Aurelia Butler

Researchers from ETH Zurich, the University of Zurich and Empa have developed a new material for an electronic component that can be used in a wider range of applications than its predecessors. Such components will help create electronic circuits that emulate the human brain and that are more efficient at performing machine-​learning tasks.
Compared with computers, the human brain is incredibly energy efficient. Scientists are therefore drawing on how the brain and its interconnected neurons function for inspiration in designing innovative computing technologies. They foresee that these brain-​inspired computing systems, will be more energy efficient than conventional ones, as well as better at performing machine-​learning tasks.
Much like neurons, which are responsible for both data storage and data processing in the brain, scientists want to combine storage and processing in a single electronic component type, known as a memristor. Their hope is that this will help to achieve greater efficiency, because moving data between the processor and the storage, as conventional computers do, is the main reason for the high energy consumption in machine learning applications.
Researchers at ETH Zurich, the University of Zurich and Empa have now developed an innovative concept for a memristor that can be used in a far wider range of applications than existing memristors. “There are different operation modes for memristors, and it is advantageous to be able to use all these modes depending on an artificial neural network’s architecture,” explains ETH postdoc Rohit John. “But previous conventional memristors had to be configured for one of these modes in advance.” The new memristors from the researchers in Zurich can now easily switch between two operation modes while in use: a mode in which the signal grows weaker over time and dies (volatile mode), and one in which the signal remains constant (non-​volatile mode).
Just like in the brain
“These two operation modes are also found in the human brain,” John says. On the one hand, stimuli at the synapses are transmitted from neuron to neuron with biochemical neurotransmitters. These stimuli start out strong and then gradually become weaker. On the other hand, new synaptic connections to other neurons form in the brain while we learn. These connections are longer-​lasting. More

Algorithms recommend products while we shop online or suggest songs we might like as we listen to music on streaming apps.
These algorithms work by using personal information like our past purchases and browsing history to generate tailored recommendations. The sensitive nature of such data makes preserving privacy extremely important, but existing methods for solving this problem rely on heavy cryptographic tools requiring enormous amounts of computation and bandwidth.
MIT researchers may have a better solution. They developed a privacy-preserving protocol that is so efficient it can run on a smartphone over a very slow network. Their technique safeguards personal data while ensuring recommendation results are accurate.
In addition to user privacy, their protocol minimizes the unauthorized transfer of information from the database, known as leakage, even if a malicious agent tries to trick a database into revealing secret information.
The new protocol could be especially useful in situations where data leaks could violate user privacy laws, like when a health care provider uses a patient’s medical history to search a database for other patients who had similar symptoms or when a company serves targeted advertisements to users under European privacy regulations.
“This is a really hard problem. We relied on a whole string of cryptographic and algorithmic tricks to arrive at our protocol,” says Sacha Servan-Schreiber, a graduate student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper that presents this new protocol. More

Researchers at Duke University have demonstrated that incorporating known physics into machine learning algorithms can help the inscrutable black boxes attain new levels of transparency and insight into material properties.
In one of the first projects of its kind, researchers constructed a modern machine learning algorithm to determine the properties of a class of engineered materials known as metamaterials and to predict how they interact with electromagnetic fields.
Because it first had to consider the metamaterial’s known physical constraints, the program was essentially forced to show its work. Not only did the approach allow the algorithm to accurately predict the metamaterial’s properties, it did so more efficiently than previous methods while providing new insights.
The results appear online the week of May 9 in the journal Advanced Optical Materials.
“By incorporating known physics directly into the machine learning, the algorithm can find solutions with less training data and in less time,” said Willie Padilla, professor of electrical and computer engineering at Duke. “While this study was mainly a demonstration showing that the approach could recreate known solutions, it also revealed some insights into the inner workings of non-metallic metamaterials that nobody knew before.”
Metamaterials are synthetic materials composed of many individual engineered features, which together produce properties not found in nature through their structure rather than their chemistry. In this case, the metamaterial consists of a large grid of silicon cylinders that resemble a Lego baseplate. More

University of Central Florida researchers are developing new photonic materials that could one day help enable low power, ultra-fast, light-based computing.
The unique materials, known as topological insulators, are like wires that have been turned inside out, where the current runs along the outside and the interior is insulated.
Topological insulators are important because they could be used in circuit designs that allow for more processing power to be crammed into a single space without generating heat, thus avoiding the overheating problem today’s smaller and smaller circuits face.
In their latest work, published in the journal Nature Materials, the researchers demonstrated a new approach to create the materials that uses a novel, chained, honeycomb lattice design.
The researchers laser etched the chained, honeycombed design onto a sample of silica, the material commonly used to make photonic circuits.
Nodes in the design allow the researchers to modulate the current without bending or stretching the photonic wires, an essential feature needed for controlling the flow of light and thus information in a circuit. More

For the majority of students in the U.S., residential addresses determine which public elementary, middle, or high school they attend. But with an influx of charter schools and state-funded voucher programs for private schools, as well as a growing number of cities that let students apply to public schools across the district (regardless of zip code), the admissions process can turn into a messy game of matchmaking.
Simultaneous applications for competitive spots and a lack of coordination among school authorities often result in some students being matched with multiple schools while others are unassigned. It can lead to unfilled seats at the start of the semester and extra stress for students and parents, as well as teachers and administrators.
Assistant Professor of Economics Bertan Turhan at Iowa State University and his co-authors outline a way to make better, more efficient matches between students and schools in their new study published in Games and Economic Behavior. Turhan says their goal was to create a fairer process that works within realistic parameters.
“There are a lot of success stories in major U.S. cities where economists and policymakers worked together to improve school choice,” said Turhan. “The algorithm we introduced builds on that and could give school groups some degree of coordination and significantly increase overall student welfare in situations where there’s a lot of competition to get into certain schools.”
A new matchmaking model
Using the researchers’ model, each student or family submits one rank-ordered list of public schools to the public school district and another rank-ordered list of private schools to the voucher program. Each school also submits a ranking of students to either the public school district or voucher program. More

Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system,’ that requires less energy consumption. The research group led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations.
Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems.
To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible.
Professor Kim said, “In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.”
This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence.
This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix.
Story Source:
Materials provided by The Korea Advanced Institute of Science and Technology (KAIST). Note: Content may be edited for style and length. More

Plasmids have extensive use in basic and applied biology. These small, circular DNA molecules are used by scientists to introduce new genes into a target organism. Well known for their applications in the production of therapeutic proteins like insulin, plasmids are broadly used in the large-scale production of many bioproducts.
However, designing and constructing plasmids remains one of the most time-consuming and labor-intensive steps in biology research.
To address this, Behnam Enghiad, Pu Xue, and other University of Illinois Urbana-Champaign researchers at the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) have developed a versatile and automated platform for plasmid design and construction called PlasmidMaker. Their work was recently published in Nature Communications.
Creating a plasmid starts with design. To aid in this design process, PlasmidMaker has a user-friendly web interface with which researchers can intuitively visualize and assemble the perfect plasmid for their needs.
Once the plasmid has been designed, it is submitted to the PlasmidMaker team, and an order for the plasmid is placed at the Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB), where the plasmid will be built. iBioFAB, located at the Carl R. Woese Institute for Genomic Biology (IGB) on the U of I campus, is a fully integrated computational and physical infrastructure that supports rapid fabrication, quality control, and analysis of genetic constructs. It features a central robotic arm that transfers labware between instruments that perform distinct operations like pipetting, incubation, or thermocycling.
The plasmid build process is automated: samples are prepared through polymerase chain reaction (PCR) and purification, the DNA sequence is assembled and transformed, and the plasmids are confirmed and frozen, all with little human involvement.
In addition to the automation and precision afforded by iBioFAB, the PlasmidMaker platform also pioneers a new highly flexible method for assembling multiple DNA fragments into a plasmid using Pyrococcus furiosus Argonaute (PfAgo)-based artificial restriction enzymes (AREs).
Restriction enzymes have long been used in plasmid construction, as they can cleave DNA molecules at specific sequences of bases, called recognition sequences. However, these recognition sequences are usually short, making them hard to work with. A short sequence is likely to occur multiple times in a DNA molecule, in which case the restriction enzyme would make too many cuts.
“In previous DNA assembly methods, it would often be hard to find the right restriction enzymes that can cut the plasmid and replace the DNA fragments,” said Huimin Zhao, co-author and the Steven L. Miller Chair of Chemical and Biomolecular Engineering (ChBE) at Illinois. “The PfAgo-based AREs offer greater flexibility and precision, as they can be programmed to seek out longer recognition sequences at virtually any site.”
With all the improvements it brings to the table, the team members at CABBI, one of four U.S. Department of Energy-funded Bioenergy Research Centers across the United States, hope that PlasmidMaker will accelerate the development of synthetic biology for biotechnological applications.
“This tool will be available to CABBI researchers, and we want to eventually make it available to all researchers at the other three Bioenergy Research Centers,” Zhao said. “If things go well, we hope to make it available to all researchers everywhere.”
The manuscript’s other co-authors are Nilmani Singh, CABBI Automation Engineer; Aashutosh Girish Boob and Chengyou Shi, CABBI graduate students in ChBE; Vassily Andrew Petrov, CABBI Software Engineer; Roy Liu, CABBI undergraduate student in Computer Engineering; Siddhartha Suryanarayana Peri, CABBI undergraduate student in ChBE; Stephan Thomas Lane, CABBI iBioFAB Manager; and Emily Danielle Gaither, former CABBI iBioFAB Technician. More

Compact and lightweight metasurfaces — which use specifically designed and patterned nanostructures on a flat surface to focus, shape and control light — are a promising technology for wearable applications, especially virtual and augmented reality systems. Today, research teams painstakingly design the specific pattern of nanostructures on the surface to achieve the desired function of the lens, whether that be resolving nanoscale features, simultaneously producing several depth-perceiving images or focusing light regardless of polarization.
If the metalens is going to be used commercially in AR and VR systems, it’s going to need to be scaled up significantly, which means the number of nanopillars will be in the billions. How can researchers design something that complex? That’s where artificial intelligence comes in.
In a recent paper, published in Nature Communications, a team of researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) and the Massachusetts Institute of Technology (MIT) described a new method for designing large-scale metasurfaces that uses techniques of machine intelligence to generate designs automatically.
“This article lays the groundwork and design approach which may influence many real-world devices,” said Federico Capasso, the Robert L. Wallace Professor of Applied Physics and Vinton Hayes Senior Research Fellow in Electrical Engineering at SEAS and senior author of the paper. “Our methods will enable new metasurface designs that can make an impact on virtual or augmented reality, self-driving cars, and machine vision for embarked systems and satellites.”
Until now, researchers needed years of knowledge and experience in the field to design a metasurface.
“We’ve been guided by intuition-based design, relying heavily on one’s training in physics, which has been limited in the number of parameters that can be considered simultaneously, bounded as we are by human working memory capacity,” said Zhaoyi Li, a research associate at SEAS and co-lead author of the paper. More