Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent advancements in cellulose-based biomaterials for management of infected wounds

ABSTRACT Introduction Chronic wounds are a substantial burden on the healthcare system. Their treatment requires advanced dressings, which can provide a moist wound environment, prevent bacterial infiltration, and act as a drug carrier. Cellulose is biocompatible, biodegradable, and can be functionalized according to specific requirements, which makes it a highly versatile biomaterial. Antimicrobial cellulose dressings are proving to be highly effective against infected wounds. Areas covered This review briefly addresses the mechanism of wound healing and its pathophysiology. It also discusses wound infections, biofilm formation, and progressive emergence of drug-resistant bacteria in chronic wounds and the treatment strategies for such types of infected wounds. It also summarizes the general properties, method of production, and types of cellulose wound dressings. It explores recent studies and advancements regarding the use of cellulose and its derivatives in wound management. Expert opinion Cellulose and its various functionalized derivatives represent a promising choice of wound dressing material. Cellulose-based dressings loaded with antimicrobials are very useful in controlling infection in a chronic wound. Recent studies showing its efficacy against drug-resistant bacteria make it a favorable choice for chronic wound infections. Further research and large-scale clinical trials are required for better clinical evidence of its efficiency.
I Think, Therefore I Act: The Influence of Critical Reasoning Ability on Trust and Behavior During the COVID‐19 Pandemic

Actively open‐minded thinking (AOT) operates in three dimensions: it serves as a norm accounting for how one should search for and use information in judgment and decision making; it is a thinking style that one may adopt in accordance with the norm; and it sets standards for evaluating the thinking of others, particularly the trustworthiness of sources that claim authority. With the first and third dimensions in mind, we explore how AOT influences trust in public health experts, risk perceptions, and compliance with recommended behaviors aimed at slowing the spread and severity of the COVID‐19 pandemic. Using survey data from a nationally representative sample of Americans (N = 857), we tested whether AOT will lead people to place greater trust public health experts (H1). Because these experts have been consistently messaging that COVID‐19 is a real and serious threat to public health, we also hypothesized that trust in experts would be positively associated with high perceived risk (H2), which should have a positive influence on (self‐reported) compliance with CDC recommendations (H3). And because AOT is a self‐directed thinking style, we also expected it to directly influence risk perceptions and, by extension, compliance (H4). Our results support all four hypotheses. We discuss the implications of these results for how risk communication and risk management efforts are designed and practiced.
On the Implementation of Approximate Randomization Tests in Linear Models with a Small Number of Clusters

Abstract This paper provides a user’s guide to the general theory of approximate randomization tests developed in Canay, Romano, and Shaikh (2017a. “Randomization Tests under an Approximate Symmetry Assumption.” Econometrica 85 (3): 1013–30) when specialized to linear regressions with clustered data. An important feature of the methodology is that it applies to settings in which the number of clusters is small – even as small as five. We provide a step-by-step algorithmic description of how to implement the test and construct confidence intervals for the parameter of interest. In doing so, we additionally present three novel results concerning the methodology: we show that the method admits an equivalent implementation based on weighted scores; we show the test and confidence intervals are invariant to whether the test statistic is studentized or not; and we prove convexity of the confidence intervals for scalar parameters. We also articulate the main requirements underlying the test, emphasizing in particular common pitfalls that researchers may encounter. Finally, we illustrate the use of the methodology with two applications that further illuminate these points: one to a linear regression with clustered data based on Meng, Qian, and Yared (2015. “The Institutional Causes of china’s Great Famine, 1959–1961.” The Review of Economic Studies 82 (4): 1568–611) and a second to a linear regression with temporally dependent data based on Munyo and Rossi (2015. “First-day Criminal Recidivism.” Journal of Public Economics 124: 81–90). The companion R and Stata packages facilitate the implementation of the methodology and the replication of the empirical exercises.
WaveBeat: End-to-end beat and downbeat tracking in the time domain

Deep learning approaches for beat and downbeat tracking have brought advancements. However, these approaches continue to rely on hand-crafted, subsampled spectral features as input, restricting the information available to the model. In this work, we propose WaveBeat, an end-to-end approach for joint beat and downbeat tracking operating directly on waveforms. This method forgoes engineered spectral features, and instead, produces beat and downbeat predictions directly from the waveform, the first of its kind for this task. Our model utilizes temporal convolutional networks (TCNs) operating on waveforms that achieve a very large receptive field ($\geq$ 30 s) at audio sample rates in a memory efficient manner by employing rapidly growing dilation factors with fewer layers. With a straightforward data augmentation strategy, our method outperforms previous state-of-the-art methods on some datasets, while producing comparable results on others, demonstrating the potential for time domain approaches.
The Impacts of Mobility on Covid-19 Dynamics: Using Soft and Hard Data

