Abstract: Suicide rates have been rising in the US over the last 2 decades. The latest data available (2018) show the highest age-adjusted suicide rate in the US since 1941.1 It is within this context that coronavirus disease 2019 (COVID-19) struck the US. Concerning disease models have led to historic and unprecedented public health actions to curb the spread of the virus. Remarkable social distancing interventions have been implemented to fundamentally reduce human contact. While these steps are expected to reduce the rate of new infections, the potential for adverse outcomes on suicide risk is high. Actions could be taken to mitigate potential unintended consequences on suicide prevention efforts, which also represent a national public health priority.

Abstract: The skin is the largest organ of the human body and can sense pressure, temperature, and other complex environmental stimuli or conditions. The mimicry of human skin's sensory ability via electronics is a topic of innovative research that could find broad applications in robotics, artificial intelligence, and human-machine interfaces, all of which promote the development of electronic skin (e-skin). To imitate tactile sensing via e-skins, flexible and stretchable pressure sensor arrays are constructed based on different transduction mechanisms and structural designs. These arrays can map pressure with high resolution and rapid response beyond that of human perception. Multi-modal force sensing, temperature, and humidity detection, as well as self-healing abilities are also exploited for multi-functional e-skins. Other recent progress in this field includes the integration with high-density flexible circuits for signal processing, the combination with wireless technology for convenient sensing and energy/data transfer, and the development of self-powered e-skins. Future opportunities lie in the fabrication of highly intelligent e-skins that can sense and respond to variations in the external environment. The rapidly increasing innovations in this area will be important to the scientific community and to the future of human life.

Abstract: Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF [9] and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT [33]). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG [6]) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.

Abstract: Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.

Abstract: We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.

Abstract: We report the weakly interacting massive particle (WIMP) dark matter search results using the first physics-run data of the PandaX-II 500 kg liquid xenon dual-phase time-projection chamber, operating at the China JinPing underground laboratory. No dark matter candidate is identified above background. In combination with the data set during the commissioning run, with a total exposure of 3.3×10^{4} kg day, the most stringent limit to the spin-independent interaction between the ordinary and WIMP dark matter is set for a range of dark matter mass between 5 and 1000 GeV/c^{2}. The best upper limit on the scattering cross section is found 2.5×10^{-46} cm^{2} for the WIMP mass 40 GeV/c^{2} at 90% confidence level.

Abstract: Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is productbased sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as “tweets”. We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features. Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of users publishing sentiment data (e.g., reviews, blogs). Although traditional classification algorithms can be used to train sentiment classifiers from manually labeled text data, the labeling work can be time-consuming and ex-pensive. Meanwhile, users often use some different words when they express sentiment in different domains. If we directly apply a classifier trained in one domain to other domains, the performance will be very low due to the differences between these domains. In this work, we develop a general solution to sentiment classification when we do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain.

Abstract: Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess1 and Go2, where a perfect simulator is available. However, in real-world problems, the dynamics governing the environment are often complex and unknown. Here we present the MuZero algorithm, which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. The MuZero algorithm learns an iterable model that produces predictions relevant to planning: the action-selection policy, the value function and the reward. When evaluated on 57 different Atari games3—the canonical video game environment for testing artificial intelligence techniques, in which model-based planning approaches have historically struggled4—the MuZero algorithm achieved state-of-the-art performance. When evaluated on Go, chess and shogi—canonical environments for high-performance planning—the MuZero algorithm matched, without any knowledge of the game dynamics, the superhuman performance of the AlphaZero algorithm5 that was supplied with the rules of the game. A reinforcement-learning algorithm that combines a tree-based search with a learned model achieves superhuman performance in high-performance planning and visually complex domains, without any knowledge of their underlying dynamics.

