Miracle-dz/UniFAR-FTUs huggingface dataset:
Single event effects on commercial SRAMs and power MOSFETs: final results of the CRUX flight experiment on APEX

The CRUX experiment on the APEX satellite monitored single event effects on 1 Mbit and 256 Kbit SRAMs and 100 volt and 200 volt power MOSFETs. The single event upsets (SEUs) on the SRAMs were mapped in geographic and geomagnetic coordinates. Other single event effects (SEEs) were observed, including multiple bits upsets (MBUs) and single hard errors ("stuck bits"). Sensitivity to programmed logic state was also analyzed. The relatively large sample sizes for most part types and almost two year flight time in a hostile radiation environment provided a data set adequate for investigation of the range in device response within the flight lots. Single event burn-out (SEB) conditions were observed on the power MOSFETs. The rates on the 200 volt devices were much higher than on the 100 volt and occurred primarily in regions of space dominated by trapped protons.
A MeerKAT-meets-LOFAR Study of MS 1455.0+2232: A 590 kiloparsec ‘Mini’-Halo in a Sloshing Cool-Core Cluster

Radio mini-haloes are poorly-understood, moderately-extended diffuse radio sources that trace the presence of magnetic fields and relativistic electrons on scales of hundreds of kiloparsecs, predominantly in relaxed clusters. With relatively few confirmed detections to-date, many questions remain unanswered. This paper presents new radio observations of the galaxy cluster MS 1455.0+2232 performed with MeerKAT (covering the frequency range 872−1712 MHz) and LOFAR (covering 120−168 MHz), the first results from a homogeneously selected mini-halo census. We find that this mini-halo extends for ∼590 kpc at 1283 MHz, significantly larger than previously believed, and has a flatter spectral index (α = −0.97 ± 0.05) than typically expected. Our X-ray analysis clearly reveals a large-scale (254 kpc) sloshing spiral in the intracluster medium. We perform a point-to-point analysis, finding a tight single correlation between radio and X-ray surface brightness with a super-linear slope of $b_{\rm 1283\, MHz} = 1.16^{+0.06}_{-0.07}$ and $b_{\rm 145\, MHz} = 1.15^{+0.09}_{-0.08}$; this indicates a strong link between the thermal and non-thermal components of the intracluster medium. Conversely, in the spectral index/X-ray surface brightness plane, we find that regions inside and outside the sloshing spiral follow different correlations. We find compelling evidence for multiple sub-components in this mini-halo for the first time. While both the turbulent (re-)acceleration and hadronic scenarios are able to explain some observed properties of the mini-halo in MS 1455.0+2232, neither scenario is able to account for all the evidence presented by our analysis.
Interaction-Aware Trajectory Prediction and Planning for Autonomous Vehicles in Forced Merge Scenarios

Merging is, in general, a challenging task for both human drivers and autonomous vehicles, especially in dense traffic, because the merging vehicle typically needs to interact with other vehicles to identify or create a gap and safely merge into. In this paper, we consider the problem of autonomous vehicle control for forced merge scenarios. We propose a novel game-theoretic controller, called the Leader-Follower Game Controller (LFGC), in which the interactions between the autonomous ego vehicle and other vehicles with a priori uncertain driving intentions is modeled as a partially observable leader-follower game. The LFGC estimates the other vehicles’ intentions online based on observed trajectories, and then predicts their future trajectories and plans the ego vehicle’s own trajectory using Model Predictive Control (MPC) to simultaneously achieve probabilistically guaranteed safety and merging objectives. To verify the performance of LFGC, we test it in simulations and with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5% in merging.
Quantum annealing for industry applications: introduction and review

Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the QA algorithm for programmable use. Specifically, QA processors produced by D-Wave systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of QA technology. We identify the advantages, limitations, and potential of QA for both researchers and practitioners from various fields.
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models

