test !isApplicable(SPNonlinearSInFactorX, [Nothing, Message{Gaussian}, Message{Gamma}])
-    @test isApplicable(SPNonlinearSInFactorX, [Message{FactorFunction}, Nothing, Message{Gaussian}, Message{Gamma}])
+@testset "SPNonlinearSInMX" begin
+    @test SPNonlinearSInMX <: SumProductRule{Nonlinear{Sampling}}
+    @test outboundType(SPNonlinearSInMX) == Message{Function}
+    @test !isApplicable(SPNonlinearSInMX, [Message{FactorFunction}, Message{Gamma}])
+    @test !isApplicable(SPNonlinearSInMX, [Message{Gaussian}, Message{Gaussian}, Nothing])
+    @test !isApplicable(SPNonlinearSInMX, [Nothing, Message{Gaussian}, Message{Gamma}])
+    @test isApplicable(SPNonlinearSInMX, [Message{FactorFunction}, Nothing, Message{Gaussian}, Message{Gamma}])
 end
 
-@testset "MNonlinearSInGX" begin
-    @test MNonlinearSInGX <: MarginalRule{Nonlinear{Sampling}}
-    @test isApplicable(MNonlinearSInGX, [Nothing, Message{Gaussian}, Message{Gaussian}])
-    @test !isApplicable(MNonlinearSInGX, [Nothing, Message{Gaussian}])
-    @test !isApplicable(MNonlinearSInGX, [Message{Gaussian}, Message{Gaussian}, ProbabilityDistribution])
+@testset "MNonlinearSInMGX" begin
+    @test MNonlinearSInMGX <: MarginalRule{Nonlinear{Sampling}}
+    @test isApplicable(MNonlinearSInMGX, [Nothing, Message{Gaussian}, Message{Gaussian}])
+    @test !isApplicable(MNonlinearSInMGX, [Nothing, Message{Gaussian}])
+    @test !isApplicable(MNonlinearSInMGX, [Message{Gaussian}, Message{Gaussian}, ProbabilityDistribution])
 end
 
 
@@ -168,17 +168,17 @@ end
 @testset "Nonlinear integration via sampling with specified variate types" begin
     fg = FactorGraph()
 
-    @RV x ~ GaussianMeanVariance(2.0, 1.0)
-    @RV y ~ GaussianMeanVariance(2.0, 3.0)
-    @RV z ~ GaussianMeanVariance(2.0, 3.0)
-    n = Nonlinear{Sampling}(z, x, y, g=g, out_variate=Multivariate, in_variates=[Univariate, Univariate])
+    @RV x ~ GaussianMeanVariance([2.0], mat(1.0))
+    @RV y ~ GaussianMeanVariance([2.0], mat(3.0))
+    @RV z ~ GaussianMeanVariance([2.0], mat(3.0))
+    n = Nonlinear{Sampling}(z, x, y, g=g, dims=[(1,), (1,), (1,)])
 
     # Forward; g_inv should not be present in call
     pfz = PosteriorFactorization(fg)
     algo = messagePassingAlgorithm(y)
     code = algorithmSourceCode(algo)
     
-    @test occursin("ruleSPNonlinearSInGX(g, Univariate, 2, messages[3], messages[2], messages[1])", code)
+    @test occursin("ruleSPNonlinearSInGX(g, 2, messages[3], messages[2], messages[1], dims=(1,))", code)
 end
 
 end # module
\ No newline at end of file
diff --git a/test/factor_nodes/test_nonlinear_unscented.jl b/test/factor_nodes/test_nonlinear_unscented.jl
index 33e34bfa..846e3334 100644
--- a/test/factor_nodes/test_nonlinear_unscented.jl
+++ b/test/factor_nodes/test_nonlinear_unscented.jl
@@ -16,6 +16,7 @@ g_inv(y::Vector{Float64}) = sqrt.(y .+ 5.0)
 
 h(x::Float64, y::Float64) = x^2 - y
 h(x::Vector{Float64}, y::Vector{Float64}) = x.^2 .- y
+h(x::Float64, y::Vector{Float64}) = x^2 .- y
 h_inv_x(z::Float64, y::Float64) = sqrt(z + y)
 h_inv_x(z::Vector{Float64}, y::Vector{Float64}) = sqrt.(z .+ y)
 