Abstract: Beyond state-of-the-art lithium-ion battery (LIB) technology with metallic lithium anodes to replace conventional ion intercalation anode materials is highly desirable because of lithium's highest specific capacity (3,860 mA/g) and lowest negative electrochemical potential (∼3.040 V vs. the standard hydrogen electrode). In this work, we report for the first time, to our knowledge, a 3D lithium-ion-conducting ceramic network based on garnet-type Li6.4La3Zr2Al0.2O12 (LLZO) lithium-ion conductor to provide continuous Li(+) transfer channels in a polyethylene oxide (PEO)-based composite. This composite structure further provides structural reinforcement to enhance the mechanical properties of the polymer matrix. The flexible solid-state electrolyte composite membrane exhibited an ionic conductivity of 2.5 × 10(-4) S/cm at room temperature. The membrane can effectively block dendrites in a symmetric Li | electrolyte | Li cell during repeated lithium stripping/plating at room temperature, with a current density of 0.2 mA/cm(2) for around 500 h and a current density of 0.5 mA/cm(2) for over 300 h. These results provide an all solid ion-conducting membrane that can be applied to flexible LIBs and other electrochemical energy storage systems, such as lithium-sulfur batteries.

Abstract: The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people ("source control") with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Abstract: Background Alzheimer’s disease is characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The humanized monoclonal antibody solanezumab was designed to increase the clearance from the brain of soluble Aβ, peptides that may lead to toxic effects in the synapses and precede the deposition of fibrillary amyloid. Methods We conducted a double-blind, placebo-controlled, phase 3 trial involving patients with mild dementia due to Alzheimer’s disease, defined as a Mini–Mental State Examination (MMSE) score of 20 to 26 (on a scale from 0 to 30, with higher scores indicating better cognition) and with amyloid deposition shown by means of florbetapir positron-emission tomography or Aβ1-42 measurements in cerebrospinal fluid. Patients were randomly assigned to receive solanezumab at a dose of 400 mg or placebo intravenously every 4 weeks for 76 weeks. The primary outcome was the change from baseline to week 80 in the score on the 14-item cognitive subscale of the Alzheimer’s Disease Assessmen...

Abstract: Zika virus (ZIKV), a mosquito-borne flavivirus with homology to Dengue virus (DENV), has become a public health emergency. By characterizing memory lymphocytes from ZIKV-infected patients, we dissected ZIKV-specific and DENV–cross-reactive immune responses. Antibodies to nonstructural protein 1 (NS1) were largely ZIKV-specific and were used to develop a serological diagnostic tool. In contrast, antibodies against E protein domain I/II (EDI/II) were cross-reactive and, although poorly neutralizing, potently enhanced ZIKV and DENV infection in vitro and lethally enhanced DENV disease in mice. Memory T cells against NS1 or E proteins were poorly cross-reactive, even in donors preexposed to DENV. The most potent neutralizing antibodies were ZIKV-specific and targeted EDIII or quaternary epitopes on infectious virus. An EDIII-specific antibody protected mice from lethal ZIKV infection, illustrating the potential for antibody-based therapy.

Abstract: PurposeThird-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have demonstrated potent activity against TKI resistance mediated by EGFR T790M. We studied whether noninvasive genotyping of cell-free plasma DNA (cfDNA) is a useful biomarker for prediction of outcome from a third-generation EGFR-TKI, osimertinib.MethodsPlasma was collected from all patients in the first-in-man study of osimertinib. Patients who were included had acquired EGFR-TKI resistance and evidence of a common EGFR-sensitizing mutation. Genotyping of cell-free plasma DNA was performed by using BEAMing. Plasma genotyping accuracy was assessed by using tumor genotyping from a central laboratory as reference. Objective response rate (ORR) and progression-free survival (PFS) were analyzed in all T790M-positive or T790M-negative patients.ResultsSensitivity of plasma genotyping for detection of T790M was 70%. Of 58 patients with T790M-negative tumors, T790M was detected in plasma of 18 (31%). ORR and median...

Abstract: Although disturbances such as fire and native insects can contribute to natural dynamics of forest health, exceptional droughts, directly and in combination with other disturbance factors, are pushing some temperate forests beyond thresholds of sustainability. Interactions from increasing temperatures, drought, native insects and pathogens, and uncharacteristically severe wildfire are resulting in forest mortality beyond the levels of 20th-century experience. Additional anthropogenic stressors, such as atmospheric pollution and invasive species, further weaken trees in some regions. Although continuing climate change will likely drive many areas of temperate forest toward large-scale transformations, management actions can help ease transitions and minimize losses of socially valued ecosystem services.

