Computers Math

Telehealth has become a critical way for doctors to still provide health care while minimizing in-person contact during COVID-19. But with phone or Zoom appointments, it’s harder for doctors to get important vital signs from a patient, such as their pulse or respiration rate, in real time.
A University of Washington-led team has developed a method that uses the camera on a person’s smartphone or computer to take their pulse and respiration signal from a real-time video of their face. The researchers presented this state-of-the-art system in December at the Neural Information Processing Systems conference.
Now the team is proposing a better system to measure these physiological signals. This system is less likely to be tripped up by different cameras, lighting conditions or facial features, such as skin color. The researchers will present these findings April 8 at the ACM Conference on Health, Interference, and Learning.
“Machine learning is pretty good at classifying images. If you give it a series of photos of cats and then tell it to find cats in other images, it can do it. But for machine learning to be helpful in remote health sensing, we need a system that can identify the region of interest in a video that holds the strongest source of physiological information — pulse, for example — and then measure that over time,” said lead author Xin Liu, a UW doctoral student in the Paul G. Allen School of Computer Science & Engineering.
“Every person is different,” Liu said. “So this system needs to be able to quickly adapt to each person’s unique physiological signature, and separate this from other variations, such as what they look like and what environment they are in.”
The team’s system is privacy preserving — it runs on the device instead of in the cloud — and uses machine learning to capture subtle changes in how light reflects off a person’s face, which is correlated with changing blood flow. Then it converts these changes into both pulse and respiration rate. More

In recent years, robots have gained artificial vision, touch, and even smell. “Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib. In a new paper, Adib’s team is pushing the technology a step further. “We’re trying to give robots superhuman perception,” he says.
The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects. The robot, called RF-Grasp, combines this powerful sensing with more traditional computer vision to locate and grasp items that might otherwise be blocked from view. The advance could one day streamline e-commerce fulfillment in warehouses or help a machine pluck a screwdriver from a jumbled toolkit.
The research will be presented in May at the IEEE International Conference on Robotics and Automation. The paper’s lead author is Tara Boroushaki, a research assistant in the Signal Kinetics Group at the MIT Media Lab. Her MIT co-authors include Adib, who is the director of the Signal Kinetics Group; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. Other co-authors include Junshan Leng, a research engineer at Harvard University, and Ian Clester, a PhD student at Georgia Tech.
As e-commerce continues to grow, warehouse work is still usually the domain of humans, not robots, despite sometimes-dangerous working conditions. That’s in part because robots struggle to locate and grasp objects in such a crowded environment. “Perception and picking are two roadblocks in the industry today,” says Rodriguez. Using optical vision alone, robots can’t perceive the presence of an item packed away in a box or hidden behind another object on the shelf — visible light waves, of course, don’t pass through walls.
But radio waves can.
For decades, radio frequency (RF) identification has been used to track everything from library books to pets. RF identification systems have two main components: a reader and a tag. The tag is a tiny computer chip that gets attached to — or, in the case of pets, implanted in — the item to be tracked. The reader then emits an RF signal, which gets modulated by the tag and reflected back to the reader. More

A new, detailed model of the surface of the SARS-CoV-2 spike protein reveals previously unknown vulnerabilities that could inform development of vaccines. Mateusz Sikora of the Max Planck Institute of Biophysics in Frankfurt, Germany, and colleagues present these findings in the open-access journal PLOS Computational Biology.
SARS-CoV-2 is the virus responsible for the COVID-19 pandemic. A key feature of SARS-CoV-2 is its spike protein, which extends from its surface and enables it to target and infect human cells. Extensive research has resulted in detailed static models of the spike protein, but these models do not capture the flexibility of the spike protein itself nor the movements of protective glycans — chains of sugar molecules — that coat it.
To support vaccine development, Sikora and colleagues aimed to identify novel potential target sites on the surface of the spike protein. To do so, they developed molecular dynamics simulations that capture the complete structure of the spike protein and its motions in a realistic environment.
