Computers Math

A team led by the University of California San Diego has developed a new system of algorithms that enables four-legged robots to walk and run on challenging terrain while avoiding both static and moving obstacles.
In tests, the system guided a robot to maneuver autonomously and swiftly across sandy surfaces, gravel, grass, and bumpy dirt hills covered with branches and fallen leaves without bumping into poles, trees, shrubs, boulders, benches or people. The robot also navigated a busy office space without bumping into boxes, desks or chairs.
The work brings researchers a step closer to building robots that can perform search and rescue missions or collect information in places that are too dangerous or difficult for humans.
The team will present its work at the 2022 International Conference on Intelligent Robots and Systems (IROS), which will take place from Oct. 23 to 27 in Kyoto, Japan.
The system provides a legged robot more versatility because of the way it combines the robot’s sense of sight with another sensing modality called proprioception, which involves the robot’s sense of movement, direction, speed, location and touch — in this case, the feel of the ground beneath its feet.
Currently, most approaches to train legged robots to walk and navigate rely either on proprioception or vision, but not both at the same time, said study senior author Xiaolong Wang, a professor of electrical and computer engineering at the UC San Diego Jacobs School of Engineering.
“In one case, it’s like training a blind robot to walk by just touching and feeling the ground. And in the other, the robot plans its leg movements based on sight alone. It is not learning two things at the same time,” said Wang. “In our work, we combine proprioception with computer vision to enable a legged robot to move around efficiently and smoothly — while avoiding obstacles — in a variety of challenging environments, not just well-defined ones.”
The system that Wang and his team developed uses a special set of algorithms to fuse data from real-time images taken by a depth camera on the robot’s head with data from sensors on the robot’s legs. This was not a simple task. “The problem is that during real-world operation, there is sometimes a slight delay in receiving images from the camera,” explained Wang, “so the data from the two different sensing modalities do not always arrive at the same time.”
The team’s solution was to simulate this mismatch by randomizing the two sets of inputs — a technique the researchers call multi-modal delay randomization. The fused and randomized inputs were then used to train a reinforcement learning policy in an end-to-end manner. This approach helped the robot to make decisions quickly during navigation and anticipate changes in its environment ahead of time, so it could move and dodge obstacles faster on different types of terrains without the help of a human operator.
Moving forward, Wang and his team are working on making legged robots more versatile so that they can conquer even more challenging terrains. “Right now, we can train a robot to do simple motions like walking, running and avoiding obstacles. Our next goals are to enable a robot to walk up and down stairs, walk on stones, change directions and jump over obstacles.”
Video: https://youtu.be/GKbTklHrq60
The team has released their code online at: https://github.com/Mehooz/vision4leg.
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Materials provided by University of California – San Diego. Original written by Liezel Labios. Note: Content may be edited for style and length. More

Although just cute little creatures at first glance, the microscopic geckos and octopuses fabricated by 3D laser printing in the molecular engineering labs at Heidelberg University could open up new opportunities in fields such as microrobotics or biomedicine. The printed microstructures are made from novel materials — known as smart polymers — whose size and mechanical properties can be tuned on demand and with high precision. These “life-like” 3D microstructures were developed in the framework of the “3D Matter Made to Order” (3DMM2O) Cluster of Excellence, a collaboration between Ruperto Carola and the Karlsruhe Institute of Technology (KIT).
“Manufacturing programmable materials whose mechanical properties can be adapted on demand is highly desired for many applications,” states Junior Professor Dr Eva Blasco, group leader at the Institute of Organic Chemistry and the Institute for Molecular Systems Engineering and Advanced Materials of Heidelberg University. This concept is known as 4D printing, and the additional fourth dimension refers to the ability of three-dimensionally printed objects to alter their properties over time. One prominent example of materials for 4D printing are shape memory polymers — smart materials that can return to their original shape from a deformed state in response to an external stimulus such as temperature.