This paper has the goal of evaluating how changes in mobility has affected the infection spread of Covid-19 throughout the 2020-2021 years. However, identifying a "clean" causal relation is not an easy task due to a high number of non-observable (behavioral) effects. We suggest the usage of Google Trends and News-based indexes as controls for some of these behavioral effects and we find that a 1\% increase in residential mobility (i.e. a reduction in overall mobility) have significant impacts for reducing both Covid-19 cases (at least 3.02\% on a one-month horizon) and deaths (at least 2.43\% at the two-weeks horizon) over the 2020-2021 sample. We also evaluate the effects of mobility on Covid-19 spread on the restricted sample (only 2020) where vaccines were not available. The results of diminishing mobility over cases and deaths on the restricted sample are still observable (with similar magnitudes in terms of residential mobility) and cumulative higher, as the effects of restricting workplace mobility turns to be also significant: a 1\% decrease in workplace mobility diminishes cases around 1\% and deaths around 2\%.
Data-Driven Detection and Identification of IoT-Enabled Load-Altering Attacks in Power Grids

Advances in edge computing are powering the development and deployment of Internet of Things (IoT) systems to provide advanced services and resource efficiency. However, large-scale IoT-based load-altering attacks (LAAs) can seriously impact power grid operations, such as destabilising the grid's control loops. Timely detection and identification of any compromised nodes are essential to minimise the adverse effects of these attacks on power grid operations. In this work, two data-driven algorithms are proposed to detect and identify compromised nodes and the attack parameters of the LAAs. The first method, based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, adopts a sparse regression framework to identify attack parameters that best describe the observed dynamics. The second method, based on physics-informed neural networks (PINN), employs neural networks to infer the attack parameters from the measurements. Both algorithms are presented utilising edge computing for deployment over decentralised architectures. Extensive simulations are performed on IEEE 6-,14- and 39-bus systems to verify the effectiveness of the proposed methods. Numerical results confirm that the proposed algorithms outperform existing approaches, such as those based on unscented Kalman filter, support vector machines (SVM), and neural networks (NN), and effectively detect and identify locations of attack in a timely manner.
Building a Noisy Audio Dataset to Evaluate Machine Learning Approaches for Automatic Speech Recognition Systems

Automatic speech recognition systems are part of people's daily lives, embedded in personal assistants and mobile phones, helping as a facilitator for human-machine interaction while allowing access to information in a practically intuitive way. Such systems are usually implemented using machine learning techniques, especially with deep neural networks. Even with its high performance in the task of transcribing text from speech, few works address the issue of its recognition in noisy environments and, usually, the datasets used do not contain noisy audio examples, while only mitigating this issue using data augmentation techniques. This work aims to present the process of building a dataset of noisy audios, in a specific case of degenerated audios due to interference, commonly present in radio transmissions. Additionally, we present initial results of a classifier that uses such data for evaluation, indicating the benefits of using this dataset in the recognizer's training process. Such recognizer achieves an average result of 0.4116 in terms of character error rate in the noisy set (SNR = 30).
Differentiable Spline Approximations

The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differentiable programming methods (such as autodiff) typically require that the machine learning models be differentiable, limiting their applicability. Our goal in this paper is to use a new, principled approach to extend gradient-based optimization to functions well modeled by splines, which encompass a large family of piecewise polynomial models. We derive the form of the (weak) Jacobian of such functions and show that it exhibits a block-sparse structure that can be computed implicitly and efficiently. Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable"layer"in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.
Evidence of X-Ray Plateaus Driven by the Magnetar Spindown Winds in Gamma-Ray Burst Afterglows

