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Messages - Abdus Sattar

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Giving robots a better feel for object manipulation
Model improves a robot’s ability to mold materials into shapes and interact with liquids and solid objects.
Rob Matheson | MIT News Office
April 16, 2019

A new learning system developed by MIT researchers improves robots’ abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch — and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi.

In robotic planning, physical simulators are models that capture how different materials respond to force. Robots are “trained” using the models, to predict the outcomes of their interactions with objects, such as pushing a solid box or poking deformable clay. But traditional learning-based simulators mainly focus on rigid objects and are unable to handle fluids or softer objects. Some more accurate physics-based simulators can handle diverse materials, but rely heavily on approximation techniques that introduce errors when robots interact with objects in the real world.

In a paper being presented at the International Conference on Learning Representations in May, the researchers describe a new model that learns to capture how small portions of different materials — “particles” — interact when they’re poked and prodded. The model directly learns from data in cases where the underlying physics of the movements are uncertain or unknown. Robots can then use the model as a guide to predict how liquids, as well as rigid and deformable materials, will react to the force of its touch. As the robot handles the objects, the model also helps to further refine the robot’s control.

In experiments, a robotic hand with two fingers, called “RiceGrip,” accurately shaped a deformable foam to a desired configuration — such as a “T” shape — that serves as a proxy for sushi rice. In short, the researchers’ model serves as a type of “intuitive physics” brain that robots can leverage to reconstruct three-dimensional objects somewhat similarly to how humans do.

“Humans have an intuitive physics model in our heads, where we can imagine how an object will behave if we push or squeeze it. Based on this intuitive model, humans can accomplish amazing manipulation tasks that are far beyond the reach of current robots,” says first author Yunzhu Li, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL). “We want to build this type of intuitive model for robots to enable them to do what humans can do.”

“When children are 5 months old, they already have different expectations for solids and liquids,” adds co-author Jiajun Wu, a CSAIL graduate student. “That’s something we know at an early age, so maybe that’s something we should try to model for robots.”

Joining Li and Wu on the paper are: Russ Tedrake, a CSAIL researcher and a professor in the Department of Electrical Engineering and Computer Science (EECS); Joshua Tenenbaum, a professor in the Department of Brain and Cognitive Sciences and a member of CSAIL and the Center for Brains, Minds, and Machines (CBMM); and Antonio Torralba, a professor in EECS and director of the MIT-IBM Watson AI Lab.

Dynamic graphs

A key innovation behind the model, called the “particle interaction network” (DPI-Nets), was creating dynamic interaction graphs, which consist of thousands of nodes and edges that can capture complex behaviors of so-called particles. In the graphs, each node represents a particle. Neighboring nodes are connected with each other using directed edges, which represent the interaction passing from one particle to the other. In the simulator, particles are hundreds of small spheres combined to make up some liquid or a deformable object.

The graphs are constructed as the basis for a machine-learning system called a graph neural network. In training, the model over time learns how particles in different materials react and reshape. It does so by implicitly calculating various properties for each particle — such as its mass and elasticity — to predict if and where the particle will move in the graph when perturbed.

The model then leverages a “propagation” technique, which instantaneously spreads a signal throughout the graph. The researchers customized the technique for each type of material — rigid, deformable, and liquid — to shoot a signal that predicts particles positions at certain incremental time steps. At each step, it moves and reconnects particles, if needed.

For example, if a solid box is pushed, perturbed particles will be moved forward. Because all particles inside the box are rigidly connected with each other, every other particle in the object moves the same calculated distance, rotation, and any other dimension. Particle connections remain intact and the box moves as a single unit. But if an area of deformable foam is indented, the effect will be different. Perturbed particles move forward a lot, surrounding particles move forward only slightly, and particles farther away won’t move at all. With liquids being sloshed around in a cup, particles may completely jump from one end of the graph to the other. The graph must learn to predict where and how much all affected particles move, which is computationally complex.

Shaping and adapting

In their paper, the researchers demonstrate the model by tasking the two-fingered RiceGrip robot with clamping target shapes out of deformable foam. The robot first uses a depth-sensing camera and object-recognition techniques to identify the foam. The researchers randomly select particles inside the perceived shape to initialize the position of the particles. Then, the model adds edges between particles and reconstructs the foam into a dynamic graph customized for deformable materials.