We study the privacy implications of training recurrent neural networks (RNNs) with sensitive training datasets. Considering membership inference attacks (MIAs)—which aim to infer whether or not specific data records have been used in training a given machine learning model—we provide empirical evidence that a neural network's architecture impacts its vulnerability to MIAs. In particular, we demonstrate that RNNs are subject to a higher attack accuracy than feed-forward neural network (FFNN) counterparts. Additionally, we study the effectiveness of two prominent mitigation methods for preempting MIAs, namely weight regularization and differential privacy. For the former, we empirically demonstrate that RNNs may only benefit from weight regularization marginally as opposed to FFNNs. For the latter, we find that enforcing differential privacy through either of the following two methods leads to a less favorable privacy-utility trade-off in RNNs than alternative FFNNs: (i) adding Gaussian noise to the gradients calculated during training as a part of the so-called extsc{DP-SGD} algorithm and (ii) adding Gaussian noise to the trainable parameters as a part of a post-training mechanism that we propose. As a result, RNNs can also be less amenable to mitigation methods, bringing us to the conclusion that the privacy risks pertaining to the recurrent architecture are higher than the feed-forward counterparts.
Biased Gradient Estimate with Drastic Variance Reduction for Meta Reinforcement Learning

Despite the empirical success of meta reinforcement learning (meta-RL), there are still a number poorly-understood discrepancies between theory and practice. Critically, biased gradient estimates are almost always implemented in practice, whereas prior theory on meta-RL only establishes convergence under unbiased gradient estimates. In this work, we investigate such a discrepancy. In particular, (1) We show that unbiased gradient estimates have variance $\Theta(N)$ which linearly depends on the sample size $N$ of the inner loop updates; (2) We propose linearized score function (LSF) gradient estimates, which have bias $\mathcal{O}(1/\sqrt{N})$ and variance $\mathcal{O}(1/N)$; (3) We show that most empirical prior work in fact implements variants of the LSF gradient estimates. This implies that practical algorithms"accidentally"introduce bias to achieve better performance; (4) We establish theoretical guarantees for the LSF gradient estimates in meta-RL regarding its convergence to stationary points, showing better dependency on $N$ than prior work when $N$ is large.
Synapse Compression for Event-Based Convolutional-Neural-Network Accelerators

Manufacturing-viable neuromorphic chips require novel compute architectures to achieve the massively parallel and efficient information processing the brain supports so effortlessly. The most promising architectures for that are spiking/event-based, which enables massive parallelism at low complexity. However, the large memory requirements for synaptic connectivity are a showstopper for the execution of modern convolutional neural networks (CNNs) on massively parallel, event-based architectures. The present work overcomes this roadblock by contributing a lightweight hardware scheme to compress the synaptic memory requirements by several thousand times—enabling the execution of complex CNNs on a single chip of small form factor. A silicon implementation in a 12-nm technology shows that the technique achieves a total memory-footprint reduction of up to 374× compared to the best previously published technique at a negligible area overhead.
Improving COVID-19 CXR Detection with Synthetic Data Augmentation

Since the beginning of the COVID-19 pandemic, researchers have developed deep learning models to classify COVID-19 induced pneumonia. As with many medical imaging tasks, the quality and quantity of the available data is often limited. In this work we train a deep learning model on publicly available COVID-19 image data and evaluate the model on local hospital chest X-ray data. The data has been reviewed and labeled by two radiologists to ensure a high quality estimation of the generalization capabilities of the model. Furthermore, we are using a Generative Adversarial Network to generate synthetic X-ray images based on this data. Our results show that using those synthetic images for data augmentation can improve the model's performance significantly. This can be a promising approach for many sparse data domains.
Bottomonia screening masses from 2 + 1 flavor QCD

