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<!DOCTYPE html>
<html lang="en">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>News | University of Sam</title>
<link rel="icon" type="image/x-icon" href="./assets/img/uos-icon.png" />
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/normalize/7.0.0/normalize.min.css"
/>
<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.2/css/all.css"
integrity="sha256-46qynGAkLSFpVbEBog43gvNhfrOj+BmwXdxFgVK/Kvc="
crossorigin="anonymous"
/>
<!--BOOTSTRAP-->
<link
href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"
rel="stylesheet"
/>
<!-- Fonts -->
<link
href="https://fonts.googleapis.com/css?family=Source+Code+Pro:400,900|Source+Sans+Pro:300,900&display=swap"
rel="stylesheet"
/>
<!-- Icons-->
<link
href="https://unpkg.com/[email protected]/css/boxicons.min.css"
rel="stylesheet"
/>
<link rel="stylesheet" href="./assets/css/style.css" />
</head>
<body>
<!-- Header -->
<header>
<nav class="nav_header" id="nav_head">
<a href="javascript:void(0);" class="icon" onclick="myFunction()">
<i class="fa fa-bars"></i>
</a>
<a href="./php/staff.php" class="nav__hlink">Staff</a>
<a href="international.html" class="nav__hlink">International</a>
<a href="alumni.html#alumni-welcome" class="nav__hlink">Alumni</a>
<a href="news.html#news" class="nav__hlink">News</a>
<a href="admission.html" class="nav__hlink">Admission</a>
<a href="contact.html" class="nav__hlink">Contact Us</a>
</nav>
</header>
<!-- Logo Section -->
<div class="Logo__Sector">
<div class="Logo__img">
<a href="home.html">
<img src="./assets/img/Logo.png" alt="" />
</a>
</div>
<button class="openBtn" onclick="openSearch()">
<i class="fas fa-search fa-2x" style="color: #000000"></i>
</button>
</div>
<div id="Searchbox" class="Searchbox">
<span class="closebtn" onclick="closeSearch()" title="Close Overlay"
>x</span
>
<div class="Searchbox-content">
<h2 class="mb-3 p-3 text-light">Search Everything Here</h2>
<form id="sub">
<input
type="text"
placeholder="Search.."
name="search"
id="searchInput"
/>
<div id="searchList"></div>
</form>
</div>
</div>
<!-- Navigation Bars -->
<nav class="nav">
<ul class="nav__list">
<li class="nav__dropdownitem">
<a href="study.html#intro" class="nav__link">Study</a>
<table class="dropdown__content">
<tr class="blank">
<td rowspan="8"></td>
</tr>
<tr class="overview">
<td colspan="2">
<a href="study.html#intro" class="nav__ddlink">Overview</a>
</td>
</tr>
<tr class="table_header">
<td>
<a href="find_course.html#intro" class="header__link"
>FIND A COURSE</a
>
</td>
<td>
<a href="study.html#info" class="header__link"
>INFORMATION FOR</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="business.html#courses" class="nav__ddlink">Business</a>
</td>
<td>
<a href="undergraduate.html#intro" class="nav__ddlink"
>Undergraduate Students</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="engineering.html#courses" class="nav__ddlink"
>Engineering</a
>
</td>
<td>
<a href="postgraduate.html#intro" class="nav__ddlink"
>Postgraduate Students</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="health.html#courses" class="nav__ddlink">Health</a>
</td>
<td>
<a href="international.html" class="nav__ddlink"
>International Students</a
>
</td>
</tr>
<tr class="contents">
<td>
<a
href="information_technology.html#courses"
class="nav__ddlink"
>Information Technology</a
>
</td>
<td>
<a href="entry_requirement.html" class="nav__ddlink"
>Entry Requirements</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="security.html#courses" class="nav__ddlink"
>Security, intelligence and criminology</a
>
</td>
<td>
<a href="fees.html#intro" class="nav__ddlink">Fees and costs</a>