Because of the learned simulations, the robot already has a good idea of how each touch, given a certain amount of force, will affect each of the particles in the graph. As the robot starts indenting the foam, it iteratively matches the real-world position of the particles to the targeted position of the particles. Whenever the particles don’t align, it sends an error signal to the model. That signal tweaks the model to better match the real-world physics of the material.

Next, the researchers aim to improve the model to help robots better predict interactions with partially observable scenarios, such as knowing how a pile of boxes will move when pushed, even if only the boxes at the surface are visible and most of the other boxes are hidden.

The researchers are also exploring ways to combine the model with an end-to-end perception module by operating directly on images. This will be a joint project with Dan Yamins’s group; Yamin recently completed his postdoc at MIT and is now an assistant professor at Stanford University. “You’re dealing with these cases all the time where there’s only partial information,” Wu says. “We’re extending our model to learn the dynamics of all particles, while only seeing a small portion.”


Teaching & Research Forum / Can science writing be automated?
« on: Yesterday at 01:11:59 PM »
Can science writing be automated?
A neural network can read scientific papers and render a plain-English summary.

David L. Chandler | MIT News Office
April 17, 2019

The work of a science writer, including this one, includes reading journal papers filled with specialized technical terminology, and figuring out how to explain their contents in language that readers without a scientific background can understand.

Now, a team of scientists at MIT and elsewhere has developed a neural network, a form of artificial intelligence (AI), that can do much the same thing, at least to a limited extent: It can read scientific papers and render a plain-English summary in a sentence or two.

Even in this limited form, such a neural network could be useful for helping editors, writers, and scientists scan a large number of papers to get a preliminary sense of what they’re about. But the approach the team developed could also find applications in a variety of other areas besides language processing, including machine translation and speech recognition.

The work is described in the journal Transactions of the Association for Computational Linguistics, in a paper by Rumen Dangovski and Li Jing, both MIT graduate students; Marin Soljačić, a professor of physics at MIT; Preslav Nakov, a senior scientist at the Qatar Computing Research Institute, HBKU; and Mićo Tatalović, a former Knight Science Journalism fellow at MIT and a former editor at New Scientist magazine.

From AI for physics to natural language

The work came about as a result of an unrelated project, which involved developing new artificial intelligence approaches based on neural networks, aimed at tackling certain thorny problems in physics. However, the researchers soon realized that the same approach could be used to address other difficult computational problems, including natural language processing, in ways that might outperform existing neural network systems.

“We have been doing various kinds of work in AI for a few years now,” Soljačić says. “We use AI to help with our research, basically to do physics better. And as we got to be  more familiar with AI, we would notice that every once in a while there is an opportunity to add to the field of AI because of something that we know from physics — a certain mathematical construct or a certain law in physics. We noticed that hey, if we use that, it could actually help with this or that particular AI algorithm.”

This approach could be useful in a variety of specific kinds of tasks, he says, but not all. “We can’t say this is useful for all of AI, but there are instances where we can use an insight from physics to improve on a given AI algorithm.”

Neural networks in general are an attempt to mimic the way humans learn certain new things: The computer examines many different examples and “learns” what the key underlying patterns are. Such systems are widely used for pattern recognition, such as learning to identify objects depicted in photos.

But neural networks in general have difficulty correlating information from a long string of data, such as is required in interpreting a research paper. Various tricks have been used to improve this capability, including techniques known as long short-term memory (LSTM) and gated recurrent units (GRU), but these still fall well short of what’s needed for real natural-language processing, the researchers say.

The team came up with an alternative system, which instead of being based on the multiplication of matrices, as most conventional neural networks are, is based on vectors rotating in a multidimensional space. The key concept is something they call a rotational unit of memory (RUM).

Essentially, the system represents each word in the text by a vector in multidimensional space — a line of a certain length pointing in a particular direction. Each subsequent word swings this vector in some direction, represented in a theoretical space that can ultimately have thousands of dimensions. At the end of the process, the final vector or set of vectors is translated back into its corresponding string of words.

“RUM helps neural networks to do two things very well,” Nakov says. “It helps them to remember better, and it enables them to recall information more accurately.”

After developing the RUM system to help with certain tough physics problems such as the behavior of light in complex engineered materials, “we realized one of the places where we thought this approach could be useful would be natural language processing,” says Soljačić,  recalling a conversation with Tatalović, who noted that such a tool would be useful for his work as an editor trying to decide which papers to write about. Tatalović was at the time exploring AI in science journalism as his Knight fellowship project.