Abstract: Importance Early-phase trials with monoclonal antibodies targeting PD-1 (programmed cell death protein 1) and PD-L1 (programmed cell death 1 ligand 1) have demonstrated durable clinical responses in patients with non–small-cell lung cancer (NSCLC). However, current assays for the prognostic and/or predictive role of tumor PD-L1 expression are not standardized with respect to either quantity or distribution of expression. Objective To demonstrate PD-L1 protein distribution in NSCLC tumors using both conventional immunohistochemistry (IHC) and quantitative immunofluorescence (QIF) and compare results obtained using 2 different PD-L1 antibodies. Design, Setting, and Participants PD-L1 was measured using E1L3N and SP142, 2 rabbit monoclonal antibodies, in 49 NSCLC whole-tissue sections and a corresponding tissue microarray with the same 49 cases. Non–small-cell lung cancer biopsy specimens from 2011 to 2012 were collected retrospectively from the Yale Thoracic Oncology Program Tissue Bank. Human melanoma Mel 624 cells stably transfected with PD-L1 as well as Mel 624 parental cells, and human term placenta whole tissue sections were used as controls and for antibody validation. PD-L1 protein expression in tumor and stroma was assessed using chromogenic IHC and the AQUA (Automated Quantitative Analysis) method of QIF. Tumor-infiltrating lymphocytes (TILs) were scored in hematoxylin-eosin slides using current consensus guidelines. The association between PD-L1 protein expression, TILs, and clinicopathological features were determined. Main Outcomes and Measures PD-L1 expression discordance or heterogeneity using the diaminobenzidine chromogen and QIF was the main outcome measure selected prior to performing the study. Results Using chromogenic IHC, both antibodies showed fair to poor concordance. The PD-L1 antibodies showed poor concordance (Cohen κ range, 0.124-0.340) using conventional chromogenic IHC and showed intra-assay heterogeneity (E1L3N coefficient of variation [CV], 6.75%-75.24%; SP142 CV, 12.17%-109.61%) and significant interassay discordance using QIF (26.6%). Quantitative immunofluorescence showed that PD-L1 expression using both PD-L1 antibodies was heterogeneous. Using QIF, the scores obtained with E1L3N and SP142 for each tumor were significantly different according to nonparametric paired test ( P P = .007) and SP142 ( P = .02). Conclusions and Relevance Objective determination of PD-L1 protein levels in NSCLC reveals heterogeneity within tumors and prominent interassay variability or discordance. This could be due to different antibody affinities, limited specificity, or distinct target epitopes. Efforts to determine the clinical value of these observations are under way.

Abstract: Lead-based halide perovskites are emerging as the most promising class of materials for next-generation optoelectronics; however, despite the enormous success of lead-halide perovskite solar cells, the issues of stability and toxicity are yet to be resolved. Here we report on the computational design and the experimental synthesis of a new family of Pb-free inorganic halide double perovskites based on bismuth or antimony and noble metals. Using first-principles calculations we show that this hitherto unknown family of perovskites exhibits very promising optoelectronic properties, such as tunable band gaps in the visible range and low carrier effective masses. Furthermore, we successfully synthesize the double perovskite Cs2BiAgCl6, perform structural refinement using single-crystal X-ray diffraction, and characterize its optical properties via optical absorption and photoluminescence measurements. This new perovskite belongs to the Fm3m space group and consists of BiCl6 and AgCl6 octahedra alternating in...

Abstract: Purpose Treatment with pembrolizumab, an anti-programmed death-1 antibody, at 10 mg/kg administered once every 2 weeks, displayed durable antitumor activity in programmed death-ligand 1 (PD-L1) -positive recurrent and/or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) in the KEYNOTE-012 trial. Results from the expansion cohort, in which patients with HNSCC, irrespective of biomarker status, received a fixed dose of pembrolizumab at a less frequent dosing schedule, are reported. Patients and Methods Patients with R/M HNSCC, irrespective of PD-L1 or human papillomavirus status, received pembrolizumab 200 mg intravenously once every 3 weeks. Imaging was performed every 8 weeks. Primary end points were overall response rate (ORR) per central imaging vendor (Response Evaluation Criteria in Solid Tumors v1.1) and safety. Secondary end points included progression-free survival, overall survival, and association of response and PD-L1 expression. Patients who received one or more doses of pembrolizumab were included in analyses. Results Of 132 patients enrolled, median age was 60 years (range, 25 to 84 years), 83% were male, and 57% received two or more lines of therapy for R/M disease. ORR was 18% (95% CI, 12 to 26) by central imaging vendor and 20% (95% CI, 13 to 28) by investigator review. Median duration of response was not reached (range, ≥ 2 to ≥ 11 months). Six-month progression-free survival and overall survival rates were 23% and 59%, respectively. By using tumor and immune cells, a statistically significant increase in ORR was observed for PD-L1-positive versus -negative patients (22% v 4%; P = .021). Treatment-related adverse events of any grade and grade ≥ 3 events occurred in 62% and 9% of patients, respectively. Conclusion Fixed-dose pembrolizumab 200 mg administered once every 3 weeks was well tolerated and yielded a clinically meaningful ORR with evidence of durable responses, which supports further development of this regimen in patients with advanced HNSCC.

