Miracle-dz/UniFAR-FTUs huggingface dataset:
TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning

Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracyof 88<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 10<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> SD) on two-way identification tests with 12 enrollment samples and accuracy of 80<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and an equal error rate (EER) of 20<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.
A New Perspective of Attention Deficit Hyperactivity Disorder Associated With Delayed Language Development: An Egyptian Sample

Objective The current study aimed to get an easy objective method to detect attention deficit hyperactivity disorder (ADHD) by investigating the simple inflammatory blood ratios platelet/lymphocyte ratio (PRL), neutrophil/lymphocyte ratio (NLR) & the monocyte/lymphocyte ratio (MLR), for the sake of receiving early management to such cases and overcoming language affection as a comorbid symptom. Methods This study was conducted on two groups: Group 1 (SG) consisted of 70 ADHD children who had delayed language development (DLD), freshly diagnosed, according to DSM-V criteria, and those patients were not on medical treatment. Group 2 healthy group (HG) consisted of 44 healthy control normal children that were both physically and mentally free of the same socio-demographic characters of the first group. Results The PLR, NLR and MLR, were significantly higher in the ADHD group than the healthy control group (HG) group, although the simple blood indices were average. Conclusion Inflammation has a role as a comorbid cause of ADHD. Simple blood inflammatory mediators may be used as comorbid factors in ADHD. This study explained that the language abilities must be taken in consideration when assessing children with ADHD. The screening tools for inflammatory markers are important when dealing with ADHD children with/ without delayed language development (DLD). Also, working memory assessment is mandatory in ADHD with DLD to assign a special program in language therapy for each child.
Andrographis paniculata mitigates first-line anti-tubercular drugs-induced nephrotoxicity in Wistar rats

Abstract Context Anti-tubercular drugs (ATDs) mediated adverse drug reactions are major concerns for clinicians to treat tuberculosis infection. This study aimed to investigate Andrographis paniculata extract-based phytotherapy to combat the nephrotoxic effects caused by ATDs therapy. Methods Reno-protective effect of A. paniculata extract in ATDs-induced rats was evaluated through LPO, GSH, CAT, SOD, GST, urea, creatinine, uric acid, and histopathological studies. Standardization of the extract was performed using RP-HPLC and FTIR analysis. Whereas, the effect of A. paniculata extract on ATDs induced genetic perturbation was analyzed using micronucleus assay. Moreover, the expression level of the xenometabolic gene was investigated using RT-PCR to explore the therapeutic mechanism. Results The nephrotoxic effect of ATDs was indicated by elevated levels of LPO and renal function markers along with the reduced activity of renal antioxidants. An up-regulated expression profile of NAT gene and histological alterations were observed in renal tissue however, micronucleated PCEs were observed in bone marrow cells. Concomitant treatment with A. paniculata extract revealed a noticeable amelioration of elevated oxidative stress markers, gene expression levels, genotoxic perturbation, and histological alterations in a dose-dependent manner. Conclusion Hence, the present study using A. paniculata leaf extract confirmed to play effective phytotherapy against ATDs induced renal toxicity.
Occurrence and clinical management of urethral obstruction in male cats under primary veterinary care in the United Kingdom in 2016

Abstract Background Urethral obstruction (UO) has a negative effect on welfare of cats. Objectives This study aimed to determine incidence, case management, and outcomes of UO in cats in primary‐care practice in the United Kingdom. Animals All male cats under veterinary care within the VetCompass database in 2016. Methods A retrospective cohort study was performed. The electronic records of all male cats with a clinical note during the study period were searched for UO cases and were manually reviewed for inclusion. Additional demographic and clinical information were extracted on cases. Results From the study cohort of 237 825 male cats, there were 1293 incident cases. The estimated UO incidence risk during 2016 was 0.54 (95% CI: 0.51‐0.57). Demographic and clinical data were available for 1108 cases. Antibiotics were administered to 641/1108 (57.9%) cases. Overall repeat catheterization rate was 253/854 (29.6%). Repeat catheterization at 48 hours was less frequent in patients with indwelling catheters (10.1%) vs those that had a catheter placed and then immediately removed (14.8%; P = .04). Death during a UO episode was 329/1108 (29.6%), and 285/329 (88.0%) deaths involved euthanasia. Conclusions and Clinical Importance Antibiotics were commonly prescribed in cats for treatment of UO despite minimal evidence in the clinical records of bacterial cystitis. Repeat catheterization was common and case fatality rate during a UO episode was high. Repeat catheterization within 48 hours of elective removal of a urethral catheter was less common in cats that had previously had indwelling catheters. The majority of cats requiring repeat catheterization survived until the end of the study.
Fourth Wave of COVID-19 in India : Statistical Forecasting