These simulations show that glycans on the spike protein act as a dynamic shield that helps the virus evade the human immune system. Similar to car windshield wipers, the glycans cover nearly the entire spike surface by flopping back and forth, even though their coverage is minimal at any given instant.
By combining the dynamic spike protein simulations with bioinformatic analysis, the researchers identified spots on the surface of the spike proteins that are least protected by the glycan shields. Some of the detected sites have been identified in previous research, but some are novel. The vulnerability of many of these novel sites was confirmed by other research groups in subsequent lab experiments.
“We are in a phase of the pandemic driven by the emergence of new variants of SARS-CoV-2, with mutations concentrated in particular in the spike protein,” Sikora says. “Our approach can support the design of vaccines and therapeutic antibodies, especially when established methods struggle.”
The method developed for this study could also be applied to identify potential vulnerabilities of other viral proteins.
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Researchers have developed a new mechanical model that simulates how whiskers bend within a follicle in response to an external force, paving the way toward better understanding of how whiskers contribute to mammals’ sense of touch. Yifu Luo and Mitra Hartmann of Northwestern University and colleagues present these findings in the open-access journal PLOS Computational Biology.
With the exception of some primates, most mammals use whiskers to explore their environment through the sense of touch. Whiskers have no sensors along their length, but when an external force bends a whisker, that deformation extends into the follicle at the base of the whisker, where the whisker pushes or pulls on sensor cells, triggering touch signals in the nervous system.
Few previous studies have examined how whiskers deform within follicles in order to impinge on the sensor cells — mechanoreceptors — inside. To better understand this process, Luo and colleagues drew on data from experimental studies of whisker follicles to create the first mechanical model capable of simulating whisker deformation within follicles.
The simulations suggest that whisker deformation within follicles most likely occurs in an “S” shape, although future experimental data may show that the deformation is “C” shaped. The researchers demonstrate that these shape estimates can be used to predict how whiskers push and pull on different kinds of mechanoreceptors located in different parts of the follicle, influencing touch signals sent to the brain.
The new model applies to both passive touch and active “whisking,” when an animal uses muscles to move its whiskers. The simulations suggest that, during active whisking, the tactile sensitivity of the whisker system is enhanced by increased blood pressure in the follicle and by increased stiffness of follicular muscle and tissue structures.
“It is exciting to use simulations, constrained by anatomical observations, to gain insights into biological processes that cannot be directly measured experimentally,” Hartmann says. “The work also underscores just how important mechanics are to understanding the sensory signals that the brain has evolved to process.”
Future research will be needed to refine the model, both computationally and by incorporating new experimental data.
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Brain-computer interfaces (BCIs) are an emerging assistive technology, enabling people with paralysis to type on computer screens or manipulate robotic prostheses just by thinking about moving their own bodies. For years, investigational BCIs used in clinical trials have required cables to connect the sensing array in the brain to computers that decode the signals and use them to drive external devices.
Now, for the first time, BrainGate clinical trial participants with tetraplegia have demonstrated use of an intracortical wireless BCI with an external wireless transmitter. The system is capable of transmitting brain signals at single-neuron resolution and in full broadband fidelity without physically tethering the user to a decoding system. The traditional cables are replaced by a small transmitter about 2 inches in its largest dimension and weighing a little over 1.5 ounces. The unit sits on top of a user’s head and connects to an electrode array within the brain’s motor cortex using the same port used by wired systems.
For a study published in IEEE Transactions on Biomedical Engineering, two clinical trial participants with paralysis used the BrainGate system with a wireless transmitter to point, click and type on a standard tablet computer. The study showed that the wireless system transmitted signals with virtually the same fidelity as wired systems, and participants achieved similar point-and-click accuracy and typing speeds.