The sequential melting of the bottomonium states is one of the important signals for the existence of a quark gluon plasma. The study of bottomonia spectral functions on the lattice is a difficult task for many reasons. Calculations based on NRQCD, that are commonly used for such purpose, are not applicable at high temperatures. In this work we propose a new method to study this problem by calculating the spatial screening masses of bottomonium states. We calculate the spatial meson correlators and extract the screening masses for mesons in different quantum channels using highly improved staggered quark (HISQ) action for bottom quarks and dynamical 2+1 flavor QCD HISQ gauge configurations. The typical lattices we choose are of size N3 s × Nτ where Ns = 4Nτ and Nτ = 8, 10, 12. We consider the temperature range T = 300-1000 MeV. We show that for T > 500 MeV the temperature dependence of the screening masses of the ground state bottomonia are compatible with the expectations based on uncorrelated quark anti-quark pairs.
Exploring Neural Models for Query-Focused Summarization

Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released datasets, such as QMSum or AQuaMuSe, facilitate research efforts in QFS, the field lacks a comprehensive study of the broad space of applicable modeling methods. In this paper we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models. Within those categories, we investigate existing models and explore strategies for transfer learning. We also present two modeling extensions that achieve state-of-the-art performance on the QMSum dataset, up to a margin of 3.38 ROUGE-1, 3.72 ROUGE2, and 3.28 ROUGE-L when combined with transfer learning strategies. Results from human evaluation suggest that the best models produce more comprehensive and factually consistent summaries compared to a baseline model. Code and checkpoints are made publicly available: https://github.com/salesforce/query-focused-sum.
A gradient sampling method with complexity guarantees for general Lipschitz functions

Zhang et al. [9] proposed a novel modiﬁcation of Goldstein’s classical subgradient method for minimizing Lipschitz continuous functions, which has ﬁnite-time eﬃciency guarantees. This work, however, makes use of a nonstandard subgradient oracle model and requires the function to be directionally diﬀerentiable. In this note, we show that both of these assumptions can be dropped by simply adding a small random perturbation in each step of their algorithm. The resulting method works on any Lipschitz function whose value and gradient can be evaluated at points of diﬀerentiablity
TRACER: Extreme Attention Guided Salient Object Tracing Network

Existing studies on salient object detection (SOD) focus on extracting distinct objects with edge features and aggregating multi-level features to improve SOD performance. However, both performance gain and computational efficiency cannot be achieved, which has motivated us to study the inefficiencies in existing encoder-decoder structures to avoid this trade-off. We propose TRACER which excludes multi-decoder structures and minimizes the learning parameters usage by employing attention guided tracing modules (ATMs), as shown in Fig. 1.
Impurity profile effects of buffer layer on PT-IGBT characteristics

The effects of Gaussian impurity profiles in the p/sup +/ anode/n-buffer layer of PT-IGBTs on device characteristics are studied numerically and analytically. The results are compared with those from the uniform impurity profiles. A better tradeoff between the forward voltage drop and the turn-off time is found in the Gaussian impurity profile.
Fast Thermalization from the Eigenstate Thermalization Hypothesis

The Eigenstate Thermalization Hypothesis (ETH) has played a major role in understanding thermodynamic phenomena in closed quantum systems. However, its connection to the timescale of thermalization for open system dynamics has remained elusive. This paper establishes a rigorous link between ETH and fast thermalization to the global Gibbs state. Specifically, we demonstrate fast thermalization for a system coupled weakly to a bath of quasi-free Fermions that we refresh periodically. To describe the joint evolution, we derive a finite-time version of Davies' generator with explicit error bounds and resource estimates. Our approach exploits a critical feature of ETH: operators in the energy basis can be modeled by independent random matrices in a near-diagonal band. This gives quantum expanders at nearby eigenstates of the Hamiltonian and reduces the problem to a one-dimensional classical random walk on the energy eigenstates. Our results explain finite-time thermalization in chaotic open quantum systems.
Self-Supervised Representation Learning Framework for Remote Physiological Measurement Using Spatiotemporal Augmentation Loss

