Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological and functional changes in skeletal muscle in type 2 diabetes mellitus: A systematic review and meta-analysis

ABSTRACT Introduction Changes in the skeletal muscle are common in patients with type 2 diabetes mellitus (T2DM). These changes impair your motor skills. Objective This systematic review aimed to investigate changes in skeletal muscle in patients with T2DM. Methods The search was carried out in the PubMed, Scopus, and Web of Science databases until December 1, 2021. Observational studies that evaluated musculoskeletal changes in people with T2DM were included. The review was based on PRISMA recommendations. The primary parameters analyzed were muscle strength, muscle mass, muscle power, and muscle endurance. Results Forty-eight studies were included, with a total of 26,042 participants. The results revealed that T2DM is associated with a reduction in handgrip [−2.64 (CI 95% = −3.33 to −1.95, Z = −7.50, p < .0001], and knee extension muscle strength [−0.56 (CI 95% = −0.76 to −0.36, Z = −5.64, p < .0001)], a higher percentage of type II fibers [11.74 (CI 95% = 6.24 to 17.25, Z = 4.18, p < .0001)], and a lower percentage of type I fibers [−15.69 (CI 95% = −18.22 to −13.16, Z = −12.16, p < .0001], in addition to a greater thickness of the calcaneus tendon (p < .0001). Conclusion Individuals with T2DM present skeletal muscle impairments, mainly reduced muscle strength, mass, and endurance; increase in the thickness of the calcaneus tendon, and alteration in the proportion of type I and II fibers, even in the initial stage of the disease.
Female genital mutilation and cutting in the Arab League and diaspora: A systematic review of preventive interventions

Female Genital Mutilation and Cutting (FGM/C) is an act of gender‐based violence (GBV) and a global public health issue with well‐documented adverse outcomes. With the rise in global migration, there is an increasing prevalence of FGM/C among Arab diaspora living in the West and Global South. What remains unclear is how to reduce the practice. This study was designed to identify interventions exerting an effect on reducing the practice of FGM/C.
Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data

Abstract Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.
Multi-Level Factors Associated With Handwashing Among Adolescents During the COVID-19 Pandemic in South Korea

The World Health Organization (WHO) emphasized the importance of handwashing as the easiest and most effective way to prevent COVID-19.1 For children or adolescents who are not currently eligible for the COVID-19 vaccination, handwashing is an essential daily tool to prevent COVID-19. Adapting the social-ecological model that suggests multilevel factors impact individuals’ health behaviors,2 this study has two aims. First, it presents the frequency of handwashing among adolescents in South Korea (hereinafter Korea), which experienced the outbreak of COVID-19 early on, but has managed to flatten the curve efficiently.3 Second, it examines multi-level factors associated with handwashing among adolescents, focusing on their perceived changes in the economic class due to COVID-19.
Association between napping status and depressive symptoms in urban residents during the COVID-19 epidemic.

：2019（COVID-19），。 ：WELL，2020482020529COVID-19，307518~80。 ，。 ：COVID-1920.6%；，（=0.61，：0.47~0.79，<0.01）、（=0.66，95%：0.50~0.88，<0.01）；，（=0.42，95%：0.21~0.82，<0.05）。 ：2019，1/5，。
Short-Term Word-Learning in a Dynamically Changing Environment

Neural sequence-to-sequence automatic speech recognition (ASR) systems are in principle open vocabulary systems, when using appropriate modeling units. In practice, however, they often fail to recognize words not seen during training, e.g., named entities, numbers or technical terms. To alleviate this problem, Huber et al. proposed to supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly. In this paper we study, a) methods to acquire important words for this memory dynamically and, b) the trade-off between improvement in recognition accuracy of new words and the potential danger of false alarms for those added words. We demonstrate significant improvements in the detection rate of new words with only a minor increase in false alarms (F1 score 0.30 $\rightarrow$ 0.80), when using an appropriate number of new words. In addition, we show that important keywords can be extracted from supporting documents and used effectively.
A Dataset for Speech Emotion Recognition in Greek Theatrical Plays

Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public. For instance, speech analytics can be adopted to automatically generate subtitles in theatrical plays, in order to (among other purposes) help people with hearing loss. Apart from a typical speech-to-text transcription with Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be used to automatically predict the underlying emotional content of speech dialogues in theatrical plays, and thus to provide a deeper understanding how the actors utter their lines. However, real-world datasets from theatrical plays are not available in the literature. In this work we present GreThE, the Greek Theatrical Emotion dataset, a new publicly available data collection for speech emotion recognition in Greek theatrical plays. The dataset contains utterances from various actors and plays, along with respective valence and arousal annotations. Towards this end, multiple annotators have been asked to provide their input for each speech recording and inter-annotator agreement is taken into account in the final ground truth generation. In addition, we discuss the results of some indicative experiments that have been conducted with machine and deep learning frameworks, using the dataset, along with some widely used databases in the field of speech emotion recognition.
Online Algorithms for Matching Platforms with Multi-Channel Traffic

Two-sided platforms rely on their recommendation algorithms to help their visitors successfully find a match. However, on platforms such as VolunteerMatch - which has facilitated tens of millions of connections between volunteers and nonprofits - a sizable fraction of website traffic arrives directly to a nonprofit's volunteering page via an external link, thus bypassing the platform's recommendation algorithm. We study how such platforms should account for this external traffic in the design of their recommendation engines, given the goal of maximizing the total number of successful matches. We model the platform's problem as a special case of online matching with stochastic rewards, where (using VolunteerMatch as a motivating example) volunteers arrive sequentially and (probabilistically) match with one opportunity, each of which has finite need for volunteers. In our framework, external traffic is interested only in their targeted opportunity; in contrast, internal traffic may be interested in many opportunities, and the platform's online algorithm selects which opportunity to recommend. In evaluating the performance of different algorithms, we take a worst-case analysis approach, yet we refine the notion of the competitive ratio by parameterizing it based on the amount of external traffic. After demonstrating the shortcomings of a commonly-used algorithm which is optimal in the absence of external traffic, we introduce a new algorithm - Adaptive Capacity (AC) - which accounts for matches differently based on whether they originate from internal or external traffic. We establish a lower bound on AC's competitive ratio that is increasing in the amount of external traffic, and we compare our lower bound to a parameterized upper bound on the competitive ratio of any online algorithm. We find that (in certain parameter regimes) AC is near-optimal regardless of the amount of external traffic, even though it does not know this amount a priori. Our analysis utilizes a path-based, pseudo-rewards approach, which we further generalize to settings where the platform can recommend a ranked set of opportunities. Beyond our theoretical results, we demonstrate the strong performance of AC in a case study motivated by VolunteerMatch data.
Training speaker embedding extractors using multi-speaker audio with unknown speaker boundaries

In this paper, we demonstrate a method for training speaker embedding extractors using weak annotation. More specifically, we are using the full VoxCeleb recordings and the name of the celebrities appearing on each video without knowledge of the time intervals the celebrities appear in the video. We show that by combining a baseline speaker diarization algorithm that requires no training or parameter tuning, a modified loss with aggregation over segments, and a two-stage training approach, we are able to train a competitive ResNet-based embedding extractor. Finally, we experiment with two different aggregation functions and analyze their behaviour in terms of their gradients.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete. Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages. In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages. However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner. To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method. Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type. As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights. Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm. Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AGA
Attacker Attribution of Audio Deepfakes

Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks. These fakes are readily being misused for slander, misinformation and fraud. For this reason, intensive research for developing countermeasures is also expanding. However, recent work is almost exclusively limited to deepfake detection - predicting if audio is real or fake. This is despite the fact that attribution (who created which fake? ) is an essential building block of a larger defense strategy, as practiced in the field of cybersecurity for a long time. This paper considers the problem of deepfake attacker attribution in the domain of audio. We present several methods for creating attacker signatures using low-level acoustic descriptors and machine learning embeddings. We show that speech signal features are inadequate for characterizing attacker signatures. However, we also demonstrate that embeddings from a recurrent neural network can successfully characterize attacks from both known and unknown attackers. Our attack signature embeddings result in distinct clusters, both for seen and unseen audio deepfakes. We show that these embeddings can be used in downstream-tasks to high-effect, scoring 97.10% accuracy in attacker-id classification.
New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor

Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic skin cancer detection model must be developed. Material and Method: This research interests to overcome automatic skin cancer detection problem. A colored skin cancer image dataset is used. This dataset contains 3297 images with two classes. An automatic multilevel textural and deep features-based model is presented. Multilevel fuse feature generation using discrete wavelet transform (DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trained DarkNet19, and DarkNet53 are utilized to generate features of the skin cancer images, top 1000 features are selected threshold value-based neighborhood component analysis (NCA). The chosen top 1000 features are classified using the 10-fold cross-validation technique. Results: To obtain results, ten-fold cross-validation is used and 91.54% classification accuracy results are obtained by using the recommended pyramidal hybrid feature generator and NCA selector-based model. Further, various training and testing separation ratios (90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classification rate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: The findings and accuracies calculated are denoted that this model can be used in dermatology and pathology clinics to simplify the skin cancer detection process and help physicians.
A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles

The number of Connected and Autonomous Vehicles (CAVs) is increasing rapidly in various smart transportation services and applications, considering many benefits to society, people, and the environment. Several research surveys for CAVs were conducted by primarily focusing on various security threats and vulnerabilities in the domain of CAVs to classify different types of attacks, impacts of attacks, attack features, cyber-risk, defense methodologies against attacks, and safety standards. However, the importance of attack detection and prevention approaches for CAVs has not been discussed extensively in the state-of-the-art surveys, and there is a clear gap in the existing literature on such methodologies to detect new and conventional threats and protect the CAV systems from unexpected hazards on the road. Some surveys have a limited discussion on Attacks Detection and Prevention Systems (ADPS), but such surveys provide only partial coverage of different types of ADPS for CAVs. Furthermore, there is a scope for discussing security, privacy, and efficiency challenges in ADPS that can give an overview of important security and performance attributes. This survey paper, therefore, presents the significance of CAVs in the market, potential challenges in CAVs, key requirements of essential security and privacy properties, various capabilities of adversaries, possible attacks in CAVs, and performance evaluation parameters for ADPS. An extensive analysis is discussed of different ADPS categories for CAVs and state-of-the-art research works based on each ADPS category that gives the latest findings in this research domain. This survey also discusses crucial and open security research problems that are required to be focused on the secure deployment of CAVs in the market.
Dark Matter and (g - 2) μ,e in radiative Dirac neutrino mass models

The origin of neutrino mass is a mystery, so is its nature, namely, whether neutrinos are Dirac or Majorana particles. On top of that, hints of large deviations of the muon and the electron anomalous magnetic moments (AMMs) are strong evidence for physics beyond the Standard Model. In this work, piecing these puzzles together, we propose a class of radiative Dirac neutrino mass models to reconcile (g - 2) μ,e anomalies with neutrino oscillation data. In this framework, a common set of new physics (NP) states run through the loops that generate non-zero neutrino mass and, due to chiral enhancement, provide substantial NP contributions to lepton AMMs. In addition, one of the three models studied in this work offers a Dark Matter candidate automatically stabilized by the residual symmetry, whose phenomenology is non-trivially connected to the other two puzzles mentioned above. Finally, our detailed numerical analysis reveals a successful resolution to these mysteries while being consistent with all colliders and cosmological constraints.
Quantitative hydrodynamic limits of the Langevin dynamics for gradient interface models

We review some recent, quantitative, progress regarding the large-scale behavior of the ∇ φ (or Ginzburg-Landau) interface model with uniformly convex potential. The arguments rely on a dynamical approach of the problem (following [32] where the Langevin dynamics associated with the model are studied) combined with recent progress in the ﬁeld of quantitative stochastic homogenization of nonlinear elliptic equations.
Analysis of Count-Min sketch under conservative update