@@ -65,6 +66,12 @@ end
     @test m_tilde == [-1.000000000174623, -1.000000000174623]
     @test V_tilde == [5.000002998916898 1.9999989990901668; 1.999998999031959 5.000002998946002]
     @test C_tilde == [0.0 0.0; 0.0 0.0; -2.9999999999998863 0.0; 0.0 -2.9999999999998863]
+
+    # Mixed variate inbounds
+    (m_tilde, V_tilde, C_tilde) = unscentedStatistics([m_x, [m_y, m_y]], [v_x, Diagonal([v_y, v_y])], h)
+    @test m_tilde == [-1.0, -1.0]
+    @test V_tilde == [5.000002000015229 2.000002000015229; 2.000002000015229 5.000002000015229]
+    @test C_tilde == [0.0 0.0; -3.0000000000001137 0.0; 0.0 -3.0000000000001137]
 end
 
 @testset "smoothRTSMessage" begin
@@ -79,22 +86,26 @@ end
 
 @testset "collectStatistics" begin
     @test collectStatistics(Message(Univariate, GaussianMeanVariance, m=0.0, v=1.0), nothing, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0)) == ([0.0, 2.0], [1.0, 3.0])
-    @test collectStatistics(Message(Univariate, GaussianMeanVariance, m=[0.0], v=mat(1.0)), nothing, Message(Univariate, GaussianMeanVariance, m=[2.0], v=mat(3.0))) == ([[0.0], [2.0]], [mat(1.0), mat(3.0)])
+    @test collectStatistics(Message(Multivariate, GaussianMeanVariance, m=[0.0], v=mat(1.0)), nothing, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0))) == ([[0.0], [2.0]], [mat(1.0), mat(3.0)])
+    @test collectStatistics(Message(Univariate, GaussianMeanVariance, m=0.0, v=1.0), nothing, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0))) == ([0.0, [2.0]], [1.0, mat(3.0)])
 end
 
 @testset "marginalizeGaussianMV" begin
-    @test marginalizeGaussianMV(Univariate, [0.0, 1.0], [2.0 0.5; 0.5 3.0], ones(Int64, 2), 1) == (0.0, 2.0)
-    @test marginalizeGaussianMV(Multivariate, [0.0, 1.0, 2.0], [2.0 0.0 0.5; 0.0 3.0 0.0; 0.5 0.0 4.0], [1, 2], 2) == ([1.0, 2.0], [3.0 0.0; 0.0 4.0])
+    @test marginalizeGaussianMV([0.0, 1.0], [2.0 0.5; 0.5 3.0], [(), ()], 1) == (0.0, 2.0) # Univariate
+    @test marginalizeGaussianMV([0.0, 1.0, 2.0], [2.0 0.0 0.5; 0.0 3.0 0.0; 0.5 0.0 4.0], [(), (2,)], 1) == (0.0, 2.0) # Univariate
+    @test marginalizeGaussianMV([0.0, 1.0, 2.0], [2.0 0.0 0.5; 0.0 3.0 0.0; 0.5 0.0 4.0], [(), (2,)], 2) == ([1.0, 2.0], [3.0 0.0; 0.0 4.0]) # Multivariate
 end
 