Abstract: This review provides an overview of the clinical issue of poorly controlled postoperative pain and therapeutic approaches that may help to address this common unresolved health-care challenge. Postoperative pain is not adequately managed in greater than 80% of patients in the US, although rates vary depending on such factors as type of surgery performed, analgesic/anesthetic intervention used, and time elapsed after surgery. Poorly controlled acute postoperative pain is associated with increased morbidity, functional and quality-of-life impairment, delayed recovery time, prolonged duration of opioid use, and higher health-care costs. In addition, the presence and intensity of acute pain during or after surgery is predictive of the development of chronic pain. More effective analgesic/anesthetic measures in the perioperative period are needed to prevent the progression to persistent pain. Although clinical findings are inconsistent, some studies of local anesthetics and nonopioid analgesics have suggested potential benefits as preventive interventions. Conventional opioids remain the standard of care for the management of acute postoperative pain; however, the risk of opioid-related adverse events can limit optimal dosing for analgesia, leading to poorly controlled acute postoperative pain. Several new opioids have been developed that modulate μ-receptor activity by selectively engaging intracellular pathways associated with analgesia and not those associated with adverse events, creating a wider therapeutic window than unselective conventional opioids. In clinical studies, oliceridine (TRV130), a novel μ-receptor G-protein pathway-selective modulator, produced rapid postoperative analgesia with reduced prevalence of adverse events versus morphine.

Abstract: Cancer continues to be one of the most difficult global healthcare problems. Although there is a large library of drugs that can be used in cancer treatment, the problem is selectively killing all the cancer cells while reducing collateral toxicity to healthy cells. There are several biological barriers to effective drug delivery in cancer such as renal, hepatic, or immune clearance. Nanoparticles loaded with drugs can be designed to overcome these biological barriers to improve efficacy while reducing morbidity. Nanomedicine has ushered in a new era for drug delivery by improving the therapeutic indices of the active pharmaceutical ingredients engineered within nanoparticles. First generation nanomedicines have received widespread clinical approval over the past two decades, from Doxil® (liposomal doxorubicin) in 1995 to Onivyde® (liposomal irinotecan) in 2015. This review highlights the biological barriers to effective drug delivery in cancer, emphasizing the need for nanoparticles for improving therapeutic outcomes. A summary of different nanoparticles used for drug delivery applications in cancer are presented. The review summarizes recent successes in cancer nanomedicine in the clinic. The clinical trials of Onivyde leading to its approval in 2015 by the Food and Drug Adminstration are highlighted as a case study in the recent clinical success of nanomedicine against cancer. Next generation nanomedicines need to be better targeted to specifically destroy cancerous tissue, but face several obstacles in their clinical development, including identification of appropriate biomarkers to target, scale-up of synthesis, and reproducible characterization. These hurdles need to be overcome through multidisciplinary collaborations across academia, pharmaceutical industry, and regulatory agencies in order to achieve the goal of eradicating cancer. This review discusses the current use of clinically approved nanomedicines, the investigation of nanomedicines in clinical trials, and the challenges that may hinder development of the nanomedicines for cancer treatment.

Abstract: This article brings together empirical academic research on public sector innovation. Via a systematic literature review, we investigate 181 articles and books on public sector innovation, published between 1990 and 2014. These studies are analysed based on the following themes: (1) the definitions of innovation, (2) innovation types, (3) goals of innovation, (4) antecedents of innovation and (5) outcomes of innovation. Based upon this analysis, we develop an empirically based framework of potentially important antecedents and effects of public sector innovation. We put forward three future research suggestions: (1) more variety in methods: moving from a qualitative dominance to using other methods, such as surveys, experiments and multi-method approaches; (2) emphasize theory development and testing as studies are often theory-poor; and (3) conduct more cross-national and cross-sectoral studies, linking for instance different governance and state traditions to the development and effects of public sector innovation.

