From 2ad7811a36bb23e27445eb5b62d089ce5eb5354c Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Wed, 28 Aug 2024 15:18:56 -0700 Subject: [PATCH 01/13] Performance: optimize the hit counter by limiting the number of documents it needs to iterate over --- .../jmhbenchmarks/HitCounterBenchmarks.scala | 92 ++++++---- .../elastiknn/search/ArrayHitCounter.java | 165 ++++++++++-------- .../elastiknn/search/KthGreatestResult.java | 18 +- .../search/MatchHashesAndScoreQuery.java | 7 +- .../search/ArrayHitCounterSpec.scala | 3 +- 5 files changed, 158 insertions(+), 127 deletions(-) diff --git a/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala b/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala index eae02df2..0525bb17 100644 --- a/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala +++ b/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala @@ -1,7 +1,9 @@ package com.klibisz.elastiknn.jmhbenchmarks -import org.openjdk.jmh.annotations._ +import com.klibisz.elastiknn.search.ArrayHitCounter +import org.openjdk.jmh.annotations.* import org.apache.lucene.internal.hppc.IntIntHashMap +import org.apache.lucene.search.DocIdSetIterator import org.eclipse.collections.impl.map.mutable.primitive.IntShortHashMap import scala.util.Random @@ -10,21 +12,16 @@ import scala.util.Random class HitCounterBenchmarksFixtures { val rng = new Random(0) val numDocs = 60000 - val numHits = 2000 - val initialMapSize = 1000 + val numHits = 30000 + val candidates = 1000 val docs: Array[Int] = (1 to numHits).map(_ => rng.nextInt(numDocs)).toArray + val maxCount = docs.groupBy(identity).keys.max } class HitCounterBenchmarks { - @Benchmark - @BenchmarkMode(Array(Mode.Throughput)) - @Fork(value = 1) - @Warmup(time = 5, iterations = 5) - @Measurement(time = 5, iterations = 5) - def arrayCountBaseline(f: HitCounterBenchmarksFixtures): Unit = { - val arr = new Array[Int](f.numDocs) - for (d <- f.docs) arr.update(d, arr(d) + 1) + private def consumeDocIdSetIterator(disi: DocIdSetIterator): Unit = { + while (disi.nextDoc() != DocIdSetIterator.NO_MORE_DOCS) disi.docID() () } @@ -32,32 +29,55 @@ class HitCounterBenchmarks { @BenchmarkMode(Array(Mode.Throughput)) @Fork(value = 1) @Warmup(time = 5, iterations = 5) - @Measurement(time = 5, iterations = 5) - def hashMapGetOrDefault(f: HitCounterBenchmarksFixtures): Unit = { - val h = new java.util.HashMap[Int, Int](f.initialMapSize, 0.99f) - for (d <- f.docs) h.put(d, h.getOrDefault(d, 0) + 1) + @Measurement(time = 5, iterations = 10) + def arrayHitCounter(f: HitCounterBenchmarksFixtures): Unit = { + val ahc = new ArrayHitCounter(f.numDocs, f.maxCount) + for (d <- f.docs) ahc.increment(d) + consumeDocIdSetIterator(ahc.docIdSetIterator(f.candidates)) () } - @Benchmark - @BenchmarkMode(Array(Mode.Throughput)) - @Fork(value = 1) - @Warmup(time = 5, iterations = 5) - @Measurement(time = 5, iterations = 5) - def luceneIntIntHashMap(f: HitCounterBenchmarksFixtures): Unit = { - val m = new IntIntHashMap(f.initialMapSize, 0.99d) - for (d <- f.docs) m.putOrAdd(d, 1, 1) - () - } - - @Benchmark - @BenchmarkMode(Array(Mode.Throughput)) - @Fork(value = 1) - @Warmup(time = 5, iterations = 5) - @Measurement(time = 5, iterations = 5) - def eclipseIntShortHashMapAddToValue(f: HitCounterBenchmarksFixtures): Unit = { - val m = new IntShortHashMap(f.initialMapSize) - for (d <- f.docs) m.addToValue(d, 1) - () - } +// @Benchmark +// @BenchmarkMode(Array(Mode.Throughput)) +// @Fork(value = 1) +// @Warmup(time = 5, iterations = 5) +// @Measurement(time = 5, iterations = 5) +// def arrayCountBaseline(f: HitCounterBenchmarksFixtures): Unit = { +// val arr = new Array[Int](f.numDocs) +// for (d <- f.docs) arr.update(d, arr(d) + 1) +// () +// } +// +// @Benchmark +// @BenchmarkMode(Array(Mode.Throughput)) +// @Fork(value = 1) +// @Warmup(time = 5, iterations = 5) +// @Measurement(time = 5, iterations = 5) +// def hashMapGetOrDefault(f: HitCounterBenchmarksFixtures): Unit = { +// val h = new java.util.HashMap[Int, Int](f.initialMapSize, 0.99f) +// for (d <- f.docs) h.put(d, h.getOrDefault(d, 0) + 1) +// () +// } +// +// @Benchmark +// @BenchmarkMode(Array(Mode.Throughput)) +// @Fork(value = 1) +// @Warmup(time = 5, iterations = 5) +// @Measurement(time = 5, iterations = 5) +// def luceneIntIntHashMap(f: HitCounterBenchmarksFixtures): Unit = { +// val m = new IntIntHashMap(f.initialMapSize, 0.99d) +// for (d <- f.docs) m.putOrAdd(d, 1, 1) +// () +// } +// +// @Benchmark +// @BenchmarkMode(Array(Mode.Throughput)) +// @Fork(value = 1) +// @Warmup(time = 5, iterations = 5) +// @Measurement(time = 5, iterations = 5) +// def eclipseIntShortHashMapAddToValue(f: HitCounterBenchmarksFixtures): Unit = { +// val m = new IntShortHashMap(f.initialMapSize) +// for (d <- f.docs) m.addToValue(d, 1) +// () +// } } diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index 60bc5a50..c717dc52 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -2,93 +2,108 @@ import org.apache.lucene.search.DocIdSetIterator; +import java.util.Arrays; + public final class ArrayHitCounter implements HitCounter { - private final short[] counts; - private int numHits; - private int minKey; - private int maxKey; + // Mapping an integer doc ID to the number of times it has occurred. + // E.g., if document 10 has been matched 11 times, then docIdToCount[10] = 11. + private final short[] docIdToCount; + + // Mapping an integer count to the number of times it has occurred. + // E.g., if there are 10 docs which have each been matched 11 times, countToCount[11] = 10. + private short[] countToCount; + + + // Mapping an integer count to the min and max documents observed to have this count. + // E.g., if documents 3, 4, and 5 each have count 10, then countToMinDocId[10] = 3 and countToMaxDocId[10] = 5. + private int[] countToMinDocId; + private int[] countToMaxDocId; + + private int maxCount = 0; + + public ArrayHitCounter(int numDocs, int expectedMaxCount) { + docIdToCount = new short[numDocs]; + countToCount = new short[expectedMaxCount + 1]; + countToMinDocId = new int[expectedMaxCount + 1]; + countToMaxDocId = new int[expectedMaxCount + 1]; + } + + public ArrayHitCounter(int numDocs) { + this(numDocs, 10); + } + + private void incrementKeyByCount(int docId, short count) { + int newCount = (docIdToCount[docId] += count); + if (newCount > maxCount) maxCount = newCount; + + // Potentially grow the count arrays. + if (newCount >= countToCount.length) { + countToCount = Arrays.copyOf(countToCount, newCount + 1); + countToMinDocId = Arrays.copyOf(countToMinDocId, newCount + 1); + countToMaxDocId = Arrays.copyOf(countToMaxDocId, newCount + 1); + } - private short maxValue; + // Update the old count. + int oldCount = newCount - count; + if (oldCount > 0) countToCount[oldCount] -= 1; - public ArrayHitCounter(int capacity) { - counts = new short[capacity]; - numHits = 0; - minKey = capacity; - maxKey = 0; - maxValue = 0; + // Update the new count. + countToCount[newCount] += 1; + if (docId < countToMinDocId[newCount]) countToMinDocId[newCount] = docId; + if (docId > countToMaxDocId[newCount]) countToMaxDocId[newCount] = docId; } @Override public void increment(int key) { - short after = ++counts[key]; - if (after == 1) { - numHits++; - minKey = Math.min(key, minKey); - maxKey = Math.max(key, maxKey); - } - if (after > maxValue) maxValue = after; + incrementKeyByCount(key, (short) 1); } @Override public void increment(int key, short count) { - short after = (counts[key] += count); - if (after == count) { - numHits++; - minKey = Math.min(key, minKey); - maxKey = Math.max(key, maxKey); - } - if (after > maxValue) maxValue = after; + incrementKeyByCount(key, count); } @Override public short get(int key) { - return counts[key]; + return docIdToCount[key]; } @Override public int capacity() { - return counts.length; - } - - - private KthGreatestResult kthGreatest(int k) { - // Find the kth greatest document hit count in O(n) time and O(n) space. - // Though the space is typically negligibly small in practice. - // This implementation exploits the fact that we're specifically counting document hit counts. - // Counts are integers, and they're likely to be pretty small, since we're unlikely to match - // the same document many times. - - // Start by building a histogram of all counts. - // e.g., if the counts are [0, 4, 1, 1, 2], - // then the histogram is [1, 2, 1, 0, 1], - // because 0 occurs once, 1 occurs twice, 2 occurs once, 3 occurs zero times, and 4 occurs once. - short[] hist = new short[maxValue + 1]; - for (short c: counts) hist[c]++; - - // Now we start at the max value and iterate backwards through the histogram, - // accumulating counts of counts until we've exceeded k. - int numGreaterEqual = 0; - short kthGreatest = maxValue; - - while (true) { - numGreaterEqual += hist[kthGreatest]; - if (kthGreatest > 1 && numGreaterEqual < k) kthGreatest--; - else break; - } - - // Finally we find the number that were greater than the kth greatest count. - // There's a special case if kthGreatest is zero, then the number that were greater is the number of hits. - int numGreater = numGreaterEqual - hist[kthGreatest]; - return new KthGreatestResult(kthGreatest, numGreater); + return docIdToCount.length; } @Override public DocIdSetIterator docIdSetIterator(int candidates) { - if (numHits == 0) return DocIdSetIterator.empty(); + if (maxCount == 0) return DocIdSetIterator.empty(); else { - KthGreatestResult kgr = kthGreatest(candidates); + // Loop backwards through countToCount to figure out a few things needed for the DocIdSetIterator: + // * the minimum count that's required for a document to be a candidate. + // * the minimum doc ID that we should start iterating at. + // * the maximum doc ID that we should start iterating at. + int kthGreatest = maxCount; + int minDocId = Integer.MAX_VALUE; + int maxDocId = Integer.MIN_VALUE; + int numGreaterEqual = 0; + while (true) { + int count = countToCount[kthGreatest]; + if (count > 0) { + numGreaterEqual += count; + int maybeNewMinDocId = countToMinDocId[kthGreatest]; + if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; + int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; + if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; + } + if (kthGreatest > 1 && numGreaterEqual < candidates) kthGreatest--; + else break; + } + // Java seems to want me to do this in order to reuse the values in the class below. + final int finalKthGreatest = kthGreatest; + final int finalMinDocId = minDocId; + final int finalMaxDocId = maxDocId; + final int numGreaterThan = numGreaterEqual - countToCount[kthGreatest]; // Return an iterator over the doc ids >= the min candidate count. return new DocIdSetIterator() { @@ -97,9 +112,15 @@ public DocIdSetIterator docIdSetIterator(int candidates) { private int docID = -1; private boolean started = false; - // Track the number of ids emitted, and the number of ids with count = kgr.kthGreatest emitted. - private int numEmitted = 0; - private int numEq = 0; + // Track the number of total IDs emitted. + private int numTotalEmitted = 0; + + // The threshold of IDs w/ count = kthGreatest that can be emitted. + private final int numEqThreshold = candidates - numGreaterThan; + + // Track the number of IDs w/ count = kthGreatest that have been emitted + private int numEqEmitted = 0; + @Override public int docID() { @@ -111,23 +132,23 @@ public int nextDoc() { if (!started) { started = true; - docID = minKey - 1; + docID = finalMinDocId - 1; } // Ensure that docs with count = kgr.kthGreatest are only emitted when there are fewer // than `candidates` docs with count > kgr.kthGreatest. while (true) { - if (numEmitted == candidates || docID + 1 > maxKey) { + if (numTotalEmitted == candidates || docID + 1 > finalMaxDocId) { docID = DocIdSetIterator.NO_MORE_DOCS; return docID; } else { docID++; - if (counts[docID] > kgr.kthGreatest) { - numEmitted++; + if (docIdToCount[docID] > finalKthGreatest) { + numTotalEmitted++; return docID; - } else if (counts[docID] == kgr.kthGreatest && numEq < candidates - kgr.numGreaterThan) { - numEq++; - numEmitted++; + } else if (docIdToCount[docID] == finalKthGreatest && numEqEmitted < numEqThreshold) { + numEqEmitted++; + numTotalEmitted++; return docID; } } @@ -142,7 +163,7 @@ public int advance(int target) { @Override public long cost() { - return maxKey - minKey; + return finalMaxDocId - finalMinDocId; } }; } diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java index 44b93ad7..66c836ab 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java @@ -1,23 +1,7 @@ package com.klibisz.elastiknn.search; -public class KthGreatestResult { - public final short kthGreatest; - public final int numGreaterThan; - public KthGreatestResult(short kthGreatest, int numGreaterThan) { - this.kthGreatest = kthGreatest; - this.numGreaterThan = numGreaterThan; - } +public record KthGreatestResult(short kthGreatest, int numGreaterThan) { - @Override - public boolean equals(Object o) { - if (o == this) { - return true; - } else if (!(o instanceof KthGreatestResult other)) { - return false; - } else { - return kthGreatest == other.kthGreatest && numGreaterThan == other.numGreaterThan; - } - } @Override public String toString() { diff --git a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java index a6269b98..0093c1e8 100644 --- a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java +++ b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java @@ -62,7 +62,12 @@ private HitCounter countHits(LeafReader reader) throws IOException { TermsEnum termsEnum = terms.iterator(); PostingsEnum docs = null; - HitCounter counter = new ArrayHitCounter(reader.maxDoc()); + int maxFrequency = 1; + for (HashAndFreq hf: hashAndFrequencies) { + if (hf.freq > maxFrequency) maxFrequency = hf.freq; + } + + HitCounter counter = new ArrayHitCounter(reader.maxDoc(), hashAndFrequencies.length * maxFrequency); for (HashAndFreq hf : hashAndFrequencies) { // We take two different paths here, depending on the frequency of the current hash. // If the frequency is one, we avoid checking the frequency of matching docs when diff --git a/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala b/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala index 900ee8df..c3d29ad9 100644 --- a/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala +++ b/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala @@ -94,6 +94,7 @@ final class ArrayHitCounterSpec extends AnyFreeSpec with Matchers { val rng = new Random(seed) val numDocs = 60000 val numMatches = numDocs / 2 + val maxCount = 10 info(s"Using seed $seed") for (_ <- 0 until 99) { val matches = (0 until numMatches).map(_ => rng.nextInt(numDocs)) @@ -103,7 +104,7 @@ final class ArrayHitCounterSpec extends AnyFreeSpec with Matchers { ref.increment(doc) ahc.increment(doc) ahc.get(doc) shouldBe ref.get(doc) - val count = rng.nextInt(10).toShort + val count = rng.nextInt(maxCount).toShort ref.increment(doc, count) ahc.increment(doc, count) ahc.get(doc) shouldBe ref.get(doc) From 1b07d89ba38a115196e65d9649db45949ef095f9 Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Wed, 28 Aug 2024 15:38:27 -0700 Subject: [PATCH 02/13] Fill the countToMinDocId with Integer.MaxValue so that we actually track the min doc IDs --- .../java/com/klibisz/elastiknn/search/ArrayHitCounter.java | 3 +++ .../org/apache/lucene/search/MatchHashesAndScoreQuery.java | 7 +------ 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index c717dc52..1a96c802 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -1,6 +1,7 @@ package com.klibisz.elastiknn.search; import org.apache.lucene.search.DocIdSetIterator; +import scala.Int; import java.util.Arrays; @@ -26,6 +27,7 @@ public ArrayHitCounter(int numDocs, int expectedMaxCount) { docIdToCount = new short[numDocs]; countToCount = new short[expectedMaxCount + 1]; countToMinDocId = new int[expectedMaxCount + 1]; + Arrays.fill(countToMinDocId, Integer.MAX_VALUE); countToMaxDocId = new int[expectedMaxCount + 1]; } @@ -41,6 +43,7 @@ private void incrementKeyByCount(int docId, short count) { if (newCount >= countToCount.length) { countToCount = Arrays.copyOf(countToCount, newCount + 1); countToMinDocId = Arrays.copyOf(countToMinDocId, newCount + 1); + countToMinDocId[newCount] = Integer.MAX_VALUE; countToMaxDocId = Arrays.copyOf(countToMaxDocId, newCount + 1); } diff --git a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java index 0093c1e8..a2fbed6c 100644 --- a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java +++ b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java @@ -62,12 +62,7 @@ private HitCounter countHits(LeafReader reader) throws IOException { TermsEnum termsEnum = terms.iterator(); PostingsEnum docs = null; - int maxFrequency = 1; - for (HashAndFreq hf: hashAndFrequencies) { - if (hf.freq > maxFrequency) maxFrequency = hf.freq; - } - - HitCounter counter = new ArrayHitCounter(reader.maxDoc(), hashAndFrequencies.length * maxFrequency); + HitCounter counter = new ArrayHitCounter(reader.maxDoc(), hashAndFrequencies.length); for (HashAndFreq hf : hashAndFrequencies) { // We take two different paths here, depending on the frequency of the current hash. // If the frequency is one, we avoid checking the frequency of matching docs when From 3c007c359af717b7ff09aae143451cc33e9b8c7d Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Wed, 28 Aug 2024 23:12:02 +0000 Subject: [PATCH 03/13] benchmark --- docs/pages/performance/fashion-mnist/plot.b64 | 2 +- docs/pages/performance/fashion-mnist/plot.png | Bin 46953 -> 42889 bytes .../performance/fashion-mnist/results.md | 16 ++++++++-------- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64 index 15c36f6a..dae7da7b 100644 --- a/docs/pages/performance/fashion-mnist/plot.b64 +++ b/docs/pages/performance/fashion-mnist/plot.b64 @@ -1 +1 @@ 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zA!Rbm60ldGO+&>=1&==n1atyUP36577Wa>(H_wA!k}3jWEvzAX9PhD@AAfjK1_}n} zF7l2&7d*y>px7VrJvdK@q~8NneP3aD1cZKw06~EX?|syl^r7Dx=G3wf5`-#5A%UO^ zL<(0T7*u`-Uxw4Y6B2y;+$-sbg-(|aX?JV@H7nrnb&U2pZ+PII3r^;9>wZInM;#RE zNK%DlTL_keGM2WHQDS`Jv%(YZrl$9px+cWbf|tP~>TJ2`MAJoDr9{smVLf~MRKkOS z6lpd}F$E>Fmaj%6rve3RUtVf(OC1m>D{vpX#+TXD0y1J@%`Q;Q<9*ZG>H5nfdDBAYoS#~i6D1CkZaqD!cdk?>~_xY4%-#R@;Ga#MBrwpr7SqIH( z!exVl`wT)0WzLeMZx%dTm$11QSSuWPz#LvB>1b%Kk)jKr)8WK!Mn_*dSZ?x?bZI=A z6+)zgEdgNx*$-k4@F^1kiX{9 Date: Wed, 28 Aug 2024 16:13:42 -0700 Subject: [PATCH 04/13] Another optimization to avoid Arrays.fill --- .../elastiknn/search/ArrayHitCounter.java | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index 1a96c802..b3947944 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -27,7 +27,6 @@ public ArrayHitCounter(int numDocs, int expectedMaxCount) { docIdToCount = new short[numDocs]; countToCount = new short[expectedMaxCount + 1]; countToMinDocId = new int[expectedMaxCount + 1]; - Arrays.fill(countToMinDocId, Integer.MAX_VALUE); countToMaxDocId = new int[expectedMaxCount + 1]; } @@ -43,7 +42,6 @@ private void incrementKeyByCount(int docId, short count) { if (newCount >= countToCount.length) { countToCount = Arrays.copyOf(countToCount, newCount + 1); countToMinDocId = Arrays.copyOf(countToMinDocId, newCount + 1); - countToMinDocId[newCount] = Integer.MAX_VALUE; countToMaxDocId = Arrays.copyOf(countToMaxDocId, newCount + 1); } @@ -52,9 +50,18 @@ private void incrementKeyByCount(int docId, short count) { if (oldCount > 0) countToCount[oldCount] -= 1; // Update the new count. - countToCount[newCount] += 1; - if (docId < countToMinDocId[newCount]) countToMinDocId[newCount] = docId; - if (docId > countToMaxDocId[newCount]) countToMaxDocId[newCount] = docId; + // We use a small trick to determine if the min and max doc IDs have been initialized + // for this count. If the old count's count was zero, that means it hasn't been initialized, + // and the new min and max are this doc ID. Otherwise, we have to fill the countToMinDocId + // array with a value larger than zero, which is a more expensive operation. + int oldCountCount = countToCount[newCount]++; + if (oldCountCount == 0) { + countToMinDocId[newCount] = docId; + countToMaxDocId[newCount] = docId; + } else { + if (docId < countToMinDocId[newCount]) countToMinDocId[newCount] = docId; + if (docId > countToMaxDocId[newCount]) countToMaxDocId[newCount] = docId; + } } @Override From 2ec50cef2258815715fa15767bb1d412c0fa5f2e Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Wed, 28 Aug 2024 16:18:09 -0700 Subject: [PATCH 05/13] Fix compiler warnings --- .../jmhbenchmarks/HitCounterBenchmarks.scala | 50 ++----------------- 1 file changed, 3 insertions(+), 47 deletions(-) diff --git a/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala b/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala index 0525bb17..e1db2644 100644 --- a/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala +++ b/elastiknn-jmh-benchmarks/src/main/scala/com/klibisz/elastiknn/jmhbenchmarks/HitCounterBenchmarks.scala @@ -2,9 +2,7 @@ package com.klibisz.elastiknn.jmhbenchmarks import com.klibisz.elastiknn.search.ArrayHitCounter import org.openjdk.jmh.annotations.