</td>
</tr>
</table>
</li>
<li class="nav__dropdownitem">
<a href="curstudent.html#intro" class="nav__link">Current Students</a>
<table class="dropdown__content">
<tr class="blank">
<td rowspan="7"></td>
</tr>
<tr class="overview">
<td colspan="3">
<a href="curstudent.html#intro" class="nav__ddlink">Overview</a>
</td>
</tr>
<tr class="table_header">
<td>
<a href="curstudent.html#oppo" class="header__link"
>OPPORTUNITIES</a
>
</td>
<td>
<a href="curstudent.html#support" class="header__link"
>SUPPORT</a
>
</td>
<td>
<a href="curstudent.html#life" class="header__link"
>Life at UOS</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="scholarship.html#intro" class="nav__ddlink"
>Scholarships</a
>
</td>
<td>
<a href="curstudent.html#access" class="nav__ddlink"
>Accessibility service</a
>
</td>
<td>
<a href="./php/student.php" class="nav__ddlink"
>Student Portal</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="curstudent.html#global" class="nav__ddlink"
>Global opportunities</a
>
</td>
<td>
<a href="curstudent.html#academic" class="nav__ddlink"
>Academic support</a
>
</td>
<td>
<a href="gallery.html#events" class="nav__ddlink">Events</a>
</td>
</tr>
<tr class="contents">
<td>
<a href="curstudent.html#career" class="nav__ddlink"
>Career development</a
>
</td>
<td>
<a href="curstudent.html#health" class="nav__ddlink"
>Health and wellbeing</a
>
</td>
</tr>
</table>
</li>
<li class="nav__dropdownitem">
<a href="gallery.html#Exhibition" class="nav__link">Gallery</a>
<table class="dropdown__content">
<tr class="blank">
<td rowspan="6"></td>
</tr>
<tr class="overview">
<td colspan="2">
<a href="gallery.html#Exhibition" class="nav__ddlink"
>Overview</a
>
</td>
</tr>
<tr class="table_header">
<td>
<a href="gallery.html#Exhibition" class="header__link"
>Exhibitions</a
>
</td>
<td>
<a href="gallery.html#events" class="header__link">Events</a>
</td>
</tr>
<tr class="contents">
<td>
<a href="gallery.html#current" class="nav__ddlink"
>Current Exhibitions</a
>
</td>
<td>
<a href="gallery.html#sports" class="nav__ddlink">Sports</a>
</td>
</tr>
<tr class="contents">
<td>
<a href="gallery.html#future" class="nav__ddlink"
>Upcoming Exhibitions</a
>
</td>
<td>
<a href="gallery.html#activities" class="nav__ddlink"
>Activities</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="gallery.html#past" class="nav__ddlink"
>Past Exhibitions</a
>
</td>
</tr>
</table>
</li>
<li class="nav__dropdownitem">
<a href="about.html#intro" class="nav__link">About Us</a>
<table class="dropdown__content">
<tr class="blank">
<td rowspan="7"></td>
</tr>
<tr class="overview">
<td colspan="3">
<a href="about.html#intro" class="nav__ddlink">Overview</a>
</td>
</tr>
<tr class="table_header">
<td>
<a href="university.html#facilities__about" class="header__link"
>About UOS</a
>
</td>
<td>
<a href="campus.html#service" class="header__link"
>Campus Services</a
>
</td>
<td>
<a href="facilities.html#intro" class="header__link"
>Facilities</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="university.html#vision" class="nav__ddlink"
>Vision and strategy</a
>
</td>
<td>
<a href="campus.html#student" class="nav__ddlink"
>Students life and support</a
>
</td>
<td>
<a href="facilities.html#library" class="nav__ddlink"
>Library</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="campus.html#intro" class="nav__ddlink">Our campus</a>
</td>
<td>
<a href="campus.html#security" class="nav__ddlink"
>Campus security</a
>
</td>
<td>
<a href="facilities.html#cafe" class="nav__ddlink"
>Café, bar and restaurant</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="faculties.html#intro" class="nav__ddlink"
>Our faculties</a
