Computers Math

In underwater acoustics, deep learning is gaining traction in improving sonar systems to detect ships and submarines in distress or in restricted waters. However, noise interference from the complex marine environment becomes a challenge when attempting to detect targeted ship-radiated sounds.
In the Journal of the Acoustical Society of America, published by the Acoustical Society of America through AIP Publishing, researchers in China and the United States explore an attention-based deep neural network (ABNN) to tackle this problem.
“We found the ABNN was highly accurate in target recognition, exceeding a conventional deep neural network, particularly when using limited single-target data to detect multiple targets,” co-author Qunyan Ren said.
Deep learning is a machine-learning method that uses artificial neural networks inspired by the human brain to recognize patterns. Each layer of artificial neurons, or nodes, learns a distinct set of features based on the information contained in the previous layer.
ABNN uses an attention module to mimic elements in the cognitive process that enable us to focus on the most important parts of an image, language, or other pattern and tune out the rest. This is accomplished by adding more weight to certain nodes to enhance specific pattern elements in the machine-learning process.
Incorporating an ABNN system in sonar equipment for targeted ship detection, the researchers tested two ships in a shallow, 135-square-mile area of the South China Sea. They compared their results with a typical deep neural network (DNN). Radar and other equipment were used to determine more than 17 interfering vessels in the experimental area.
They found the ABNN increases its predictions considerably as it gravitates toward the features closely correlated with the training goals. Detection becomes more pronounced as the network continually cycles through the entire training dataset, accentuating the weighted nodes and disregarding irrelevant information.
While the ABNN accuracy of detecting ships A and B separately was slightly higher than the DNN (98% and 97.4%, respectively), the ABNN accuracy of detecting both ships in the same vicinity was significantly higher (74% and 58.4%).
For multiple-target identification, a traditional ABNN model is generally trained using multiship data, but this can be a complicated and computationally costly process. The researchers trained their ABNN model to detect each target separately. The individual-target datasets then merge as the output layer of the network is extended.
“The need to detect multiple ships at one time is a common scenario, and our model significantly exceeds DNN in detecting two ships in the same vicinity,” Ren said. “Moreover, our ABNN focused on the inherent features of the two ships simultaneously.”
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Scientists from Nara Institute of Science and Technology (NAIST) used the mathematical method called automatic differentiation to find the optimal fit of experimental data up to four times faster. This research can be applied to multivariable models of electronic devices, which may allow them to be designed with increased performance while consuming less power.
Wide bandgap devices, such as silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFET), are a critical element for making converters faster and more sustainable. This is because of their larger switching frequencies with smaller energy losses under a wide range of temperatures when compared with conventional silicon-based devices. However, calculating the parameters that determine how the electrical current in a MOSFET responds as a function of the applied voltage remains difficult in a circuit simulation. A better approach for fitting experimental data to extract the important parameters would provide chip manufacturers the ability to design more efficient power converters.
Now, a team of scientists led by NAIST has successfully used the mathematical method called automatic differentiation (AD) to significantly accelerate these calculations. While AD has been used extensively when training artificial neural networks, the current project extends its application into the area of model parameter extraction. For problems involving many variables, the task of minimizing the error is often accomplished by a process of “gradient descent,” in which an initial guess is repeatedly refined by making small adjustments in the direction that reduces the error the quickest. This is where AD can be much faster than previous alternatives, such as symbolic or numerical differentiation, at finding direction with the steepest “slope.” AD breaks down the problem into combinations of basic arithmetic operations, each of which only needs to be done once. “With AD, the partial derivatives with respect to each of the input parameters are obtained simultaneously, so there is no need to repeat the model evaluation for each parameter,” first author Michihiro Shintani says. By contrast, symbolic differentiation provides exact solutions, but uses a large amount of time and computational resources as the problem becomes more complex.
To show the effectiveness of this method, the team applied it to experimental data collected from a commercially available SiC MOSFET. “Our approach reduced the computation time by 3.5× in comparison to the conventional numerical-differentiation method, which is close to the maximum improvement theoretically possible,” Shintani says. This method can be readily applied in many other areas of research involving multiple variables, since it preserves the physical meanings of the model parameters. The application of AD for the enhanced extraction of model parameters will support new advances in MOSFET development and improved manufacturing yields.
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Stress enhances the properties of a promising material for future technologies.
UNSW researchers find a new exotic state of one of the most promising multiferroic materials, with exciting implications for future technologies using these enhanced properties.
Combining a careful balance of thin-film strain, distortion, and thickness, the team has stabilised a new intermediate phase in one of the few known room-temperature multiferroic materials.
