Miracle-dz/UniFAR-FTUs huggingface dataset:
The Psychosocial Benefits of Sport Participation During COVID-19 Are Only Partially Explained by Increased Physical Activity

The purpose of this study was to determine whether physical activity (PA) increases were responsible for the improvements in mental health and quality of life (QOL) seen among adolescents who returned to sport during the COVID-19 pandemic. Adolescent athletes were asked to complete a survey in October 2020 regarding demographic information, whether they had returned to sport participation (no [DNP], yes [PLY]), school instruction type (virtual, in-person, hybrid), anxiety, depression, QOL, and PA. Anxiety, depression, QOL and PA were compared between PLY and DNP using least squares means from linear models adjusted for age, gender, and instruction type. Mediation analysis assessed whether the relationship between sport status and anxiety, depression, and QOL was mediated by PA. 171 athletes had returned to play, while 388 had not. PLY athletes had significantly lower anxiety (3.6 +/- 0.4 v 8.2 +/- 0.6, p < 0.001) and depression (4.2 +/- 0.4 v 7.3 +/- 0.6, p < 0.001), and significantly higher QOL (88.1 +/- 1.0 v 80.2 +/- 1.4, p < 0.001) and PA (24.0 +/- 0.5 v 16.3 +/- 0.7, p < 0.001). PA explained a significant, but relatively small portion of the difference in depression (22.1%, p = 0.02) and QOL (16.0%, p = 0.048) between PLY and DNP athletes, but did not explain the difference in anxiety (6.6%, p = 0.20). Increased PA is only responsible for a small portion of the improvements in depression and QOL among athletes who returned to sports and unrelated to improvements in anxiety. This suggests that the majority of the mental health benefits of sport participation for adolescents during the COVID-19 pandemic are independent of, and in addition to, the benefits of increased PA.
COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation

Background: An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions. Research objective: To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to APACHE IVa and SOFA. Methods: A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. AUROC was calculated for C-TIME, APACHE IVa and SOFA. Results: The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72-0.79), vs 0.67 (0.64-0.71) and 0.59 (0.55-0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001). Conclusions: C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona. edu/ctime.
The E-Commerce Book: Building the E-Empire, Second Edition

From the Publisher: The E-Commerce Book: Building the E-Empire will lead you through e-commerce basics, explaining how both large and small companies are riding the wave to huge success. This book provides the opportunity to participate in what many have called the next "Industrial Revolution. " This book is direct result of over 10 years of industry experience and "The E-Commerce Program," developed for professionals in the area of e-commerce. Primarily, the book focuses on business concepts and how to apply this technology in order to be successful. The book covers globalizing your company, marketing and advertising, market trends, vendor solutions and must-know technologies such as credit card verification systems, security, auction technologies, storefronts, and overall technology architecture. The final chapter focuses how to get and deploy e-commerce solutions from process re-engineering to actual deployment and testing. Key Features The E-Commerce Book: Building the E-Empire will lead you through e-commerce basics, explaining how both large and small companies are riding the wave to huge success. This book provides the opportunity to participate in what many have called the next "Industrial Revolution. " This book is direct result of over 10 years of industry experience and "The E-Commerce Program," developed for professionals in the area of e-commerce. Primarily, the book focuses on business concepts and how to apply this technology in order to be successful. The book covers globalizing your company, marketing and advertising, market trends, vendor solutions and must-know technologies such as credit card verification systems, security, auction technologies, storefronts, and overall technology architecture. The final chapter focuses how to get and deploy e-commerce solutions from process re-engineering to actual deployment and testing. Key Features The first complete book on deploying an e-commerce solution for small, medium and large businesses Reveals how to open your first e-commerce business and provides a globalized site and deployment planning Walks you through the business aspects of e-commerce and translates these into the overall architecture and design solution Will help any corporation, small business, or entrepreneur to move their company into the 21st century The first complete book on deploying an e-commerce solution for small, medium and large businesses Reveals how to open your first e-commerce business and provides a globalized site and deployment planning Walks you through the business aspects of e-commerce and translates these into the overall architecture and design solution Will help any corporation, small business, or entrepreneur to move their company into the 21st century
Ritus functions for graphene-like systems with magnetic fields generated by first-order intertwining operators