>
</td>
<td>
<a href="campus.html#security" class="nav__ddlink"
>Children and youth</a
>
</td>
</tr>
<tr class="contents">
<td>
<a href="contact.html" class="nav__ddlink"
>Location and contacts</a
>
</td>
</tr>
</table>
</li>
</ul>
</nav>
<!--News Area-->
<div class="news__site">
<div class="card" id="banner">
<img class="card-img-top" src="./assets/img/news-banner.jpg" alt="" />
<div class="card-img-overlay">
<h2>News</h2>
</div>
</div>
<div class="container">
<nav
style="--bs-breadcrumb-divider: '>'; font-weight: 500"
aria-label="breadcrumb"
>
<ol class="breadcrumb">
<li class="breadcrumb-item"><a href="home.html">Home</a></li>
<li class="breadcrumb-item active" aria-current="page">News</li>
</ol>
</nav>
</div>
<div class="container-fluid text-center mt-3" id="news">
<h2>News</h2>
<div class="container mt-3">
<div class="row">
<div class="col-md-6">
<div class="news-grid">
<div class="news-grid-image">
<img src="./assets/img/news1.jpg" alt="" />
<div class="news-grid-box">
<h1>4</h1>
<p>Oct</p>
</div>
</div>
<div class="news-grid-txt">
<span>Robotics</span>
<h2>Finger-shaped sensor enables more dexterous robots</h2>
<ul>
<li>
<i class="fa fa-calendar" aria-hidden="true"></i> Oct 04,
2023
</li>
<li><i class="fa fa-user" aria-hidden="true"></i> Admin</li>
</ul>
<p>
Imagine grasping a heavy object, like a pipe wrench, with
one hand. You would likely grab the wrench using your entire
fingers, not just your fingertips. Sensory receptors,...
</p>
<a data-bs-toggle="modal" data-bs-target="#myModal1"
>Read More...</a
>
</div>
</div>
</div>
<div class="col-md-6">
<div class="news-grid">
<div class="news-grid-image">
<img src="./assets/img/news2.jpg" alt="" />
<div class="news-grid-box">
<h1>2</h1>
<p>Oct</p>
</div>
</div>
<div class="news-grid-txt">
<span>Computer Science</span>
<h2>
A more effective experimental design for engineering a cell
</h2>
<ul>
<li>
<i class="fa fa-calendar" aria-hidden="true"></i> Oct 02,
2023
</li>
<li><i class="fa fa-user" aria-hidden="true"></i> Admin</li>
</ul>
<p>
By focusing on causal relationships in genome regulation, a
new AI method could help scientists identify new
immunotherapy techniques or regenerative therapies.
</p>
<a data-bs-toggle="modal" data-bs-target="#myModal2"
>Read More...</a
>
</div>
</div>
</div>
</div>
<div class="row mt-3">
<div class="col-md-6">
<div class="news-grid">
<div class="news-grid-image">
<img src="./assets/img/news3.jpg" alt="" />
<div class="news-grid-box">
<h1>6</h1>
<p>Oct</p>
</div>
</div>
<div class="news-grid-txt">
<span>Robotics</span>
<h2>A 130g soft robot gripper that can lift 100kg</h2>
<ul>
<li>
<i class="fa fa-calendar" aria-hidden="true"></i> Oct 06,
2023
</li>
<li><i class="fa fa-user" aria-hidden="true"></i> Admin</li>
</ul>
<p>
Utilizing soft, flexible materials such as cloth, paper, and
silicone, soft robotic grippers act like a robot's hand to
perform functions such as safely grasping and releasing
objects,...
</p>
<a data-bs-toggle="modal" data-bs-target="#myModal3"
>Read More...</a
>
</div>
</div>
</div>
<div class="col-md-6">
<div class="news-grid">
<div class="news-grid-image">
<img src="./assets/img/news4.jpg" alt="" />
<div class="news-grid-box">
<h1>17</h1>
<p>Aug</p>
</div>
</div>
<div class="news-grid-txt">
<span>Business</span>
<h2>7 Rules to Follow for a Successful Internship</h2>
<ul>
<li>
<i class="fa fa-calendar" aria-hidden="true"></i> Aug 17,
2023
</li>
<li><i class="fa fa-user" aria-hidden="true"></i> Admin</li>
</ul>
<p>
Internships are such an important part of the college
experience, as these work opportunities not only help you
gain more experience, develop specific skills,...