The theoretical and experimental US-Australian study shows that this new phase has an electromechanical figure of merit over double its usual value, and that we can even transform between this intermediate phase to other phases easily using an electric field.
As well as providing a valuable new technique to the toolkit of all international material scientists working with multiferroics and epitaxy, the results finally shed light on how epitaxial techniques can be used to enhance functional response of materials for future application in next -generation devices.
STRESS CHANGES EVERYTHING
If 2020-21 has taught us anything, it’s that stress changes everything. Even the most ‘together’ person can struggle and change given enough stress in their life. More

In high-performance CPUs used in large servers and power semiconductors used in inverters for hybrid electric vehicles (HEVs), as the integration density rises and the higher the power consumption becomes, the semiconductor package is also becoming smaller. Therefore, the power consumption per area of the semiconductor increases. As a result, the heat generation density increases, and the current situation is that the heat removal limit from the device is approaching.
JST commissioned the company-led phase NexTEP-B type development project of Adaptable and Seamless Technology Transfer Program through Target-driven R&D (A-STEP*) “high-performance in-vehicle cooler with spontaneous cooling promotion mechanism” to Lotus Thermal Solutions Co., Ltd. to proceed the practical development based on the research results of Professor Kazuhisa Yuki et al. of Sanyo-Onoda City University.
In the research by Professor Yuki et al., they realized the structure hard for occurring the film boiling by engraving about 1 mm wide grooves at a regular interval on a heat conductor, such a copper, which contacts with a heating element, and combining it with a lotus metal. Lotus Thermal Solution has established a method that determines the appropriate groove cross-sectional areas and pore diameters according to the refrigerant, and developed a highly efficient boiling immersion cooler (1) using lotus metals (2). Silicon carbide (SiC), which is expected as a next-generation power semiconductor, has a heat generation density of 300 to 500 watts per square centimeter (W/cm2). Thus, to use SiC in devices, a cooler with a critical heat flux (CHF) (3) larger than this heat generation density is required. In this development, we succeeded in increasing CHF from about 200 W/cm2 of the conventional cooler to 530 W/cm2 or more by using the boiling promotion technology using lotus metals.
The boiling immersion cooler prototyped in this development has the capability to cool the inverter with Si semiconductors and SiC semiconductors and is expected as a technology to solve the heat concentration problem of in-vehicle power semiconductors with increasingly high heat generation density. Furthermore, this technology is considered as a highly efficient cooling technology for CPUs for conventional workstations and large-scale servers.
(1) Boiling immersion cooling
This is a method that a liquid refrigerant is boiled with the heat of a heat source and then cooled. The conventional cooling method uses the temperature difference for heat transfer from the heat source to the refrigerant such as water or air, and it cools by natural convection or forced convection. However boiling cooling can utilize latent heat of evaporation (heat of vaporization) when vaporizing; therefore, it is said to have cooling capacity several times that of the conventional method.
(2) Lotus metal
This is a lotus root-like porous metal in which many elongated pores are arranged in the same direction. It has cooling characteristics owing to a refrigerant flowing through the pores. When a molten metal containing hydrogen is solidified, pores are formed by hydrogen that cannot completely be dissolved in the molten metal. Utilizing this phenomenon, lotus metals can be produced at a low cost.
(3) Critical Heat Flux CHF
When the heat load increases in boiling immersion cooling, the nucleate boiling with a good heat transfer efficiency cannot be maintained at certain point, and suddenly transitions to film boiling where the heating surface is covered with a vapor film. The heat flux (heat flow per unit area, unit [W/cm2]) at the transition point is called the critical heat flux.
*A-STEP is a technology transfer support program whose aim is to put the research results by public research institutes into practical applications as important technology in the national economy, and thus to give some of their profit back to society.
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A novel electronic component from TU Wien (Vienna) could be an important key to the era of quantum information technology: Using a tailored manufacturing process, pure germanium is bonded with aluminium in a way that atomically sharp interfaces are created. This results in a so-called monolithic metal-semiconductor-metal heterostructure.
This structure shows unique effects that are particularly evident at low temperatures. The aluminium becomes superconducting — but not only that, this property is also transferred to the adjacent germanium semiconductor and can be specifically controlled with electric fields. This makes it excellently suited for complex applications in quantum technology, such as processing quantum bits. A particular advantage is that using this approach, it is not necessary to develop completely new fabrication technologies. Instead, well established semiconductor fabrication techniques can be used to enable germanium-based quantum electronics. The results have now been published in the journal Advanced Materials.