Abstract: In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

Abstract: This paper explores four issues that have emerged from the research on social, cognitive and teaching presence in an online community of inquiry. The early research in the area of online communities of inquiry has raised several issues with regard to the creation and maintenance of social, cognitive and teaching presence that require further research and analysis. The other overarching issue is the methodological validity associated with the community of inquiry framework. The first issue is about shifting social presence from socio-emotional support to a focus on group cohesion (from personal to purposeful relationships). The second issue concerns the progressive development of cognitive presence (inquiry) from exploration to resolution. That is, moving discussion beyond the exploration phase. The third issue has to do with how we conceive of teaching presence (design, facilitation, direct instruction). More specifically, is there an important distinction between facilitation and direct instruction? Finally, the methodological issue concerns qualitative transcript analysis and the validity of the coding protocol.

Abstract: Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. Recent work has shown that self-attention is an effective way of modeling textual sequences. In this work, we generalize a recently proposed model architecture based on self-attention, the Transformer, to a sequence modeling formulation of image generation with a tractable likelihood. By restricting the self-attention mechanism to attend to local neighborhoods we significantly increase the size of images the model can process in practice, despite maintaining significantly larger receptive fields per layer than typical convolutional neural networks. While conceptually simple, our generative models significantly outperform the current state of the art in image generation on ImageNet, improving the best published negative log-likelihood on ImageNet from 3.83 to 3.77. We also present results on image super-resolution with a large magnification ratio, applying an encoder-decoder configuration of our architecture. In a human evaluation study, we find that images generated by our super-resolution model fool human observers three times more often than the previous state of the art.

Abstract: The new ICD-10-CM (M62.84) code for sarcopenia represents a major step forward in recognizing sarcopenia as a disease. This should lead to an increase in availability of diagnostic tools and the enthusiasm for pharmacological companies to develop drugs for sarcopenia.

Abstract: In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs...

Abstract: Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the task of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images. This secondary task helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task. Multiple experiments on the PACS, VLCS, Office-Home and digits datasets confirm our intuition and show that this simple method outperforms previous domain generalization and adaptation solutions. An ablation study further illustrates the inner workings of our approach.

Abstract: We review lattice results related to pion, kaon, D- and B-meson physics with the aim of making them easily accessible to the particle-physics community. More specifically, we report on the determination of the light-quark masses, the form factor [Formula: see text], arising in the semileptonic [Formula: see text] transition at zero momentum transfer, as well as the decay constant ratio [Formula: see text] and its consequences for the CKM matrix elements [Formula: see text] and [Formula: see text]. Furthermore, we describe the results obtained on the lattice for some of the low-energy constants of [Formula: see text] and [Formula: see text] Chiral Perturbation Theory. We review the determination of the [Formula: see text] parameter of neutral kaon mixing as well as the additional four B parameters that arise in theories of physics beyond the Standard Model. The latter quantities are an addition compared to the previous review. For the heavy-quark sector, we provide results for [Formula: see text] and [Formula: see text] (also new compared to the previous review), as well as those for D- and B-meson-decay constants, form factors, and mixing parameters. These are the heavy-quark quantities most relevant for the determination of CKM matrix elements and the global CKM unitarity-triangle fit. Finally, we review the status of lattice determinations of the strong coupling constant [Formula: see text].

Abstract: Detecting single-photon level signals—carriers of both classical and quantum information—is particularly challenging for low-energy microwave frequency excitations. Here we introduce a superconducting amplifier based on a Josephson junction transmission line. Unlike current standing-wave parametric amplifiers, this traveling wave architecture robustly achieves high gain over a bandwidth of several gigahertz with sufficient dynamic range to read out 20 superconducting qubits. To achieve this performance, we introduce a subwavelength resonant phase-matching technique that enables the creation of nonlinear microwave devices with unique dispersion relations. We benchmark the amplifier with weak measurements, obtaining a high quantum efficiency of 75% (70% including noise added by amplifiers following the Josephson amplifier). With a flexible design based on compact lumped elements, this Josephson amplifier has broad applicability to microwave metrology and quantum optics.