“And so we tried a few natural language processing tasks on it,” Soljačić says. “One that we tried was summarizing articles, and that seems to be working quite well.”

The proof is in the reading

As an example, they fed the same research paper through a conventional LSTM-based neural network and through their RUM-based system. The resulting summaries were dramatically different.

The LSTM system yielded this highly repetitive and fairly technical summary: “Baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat and has caused disease like blindness or severe consequences. This infection, termed “baylisascariasis,” kills mice, has endangered the allegheny woodrat.

Based on the same paper, the RUM system produced a much more readable summary, and one that did not include the needless repetition of phrases: Urban raccoons may infect people more than previously assumed. 7 percent of surveyed individuals tested positive for raccoon roundworm antibodies. Over 90 percent of raccoons in Santa Barbara play host to this parasite.

Already, the RUM-based system has been expanded so it can “read” through entire research papers, not just the abstracts, to produce a summary of their contents. The researchers have even tried using the system on their own research paper describing these findings — the paper that this news story is attempting to summarize.

Here is the new neural network’s summary: Researchers have developed a new representation process on the rotational unit of RUM, a recurrent memory that can be used to solve a broad spectrum of the neural revolution in natural language processing.

It may not be elegant prose, but it does at least hit the key points of information.

Çağlar Gülçehre, a research scientist at the British AI company Deepmind Technologies, who was not involved in this work, says this research tackles an important problem in neural networks, having to do with relating pieces of information that are widely separated in time or space. “This problem has been a very fundamental issue in AI due to the necessity to do reasoning over long time-delays in sequence-prediction tasks,” he says. “Although I do not think this paper completely solves this problem, it shows promising results on the long-term dependency tasks such as question-answering, text summarization, and associative recall.”

Gülçehre adds, “Since the experiments conducted and model proposed in this paper are released as open-source on Github, as a result many researchers will be interested in trying it on their own tasks. … To be more specific, potentially the approach proposed in this paper can have very high impact on the fields of natural language processing and reinforcement learning, where the long-term dependencies are very crucial.”

The research received support from the Army Research Office, the National Science Foundation, the MIT-SenseTime Alliance on Artificial Intelligence, and the Semiconductor Research Corporation. The team also had help from the Science Daily website, whose articles were used in training some of the AI models in this research.


Informative. Thank for Sharing

Internet of Things / Re: How Blockchains Help IoT
« on: Yesterday at 01:05:43 PM »
Informative. Thank for Sharing

Informative. Thank for Sharing

Machine Learning/ Deep Learning / Re: Transfer Learning
« on: Yesterday at 01:04:42 PM »
Informative. Thank for Sharing

Thanks for sharing

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ভালো তথ্য।

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কথাটির সাথে সহমত পোষন করছি।

Faculty Sections / In China, a link between happiness and air quality
« on: January 24, 2019, 11:25:01 AM »
In China, a link between happiness and air quality
Moods expressed on social media tend to decline when air pollution gets worse, study finds.

Helen Knight | MIT News correspondent
January 21, 2019

For many years, China has been struggling to tackle high pollution levels that are crippling its major cities. Indeed, a recent study by researchers at Chinese Hong Kong University has found that air pollution in the country causes an average of 1.1 million premature deaths each year and costs its economy $38 billion.

Now researchers at MIT have discovered that air pollution in China’s cities may be contributing to low levels of happiness amongst the country’s urban population.

In a paper published today in the journal Nature Human Behaviour, a research team led by Siqi Zheng, the Samuel Tak Lee Associate Professor in MIT’s Department of Urban Studies and Planning and Center for Real Estate, and the Faculty Director of MIT China Future City Lab, reveals that higher levels of pollution are associated with a decrease in people’s happiness levels.

The paper also includes co-first author Jianghao Wang of the Chinese Academy of Sciences, Matthew Kahn of the University of Southern California, Cong Sun of the Shanghai University of Finance and Economics, and Xiaonan Zhang of Tsinghua University in Beijing.

Despite an annual economic growth rate of 8 percent, satisfaction levels amongst China’s urban population have not risen as much as would be expected.

Alongside inadequate public services, soaring house prices, and concerns over food safety, air pollution — caused by the country’s industrialization, coal burning, and increasing use of cars — has had a significant impact on quality of life in urban areas.