 @testset "concatenateGaussianMV" begin
-    @test concatenateGaussianMV([1.0, 2.0, 3.0], [4.0, 5.0, 6.0]) == ([1.0, 2.0, 3.0], Diagonal([4.0, 5.0, 6.0]), ones(Int64, 3))
-    @test concatenateGaussianMV([[1.0], [2.0, 3.0]], [mat(4.0), Diagonal([5.0, 6.0])]) == ([1.0, 2.0, 3.0], [4.0 0.0 0.0; 0.0 5.0 0.0; 0.0 0.0 6.0], [1, 2])
-    @test concatenateGaussianMV([1.0, [2.0, 3.0]], [4.0, Diagonal([5.0, 6.0])]) == ([1.0, 2.0, 3.0], [4.0 0.0 0.0; 0.0 5.0 0.0; 0.0 0.0 6.0], [1, 2])
+    @test concatenateGaussianMV([1.0, 2.0, 3.0], [4.0, 5.0, 6.0]) == ([1.0, 2.0, 3.0], Diagonal([4.0, 5.0, 6.0]), [(), (), ()])
+    @test concatenateGaussianMV([[1.0], [2.0, 3.0]], [mat(4.0), Diagonal([5.0, 6.0])]) == ([1.0, 2.0, 3.0], [4.0 0.0 0.0; 0.0 5.0 0.0; 0.0 0.0 6.0], [(1,), (2,)])
+    @test concatenateGaussianMV([1.0, [2.0, 3.0]], [4.0, Diagonal([5.0, 6.0])]) == ([1.0, 2.0, 3.0], [4.0 0.0 0.0; 0.0 5.0 0.0; 0.0 0.0 6.0], [(), (2,)])
 end
 
 @testset "split" begin
-    @test split([1.0, 2.0, 3.0], [1, 2]) == [[1.0], [2.0, 3.0]]
+    @test split([1.0, 2.0], [(), ()]) == [1.0, 2.0]
+    @test split([1.0, 2.0, 3.0, 4.0], [(2,), (2,)]) == [[1.0, 2.0], [3.0, 4.0]]
+    @test split([1.0, 2.0, 3.0], [(), (2,)]) == [1.0, [2.0, 3.0]]
 end
 
 @testset "requiresBreaker and breakerParameters" begin
@@ -173,7 +184,7 @@ end
     @test isApplicable(SPNonlinearUTOutNGX, [Nothing, Message{Gaussian}, Message{Gaussian}]) 
     @test !isApplicable(SPNonlinearUTOutNGX, [Message{Gaussian}, Nothing, Message{Gaussian}]) 
 
-    @test ruleSPNonlinearUTOutNGX(h, nothing, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianMeanVariance, m=1.9999999997671694, v=67.00000899797305)
+    @test ruleSPNonlinearUTOutNGX(h, nothing, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianMeanVariance, m=1.9999999997671694, v=67.00000899657607)
     @test ruleSPNonlinearUTOutNGX(h, nothing, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0)), Message(Multivariate, GaussianMeanVariance, m=[5.0], v=mat(1.0))) == Message(Multivariate, GaussianMeanVariance, m=[1.9999999997671694], v=mat(67.00000899657607))
 end
 
@@ -203,7 +214,7 @@ end
     @test isApplicable(SPNonlinearUTInGX, [Message{Gaussian}, Nothing, Message{Gaussian}]) 
 
     # Without given inverse
-    @test ruleSPNonlinearUTInGX(h, 1, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), Message(Univariate, GaussianMeanVariance, m=2.0, v=1.0), Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianWeightedMeanPrecision, xi=6.666665554160243, w=2.6666662217033044)
+    @test ruleSPNonlinearUTInGX(h, 1, Message(Univariate, GaussianMeanVariance, m=2.0, v=3.0), Message(Univariate, GaussianMeanVariance, m=2.0, v=1.0), Message(Univariate, GaussianMeanVariance, m=5.0, v=1.0)) == Message(Univariate, GaussianWeightedMeanPrecision, xi=6.666665554127903, w=2.6666662216903676)
     @test ruleSPNonlinearUTInGX(h, 1, Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(3.0)), Message(Multivariate, GaussianMeanVariance, m=[2.0], v=mat(1.0)), Message(Multivariate, GaussianMeanVariance, m=[5.0], v=mat(1.0))) == Message(Multivariate, GaussianWeightedMeanPrecision, xi=[6.666665554127903], w=mat(2.666666221690368))
 