</p>
<a data-bs-toggle="modal" data-bs-target="#myModal4"
>Read More...</a
>
</div>
</div>
</div>
</div>
<div class="row mt-3">
<div class="col-md-6">
<div class="news-grid">
<div class="news-grid-image">
<img src="./assets/img/news5.jpg" alt="" />
<div class="news-grid-box">
<h1>6</h1>
<p>Feb</p>
</div>
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<h2>Knee Injections for Osteoarthritis Pain Relief</h2>
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<p>
Are steroid injections the best option for osteoarthritis
knee pain relief? Studies show that knee injections may
cause long term issues.
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<p>Oct</p>
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<h2>
AI copilot enhances human precision for safer aviation
</h2>
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<i class="fa fa-calendar" aria-hidden="true"></i> Oct 03,
2023
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<p>
Designed to ensure safer skies, “Air-Guardian” blends human
intuition with machine precision, creating a more symbiotic
relationship between pilot and aircraft.
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Finger-shaped sensor enables more dexterous robots
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<p><span>OCTOBER 4, 2023</span></p>
<h3>Finger-shaped sensor enables more dexterous robots</h3>
<p>
<span>by Adam Zewe, Massachusetts Institute of Technology</span>
</p>
<img src="./assets/img/news1.jpg" alt="" />
<p>
Imagine grasping a heavy object, like a pipe wrench, with one
hand. You would likely grab the wrench using your entire fingers,
not just your fingertips. Sensory receptors in your skin, which
run along the entire length of each finger, would send information
to your brain about the tool you are grasping.
</p>
<p>
In a robotic hand, tactile sensors that use cameras to obtain
information about grasped objects are small and flat, so they are
often located in the fingertips. These robots, in turn, use only
their fingertips to grasp objects, typically with a pinching
motion. This limits the manipulation tasks they can perform.
</p>
<p>
MIT researchers have developed a camera-based touch sensor that is
long, curved, and shaped like a human finger. Their device
provides high-resolution tactile sensing over a large area. The
sensor, called the GelSight Svelte, uses two mirrors to reflect
and refract light so that one camera, located in the base of the
sensor, can see along the entire finger's length. The work has
been published on the pre-print server arXiv.
</p>
<p>
In addition, the researchers built the finger-shaped sensor with a
flexible backbone. By measuring how the backbone bends when the
finger touches an object, they can estimate the force being placed
on the sensor.
</p>
<p>
They used GelSight Svelte sensors to produce a robotic hand that
was able to grasp a heavy object like a human would, using the
entire sensing area of all three of its fingers. The hand could
also perform the same pinch grasps common to traditional robotic
grippers.
</p>
<p>
"Because our new sensor is human finger-shaped, we can use it to
do different types of grasps for different tasks, instead of using
pinch grasps for everything. There's only so much you can do with
a parallel jaw gripper. Our sensor really opens up some new
possibilities on different manipulation tasks we could do with
robots," says Alan (Jialiang) Zhao, a mechanical engineering
graduate student and lead author of a paper on GelSight Svelte.
</p>
<p>
Zhao wrote the paper with senior author Edward Adelson, the John
and Dorothy Wilson Professor of Vision Science in the Department
of Brain and Cognitive Sciences and a member of the Computer
Science and Artificial Intelligence Laboratory (CSAIL). The
research will be presented at the IEEE Conference on Intelligent
Robots and Systems.
</p>
<p>
Cameras used in tactile sensors are limited by their size, the
focal distance of their lenses, and their viewing angles.
Therefore, these tactile sensors tend to be small and flat, which
confines them to a robot's fingertips.
</p>
<p>
With a longer sensing area, one that more closely resembles a
human finger, the camera would need to sit farther from the
sensing surface to see the entire area. This is particularly
challenging due to size and shape restrictions of a robotic
gripper.
</p>
<p>
Zhao and Adelson solved this problem using two mirrors that
reflect and refract light toward a single camera located at the
base of the finger.
</p>
<p>
GelSight Svelte incorporates one flat, angled mirror that sits
across from the camera and one long, curved mirror that sits along
the back of the sensor. These mirrors redistribute light rays from
the camera in such a way that the camera can see the along the
entire finger's length.