Research has previously shown that air pollution is damaging to health, cognitive performance, labor productivity, and educational outcomes. But air pollution also has a broader impact on people’s social lives and behavior, according to Zheng.

To avoid high levels of air pollution, for example, people may move to cleaner cities or green buildings, buy protective equipment such as face masks and air purifiers, and spend less time outdoors.

“Pollution also has an emotional cost,” Zheng says. “People are unhappy, and that means they may make irrational decisions.”

On polluted days, people have been shown to be more likely to engage in impulsive and risky behavior that they may later regret, possibly as a result of short-term depression and anxiety, according to Zheng.

“So we wanted to explore a broader range of effects of air pollution on people’s daily lives in highly polluted Chinese cities,” she says.

To this end, the researchers used real-time data from social media to track how changing daily pollution levels impact people’s happiness in 144 Chinese cities.

In the past, happiness levels have typically been measured using questionnaires. However, such surveys provide only a single snapshot; people’s responses tend to reflect their overall feeling of well-being, rather than their happiness on particular days.

“Social media gives a real-time measure of people’s happiness levels and also provides a huge amount of data, across a lot of different cities,” Zheng says.

The researchers used information on urban levels of ultrafine particulate matter — PM 2.5 concentration — from the daily air quality readings released by China’s Ministry of Environmental Protection. Airborne particulate matter has become the primary air pollutant in Chinese cities in recent years, and PM 2.5 particles, which measure less than 2.5 microns in diameter, are particularly dangerous to people’s lungs.

To measure daily happiness levels for each city, the team applied a machine-learning algorithm to analyze the 210 million geotagged tweets from China’s largest microblogging platform, Sina Weibo.

The tweets cover a period from March to November 2014. For each tweet, the researchers applied the machine-trained sentiment analysis algorithm to measure the sentiment of the post. They then calculated the median value for that city and day, the so-called expressed happiness index, ranging from 0 to 100, with 0 indicating a very negative mood, and 100 a very positive one.

Finally, the researchers merged this index with the daily PM2.5 concentration and weather data.

They found a significantly negative correlation between pollution and happiness levels. What’s more, women were more sensitive to higher pollution levels than men, as were those on higher incomes.

When the researchers looked at the type of cities that the tweets originated from, they found that people from the very cleanest and very dirtiest cities were the most severely affected by pollution levels.

This may be because those people who are particularly concerned about their health and air quality tend to move to clean cities, while those in very dirty cities are more aware of the damage to their health from long-term exposure to pollutants, Zheng says.

Through a creative use of social media data, the authors convincingly demonstrate a strong relationship between air quality and expressed happiness, a subjective measure of well-being, says Shanjun Li, a professor of environmental economics at Cornell University, who was not involved in the research.

“The study adds to the growing scientific knowledge on the social cost of air pollution by focusing on the cost borne by the ‘silent majority’ who do not typically show up in the studies based on morbidity and mortality outcomes,” Li says.

Zheng now hopes to continue her research into the impact of pollution on people’s behavior, and to investigate how China’s politicians will respond to the increasing public demand for cleaner air.


Faculty Sections / Merging engineering and education
« on: January 24, 2019, 11:23:48 AM »
Merging engineering and education
Senior and first-generation student Nikayah Etienne aims to incorporate hands-on science in under-resourced classrooms.

Gina Vitale | MIT News correspondent
January 23, 2019

Nikayah Etienne’s mother, an immigrant from the Caribbean island of Dominica, was passionate about her daughter’s education. At her mother’s insistence, Etienne spent her Saturday mornings in the classroom for additional schooling throughout middle school. She wasn’t a fan of the extra education at the time — but looking back, she thinks it paved the way for her to become the first person in her family to earn a bachelor’s degree.

Etienne grew up in a largely Caribbean neighborhood in Brooklyn, New York, and attended one of the city’s magnet high schools, where she excelled in the STEM fields. At first, it seemed everyone was telling her to become a doctor — but her calculus teacher recognized her talent for math and science, and encouraged her to consider engineering. That’s when Etienne started looking into MIT.

After her junior year of high school, she participated in the intensive Minority Introduction to Engineering and Science (MITES) program at MIT, where she stayed in Simmons Hall and attended six weeks of classes. It was there that she took her first real engineering class — underwater robotics.

