Computers Math

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power.
“Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task,” says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. “But they have mostly been treated separately. We found that combining them made them more efficient and effective.”
To test their AN module, the researchers plugged it into four of the most widely used neural network architectures: ResNets, DenseNets, MobileNetsV2 and AOGNets. They then tested the networks against two industry standard benchmarks: the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark.
“We found that AN improved performance for all four architectures on both benchmarks,” Wu says. “For example, top-1 accuracy in the ImageNet-1000 improved by between 0.5% and 2.7%. And Average Precision (AP) accuracy increased by up to 1.8% for bounding box and 2.2% for semantic mask in MS-COCO.
“Another advantage of AN is that it facilitates better transfer learning between different domains,” Wu says. “For example, from image classification in ImageNet to object detection and semantic segmentation in MS-COCO. This is illustrated by the performance improvement in the MS-COCO benchmark, which was obtained by fine-tuning ImageNet-pretrained deep neural networks in MS-COCO, a common workflow in state-of-the-art computer vision.
“We have released the source code and hope our AN will lead to better integrative design of deep neural networks.”

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Researchers have developed an advanced spectrometer that can acquire data with exceptionally high speed. The new spectrometer could be useful for a variety of applications including remote sensing, real-time biological imaging and machine vision.
Spectrometers measure the color of light absorbed or emitted from a substance. However, using such systems for complex and detailed measurement typically requires long data acquisition times.
“Our new system can measure a spectrum in mere microseconds,” said research team leader Scott B. Papp from the National Institute of Standards and Technology and the University of Colorado, Boulder. “This means it could be used for chemical studies in the dynamic environment of power plants or jet engines, for quality control of pharmaceuticals or semiconductors flying by on a production line, or for video imaging of biological samples.”
In The Optical Society (OSA) journal Optics Express, lead author David R. Carlson and colleagues Daniel D. Hickstein and Papp report the first dual-comb spectrometer with a pulse repetition rate of 10 gigahertz. They demonstrate it by carrying out spectroscopy experiments on pressurized gases and semiconductor wafers.
“Frequency combs are already known to be useful for spectroscopy,” said Carlson. “Our research is focused on building new, high-speed frequency combs that can make a spectrometer that operates hundreds of times faster than current technologies.”
Getting data faster
Dual-comb spectroscopy uses two optical sources, known as optical frequency combs that emit a spectrum of colors — or frequencies — perfectly spaced like the teeth on a comb. Frequency combs are useful for spectroscopy because they provide access to a wide range of colors that can be used to distinguish various substances.

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To create a dual-comb spectroscopy system with extremely fast acquisition and a wide range of colors, the researchers brought together techniques from several different disciplines, including nanofabrication, microwave electronics, spectroscopy and microscopy.
The frequency combs in the new system use an optical modulator driven by an electronic signal to carve a continuous laser beam into a sequence of very short pulses. These pulses of light pass through nanophotonic nonlinear waveguides on a microchip, which generates many colors of light simultaneously. This multi-color output, known as a supercontinuum, can then be used to make precise spectroscopy measurements of solids, liquids and gases.
The chip-based nanophotonic nonlinear waveguides were a key component in this new system. These channels confine light within structures that are a centimeter long but only nanometers wide. Their small size and low light losses combined with the properties of the material they are made from allow them to convert light from one wavelength to another very efficiently to create the supercontinuum.
“The frequency comb source itself is also unique compared to most other dual-comb systems because it is generated by carving a continuous laser beam into pulses with an electro-optic modulator,” said Carlson. “This means the reliability and tunability of the laser can be exceptionally high across a wide range of operating conditions, an important feature when looking at future applications outside of a laboratory environment.”
Analyzing gases and solids
To demonstrate the versatility of the new dual-comb spectrometer, the researchers used it to perform linear absorption spectroscopy on gases of different pressure. They also operated it in a slightly different configuration to perform the advanced analytical technique known as nonlinear Raman spectroscopy on semiconductor materials. Nonlinear Raman spectroscopy, which uses pulses of light to characterize the vibrations of molecules in a sample, has not previously been performed using an electro-optic frequency comb.