Recent advances in supervised deep learning methods are enabling remote measurements of photoplethysmography-based physiological signals using facial videos. The performance of these supervised methods, however, are dependent on the availability of large labelled data. Contrastive learning as a self-supervised method has recently achieved state-of-the-art performances in learning representative data features by maximising mutual information between different augmented views. However, existing data augmentation techniques for contrastive learning are not designed to learn physiological signals from videos and often fail when there are complicated noise and subtle and periodic colour/shape variations between video frames. To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data. Firstly, we propose a landmark-based spatial augmentation that splits the face into several informative parts based on the Shafer’s dichromatic reﬂection model to characterise subtle skin colour fluctuations. We also formulate a sparsity-based temporal augmentation exploiting Nyquist–Shannon sampling theorem to effectively capture periodic temporal changes by modelling physiological signal features. Furthermore, we introduce a constrained spatiotemporal loss which generates pseudo-labels for augmented video clips. It is used to regulate the training process and handle complicated noise. We evaluated our framework on 3 public datasets and demonstrated superior performances than other self-supervised methods and achieved competitive accuracy compared to the state-of-the-art supervised methods. Code is available at https://github.com/Dylan-H-Wang/SLF-RPM.
Expansion of channel number in optical demultiplexer using cascaded photopolymer volume gratings

A method to expand the number of channels in the optical demultiplexer (DMUX) using two cascaded photopolymer volume gratings is proposed and presented. A 0.4-nm-spaced 130-channel DMUX with the channel uniformity of 3.5 dB, the 3-dB bandwidth of 0.12 nm, and the channel crosstalk of -20 dB is experimentally demonstrated.
Understanding Patients’ and Living Donors’ Kidney Paired Donation Educational Experiences and Recommendations for Improvement

Introduction Educating potential kidney patients and living donors about the risks and benefits of kidney paired donation to ensure they make informed decisions is complex. This study aimed to increase understanding of patients’ and donors’ decision-making about donation, the educational content they received, and their recommendations for educational improvements. Method We conducted a mixed methods study, including semistructured interviews and quantitative surveys, with 43 participants (25 living donors; 18 kidney recipients). Findings Participants reported that the benefits of paired donation motivated them to participate (ie, helping multiple people, receiving a transplant sooner, flexible timing of donation). Although deciding to participate in paired donation was a systematic, logical, and carefully considered process for some. For most, it was a quickly made, often emotion-based decision. Paired donation educational content on different topics varied, with recipients reporting receiving less information than donors about donor protections and processes to ameliorate the challenges faced, such as broken swaps and chains, and delays in matching. Those who faced challenges requested more information about donor protections and support during and after paired donation. Although many acknowledged their transplant coordinators’ helpfulness, participants also recommended being more proactive in learning about kidney paired donation and speaking to former donors and recipients beforehand. Discussion Standardized, health literate educational content addressing the gaps and variability in education received may help increase paired donation informed decision-making.
Sudden death related to the gastrointestinal system in Tunisia: A 13 year autopsy study

Sudden gastrointestinal (GI) death is an unexpected death due to digestive system causes mainly found after autopsy. The literature is rich in articles that studied sudden death due to cardiac causes while sudden GI deaths remain less well‐documented. We retrospectively investigated all cases of gastrointestinal death at the Department of Forensic Medicine in Tunis, over 13 years (January 1, 2006 to December 31, 2018). Two hundred and eight cases were collected. The mean age of our series was 51.06 ± 20.99 years. No history of digestive disorders was reported in 78.4%, and no family history of sudden death was found in any cases. A male predominance was found in most epidemiological characteristics of the sample with a significant statistical rate in some features. Non‐specific abdominal pain was the most described symptom (n = 92). Perforation of GI tract was the common mechanism involved in the death of 55 cases, of which 44 were related to ulcer perforation. These ulcer perforations were statistically more reported in smokers and people suffering from schizophrenia. Intestinal obstruction was the second commonest cause of death, mainly found in the elderly. Sudden death in children was most frequently caused by acute intussusception. This study highlights that systematic study of sudden death due to GI causes might provide opportunities to identify avenues for overall health improvement.
Association Between MTHFD1 1958G > A Variant and non-Syndromic Cleft lip and Palate: An Updated Meta-Analysis