* -import org.apache.lucene.internal.hppc.IntIntHashMap import org.apache.lucene.search.DocIdSetIterator -import org.eclipse.collections.impl.map.mutable.primitive.IntShortHashMap import scala.util.Random @@ -21,7 +19,9 @@ class HitCounterBenchmarksFixtures { class HitCounterBenchmarks { private def consumeDocIdSetIterator(disi: DocIdSetIterator): Unit = { - while (disi.nextDoc() != DocIdSetIterator.NO_MORE_DOCS) disi.docID() + while (disi.nextDoc() != DocIdSetIterator.NO_MORE_DOCS) { + val _ = disi.docID() + } () } @@ -36,48 +36,4 @@ class HitCounterBenchmarks { consumeDocIdSetIterator(ahc.docIdSetIterator(f.candidates)) () } - -// @Benchmark -// @BenchmarkMode(Array(Mode.Throughput)) -// @Fork(value = 1) -// @Warmup(time = 5, iterations = 5) -// @Measurement(time = 5, iterations = 5) -// def arrayCountBaseline(f: HitCounterBenchmarksFixtures): Unit = { -// val arr = new Array[Int](f.numDocs) -// for (d <- f.docs) arr.update(d, arr(d) + 1) -// () -// } -// -// @Benchmark -// @BenchmarkMode(Array(Mode.Throughput)) -// @Fork(value = 1) -// @Warmup(time = 5, iterations = 5) -// @Measurement(time = 5, iterations = 5) -// def hashMapGetOrDefault(f: HitCounterBenchmarksFixtures): Unit = { -// val h = new java.util.HashMap[Int, Int](f.initialMapSize, 0.99f) -// for (d <- f.docs) h.put(d, h.getOrDefault(d, 0) + 1) -// () -// } -// -// @Benchmark -// @BenchmarkMode(Array(Mode.Throughput)) -// @Fork(value = 1) -// @Warmup(time = 5, iterations = 5) -// @Measurement(time = 5, iterations = 5) -// def luceneIntIntHashMap(f: HitCounterBenchmarksFixtures): Unit = { -// val m = new IntIntHashMap(f.initialMapSize, 0.99d) -// for (d <- f.docs) m.putOrAdd(d, 1, 1) -// () -// } -// -// @Benchmark -// @BenchmarkMode(Array(Mode.Throughput)) -// @Fork(value = 1) -// @Warmup(time = 5, iterations = 5) -// @Measurement(time = 5, iterations = 5) -// def eclipseIntShortHashMapAddToValue(f: HitCounterBenchmarksFixtures): Unit = { -// val m = new IntShortHashMap(f.initialMapSize) -// for (d <- f.docs) m.addToValue(d, 1) -// () -// } } From 84030a856f7af18c37af0b9e3147cf5f126ad8e2 Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Thu, 29 Aug 2024 00:11:40 +0000 Subject: [PATCH 06/13] benchmark --- docs/pages/performance/fashion-mnist/plot.b64 | 2 +- docs/pages/performance/fashion-mnist/plot.png | Bin 42889 -> 40561 bytes .../performance/fashion-mnist/results.md | 16 ++++++++-------- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64 index dae7da7b..3a459343 100644 --- a/docs/pages/performance/fashion-mnist/plot.b64 +++ b/docs/pages/performance/fashion-mnist/plot.b64 @@ -1 +1 @@ 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z369fp*;>D_)NCC1(Bb-k4C%ak@DQWJ!osx6C}`FHMDU~B$WQJO0RW&xH$@1jp{Ey2 zsT-K5WV1BTZtWHWaole2rOLsv6&3qjer48^W*dB=h2OZ{exHR?g^)tEo>ScL&x@N) zVVgi2x*Vv`^}Eaka2hFoW`yf;?OGLB7QI-Mz_i%*xHUQk4O;l98xi7>*9aJCIoah6 zkE{m>WZO`4sl-ba#Fc^h{uO49*9Ga>VQsF!mwj&CiXuxZ2nm)dV{{5c?`LL(I3fOp rn%S_hg#TP$W8-iCwdD4H{n6lN*+9iQ^`TY;I Date: Wed, 28 Aug 2024 18:05:08 -0700 Subject: [PATCH 07/13] Remove an unused import --- .../com/klibisz/elastiknn/search/ArrayHitCounter.java | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index b3947944..5101939f 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -1,7 +1,6 @@ package com.klibisz.elastiknn.search; import org.apache.lucene.search.DocIdSetIterator; -import scala.Int; import java.util.Arrays; @@ -101,10 +100,12 @@ public DocIdSetIterator docIdSetIterator(int candidates) { int count = countToCount[kthGreatest]; if (count > 0) { numGreaterEqual += count; - int maybeNewMinDocId = countToMinDocId[kthGreatest]; - if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; - int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; - if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; + if (numGreaterEqual < candidates) { + int maybeNewMinDocId = countToMinDocId[kthGreatest]; + if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; + int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; + if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; + } } if (kthGreatest > 1 && numGreaterEqual < candidates) kthGreatest--; else break; From 71edd31434b79ffe2c68a7c48ca5b9a14c32b98e Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Wed, 28 Aug 2024 19:06:17 -0700 Subject: [PATCH 08/13] Fix --- .../com/klibisz/elastiknn/search/ArrayHitCounter.java | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index 5101939f..b3947944 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -1,6 +1,7 @@ package com.klibisz.elastiknn.search; import org.apache.lucene.search.DocIdSetIterator; +import scala.Int; import java.util.Arrays; @@ -100,12 +101,10 @@ public DocIdSetIterator docIdSetIterator(int candidates) { int count = countToCount[kthGreatest]; if (count > 0) { numGreaterEqual += count; - if (numGreaterEqual < candidates) { - int maybeNewMinDocId = countToMinDocId[kthGreatest]; - if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; - int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; - if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; - } + int maybeNewMinDocId = countToMinDocId[kthGreatest]; + if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; + int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; + if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; } if (kthGreatest > 1 && numGreaterEqual < candidates) kthGreatest--; else break; From 2624988d415ab4f5f897cc48464e816d53c6e903 Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Thu, 29 Aug 2024 10:43:32 -0700 Subject: [PATCH 09/13] Stop tracking countToMin/MaxDocId --- .../elastiknn/search/ArrayHitCounter.java | 43 +++++-------------- 1 file changed, 10 insertions(+), 33 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index b3947944..a2b5b081 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -1,7 +1,6 @@ package com.klibisz.elastiknn.search; import org.apache.lucene.search.DocIdSetIterator; -import scala.Int; import java.util.Arrays; @@ -15,19 +14,16 @@ public final class ArrayHitCounter implements HitCounter { // E.g., if there are 10 docs which have each been matched 11 times, countToCount[11] = 10. private short[] countToCount; - - // Mapping an integer count to the min and max documents observed to have this count. - // E.g., if documents 3, 4, and 5 each have count 10, then countToMinDocId[10] = 3 and countToMaxDocId[10] = 5. - private int[] countToMinDocId; - private int[] countToMaxDocId; + private int minDocId; + private int maxDocId; private int maxCount = 0; public ArrayHitCounter(int numDocs, int expectedMaxCount) { docIdToCount = new short[numDocs]; countToCount = new short[expectedMaxCount + 1]; - countToMinDocId = new int[expectedMaxCount + 1]; - countToMaxDocId = new int[expectedMaxCount + 1]; + minDocId = Integer.MAX_VALUE; + maxDocId = 0; } public ArrayHitCounter(int numDocs) { @@ -41,8 +37,6 @@ private void incrementKeyByCount(int docId, short count) { // Potentially grow the count arrays. if (newCount >= countToCount.length) { countToCount = Arrays.copyOf(countToCount, newCount + 1); - countToMinDocId = Arrays.copyOf(countToMinDocId, newCount + 1); - countToMaxDocId = Arrays.copyOf(countToMaxDocId, newCount + 1); } // Update the old count. @@ -50,18 +44,11 @@ private void incrementKeyByCount(int docId, short count) { if (oldCount > 0) countToCount[oldCount] -= 1; // Update the new count. - // We use a small trick to determine if the min and max doc IDs have been initialized - // for this count. If the old count's count was zero, that means it hasn't been initialized, - // and the new min and max are this doc ID. Otherwise, we have to fill the countToMinDocId - // array with a value larger than zero, which is a more expensive operation. - int oldCountCount = countToCount[newCount]++; - if (oldCountCount == 0) { - countToMinDocId[newCount] = docId; - countToMaxDocId[newCount] = docId; - } else { - if (docId < countToMinDocId[newCount]) countToMinDocId[newCount] = docId; - if (docId > countToMaxDocId[newCount]) countToMaxDocId[newCount] = docId; - } + countToCount[newCount]++; + + // Update min/max doc IDs. + if (docId < minDocId) minDocId = docId; + if (docId > maxDocId) maxDocId = docId; } @Override @@ -94,18 +81,9 @@ public DocIdSetIterator docIdSetIterator(int candidates) { // * the minimum doc ID that we should start iterating at. // * the maximum doc ID that we should start iterating at. int kthGreatest = maxCount; - int minDocId = Integer.MAX_VALUE; - int maxDocId = Integer.MIN_VALUE; int numGreaterEqual = 0; while (true) { - int count = countToCount[kthGreatest]; - if (count > 0) { - numGreaterEqual += count; - int maybeNewMinDocId = countToMinDocId[kthGreatest]; - if (maybeNewMinDocId < minDocId) minDocId = maybeNewMinDocId; - int maybeNewMaxDocId = countToMaxDocId[kthGreatest]; - if (maybeNewMaxDocId > maxDocId) maxDocId = maybeNewMaxDocId; - } + numGreaterEqual += countToCount[kthGreatest]; if (kthGreatest > 1 && numGreaterEqual < candidates) kthGreatest--; else break; } @@ -178,5 +156,4 @@ public long cost() { }; } } - } \ No newline at end of file From