The team led by Prof. Blasco recently introduced one of the first examples of 3D printed shape memory polymers at the microscale. In cooperation with the working group of biophysicist Prof. Dr Joachim Spatz, a scientist at Ruperto Carola and Director at the Max Planck Institute for Medical Research, the researchers developed a new shape memory material that can be 3D printed with high resolution both at the macro and at the microscale. The structures produced include box-shaped microarchitectures whose lids close in response to heat and can then be reopened. “These tiny structures show unusual shape memory properties at low activation temperatures, which is extremely interesting for bioapplications,” explains Christoph Spiegel, a doctoral researcher in the working group of Eva Blasco.
Using adaptive materials, the researchers succeeded in a follow-up study in producing much more complex 3D microstructures like geckos, octopuses, and even sunflowers with “life-like” properties. These materials are based on dynamic chemical bonds. The Heidelberg researchers report that alkoxyamines are particularly suitable for this purpose. After the printing process, these dynamic bonds allow for the complex, micrometric structures to grow eight-fold in just a few hours and to harden, while maintaining their shape. “Conventional inks do not offer such features,” emphasises Prof. Blasco. “Adaptive materials containing dynamic bonds have a bright future in the field of 3D printing,” adds the chemist.
Materials scientists at the Karlsruhe Institute of Technology (KIT) also participated in the research on adaptable materials with “life-like” properties. The German Research Foundation and the Carl Zeiss Foundation funded the work, which was carried out within the framework of the 3DMM2O Cluster of Excellence. The results were published in two papers in the journal Advanced Functional Materials.
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A milestone report from the University of Kansas appearing this week in the Proceedings of the National Academy of Sciences proposes a new technique for modeling molecular life with computers.
According to lead author Ilya Vakser, director of the Computational Biology Program and Center for Computational Biology and professor of molecular biosciences at KU, the investigation into computer modeling of life processes is a major step toward creating a working simulation of a living cell at atomic resolution. The advance promises new insights into the fundamental biology of a cell, as well as faster and more precise treatment of human disease.
“It is about tens or hundreds of thousands of times faster than the existing atomic resolution techniques,” Vakser said. “This provides unprecedented opportunities to characterize physiological mechanisms that now are far beyond the reach of computational modeling, to get insights into cellular mechanisms and to use this knowledge to improve our ability to treat diseases.”
Until now, a major hurdle to modeling cells via computer has been how to approach proteins and their interactions that lie at the heart of cellular processes. To date, established techniques for modeling protein interactions have depended on either “protein docking” or “molecular simulation.”
According to the investigators, both approaches have advantages and drawbacks. While protein docking algorithms are great for sampling spatial coordinates, they do not account for the “time coordinate,” or dynamics of protein interactions. By contrast, molecular simulations model dynamics well, but these simulations are too slow or low-resolution.
“Our proof-of-concept study bridges the two modeling methodologies, developing an approach that can reach unprecedented simulation timescales at all-atom resolution,” the authors wrote. More

Research led by Carnegie Mellon University has developed a model that can accurately predict how stay-at-home orders like those put in place during the COVID-19 pandemic affect the mental health of people with chronic neurological disorders such as multiple sclerosis.
Researchers from CMU, the University of Pittsburgh and the University of Washington gathered data from the smartphones and fitness trackers of people with MS both before and during the early wave of the pandemic. Specifically, they used the passively collected sensor data to build machine learning models to predict depression, fatigue, poor sleep quality and worsening MS symptoms during the unprecedented stay-at-home period.
Before the pandemic began, the original research question was whether digital data from the smartphones and fitness trackers of people with MS could predict clinical outcomes. By March 2020, as study participants were required to stay at home, their daily behavior patterns were significantly altered. The research team realized the data being collected could inform the effect of the stay-at-home orders on people with MS.
“It presented us with an exciting opportunity,” said Mayank Goel, head of the Smart Sensing for Humans (SMASH) Lab at CMU. “If we look at the data points before and during the stay-at-home period, can we identify factors that signal changes in the health of people with MS?”
The team gathered data passively over three to six months, collecting information such as the number of calls on the participants’ smartphones and the duration of those calls; the number of missed calls; and the participants’ location and screen activity data. The team also collected heart rate, sleep information and step count data from their fitness trackers. The research, “Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-Home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping,” was recently published in the Journal of Medical Internet Research Mental Health. Goel, an associate professor in the School of Computer Science’s Software and Societal Systems Department (S3D) and Human-Computer Interaction Institute (HCII), collaborated with Prerna Chikersal, a Ph.D. student in the HCII; Dr. Zongqi Xia, an associate professor of Neurology and director of the Translational and Computational Neuroimmunology Research Program at the University of Pittsburgh; and Anind Dey, a professor and dean of the University of Washington’s Information School.