Count-Min sketch is a hash-based data structure to represent a dynamically changing associative array of counters. Here we analyse the counting version of Count-Min under a stronger update rule known as conservative update , assuming the uniform distribution of input keys. We show that the accuracy of conservative update strategy undergoes a phase transition, depending on the number of distinct keys in the input as a fraction of the size of the Count-Min array. We prove that below the threshold, the relative error is asymptotically o (1) (as opposed to the regular Count-Min strategy), whereas above the threshold, the relative error is Θ(1). The threshold corresponds to the peelability threshold of random k -uniform hypergraphs. We demonstrate that even for small number of keys, peelability of the underlying hypergraph is a crucial property to ensure the o (1) error.
A Generalized Cluster-Free NOMA Framework Towards Next-Generation Multiple Access

A generalized downlink multi-antenna non-orthogonal multiple access (NOMA) transmission framework is proposed with the novel concept of cluster-free successive interference cancellation (SIC). In contrast to conventional NOMA approaches, where SIC is successively carried out within the same cluster, the key idea is that the SIC can be ﬂexibly implemented between any arbitrary users to achieve efﬁcient interference elimination. Based on the proposed framework, a sum rate maximization problem is formulated for jointly optimizing the transmit beamforming and the SIC operations between users, subject to the SIC decoding conditions and users’ minimal data rate requirements. To tackle this highly-coupled mixed-integer nonlinear programming problem, an alternating direction method of multipliers-successive convex approximation (ADMM-SCA) algorithm is developed. The original problem is ﬁrst reformulated into a tractable biconvex augmented Lagrangian (AL) problem by handling the non-convex terms via SCA. Then, this AL problem is decomposed into two subproblems that are iteratively solved by the ADMM to obtain the stationary solution. Furthermore, to reduce the computational complexity and alleviate the parameter initialization sensitivity of ADMM-SCA, a Matching-SCA algorithm is proposed. The intractable binary SIC operations are solved through an extended many-to-many matching, which is jointly combined with an SCA process to optimize the transmit beamforming. The proposed Matching-SCA can converge to an enhanced exchange-stable matching that guarantees the local optimality. Numerical results demonstrate that: i) the proposed Matching-SCA algorithm achieves comparable performance and a faster convergence compared to ADMM-SCA; ii) the proposed generalized framework realizes scenario-adaptive communications and outperforms traditional multi-antenna NOMA approaches in various communication regimes.
BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information

Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to directly regress the SMAL animal model, with additional limb scale parameters, from images. Our method, called BARC (Breed-Augmented Regression using Classification), goes beyond prior work in several important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. But, even with a better shape model, the problem of regressing dog shape from an image is still challenging because we lack paired images with 3D ground truth. To compensate for the lack of paired data, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss consisting of two parts: One term encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one, a breed classification loss, helps to produce recognizable breed-specific shapes. Through ablation studies, we find that our breed losses significantly improve shape accuracy over a baseline without them. We also compare BARC qualitatively to WLDO with a perceptual study and find that our approach produces dogs that are significantly more realistic. This work shows that a-priori information about genetic similarity can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. Our code is publicly available for research purposes at https://barc.is. tue.mpg. de/.
(Nearly) All Cardinality Estimators Are Differentially Private

We consider privacy in the context of streaming algorithms for cardinality estimation. We show that a large class of algorithms all satisfy ǫ -diﬀerential privacy, so long as (a) the algorithm is combined with a simple down-sampling procedure, and (b) the cardinality of the input stream is Ω( k/ǫ ). Here, k is a certain parameter of the sketch that is always at most the sketch size in bits, but is typically much smaller. We also show that, even with no modiﬁcation, algorithms in our class satisfy ( ǫ, δ )-diﬀerential privacy, where δ falls exponentially with the stream cardinality. Our analysis applies to essentially all popular cardinality estimation algorithms, and substantially generalizes and tightens privacy bounds from earlier works.