</p>
<p>
To optimize the shape, angle, and curvature of the mirrors, the
researchers designed software to simulate reflection and
refraction of light.
</p>
<p>
"With this software, we can easily play around with where the
mirrors are located and how they are curved to get a sense of how
well the image will look after we actually make the sensor," Zhao
explains.
</p>
<p>
The mirrors, camera, and two sets of LEDs for illumination are
attached to a plastic backbone and encased in a flexible skin made
from silicone gel. The camera views the back of the skin from the
inside; based on the deformation, it can see where contact occurs
and measure the geometry of the object's contact surface.
</p>
<p>
In addition, the red and green LED arrays give a sense of how
deeply the gel is being pressed down when an object is grasped,
due to the saturation of color at different locations on the
sensor.
</p>
<p>
The researchers can use this color saturation information to
reconstruct a 3D depth image of the object being grasped.
</p>
<p>
The sensor's plastic backbone enables it to determine
proprioceptive information, such as the twisting torques applied
to the finger. The backbone bends and flexes when an object is
grasped. The researchers use machine learning to estimate how much
force is being applied to the sensor, based on these backbone
deformations.
</p>
<p>
However, combining these elements into a working sensor was no
easy task, Zhao says.
</p>
<p>
"Making sure you have the correct curvature for the mirror to
match what we have in simulation is pretty challenging. Plus, I
realized there are some kinds of superglue that inhibit the curing
of silicon. It took a lot of experiments to make a sensor that
actually works," he adds.
</p>
<p>
This gif shows a robotic hand that incorporates three,
finger-shaped GelSight Svelte sensors. The sensors, which provide
high-resolution tactile sensing over a large area, enable the hand
to perform multiple grasps, including pinch grasps that use only
the fingertips and a power grasp that uses the entire sensing area
of all three fingers. Credit: Massachusetts Institute of
Technology
</p>
<h4>Versatile grasping</h4>
<p>
Once they had perfected the design, the researchers tested the
GelSight Svelte by pressing objects, like a screw, to different
locations on the sensor to check image clarity and see how well it
could determine the shape of the object.
</p>
<p>
They also used three sensors to build a GelSight Svelte hand that
can perform multiple grasps, including a pinch grasp, lateral
pinch grasp, and a power grasp that uses the entire sensing area
of the three fingers. Most robotic hands, which are shaped like
parallel jaw drippers, can only perform pinch grasps.
</p>
<p>
A three-finger power grasp enables a robotic hand to hold a
heavier object more stably. However, pinch grasps are still useful
when an object is very small. Being able to perform both types of
grasps with one hand would give a robot more versatility, he says.
</p>
<p>
Moving forward, the researchers plan to enhance the GelSight
Svelte so the sensor is articulated and can bend at the joints,
more like a human finger.
</p>
<p>
"Optical-tactile finger sensors allow robots to use inexpensive
cameras to collect high-resolution images of surface contact, and
by observing the deformation of a flexible surface the robot
estimates the contact shape and forces applied. This work
represents an advancement on the GelSight finger design, with
improvements in full-finger coverage and the ability to
approximate bending deflection torques using image differences and
machine learning," says Monroe Kennedy III, assistant professor of
mechanical engineering at Stanford University, who was not
involved with this research.
</p>
<p>
"Improving a robot's sense of touch to approach human ability is a
necessity and perhaps the catalyst problem for developing robots
capable of working on complex, dexterous tasks."
</p>
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A more effective experimental design for engineering a cell into a
new state
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<h3>
A more effective experimental design for engineering a cell into a
new state
</h3>
<p>
By focusing on causal relationships in genome regulation, a new AI
method could help scientists identify new immunotherapy techniques
or regenerative therapies.
</p>
<p><span>Adam Zewe | MIT News</span></p>
<p><span>October 2, 2023</span></p>
<img src="./assets/img/news2.jpg" alt="" />
<p>
A strategy for cellular reprogramming involves using targeted
genetic interventions to engineer a cell into a new state. The
technique holds great promise in immunotherapy, for instance,
where researchers could reprogram a patient’s T-cells so they are
more potent cancer killers. Someday, the approach could also help
identify life-saving cancer treatments or regenerative therapies
that repair disease-ravaged organs.
