Aurelia Butler

Researchers have pioneered a technique that can dramatically accelerate certain types of computer programs automatically, while ensuring program results remain accurate.
Their system boosts the speeds of programs that run in the Unix shell, a ubiquitous programming environment created 50 years ago that is still widely used today. Their method parallelizes these programs, which means that it splits program components into pieces that can be run simultaneously on multiple computer processors.
This enables programs to execute tasks like web indexing, natural language processing, or analyzing data in a fraction of their original runtime.
“There are so many people who use these types of programs, like data scientists, biologists, engineers, and economists. Now they can automatically accelerate their programs without fear that they will get incorrect results,” says Nikos Vasilakis, research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.
The system also makes it easy for the programmers who develop tools that data scientists, biologists, engineers, and others use. They don’t need to make any special adjustments to their program commands to enable this automatic, error-free parallelization, adds Vasilakis, who chairs a committee of researchers from around the world who have been working on this system for nearly two years.
Vasilakis is senior author of the group’s latest research paper, which includes MIT co-author and CSAIL graduate student Tammam Mustafa and will be presented at the USENIX Symposium on Operating Systems Design and Implementation.Co-authors include lead author Konstantinos Kallas, a graduate student at the University of Pennsylvania; Jan Bielak, a student at Warsaw Staszic High School; Dimitris Karnikis, a software engineer at Aarno Labs; Thurston H.Y. Dang, a former MIT postdoc who is now a software engineer at Google; and Michael Greenberg, assistant professor of computer science at the Stevens Institute of Technology. More

A team of engineers at the University of California San Diego has demonstrated for the first time that the Bluetooth signals emitted constantly by our mobile phones have a unique fingerprint that can be used to track individuals’ movements.
Mobile devices, including phones, smartwatches and fitness trackers, constantly transmit signals, known as Bluetooth beacons, at the rate of roughly 500 beacons per minute.These beacons enable features like Apple’s “Find My” lost device tracking service; COVID-19 tracing apps; and connect smartphones to other devices such as wireless earphones.
Prior research has shown that wireless fingerprinting exists in WiFi and other wireless technologies. The critical insight of the UC San Diego team was that this form of tracking can also be done with Bluetooth, in a highly accurate way.
“This is important because in today’s world Bluetooth poses a more significant threat as it is a frequent and constant wireless signal emitted from all our personal mobile devices,” said Nishant Bhaskar, a Ph.D. student in the UC San Diego Department of Computer Science and Engineering and one of the paper’s lead authors.
The team, which includes researchers from the Departments of Computer Science and Engineering and Electrical and Computer Engineering, presented its findings at the IEEE Security & Privacy conference in Oakland, Calif., on May 24, 2022.
All wireless devices have small manufacturing imperfections in the hardware that are unique to each device. These fingerprints are an accidental byproduct of the manufacturing process. These imperfections in Bluetooth hardware result in unique distortions, which can be used as a fingerprint to track a specific device. For Bluetooth, this would allow an attacker to circumvent anti-tracking techniques such as constantly changing the address a mobile device uses to connect to Internet networks. More