The COVID-19 pandemic is spreading in waves. This article proposes a statistical methodology to study and forecast the future waves. The methodology is applied to COVID-19 data from India to statistically forecast the occurrence of fourth wave in India. In the course of this study, the data is fitted by the mixture of Gaussian distribution, and Bootstrap methodology is used to compute the confidence interval of the time point of peak of the fourth wave. This methodology can also be used to forecast the fourth and other waves in other countries also.
Probing inter-layer pairing via density imbalance in exciton bilayers

Layered bosonic dipolar fluids have been suggested to host a condensate of inter-layer molecular bound states. However, its experimental observation has remained elusive. Motivated by two recent experimental works [Hubert et al., Phys. Rev. X 9, 021026 (2019) and D. J. Choksy et al., Phys. Rev. B 103 045126 (2021)], we theoretically study, using numerically exact quantum Monte Carlo calculations, the experimental signatures of collective inter-layer pairing in indirect exciton (IX) bilayers. We find that IX energy shifts associated with each layer evolve non-trivially as a function of density imbalance following a non-monotonic trend with a jump discontinuity at density balance, identified with the inter-layer IX molecule gap. This behavior discriminates between the superfluidity of inter-layer bound pairs and independent dipole condensation in distinct layers. Considering finite temperature and finite density imbalance conditions, we find a cascade of Berezinskii–Kosterlitz– Thouless (BKT) transitions, initially into a pair superfluid and only then, at lower temperatures, into complete superfluidity of both layers. Our results may provide a theoretical interpretation of existing experimental observations in GaAs double quantum well (DQW) bilayer structures. Furthermore, to optimize the visibility of pairing dynamics in future studies, we present a detailed analysis of realistic experimental settings in GaAs and transition-metal dichalcogenide (TMD) bilayer DQW heterostructures.
Activation Functions: Dive into an optimal activation function

Activation functions have come up as one of the essential components of neural networks. The choice of adequate activation function can impact the accuracy of these methods. In this study, we experiment for finding an optimal activation function by defining it as a weighted sum of existing activation functions and then further optimizing these weights while training the network. The study uses three activation functions, ReLU, tanh, and sin, over three popular image datasets, MNIST, FashionMNIST, and KMNIST. We observe that the ReLU activation function can easily overlook other activation functions. Also, we see that initial layers prefer to have ReLU or LeakyReLU type of activation functions, but deeper layers tend to prefer more convergent activation functions.
A Timing Yield Model for SRAM Cells at Sub/Near-Threshold Voltages Based on a Compact Drain Current Model

Sub/near-threshold static random-access memory (SRAM) design is crucial for addressing the memory bottleneck in power-constrained applications. However, the high integration density and reliability under process variations demand an accurate estimation of extremely small failure probabilities. To capture such a “rare event” in memory circuits, the time and storage overhead of conventional simulations based on the Monte Carlo (MC) analysis cannot be tolerated. On the other hand, classic analytical methods predicting failure probabilities from a physical expression become inaccurate in the sub/near-threshold voltage domain due to the hypothetical distribution or the oversimplified drain current (<inline-formula> <tex-math notation="LaTeX">$I_{ds}$ </tex-math></inline-formula>) model for nanoscale devices. This work first proposes a simple but efficient empirical <inline-formula> <tex-math notation="LaTeX">$I_{ds}$ </tex-math></inline-formula> model to describe the drain-induced barrier lowering (DIBL) effect. Based on that, the probability density functions of the interest metrics in SRAM are derived. Two analytical models are then put forward to evaluate SRAM dynamic stabilities, including the access time failure and the write failure. The proposed models can be extended easily to different types of SRAM with different read/write-assist circuits. The models are validated against MC simulations across different operating voltages and temperatures. The average relative errors at 0.5-V <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm{ DD}}$ </tex-math></inline-formula> are only 8.8% for the access-time failure model and 10.4% for the write failure model. The size of the required sample data set is <inline-formula> <tex-math notation="LaTeX">$43.6\times $ </tex-math></inline-formula> smaller than that of the state-of-the-art method.
A Greedy Approach to the Canny-Emiris Formula