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The high data acquisition speeds that are possible with electro-optic combs operating at gigahertz pulse rates are ideal for making spectroscopy measurements of fast and non-repeatable events.
“It may be possible to analyze and capture the chemical signatures during an explosion or combustion event,” said Carlson. “Similarly, in biological imaging the ability to create images in real time of living tissues without requiring chemical labeling would be immensely valuable to biological researchers.”
The researchers are now working to improve the system’s performance to make it practical for applications like real-time biological imaging and to simplify and shrink the experimental setup so that it could be operated outside of the lab.

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Colloidal particles have become increasingly important for research as vehicles of biochemical agents. In future, it will be possible to study their behaviour much more efficiently than before by placing them on a magnetised chip. A research team from the University of Bayreuth reports on these new findings in the journal Nature Communications. The scientists have discovered that colloidal rods can be moved on a chip quickly, precisely, and in different directions, almost like chess pieces. A pre-programmed magnetic field even enables these controlled movements to occur simultaneously.
For the recently published study, the research team, led by Prof. Dr. Thomas Fischer, Professor of Experimental Physics at the University of Bayreuth, worked closely with partners at the University of Poznán and the University of Kassel. To begin with, individual spherical colloidal particles constituted the building blocks for rods of different lengths. These particles were assembled in such a way as to allow the rods to move in different directions on a magnetised chip like upright chess figures — as if by magic, but in fact determined by the characteristics of the magnetic field.
In a further step, the scientists succeeded in eliciting individual movements in various directions simultaneously. The critical factor here was the “programming” of the magnetic field with the aid of a mathematical code, which in encrypted form, outlines all the movements to be performed by the figures. When these movements are carried out simultaneously, they take up to one tenth of the time needed if they are carried out one after the other like the moves on a chessboard.
“The simultaneity of differently directed movements makes research into colloidal particles and their dynamics much more efficient,” says Adrian Ernst, doctoral student in the Bayreuth research team and co-author of the publication. “Miniaturised laboratories on small chips measuring just a few centimetres in size are being used more and more in basic physics research to gain insights into the properties and dynamics of materials. Our new research results reinforce this trend. Because colloidal particles are in many cases very well suited as vehicles for active substances, our research results could be of particular benefit to biomedicine and biotechnology,” says Mahla Mirzaee-Kakhki, first author and Bayreuth doctoral student.