In this work, we construct the exact propagator for Dirac fermions in graphene-like systems immersed in external static magnetic fields with non-trivial spatial dependence. Such field profiles are generated within a first-order supersymmetric framework departing from much simpler (seed) magnetic field examples. The propagator is spanned on the basis of the Ritus eigenfunctions, corresponding to the Dirac fermion asymptotic states in the non-trivial magnetic field background which nevertheless admits a simple diagonal form in momentum space. This strategy enlarges the number of magnetic field profiles in which the fermion propagator can be expressed in a closed-form. Electric charge and current densities are found directly from the corresponding propagator and compared against similar findings derived from other methods.
Multi-Hypothesis Scan Matching through Clustering

Graph-SLAM is a well-established algorithm for constructing a topological map of the environment while simultaneously attempting the localisation of the robot. It relies on scan matching algorithms to align noisy observations along robot's movements to compute an estimate of the current robot's location. We propose a fundamentally different approach to scan matching tasks to improve the estimation of roto-translation displacements and therefore the performance of the full SLAM algorithm. A Monte-Carlo approach is used to generate weighted hypotheses of the geometrical displacement between two scans, and then we cluster these hypotheses to compute the displacement that results in the best alignment. To cope with clusterization on roto-translations, we propose a novel clustering approach that robustly extends Gaussian Mean-Shift to orientations by factorizing the kernel density over the roto-translation components. We demonstrate the effectiveness of our method in an extensive set of experiments using both synthetic data and the Intel Research Lab's benchmarking datasets. The results confirms that our approach has superior performance in terms of matching accuracy and runtime computation than the state-of-the-art iterative point-based scan matching algorithms.
Plasmon resonances in multilayer Fanoshells

We develop a theoretical framework, based on a multipole, quasi-static approach, for the prediction of the localized surface plasmon resonances in Fanoshells formed via geometrical symmetry-breaking in multilayer nanoshells consisting of a metallic core, a dielectric inner shell, and a metallic outer shell. By tuning the core and shell offsets of a gold-silica-gold multilayer nanoshell, we show that the theoretical model is in good agreement with electrodynamic simulations. The dipolar resonances are more suppressed when the core and the outer shell are concurrently offset, and less suppressed when either the core, the inner shell, or the outer shell is offset. We attribute the former to coupling constants arising from dual symmetry-breaking, and the latter to coupling constants due to single symmetry-breaking. Using three performance parameters, we propose the outer shell offset as the optimal Fanoshell for sensing applications. This study systematically investigates all types of offset-based, symmetry-broken, metal-dielectric-metal multilayer nanoshells within the Rayleigh regime.
An Efficient Locally Reactive Controller for Safe Navigation in Visual Teach and Repeat Missions

To achieve successful field autonomy, mobile robots need to freely adapt to changes in their environment. Visual navigation systems such as Visual Teach and Repeat (VT&R) often assume the space around the reference trajectory is free, but if the environment is obstructed path tracking can fail or the robot could collide with a previously unseen obstacle. In this work, we present a locally reactive controller for a VT&R system that allows a robot to navigate safely despite physical changes to the environment. Our controller uses a local elevation map to compute vector representations and outputs twist commands for navigation at 10 Hz. They are combined in a Riemannian Motion Policies (RMP) controller that requires <2ms to run on a CPU. We integrated our controller with a VT&R system onboard an ANYmal C robot and tested it in indoor cluttered spaces and a large-scale underground mine. We demonstrate that our locally reactive controller keeps the robot safe when physical occlusions or loss of visual tracking occur such as when walking close to walls, crossing doorways, or traversing narrow corridors.
Tackling Multipath and Biased Training Data for IMU-Assisted BLE Proximity Detection

Proximity detection is to determine whether an IoT receiver is within a certain distance from a signal transmitter. Due to its low cost and high popularity, Bluetooth low energy (BLE) has been used to detect proximity based on the received signal strength indicator (RSSI). To address the fact that RSSI can be markedly influenced by device carriage states, previous works have incorporated RSSI with inertial measurement unit (IMU) using deep learning. However, they have not sufficiently accounted for the impact of multipath. Furthermore, due to the special setup, the IMU data collected in the training process may be biased, which hampers the system’s robustness and generalizability. This issue has not been studied before.We propose PRID, an IMU-assisted BLE proximity detection approach robust against RSSI fluctuation and IMU data bias. PRID histogramizes RSSI to extract multipath features and uses carriage state regularization to mitigate overfitting due to IMU data bias. We further propose PRID-lite based on a binarized neural network to substantially cut memory requirements for resource-constrained devices. We have conducted extensive experiments under different multipath environments, data bias levels, and a crowdsourced dataset. Our results show that PRID significantly reduces false detection cases compared with the existing arts (by over 50%). PRID-lite further reduces over 90% PRID model size and extends 60% battery life, with a minor compromise in accuracy (7%).
Captcha Attack: Turning Captchas Against Humanity