“We’ve demonstrated that this wireless system is functionally equivalent to the wired systems that have been the gold standard in BCI performance for years,” said John Simeral, an assistant professor of engineering (research) at Brown University, a member of the BrainGate research consortium and the study’s lead author. “The signals are recorded and transmitted with appropriately similar fidelity, which means we can use the same decoding algorithms we used with wired equipment. The only difference is that people no longer need to be physically tethered to our equipment, which opens up new possibilities in terms of how the system can be used.”
The researchers say the study represents an early but important step toward a major objective in BCI research: a fully implantable intracortical system that aids in restoring independence for people who have lost the ability to move. While wireless devices with lower bandwidth have been reported previously, this is the first device to transmit the full spectrum of signals recorded by an intracortical sensor. That high-broadband wireless signal enables clinical research and basic human neuroscience that is much more difficult to perform with wired BCIs.
The new study demonstrated some of those new possibilities. The trial participants — a 35-year-old man and a 63-year-old man, both paralyzed by spinal cord injuries — were able to use the system in their homes, as opposed to the lab setting where most BCI research takes place. Unencumbered by cables, the participants were able to use the BCI continuously for up to 24 hours, giving the researchers long-duration data including while participants slept. More

When it comes to powering mobile robots, batteries present a problematic paradox: the more energy they contain, the more they weigh, and thus the more energy the robot needs to move. Energy harvesters, like solar panels, might work for some applications, but they don’t deliver power quickly or consistently enough for sustained travel.
James Pikul, assistant professor in Penn Engineering’s Department of Mechanical Engineering and Applied Mechanics, is developing robot-powering technology that has the best of both worlds. His environmentally controlled voltage source, or ECVS, works like a battery, in that the energy is produced by repeatedly breaking and forming chemical bonds, but it escapes the weight paradox by finding those chemical bonds in the robot’s environment, like a harvester. While in contact with a metal surface, an ECVS unit catalyzes an oxidation reaction with the surrounding air, powering the robot with the freed electrons.
Pikul’s approach was inspired by how animals power themselves through foraging for chemical bonds in the form of food. And like a simple organism, these ECVS-powered robots are now capable of searching for their own food sources despite lacking a “brain.”
In a new study published as an Editor’s Choice article in Advanced Intelligent Systems, Pikul, along with lab members Min Wang and Yue Gao, demonstrate a wheeled robot that can navigate its environment without a computer. By having the left and right wheels of the robot powered by different ECVS units, they show a rudimentary form of navigation and foraging, where the robot will automatically steer toward metallic surfaces it can “eat.”
Their study also outlines more complicated behavior that can be achieved without a central processor. With different spatial and sequential arrangements of ECVS units, a robot can perform a variety of logical operations based on the presence or absence of its food source.
“Bacteria are able to autonomously navigate toward nutrients through a process called chemotaxis, where they sense and respond to changes in chemical concentrations,” Pikul says. “Small robots have similar constraints to microorganisms, since they can’t carry big batteries or complicated computers, so we wanted to explore how our ECVS technology could replicate that kind of behavior.”
In the researchers’ experiments, they placed their robot on aluminum surfaces capable of powering its ECVS units. By adding “hazards” that would prevent the robot from making contact with the metal, they showed how ECVS units could both get the robot moving and navigate it toward more energy-rich sources. More

A thermos bottle has the task of preserving the temperature — but sometimes you want to achieve the opposite: Computer chips generate heat that must be dissipated as quickly as possible so that the chip is not destroyed. This requires special materials with particularly good heat conduction properties.
In collaboration with groups from China and the United States, a research team from TU Wien therefore set out to find the optimal heat conductor. They finally found what they were looking for in a very specific form of tantalum nitride — no other known metallic material has a higher thermal conductivity. In order to be able to identify this record-breaking material, they first had to analyse which processes play a role in heat conduction in such materials at the atomic level. The results have now been published in the scientific journal Physical Review Letters.