As electronic, thermoelectric and computer technologies have been miniaturized to nanometer scale, engineers have faced a challenge studying fundamental properties of the materials involved; in many cases, targets are too small to be observed with optical instruments.
Using cutting-edge electron microscopes and novel techniques, a team of researchers at the University of California, Irvine, the Massachusetts Institute of Technology and other institutions has found a way to map phonons — vibrations in crystal lattices — in atomic resolution, enabling deeper understanding of the way heat travels through quantum dots, engineered nanostructures in electronic components.
To investigate how phonons are scattered by flaws and interfaces in crystals, the researchers probed the dynamic behavior of phonons near a single quantum dot of silicon-germanium using vibrational electron energy loss spectroscopy in a transmission electron microscope, equipment housed in the Irvine Materials Research Institute on the UCI campus. The results of the project are the subject of a paper published today in Nature.
“We developed a novel technique to differentially map phonon momenta with atomic resolution, which enables us to observe nonequilibrium phonons that only exist near the interface,” said co-author Xiaoqing Pan, UCI professor of materials science and engineering and physics, Henry Samueli Endowed Chair in Engineering, and IMRI director. “This work marks a major advance in the field because it’s the first time we have been able to provide direct evidence that the interplay between diffusive and specular reflection largely depends on the detailed atomistic structure.”
According to Pan, at the atomic scale, heat is transported in solid materials as a wave of atoms displaced from their equilibrium position as heat moves away from the thermal source. In crystals, which possess an ordered atomic structure, these waves are called phonons: wave packets of atomic displacements that carry thermal energy equal to their frequency of vibration.
Using an alloy of silicon and germanium, the team was able to study how phonons behave in the disordered environment of the quantum dot, in the interface between the quantum dot and the surrounding silicon, and around the dome-shaped surface of the quantum dot nanostructure itself. More

What’s the relationship between people’s perception of beauty and animals’ conservation needs? According to a machine-learning study by Nicolas Mouquet at the University of Montpellier, France, and colleagues, publishing June 7thin the open-access journal PLOS Biology, the reef fishes that people find most beautiful tend to be the lowest priority for conservation support.
The researchers asked 13,000 members of the public to rate the aesthetic attractiveness of 481 photographs of ray-finned reef fishes in an online survey and used this data to train a convolutional neural network. They then used the trained neural network to generate predictions for additional 4,400 photographs featuring 2,417 of the most encountered reef fish species.
Combining the public’s ratings with the neural network’s predictions, they found that bright, colorful fish species with rounder bodies tended to be rated as the most beautiful. However, the species that were ranked as more attractive tended to be less distinctive in terms of their ecological traits and evolutionary history. Furthermore, species listed on the IUCN Red List as “Threatened” or whose conservation status has not yet been evaluated had lower aesthetic value on average than species categorized as “Least Concern.” Unattractive species were also of greater commercial interest, whereas aesthetic value was not correlated with a species’ importance for subsistence fisheries.
Our innate preferences for shape and color are probably a consequence of the way the human brain processes colors and patterns, the authors say, but mismatches between aesthetic value, ecological function, and extinction vulnerability may mean that the species most in need of public support are the least likely to receive it. The ecological and evolutionary distinctiveness of unattractive fishes makes them important for the functioning of the whole reef, and their loss could have a disproportionate impact on these high-biodiversity ecosystems.
Mouquet adds, “Our study provides, for the first time, the aesthetic value of 2,417 reef fish species. We found that less beautiful fishes are the most ecologically and evolutionary distinct species and those recognized as threatened. Our study highlights likely important mismatches between potential public support for conservation and the species most in need of this support.”
Story Source:
Materials provided by PLOS. Note: Content may be edited for style and length. More