     # With given inverse
diff --git a/test/factor_nodes/test_poisson.jl b/test/factor_nodes/test_poisson.jl
index 8b1e29ba..51228b06 100644
--- a/test/factor_nodes/test_poisson.jl
+++ b/test/factor_nodes/test_poisson.jl
@@ -21,7 +21,7 @@ end
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Poisson)) == 1
+    @test dims(ProbabilityDistribution(Poisson)) == ()
 end
 
 @testset "slug" begin
diff --git a/test/factor_nodes/test_sample_list.jl b/test/factor_nodes/test_sample_list.jl
index be8b2060..ea3d8d30 100644
--- a/test/factor_nodes/test_sample_list.jl
+++ b/test/factor_nodes/test_sample_list.jl
@@ -6,8 +6,8 @@ using ForneyLab: outboundType, isApplicable, prod!, unsafeMean, unsafeCov, unsaf
 using ForneyLab: SPSampleListOutNPP, VBSampleListOut
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, SampleList, s=[0.0, 1.0], w=[0.5, 0.5])) == 1
-    @test dims(ProbabilityDistribution(Multivariate, SampleList, s=[[0.0], [1.0]], w=[0.5, 0.5])) == 1
+    @test dims(ProbabilityDistribution(Univariate, SampleList, s=[0.0, 1.0], w=[0.5, 0.5])) == ()
+    @test dims(ProbabilityDistribution(Multivariate, SampleList, s=[[0.0], [1.0]], w=[0.5, 0.5])) == (1,)
     @test dims(ProbabilityDistribution(MatrixVariate, SampleList, s=[mat(0.0), mat(1.0)], w=[0.5, 0.5])) == (1,1)
 end
 
diff --git a/test/factor_nodes/test_wishart.jl b/test/factor_nodes/test_wishart.jl
index 378c0b5f..ef900321 100644
--- a/test/factor_nodes/test_wishart.jl
+++ b/test/factor_nodes/test_wishart.jl
@@ -7,11 +7,10 @@ using ForneyLab: SPWishartOutNPP, VBWishartOut
 using SpecialFunctions: digamma
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(MatrixVariate, Wishart, v=diageye(3), nu=4.0)) == (3, 3)
+    @test dims(ProbabilityDistribution(MatrixVariate, Wishart, v=diageye(3), nu=4.0)) == (3,3)
 end
 
 @testset "vague" begin
-    @test vague(Wishart, 3) == ProbabilityDistribution(MatrixVariate, Wishart, v=huge*diageye(3), nu=3.0)
     @test vague(Wishart, (3,3)) == ProbabilityDistribution(MatrixVariate, Wishart, v=huge*diageye(3), nu=3.0)
     @test vague(Union{Gamma, Wishart}, (3,3)) == ProbabilityDistribution(MatrixVariate, Wishart, v=huge*diageye(3), nu=3.0)
     @test vague(Union{Gamma, Wishart}, ()) == ProbabilityDistribution(Univariate, Gamma, a=1.0, b=tiny)
diff --git a/test/test_probability_distribution.jl b/test/test_probability_distribution.jl
index ca3a8a4a..0395c27e 100644
--- a/test/test_probability_distribution.jl
+++ b/test/test_probability_distribution.jl
@@ -106,10 +106,9 @@ end
 end
 
 @testset "dims" begin
-    @test dims(ProbabilityDistribution(Univariate, PointMass, m=0.0)) == 1
-    @test dims(ProbabilityDistribution(Multivariate, PointMass, m=ones(2))) == 2
-    @test dims(ProbabilityDistribution(MatrixVariate, PointMass, m=eye(2))) == (2, 2)
-    @test dims(ProbabilityDistribution(MatrixVariate, PointMass, m=diageye(2))) == (2, 2)
+    @test dims(ProbabilityDistribution(Univariate, PointMass, m=0.0)) == ()
+    @test dims(ProbabilityDistribution(Multivariate, PointMass, m=ones(2))) == (2,)
+    @test dims(ProbabilityDistribution(MatrixVariate, PointMass, m=eye(2))) == (2,2)
 end
 
 @testset "gaussianQuadrature" begin