Germanium: difficult to form high-quality contacts
“Germanium is a material which will definitely play an important role in semiconductor technology for the development of faster and more energy-efficient components,” says Dr. Masiar Sistani from the Institute for Solid State Electronics at TU Wien. However, if it is used to produce components on a nanometre scale, major problems arise: the material makes it extremely difficult to produce high-quality electrical contacts. This is related to the high impact of even smallest impurities at the contact points that significantly alter the electrical properties. “We have therefore set ourselves the task of developing a new manufacturing method that enables reliable and reproducible contact properties,” says Masiar Sistani.
Diffusing atoms
The key is temperature: when nanometre-structured germanium and aluminium are brought into contact and heated, the atoms of both materials begin to diffuse into the neighbouring material — but to very different extents: the germanium atoms move rapidly into the aluminium, whereas aluminium hardly diffuses into the germanium at all. “Thus, if you connect two aluminium contacts to a thin germanium nanowire and raise the temperature to 350 degrees Celsius, the germanium atoms diffuse off the edge of the nanowire. This creates empty spaces into which the aluminium can then easily penetrate,” explains Masiar Sistani. “In the end, only a few nanometre area in the middle of the nanowire consists of germanium, the rest has been filled up by aluminium.”
Normally, aluminium is made up of tiny crystal grains, but this novel fabrication method forms a perfect single crystal in which the aluminium atoms are arranged in a uniform pattern. As can be seen under the transmission electron microscope, a perfectly clean and atomically sharp transition is formed between germanium and aluminium, with no disordered region in between. In contrast to conventional methods where electrical contacts are applied to a semiconductor, for example by evaporating a metal, no oxides can form at the boundary layer.
Quantum transport in Grenoble
In order to take a closer look at the properties of this monolithic metal-semiconductor heterostructure of germanium and aluminium at low temperature, we collaborated with Dr. Olivier Buisson and Dr. Cécile Naud from the quantum electronics circuits group at Néel Institute — CNRS-UGA in Grenoble. It turned out that the novel structure indeed has quite remarkable properties: “Not only were we able to demonstrate superconductivity in pure, undoped germanium for the first time, we were also able to show that this structure can be switched between quite different operating states using electric fields. Such a germanium quantum dot device can not only be superconducting but also completely insulating, or it can behave like a Josephson transistor, an important basic element of quantum electronic circuits,” explains Masiar Sistani.
This new heterostructure combines a whole range of advantages: The structure has excellent physical properties needed for quantum technologies, such as high carrier mobility and excellent manipulability with electric fields, and it has the additional advantage of fitting well with already established microelectronics technologies: Germanium is already used in current chip architectures and the temperatures required for heterostructure formation are compatible with well-established semiconductor processing schemes. The novel structures not only have theoretically interesting quantum properties, but also opens up a technologically very realistic possibility of enabling further novel and energy-saving devices.
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Materials consisting of a few atomic layers display properties determined by quantum physics. In a stack of such layers, vibrations of the atoms can be triggered by infrared light. New experimental and theoretical work shows that atomic vibrations within the layers of hexagonal boron nitride, the so-called transverse optical phonons, couple directly to motions of the layers against each other. For a period of some 20 ps, the coupling results in a frequency down-shift of the optical phonons and their optical resonance. This behavior is a genuine property of the quantum material and of interest for applications in high-frequency optoelectronics.
Hexagonal boron nitride consist of layers in which covalently bonded boron and nitrogen atoms form a regular array of six-rings. Neighboring layers are coupled via the much weaker van der Waals interaction. Vibrations of boron and nitrogen atoms in the layer, the so-called transverse optical (TO) phonons, show an oscillation frequency on the order of 40 Terahertz (THz, 4×1013 vibrations per second) which is ten to hundred times higher than that of shear and breathing motions of the layers relative to each other. So far, there was nearly no insight into the lifetime of such motions after optical excitation and into their coupling.
An international collaboration of scientists from Berlin, Montpellier, Nantes, Paris and Ithaca (USA) now presents detailed experimental and theoretical results on ultrafast dynamics of coupled phonons in few-layer hexagonal boron nitride. Transverse optical (TO) phonons in a stack of 8 to 9 boron nitride layers display a lifetime of 1.2 ps (1 ps = 10-12 s), while shear and breathing modes show a decay time of 22 ps. Such lifetimes were directly measured in femtosecond pump-probe experiments and are in very good agreement with values derived from a theoretical analysis of the phonon decay channels.