Electro-optic modulators, which control aspects of light in response to electrical signals, are essential for everything from sensing to metrology and telecommunications. Today, most research into these modulators is focused on applications that take place on chips or within fiber optic systems. But what about optical applications outside the wire and off the chip, like distance sensing in vehicles?
Current technologies to modulate light in free space are bulky, slow, static, or inefficient. Now, researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), in collaboration with researchers at the department of Chemistry at the University of Washington, have developed a compact and tunable electro-optic modulator for free space applications that can modulate light at gigahertz speed.
“Our work is the first step toward a class of free-space electro-optic modulators that provide compact and efficient intensity modulation at gigahertz speed of free-space beams at telecom wavelengths,” said Federico Capasso, Robert L. Wallace Professor of Applied Physics and Vinton Hayes Senior Research Fellow in Electrical Engineering, senior author of the paper.
The research is published in Nature Communications.
Flat, compact metasurfaces are ideal platforms for controlling light in free space but most are static, meaning they can’t switch on and off — a key functionality for modulators. Some active metasurfaces can effectively modulate light, but only at low speeds, just a few megahertz.
For applications such as sensing or free-space communications, you need short, fast bursts of light, on the scale of gigahertz.
The high-speed modulator developed by Capasso and his team brings together metasurface resonators with high-performance organic electro-optical materials and high-frequency electronic design to efficiently modulate the intensity of light in free space.
The modulator consists of a thin layer of an organic electro-optic material deposited on top of a metasurface etched with sub-wavelength resonators integrated with microwave electronics. When a microwave field is applied to the electro-optical material, its refractive index changes, changing the intensity of light that is being transmitted by the metasurface in mere nanoseconds.
“With this design, we now can modulate light 100 to 1,000 times faster than previously,” said Ileana-Cristina Benea-Chelmus, a research associate in the Capasso Lab and first author of the paper. “This speed advance opens new possibilities in computing or communications and the tunability of the metasurface opens up a vast application space for custom-tailored, ultracompact photonics that may in the future be deposited onto any nanoscale free-space optical product.”
Next, the researchers aim to see if they can modulate light even faster and, by changing the design of the metasurface, modulate other aspects of light such as phase or polarization.
The Harvard Office of Technology Development has protected the intellectual property associated with this project.
The research was co-authored by Sydney Mason, Maryna L. Meretska, Dmitry Kazakov, Amirhassan Shams-Ansari from SEAS, and Larry R. Dalton and Delwin Elder of the University of Washington. It was supported in part by the Air Force Office of Scientific Research under award numbers FA9550-19-1-0352 and FA9550-19-1-0069 and the Office of Naval Research (ONR) MURI program, under grant number N00014-20-1-2450. This work was performed in part at the Harvard University Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF award no. ECCS-2025158. More

A new theory of economic decision-making from Mina Mahmoudi, a lecturer in the Department of Economics at Rensselaer Polytechnic Institute, offers an explanation as to why humans, in general, make decisions that are simply adequate, not optimal.
In research published today in the Review of Behavioral Economics, Dr. Mahmoudi theorizes an aspect of relative thinking explaining people may use ratios in their decision-making when they should only use absolute differences. The inverse is also possible.
To explain this behavioral anomaly, Dr. Mahmoudi has developed a ratio-difference theory that gives weight to both ratio and difference comparisons. This theory seeks to more accurately capture the manner by which a boundedly rational decision-maker might operationally distinguish whether one alternative is better than another.
“Effectively solving some economic problems requires one to think in terms of differences while others require one to think in terms of ratios,” Dr. Mahmoudi said. “Because both types of thinking are necessary, it is reasonable to think people develop and apply both types. However, it is also reasonable to expect that people misapply the two types of thinking, especially when less experienced with the context.”
Past studies have shown that when given the opportunity to save, for example, $5 on a $25 item or a $500 item, people in general would put in more effort to save the money on the lower-cost product than the more expensive item. They believe they are getting a better deal because the ratio of cost to savings is higher. In fact, the $5 saved is the same for both items and the perfect, or optimal choice, would be to look at the absolute savings and work equally hard to save each $5. People should use differences to solve this problem, but many seem to make unreasonable decisions because they apply ratio thinking.
“Understanding how the cognitive and motivational characteristics of human beings and the operating procedures of organizations influence the working of economic systems is of critical importance,” Dr. Mahmoudi said. “Many economic behaviors such as imitation occur and many economic institutions like inventories exist because people cannot maximize or because markets are not in equilibrium. Our model provides an example of a behavior that occurs because people cannot maximize.”
This model can be applied to a variety of behavioral economic experiments in the gambling industry and financial markets among others.
Story Source:
Materials provided by Rensselaer Polytechnic Institute. Original written by Jeanne Hedden Gallagher. Note: Content may be edited for style and length. More