cbd3714dfa91f85ef1995a78378bd626d24fffda Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Thu, 29 Aug 2024 10:50:56 -0700 Subject: [PATCH 10/13] ForceInline the incrementKeyByCount method --- build.sbt | 4 ++++ .../java/com/klibisz/elastiknn/search/ArrayHitCounter.java | 2 ++ 2 files changed, 6 insertions(+) diff --git a/build.sbt b/build.sbt index f5108d6b..55e0e808 100644 --- a/build.sbt +++ b/build.sbt @@ -76,6 +76,10 @@ lazy val `elastiknn-lucene` = project "org.apache.lucene" % "lucene-core" % LuceneVersion, "org.apache.lucene" % "lucene-analysis-common" % LuceneVersion % Test ), + javacOptions ++= Seq( + "--add-exports", + "java.base/jdk.internal.vm.annotation=ALL-UNNAMED" + ), TestSettings ) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index a2b5b081..d5aafeed 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -1,5 +1,6 @@ package com.klibisz.elastiknn.search; +import jdk.internal.vm.annotation.ForceInline; import org.apache.lucene.search.DocIdSetIterator; import java.util.Arrays; @@ -30,6 +31,7 @@ public ArrayHitCounter(int numDocs) { this(numDocs, 10); } + @ForceInline private void incrementKeyByCount(int docId, short count) { int newCount = (docIdToCount[docId] += count); if (newCount > maxCount) maxCount = newCount; From afb0b4163a8ae2c7d4c5172e7f928cfe1224fb7a Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Thu, 29 Aug 2024 18:00:57 +0000 Subject: [PATCH 11/13] partial benchmark --- docs/pages/performance/fashion-mnist/plot.b64 | 2 +- docs/pages/performance/fashion-mnist/plot.png | Bin 40561 -> 34235 bytes .../performance/fashion-mnist/results.md | 10 ++-------- 3 files changed, 3 insertions(+), 9 deletions(-) diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64 index 3a459343..bcbea6b3 100644 --- a/docs/pages/performance/fashion-mnist/plot.b64 +++ b/docs/pages/performance/fashion-mnist/plot.b64 @@ -1 +1 @@ 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|Model|Parameters|Recall|Queries per Second| |---|---|---|---| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.378|385.500| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.447|326.569| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.634|297.646| -|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.716|262.916| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.767|328.729| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.847|285.492| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.922|206.483| -|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|187.749| +|eknn-l2lsh|L=128 k=8 w=4096 candidates=256 probes=8|0.940|126.091| +|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|195.211| From ce6a4dbf1392950ee0deed064a759b3b0b60971e Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Thu, 29 Aug 2024 12:37:26 -0700 Subject: [PATCH 12/13] Remove stale comment --- .../java/com/klibisz/elastiknn/search/ArrayHitCounter.java | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java index d5aafeed..475a3cae 100644 --- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java +++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java @@ -78,10 +78,8 @@ public DocIdSetIterator docIdSetIterator(int candidates) { if (maxCount == 0) return DocIdSetIterator.empty(); else { - // Loop backwards through countToCount to figure out a few things needed for the DocIdSetIterator: - // * the minimum count that's required for a document to be a candidate. - // * the minimum doc ID that we should start iterating at. - // * the maximum doc ID that we should start iterating at. + // Loop backwards through countToCount to figure out the minimum count that's required for a + // document to be a candidate. int kthGreatest = maxCount; int numGreaterEqual = 0; while (true) { From 34f9e1da173642b764cf0780893b2589d68f66e9 Mon Sep 17 00:00:00 2001 From: Alex Klibisz Date: Sat, 31 Aug 2024 18:57:52 +0000 Subject: [PATCH 13/13] Benchmarks --- docs/pages/performance/fashion-mnist/plot.b64 | 2 +- docs/pages/performance/fashion-mnist/plot.png | Bin 41761 -> 42101 bytes .../performance/fashion-mnist/results.md | 6 +++--- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64 index e489e20a..6d28f658 100644 --- a/docs/pages/performance/fashion-mnist/plot.b64 +++ b/docs/pages/performance/fashion-mnist/plot.b64 @@ -1 +1 @@ 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lGIbx9NNPG5KMgQMHOmv5HY+fffaZIcl46aWXTPv17dvXsNlsxh9//OGstW/f3mjYsGGu59WjR48c9at18fvZhc934blmv9+0bNnSSE9Pd9Zfe+01Q5KxfPnySz5Pbq/9hx9+aEgyNm7c6Kxlv99d/L7bu3dvo0yZMs7Hv/zyiyHJeOSRR0z73XvvvXme04W2bt1qSDLmzZtnqqenpxsRERFGo0aNjJSUFGf9yy+/NCQZzz333CWPm5+xeeH5nj592vDx8TH+9a9/OWv5HVd5udTXwMWf11GjRhk2m83YsWOHs3bu3DkjPDw8x/e1/Oa/1GuQm4u/x+Xn/TgvkgxJxs8//+ysHTlyxPD19TV69+7trA0ZMsSoUKGCcfbsWdPH33PPPUZISIhzvOY1TgzDMHr06GH6Xpktv98rsvPa7XZjz549Lp8rAJQmXMYFuJHOnTurXLlyqlKlivr27auAgAB9/vnnqly5siQpJiZG69evV79+/ZSYmKizZ8/q7NmzOnfunLp27aoDBw44p9wvW7ZMTZs2dc70uVD2X5M9PDyca7k4HA7FxMQoMzNTrVq10vbt2wvsvBITExUUFHTJfbK3JyYmunTsNWvWKC4uTv3793e+HmfPnpWHh4euu+66XC9Jy89C0EuWLFFISIhuueUW03FbtmypwMBA53Gz/9r95ZdfKiMjw6XsklSxYkXT5yj7EpEdO3bo5MmTziw33nijwsLCTFk6d+6srKysHFP1+/Tpk68ZENnZly9fnudlEkuWLFH9+vVVr14903Nnry+T/Tp89tlncjgceu6553Jc+pQ93tauXav09HSNHTvWtM/QoUMVHBysFStWmD4uMDBQ9913n/Oxt7e3rr32Wv3555/O2sqVK1WhQgX17dvXWfP399ewYcMue/7SP7c/9/T0dP51+kqtXLlSN998s3ONHFfGRfbMvezLK69UjRo11LVr1xz1C9ftiY+P19mzZ9W+fXv9+eefio+Pl/S/z82oUaNMs01yW4Q1v+Nx5cqV8vDw0OjRo00f/69//UuGYeirr766qvMtSsOGDTPNOhsxYoQ8PT21cuXKS37cha99amqqzp4961yXLLf32Icfftj0+MYbb9S5c+eUkJAgSc7nu/g1vdrFcn/++WedPn1ajzzyiGlmZY8ePVSvXr0cX5uuatCggXOGiPTPDK26deuavpZdfZ/LTV5fAxdbtWqV2rZta5q9Gh4e7rys90ryX638vB9fStu2bdWyZUvn46pVq+qOO+7Q119/raysLBmGoWXLlun222+XYRim17hr166Kj4+/qu/7+f1eka19+/asxwUAl8FlXIAbmTFjhq655hrFx8dr7ty52rhxo2la+R9//CHDMPTvf/87z9s1nz59WpUqVdLBgwfVp0+fyz7nggUL9MYbb+i3334z/VJao0YNl7LHx8crJSXF+djb29t5+VBQUNBlmzjZ2yMiIlx63gMHDkhSnovbBgcHmx57eno6m2eXO258fHyeeU6fPi3pnx9Y+/TpowkTJmjy5Mnq0KGDevXqpXvvvTdfi4LWrl07x6Uc11xzjaR/LuOLjIzUgQMHtGvXrjwbONlZsuX3c3f33XfrnXfe0UMPPaSnnnpKnTp10p133qm+ffs6mzEHDhzQvn37LvvcBw8elN1uv+QP70eOHJGkHJdWeHt7q2bNms7t2SpXrpzjtQkLC9OuXbtMx8ztNczr8o3CkJGRoTVr1jgvQZJcGxfG/69fc6nLgfIjr8/7pk2b9Pzzz2vz5s051n+Jj49XSEiI87WvU6eOaXu5cuVMl5FKyvd4PHLkiCpWrJij0Zt9acvFn++rERMTY7rczM/PTyEhIQV2/Itfl8DAQFWoUOGyawjFxMRowoQJ+uijj3J8nWY32i5UtWpV0+Ps1z42NlbBwcE6cuSI7HZ7jrXPrna85/W1KUn16tXT999/f1XHv/i8pH/O7cJ1j1x9n8tNft/7jhw5orZt2+ao165dO9f985P/auXn/fhSLh6j0j/fS5KTk3XmzBnZ7XbFxcVp9uzZmj17dq7HyM9rnJf8fq/I5urPGABQGtHsAdzItdde67wbV69evdSuXTvde++92r9/vwIDA51/7Xvsscfy/OtlXj+s5ub999/Xgw8+qF69eunxxx9XRESEPDw8NHHixByLQl/OmDFjnAstS//8spu9OGWDBg20fft2paWl5dkA2bVrl7y9vVWpUiVJef/im5WVZXqc/ZosXLhQkZGROfa/+G4nPj4++frB2eFwKCIiQh988EGu27N/oLXZbFq6dKm2bNmiL774Ql9//bUGDx6sN954Q1u2bLnqGSPZWW655RY98cQTuW7Pbg5ly+8dmPz8/LRx40Zt2LBBK1as0KpVq7R48WJ17NhRq1evloeHhxwOhxo3bqw333wz12NUqVLFtZNxgYeHR6514xILGbuqTJkyyszMzNfss7x8//33SkhIUPfu3Z01V8ZF9i+MZcuWvapzye3zfvDgQXXq1En16tXTm2++qSpVqsjb21srV67U5MmTr2gGgavjsSjceeed+vbbb52PBw4caFp4OL8ufn+5Wv369dMPP/ygxx9/XM2aNXO+j3fr1i3X174oxrwV8nNeBTGuCuvuc0XxecnP+/HVyB5v9913nwYOHJjrPk2aNLmq47vyvaKk3CkQAKxEswdwU9lNl5tvvlnTp0/XU089pZo1a0qSvLy8cr170IVq1aql3bt3X3KfpUuXqmbNmvrkk09MzZXnn3/e5bxPPPGE6ZKbC2cD3H777frhhx+0ZMkS0z7ZDh8+rO+++0533HGH8wfA7I+Pi4szLQx68WyA7L9wR0REXPY1cUWtWrW0du1a3XDDDfn6obRNmzZq06aN/vvf/2rRokUaMGCAPvroIz300EOX/Ljs2VoXvv6///67JDkXrKxVq5aSkpIK9Pyy2e12derUSZ06ddKbb76pl19+Wc8884w2bNigzp07q1atWtq5c6c6dep0yZkntWrVksPh0N69e/Nc2LtatWqS/llENXssS//cFe7QoUNXdH7VqlXT7t27c7yG+/fvz9fH16tXT9I/d+W60l90VqxYoQYNGuS6wGh+xsWhQ4ckXX4x1yuZ+fPFF18oLS1Nn3/+uWl2wsWXVGR/bg4cOGD63Jw5cybH7IX8jsdq1app7dq1ORppv/32m+k5C8Ibb7xhylmxYsVL7h8WFpbjDnDp6ek6ceJErvsfOHBAN998s/NxUlKSTpw4YWrwXSw2Nlbr1q3ThAkT9Nxzz5mOdaWqVasmh8OhgwcPmmbh5He85zWGLvzavHiW5P79+y/7ubraWWlS4b7PXaxatWr6448/ctRzq+VXQbwGl3s/vpTcxtXvv/8uf39/5x8ngoKClJWVddljXepc8tqW3+8VAID8Y80ewI116NBB1157raKjo5WamqqIiAh16NBBb7/9dq6/lJw5c8b5/3369NHOnTv16aef5tgv+6+R2X8pvPCvkz/++KM2b97sctYGDRqoc+fOzn8Xrh0wfPhwRUZG6vHHH8+xxkFqaqrz9q4X/kU3u4lz4ToN58+fN80ekqSuXbsqODhYL7/8cq5ro1z4mriiX79+ysrK0n/+858c2zIzM52/KMbGxub46252syM/t/8+fvy46XOUkJCg9957T82aNXPOVOrXr582b96sr7/+OsfHx8XFKTMzM7+nZRITE5OjdnH2fv366e+//9acOXNy7JuSkqLz589L+mcmmt1u14svvphjxkL269O5c2d5e3tr6tSpptfs3XffVXx8fL7uKHSx7t276/jx41q6dKmzlpycnOdlChfLvpTj559/dvm5s61cuTJHdlfGxbZt2xQSEuK8K1FeAgICJClHk+JScvsaj4+P17x580z7de7cWV5eXpo2bZpp39zugJTf8di9e3dlZWVp+vTppn0mT54sm83mvKNaQWjZsqXp/edya4HUqlUrxxows2fPznNmz+zZs03vLzNnzlRmZuYlzyG3117K/TXNr+znmzp16hUdM68x1KpVK0VERGjWrFmm8fnVV19p3759l/3avJKxebHCep/LTdeuXbV582b98ssvzlpMTEyeMznz42pfg/y8H1/K5s2bTWvu/PXXX1q+fLm6dOkiDw8PeXh4qE+fPlq2bFmufwi68Hvlpc4lICAg10sQ8/u9AgCQf8zsAdzc448/rrvuukvz58/Xww8/rBkzZqhdu3Zq3Lixhg4dqpo1a+rUqVPavHmzjh07pp07dzo/bunSpbrrrrs0ePBgtWzZUjExMfr88881a9YsNW3aVLfddps++eQT9e7dWz169NChQ4c0a9YsNWjQQElJSQV2DmFhYVq6dKm6d++uFi1a6KGHHlKDBg108uRJzZ8/X3/++aemT5+u6667zvkxXbp0UdWqVTVkyBA9/vjj8vDw0Ny5c1WuXDkdPXrUuV9wcLBmzpyp+++/Xy1atNA999zj3GfFihW64YYbcvyymR/t27fX8OHDNXHiRP3yyy/q0qWLvLy8dODAAS1ZskRTpkxR3759tWDBAr311lvq3bu3atWqpcTERM2ZM0fBwcGX/Kt/tmuuuUZDhgzR1q1bVb58ec2dO1enTp0y/TL++OOP6/PPP9dtt93mvN3v+fPn9euvv2rp0qU6fPjwFV0C9OKLL2rjxo3q0aOHqlWrptOnT+utt95S5cqV1a5dO0nS/fffr48//lgPP/ywNmzYoBtuuEFZWVn67bff9PHHH+vrr79Wq1atVLt2bT3zzDP6z3/+oxtvvFF33nmnfHx8tHXrVlWsWFETJ05UuXLlNH78eE2YMEHdunVTz549tX//fr311ltq3bp1rrO+Lmfo0KGaPn26HnjgAW3btk0VKlTQwoUL5e/vn6+Pr1mzpho1aqS1a9dq8ODBpm0bN250NgTOnDmj8+fP66WXXpL0z+2mb7rpJh06dEj79u3TzJkzTR/ryrhYs2aNbr/99sv+NbxZs2by8PDQq6++qvj4ePn4+Khjx46XXOeqS5cu8vb21u23367hw4crKSlJc+bMUUREhKlhXK5cOT322GOaOHGibrvtNnXv3l07duzQV199lWNs5Xc83n777br55pv1zDPP6PDhw2ratKlWr16t5cuXa+zYsTnWnSlKDz30kB5++GH16dNHt9xyi3bu3Kmvv/46z6+j9PR0derUSf369XOO2Xbt2qlnz555PkdwcLBuuukmvfbaa8rIyFClSpW0evVq50yuK9GsWTP1799fb731luLj43X99ddr3bp1+Z6RUqtWLYWGhmrWrFkKCgpSQECArrvuOtWoUUOvvvqqBg0apPbt26t///7OW69Xr15d48aNu2wuV8fmxQrrfS43TzzxhN5//33dcsstGjVqlPPW61WrVlVMTMwVzUy52tcgP+/Hl9KoUSN17drVdOt1SZowYYJzn1deeUUbNmzQddddp6FDh6pBgwaKiYnR9u3btXbtWmfD6VLjpGXLllq8eLEeffRRtW7dWoGBgbr99tvz/b0CAOCCor35F4DCkH1739xu05yVlWXUqlXLqFWrlpGZmWkYhmEcPHjQeOCBB4zIyEjDy8vLqFSpknHbbbcZS5cuNX3suXPnjJEjRxqVKlUyvL29jcqVKxsDBw503nbV4XAYL7/8slGtWjXDx8fHaN68ufHll1/muA25YVzdrdezHT582Bg2bJhRtWpVw9PT03m72LVr1+a6/7Zt24