The Canny-Emiris formula [3] gives the sparse resultant as a ratio between the determinant of a Sylvester-type matrix and a minor of it, by a subdivision algorithm. The most complete proof of the formula was given by D'Andrea et al. in [9] under general conditions on the underlying mixed subdivision. Before the proof, Canny and Pedersen had proposed [5] a greedy algorithm which provides smaller matrices, in general. The goal of this paper is to give an explicit class of mixed subdivisions for the greedy approach such that the formula holds, and the dimensions of the matrices are reduced compared to the subdivision algorithm. We measure this reduction for the case when the Newton polytopes are zonotopes generated by n line segments (where n is the rank of the underlying lattice), and for the case of multihomogeneous systems. This article comes with a JULIA implementation of the treated cases.
A Method for Waste Segregation using Convolutional Neural Networks

Segregation of garbage is a primary concern in many nations across the world. In spite of being in the modern era, many people still do not know how to distinguish between organic and recyclable waste. It is because of this that the world is facing a major crisis of waste disposal. In this paper, deep learning algorithms have been used to help solve this problem of waste classification. The waste is classified into two categories like organic and recyclable. Our proposed model achieves an accuracy of 94.9%. Although the other two models also show promising results, the Proposed Model stands out with the greatest accuracy. With the help of deep learning, one of the greatest obstacles to efficient waste management can finally be removed.
Multi-Modal Legged Locomotion Framework With Automated Residual Reinforcement Learning

While quadruped robots usually have good stability and load capacity, bipedal robots offer a higher level of flexibility / adaptability to different tasks and environments. A multi-modal legged robot can take the best of both worlds. In this paper, we propose a multi-modal locomotion framework that is composed of a hand-crafted transition motion and a learning-based bipedal controller—learnt by a novel algorithm called Automated Residual Reinforcement Learning. This framework aims to endow arbitrary quadruped robots with the ability to walk bipedally. In particular, we 1) design an additional supporting structure for a quadruped robot and a sequential multi-modal transition strategy; 2) propose a novel class of Reinforcement Learning algorithms for bipedal control and evaluate their performances in both simulation and the real world. Experimental results show that our proposed algorithms have the best performance in simulation and maintain a good performance in a real-world robot. Overall, our multi-modal robot could successfully switch between biped and quadruped, and walk in both modes.
From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics Systems

Our work is the first attempt to apply Natural Language Processing to automate the development of simulation models of systems vitally important for logistics. We demonstrated that the framework built on top of the fine-tuned GPT-3 Codex, a Transformer-based language model, could produce functionally valid simulations of queuing and inventory control systems given the verbal description. In conducted experiments, GPT-3 Codex demonstrated convincing expertise in Python as well as an understanding of the domain-specific vocabulary. As a result, the language model could produce simulations of a single-product inventory-control system and single-server queuing system given the domain-specific context, a detailed description of the process, and a list of variables with the corresponding values. The demonstrated results, along with the rapid improvement of language models, open the door for significant simplification of the workflow behind the simulation model development, which will allow experts to focus on the high-level consideration of the problem and holistic thinking.
A Unified Framework for Campaign Performance Forecasting in Online Display Advertising

Advertisers usually enjoy the flexibility to choose criteria like target audience, geographic area and bid price when planning an campaign for online display advertising, while they lack forecast information on campaign performance to optimize delivery strategies in advance, resulting in a waste of labour and budget for feedback adjustments. In this paper, we aim to forecast key performance indicators for new campaigns given any certain criteria. Interpretable and accurate results could enable advertisers to manage and optimize their campaign criteria. There are several challenges for this very task. First, platforms usually offer advertisers various criteria when they plan an advertising campaign, it is difficult to estimate campaign performance unifiedly because of the great difference among bidding types. Furthermore, complex strategies applied in bidding system bring great fluctuation on campaign performance, making estimation accuracy an extremely tough problem. To address above challenges, we propose a novel Campaign Performance Forecasting framework, which firstly reproduces campaign performance on historical logs under various bidding types with a unified replay algorithm, in which essential auction processes like match and rank are replayed, ensuring the interpretability on forecast results. Then, we innovatively introduce a multi-task learning method to calibrate the deviation of estimation brought by hard-to-reproduce bidding strategies in replay. The method captures mixture calibration patterns among related forecast indicators to map the estimated results to the true ones, improving both accuracy and efficiency significantly. Experiment results on a dataset from Taobao.com demonstrate that the proposed framework significantly outperforms other baselines by a large margin, and an online A/B test verifies its effectiveness in the real world.
The low energy spectrum of trapped bosons in the Gross–Pitaevskii regime