Introduction Non-syndromic cleft lip and palate (NSCLP) is one of the most common and challenging congenital deformities worldwide. Previous research has linked the methylenetetrahydrofolate dehydrogenase1 (MTHFD1) gene to orofacial cleft (OFC) susceptibility via a complex metabolism. Studies analyzing the MTHFD1 1958G > A variant and NSCLP are contradictory. This study aims to evaluate the association between the MTHFD1 1958G > A variant and NSCLP by meta-analysis. Methods PubMed, Web of Science, MEDLINE, and Google Scholar databases were searched to retrieve the eligible studies. A fixed- or random-effect model was used to calculate pooled odds ratio (OR) and 95% confidence interval (CI). All analyses were calculated by Metagenyo software. To detect heterogeneity, the Cochrane Q and I2 statistics were used. The publication bias was estimated using funnel plots and Egger’s test. Results Our study suggested that the MTHFD1 1958G > A variant allele “A” does not appear to increase the risk of NSCLP (A vs G random effect model: Overall P = .501, OR = 1.07, CI = 0.88–1.31; Asians P = .245, OR = 1.29, CI = 0.84–1.97; Caucasians P = .658, OR = 0.95, CI = 0.76–1.19). Similarly, mutant genotypes also did not exhibit increased risk for NSCLP in the overall populations as well in subgroup analysis by ethnicity (AA + AG vs GG: Overall P = .684, OR = 1.06, CI = 0.80–1.39; Asians P = .240, OR = 1.47, CI = 0.77–2.78; Caucasians P = .923, OR = 0.99, CI = 0.85–1.16). Conclusions Our data suggest no association between the MTHFD1 1958G > A variant and NSCLP. Additional well-designed studies are needed to better understand the role of MTHFD1 polymorphisms in the etiopathogenesis of NSCLP.
Benefit of Postoperative Radiotherapy for Patients With Nonmetastatic Adrenocortical Carcinoma: A Population-Based Analysis.

BACKGROUND Adrenocortical carcinoma (ACC) is an aggressive cancer with high recurrence rates and poor prognosis, even after radical surgery. The survival benefit of adjuvant radiotherapy (RT) in patients with ACC has not been well explored. The aim of this study was to evaluate the effect of adjuvant RT on the survival outcome of patients with ACC. PATIENTS AND METHODS All patients with nonmetastatic ACC who underwent complete resection were identified from the SEER database (2004-2016). Overall survival (OS) was estimated using the Kaplan-Meier method. Multivariable Cox regression analysis was performed to identify prognostic factors associated with survival. RESULTS Of 365 patients with nonmetastatic ACC, 55 (15.1%) received adjuvant RT and the remainder underwent surgery alone. Patient characteristics were similar between the 2 groups, but those with a higher disease stage were more likely to receive adjuvant RT. Overall, patients receiving RT seemed to have better survival compared with the non-RT group (3-year OS rate, 63.1% vs 52.8%; P<.062). After adjustment for confounding factors, adjuvant RT was indeed associated with a 48% decreased risk of death (hazard ratio, 0.52; 95% CI, 0.29-0.91; P=.023) for all patients. In addition, adjuvant RT may confer a survival benefit only in patients with a high risk of recurrence (3-year OS rate, 55.1% vs 40.0%; P=.048) rather than in those with low/moderate-risk ACC (P=.559). CONCLUSIONS Our findings suggest that adjuvant RT may be associated with improved survival in patients with nonmetastatic ACC who underwent radical surgery, especially those with high risk of recurrence.