Recently, electric vehicles (EVs) are seen everywhere, from passenger cars, buses, to taxis. EVs have the advantage of being eco-friendly and having low maintenance costs; but their owners must remain wary of fatal accidents in case the battery runs out or reaches the end of its life. Therefore, precise capacity and lifespan predictions for the lithium-ion batteries — commonly used in EVs — are vital.
A POSTECH research team led by Professor Seungchul Lee, and Ph.D. candidate Sung Wook Kim (Department of Mechanical Engineering) collaborated with Professor Ki-Yong Oh of Hanyang University to develop a novel artificial intelligence (AI) technology that can accurately predict the capacity and lifespan of lithium-ion batteries. This research breakthrough, which considerably improved the prediction accuracy by merging physical domain knowledge with AI, has recently been published in Applied Energy, an international academic journal in the energy field.
There are two methods of predicting the battery capacity: a physics-based model, which simplifies the intricate internal structure of batteries, and an AI model, which uses the electrical and mechanical responses of batteries. However, the conventional AI model required large amounts of data for training. In addition, when applied to untrained data, its prediction accuracy was very low, which desperately called for the emergence of a next-generation AI technology.
To effectively predict battery capacity with less training data, the research team combined a feature extraction strategy that differs from conventional methods with physical domain knowledge-based neural networks. As a result, the battery prediction accuracy for testing batteries with various capacities and lifespan distributions improved by up to 20%. Its reliability was ensured by confirming the consistency of the results. These outcomes are anticipated to lay the foundation for applying highly dependable physical domain knowledge-based AI to various industries.
Professor Lee of POSTECH remarked, “The limitations of data-based AI have been overcome using physics knowledge. The difficulty of building big data has also been alleviated thanks to the development of the differentiated feature extraction technique.”
Professor Oh of Hanyang University added, “Our research is significant in that it will contribute in propagating EVs to the public by enabling accurate predictions of remaining lifespan of batteries in next-generational EVs.”
This study was supported by the Institute of Civil Military Technology Cooperation and the National Research Foundation of Korea.
Story Source:
Materials provided by Pohang University of Science & Technology (POSTECH). Note: Content may be edited for style and length. More

Sensors are a constant feature of our everyday lives. Although they often go unperceived, sensors provide critical information essential to modern healthcare, security, and environmental monitoring. Modern cars alone contain over 100 sensors and this number will only increase.
Quantum sensing is poised to revolutionise today’s sensors, significantly boosting the performance they can achieve. More precise, faster, and reliable measurements of physical quantities can have a transformative effect on every area of science and technology, including our daily lives.
However, the majority of quantum sensing schemes rely on special entangled or squeezed states of light or matter that are hard to generate and detect. This is a major obstacle to harnessing the full power of quantum-limited sensors and deploying them in real-world scenarios.
In a paper published today, a team of physicists at the Universities of Bristol, Bath and Warwick have shown it is possible to perform high precision measurements of important physical properties without the need for sophisticated quantum states of light and detection schemes.
The key to this breakthrough is the use of ring resonators — tiny racetrack structures that guide light in a loop and maximize its interaction with the sample under study. Importantly, ring resonators can be mass manufactured using the same processes as the chips in our computers and smartphones.
Alex Belsley, Quantum Engineering Technology Labs (QET Labs) PhD student and lead author of the work, said: “We are one step closer to all integrated photonic sensors operating at the limits of detection imposed by quantum mechanics.”
Employing this technology to sense absorption or refractive index changes can be used to identify and characterise a wide range of materials and biochemical samples, with topical applications from monitoring greenhouse gases to cancer detection.
Associate Professor Jonathan Matthews, co-Director of QET Labs and co-author of the work, stated: “We are really excited by the opportunities this result enables: we now know how to use mass manufacturable processes to engineer chip scale photonic sensors that operate at the quantum limit.”
Story Source:
Materials provided by University of Bristol. Note: Content may be edited for style and length. More