“MITES definitely solidified the fact that I should pursue engineering, especially since I’m the kind of person that likes hands-on learning instead of lecture-style learning,” she says.

Now a senior, Etienne is majoring in mechanical engineering with a concentration in education. Her aim is to identify ways to merge the two.

“Right now, I’m trying to focus on learning about different equitable teaching practices and  different education technology platforms, in an effort to see the overlap between engineering and education and how it can be improved in underresourced communities,” she says.

Researching teaching

During Etienne’s first research project at MIT, she was on a team of four in the Teaching Systems Lab analyzing online learning platforms. Basically, a group of students would join an online forum and participate in discussions. Each of these students had identified their political affinity — say, Democrat or Republican — to the researchers, but not to each other. Etienne and her team then analyzed how those students interacted.

This past January, Etienne shifted her research to be more hands-on. Through a grant from the Priscilla King Gray Public Service Center, she travelled to Dominica, her mother’s original home, to see how Hurricane Maria had impacted education there.

She spent six weeks in a school helping with a class of third graders, while observing the teaching style of the instructors, the learning style of the students, and other dynamics of the classroom. The second and third grade classes shared a UNICEF tent and were separated with a divider. Books had been lost in the hurricane, and donations had not fully restored the inventory. The instructor taught on a very small whiteboard, and there was no digital technology to speak of. Etienne noticed there weren’t a lot of hands-on learning opportunities for the students.

“That experience made me realize that I do want to introduce engineering or STEM in general to underrepresented communities, because I really saw the challenges that occurred from not having resources in schools,” she says.

Currently, Etienne has returned to the Teaching Systems Lab to work on its equity team. On one project, she assists in developing educational interfaces that train community members, law enforcement and criminal justice officials, and educators to recognize their biases and to better contextualize social media posts by young people of color experiencing violence. In her second project, she focuses on designing multimedia “practice spaces,” or immersive simulations, for teachers in training to develop equitable teaching approaches and mindsets.

Service in the school and the city

Community service plays a large role in Etienne’s life outside of her studies. In the fall of her first year, she became a counselor for Camp Kesem, a summer camp for kids whose parents are affected by cancer. In her sophomore year, she served as the treasurer for the Black Women’s Alliance, helping to plan their 50-year reunion.

Starting in the spring of her second year, she also worked for three semesters as a lab assistant for 2.678 (Electronics for Mechanical Systems), helping teach students how to build electronic systems. Additionally, Etienne served as a STEM mentor for the Office of Engineering Outreach Programs, working with a middle schooler every other Saturday on little engineering projects.

In the fall of her junior year, Etienne became an admissions ambassador, helping to identify minority students that would be great fits for the MIT community. In the spring, she was elected to be the social chair of MIT Class Awareness Support and Equality (CASE), and she currently serves as vice president of the student group.

“Now, students have a say in what initiatives get implemented for low income students. MIT’s efforts in trying to better the college experience of low income students at MIT is very transformative,” she says. “[It’s] something that’s really never been done before, or hasn’t been done to the extent that CASE has been doing, so I’m glad I’m helping with those efforts.”

This fall, Etienne joined the citywide chapter of public service sorority Delta Sigma Theta Sorority, which spans five local colleges and universities. The organization’s focus is to provide service to the black community around Cambridge, both on college campuses and to the general public. It has held food and clothing drives, educated students about AIDS on World Aids Day, and is working on holding a black-owned business pop-up shop in the future. She currently serves as the second vice president of her chapter. Etienne’s favorite part of being in this sorority is the strong sisterhood that she knows will continue to support her throughout her lifetime.

“Now I’m serving an area that is way bigger than just the realm of MIT,” she says. “So I’m really glad that I’m able to provide programming to our service schools and serve underrepresented communities around Cambridge.”

Fun and future

For fun, Etienne loves spending time in the city. Her preferred activities downtown include escape rooms, paint bars, and sports tournaments. An enthusiast of music and dancing, she also frequents concerts, performances, and workshops led by professional dancers. And she enjoys checking out new restaurants — her current favourite is La Fabrica in Central Square.

Whether or not she remains in the city she’s had these adventures in, Etienne aims to get some experience in the engineering industry before continuing her education.

“For right now I want the engineering experience to develop my engineering skills and mindset,” she says.

After that, she wants to go to graduate school, although she hasn’t decided exactly what type of program to apply for.


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