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Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge 2 Sb 2 Te 5 during phase transition to control the waveguide spatial modes with a very high precision of up to 64 levels in modal contrast. This contrast is used to represent the matrix elements, with 6-bit resolution and both positive and negative values, to perform MVM computation in neural network algorithms. We demonstrate a prototypical optical convolutional neural network that can perform image processing and recognition tasks with high accuracy. With a broad operation bandwidth and a compact device footprint, the demonstrated multimode photonic core is promising toward large-scale photonic neural networks with ultrahigh computation throughputs. Integrated optical computing requires programmable photonic and nonlinear elements. The authors demonstrate a phase-change metasurface mode converter, which can be programmed to control the waveguide mode contrast, and build an optical convolutional neural network to perform image processing tasks.

Active learning—the field of machine learning (ML) dedicated to optimal experiment design—has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material. Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new phase- change materials using X-ray diffraction experiments.

Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features. It has a coverage guarantee at any nominal level without additional assumptions on their distribution. Its computation deplorably requires a refitting procedure for all replacement candidates of the target response. In regression settings, this corresponds to an infinite number of model fits. Apart from relatively simple estimators that can be written as pieces of linear function of the response, efficiently computing such sets is difficult, and is still considered as an open problem. We exploit the fact that, often , conformal prediction sets are intervals whose boundaries can be efficiently approximated by classical root-finding algorithms. We investigate how this approach can overcome many limitations of formerly used strategies; we discuss its complexity and drawbacks.

Dear Editor We report the current situation regarding the case of multiple patients who tested positive via polymerase chain reaction (PCR) for the novel coronavirus infection and were transported from the Diamond Princess cruise ship, from the viewpoint of medical facilities in Yokohama City to serve as a reference for future development of emergency medical care systems in each region. There was a case of a foreign national admitted to a medical facility in the City who developed gastrointestinal perforation, requiring an emergency surgery. Because emergency surgery of PCR‐positive patients was considered difficult in general hospitals due to lack of staff, the patient was transported to the abovementioned medical center for emergency surgery. [...]these were healthy people without any underlying conditions, and emergency calls have drastically decreased to 1–2 per day. Because the Philippine government had their crews return back to their country, it is likely that the transportation of patients from the Diamond Princess cruise ship in Yokohama has settled down. On February 20, there was a request to transport ECMO patients from hospital A in Yokosuka City; however, advanced medical facilities in Yokohama City, Kanagawa Prefecture, were unable to accept critical patients from the Diamond Princess cruise ship. [...]these patients were transported from Yokosuka to referral hospitals in Tama, Tokyo Prefecture (This was performed by collaborating with COVID‐19 Countermeasure ECMOnet Project established by six associations such as Japanese Association for Acute Medicine and The Japanese Society of Intensive Care Medicine, as needed, and paramedics of Yokohama City University went to Yokohama and rode with patients as they were transported from Yokosuka to Tama to secure their safety.).

Elastocaloric cooling, a solid-state cooling technology, exploits the latent heat released and absorbed by stress-induced phase transformations. Hysteresis associated with transformation, however, is detrimental to efficient energy conversion and functional durability. We have created thermodynamically efficient, low-hysteresis elastocaloric cooling materials by means of additive manufacturing of nickel-titanium. The use of a localized molten environment and near-eutectic mixing of elemental powders has led to the formation of nanocomposite microstructures composed of a nickel-rich intermetallic compound interspersed among a binary alloy matrix. The microstructure allowed extremely small hysteresis in quasi-linear stress-strain behaviors—enhancing the materials efficiency by a factor of four to seven—and repeatable elastocaloric performance over 1 million cycles. Implementing additive manufacturing to elastocaloric cooling materials enables distinct microstructure control of high-performance metallic refrigerants with long fatigue life.

The advent of caloric materials for magnetocaloric, electrocaloric, and elastocaloric cooling is changing the landscape of solid state cooling technologies with potentials for high-efficiency and environmentally friendly residential and commercial cooling and heat-pumping applications. Given that caloric materials are ferroic materials that undergo first (or second) order phase transitions near room temperature, they open up intriguing possibilities for multiferroic devices with hitherto unexplored functionalities coupling their thermal properties with different fields (magnetic, electric, and stress) through composite configurations. Here we demonstrate a magneto-elastocaloric effect with ultra-low magnetic field (0.16 T) in a compact geometry to generate a cooling temperature change as large as 4 K using a magnetostriction/superelastic alloy composite. Such composite systems can be used to circumvent shortcomings of existing technologies such as the need for high-stress actuation mechanism for elastocaloric materials and the high magnetic field requirement of magnetocaloric materials, while enabling new applications such as compact remote cooling devices. The broad use of elastocaloric materials in cooling applications is hindered by the need to exert large forces onto the material. Compressing a magnetostrictive-elastocaloric composite using a low magnetic field of 0.16 T, temperature changes up to 4 K are achieved without applying external forces.

A novel coronavirus (severe acute respiratory syndrome coronavirus 2) causes a cluster of pneumonia cases in Wuhan, China. It spread rapidly and globally. CT imaging is helpful for the evaluation of the novel coronavirus disease 2019 (COVID-19) pneumonia. Infection control inside the CT suites is also important to prevent hospital-related transmission of COVID-19. We present our experience with infection control protocol for COVID-19 inside the CT suites.

Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.

Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequence. However, in sport, these techniques cannot determine the importance of particular patterns of play to good or bad outcomes, which is often of greater interest to coaches and performance analysts. In this study, we apply a recently proposed supervised sequential pattern mining algorithm called safe pattern pruning (SPP) to 490 labelled event sequences representing passages of play from one rugby team’s matches in the 2018 Japan Top League season. We obtain patterns that are the most discriminative between scoring and non-scoring outcomes from both the team’s and opposition teams’ perspectives using SPP, and compare these with the most frequent patterns obtained with well-known unsupervised sequential pattern mining algorithms when applied to subsets of the original dataset, split on the label. From our obtained results, line breaks, successful line-outs, regained kicks in play, repeated phase-breakdown play, and failed exit plays by the opposition team were found to be the patterns that discriminated most between the team scoring and not scoring. Opposition team line breaks, errors made by the team, opposition team line-outs, and repeated phase-breakdown play by the opposition team were found to be the patterns that discriminated most between the opposition team scoring and not scoring. It was also found that, probably because of the supervised nature and pruning/safe-screening mechanisms of SPP, compared to the patterns obtained by the unsupervised methods, those obtained by SPP were more sophisticated in terms of containing a greater variety of events, and when interpreted, the SPP-obtained patterns would also be more useful for coaches and performance analysts.

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest. Ferroelectric domain wall profiles can be modeled by phenomenological Ginzburg-Landau theory, with different candidate models and parameters. Here, the authors solve the problem of model selection by developing a Bayesian inference framework allowing for uncertainty quantification and apply it to atomically resolved images of walls. This analysis can also predict the level of microscope performance needed to detect specific physical phenomena.