Electrons and lattice vibrations
“Basically, there are two mechanisms by which heat propagates in a material,” explains Prof. Georg Madsen from the Institute of Materials Chemistry at TU Wien. “Firstly, through the electrons that travel through the material, taking energy with them. This is the main mechanism in good electrical conductors. And secondly through the phonons, which are collective lattice vibrations in the material.” The atoms move, causing other atoms to wobble. At higher temperatures, heat conduction through propagation of these vibrations is usually the decisive effect.
But neither the electrons nor the lattice vibrations can propagate completely unhindered through the material. There are various processes that slow down this propagation of thermal energy. Electrons and lattice vibrations can interact with each other, they can scatter, they can be stopped by irregularities in the material.
In some cases, heat conduction can even be dramatically limited by the fact that different isotopes of an element are built into the material — i.e. similar atoms with different numbers of neutrons. In that case, the atoms do not have exactly the same mass, and this affects the collective vibrational behaviour of the atoms in the material. More

Last year, a team of biologists and computer scientists from Tufts University and the University of Vermont (UVM) created novel, tiny self-healing biological machines from frog cells called “Xenobots” that could move around, push a payload, and even exhibit collective behavior in the presence of a swarm of other Xenobots.
Get ready for Xenobots 2.0.
The same team has now created life forms that self-assemble a body from single cells, do not require muscle cells to move, and even demonstrate the capability of recordable memory. The new generation Xenobots also move faster, navigate different environments, and have longer lifespans than the first edition, and they still have the ability to work together in groups and heal themselves if damaged. The results of the new research were published today in Science Robotics.
Compared to Xenobots 1.0, in which the millimeter-sized automatons were constructed in a “top down” approach by manual placement of tissue and surgical shaping of frog skin and cardiac cells to produce motion, the next version of Xenobots takes a “bottom up” approach. The biologists at Tufts took stem cells from embryos of the African frog Xenopus laevis (hence the name “Xenobots”) and allowed them to self-assemble and grow into spheroids, where some of the cells after a few days differentiated to produce cilia — tiny hair-like projections that move back and forth or rotate in a specific way. Instead of using manually sculpted cardiac cells whose natural rhythmic contractions allowed the original Xenobots to scuttle around, cilia give the new spheroidal bots “legs” to move them rapidly across a surface. In a frog, or human for that matter, cilia would normally be found on mucous surfaces, like in the lungs, to help push out pathogens and other foreign material. On the Xenobots, they are repurposed to provide rapid locomotion.
“We are witnessing the remarkable plasticity of cellular collectives, which build a rudimentary new ‘body’ that is quite distinct from their default — in this case, a frog — despite having a completely normal genome,” said Michael Levin, Distinguished Professor of Biology and director of the Allen Discovery Center at Tufts University, and corresponding author of the study. “In a frog embryo, cells cooperate to create a tadpole. Here, removed from that context, we see that cells can re-purpose their genetically encoded hardware, like cilia, for new functions such as locomotion. It is amazing that cells can spontaneously take on new roles and create new body plans and behaviors without long periods of evolutionary selection for those features.”
“In a way, the Xenobots are constructed much like a traditional robot. Only we use cells and tissues rather than artificial components to build the shape and create predictable behavior.” said senior scientist Doug Blackiston, who co-first authored the study with research technician Emma Lederer. “On the biology end, this approach is helping us understand how cells communicate as they interact with one another during development, and how we might better control those interactions.”
While the Tufts scientists created the physical organisms, scientists at UVM were busy running computer simulations that modeled different shapes of the Xenobots to see if they might exhibit different behaviors, both individually and in groups. Using the Deep Green supercomputer cluster at UVM’s Vermont Advanced Computing Core, the team, led by computer scientists and robotics experts Josh Bongard and under hundreds of thousands of random environmental conditions using an evolutionary algorithm. These simulations were used to identify Xenobots most able to work together in swarms to gather large piles of debris in a field of particles. More