zrrrvO8Pb2NqpWrWq8+eabeT7vhg0bjK5duxohISGGr6+vUatWLePBBx803Yp24MCBRkBAQK7Plds5G4ZhzJ4922jZsqXh5+dnBAUFGY0bNzaeeOIJ4/jx44ZhGMb27duN/v37G1WrVjV8fHyMiIgI47bbbjM9b16ybyv99ddfG02aNDF8fHyMevXqGUuWLMmxb2JiojF+/Hijdu3ahre3t1G2bFnj+uuvNyZNmuS8JXT2rddff/31yz63YRjGunXrjDvuuMOoWLGi4e3tbVSsWNHo379/jlsfp6enG6+++qrRsGFDw8fHxwgLCzNatmxpTJgwwYiPjzftO3fuXKN58+bO/dq3b2+sWbPGtM/06dONevXqGV5eXkb58uWNESNGGLGxsaZ98roVd26fpyNHjhg9e/Y0/P39jbJlyxpjxowxVq1ale/bIL/55ptGYGBgjttkZ98aObd/2V8L06dPN0JCQoyMjAzTx+Z3XOzbt++SXwMXmzNnjlGzZk3Dw8PDdH6XukX5559/bjRp0sTw9fU1qlevbrz66qvG3Llzc3wdZWVlGRMmTDAqVKhg+Pn5GR06dDB2796d47bVhpG/8Zi937hx44yKFSsaXl5eRp06dYzXX3/ddHt3wyj6W69nZWUZTz75pFG2bFnD39/f6Nq1q/HHH3/keev1b7/91hg2bJgRFhZmBAYGGgMGDDDOnTt32ec5duyY0bt3byM0NNQICQkx7rrrLuP48eM53k+zx9rFt93O7f0uJSXFGD16tFGmTBkjICDAuP32242//vorX7deNwzDWL58udGgQQPne/CFt9devHix8+s3PDzcGDBggHHs2LHLHtMwXB+b7du3N9q3b2+q5Xdc5eZSYyW3Mbxjxw7jxhtvNHx8fIzKlSsbEydONKZOnWpIMk6ePHnZ4+aWP6/XIDcX33o9v+/HuZFkREVFGe+//75Rp04d5/fz3J7/1KlTRlRUlFGlShXDy8vLiIyMNDp16mTMnj3btF9e4yQpKcm49957jdDQUEOS6f04v98rsvMCAC7NZhglfNU+AKXWunXr1L17d7Vr105fffWV8zbwpUX16tXVqFEjffnll1ZHKdXi4+NVs2ZNvfbaaxoyZIhLH9u9e3cFBgbq448/vqLnHjt2rDZu3Kht27axzkUxNH/+fA0aNEhbt25lVkIpMHbsWL399ttKSkq66gWRAQC4WqzZA6DE6tSpkxYsWKANGzZo0KBBJf6OMyiZQkJC9MQTT+j11193+e5UHTp0uOwlLnk5d+6c3nnnHb300ks0eoAilpKSYnp87tw5LVy4UO3ataPRAwAoFpjZAwAlFDN7gOKNmT3uq1mzZurQoYPq16+vU6dO6d1339Xx48e1bt063XTTTVbHAwCABZoBAAAAV3Tv3l1Lly7V7NmzZbPZ1KJFC7377rs0egAAxQYzewAAAAAAANwIa/YAAAAAAAC4EZo9AAAAAAAAboQ1eyQ5HA4dP35cQUFB3NEEAAAAAFDoDMNQYmKiKlasKLudeRgoWDR7JB0/flxVqlSxOgYAAAAAoJT566+/VLlyZatjwM3Q7JEUFBQkSTp06JDCw8MtTgMUjoyMDH322Wfq1auXvLy8rI4DFArGOUoDxjlKA8Y5SoOYmBjVqFHD+fsoUJBo9kjOS7eCgoIUHBxscRqgcGRkZMjf31/BwcH80AS3xThHacA4R2nAOEdpkJGRIUksJYJCwYWBAAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGaPYAAAAAAAC4EZo9AAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGaPYAAAAAAAC4EZo9AAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGaPYAAAAAAAC4EZo9AAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGaPYAAAAAAAC4EZo9AAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGaPYAAAAAAAC4EZo9AAAAAAAAboRmDwAAAAAAgBuh2QMAAAAAAOBGPK0OAAAAgCuXlZio0489ZqpFTJokj6AgixIBAACrMbMHAAAAAADAjTCzBwAAoARyJCYqy8tLjsTEXLdlY4YPAAClD80eAACAEihm/Pg8t52dMMH5/xXefrso4gAAgGKEy7gAAAAAAADcCM0eAAAAAAAAN0KzBwAAAAAAwI2wZg8AAEAJFD5xojz/f4HmC9foMfHwUNr+/fKpW7dowwEAAEsxswcAAKAEsgcFySMoSPZL3W0rK0uxM2Yo/fDhIssFAACsR7MHAADAjRlpaYqZOlUZx49bHQUAABQRLuMCAAAowTyCgky3VzeyshQ3Z45Sd+z4X+38ecVMmaIyjz8uz7JlrYgJAACKEDN7AAAA3IjNw0OhQ4bIu359U90RF6eY6GhlxcdblAwAABQVmj0AAABuxublpbCHH5ZXjRqmetaZM4qZMkWO8+ctSgYAAIoCzR4AAAA3ZPf1VfioUfKsVMlUz/z7b8VMmyZHaqpFyQAAQGGj2QMAAOCm7AEBCh8zRh7lypnqGYcOKXbWLBkZGRYlAwAAhcnSZs/MmTPVpEkTBQcHKzg4WG3bttVXX33l3J6amqqoqCiVKVNGgYGB6tOnj06dOmU6xtGjR9WjRw/5+/srIiJCjz/+uDIzM4v6VAAAAIolj5AQhY8dK3toqKmevm+f4t59V0ZWljXBAABAobG02VO5cmW98sor2rZtm37++Wd17NhRd9xxh/bs2SNJGjdunL744gstWbJE3377rY4fP64777zT+fFZWVnq0aOH0tPT9cMPP2jBggWaP3++nnvuOatOCQAAoNjxLFtW4WPGyBYQYKqn7tih+IULZTgcFiUDAACFwdJmz+23367u3burTp06uuaaa/Tf//5XgYGB2rJli+Lj4/Xuu+/qzTffVMeOHdWyZUvNmzdPP/zwg7Zs2SJJWr16tfbu3av3339fzZo106233qr//Oc/mjFjhtLT0608NQAAgGLFq2JFhY8eLZuvr6mesnmzEpculWEYFiUDAAAFzdPqANmysrK0ZMkSnT9/Xm3bttW2bduUkZGhzp07O/epV6+eqlatqs2bN6tNmzbavHmzGjdurPLlyzv36dq1q0aMGKE9e/aoefPmuT5XWlqa0tLSnI8TEhIkSRkZGcrg2nW4qeyxzRiHO2OcozS4mnFuq1RJQcOGKeGtt6QLLns/v26dDF9f+d96a4HlBK4G7+coDRjfKEyWN3t+/fVXtW3bVqmpqQoMDNSnn36qBg0a6JdffpG3t7dCL7q+vHz58jp58qQk6eTJk6ZGT/b27G15mThxoiZMmJCjvmLFCvn7+1/lGQHF2/Lly62OABQ6xjlKg6sZ52Xq1VOj3btlu6CWvGKFdu7fr78rV776cEAB4f0c7iw5OdnqCHBjljd76tatq19++UXx8fFaunSpBg4cqG+//bZQn3P8+PF69NFHnY8TEhJUpUoV9ejRQ2XKlCnU5waskpGRoeXLl+uOO+6Ql5eX1XGAQsE4R2lQUOM8detWJb33nnTB5Vt1/vhDzdu2le911xVEVOCK8X6O0uDcuXNWR4Abs7zZ4+3trdq1a0uSWrZsqa1bt2rKlCm6++67lZ6erri4ONPsnlOnTikyMlKSFBkZqZ9++sl0vOy7dWXvkxsfHx/5+PjkqHt5efHNBG6PcY7SgHGO0uBqx7nX9dfLnp6uhA8/NNWTPvhAXoGB8m3W7CoTAleP93O4M8Y2CpOlCzTnxuFwKC0tTS1btpSXl5fWrVvn3LZ//34dPXpUbdu2lSS1bdtWv/76q06fPu3cZ82aNQoODlaDBg2KPDsAAEBJEtChg4LuuMNcdDgUO2eO0vbtsyYUAAC4apbO7Bk/frxuvfVWVa1aVYmJiVq0aJG++eYbff311woJCdGQIUP06KOPKjw8XMHBwRo1apTatm2rNm3aSJK6dOmiBg0a6P7779drr72mkydP6tlnn1VUVFSuM3cAAABgFnDrrXIkJ+v8mjX/K2ZmKnbmTIWPGyfvGjWsCwcAAK6Ipc2e06dP64EHHtCJEycUEhKiJk2a6Ouvv9Ytt9wiSZo8ebLsdrv69OmjtLQ0de3aVW+99Zbz4z08PPTll19qxIgRatu2rQICAjRw4EC9+OKLVp0SAABAiWKz2RTUp48cKSlK+f57Z91IS1PM1Kkq89hj8qpUycKEAADAVZY2e959991Lbvf19dWMGTM0Y8aMPPepVq2aVq5cWdDRAAAASg2bzaaQAQNkpKQodds2Z91ITlbMlCkq8/jj8ixXzsKEAADAFcVuzR4AAAAUPZvdrtDBg+Vz0bqHjvh4xURHKysuzppgAADAZTR7AAAAIEmyeXoq9OGH5VWrlqmedfasYqZMkSMpyaJkAADAFTR7AAAA4GT38VH4yJHyrFLFVM88flwx06bJkZpqUTIAAJBfNHsAAABgYvf3V/jo0fKIiDDVMw4fVuxbb8nIyLAoGQAAyA+aPQAAAMjBIzhYZcaNkz0szFRP379fsXPmyMjKsigZAAC4HJo9AAAAyJVHeLjKjB0re2CgqZ62c6fi33tPhsNhUTIAAHApNHsAAACQJ8/ISIWPGSObr6+pnrJlixI+/liGYViUDAAA5IVmDwAAAC7Jq2pVhUVFSV5epnryhg1K+uILi1IBAIC80OwBAADAZflcc43Chg+X7OYfH5NWrFDS2rUWpQIAALmh2QMAAIB88W3cWKGDB0s2m6meuGSJkjdtsigVAAC4GM0eAAAA5Jtf69YKuffeHPX4hQuVsn27BYkAAMDFaPYAAADAJf433aSg3r3NRcNQ3LvvKm3vXmtCAQAAJ5o9AAAAcFlgt24K6NrVXMzMVOzMmUo/eNCaUAAAQBLNHgAAAFyhoN695X/TTaaakZ6umOnTlXHsmEWpAAAAzR4AAABcEZvNpuD+/eXbqpWpbiQnK2bKFGWePm1RMgAASjeaPQAAALhiNrtdoYMGyadRI1PdkZCgmOhoZcXGWpQMAIDSi2YPAAAArorN01Nhw4fLu3ZtUz3r3DnFTJkiR1KSRckAACidaPYAAADgqtm8vRU2cqQ8q1Y11TNPnPin4ZOSYlEyAABKH5o9AAAAKBB2Pz+Fjx4tj/LlTfWMo0cV+9ZbMtLTLUoGAEDpQrMHAAAABcYjKEhlxo6VR3i4qZ7++++KnT1bRlaWRckAACg9aPYAAACgQHmEhyt87FjZg4JM9bRff1Xc/PkyHA6LkgEAUDrQ7AEAAECB8yxfXuFjxsjm52eqp/70kxI++kiGYViUDAAA90ezBwAAAIXCq0oVhY8cKXl5merJ336rxOXLLUoFAID7o9kDAACAQuNdu7bCR4yQPDxM9fNffaWk1astSgUAgHuj2QMAAIBC5dOwoUKHDJFsNlM9cdkyJX//vUWpAABwXzR7AAAAUOj8WrZUyH335ajHv/++UrZtsyARAADui2YPAAAAioR/u3YK6tPHXDQMxb37rlJ377YmFAAAbohmDwAAAIpMYJcuCrz1VnMxK0uxs2Yp/Y8/rAkFAICbodkDAACAIhV4xx3y79DBXMzIUMz06cr46y9LMgEA4E5o9gAAAKBI2Ww2Bd99t3yvvdZUN1JSFDNlijJPnbIoGQAA7oFmDwAAAIqczW5X6IMPyqdJE1PdkZioc5MnKysmxqJkAACUfDR7AAAAYAmbh4fChg6V9zXXmOqO2Fidi45WVkKCRckAACjZaPYAAADAMjZvb4U98oi8qlUz1bNOnVLM1KlypKRYlAwAgJKLZg8AAAAsZffzU/jo0fKsUMFUz/zrL8VOny4jPd2iZAAAlEw0ewAAAGA5e2CgwseMkUeZMqZ6+h9/KPbtt2VkZlqUDACAkodmDwAAAIoFj7AwhY8dK3twsKmetnu34ubNk+FwWJQMAICShWYPAAAAig3PiAiFjxkjm7+/qZ76889KWLRIhmFYlAwAgJKDZg8AAACKFa/KlRU+cqRs3t6mevJ33ynx008tSgUAQMlBswcAAADFjnetWgobMULy9DTVz3/9tZJWrbIoFQAAJQPNHgAAABRLPg0aKHTIEMlmM9UTP/1U5zdutCgVAADFH80eAAAAFFt+LVoo5IEHctQTFi1Syk8/WZAIAIDij2YPAAAAijX/669X8F13mYuGobh585T666/WhAIAoBij2QMAAIBiL6BzZwX26GEuOhyKffttpf3+uzWhAAAopmj2AAAAoEQIvP12+d98s7mYkaHYGTOUcfSoNaEAACiGaPYAAACgRLDZbAru109+bdqY6kZqqmKmTFHmyZMWJQMAoHih2QMAAIASw2a3K+SBB+TTrJmp7khK0rnoaGWeO2dNMAAAihGaPQAAAChRbB4eCnvoIXnXrWuqO2JjFRMdrayEBIuSAQBQPNDsAQAAQIlj8/JS2COPyKt6dVM96/RpxUyZIkdysjXBAAAoBmj2AAAAoESy+/oqfPRoeVasaKpnHjummOnT5UhLsygZAADWotkDAACAEsseEKDwMWPkUbasqZ5x8KDiZs2SkZlpUTIAAKxDswcAAAAlmkdoqMLHjpU9JMRUT9u7V3Fz58pwOCxKBgCANWj2AAAAoMTzLFdO4WPGyBYQYKqnbtum+Pffl2EYFiUDAKDo0ewBAACAW/CqVEnho0bJ5uNjqqds2qTEZcto+AAASg2aPQAAAHAb3jVqKOyRRyRPT1P9/Jo1SvrqK4tSAQBQtGj2AAAAwK341KunsKFDJbv5R92k5ct1fsMGi1IBAFB0aPYAAADA7fg2a6aQBx7IUU/46COl/PijBYkAACg6NHsAAADglvzbtlXw3XfnqMfNn6/UnTstSAQAQNGg2QMAAAC3FdCxowJ79jQXHQ7Fzp6ttP37rQkFAEAho9kDAAAAtxbYvbsCOnUyFzMzFTtjhtIPH7YkEwAAhYlmDwAAANyazWZT0F13ye/66011Iy1NMVOnKuP4cYuSAQBQOGj2AAAAwO3ZbDaF3HeffJs3N9WN8+cVM2WKMs+etSgZAAAFj2YPAAAASgWbh4dChwyRd/36projLk4x0dHKio+3KBkAAAWLZg8AAABKDZuXl8IeflheNWqY6llnzihmyhQ5zp+3KBkAAAWHZg8AAABKFbuvr8JHjZJnpUqmeubffytm2jQ5UlMtSgYAQMGg2QMAAIBSxx4QoPAxY+QREWGqZxw6pNiZM2VkZFiUDACAq0ezBwAAAKWSR0iIwseMkT001FRP/+03xb7zjoysLGuCAQBwlWj2AAAAoNTyLFtW4WPHyhYQYKqn/fKL4hculOFwWJQMAIArR7MHAAAApZpXhQoKHz1aNl9fUz1l82YlLl0qwzAsSgYAwJWh2QMAAIBSz7t6dYU98ojk6Wmqn1+3TkkrVliUCgCAK0OzBwAAAJDkU7euwoYPl+zmH5GTvvhC59evtygVAACuo9kDAAAA/D/fJk0UOmiQZLOZ6gmLFyt582aLUgEA4BqaPQAAAMAF/K69VsH9++eox7/3nlJ/+aXoAwEA4CKaPQAAAMBFAtq3V9Add5iLDodi58xR2r591oQCACCfaPYAAAAAuQi49VYF3HKLuZiZqdiZM5V+6JA1oQAAyAeaPQAAAEAubDabgvr0kV+7dqa6kZammKlTlfH33xYlAwDg0mj2AAAAAHmw2WwKGTBAvi1bmupGcrJipkxR5pkzFiUDACBvNHsAAACAS7DZ7QodPFg+DRua6o74eMVERysrLs6aYAAA5IFmDwAAAHAZNk9PhT38sLxq1TLVs86eVUx0tBxJSRYlAwAgJ5o9AAAAQD7YvL0VPnKkPKtUMdUzT5xQzLRpcqSmWpQMAAAzS5s9EydOVOvWrRUUFKSIiAj16tVL+/fvN+1z8OBB9e7dW+XKlVNwcLD69eunU6dOmfaJiYnRgAEDFBwcrNDQUA0ZMkRJ/HUFAAAABczu76/w0aPlERFhqmccPqzYt96SkZFhUTIAAP7H0mbPt99+q6ioKG3ZskVr1qxRRkaGunTpovPnz0uSzp8/ry5dushms2n9+vXatGmT0tPTdfvtt8vhcDiPM2DAAO3Zs0dr1qzRl19+qY0bN2rYsGFWnRYAAADcmEdwsMqMGyd7WJipnr5/v2LnzJGRlWVRMgAA/uFp5ZOvWrXK9Hj+/PmKiIjQtm3bdNNNN2nTpk06fPiwduzYoeDgYEnSggULFBYWpvXr16tz587at2+fVq1apa1bt6pVq1aSpGnTpql79+6aNGmSKlasWOTnBQAAAPfmER6uMmPH6tykSXIkJjrraTt3Kv699xQycKBsdlZMAABYw9Jmz8Xi4+MlSeHh4ZKktLQ02Ww2+fj4OPfx9fWV3W7X999/r86dO2vz5s0KDQ11NnokqXPnzrLb7frxxx/Vu3fvHM+TlpamtLQ05+OEhARJUkZGhjKYegs3lT22GeNwZ4xzlAaM82KkTBkFP/KI4qdMkXHBej0pW7bI8PZWwF13yWazWRiw5GKcozRgfKMwFZtmj8Ph0NixY3XDDTeoUaNGkqQ2bdooICBATz75pF5++WUZhqGnnnpKWVlZOnHihCTp5MmTirjommlPT0+Fh4fr5MmTuT7XxIkTNWHChBz1FStWyN/fv4DPDCheli9fbnUEoNAxzlEaMM6Lj5B69dRk1y55XLDMQOrGjfrtyBEdrlHDwmQlH+Mc7iw5OdnqCHBjxabZExUVpd27d+v777931sqVK6clS5ZoxIgRmjp1qux2u/r3768WLVrIfhXTYsePH69HH33U+TghIUFVqlRRjx49VKZMmas6D6C4ysjI0PLly3XHHXfIy8vL6jhAoWCcozRgnBdP6Xv2KOHtt6ULGj7VjxxRw5Yt5dexo4XJSibGOUqDc+fOWR0BbqxYNHtGjhzpXFi5cuXKpm1dunTRwYMHdfbsWXl6eio0NFSRkZGqWbOmJCkyMlKnT582fUxmZqZiYmIUGRmZ6/P5+PiYLg3L5uXlxTcTuD3GOUoDxjlKA8Z58eLVrJnsgwcr7t13JcNw1s9/8ok8AwPlf8MNFqYruRjncGeMbRQmS1eNMwxDI0eO1Keffqr169erxiWmuZYtW1ahoaFav369Tp8+rZ49e0qS2rZtq7i4OG3bts257/r16+VwOHTdddcV+jkAAAAAkuTXurVC7r03Rz1+4UKlbN9uQSIAQGll6cyeqKgoLVq0SMuXL1dQUJBzjZ2QkBD5+flJkubNm6f69eurXLly2rx5s8aMGaNx48apbt26kqT69eurW7duGjp0qGbNmqWMjAyNHDlS99xzD3fiAgAAQJHyv+kmOVJSlPjJJ/8rGobi3n1Xdl9f+TRoYF04AECpYenMnpkzZyo+Pl4dOnRQhQoVnP8WL17s3Gf//v3q1auX6tevrxdffFHPPPOMJk2aZDrOBx98oHr16qlTp07q3r272rVrp9mzZxf16QAAAAAK7NpVAd26mYuZmYqdOVPpBw9aEwoAUKpYOrPHuOB65ry88soreuWVVy65T3h4uBYtWlRQsQAAAICrEtSrl4zkZCVv3OisGenpipk+XWX+9S95XbROJQAABcnSmT0AAACAO7LZbAru31++rVub6kZysmKmTFHmqVMWJQMAlAY0ewAAAIBCYLPbFTpokHwaNTLVHQkJipkyRVmxsRYlAwC4O5o9AAAAQCGxeXgobPhwedeubapnnTunmClT5EhKsigZAMCd0ewBAAAACpHN21thI0fKs2pVUz3zxIl/Gj4pKRYlAwC4K5o9AAAAQCGz+/kpfPRoeURGmuoZR48q9q23ZKSnW5QMAOCOaPYAAAAARcAjKEhlxoyRR3i4qZ7++++KnT1bRlaWRckAAO6GZg8AAABQRDzCwxU+dqzsQUGmetqvvypu/nwZDodFyQAA7oRmDwAAAFCEPMuXV/iYMbL5+ZnqqT/9pISPPpJhGBYlAwC4C5o