</p>
<p>
But the human body has about 20,000 genes, and a genetic
perturbation could be on a combination of genes or on any of the
over 1,000 transcription factors that regulate the genes. Because
the search space is vast and genetic experiments are costly,
scientists often struggle to find the ideal perturbation for their
particular application.
</p>
<p>
Researchers from MIT and Harvard University developed a new,
computational approach that can efficiently identify optimal
genetic perturbations based on a much smaller number of
experiments than traditional methods.
</p>
<p>
Their algorithmic technique leverages the cause-and-effect
relationship between factors in a complex system, such as genome
regulation, to prioritize the best intervention in each round of
sequential experiments.
</p>
<p>
The researchers conducted a rigorous theoretical analysis to
determine that their technique did, indeed, identify optimal
interventions. With that theoretical framework in place, they
applied the algorithms to real biological data designed to mimic a
cellular reprogramming experiment. Their algorithms were the most
efficient and effective.
</p>
<p>
“Too often, large-scale experiments are designed empirically. A
careful causal framework for sequential experimentation may allow
identifying optimal interventions with fewer trials, thereby
reducing experimental costs,” says co-senior author Caroline
Uhler, a professor in the Department of Electrical Engineering and
Computer Science (EECS) who is also co-director of the Eric and
Wendy Schmidt Center at the Broad Institute of MIT and Harvard,
and a researcher at MIT’s Laboratory for Information and Decision
Systems (LIDS) and Institute for Data, Systems and Society (IDSS).
</p>
<p>
Joining Uhler on the paper, which appears today in Nature Machine
Intelligence, are lead author Jiaqi Zhang, a graduate student and
Eric and Wendy Schmidt Center Fellow; co-senior author
Themistoklis P. Sapsis, professor of mechanical and ocean
engineering at MIT and a member of IDSS; and others at Harvard and
MIT.
</p>
<h4>Active learning</h4>
<p>
When scientists try to design an effective intervention for a
complex system, like in cellular reprogramming, they often perform
experiments sequentially. Such settings are ideally suited for the
use of a machine-learning approach called active learning. Data
samples are collected and used to learn a model of the system that
incorporates the knowledge gathered so far. From this model, an
acquisition function is designed — an equation that evaluates all
potential interventions and picks the best one to test in the next
trial.
</p>
<p>
This process is repeated until an optimal intervention is
identified (or resources to fund subsequent experiments run out).
</p>
<p>
“While there are several generic acquisition functions to
sequentially design experiments, these are not effective for
problems of such complexity, leading to very slow convergence,”
Sapsis explains.
</p>
<p>
Acquisition functions typically consider correlation between
factors, such as which genes are co-expressed. But focusing only
on correlation ignores the regulatory relationships or causal
structure of the system. For instance, a genetic intervention can
only affect the expression of downstream genes, but a
correlation-based approach would not be able to distinguish
between genes that are upstream or downstream.
</p>
<p>
“You can learn some of this causal knowledge from the data and use
that to design an intervention more efficiently,” Zhang explains.
</p>
<p>
The MIT and Harvard researchers leveraged this underlying causal
structure for their technique. First, they carefully constructed
an algorithm so it can only learn models of the system that
account for causal relationships.
</p>
<p>
Then the researchers designed the acquisition function so it
automatically evaluates interventions using information on these
causal relationships. They crafted this function so it prioritizes
the most informative interventions, meaning those most likely to
lead to the optimal intervention in subsequent experiments.
</p>
<p>
“By considering causal models instead of correlation-based models,
we can already rule out certain interventions. Then, whenever you
get new data, you can learn a more accurate causal model and
thereby further shrink the space of interventions,” Uhler
explains.
</p>
<p>
This smaller search space, coupled with the acquisition function’s
special focus on the most informative interventions, is what makes
their approach so efficient.
</p>
<p>
The researchers further improved their acquisition function using
a technique known as output weighting, inspired by the study of
extreme events in complex systems. This method carefully
emphasizes interventions that are likely to be closer to the
optimal intervention.