The work was based on previous studies from Goel’s and Dey’s research groups. In 2020, a CMU team published research that presented a machine learning model that could identify depression in college students at the end of the semester using smartphone and fitness tracker data. Participants in the earlier study, specifically 138 first-year CMU students, were relatively similar to each other when compared to the larger population beyond the university. The researchers set out to test whether their modeling approach could accurately predict clinically relevant health outcomes in a real-world patient population with greater demographic and clinical diversity, leading them to collaborate with Xia’s MS research program.
People with MS can experience several chronic comorbidities, which gave the team a chance to test if their model could predict adverse health outcomes such as severe fatigue, poor sleep quality and worsening of MS symptoms in addition to depression. Building on this study, the team hopes to advance precision medicine for people with MS by improving early detection of disease progression and implementing targeted interventions based on digital phenotyping.
The work could also help inform policymakers tasked with issuing future stay-at-home orders or other similar responses during pandemics or natural disasters. When the original COVID-19 stay-at-home orders were issued, there were early concerns about its economic impacts but only a belated appreciation for the toll on peoples’ mental and physical health — particularly among vulnerable populations such as those with chronic neurological conditions.
“We were able to capture the change in people’s behaviors and accurately predict clinical outcomes when they are forced to stay at home for prolonged periods,” Goel said. “Now that we have a working model, we could evaluate who is at risk for worsening mental health or physical health, inform clinical triage decisions, or shape future public health policies.”
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Materials provided by Carnegie Mellon University. Original written by Aaron Aupperlee. Note: Content may be edited for style and length. More

After a decade of work, scientists at UC San Francisco’s Neuroscape Center have developed a suite of video game interventions that improve key aspects of cognition in aging adults.
The games, which co-creator Adam Gazzaley, MD, PhD, says can be adapted to clinical populations as a new form of “experiential medicine,” showed benefits on an array of important cognitive processes, including short-term memory, attention and long-term memory.
Each employs adaptive closed-loop algorithms that Gazzaley’s lab pioneered in the widely cited 2013 Neuroracer study published in Nature, which first demonstrated it was possible to restore diminished mental faculties in older people with just four weeks of training on a specially designed video game.
These algorithms achieve better results than commercial games by automatically increasing or decreasing in difficulty, depending on how well someone is playing the game. That keeps less skilled players from becoming overwhelmed, while still challenging those with greater ability. The games using these algorithms recreate common activities, such as driving, exercising and playing a drum, and use the skills each can engender to retrain cognitive processes that become deficient with age.
“All of these are taking experiences and delivering them in a very personalized, fun manner, and our brains respond through a process called plasticity,” said Gazzaley, who is professor of neurology in the UCSF Weill Institute for Neurosciences and the founder and executive director of Neuroscape. “Experiences are a powerful way of changing our brain, and this form of experience allows us to deliver it in a manner that’s very accessible.”
The lab’s most recent invention is a musical rhythm game, developed in consultation with drummer Mickey Hart, that not only taught the 60 to 79-year-old participants how to drum, but also improved their ability to remember faces. The study appears Oct. 3, 2022, in PNAS. More

Robotics and wearable devices might soon get a little smarter with the addition of a stretchy, wearable synaptic transistor developed by Penn State engineers. The device works like neurons in the brain to send signals to some cells and inhibit others in order to enhance and weaken the devices’ memories.
Led by Cunjiang Yu, Dorothy Quiggle Career Development Associate Professor of Engineering Science and Mechanics and associate professor of biomedical engineering and of materials science and engineering, the team designed the synaptic transistor to be integrated in robots or wearables and use artificial intelligence to optimize functions. The details were published on Sept. 29 in Nature Electronics.
“Mirroring the human brain, robots and wearable devices using the synaptic transistor can use its artificial neurons to ‘learn’ and adapt their behaviors,” Yu said. “For example, if we burn our hand on a stove, it hurts, and we know to avoid touching it next time. The same results will be possible for devices that use the synaptic transistor, as the artificial intelligence is able to ‘learn’ and adapt to its environment.”
