First-principles study on the structural and electronic properties of single-layer MoSi2N4

Motivated by the successful exfoliation of a novel two-dimensional MoSi2N4 materials, in this work, we investigate the structural and electronic properties of a novel single-layer MoSi2N4 and the effect of strain engineering by using the first-principles calculations based on the density functional theory. The single-layer MoSi2N4 has a hexagonal structure with a space group of P6m1, which is dynamically stable. The material exhibits a semiconducting characteristic with an indirect band gap of 1.80/2.36 eV calculated by using the PBE/HSE functional. The conduction band minimum at the K point of the material originates from the Mo atom, while its valence band maximum at the G point is contributed by the hybridization between the Mo and N atoms. The electronic properties of the single-layer MoSi2N4 can be modulated with strain engineering, giving rise to a transition from a semiconductor to a metal and tending to a change in the band gap. Our results demonstrate that the single-layer MoSi2N4 is a promising candidate for electronic and optoelectronic applications

Правові та організаційні особливості планування законотворчої діяльності органів виконавчої влади за кордоном (на прикладі Соціалістичної Республіки В’єтнам)

This article analyses the laws of the Socialistic Republic of Vietnam in the context of defining the specifics of the involvement of public administration bodies (namely, the central executive authorities) in the process of public law-making planning in general and departmental law-making planning in particular. It contains positive aspects of foreign experience, which should be realised in the Ukrainian law-making process.В статье приведен анализ законодательства Социалистической Республики Вьетнам в контексте определения специфики участия органов публичной администрации (а именно, центральных органов исполнительной власти) в процессе планирования государственного законотворчества в целом и ведомственного законотворческого планирования в частности. Указаны положительные черты зарубежного опыта, заслуживающие внимания при внедрении в украинское законотворчество.У статті наведений аналіз законодавства Соціалістичної Республіки В’єтнам в контексті визначення специфіки участі органів публічної адміністрації (а саме, центральних органів виконавчої влади) в процесі планування державної законотворчості загалом, та відомчого законотворчого планування зокрема. Вказані позитивні риси зарубіжного досвіду, що заслуговують уваги та втілення в українську законотворчість

EnSolver: Uncertainty-Aware CAPTCHA Solver Using Deep Ensembles

The popularity of text-based CAPTCHA as a security mechanism to protect websites from automated bots has prompted researches in CAPTCHA solvers, with the aim of understanding its failure cases and subsequently making CAPTCHAs more secure. Recently proposed solvers, built on advances in deep learning, are able to crack even the very challenging CAPTCHAs with high accuracy. However, these solvers often perform poorly on out-of-distribution samples that contain visual features different from those in the training set. Furthermore, they lack the ability to detect and avoid such samples, making them susceptible to being locked out by defense systems after a certain number of failed attempts. In this paper, we propose EnSolver, a novel CAPTCHA solver that utilizes deep ensemble uncertainty estimation to detect and skip out-of-distribution CAPTCHAs, making it harder to be detected. We demonstrate the use of our solver with object detection models and show empirically that it performs well on both in-distribution and out-of-distribution data, achieving up to 98.1% accuracy when detecting out-of-distribution data and up to 93% success rate when solving in-distribution CAPTCHAs.Comment: Epistemic Uncertainty - E-pi UAI 2023 Worksho

Electronic Scan Strategy for Phased Array Weather Radar Using a Space–Time Characterization Model

AbstractThis paper presents an adaptive scan strategy concept for phased array weather radars (PAWR) with the objective of increasing the scan speed and capturing features of the storm system while maintaining the measurement accuracy. The adaptive scan strategy is developed based on the space–time variability of the storm under observation. Quickly evolving regions are scanned more often and the spatial sampling resolution is matched to the spatial scale. A model that includes the interaction between space and time is used to extract spatial and temporal scales of the medium and to define scanning regions. The temporal scale constrains the radar revisit time, while the measurement accuracy controls the radar's dwell time. These conditions are employed in a task scheduler that works on a ray-by-ray basis and is designed to balance task priority and radar resources. The scheduler algorithm also includes an optimization procedure for minimizing radar scan time. The model and the scan strategy are demonstrated using simulation data. The results show that the proposed scan strategy can reduce the scan time significantly without compromising data quality

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

Simple Transferability Estimation for Regression Tasks

We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationally efficient approaches that estimate transferability based on the negative regularized mean squared error of a linear regression model. We prove novel theoretical results connecting our approaches to the actual transferability of the optimal target models obtained from the transfer learning process. Despite their simplicity, our approaches significantly outperform existing state-of-the-art regression transferability estimators in both accuracy and efficiency. On two large-scale keypoint regression benchmarks, our approaches yield 12% to 36% better results on average while being at least 27% faster than previous state-of-the-art methods.Comment: Paper published at The 39th Conference on Uncertainty in Artificial Intelligence (UAI) 202

Explainable Severity ranking via pairwise n-hidden comparison: a case study of glaucoma

Primary open-angle glaucoma (POAG) is a chronic and progressive optic nerve condition that results in an acquired loss of optic nerve fibers and potential blindness. The gradual onset of glaucoma results in patients progressively losing their vision without being consciously aware of the changes. To diagnose POAG and determine its severity, patients must undergo a comprehensive dilated eye examination. In this work, we build a framework to rank, compare, and interpret the severity of glaucoma using fundus images. We introduce a siamese-based severity ranking using pairwise n-hidden comparisons. We additionally have a novel approach to explaining why a specific image is deemed more severe than others. Our findings indicate that the proposed severity ranking model surpasses traditional ones in terms of diagnostic accuracy and delivers improved saliency explanations.Comment: 4 page