—Nowadays, people generate and share massive con- tent on online platforms (e.g., social networks, blogs). In 2021, the 1.9 billion daily active Facebook users posted around 150 thousand photos every minute. Content moderators constantly monitor these online platforms to prevent the spreading of inappropriate content (e.g., hate speech, nudity images). Based on deep learning (DL) advances, Automatic Content Moderators (ACM) help human moderators handle high data volume. Despite their advantages, attackers can exploit weaknesses of DL com- ponents (e.g., preprocessing, model) to affect their performance. Therefore, an attacker can leverage such techniques to spread inappropriate content by evading ACM. While surﬁng the Web, we observed users posting hateful or sexually explicit content with simple ‘obfuscation’ techniques that recall classical textual captchas. Through our investigation, we revisit the concept of textual captchas and propose a taxonomy of obfuscations applied by Web users. Then, inspired by these observations, we propose CAPA (CAPtcha Attack), an adversarial technique that allows users to spread inappropriate text online by evading ACM controls. CAPA, by generating custom textual CAPTCHAs, exploits ACM’s careless design implementations and internal procedures vulnerabilities. We test our attack on real-world ACM, and the results conﬁrm the ferocity of our simple yet effective attack, reaching up to a 100% evasion success in most cases. At the same time, we demonstrate the difﬁculties in designing CAPA mitigations, opening new challenges in CAPTCHAs research area. As a result of our experiments, we found that outlier detection approaches were effective in spotting captchas over usual social media content, exceeding 80%
Entropic Optimal Transport in Random Graphs

In graph analysis, a classic task consists in computing similarity measures between (groups of) nodes. In latent space random graphs, nodes are associated to unknown latent variables. One may then seek to compute distances directly in the latent space, using only the graph structure. In this paper, we show that it is possible to consistently estimate entropic-regularized Optimal Transport (OT) distances between groups of nodes in the latent space. We provide a general stability result for entropic OT with respect to perturbations of the cost matrix. We then apply it to several examples of random graphs, such as graphons or $\epsilon$-graphs on manifolds. Along the way, we prove new concentration results for the so-called Universal Singular Value Thresholding estimator, and for the estimation of geodesic distances on a manifold.
Tracing pebble drift and trapping using radial carbon depletion profiles in protoplanetary disks

Context. The composition of planets may be largely determined by the chemical processing and accretion of icy pebbles in protoplan- etary disks. Recent observations of protoplanetary disks hint at wide-spread depletion of gaseous carbon. The missing volatile carbon is likely frozen in CO and/or CO 2 ice on grains and locked into the disk through pebble trapping in pressure bumps or planetesimals. Aims. We aim to measure the total elemental C/H ratio in the outer region of seven disks, four of which have been previously shown to be depleted of carbon gas interior to 0.1 AU through near-infrared spectroscopy. Methods. We present the results of the ﬁrst successful Atacama Compact Array (ACA) [C I] J = 1–0 mini-survey of seven protoplanetary disks. Using tailored azimuthally symmetric Dust And LInes thermo-chemical disk models, supported by the [C I] J = 1–0 and resolved CO isotopologue data, we determine the system-averaged elemental volatile carbon abundance in the outer disk of three sources. Results. Six out of the seven sources are detected in [C I] J = 1–0 with ACA, four of which show a distinct disk component. Based on the modeling we ﬁnd severe cold gaseous carbon depletion by a factor of 157 + 17 − 15 in the outer disk of DL Tau and moderate depletion in the outer disks of DR Tau and DO Tau, by factors of 5 + 2 − 1 and 17 + 3 − 2 , respectively. The carbon abundance is in general expected to be higher in the inner disk if carbon-rich ices drift on large grains toward the star. Combining the outer and inner disk carbon abundances, we demonstrate deﬁnitive evidence for radial drift in the disk of DL Tau, where the existence of multiple dust rings points to either short-lived or leaky dust traps. We ﬁnd dust locking in the compact, smooth disks of DO Tau and DR Tau, which hints at unresolved dust substructure. Comparing our results with the inner and outer disk carbon depletion around stars of different ages and luminosities, we identify an observational evolutionary trend in gaseous carbon depletion that is consistent with dynamical models of CO depletion processes. Conclusions. The transport efﬁciency of solids in protoplanetary disks can signiﬁcantly differ from what we expect based on the current resolved substructure in the continuum observations. This has important implications for our understanding of the impact of radial drift and pebble accretion on planetary compositions.
Building Bridges: Establishing a Dialogue Between Software Engineering Research and Computational Science