According to Yu, the artificial neurons in the device were designed to perform like neurons in the ventral tegmental area, a tiny segment of the human brain located in the uppermost part of the brain stem. Neurons process and transmit information by releasing neurotransmitters at their synapses, typically located at the neural cell ends. Excitatory neurotransmitters trigger the activity of other neurons and are associated with enhancing memories, while inhibitory neurotransmitters reduce the activity of other neurons and are associated with weakening memories.
“Unlike all other areas of the brain, neurons in the ventral tegmental area are capable of releasing both excitatory and inhibitory neurotransmitters at the same time,” Yu said. “By designing the synaptic transistor to operate with both synaptic behaviors simultaneously, fewer transistors are needed compared to conventional integrated electronics technology, which simplifies the system architecture and allows the device to conserve energy.”
To model soft, stretchy biological tissues, the researchers used stretchable bilayer semiconductor materials to fabricate the device, allowing it to stretch and twist while in use, according to Yu. Conventional transistors, on the other hand, are rigid and will break when deformed.
“The transistor is mechanically deformable and functionally reconfigurable, yet still retains its functions when stretched extensively,” Yu said. “It can attach to a robot or wearable device to serve as their outermost skin.”
In addition to Yu, other contributors include Hyunseok Shim and Shubham Patel, Penn State Department of Engineering Science and Mechanics; Yongcao Zhang, the University of Houston Materials Science and Engineering Program; Faheem Ershad, Penn State Department of Biomedical Engineering and University of Houston Department of Biomedical Engineering; Binghao Wang, School of Electronic Science and Engineering, Southeast University and Department of Chemistry and the Materials Research Center, Northwestern University; Zhihua Chen, Flexterra Inc.; Tobin J. Marks, Department of Chemistry and the Materials Research Center, Northwestern University; Antonio Facchetti, Flexterra Inc. and Northwestern University’s Department of Chemistry and Materials Research Center.
The Office of Naval Research, the Air Force Office of Scientific Research and the National Science Foundation supported this work.
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Materials provided by Penn State. Original written by Mariah Chuprinski. Note: Content may be edited for style and length. More

Researchers in public policy and education recently found that young adults who use more social media are significantly more likely to develop depression within six months, regardless of personality type.
Published in the Journal of Affective Disorders Reports, the study, “Associations between social media use, personality structure, and development of depression,” was co-authored by Renae Merrill, a doctoral student in the Public Policy Program at the University of Arkansas.
Merrill wrote the paper with dean of the College of Public Health and Human Sciences at Oregon State University, Brian Primack, and Chunhua Cao, an assistant professor in the College of Education at the University of Alabama.
“Previous research has linked the development of depression with numerous factors,” the authors noted. “However, the literature has been lacking in studies that focus on how various personality characteristics may interact with social media use and depression. This new study addressed these important research questions, finding strong and linear associations of depression across all personality traits.”
Among the study’s findings was that people with high agreeableness were 49 percent less likely to become depressed than people with low agreeableness. Additionally, those with high neuroticism were twice as likely to develop depression than those with low neuroticism when using more than 300 minutes of social media per day. More importantly, for each personality trait, social media use was strongly associated with the development of depression.
The sample of more than 1,000 U.S. adults between the ages of 18 to 30 was from 2018 data collected by Primack and his colleagues at the University of Pittsburgh.
Depression was measured using the Patient Health Questionnaire. Social media was measured by asking participants how much daily time was spent using popular social media platforms, and personality was measured using the Big Five Inventory, which assessed openness, conscientiousness, extraversion, agreeableness and neuroticism.
The authors suggest that problematic social comparison can enhance negative feelings of oneself and others, which could explain how risk of depression increases with increased social media use. Engaging primarily in negative content can also enhance these feelings. And lastly, engaging in more social media reduces opportunities for in-person interactions and activities outside of the home.
Depression has been noted as the leading cause of disability and mortality worldwide. This makes these findings even more pronounced for creating health interventions and prevention efforts.