Excitations of shear and breathing modes induce a characteristic spectral down-shift of the TO phonon resonance in the optical spectra . Theoretical calculations give the coupling energy between the different modes of the layer stack and show that the corresponding coupling is negligibly small in a bulk boron nitride crystal consisting of many layers. Thus, the observed coupled vibrational dynamics represent a genuine property of the quantum material.
The spectral shift of the TO phonon resonance in the optical spectra is a nonlinear optical effect which can be induced by light of moderate power. This is of interest for applications in optoelectronics and holds potential for optical modulators and switches in the giga- to terahertz frequency range.
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A new study from UCLA finds a visit from human-controlled robot encourages a positive outlook and improves medical interactions for hospitalized children.
Robin is a social companion robot that stands at about 4 feet tall and has the capabilities to move, talk and play with others while being remotely controlled by humans. Specialists from UCLA Mattel Children’s Hospital’s Chase Child Life Program conducted hour-long video visits with young patients using Robin, comparing it to interactions using a standard tablet, from October 2020 to April 2021. At the conclusion of the study period, children and their parents were interviewed about their experiences and child life specialists provided feedback in a focus group. Researchers then used a transcript of the discussion to identify recurrent and salient themes.
Ninety percent of parents who had a visit with Robin indicated they were “extremely likely” to request another visit, compared to 60% of parents whose children interacted with the tablet. Children reported a 29% increase in positive affect — described as the tendency to experience the world in a positive way, including emotions, interactions with others and with life’s challenges — after a visit with Robin and a 33% decrease in negative affect. Children who had a tablet visit reported a 43% decrease in positive affect and a 33% decrease in negative affect.
Parents whose children had a visit from Robin reported their children had no change in positive affect and a 75% decrease in negative affect. Parents whose children had a tablet visit reported their children had a 16% increase in positive affect and no change in negative affect.
The study is being presented on October 11 at the American Academy of Pediatrics (AAP) National Conference.
Child life specialists who oversaw visits with Robin reported benefits that included a greater display of intimacy and interactivity during play, increased control over their hospital experience and the formation of a new, trusting friendship.
“Our team has demonstrated that a social companion robot can go beyond video chats on a tablet to give us a more imaginative and profound way to make the hospital less stressful,” said Justin Wagner, MD, a pediatric surgeon at UCLA Mattel Children’s Hospital and senior author of the study. “As the pandemic continues, our patients are still feeling anxious and vulnerable in a variety of ways, so it’s critical that we be as creative as possible to make their experiences easier when they need our help.”
“We saw the positive effect in children, their families and healthcare workers,” adds Wagner. The analysis also suggests benefits to staff, including an increased sense of intimacy with and focus on the patient, increased staff engagement in social care and relative ease in maintaining infection control practices.
In the study, child life specialists also reported the challenges of limited time for patient encounters and a learning curve for operating Robin.
The authors say the evidence illustrates benefits for young patients and supports the incorporation of a social robot like Robin in an inpatient pediatric multidisciplinary care setting.
The study’s other authors are Dr. Gabriel Oland, Joseph Wertz, W. Scott Comulada, Valentina Ogaryan, Megan Pike, and Dr. Shant Shekherdimian of UCLA. More

Researchers at the National Institute of Standards and Technology (NIST) and collaborators have devised and tested a new, highly sensitive method of detecting and counting defects in transistors — a matter of urgent concern to the semiconductor industry as it develops new materials for next-generation devices. These defects limit transistor and circuit performance and can affect product reliability.
A typical transistor is, for most uses, basically a switch. When it’s on, current flows from one side of a semiconductor to the other; switching it off stops the current. Those actions respectively create the binary 1s and 0s of digital information.
Transistor performance critically depends on how reliably a designated amount of current will flow. Defects in the transistor material, such as unwanted “impurity” regions or broken chemical bonds, interrupt and destabilize the flow. These defects can manifest themselves immediately or over a period of time while the device is operating.
Over many years, scientists have found numerous ways to classify and minimize those effects.
But defects become harder to identify as transistor dimensions become almost unimaginably small and switching speeds very high. For some promising semiconductor materials in development — such as silicon carbide (SiC) instead of silicon (Si) alone for novel high-energy, high-temperature devices — there has been no simple and straightforward way to characterize defects in detail.
“The method we developed works with both traditional Si and SiC, allowing us for the first time to identify not only the type of defect but the number of them in a given space with a simple DC measurement,” said NIST’s James Ashton, who conducted the research with colleagues at NIST and Pennsylvania State University. They published their results on October 6 in the Journal of Applied Physics. The research focuses on interactions between the two kinds of electrical charge carriers in a transistor: negatively charged electrons and positively charged “holes,” which are spaces where an electron is missing from the local atomic structure. More