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Before autonomous vehicles participate in road traffic, they must demonstrate conclusively that they do not pose a danger to others. New software developed at the Technical University of Munich (TUM) prevents accidents by predicting different variants of a traffic situation every millisecond.
A car approaches an intersection. Another vehicle jets out of the cross street, but it is not yet clear whether it will turn right or left. At the same time, a pedestrian steps into the lane directly in front of the car, and there is a cyclist on the other side of the street. People with road traffic experience will in general assess the movements of other traffic participants correctly.
“These kinds of situations present an enormous challenge for autonomous vehicles controlled by computer programs,” explains Matthias Althoff, Professor of Cyber-Physical Systems at TUM. “But autonomous driving will only gain acceptance of the general public if you can ensure that the vehicles will not endanger other road users — no matter how confusing the traffic situation.”
Algorithms that peer into the future
The ultimate goal when developing software for autonomous vehicles is to ensure that they will not cause accidents. Althoff, who is a member of the Munich School of Robotics and Machine Intelligence at TUM, and his team have now developed a software module that permanently analyzes and predicts events while driving. Vehicle sensor data are recorded and evaluated every millisecond. The software can calculate all possible movements for every traffic participant — provided they adhere to the road traffic regulations — allowing the system to look three to six seconds into the future.
Based on these future scenarios, the system determines a variety of movement options for the vehicle. At the same time, the program calculates potential emergency maneuvers in which the vehicle can be moved out of harm’s way by accelerating or braking without endangering others. The autonomous vehicle may only follow routes that are free of foreseeable collisions and for which an emergency maneuver option has been identified.
Streamlined models for swift calculations
This kind of detailed traffic situation forecasting was previously considered too time-consuming and thus impractical. But now, the Munich research team has shown not only the theoretical viability of real-time data analysis with simultaneous simulation of future traffic events: They have also demonstrated that it delivers reliable results.
The quick calculations are made possible by simplified dynamic models. So-called reachability analysis is used to calculate potential future positions a car or a pedestrian might assume. When all characteristics of the road users are taken into account, the calculations become prohibitively time-consuming. That is why Althoff and his team work with simplified models. These are superior to the real ones in terms of their range of motion — yet, mathematically easier to handle. This enhanced freedom of movement allows the models to depict a larger number of possible positions but includes the subset of positions expected for actual road users.
Real traffic data for a virtual test environment
For their evaluation, the computer scientists created a virtual model based on real data they had collected during test drives with an autonomous vehicle in Munich. This allowed them to craft a test environment that closely reflects everyday traffic scenarios. “Using the simulations, we were able to establish that the safety module does not lead to any loss of performance in terms of driving behavior, the predictive calculations are correct, accidents are prevented, and in emergency situations the vehicle is demonstrably brought to a safe stop,” Althoff sums up.
The computer scientist emphasizes that the new security software could simplify the development of autonomous vehicles because it can be combined with all standard motion control programs.

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Machine learning can help public health officials identify children most at risk of lead poisoning, enabling them to concentrate their limited resources on preventing poisonings rather than remediating homes only after a child suffers elevated blood lead levels, a new study shows.
Rayid Ghani, Distinguished Career Professor in Carnegie Mellon University’s Machine Learning Department and Heinz College of Information Systems and Public Policy, said the Chicago Department of Public Health (CDPH) has implemented an intervention program based on the new machine learning model and Chicago hospitals are in the midst of doing the same. Other cities also are considering replicating the program to address lead poisoning, which remains a significant environmental health issue in the United States.
In a study published today in the journal JAMA Network Open, Ghani and colleagues at the University of Chicago and CDPH report that their machine learning model is about twice as accurate in identifying children at high risk than previous, simpler models, and equitably identifies children regardless of their race or ethnicity.
Elevated blood lead levels can cause irreversible neurological damage in children, including developmental delays and irritability. Lead-based paint in older housing is the typical source of lead poisoning. Yet the standard public health practice has been to wait until children are identified with elevated lead levels and then fix their living conditions.
“Remediation can help other children who will live there, but it doesn’t help the child who has already been injured,” said Ghani, who was a leader of the study while on the faculty of the University of Chicago. “Prevention is the only way to deal with this problem. The question becomes: Can we be proactive in allocating limited inspection and remediation resources?”
Early attempts to devise predictive computer models based on factors such as housing, economic status, race and geography met with only limited success, Ghani said. By contrast, the machine learning model his team devised is more complicated and takes into account more factors, including 2.5 million surveillance blood tests, 70,000 public health lead investigations, 2 million building permits and violations, as well as age, size and condition of housing, and sociodemographic data from the U.S. Census.
This more sophisticated approach correctly identified the children at highest risk of lead poisoning 15.5% of the time — about twice the rate of previous predictive models. That’s a significant improvement, Ghani said. Of course, most health departments currently aren’t identifying any of these children proactively, he added.
The study also showed that the machine learning model identified these high-risk children equitably. That’s a problem with the current system, where Black and Hispanic children are less likely to be tested for blood lead levels than are white children, Ghani said.
In addition to Ghani, the research team included Eric Potash and Joe Walsh of the University of Chicago Harris School of Public Policy; Emile Jorgensen, Nik Prachand and Raed Manour of CDPH; and Corland Lohff of the Southern Nevada Health District. The Robert Wood Johnson Foundation supported this research.