There has been growing interest within the computational science and engineering (CSE) community in engaging with software engineering research -- the systematic study of software systems and their development, operation, and maintenance -- to solve challenges in scientific software development. Historically, there has been little interaction between scientific computing and the field, which has held back progress. With the ranks of scientific software teams expanding to include software engineering researchers and practitioners, we can work to build bridges to software science and reap the rewards of evidence-based practice in software development.
Modelling, analysis and control design of a two-stage photovoltaic generation system

Photovoltaic (PV) generation systems with two-stage topology are recently emerged due to its flexibility of installation. However, most studies on dynamic stability of the PV generation system are based either on the first DC/DC stage or the second DC/AC stage in previous literature. A system-level modelling and stability has not been reported significantly, which is a crucial issue for the design of the PV system controllers. In this study, an integrated small-signal model for a two-stage PV generation system is derived to investigate the system stability and sensitivity. The proposed model takes into account the dynamics of the DC-link capacitor that was generally regarded as an ideal constant voltage source in previous researches. A v 2 feedback based DC-link voltage control scheme is presented to address the non-linear relationship between capacitor energy and its voltage and thus a high performance DC-link voltage regulation is achieved. Hardware-in-loop tests using real time digital simulator are presented to validate the feasibility of the proposed method.
ValueNetQP: Learned one-step optimal control for legged locomotion

Optimal control is a successful approach to generate motions for complex robots, in particular for legged locomotion. However, these techniques are often too slow to run in real time for model predictive control or one needs to drastically simplify the dynamics model. In this work, we present a method to learn to predict the gradient and hessian of the problem value function, enabling fast resolution of the predictive control problem with a one-step quadratic program. In addition, our method is able to satisfy constraints like friction cones and unilateral constraints, which are important for high dynamics locomotion tasks. We demonstrate the capability of our method in simulation and on a real quadruped robot performing trotting and bounding motions.
Learning what to remember

We consider a lifelong learning scenario in which a learner faces a neverending and arbitrary stream of facts and has to decide which ones to retain in its limited memory. We introduce a mathematical model based on the online learning framework, in which the learner measures itself against a collection of experts that are also memory-constrained and that reflect different policies for what to remember. Interspersed with the stream of facts are occasional questions, and on each of these the learner incurs a loss if it has not remembered the corresponding fact. Its goal is to do almost as well as the best expert in hindsight, while using roughly the same amount of memory. We identify difficulties with using the multiplicative weights update algorithm in this memory-constrained scenario, and design an alternative scheme whose regret guarantees are close to the best possible.
High throughput multidimensional tridiagonal system solvers on FPGAs

We present a high performance tridiagonal solver library for Xilinx FPGAs optimized for multiple multi-dimensional systems common in real-world applications. An analytical performance model is developed and used to explore the design space and obtain rapid performance estimates that are over 85% accurate. This library achieves an order of magnitude better performance when solving large batches of systems than previous FPGA work. A detailed comparison with a current state-of-the-art GPU library for multi-dimensional tridiagonal systems on an Nvidia V100 GPU shows the FPGA achieving competitive or better runtime and significant energy savings of over 30%. Through this design, we learn lessons about the types of applications where FPGAs can challenge the current dominance of GPUs.
Music2Video: Automatic Generation of Music Video with fusion of audio and text

Creation of images using generative adversarial networks has been widely adapted into multi-modal regime with the advent of multi-modal representation models pre-trained on large corpus. Various modalities sharing a common representation space could be utilized to guide the generative models to create images from text or even from audio source. Departing from the previous methods that solely rely on either text or audio, we exploit the expressiveness of both modality. Based on the fusion of text and audio, we create video whose content is consistent with the distinct modalities that are provided. A simple approach to automatically segment the video into variable length intervals and maintain time consistency in generated video is part of our method. Our proposed framework for generating music video shows promising results in application level where users can interactively feed in music source and text source to create artistic music videos. Our code is available at https://github.com/joeljang/music2video.