System Bits: Nov. 20


Designing transistors that don’t overheat In order to avoid heat-induced voids and cracking that can cause chips and circuits to fail, Stanford University and University of California at Davis researchers have developed a way to not only manage heat, but help route it away from delicate devices that leverages a thermal transistor, which is a nanoscale switch that can conduct heat away from ... » read more

System Bits: Nov. 13


Deep learning device identifies airborne allergens To identify and measure airborne biological particles, or bioaerosols, that originate from living organisms such as plants or fungi, UCLA researchers have invented a portable device that uses holograms and machine learning. The device is trained to recognize five common allergens — pollen from Bermuda grass, oak, ragweed and spores from t... » read more

System Bits: Nov. 6


Keeping data private To preserve privacy during data collection from the Internet, Stanford University researchers have developed a new technique that maintains personal privacy given that the many devices part of our daily lives collect information about how we use them. Stanford computer scientists Dan Boneh and Henry Corrigan-Gibbs created the Prio method for keeping collected data priva... » read more

System Bits: Oct. 30


Ethics, regional differences for programming autonomous vehicles MIT researchers have revealed some distinct global preferences concerning the ethics of autonomous vehicles, as well as some regional variations in those preferences based on a recently completed survey. [caption id="attachment_24139620" align="alignleft" width="300"] Ethical questions involving autonomous vehicles are the foc... » read more

System Bits: Oct. 23


Adapting machine learning for use in scientific research To better tailor machine learning for effective use in scientific research, the U.S. Department of Energy has awarded a collaborative grant to a group of researchers, including UC Santa Barbara mathematician Paul Atzberger, to establish a new data science research center. According to UCSB, the Physics-Informed Learning Machines for M... » read more

System Bits: Oct. 16


Solving the quantum verification problem UC Berkeley doctoral candidate Urmila Mahadev spent 8 years in graduate school solving one of the most basic questions in quantum computation, which is how to know whether a quantum computer has done anything quantum at all, according to Quanta Magazine. In her paper, Mahadev presents the first protocol allowing a classical computer to interactively ... » read more

System Bits: Oct. 9


Sensing with light pulses In a development expected to be useful in applications including distance measurement, molecular fingerprinting and ultrafast sampling, EPFL researchers have found a way to implement an optical sensing system by using spatial multiplexing, a technique originally developed in optical-fiber communication, which produces three independent streams of ultrashort optical pu... » read more

System Bits: Oct. 2


Computer algorithms exhibit prejudice based on datasets Researchers at Cardiff University and MIT have shown that groups of autonomous machines are capable of demonstrating prejudice by identifying, copying, and learning this behavior from one another. The team noted that while it may seem that prejudice is a human-specific phenomenon that requires human cognition to form an opinion of, or ... » read more

System Bits: Sept. 25


Schottky diodes: One 2D material equation to rule them all Specifying the right materials for the heterostructure of 2D Schottky diodes—which consist of a metal touching a semiconductor—means designers have to wade through sometimes conflicting theoretical models to select materials. “It is not uncommon to see a model, whose underlying physics fundamentally contradicts with the physical ... » read more

System Bits: Sept. 18


Better AI technique for chemistry predictions CalTech researchers have found a new technique that uses machine learning more effectively to predict how complex chemicals will react to reagents. The tool is a new twist on similar machine learning techniques to find more effective catalysts without having the time-consuming trial-and-error research, making it a time-saver for drug researchers. ... » read more

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