The central engine of gamma-ray bursts (GRBs) remains an open and cutting-edge topic in the era of multimessenger astrophysics. X-ray plateaus appear in some GRB afterglows, which are widely considered to originate from the spindown of magnetars. According to the stable magnetar scenario of GRBs, an X-ray plateau and a decay phase ∼t −2 should appear in X-ray afterglows. Meanwhile, the “normal” X-ray afterglow is produced by the external shock from a GRB fireball. We analyze the Neil Gehrels Swift GRB data, then find three gold samples that have an X-ray plateau and a decay phase ∼t −2 superimposed on the jet-driven normal component. Based on these features of the lightcurves, we argue that the magnetars should be the central engines of these three GRBs. Future joint multimessenger observations might further test this possibility, which can then be beneficial to constrain GRB physics.
SecurePtrs: Proving Secure Compilation with Data-Flow Back-Translation and Turn-Taking Simulation

Proving secure compilation of partial programs typically requires back-translating an attack against the compiled program to an attack against the source program. To prove back-translation, one can syntactically translate the target attacker to a source one-i.e., syntax-directed back-translation-or show that the interaction traces of the target attacker can also be emitted by source attackers—i.e., trace-directed back-translation. Syntax-directed back-translation is not suitable when the target attacker may use unstructured control flow that the source language cannot directly represent. Trace-directed back-translation works with such syntactic dissimilarity because only the external interactions of the target attacker have to be mimicked in the source, not its internal control flow. Revealing only external interactions is, however, inconvenient when sharing memory via unforgeable pointers, since information about shared pointers stashed in private memory is not present on the trace. This made prior proofs unnecessarily complex, since the generated attacker had to instead stash all reachable pointers. In this work, we introduce more informative data-flow traces, combining the best of syntax- and trace-directed back-translation in a simpler technique that handles both syntactic dissimilarity and memory sharing well, and that is proved correct in Coq. Additionally, we develop a novel turn-taking simulation relation and use it to prove a recomposition lemma, which is key to reusing compiler correctness in such secure compilation proofs. We are the first to mechanize such a recomposition lemma in the presence of memory sharing. We use these two innovations in a secure compilation proof for a code generation compiler pass between a source language with structured control flow and a target language with unstructured control flow, both with safe pointers and components.
Stanford Pupper: A Low-Cost Agile Quadruped Robot for Benchmarking and Education

We present Stanford Pupper, an easily-replicated open source quadruped robot designed specifically as a benchmark platform for legged robotics research. The robot features torque-controllable brushless motors with high specific power that enable testing of impedance and torque-based machine learning and optimization control approaches. Pupper can be built from the ground up in under 8 hours for a total cost under $2000, with all components either easily purchased or 3D printed. To rigorously compare control approaches, we introduce two benchmarks, Sprint and Scramble with a leader board maintained by Stanford Student Robotics. These benchmarks test high-speed dynamic locomotion capability, and robustness to unstructured terrain. We provide a reference controller with dynamic, omnidirectional gaits that serves as a baseline for comparison. Reproducibility is demonstrated across multiple institutions with robots made independently. All material is available at https://stanfordstudentrobotics.org/quadruped-benchmark.
Modified Stoney formula for determining stress within thin films on large-deformation isotropic circular plates

Stoney formula is widely used to obtain the residual stress in the films on isotropic circular plates. However, significant errors would be introduced for large deformations because of the assumption of small deformation in the Stoney formula. In this study, a modified Stoney formula was proposed to extend its scope of application to the nonlinear domain. One-phase exponential decay function with coefficient p was used to relate the curvature of the substrate to the stress in the film. The coefficient p could be expressed as a function of the thickness, diameter, Young’s modulus, and Poisson’s ratio of the circular plate. The linear fitting technique was applied to ascertain the relationship between the coefficient p and the aforementioned parameters. The simple yet accurate modified Stoney formula could be used to calculate the residual stress in the film directly from the measured curvature of isotropic circular plates with various dimensions and materials.
Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis

We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( ( 1 / k ) \log D )$ rate, where $k$ denotes the number of iterations and $D$ denotes the dimension of the quantum state. The per-iteration computational complexity of the algorithm is $O ( D ^ 3 + N D ^2 )$, where $N$ denotes the number of measurement outcomes. The algorithm can be considered as a parameter-free correction of the $R \rho R$ method [A. I. Lvovsky. Iterative maximum-likelihood reconstruction in quantum homodyne tomography. \textit{J. Opt. B: Quantum Semiclass. Opt. } 2004] [G. Molina-Terriza et al. Triggered qutrits for quantum communication protocols. \textit{Phys. Rev. Lett. } 2004. ].
Cuttlefish: Library for Achieving Energy Efficiency in Multicore Parallel Programs