“Findings from this study are important during a time of technology expansion and integration,” Merrill said. “Connecting to people virtually may increase the risk of miscommunication or misperception that leads to relationship difficulties and potential risk for developing mental health problems.”
“People have innate emotional needs for social connection and understanding,” Merrill added. “For example, social media experiences can be improved by becoming more aware of our emotions and our connection with others in various life circumstances. This awareness helps improve relationship quality by simply reaching shared meaning and understanding through more effective communication and concern for others and ourselves. Despite our differences, we have the ability to create a culture of empathy and kindness.”
Research support was received by the Fine Foundation.
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If you ask someone if they are a math or science person, they may quickly tell you yes or no. It turns out that how people answer that question in ninth grade and even earlier not only can tell you what subjects they prefer in school, but how likely they are to go on to study STEM subjects in college and work in those fields as adults. The results of a new study from the University of Kansas suggest the importance of fostering positive attitudes toward math and science early in students’ life to address gender and socioeconomic gaps in STEM.
KU researchers analyzed a nationwide data set that asked students if they consider themselves a math and/or science person in ninth grade in 2009. The survey then followed up with those students in 11th grade to ask the same question, then three years after graduation to see who had enrolled in science, technology, engineering and math (STEM) majors, and whether they intended to have a related career when they turned 30. The results not only support the importance of student attitudes on academic outcomes, they also suggest efforts should be focused more on cultivating positive attitudes earlier in student careers, before they get to college, where most of such efforts happen currently.
Rafael Quintana, assistant professor of educational psychology, and Argun Saatcioglu, professor of educational policy and sociology, both at KU, conducted a study in which they analyzed data from the High School Longitudinal Study of 2009. The data set includes responses from more than 21,000 students from about 940 schools across the United States. The study was published in the journal Socius: Sociological Research for a Dynamic World.
Results showed that the odds of enrolling in a STEM major were 1.78 times larger for students with a science identity in ninth grade and 1.66 times larger for those with a math identity than those who did not identify with the subjects. The odds of expecting a career in STEM was 1.69 times larger and 1.6 times larger for those with high science and math identities, respectively.
Those numbers are illustrative of how having positive experiences with math and science early can be influential both in higher education and later in life, the researchers said.
“What do we mean when we say education has long-lasting effects? That’s something we want to think about longitudinally,” Quintana said. “Those early experiences get ‘under the skin,’ as they are related to later outcomes independently of how these attitudes developed later. What this suggests is one, the importance of identity beliefs for career-related decisions, and two, that early experiences can have long-lasting, potentially irreversible effects.”
The data also showed that, when controlling for all other variables, the odds of expecting a career in a STEM field was about 50% lower for women than men and that there was a significant interaction between science identity in school and gender when predicting STEM occupation. In other words, it was more consequential for men to identify with science in ninth grade, as they were more likely to go on to a career in the sciences. Research has long noted a gender gap and socioeconomic inequalities in STEM, but most efforts have focused on how to address them among college students. While those efforts are just, Quintana said, the study results suggest it is important to take measures to address math and science inequities earlier in life as well.
Schools can play a long-term role in helping students believe they can have a career in STEM and visualize such a possibility. By providing equitable access to math and science programs, they can also provide chances to those who may not otherwise get them, the researchers said.
“We want schools to matter and have a consequential effect,” Saatcioglu said. “If you can get kids thinking they are a math or science person through positive experiences, that can have long-term effects. If you can get students to feel that way, it can be beneficial. The key in this study was Rafael was able to isolate the long-term effects of attitudes from ninth grade.”
The attitudes students hold in early high school are key, as they have a cascading effect.
“For example, individuals’ self- perceptions can affect the courses they take, the effort and time they spend on specific subjects and the interests and aspirations they develop,” the authors wrote. “These attitudes and behaviors can shape individuals career trajectories independently of their future identity beliefs. This ramification of causal effects is what generates the cascading and potentially irreversible consequences of early-life experiences.”
Quintana, who uses longitudinal data analysis to study problems in education and human development, said he also hopes to revisit the data in the future to see where those in the data set are now, and how many are still working in STEM fields. Such analysis could also be applied to understand other early educational experiences such as bullying and how they influence later choices, attitudes and career pathways. More