Bogoliubov theory {N. N. Bogoliubov, Izv. Akad. Nauk Ser. Fiz. 11, 77 (1947) [J. Phys. (USSR) 11, 23 (1947) (in English)]} provides important predictions for the low energy properties of the weakly interacting Bose gas. Recently, Bogoliubov’s predictions were justified rigorously by Boccato et al. [Acta Math. 222(2), 219–335 (2019)] for translation invariant systems in the Gross–Pitaveskii regime, where N bosons in [Formula: see text] interact through a potential whose scattering length is of size N−1. In this article, we review recent results from the work of Brennecke et al. [Ann. Henri Poincaré 23, 1583–1658 (2022)], a joint work with Schlein and Schraven, which extends the analysis for translation invariant systems to systems of bosons in [Formula: see text] that are trapped by an external potential.
All-Electrical Control and Temperature Dependence of the Spin and Valley Hall Effect in Monolayer WSe2 Transistors

Heavy metal-based two-dimensional van der Waals materials have a large, coupled spin and valley Hall effect (SVHE) that has potential use in spintronics and valleytronics. Optical measurements of the SVHE have largely been performed below 30 K and understanding of the SVHE-induced spin/valley polarizations that can be electrically generated is limited. Here, we study the SVHE in monolayer p-type tungsten diselenide (WSe2). Kerr rotation (KR) measurements show the spatial distribution of the SVHE at different temperatures, its persistence up to 160 K, and that it can be electrically modulated via gate and drain bias. A spin/valley drift and diffusion model together with reflection spectra data is used to interpret the KR data and predict a lower-bound spin/valley lifetime of 4.1 ns below 90 K and 0.26 ns at 160 K. The excess spin and valley per unit length along the edge is calculated to be 109 per micron at 45 K, which corresponds to a spin/valley polarization on the edge of 6%. These results are important steps towards practical use of the SVHE.
Proactive Libraries: Enforcing Correct Behaviors in Android Apps

The Android framework provides a rich set of APIs that can be exploited by developers to build their apps. However, the rapid evolution of these APIs jointly with the specific characteristics of the lifecycle of the Android components challenge developers, who may release apps that use APIs incorrectly.In this demo, we present Proactive Libraries, a tool that can be used to decorate regular libraries with the capability of proactively detecting and healing API misuses at runtime. Proactive Libraries blend libraries with multiple proactive modules that collect data, check the compliance of API usages with correctness policies, and heal executions as soon as the possible violation of a policy is detected. The results of our evaluation with 27 possible API misuses show the effectiveness of Proactive Libraries in correcting API misuses with negligible runtime overhead.Video: https://youtu.be/rkfZ38mPgV0Repo: https://gitlab.com/learnERC/proactivelibrary
Computer Aided Diagnosis and Out-of-Distribution Detection in Glaucoma Screening Using Color Fundus Photography

Artificial Intelligence for RObust Glaucoma Screening (AIROGS) Challenge is held for developing solutions for glaucoma screening from color fundus photography that are robust to real-world scenarios. This report describes our method submitted to the AIROGS challenge. Our method employs convolutional neural networks to classify input images to"referable glaucoma"or"no referable glaucoma". In addition, we introduce an inference-time out-of-distribution (OOD) detection method to identify ungradable images. Our OOD detection is based on an energy-based method combined with activation rectification.
Googling for Abortion: Search Engine Mediation of Abortion Accessibility in the United States

Among the myriad barriers to abortion access, crisis pregnancy centers (CPCs) pose an additional difficulty by targeting women with unexpected or “crisis” pregnancies in order to dissuade them from the procedure. Web search engines may prove to be another barrier, being in a powerful position to direct their users to health information, and above all, health services. In this study we ask, to what degree does Google Search provide quality responses to users searching for an abortion provider, specifically in terms of directing them to abortion clinics (ACs) or CPCs. To answer this question, we considered the scenario of a woman searching for abortion services online, and conducted 10 abortion-related queries from 467 locations across the United States once a week for 14 weeks. Overall, among Google’s location results that feature businesses alongside a map, 79.4% were ACs, and 6.9% were CPCs. When an AC was returned, it was the closest known AC location 86.9% of the time. However, when a CPC appeared in a result set, it was the closest one to the search location 75.9% of the time. Examining correlates of AC results, we found that fewer AC results were returned for searches from poorer and rural areas, and those with TRAP laws governing AC facility and clinician requirements. We also observed that Google’s performance on our queries significantly improved following a major algorithm update. These results have important implications concerning health access quality and equity, both for individual users and public health policy.