A low-cap power budget is challenging for exascale computing. Dy-namic Voltage and Frequency Scaling (DVFS) and Uncore Frequency Scaling (UFS) are the two widely used techniques for limiting the HPC application's energy footprint. However, existing approaches fail to provide a unified solution that can work with different types of parallel programming models and applications. This paper proposes Cuttlefish, a programming model oblivious C/C++ library for achieving energy efficiency in multicore parallel programs running over Intel processors. An online profiler periodi-cally profiles model-specific registers to discover a running appli-cation's memory access pattern. Using a combination of DVFS and UFS, Cuttlefish then dynamically adapts the processor's core and uncore frequencies, thereby improving its energy efficiency. The evaluation on a 20-core Intel Xeon processor using a set of widely used OpenMP benchmarks, consisting of several irregular-tasking and work-sharing pragmas, achieves geometric mean energy savings of 19.4% with a 3.6% slowdown.
Graph-based multiple change-point detection

We propose a new multiple change-point detection framework for multivariate and non-Euclidean data. First, we combine graph-based statistics with wild binary segmentation or seeded binary segmentation to search for a pool of candidate change-points. We then prune the candidate change-points through a novel goodness-of-fit statistic. Numerical studies show that this new framework outperforms existing methods under a wide range of settings. The resulting change-points can further be arranged hierarchically based on the goodness-of-fit statistic. The new framework is illustrated on a Neuropixels recording of an awake mouse.
Optimal Resource Allocation and Beamforming for Two-User Miso WPCNS for a Non-Linear Circuit-Based EH Model : (Invited Paper)

We study two-user multiple-input single-output (MISO) wireless powered communication networks (WPCNs), where the user devices are equipped with non-linear energy harvesting (EH) circuits. We consider time-division duplex (TDD) transmission, where the users harvest power from the signal received in the downlink phase, and then, utilize this harvested power for information transmission in the uplink phase. In contrast to existing works, we adopt a non-linear model of the harvested power based on a precise analysis of the employed EH circuit. We jointly optimize the beamforming vectors in the downlink and the time allocated for downlink and uplink transmission to minimize the average transmit power in the downlink under per-user data rate constraints in the uplink. We provide conditions for the feasibility of the resource allocation problem and the existence of a trivial solution, respectively. For the case where the resource allocation has a non-trivial solution, we show that it is optimal to employ no more than three beamforming vectors for power transfer in the downlink. To determine these beamforming vectors, we develop an iterative algorithm based on semi-definite relaxation (SDR) and successive convex approximation (SCA). Our simulation results reveal that the proposed resource allocation scheme outperforms two baseline schemes based on linear and sigmoidal EH models, respectively.
Coverage Axis: Inner Point Selection for 3D Shape Skeletonization

In this paper, we present a simple yet effective formulation called Coverage Axis for 3D shape skeletonization. Inspired by the set cover problem, our key idea is to cover all the surface points using as few inside medial balls as possible. This formulation inherently induces a compact and expressive approximation of the Medial Axis Transform (MAT) of a given shape. Different from previous methods that rely on local approximation error, our method allows a global consideration of the overall shape structure, leading to an efficient high‐level abstraction and superior robustness to noise. Another appealing aspect of our method is its capability to handle more generalized input such as point clouds and poor‐quality meshes. Extensive comparisons and evaluations demonstrate the remarkable effectiveness of our method for generating compact and expressive skeletal representation to approximate the MAT.
A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines: From Medical to Remote Sensing

We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN’s applications in different field.
Planning 5G Networks for Rural Fixed Wireless Access

We study the planning of a rural 5G multi-user massive MIMO fixed wireless access system to offer fixed broadband service to homes. Specifically, we aim to determine the user limit, i.e., the maximum number of homes that can simultaneously receive target minimum bit rates (MBRs) on the downlink (DL) and on the uplink (UL) given a set of network resources and a cell radius. To compute that limit, we must understand how resources should be shared between the DL and UL and how user and stream selection, precoding and combining, and power distribution should be performed. We use block diagonalization and propose a static grouping strategy that organizes homes into fixed groups (of possibly different sizes) in the DL and UL; then we develop a simple approach to compute the user limit that we validate numerically. We study the impact of group size and show that smaller groups yield larger user limits in a 3.5 GHz band. We show how the user limit at different cell radii is impacted by the system bandwidth, the number of antennas at the base station and homes, the transmit power, and the MBRs. Lastly, we offer insights into how the network could be operated.