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Materials provided by Carnegie Mellon University. Original written by Byron Spice. Note: Content may be edited for style and length. More

Optical signals produced by laser sources are extensively used in fiber-optic communications, which work by pulsing information packaged as light through cables, even at great distances, from a transmitter to a receiver. Through this technology it is possible to transmit telephone conversations, internet messages and cable television images. The great advantage of this technology over electrical signal transmission is its bandwidth — namely, the amount of information that can be broadcast.
New research from a collaboration between Michigan Technological University and Argonne National Laboratory further improves optical signal processing, which could lead to the fabrication of even smaller fiber-optic devices.
The article, unveiling an unexpected mechanism in optical nonreciprocity — developed by the research group of Miguel Levy, professor of physics at Michigan Tech — has been published in the journal Optica. “Boosting Optical Nonreciprocity: Surface Reconstruction in Iron Garnets” explains the quantum and crystallographic origins of a novel surface effect in nonreciprocal optics that improves the processing of optical signals.
An optical component called the magneto-optic isolator appears ubiquitously in these optical circuits. Its function is to protect the laser source — the place where light is generated before transmission — from unwanted light that might be reflected back from downstream. Any such light entering the laser cavity endangers the transmitted signal because it creates the optical equivalent of noise.
“Optical isolators work on a very simple principle: light going in the forward direction is allowed through; light going in the backwards direction is stopped,” Levy said. “This appears to violate a physical principle called time-reversal symmetry. The laws of physics say that if you reverse the direction of time — if you travel backwards in time — you end up exactly where you started. Therefore, the light going back should end up inside the laser.”
But the light doesn’t. Isolators achieve this feat by being magnetized. North and south magnetic poles in the device do not switch places for light coming back.
“So forward and backward directions actually look different to the traveling light. This phenomenon is called optical nonreciprocity,” Levy said.
Optical isolators need to be miniaturized for on-chip integration into optical circuits, a process similar to the integration of transistors into computer chips. But that integration requires the development of materials technologies that can produce more efficient optical isolators than presently available.
Recent work by Levy’s research group has demonstrated an order-of-magnitude improvement in the physical effect responsible for isolator operation. This finding, observable in nanoscale iron garnet films, opens up the possibility of much tinier devices. New materials technology development of this effect hinges on understanding its quantum basis.
The research group’s findings provide precisely this type of understanding. This work was done in collaboration with physics graduate student Sushree Dash, Applied Chemical and Morphological Analysis Laboratory staff engineer Pinaki Mukherjee and Argonne National Laboratory staff scientists Daniel Haskel and Richard Rosenberg.
The Optica article explains the role of the surface in the electronic transitions responsible for the observed enhanced magneto-optic response. These were observed with the help of Argonne’s Advanced Photon Source. Mapping the surface reconstruction underlying these effects was made possible through the state-of-the-art scanning transmission electron microscope acquired by Michigan Tech two years ago.
The new understanding of magneto-optic response provides a powerful tool for the further development of improved materials technologies to advance the integration of nonreciprocal devices in optical circuits.

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An Australian collaboration has reviewed the fundamental theories underpinning the quantum anomalous Hall effect (QAHE).
QAHE is one of the most fascinating and important recent discoveries in condensed-matter physics.
It is key to the function of emerging ‘quantum’ materials, which offer potential for ultra-low energy electronics.
QAHE causes the flow of zero-resistance electrical current along the edges of a material.
QAHE IN TOPOLOGICAL MATERIALS: KEY TO LOW-ENERGY ELECTRONICS
Topological insulators, recognised by the Nobel Prize in Physics in 2016, are based on a quantum effect known as the quantum anomalous Hall effect (QAHE).