9AAAAQBHzqlJF4SNHSl5epnryt98qcflyi1IBANwFzR4AAADAAt61ayt8xAjJw8NUP//VV0pavdqiVAAAd0CzBwAAALCIT8OGCh0yRLLZTPXEZcuU/N13FqUCAJR0NHsAAAAAC/m1bKmQ++7LUY//4AOl/PyzBYkAACUdzR4AAADAYv7t2imob19z0TAUN3euUnfvtiYUAKDEotkDAAAAFAOBt9yiwFtvNRezshQ7a5bS//jDmlAAgBKJZg8AAABQTATecYf8O3QwFzMyFDN9ujL++suSTACAkodmDwAAAFBM2Gw2Bd99t/yuu85UN1JSFDNlijJPnbIoGQCgJKHZAwAAABQjNrtdIQMHyqdJE1PdkZioc5MnKysmxqJkAICSgmYPAAAAUMzYPDwUNmyYvK+5xlR3xMbqXHS0shISLEoGACgJaPYAAAAAxZDNy0thUVHyqlbNVM86dUoxU6fKkZJiUTIAQHFHswcAAAAopuy+vgofPVqeFSqY6pl//aWY6dNlpKdblAwAUJzR7AEAAACKMXtgoMLHjJFHmTKmesYffyj27bdlZGZalAwAUFzR7AEAAACKOY+wMIWPHSt7cLCpnrZ7t+LmzZPhcFiUDABQHNHsAQAAAEoAz4gIhY8dK5u/v6me+vPPSli0SIZhWJQMAFDc0OwBAAAASgivSpUUPmqUbD4+pnryd98p8ZNPLEoFAChuaPYAAAAAJYh3zZoKGzFC8vQ01c+vXq2kVassSgUAKE5o9gAAAAAljE/9+godMkSy2Uz1xE8/1flvv7UoFQCguKDZAwAAAJRAfi1aKOSBB3LUEz78UCk//WRBIgBAcUGzBwAAACih/K+/XsH9+pmLhqG4efOU+uuv1oQCAFiOZg8AAABQggV06qTA224zFx0Oxb79ttJ+/92aUAAAS9HsAQAAAEq4wNtuk3/HjuZiRoZiZ8xQxpEj1oQCAFiGZg8AAABQwtlsNgXfdZf82rY11Y3UVMVMnarMkyctSgYAsALNHgAAAMAN2Ox2hdx/v3yaNTPVHUlJOhcdrcxz56wJBgAocjR7AAAAADdh8/BQ2EMPybtuXVPdERurmOhoZSUkWJQMAFCUaPYAAAAAbsTm5aWwRx6RV40apnrW6dOKmTJFjuRki5IBAIoKzR4AAADAzdh9fRU+apQ8K1Y01TOPHVPM9OlypKVZlAwAUBRo9gAAAABuyB4QoPCxY+VRtqypnnHwoGJnzZKRkWFRMgBAYaPZAwAAALgpj5AQhY8bJ3tIiKmevnev4ubOleFwWJQMAFCYaPYAAAAAbsyzbFmFjx0rW0CAqZ66fbvi339fhmFYlAwAUFho9gAAAABuzqtiRYWPGiWbj4+pnrJpkxKXLaPhAwBuhmYPAAAAUAp416ihsKgoydPTVD+/Zo2SvvrKolQAgMJAswcAAAAoJXzq1lXYsGGS3fxrQNLy5Tq/YYNFqQAABY1mDwAAAFCK+DZtqpCBA3PUEz76SMlbtliQCABQ0Gj2AAAAAKWMf5s2Cr7nnhz1+AULlLpzpwWJAAAFiWYPAAAAUAoF3HyzAnv2NBcdDsXOnq20/futCQUAKBA0ewAAAIBSKrB7dwV06mQuZmYqdsYMpR8+bEkmAMDVo9kDAAAAlFI2m01Bd90lvxtuMNWNtDTFTJ2qjOPHLUoGALgaNHsAAACAUsxmsynkvvvk26KFqW6cP6+YKVOUefasRckAAFeKZg8AAABQytnsdoUOHizvBg1MdUdcnGKio5UVH29RMgDAlaDZAwAAAEA2Ly+FPfywvGrWNNWzzpxRTHS0HOfPW5QMAOAqmj0AAAAAJEl2Hx+Fjxwpz8qVTfXM48cVM22aHKmpFiUDALiCZg8AAAAAJ3tAgMJHj5ZHRISpnnHokGJnzpSRkWFRMgBAftHsAQAAAGDiERKi8DFjZA8NNdXTf/tNse+8IyMry5pgAIB8odkDAAAAIAfPsmUVPnas7IGBpnraL78ofuFCGQ6HRckAAJdDswcAAABArrwqVFD46NGy+fqa6imbNythyRIZhmFRMgDApdDsAQAAAJAnr2rVFBYVJXl5merJ69cr6csvLUoFALgUmj0AAAAALsnnmmsUNmyYZDf/+pD05Zc6v26dRakAAHmh2QMAAADgsnybNFHooEGSzWaqJ3z8sZI3b7YoFQAgNzR7AAAAAOSL37XXKrh//xz1+PfeU+ovvxR9IABArmj2AAAAAMi3gPbtFdSrl7nocCh2zhyl7dtnSSYAgBnNHgAAAAAuCejWTQFdupiLmZmKnTlT6YcOWRMKAOBEswcAAACAS2w2m4LuvFN+7dqZ6kZammKmTlXG339blAwAINHsAQAAAHAFbDabQgYMkG/Llqa6kZysmOhoZZ45Y1EyAADNHgAAAABXxGa3K3TwYPk0bGiqOxISFBMdray4OGuCAUApR7MHAAAAwBWzeXoq7OGH5VW7tqmedfasYqKj5UhKsigZAJReNHsAAAAAXBWbt7fCo6LkWaWKqZ554oRipk2TIzXVomQAUDrR7AEAAABw1ez+/gofPVoe5cub6hmHDyv2rbdkZGRYlAwASh+aPQAAAAAKhEdwsMqMHSt7WJipnr5/v2LnzJGRlWVRMgAoXWj2AAAAACgwHuHh/zR8goJM9bSdOxW/YIEMh8OiZABQetDsAQAAAFCgPCMjFT56tGy+vqZ6yo8/KmHxYhmGYVEyACgdaPYAAAAAKHBeVasqfORIycvLVE/+5hslffGFRakAoHSg2QMAAACgUHjXqaOwhx+W7OZfO5JWrFDS2rUWpQIA90ezBwAAAECh8W3USKGDB0s2m6meuGSJkjdtsigVALg3mj0AAAAACpVf69YKGTAgRz1+4UKlbNtmQSIAcG80ewAAAAAUOv8bb1TQnXeai4ahuHffVdrevdaEAgA3RbMHAAAAQJEI7NpVAd26mYtZWYqdOVPpBw9aEwoA3BDNHgAAAABFJqhXL/nfdJOpZqSnK2b6dGUcO2ZRKgBwLzR7AAAAABQZm82m4P795du6taluJCcrZsoUZZ46ZVEyAHAfNHsAAAAAFCmb3a7QQYPk07ixqe5ISFDMlCnKio21KBkAuAeaPQAAAACKnM3DQ2HDhsm7Th1TPevcOSVMny6v9HSLkgFAyWdps2fixIlq3bq1goKCFBERoV69emn//v2mfU6ePKn7779fkZGRCggIUIsWLbRs2TLTPjExMRowYICCg4MVGhqqIUOGKCkpqShPBQAAAICLbN7eCouKkmfVqqZ61qlTarJrlxwpKRYlA4CSzdJmz7fffquoqCht2bJFa9asUUZGhrp06aLz588793nggQe0f/9+ff755/r111915513ql+/ftqxY4dznwEDBmjPnj1as2aNvvzyS23cuFHDhg2z4pQAAAAAuMDu56fw0aPlERlpqgclJSnh7bdlMMMHAFxmabNn1apVevDBB9WwYUM1bdpU8+fP19GjR7Vt2zbnPj/88INGjRqla6+9VjVr1tSzzz6r0NBQ5z779u3TqlWr9M477+i6665Tu3btNG3aNH300Uc6fvy4VacGAAAAIJ88goJUZswYeYSHm+qZf/yh2NmzZWRlWZQMAEomT6sDXCg+Pl6SFH7Bm/z111+vxYsXq0ePHgoNDdXHH3+s1NRUdejQQZK0efNmhYaGqlWrVs6P6dy5s+x2u3788Uf17t07x/OkpaUpLS3N+TghIUGSlJGRoYyMjMI4NcBy2WObMQ53xjhHacA4h9sKClLwyJGKmzxZRmKis5z266+KnTtXgQ88IJudJUfhPngfR2EqNs0eh8OhsWPH6oYbblCjRo2c9Y8//lh33323ypQpI09PT/n7++vTTz9V7dq1Jf2zpk9ERITpWJ6engoPD9fJkydzfa6JEydqwoQJOeorVqyQv79/AZ4VUPwsX77c6ghAoWOcozRgnMNdBdStq2a//CKvzExnLe3nn/Xn8eM6UKeOZLNZmA4oOMnJyVZHgBsrNs2eqKgo7d69W99//72p/u9//1txcXFau3atypYtq88++0z9+vXTd999p8YX3aoxv8aPH69HH33U+TghIUFVqlRRjx49VKZMmas6D6C4ysjI0PLly3XHHXfIy8vL6jhAoWCcozRgnKM0SPn9dyVMny4Ph8NZq3T8uGo3aqSAnj0tTAYUnHPnzlkdAW6sWDR7Ro4c6VxYuXLlys76wYMHNX36dO3evVsNGzaUJDVt2lTfffedZsyYoVmzZikyMlKnT582HS8zM1MxMTGKvGiRt2w+Pj7y8fHJUffy8uKHJrg9xjlKA8Y5SgPGOdzaNdfoh0aN1HTPHumC9XpSVq+WZ2CgArt2tTAcUDB4D0dhsvSiV8MwNHLkSH366adav369atSoYdqePa3NftG1uR4eHnL8f5e/bdu2iouLMy3qvH79ejkcDl133XWFfAYAAAAACkNseLiCHnwwx2VbiZ98ouTvvrMmFACUEJY2e6KiovT+++9r0aJFCgoK0smTJ3Xy5EmlpKRIkurVq6fatWtr+PDh+umnn3Tw4EG98cYbWrNmjXr16iVJql+/vrp166ahQ4fqp59+0qZNmzRy5Ejdc889qlixooVnBwAAAOBq+DRvrpD77stRj//gA6X8/LMFiQCgZLC02TNz5kzFx8erQ4cOqlChgvPf4sWLJf0zrW3lypUqV66cbr/9djVp0kTvvfeeFixYoO7duzuP88EHH6hevXrq1KmTunfvrnbt2mn27NlWnRYAAACAAuLfrp2C+vY1Fw1DcXPnKnX3bmtCAUAxZ+maPYZhXHafOnXqaNmyZZfcJzw8XIsWLSqoWAAAAACKkcBbbpGRnKyklSv/V8zKUuysWSozdqy8//9OvQCAf1g6swcAAAAA8iOwZ0/5d+hgLmZkKGbaNGUcPWpJJgAormj2AAAAACj2bDabgu++W34X3YTFSE1VzNSpyjx50qJkAFD80OwBAAAAUCLY7HaFDBwonyZNTHVHYqLORUcrKybGomQAULzQ7AEAAABQYtg8PBQ2bJi8r7nGVHfExv7T8ElIsCgZABQfNHsAAAAAlCg2Ly+FRUXJq3p1Uz3r1CnFTJ0qR0qKNcEAoJig2QMAAACgxLH7+ip81Ch5Vqhgqmf+9Zdipk+XkZ5uUTIAsB7NHgAAAAAlkj0wUOFjxsijTBlTPeOPPxQ7a5aMzEyLkgGAtWj2AAAAACixPMLCFD5unOzBwaZ62p49ips7V4bDYVEyALAOzR4AAAAAJZpnuXIKHztWNn9/Uz112zbFf/CBDMOwKBkAWINmDwAAAIASz6tSJYWPGiWbj4+pnvL990r85BOLUgGANWj2AAAAAHAL3jVrKmzECMnT01Q/v3q1klatsigVABQ9mj0AAAAA3IZP/foKe+ghyWYz1RM//VTnv/3WolQAULRo9gAAAABwK77Nmytk4MAc9YQPP1TKTz9ZkAgAihbNHgAAAABux79tWwX362cuGobi5s1T6q+/WhMKAIoIzR4AAAAAbimgUycF3nabuehwKPbtt5X2++/WhAKAIkCzBwAAAIDbCrztNvl37GguZmQodsYMZRw5Yk0oAChkNHsAAAAAuC2bzabgu+6SX9u2prqRmqqYqVOVefKkRckAoPDQ7AEAAADg1mx2u0Luv18+zZqZ6o6kJJ2LjlbmuXPWBAOAQkKzBwAAAIDbs3l4KOyhh+Rdr56p7oiNVUx0tLISEixKBgAFj2YPAAAAgFLB5uWlsBEj5FWjhqmedfq0YqZMkeP8eYuSAUDBotkDAAAAoNSw+/oqfNQoeVasaKpnHjummOnT5UhLsygZABQcmj0AAAAAShV7QIDCx46VR9mypnrGn38qdtYsGRkZFiUDgIJBswcAAABAqeMREqLwceNkDw011dP37lXc3LkyHA5rggFAAaDZAwAAAKBU8ixbVuFjxsgWEGCqp27frvj335dhGBYlA4CrQ7MHAAAAQKnlVbGiwkePls3Hx1RP2bRJiUuX0vABUCLR7AEAAABQqnlXr66wqCjJ09NUP792rZJWrrQoFQBcOZo9AAAAAEo9n7p1FTZsmGQ3/4qU9PnnOr9hg0WpAODK0OwBAAAAAEm+TZsqZODAHPWEjz5S8pYtFiQCgCtDswcAAAAA/p9/mzYKvueeHPX4BQuUunOnBYkAwHU0ewAAAADgAgE336zAnj3NRYdDsbNnK23/fmtCAYALaPYAAAAAwEUCu3dXQOfO5mJmpmJnzFD64cOWZAKA/LqiZs/Ro0f13Xff6euvv9b27duVlpZW0LkAAAAAwDI2m01BffvK74YbTHUjLU0xU6cq4/hxi5IBwOXlu9lz+PBhPfnkk6pWrZpq1Kih9u3b69Zbb1WrVq0UEhKiW265RUuWLJHD4SjMvAAAAABQJGw2m0Luu0++LVqY6sb584qZMkWZZ89alAwALi1fzZ7Ro0eradOmOnTokF566SXt3btX8fHxSk9P18mTJ7Vy5Uq1a9dOzz33nJo0aaKtW7cWdm4AAAAAKHQ2u12hgwfLu0EDU90RF6eYyZOVFR9vUTIAyJtnfnYKCAjQn3/+qTJlyuTYFhERoY4dO6pjx456/vnntWrVKv31119q3bp1gYcFAAAAgKJm8/JS2MMPK2bKFGUcPOisZ509q5joaJV57DHZAwIsTAgAZvma2TNx4sRcGz256datm+68886rCgUAAAAAxYndx0fhUVHyrFzZVM88flwx06bJkZpqUTIAyMnlBZpTUlKUnJzsfHzkyBFFR0fr66+/LtBgAAAAAFCc2AMCFD5mjDwiIkz1jEOHFDtzpoyMDIuSAYCZy82eO+64Q++9954kKS4uTtddd53eeOMN9erVSzNnzizwgAAAAABQXHgEByt87FjZQ0NN9fTfflPsO+/IyMqyJhgAXMDlZs/27dt14403SpKWLl2q8uXL68iRI3rvvfc0derUAg8IAAAAAMWJZ5ky/zR8AgNN9bRfflH8woUyuEMxAIu53OxJTk5WUFCQJGn16tW68847Zbfb1aZNGx05cqTAAwIAAABAceNVoYLCR4+WzdfXVE/ZvFkJS5bIMAyLkgHAFTR7ateurc8++0x//fWXvv76a3Xp0kWSdPr0aQUHBxd4QAAAAAAojryqVVNYVJTk5WWqJ69fr6Qvv7QoFQBcQbPnueee02OPPabq1avruuuuU9u2bSX9M8unefPmBR4QAAAAAIorn2uuUdjw4ZLd/KtV0pdf6vy6dRalAlDaudzs6du3r44ePaqff/5Zq1atctY7deqkyZMnF2g4AAAAACjufBs3VuigQZLNZqonfPyxkjdvtigVgNLMM787Vq1aVT179lTPnj3VsWNHRUZGmrZfe+21BR4OAAAAAEoCv2uvlSMlRQmLFpnq8QsWyO7rK1+uggBQhPI9s2fhwoXy8fFRVFSUypYtq7vvvlsffPCB4uLiCjEeAAAAAJQMAe3bK6hXL3PRMBT7zjtK27fPkkwASqd8N3vat2+vN954QwcOHNCmTZvUrFkzTZs2TZGRkerYsaOio6P1559/FmZWAAAAACjWArp1U8D/38TGKTNTsTNnKp3flwAUEZfX7JGkhg0bavz48dqyZYsOHz6s/v37a926dWrUqJEaNWqkFStWFHROAAAAACj2bDabgu68U37t2pnqRlqaYqZNU8bff1uUDEBpckXNngtFRkZq6NCh+uKLL3T27Fn95z//kY+PT0FkAwAAAIASx2azKWTAAPm2amWqG8nJiomOVuaZMxYlA1BaXHWzJ1tmZqbOnj2r3r17q3PnzgV1WAAAAAAocWx2u0IHDZJPo0amuiMhQTHR0cqKjbUoGYDSoMCaPXv27FGNGjUK6nAAAAAAUKLZPD0VNny4vGrXNtWzzp5VzJQpciQlWZQMgLsrsGYPAAAAAMDM5u2t8KgoeVapYqpnnjihmGnT5EhNtSgZAHfmmd8dW7RoccntKSkpVx0GAAAAANyN3d9f4aNH69ykSco6dcpZzzh8WLFvvaXwUaNk8/KyMCEAd5PvZs/evXt1zz335Hmp1okTJ/T7778XWDAAAAAAcBcewcEqM3aszr72mhwXrNeTvn+/YufMUdjw4bJ5eFiYEIA7yXezp1GjRrruuus0YsSIXLf/8ssvmjNnToEFAwAAAAB34hEerjLjxunc66/LkZjorKft3Kn4BQsU8uCDstlZaQPA1cv3O8kNN9yg/fv357k9KChIN910U4GEAgAAAAB35Fm+vMJHj5bN19dUT/nxRyUsXizDMCxKBsCd5Htmz5QpUy65vVatWtqwYcNVBwIAAAAAd+ZVtarCR43SuehoKSPDWU/+5hvZ/f0VdMcd1oUD4BaYIwgAAAAARcy7dm2FPfywdNFlW0krVyppzRqLUgFwF/lq9hw9etSlg/79999XFAYAAAAASgvfRo0UOniwZLOZ6olLlyp50yaLUgFwB/lq9rRu3VrDhw/X1q1b89wnPj5ec+bMUaNGjbRs2bICCwgAAAAA7sqvdWuFDBiQox6/cKFStm2zIBEAd5CvNXv27t2r//73v7rlllvk6+urli1bqmLFivL19VVsbKz27t2rPXv2qEWLFnrttdfUvXv3ws4NAAAAAG7B/8Yb5UhOVuInn/yvaBiKe/dd2f385NOggXXhAJRI+ZrZU6ZMGb355ps6ceKEpk+frjp16ujs2bM6cOCAJGnAgAHatm2bNm/eTKMHAAAAAFwU2LWrArp1MxezshQ7c6bSDx60JhSAEivfd+OSJD8/P/Xt21d9+/YtrDwAAAAAUCoF9eolIyVFyd9+66wZ6emKmT5dZR59VF5VqliYDkBJwt24AAAAAKAYsNlsCr7nHvm2bm2qG8nJipkyRZmnTlmUDEBJQ7MHAAAAAIoJm92u0EGD5NO4sanuSExUTHS0smJjLUoGoCSh2QMAAAAAxYjNw0Nhw4bJu04dUz0rJkbnoqOVlZhoUTIAJQXNHgAAAAAoZmze3gqLipJX1aqmetbJk4qdOlWOlBSLkgEoCWj2AAAAAEAxZPfzU/iYMfKIjDTVM44eVeyMGTLS0y1KBqC4y9fduD7//PN8H7Bnz55XHAYAAAAA8D/2wECVGTtW515/XVnnzjnr6QcOKHb2bIWNGCGbh4eFCQEUR/lq9vTq1cv02GazyTAM0+NsWVlZBZMMAAAAACCPsDCFjxmjc5MmyZGQ4Kyn/fqr4ubNU+jgwbLZuWgDwP/k6x3B4XA4/61evVrNmjXTV199pbi4OMXFxWnlypVq0aKFVq1aVdh5AQAAAKDU8SxfXuGjR8vm72+qp27dqoQPPzT9MR4A8jWz50Jjx47VrFmz1K5dO2eta9eu8vf317Bhw7Rv374CDQgAAAAAkLyqVFH4yJGKiY42rdeTvHGjbP7+Cu7d28J0AIoTl+f6HTx4UKGhoTnqISEhOnz4cAFEAgAAAADkxrtWLYWNGCFdtE7P+VWrlPT11xalAlDcuNzsad26tR599FGdOnXKWTt16pQef/xxXXvttQUaDgAAAABg5tOggUIfeki6YO1USUr85BMlf/edRakAFCcuN3vmzp2rEydOqGrVqqpdu7Zq166tqlWr6u+//9a7775bGBkBAAAAABfwa9FCIfffn6Me/8EHStm61YJEAIoTl9fsqV27tnbt2qU1a9bot99+kyTVr19fnTt3Nt2VCwAAAABQePxvuEGO5GQlLl36v6JhKG7uXNn8/OTbqJF14QBYyuVmj/TPrda7dOmiLl26FHQeAAAAAEA+Bd5yi4zkZCWtXPm/osOh2FmzVGbMGHnXqWNdOACWuaJmz7p167Ru3TqdPn1aDofDtG3u3LkFEgwAAAAAcHmBPXvKkZys5G+++V8xI0Mx06erzL/+Ja+qVS3LBsAaLq/ZM2HCBHXp0kXr1q3T2bNnFRsba/oHAAAAACg6NptNwXffLb82bUx1IzVVMVOnKvPkSYuSAbCKyzN7Zs2apfnz5+v+XBYDAwAAAAAUPZvdrpAHHpAjJUVpO3c6647ERJ2LjlbZJ56QR3i4hQkBFCWXZ/akp6fr+uuvL5Annzhxolq3bq2goCBFRESoV69e2r9/v3P74cOHZbPZcv23ZMkS535Hjx5Vjx495O/vr4iICD3++OPKzMwskIwAAAAAUBLYPDwUNnSovOvWNdUdsbE6Fx2trIQEi5IBKGouN3seeughLVq0qECe/Ntvv1VUVJS2bNmiNWvWKCMjQ126dNH58+clSVWqVNGJEydM/yZMmKDAwEDdeuutkqSsrCz16NFD6enp+uGHH7RgwQLNnz9fzz33XIFkBAAAAICSwublpbBHHpFX9eqmetapU4qZOlWO5GRrggEoUi5fxpWamqrZs2dr7dq1atKkiby8vEzb33zzzXwfa9WqVabH8+fPV0REhLZt26abbrpJHh4eioyMNO3z6aefql+/fgoMDJQkrV69Wnv37tXatWtVvnx5NWvWTP/5z3/05JNP6oUXXpC3t7erpwgAAAAAJZbd11fho0bp3KRJyjxxwlnP/OsvxcyYoTJjxsjG70mAW3O52bNr1y41a9ZMkrR7927TNpvNdlVh4uPjJUnheVxLum3bNv3yyy+aMWOGs7Z582Y1btxY5cuXd9a6du2qESNGaM+ePWrevHmO46SlpSktLc35OOH/pzNmZGQoIyPjqs4BKK6yxzZjHO6McY7SgHGO0oBxXgB8fBQUFaX4yZPlOHfOWc744w+dmzlTwcOGyeZ5RTdnRgFhfKMw2QzDMKwOIUkOh0M9e/ZUXFycvv/++1z3eeSRR/TNN99o7969ztqwYcN05MgRff31185acnKyAgICtHLlSuflXhd64YUXNGHChBz1d955R/7+/gVwNgAAAABgPd+UFDXfsUM+6emm+uly5bS3QQPpKv9gjyuXnJyshx56SPHx8QoODrY6DtzMVbVyjx07JkmqXLnyVQeJiorS7t2782z0pKSkaNGiRfr3v/991c81fvx4Pfroo87HCQkJqlKlinr06KEyZcpc9fGB4igjI0PLly/XHXfckePyS8BdMM5RGjDOURowzgtWZqdOio+OlnHBej0RZ86oSkaGAvv3v+orNHBlzl0w4wooaC43exwOh1566SW98cYbSkpKkiQFBQXpX//6l5555hnZ7S6v+ayRI0fqyy+/1MaNG/NsHC1dulTJycl64IEHTPXIyEj99NNPptqpU6ec23Lj4+MjHx+fHHUvLy++mcDtMc5RGjDOURowzlEaMM4Lhle1avIYPVoxkyfLuGA5i7QffpBnYKCC7ryTho8FGNsoTC53Zp555hlNnz5dr7zyinbs2KEdO3bo5Zdf1rRp01yedWMYhkaOHKlPP/1U69evV40aNfLc991331XPnj1Vrlw5U71t27b69ddfdfr0aWdtzZo1Cg4OVoMGDVw7OQAAAABwQ941aihsxAjponV6zq9erfMX3TgHQMnn8syeBQsW6J133lHPnj2dtSZNmqhSpUp65JFH9N///jffx4qKitKiRYu0fPlyBQUF6eTJk5KkkJAQ+fn5Off7448/tHHjRq1cuTLHMbp06aIGDRro/vvv12uvvaaTJ0/q2WefVVRUVK6zdwAAAACgNPKpX19hDz2k2Lffli5YujXxs89k8/dXQPv2FqYDUJBcntkTExOjevXq5ajXq1dPMTExLh1r5syZio+PV4cOHVShQgXnv8WLF5v2mzt3ripXrqwuXbrkOIaHh4e+/PJLeXh4qG3btrrvvvv0wAMP6MUXX3TtxAAAAADAzfk2b66QgQNz1BM+/FApFy2PAaDkcnlmT9OmTTV9+nRNnTrVVJ8+fbqaNm3q0rHyeyOwl19+WS+//HKe26tVq5brrB8AAAAAgJl/27YyUlKUcOEf2Q1DcfPmyebnJ9/Gja0LB6BAuNzsee2119SjRw+tXbtWbdu2lSRt3rxZf/31Fw0XAAAAACgBAjp2lOP8eSV9+eX/ig6HYt9+W+GjR8vnmmusCwfgqrl8GVf79u21f/9+9e7dW3FxcYqLi9Odd96p/fv368YbbyyMjAAAAACAAhZ4223y79jRXMzIUOyMGco4csSaUAAKhMszeySpUqVKLi3EDAAAAAAoXmw2m4LvuktGSopSNm921o3UVJ2bMkVlHn9cXhUqWJgQwJVyeWbPvHnztGTJkhz1JUuWaMGCBQUSCgAAAABQ+Gx2u0Luv18+zZqZ6sb584qJjlbm2bPWBANwVVxu9kycOFFly5bNUY+IiLjkIsoAAAAAgOLH5uGhsIceknf9+qa6Iy5OMVOmKCs+3qJkAK6Uy82eo0ePqkaNGjnq1apV09GjRwskFAAAAACg6Ni8vBT28MPyuuh3vazTpxUzdaoc589blAzAlXC52RMREaFdu3blqO/cuVNlypQpkFAAAAAAgKJl9/VV+KhR8qxY0VTPPHZMMdOny5GWZlEyAK5yudnTv39/jR49Whs2bFBWVpaysrK0fv16jRkzRvfcc09hZAQAAAAAFAF7QIDCx46VR7lypnrGn38qdtYsGRkZFiUD4AqXmz3/+c9/dN1116lTp07y8/OTn5+funTpoo4dO7JmDwAAAACUcB4hIQofO1b20FBTPX3vXsXNnSsjK8uaYADyzeVmj7e3txYvXqzffvtNH3zwgT755BMdPHhQc+fOlbe3d2FkBAAAAAAUIc+yZRU+ZoxsAQGmeur27Yr/4AMZhmFRMgD54XmlH1i9enUZhqFatWrJ0/OKDwMAAAAAKIa8KlZU+OjRinnzTRkXrNeTsmmT7H5+CurbVzabzcKEAPLi8sye5ORkDRkyRP7+/mrYsKHzDlyjRo3SK6+8UuABAQAAAADW8K5eXWFRUdJFf+A/v3atklautCgVgMtxeUrO+PHjtXPnTn3zzTfq1q2bs965c2e98MILeuqppwo0IAAAAADAOj516yps2DDFzpolORzOetLnn8vu76+Am2+2MJ37ysrKUgYLYuMCHh4e8vT0zNeMOpebPZ999pkWL16sNm3amJ6gYcOGOnjwoKuHAwAAAAAUc75Nmypk4EDFz5tnqid89JFsfn7yb9PGomTuKSkpSceOHWNtJOTg7++vChUqXHbNZJebPWfOnFFERESO+vnz57leEwAAAADclH+bNjJSUpTw0UemevyCBbL7+sq3WTNrgrmZrKwsHTt2TP7+/ipXrhy/Z0OSZBiG0tPTdebMGR06dEh16tSR3Z73yjwuN3tatWqlFStWaNSoUZLkHHjvvPOO2rZte4WxAQAAAADFXcDNN8uRnKykzz//X9HhUOycOQofPVo+detaF85NZGRkyDAMlStXTn5+flbHQTHi5+cnLy8vHTlyROnp6fL19c1zX5ebPS+//LJuvfVW7d27V5mZmZoyZYr27t2rH374Qd9+++1VBQcAAAAAFG+B3bvLSEnR+TVr/lfMzFTsjBkKHzdO3jVqWBfOjTCjB7m51GyeC7nc7GnXrp1++eUXvfLKK2rcuLFWr16tFi1aaPPmzWrcuLHLQQEAAAAAJYfNZlNQnz5yJCcrZdMmZ91IS9O5i+7QHDFpkjyCgoo6IlDqudzskaRatWppzpw5BZ0FAAAAAFAC2Gw2hdx3n4yUFKVu3251HPy/rMREnX7sMVPNiobb4cOHVaNGDcXGxio0NLRAj/3yyy/r119/1Ycffijpn7G4Y8cONSuCNaNeeOEF/fLLL/rss88uu29+X4PCeq3yN//nAtu3b9evv/7qfLx8+XL16tVLTz/9tNLT0wssGAAAAACg+LLZ7QodPFhe11yT5z6OxERl/f8/wFXz58/P0cR5+umnnY0eK50+fVoDBgxQ5cqVFRwcrObNm+vzC9eyspjLM3uGDx+up556So0bN9aff/6pu+++W3feeaeWLFmi5ORkRUdHF0JMAAAAAEBxY/PyUsbvv+e5/eyECc7/r/D220URya0YDocc589fdj9HUpLpvxfKPHnSWbcHBubree0BAbLlc22Y0iopKUnNmzfXq6++qooVK2rFihW65557tHXrVjVo0MDqeK43e37//XdnZ23JkiVq3769Fi1apE2bNumee+6h2QMAAAAAQAFwnD+f47IsV8VMmuTyx+T30q+kpCQ99dRT+vzzz5Wamqpu3bpp2rRpOfZbvXq1nn76aR04cED+/v7q3bu33njjDefdxt58801FR0crNjZWZcqU0bPPPquWLVvq4YcfVkZGhgL/v0m1d+9ezZ07N89LqX7//XfdeuutGjdunEaOHKkOHTqobdu22r59u3744QfVqVNHCxYscK43XL16dT3yyCP65JNPtGfPHrVo0ULvv/++qlSpctlzr1mzph674HNz++23q27dutqyZUuuzZ41a9boX//6lw4dOiR/f3/deeedmjlzpnP7F198oQkTJujs2bPq1auX5syZIy8vr8vmyIvLrTrDMORwOCRJa9euVffu3SVJVapU0dmzZ684CAAAAAAAKDkGDx6smJgY7dq1S4cOHVJGRoZGjhyZYz8/Pz/NmTNHMTEx2rRpkzZs2KA333xT0j8NmmeffVarV69WYmKifvzxR1177bVq3ry5Zs2apcaNGyspKUlJSUmqWrVqnll++ukndezYURMnTjRlWLhwoV577TXFxsaqVatWGjVqlOnj3n//fX344Yc6c+aMAgIC9O9///uKXovTp09r3759atKkSa7bBw4cqMcff1yJiYn6888/df/995u2f/XVV9qxY4f27t2rdevW6YMPPriiHNlcbva0atVKL730khYuXKhvv/1WPXr0kCQdOnRI5cuXv6owAAAAAACg+Dtz5oyWLVumGTNmKDQ0VAEBAXrxxRe1ePFiZWVlmfa98cYb1bx5c3l4eKhmzZoaPny4vvnmG0mSh4eHDMPQnj17lJKSovLly+fZMMnLqlWr1KtXL7333nvq16+fadt9992npk2bytPTUwMHDtS2bdtM2x955BHVqFFDvr6+GjBgQI7t+ZGenq577rlH/fr1U6tWrXLdx8vLS3/88YezqXT99debtj/33HMKCgpSxYoV1a1btyvKcSGXmz3R0dHavn27Ro4cqWeeeUa1a9eWJC1dujRHWAAAAACAe4uYNEkRkyap7PPP59jmERnp3A73cvjwYTkcDtWoUUOhoaEKDQ1V69atZbfbdfLkSdO+W7duVefOnVW+fHkFBwfr6aefdl4ZVKtWLS1YsEDTp09X+fLl1aVLF/3yyy8uZYmOjtbNN9+sjh075tgWGRnp/P+AgAAlXbSu0cXbE/9/MfGXX35ZgYGBCgwM1K233prnc6enp6tv377y9/e/5F3LP/30U+3evVt169ZV8+bN9fHHH+crx5Vyec2eJk2amO7Gle3111+Xh4fHVYUBAAAAAJQsl1rbJSs+vshv++1O7AEB+WqUXbhA88Vr9IQ/9phzYWZXFmi+nCpVqshut+v48ePy9/c3bTt8+LDpcf/+/TVo0CAtX75cAQEBio6O1vz5853b+/Xrp379+iklJUXPPfec7r//fv3666+y53OR6EWLFumxxx7TqFGjcl0z6Eo8/fTTevrppy+5T3p6uu666y6lp6dr+fLl8vb2znPfFi1aaNmyZXI4HPrss8/Ur18/tW/fvkCy5qbAltf29fW9qsWDAAAAAABuJiVFjtRUq1OUWDa7XR5BQZf951WhgrwqVJDnBbNDsnlGRjq35+dYHkFB+boTV2RkpHr16qWRI0c6Z+mcPHlSn376aY59ExISnJd67du3z7Qw8f79+7VmzRqlpKTI29tbgYGB8vT8Z15K+fLldeLECaWkpFwyS3h4uNatW6fNmzdrxIgRMgzjsvmvVkZGhvr166fz58/rs88+k4+PT577pqena+HChYqNjZXdbldoaKgkOc+zMHAvNQAAAADAVfMIClLk9Ok56lnnzlmQBkVh/vz5zsu3goODdeONN+a61szbb7+tSZMmKTAwUA8//LDuuece57b09HT9+9//Vvny5VWmTBmtX7/eOeunY8eOatOmjSpVqqTQ0FAdPXo0zyxhYWFau3attm/frmHDhhV6w+eHH37Q8uXLtWnTJpUtW9Z5ydfLL7+c6/6LFi1S7dq1FRQUpFGjRmnRokUqU6ZMoeWzGUXR8irmEhISFBISorNnzxbqiw1YKSMjQ0uXLlXfvn2ZhQe3xThHacA4R2nAOC/ZTj3+uBwJCc7HYSNHyvf/b3WN/zl37pzKli2r+Ph4BQcHO+upqak6dOiQc9Fg4EL5HR/M7AEAAAAAFBiP8HDTY2b2AEXPpWZPRkaGatWqpX379hVWHgAAAABACUazB7CeS80eLy8vpbK4FgAAAAAgDx4XLY2RFRtrURKg9HL5Mq6oqCi9+uqryszMLIw8AAAAAIASjJk9gPVcvs/X1q1btW7dOq1evVqNGzdWQECAafsnn3xSYOEAAAAAACVLjpk9MTEWJSnZuJcScpPfceFysyc0NFR9+vRxORAAAAAAwP1dPLPHER8vIzNTNk+Xf/0slTw8PCT9c0tyPz8/i9OguElOTpaky96p0OWvtnnz5l1ZIgAAAACA27t4Zo8MQ1mxsfIsV86aQCWMp6en/P39debMGXl5eclu5yba+GdGT3Jysk6fPq3Q0FBnUzAvV9RazczM1DfffKODBw/q3nvvVVBQkI4fP67g4GAFBgZeUXAAAAAAQMln9/eXzddXxgU398k6d45mTz7ZbDZVqFBBhw4d0pEjR6yOg2ImNDRUkZGRl93P5WbPkSNH1K1bNx09elRpaWm65ZZbFBQUpFdffVVpaWmaNWvWFQUGAAAAALgHjzJllPn3387HLNLsGm9vb9WpU0fp6elWR0Ex4uXlddkZPdlcbvaMGTNGrVq10s6dO1Xmgul5vXv31tChQ109HAAAAADAzXiEh5ubPSzS7DK73S5fX1+rY6CEcrnZ89133+mHH36Qt7e3qV69enX9fcEXMwAAAACgdOL264C1XF7pyeFwKCsrK0f92LFjCgoKKpBQAAAAAICSi9uvA9ZyudnTpUsXRUdHOx/bbDYlJSXp+eefV/fu3QsyGwAAAACgBMoxs4dmD1CkXL6M64033lDXrl3VoEEDpaam6t5779WBAwdUtmxZffjhh4WREQAAAABQguSY2RMbK8PhkI3biANFwuVmT+XKlbVz50599NFH2rVrl5KSkjRkyBANGDBAfn5+hZERAAAAAFCCXNzsUWamHAkJ8ggNtSQPUNq43OyRJE9PT913330FnQUAAAAA4AbsQUGSp6eUmemsZZ07R7MHKCJX1OzZv3+/pk2bpn379kmS6tevr5EjR6pevXoFGg4AAAAAUPLY7HZ5hIcr6/RpZy0rJkaqVcvCVEDp4fIFk8uWLVOjRo20bds2NW3aVE2bNtX27dvVuHFjLVu2rDAyAgAAAABKGI+wMNNjbr8OFB2XZ/Y88cQTGj9+vF588UVT/fnnn9cTTzyhPn36FFg4AAAAAEDJxO3XAeu4PLPnxIkTeuCBB3LU77vvPp04caJAQgEAAAAASjZuvw5Yx+VmT4cOHfTdd9/lqH///fe68cYbCyQUAAAAAKBkyzGzh8u4gCLj8mVcPXv21JNPPqlt27apTZs2kqQtW7ZoyZIlmjBhgj7//HPTvgAAAACA0ie3y7gMw5DNZrMoEVB6uNzseeSRRyRJb731lt56661ct0mSzWZTVlbWVcYDAAAAAJREF1/GZaSmykhOli0gwKJEQOnhcrPH4XAURg4AAAAAgBvxCAuTbDbJMJy1rJgY2Wn2AIXO5TV7AAAAAAC4HJunp+whIaYa6/YARYNmDwAAAACgUHD7dcAaNHsAAAAAAIUix+3XmdkDFAmaPQAAAACAQpGj2cPMHqBI0OwBAAAAABQKLuMCrOFys2f79u369ddfnY+XL1+uXr166emnn1Z6enqBhgMAAAAAlFxcxgVYw+Vmz/Dhw/X7779Lkv7880/dc8898vf315IlS/TEE08UeEAAAAAAQMl08cweR2KiDCYJAIXO5WbP77//rmbNmkmSlixZoptuukmLFi3S/PnztWzZsoLOBwAAAAAooS6e2SNxKRdQFFxu9hiGIYfDIUlau3atunfvLkmqUqWKzp49W7DpAAAAAAAllt3XV7aAAFONZg9Q+Fxu9rRq1UovvfSSFi5cqG+//VY9evSQJB06dEjly5cv8IAAAAAAgJKLdXuAoudysyc6Olrbt2/XyJEj9cwzz6h27dqSpKVLl+r6668v8IAAAAAAgJIrxx25aPYAhc7T1Q9o0qSJ6W5c2V5//XV5eHgUSCgAAAAAgHvIMbOHy7iAQufyzB5JiouL0zvvvKPx48cr5v+/UPfu3avTp08XaDgAAAAAQMnGZVxA0XN5Zs+uXbvUqVMnhYaG6vDhwxo6dKjCw8P1ySef6OjRo3rvvfcKIycAAAAAoATKcRlXbKxFSYDSw+WZPY8++qgGDRqkAwcOyNfX11nv3r27Nm7cWKDhAAAAAAAlW46ZPbGxMrKyLEoDlA4uN3u2bt2q4cOH56hXqlRJJ0+eLJBQAAAAAAD3cPHMHjkccsTHWxMGKCVcbvb4+PgoISEhR/33339XuXLlCiQUAAAAAMA92AMDJS8vU411e4DC5XKzp2fPnnrxxReVkZEhSbLZbDp69KiefPJJ9enTp8ADAgAAAABKLpvNlmN2TybNHqBQudzseeONN5SUlKSIiAilpKSoffv2ql27toKCgvTf//63MDICAAAAAEowT26/DhQpl+/GFRISojVr1uj777/Xrl27lJSUpBYtWqhz586FkQ8AAAAAUMLluCMXM3uAQuVysydbu3bt1K5du4LMAgAAAABwQ3Zm9gBFKl/NnqlTp2rYsGHy9fXV1KlTL7nv6NGjCyQYAAAAAMA95Lj9OjN7gEKVr2bP5MmTNWDAAPn6+mry5Ml57mez2Wj2AAAAAABMPC++jCsmRoZhyGazWZQIcG/5avYcOnQo1/8HAAAAAOByLl6zRxkZciQlySMoyJpAgJtz6W5cGRkZqlWrlvbt21dYeQAAAAAAbsYeEiLZzb9+cikXUHhcavZ4eXkpNTW1sLIAAAAAANyQzcNDHmFhphqLNAOFx6VmjyRFRUXp1VdfVWZm5lU/+cSJE9W6dWsFBQUpIiJCvXr10v79+3Pst3nzZnXs2FEBAQEKDg7WTTfdpJSUFOf2mJgYDRgwQMHBwQoNDdWQIUOUlJR01fkAAAAAAAWDRZqBouPyrde3bt2qdevWafXq1WrcuLECAgJM2z/55JN8H+vbb79VVFSUWrdurczMTD399NPq0qWL9u7d6zzu5s2b1a1bN40fP17Tpk2Tp6endu7cKfsFUwAHDBigEydOaM2aNcrIyNCgQYM0bNgwLVq0yNXTAwAAAAAUAo8yZaQDB5yPmdkDFB6Xmz2hoaHq06dPgTz5qlWrTI/nz5+viIgIbdu2TTfddJMkady4cRo9erSeeuop535169Z1/v++ffu0atUqbd26Va1atZIkTZs2Td27d9ekSZNUsWLFAskKAAAAALhyzOwBio7LzZ558+YVRg5JUnx8vCQp/P/fBE6fPq0ff/xRAwYM0PXXX6+DBw+qXr16+u9//6t27dpJ+mfmT2hoqLPRI0mdO3eW3W7Xjz/+qN69e+d4nrS0NKWlpTkfJyQkSPpnAeqMjIxCOz/AStljmzEOd8Y4R2nAOEdpwDh3UyEhpoeZ586V6s9xaT53FD6Xmz2SlJmZqW+++UYHDx7Uvffeq6CgIB0/flzBwcEKDAy8oiAOh0Njx47VDTfcoEaNGkmS/vzzT0nSCy+8oEmTJqlZs2Z677331KlTJ+3evVt16tTRyZMnFRERYT4pT0+Fh4fr5MmTuT7XxIkTNWHChBz1FStWyN/f/4ryAyXF8uXLrY4AFDrGOUoDxjlKA8a5ewmLiVHTCx6nnjyppUuXWpbHasnJyVZHgBtzudlz5MgRdevWTUePHlVaWppuueUWBQUF6dVXX1VaWppmzZp1RUGioqK0e/duff/9986aw+GQJA0fPlyDBg2SJDVv3lzr1q3T3LlzNXHixCt6rvHjx+vRRx91Pk5ISFCVKlXUo0cPlSlT5