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“Topological insulators conduct electricity only along their edges, where one-way ‘edge paths’ conducts electrons without the scattering that causes dissipation and heat in conventional materials,” explains lead author Muhammad Nadeem.
QAHE was first proposed by 2016 Nobel-recipient Prof Duncan Haldane (Manchester) in the 1980s, but it subsequently proved challenging to realize QAHE in real materials. Magnetic-doped topological insulators and spin-gapless semiconductors are the two best candidates for QAHE.
It’s an area of great interest for technologists,” explains Xiaolin Wang. “They are interested in using this significant reduction in resistance to significantly reduce the power consumption in electronic devices.”
“We hope this study will shed light on the fundamental theoretical perspectives of quantum anomalous Hall materials,” says co-author Prof Michael Fuhrer (Monash University), who is Director of FLEET.
THE STUDY
The collaborative, theoretical study concentrates on these two mechanisms:
large spin-orbit coupling (interaction between electrons’ movement and their spin)
strong intrinsic magnetization (ferromagnetism)
The study was supported by the Australian Research Council (Centres of Excellence and Future Fellowship projects).

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Biosensors that are wearable on human skin or safely used inside the body are increasingly prevalent for both medical applications and everyday health monitoring. Finding the right materials to bind the sensors together and adhere them to surfaces is also an important part of making this technology better. A recent study from Binghamton University, State University of New York offers one possible solution, especially for skin applications.
Matthew S. Brown, a fourth-year PhD student with Assistant Professor Ahyeon Koh’s lab in the Department of Biomedical Engineering, served as the lead author for “Electronic?ECM: A Permeable Microporous Elastomer for an Advanced Bio-Integrated Continuous Sensing Platform,” published in the journal Advanced Materials Technology.
The study utilizes polydimethylsiloxane (PDMS), a silicone material popular for use in biosensors because of its biocompatibility and soft mechanics. It’s generally utilized as a solid film, nonporous material, which can lead to problems in sensor breathability and sweat evaporation.
“In athletic monitoring, if you have a device on your skin, sweat can build up under that device,” Brown said. “That can cause inflammation and also inaccuracies in continuous monitoring applications.
“For instance, one experiment with electrocardiogram (ECG) analysis showed that the porous PDMS allowed for the evaporation of sweat during exercise, capable of maintaining a high-resolution signal. The nonporous PDMS did not provide the ability for the sweat to readily evaporate, leading to a lower signal resolution after exercise.
The team created a porous PDMS material through electrospinning, a production method that makes nanofibers through the use of electric force.

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During mechanical testing, the researchers found that this new material acted like the collagen and elastic fibers of the human epidermis. The material was also capable of acting as a dry adhesive for the electronics to strongly laminate on the skin, for adhesive-free monitoring. Biocompatibility and viability testing also showed better results after seven days of use, compared to the nonporous PDMS film.
“You can use this in a wide variety of applications where you need fluids to passively transfer through the material — such as sweat — to readily evaporate through the device,” Brown said.
Because the material’s permeable structure is capable of biofluid, small-molecule and gas diffusion, it can be integrated with soft biological tissue such as skin, neural and cardiac tissue with reduced inflammation at the application site.
Among the applications that Brown sees are electronics for healing long-term, chronic wounds; breathable electronics for oxygen and carbon dioxide respiratory monitoring; devices that integrate human cells within implantable electronic devices; and real-time, in-vitro chemical and biological monitoring.
Koh — whose recent projects include sweat-assisted battery power and biomonitoring — described the porous PDMS study as “a cornerstone of my research.”
“My lab is very interested in developing a biointegrated sensing system beyond wearable electronics,” she said. “At the moment, technologies have advanced to develop durable and flexible devices over the past 10 years. But we always want to go even further, to create sensors that can be used in more nonvisible systems that aren’t just on the skin.
“Koh also sees the possibilities for this porous PDMS material in another line of research she is pursuing with Associate Professor Seokheun Choi from the Department of Electrical and Computer Engineering. She and Choi are combining their strengths to create stretchable papers for soft bioelectronics, enabling us to monitor physiological statuses.

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Materials provided by Binghamton University. Original written by Chris Kocher. Note: Content may be edited for style and length. More