oqOCRR3GRkZWr58ue644w55eXlZHQcoFIxzlAaMc5QGjHP3lHnqlOJ27XI+9srM1J233Sa7r6+FqaxzjsvYUIhcbvaMGTNGrVq10s6dO02Nkd69e2vo0KFXFGLkyJH68ssvtXHjRlWuXNlZr1ChgiSpQYMGpv3r16+vo0ePSpIiIyN1+vRp0/bMzEzFxMQoMjIy1+fz8fGRj49PjrqXlxffTOD2GOcoDRjnKA0Y5ygNGOfuxfOiKzIkyZ6YKK+gIAvSWI+xjcLk8q3Xv/vuOz377LPy9vY21atXr66///7bpWMZhqGRI0fq008/1fr161WjRo0cx6xYsWKO27H//vvvqlatmiSpbdu2iouL07Zt25zb169fL4fDoeuuu86lPAAAAACAwmHz9pb9osYOizQDhcPlmT0Oh0NZWVk56seOHVOQix3ZqKgoLVq0SMuXL1dQUJBzjZ2QkBD5+fnJZrPp8ccf1/PPP6+mTZuqWbNmWrBggX777TfntZ3169dXt27dNHToUM2aNUsZGRkaOXKk7rnnHu7EBQAAAADFiEeZMnIkJjofc/t1oHC4PLOnS5cuio6Odj622WxKSkrS888/r+7du7t0rJkzZyo+Pl4dOnRQhQoVnP8WL17s3Gfs2LEaP368xo0bp6ZNm2rdunVas2aNatWq5dzngw8+UL169dSpUyd1795d7dq10+zZs109NQAAAABAIeL260DRcHlmzxtvvKGuXbuqQYMGSk1N1b333qsDBw6obNmy+vDDD106lmEY+drvqaee0lNPPZXn9vDwcC1atMil5wYAAAAAFC2Pi26Iw8weoHC43OypXLmydu7cqY8++ki7du1SUlKShgwZogEDBsjPz68wMgIAAAAA3AAze4Ci4XKzR5I8PT113333FXQWAAAAAIAby9HsYWYPUChcbva89957l9z+wAMPXHEYAAAAAID7uvgyLkd8vIzMTNk8r2geAoA8uPwVNWbMGNPjjIwMJScny9vbW/7+/jR7AAAAAAC5unhmjwxDWbGx8ixXzppAgJty+W5csbGxpn9JSUnav3+/2rVr5/ICzQAAAACA0sPm7y+br6+pxqVcQMFzudmTmzp16uiVV17JMesHAAAAAIBsNpuNRZqBIlAgzR7pn0Wbjx8/XlCHAwAAAAC4oRy3X6fZAxQ4l9fs+fzzz02PDcPQiRMnNH36dN1www0FFgwAAAAA4H64IxdQ+Fxu9vTq1cv02GazqVy5curYsaPeeOONgsoFAAAAAHBDzOwBCp/LzR6Hw1EYOQAAAAAApQAze4DCd8Vr9pw9e1YJCQkFmQUAAAAA4OZyNHtiY2UwqQAoUC41e+Li4hQVFaWyZcuqfPnyCgsLU2RkpMaPH6/k5OTCyggAAAAAcBMXX8alzEw5EhOtCQO4qXxfxhUTE6O2bdvq77//1oABA1S/fn1J0t69ezVt2jStWbNG33//vXbt2qUtW7Zo9OjRhRYaAAAAAFAy2YODJU9PKTPTWcs6d04eISEWpgLcS76bPS+++KK8vb118OBBlS9fPse2Ll266P7779fq1as1derUAg8KAAAAACj5bHa7PMLClHXmjLOWde6cVLOmhakA95Lvy7g+++wzTZo0KUejR5IiIyP12muvadmyZXr00Uc1cODAAg0JAAAAAHAfLNIMFK58N3tOnDihhg0b5rm9UaNGstvtev755wskGAAAAADAPXH7daBw5bvZU7ZsWR0+fDjP7YcOHVJERERBZAIAAAAAuDFm9gCFK9/Nnq5du+qZZ55Renp6jm1paWn697//rW7duhVoOAAAAACA+2FmD1C4XFqguVWrVqpTp46ioqJUr149GYahffv26a233lJaWpree++9wswKAAAAAHADuc3sMQxDNpvNokSAe8l3s6dy5cravHmzHnnkEY0fP16GYUiSbDabbrnlFk2fPl1Vq1YttKAAAAAAAPdw8cweIzVVRnKybAEBFiUC3Eu+mz2SVKNGDX311VeKjY3VgQMHJEm1a9dW+EVdWQAAAAAA8uIRFibZbNL/TyKQ/pndY6fZAxQIl5o92cLCwnTttdcWdBYAAAAAQClg8/SUPSREjrg4Zy0rJkZeVapYFwpwI/leoBkAAAAAgIKSY90eFmkGCgzNHgAAAABAkctxRy5uvw4UGJo9AAAAAIAix8weoPDQ7AEAAAAAFDlm9gCFh2YPAAAAAKDIMbMHKDw0e4D/a+/eg+ys6/uBv89uNptENxcumwRYMUbU8uMqZTRSbi2ElkjJjNYrgY5aqt10ShAMYiRcLKEUq05FvFFCnaEwdYTYwESCATQCWgMZucSoiTTKsCAISdxAstk9vz8YVvYCySa7+5zL6zWzMzzPec45n2fmY+K+8/l+HwAAYNT1D3t6tm5NeceOgqqB2iLsAQAAYNT1X8aVWMoFw0XYAwAAwKhrGDcupQkT+pwT9sDwEPYAAABQCJs0w8gQ9gAAAFAImzTDyBD2AAAAUAiTPTAyhD0AAAAUwmQPjAxhDwAAAIUw2QMjQ9gDAABAIQZM9jz3XMrd3QVVA7VD2AMAAEAh+oc96elJz+bNxRQDNUTYAwAAQCEaWlqSpqY+5+zbA3tP2AMAAEAhSqXSwKVc9u2BvSbsAQAAoDADNmk22QN7TdgDAABAYUz2wPAT9gAAAFAYkz0w/IQ9AAAAFKb/ZM9Okz2w14Q9AAAAFGbMIJM95XK5oGqgNgh7AAAAKExDv8medHWl5w9/KKYYqBHCHgAAAArTOHly0tD3V1P79sDeEfYAAABQmFJj40uBzyt4IhfsHWEPAAAAhfL4dRhewh4AAAAK5fHrMLyEPQAAABTKZA8ML2EPAAAAhTLZA8NL2AMAAEChBoQ9Jntgrwh7AAAAKFT/ZVzlzs70vPhiQdVA9RP2AAAAUKj+YU9iugf2hrAHAACAQpXGjk1DS0ufc/btgT0n7AEAAKBwnsgFw0fYAwAAQOFs0gzDR9gDAABA4QZM9ljGBXtM2AMAAEDhTPbA8BH2AAAAUDiTPTB8hD0AAAAUrv9kT8/mzSnv3FlQNVDdhD0AAAAUrv9kT8rldD/3XDHFQJUT9gAAAFC40oQJKTU39zln3x7YM8IeAAAAClcqlQZu0mzfHtgjwh4AAAAqwoBNmk32wB4R9gAAAFARPH4dhoewBwAAgIrg8eswPIQ9AAAAVASTPTA8hD0AAABUhMH27Cn39BRUDVQvYQ8AAAAVof9kT3buTM/WrcUUA1VM2AMAAEBFaJg4MWls7HPOvj0wdMIeAAAAKkKpoSGNU6b0OSfsgaET9gAAAFAxbNIMe0/YAwAAQMUYbJNmYGiEPQAAAFSMAZM9lnHBkAl7AAAAqBgme2DvCXsAAACoGCZ7YO8JewAAAKgY/cOe8osvpmfbtoKqgeok7AEAAKBiNE6enJRKfc6Z7oGhEfYAAABQMUpNTWmYOLHPOfv2wNAUGvYsWbIkxx57bFpaWtLa2pq5c+dm/fr1fa456aSTUiqV+vx8/OMf73PNpk2bMmfOnEyYMCGtra258MILs3PnztG8FQAAAIbJgE2aTfbAkBQa9tx7771pb2/PAw88kJUrV6arqyuzZ89OZ2dnn+v+7u/+Lk8++WTvz9VXX937Wnd3d+bMmZMdO3bkvvvuy4033pilS5fmkksuGe3bAQAAYBgM2KTZZA8MyZgiv3zFihV9jpcuXZrW1tasWbMmJ5xwQu/5CRMmZNq0aYN+xp133pnHHnssd911V6ZOnZqjjjoqV1xxRRYuXJhLL700Y8eOHfCe7du3Z/v27b3HW7ZsSZJ0dXWlq6trOG4NKs7Lva3HqWX6nHqgz6kH+pzS5Ml9jrueeabm+qHW7ofKUiqXy+Wii3jZr371qxxyyCF5+OGHc9hhhyV5aRnXo48+mnK5nGnTpuWMM87IZz/72UyYMCFJcskll+S73/1u1q5d2/s5v/71r/OmN70pDz74YI4++ugB33PppZfmsssuG3D+m9/8Zu/nAgAAUIwDnngib/nlL3uPt7S05MFjjimwouG3bdu2fOxjH8vmzZszsd8eRbC3Cp3seaWenp6cd955Oe6443qDniT50Ic+lIMPPjgHHHBAfvazn2XhwoVZv359vvOd7yRJOjo6MnXq1D6f9fJxR0fHoN/16U9/Oueff37v8ZYtW9LW1pY5c+Zk337jglArurq6smzZspx55plpamoquhwYEfqceqDPqQf6nB2PPJItrwh7JiV573vfW1xBI+BZ+xAxgiom7Glvb88jjzyS1atX9zl/7rnn9v734YcfnunTp+cv/uIvsmHDhsycOXOPvqu5uTnNzc0Dzjc1NfnLhJqnz6kH+px6oM+pB/q8jrW29jksb92aMXnpSV21Qm8zkiri0evz58/P8uXLc/fdd+eggw56zWvf8Y53JHlpyVeSTJs2LU899VSfa14+frV9fgAAAKhc/TdoTmzSDENRaNhTLpczf/783HrrrVm1alVmzJixy/e8vDfP9OnTkySzZs3Kww8/nKeffrr3mpUrV2bixIk59NBDR6RuAAAARk7DuHEp9dtP1ePXYfcVuoyrvb09N910U5YtW5aWlpbePXYmTZqU8ePHZ8OGDbnpppty+umnZ999983PfvazLFiwICeccEKOOOKIJMns2bNz6KGHZt68ebn66qvT0dGRRYsWpb29fdClWgAAAFS+xn32yc5t23qPTfbA7it0sue6667L5s2bc9JJJ2X69Om9P7fcckuSZOzYsbnrrrsye/bsvO1tb8snP/nJvOc978n//M//9H5GY2Njli9fnsbGxsyaNStnnXVWzj777Fx++eVF3RYAAAB7qf9SLpM9sPsKnezZ1VPf29racu+99+7ycw4++ODccccdw1UWAAAABWvcZ58+xyZ7YPdVxAbNAAAA8EoDJnuEPbDbhD0AAABUnAGTPZZxwW4T9gAAAFBxBkz2PPdcyj09BVUD1UXYAwAAQMXpP9mTnp70PP98IbVAtRH2AAAAUHEaWlqSpqY+5+zbA7tH2AMAAEDFKZVK9u2BPSTsAQAAoCIJe2DPCHsAAACoSB6/DntG2AMAAEBFGjDZI+yB3SLsAQAAoCKZ7IE9I+wBAACgIg22Z0+5XC6oGqgewh4AAAAqUv/JnvKOHSl3dhZUDVQPYQ8AAAAVqXHy5KSh76+tnsgFuybsAQAAoCKVGhtfCnxeYaewB3ZJ2AMAAEDF8kQuGDphDwAAABWrYZBNmoHXJuwBAACgYnn8OgydsAcAAICKNcYyLhgyYQ8AAAAVa8Bkj2VcsEvCHgAAACpW/7Cn3NmZnu3bC6oGqoOwBwAAgIrV/2lcieke2BVhDwAAABWrNHZsGlpa+pyzbw+8NmEPAAAAFa3/dI/JHnhtwh4AAAAq2oCwx2QPvCZhDwAAABXNZA8MjbAHAACAijbg8esme+A1CXsAAACoaMIeGBphDwAAABWt/zKunuefT7m7u6BqoPIJewAAAKho/Sd7Ui6n+7nniikGqoCwBwAAgIpWmjAhpebmPuds0gyvTtgDAABARSuVSvbtgSEQ9gAAAFDxPH4ddp+wBwAAgIo3IOwx2QOvStgDAABAxRuwjMtkD7wqYQ8AAAAVz2QP7D5hDwAAABVvsA2ayz09BVUDlU3YAwAAQMXrP9mTnTvTs3VrMcVAhRP2AAAAUPEaJk1KGhv7nLOUCwYn7AEAAKDilRoa0jhlSp9zNmmGwQl7AAAAqAqeyAW7R9gDAABAVfBELtg9wh4AAACqwoCwx2QPDErYAwAAQFUY7PHrwEDCHgAAAKqCZVywe4Q9AAAAVIX+kz3lF15IzwsvFFQNVC5hDwAAAFWh/6PXE/v2wGCEPQAAAFSFUlNTGiZN6nNO2AMDCXsAAACoGvbtgV0T9gAAAFA1BjyRy2QPDCDsAQAAoGqY7IFdE/YAAABQNQaEPSZ7YABhDwAAAFVjwDIukz0wgLAHAACAqtE/7OnZsiXlrq6CqoHKJOwBAACgavRfxpWY7oH+hD0AAABUjYbx41OaMKHPOWEP9CXsAQAAoKrYpBlem7AHAACAqmKTZnhtwh4AAACqiskeeG3CHgAAAKrKgLDHZA/0IewBAACgqgxYxmWyB/oQ9gAAAFBVBkz2PPdcyj09BVUDlUfYAwAAQFXpP9mTnp70bN5cTDFQgYQ9AAAAVJWGlpakqanPOUu54I+EPQAAAFSVUqlkk2Z4DcIeAAAAqo7Hr8OrE/YAAABQdQY8kctkD/QS9gAAAFB1TPbAqxP2AAAAUHXs2QOvTtgDAABA1RmwjOvZZ1MulwuqBiqLsAcAAICq03+yp7xjR8qdnQVVA5VF2AMAAEDVaZwyJSmV+pyzlAteIuwBAACg6pQaG9MweXKfczZphpcIewAAAKhKg+3bAwh7AAAAqFKeyAWDE/YAAABQlfpP9uw02QNJhD0AAABUqTEme2BQwh4AAACqUkP/sMdkDyQR9gAAAFCl+i/jKnd2pmf79oKqgcoh7AEAAKAq9d+gObGUC5KCw54lS5bk2GOPTUtLS1pbWzN37tysX79+0GvL5XL+6q/+KqVSKbfddluf1zZt2pQ5c+ZkwoQJaW1tzYUXXpidO3eOwh0AAABQlIbm5jS8/vV9zlnKBQWHPffee2/a29vzwAMPZOXKlenq6srs2bPT2dk54NovfvGLKZVKA853d3dnzpw52bFjR+67777ceOONWbp0aS655JLRuAUAAAAK5PHrMNCYIr98xYoVfY6XLl2a1tbWrFmzJieccELv+bVr1+bzn/98fvrTn2b69Ol93nPnnXfmsccey1133ZWpU6fmqKOOyhVXXJGFCxfm0ksvzdixYwd87/bt27P9Fes4t2zZkiTp6upKV1fXcN4iVIyXe1uPU8v0OfVAn1MP9DlDUZoyJdm0qfe463e/q4reqYYaqV6Fhj39bd68OUmyzyuS2W3btuVDH/pQrr322kybNm3Ae+6///4cfvjhmTp1au+50047LZ/4xCfy6KOP5uijjx7wniVLluSyyy4bcP7222/PhAkThuNWoGItW7as6BJgxOlz6oE+px7oc3bHzGefTdsrjh9fuzbrduworJ7dtW3btqJLoIZVTNjT09OT8847L8cdd1wOO+yw3vMLFizIu971rpx55pmDvq+jo6NP0JOk97ijo2PQ93z605/O+eef33u8ZcuWtLW1Zc6cOdm3327uUCu6urqybNmynHnmmWlqaiq6HBgR+px6oM+pB/qcoXjh7rvT+dvf9h4f+PrX5/+9970FVrR7nrW3ECOoYsKe9vb2PPLII1m9enXvue9+97tZtWpVHnrooWH9rubm5jQ3Nw8439TU5C8Tap4+px7oc+qBPqce6HN2R3dra16562vPc89VRd9UQ41Ur4p49Pr8+fOzfPny3H333TnooIN6z69atSobNmzI5MmTM2bMmIwZ81I29Z73vCcnnXRSkmTatGl56qmn+nzey8eDLfsCAACgdvTfoLnn+edT7u4uqBqoDIWGPeVyOfPnz8+tt96aVatWZcaMGX1ev+iii/Kzn/0sa9eu7f1Jki984Qu54YYbkiSzZs3Kww8/nKeffrr3fStXrszEiRNz6KGHjtq9AAAAMPr6hz0pl9P93HPFFAMVotBlXO3t7bnpppuybNmytLS09O6xM2nSpIwfPz7Tpk0bdDrnDW94Q28wNHv27Bx66KGZN29err766nR0dGTRokVpb28fdKkWAAAAtaP0utel1Nyc8iueuNz9+99nzH77FVgVFKvQyZ7rrrsumzdvzkknnZTp06f3/txyyy27/RmNjY1Zvnx5GhsbM2vWrJx11lk5++yzc/nll49g5QAAAFSCUqk0YLqn2+bH1LlCJ3vK5fKwvOfggw/OHXfcMRwlAQAAUGUa9903O598sve4+/e/L7AaKF5FbNAMAAAAe8pkD/Ql7AEAAKCqNe67b59jkz3UO2EPAAAAVc1kD/Ql7AEAAKCqDQh7fv/7lHt6CqoGiifsAQAAoKr1X8aVnTvTs3VrMcVABRD2AAAAUNUaJk1KGvr+emvfHuqZsAcAAICqVmpoGHQpF9QrYQ8AAABVzybN8EfCHgAAAKregMevC3uoY8IeAAAAqp5lXPBHwh4AAACq3oDJHmEPdUzYAwAAQNWzZw/8kbAHAACAqtc/7Cm/8EJ6XnihoGqgWMIeAAAAql7/sCcx3UP9EvYAAABQ9UpNTWmYOLHPOWEP9UrYAwAAQE2wSTO8RNgDAABATfD4dXiJsAcAAICaMGCyxzIu6pSwBwAAgJpgsgdeIuwBAACgJpjsgZcIewAAAKgJ/cOeni1bUu7qKqgaKI6wBwAAgJrQfxlXYikX9UnYAwAAQE1oGD8+pfHj+5wT9lCPhD0AAADUDPv2gLAHAACAGuKJXCDsAQAAoIYIe0DYAwAAQA2xjAuEPQAAANSQAWGPyR7qkLAHAACAmjHYMq5yT09B1UAxhD0AAADUjP6TPenpSc/mzcUUAwUR9gAAAFAzGl7/+mTMmD7n7NtDvRH2AAAAUDNKDQ2eyEXdE/YAAABQU2zSTL0T9gAAAFBTBkz2WMZFnRH2AAAAUFNM9lDvhD0AAADUFJM91DthDwAAADVlsMmecrlcUDUw+oQ9AAAA1JT+kz3l7dtT7uwsqBoYfcIeAAAAakrjlClJqdTnnH17qCfCHgAAAGpKqbExDZMn9zln3x7qibAHAACAmmOTZuqZsAcAAICa4/Hr1DNhDwAAADVnwGSPsIc6IuwBAACg5gyY7LGMizoi7AEAAKDmmOyhngl7AAAAqDn9J3t6/vCH9GzfXlA1MLqEPQAAANSc/pM9ieke6oewBwAAgJrT0Nyc0ute1+ecfXuoF8IeAAAAapLHr1OvhD0AAADUpAGbNJvsoU4IewAAAKhJY0z2UKeEPQAAANQkj1+nXgl7AAAAqEkD9uyxjIs6IewBAACgJvUPe3qefz7l7u6CqoHRI+wBAACgJvVfxpVyOd3PPVdMMTCKhD0AAADUpNLrXpdSc3Ofc/btoR4IewAAAKhJpVLJ49epS8IeAAAAapYnclGPhD0AAADULE/koh4JewAAAKhZJnuoR8IeAAAAataAyR5hD3VA2AMAAEDNGmyyp1wuF1QNjA5hDwAAADWr/2RPurrSs3VrMcXAKBH2AAAAULMaJk1KSqU+556+8MJ0C3yoYcIeAAAAalapoSENU6YUXQaMqjFFFwAAAAAj4eXpncaWlvT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a/docs/pages/performance/fashion-mnist/results.md b/docs/pages/performance/fashion-mnist/results.md index 70f9b14a..4343a027 100644 --- a/docs/pages/performance/fashion-mnist/results.md +++ b/docs/pages/performance/fashion-mnist/results.md @@ -1,5 +1,5 @@ |Model|Parameters|Recall|Queries per Second| |---|---|---|---| -|eknn-l2lsh|L=175 k=7 w=3900 candidates=100 probes=0|0.607|304.462| -|eknn-l2lsh|L=175 k=7 w=3900 candidates=500 probes=0|0.921|269.909| -|eknn-l2lsh|L=175 k=7 w=3900 candidates=1000 probes=0|0.962|239.598| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=100 probes=0|0.607|301.546| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=500 probes=0|0.921|266.820| +|eknn-l2lsh|L=175 k=7 w=3900 candidates=1000 probes=0|0.962|231.592|