Miracle-dz/UniFAR-FTUs huggingface dataset:
Incremental Aggregated Riemannian Gradient Method for Distributed PCA

We consider the problem of distributed principal component analysis (PCA) where the data samples are dispersed across different agents. Despite the rich literature on this problem under various speciﬁc settings, there is still a lack of efﬁcient algorithms that are amenable to decentralized and asynchronous implementations. In this paper, we extend the incremental aggregated gradient (IAG) method in convex optimization to the nonconvex PCA problems based on an Riemannian gradient-type method named IARG-PCA. The IARG-PCA method admits low per-iteration computational and communication cost and can be readily implemented in a decentralized and asynchronous manner. Moreover, we show that the IARG-PCA method converges linearly to the leading eigenvector of the sample covariance of the whole dataset with a constant step size. The iteration complexity coincides with the best-known result of the IAG method in terms of the linear dependence on the number of agents. Meanwhile, the communication complexity is much lower than the state-of-the-art decentralized PCA algorithms if the eigengap of the sample covariance is moderate. Numerical experiments on synthetic and real datasets show that our IARG-PCA method exhibits substantially lower communication cost and comparable computational cost compared with other existing algorithms.
Sample Complexity of Distinguishing Cause from Effect

We study the sample complexity of causal structure learning on a two-variable system with observational and experimental data. Speciﬁcally, for two variables X and Y , we consider the classical scenario where either X causes Y , Y causes X , or there is an unmeasured confounder between X and Y . Let m 1 be the number of observational samples of ( X, Y ) , and let m 2 be the number of interventional samples where either X or Y has been subject to an external intervention. We show that if X and Y are over a ﬁnite domain of size k and are signiﬁcantly correlated, the minimum m 2 needed is sublinear in k . Moreover, as m 1 grows, the minimum m 2 needed to identify the causal structure decreases. In fact, we can give a tight characterization of the tradeoff between m 1 and m 2 when m 1 = O ( k ) or is sufﬁciently large. We build upon techniques for closeness testing when m 1 is small (e.g., sublinear in k ), and for non-parametric density estimation when m 1 is large. Our hardness results are based on carefully constructing causal models whose marginal and interventional distributions form hard instances of canonical results on property testing.
Learning with Partial Forgetting in Modern Hopfield Networks

It has been known by neuroscience studies that partial and transient forgetting of memory often plays an important role in the brain to improve performance for certain intellectual activities. In machine learning, associative memory models such as classical and modern Hopﬁeld networks have been proposed to express memories as at-tractors in the feature space of a closed recurrent network. In this work, we propose learning with partial forgetting (LwPF), where a partial forgetting functionality is designed by element-wise non-bijective projections, for memory neurons in modern Hopﬁeld networks to improve model performance. We incorporate LwPF into the attention mechanism also, whose process has been shown to be identical to the update rule of a certain modern Hopﬁeld network, by modifying the corresponding Lagrangian. We evaluated the effectiveness of LwPF on three diverse tasks such as bit-pattern classiﬁcation, immune repertoire clas-siﬁcation for computational biology, and image classiﬁcation for computer vision, and conﬁrmed that LwPF consistently improves the performance of existing neural networks including DeepRC and vision transformers.
Recursive Control Variates for Inverse Rendering

We present a method for reducing errors---variance and bias---in physically based differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as part of an optimization loop involving gradient descent. The actual change introduced by each gradient descent step is often relatively small, causing a significant degree of redundancy in this computation. We exploit this redundancy by formulating a gradient estimator that employs a recursive control variate, which leverages information from previous optimization steps. The control variate reduces variance in gradients, and, perhaps more importantly, alleviates issues that arise from differentiating loss functions with respect to noisy inputs, a common cause of drift to bad local minima or divergent optimizations. We experimentally evaluate our approach on a variety of path-traced scenes containing surfaces and volumes and observe that primal rendering efficiency improves by a factor of up to 10.
Effects of modular new prone positioning tools in patients with acute respiratory distress syndrome due to COVID-19: A randomized controlled trial (preprint)

Background Prone position (PP) ventilation has become an effective and simple treatment for acute respiratory distress syndrome (ARDS) due to COVID-19; however, prolonged prone position not only leads to patient discomfort, but also reduces patient compliance, and is prone to causing adverse events such as pressure injuries, pain, and dizziness. We aim to explore the effects of modular new prone positioning tools in patients with ARDS due to COVID-19.Methods 168 patients with ARDS due to COVID-19 were selected; however, 92 were later disqualified. 76 patients were randomly assigned to the observation group (n = 38) and the control group (n = 38). The observation group used modular new prone positioning tools to implement prone ventilation therapy while the control group used soft pillows to implement prone ventilation therapy. Comfort indicators (including time spent implementing PP, duration of PP, number of postural adjustments during PP, and duration time when first needing to adjust position), adverse events (including artificial airway kinking, shortness of breath, dizziness, and stress injury), and efficacy indicators (including intubation and mortality) were collected. The feeling of comfort, the occurrence of adverse events, and the efficacy of two groups of patients were also evaluated.Results The observation group had shorter time spent implementing PP(2.74 ± 0.86 vs. 4.64 ± 1.02, P < 0.001), longer duration of PP (14.02 ± 1.01 vs. 13.03 ± 0.66, P < 0.001), duration time when first needing to adjust position (59.89 ± 12.73 vs. 36.57 ± 8.69, P < 0.001), and lower number of postural adjustments during PP (11.03 ± 2.67 vs. 17.95 ± 2.58, P < 0.001) in comparison with the control group. No significant differences in intubation (9 vs. 11, P = 0.602) and mortality (4 vs. 6, P = 0.602) were found in both groups. However, in terms of adverse events, the observation group showed lower artificial airway kinking (5 vs. 23, P < 0.001), pain (7 vs. 21, P = 0.001), shortness of breath (2 vs. 9, P = 0.022), dizziness (0 vs. 5, P = 0.021), and stress injury (7 vs. 26, P < 0.001) than the control group.Conclusion Utilizing modular new prone position tools to implement prone ventilation therapy not only improves the efficiency of prone position execution and patient comfort, but also reduces the incidence of adverse events. However, it cannot change the intubation rate and mortality rate of patients.
Virtual and Augmented Reality Mobile Computing Machine Learning

ing and Library Use: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy for private use of patrons, provided the per-copy fee indicated in the code at the bottom of the first page is paid through the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. Unsubscribe: If you no longer wish to receive this ComputingEdge mailing, please email IEEE Computer Society Customer Service at help@ computer.org and type “unsubscribe ComputingEdge” in your subject line. IEEE prohibits discrimination, harassment, and bullying. For more information, visit www.ieee. org/web/aboutus/whatis/policies/p9-26.html. IEEE COMPUTER SOCIETY computer.org IEEE Computer Society Magazine Editors in Chief Computer Jeff Voas, NIST Computing in Science & Engineering Lorena A. Barba, George Washington University IEEE Annals of the History of Computing Gerardo Con Diaz, University of California, Davis IEEE Computer Graphics and Applications Torsten Möller, Universität Wien IEEE Intelligent Systems Longbing Cao, University of Technology Sydney IEEE Internet Computing George Pallis, University of Cyprus IEEE Micro Lizy Kurian John, University of Texas at Austin IEEE MultiMedia Shu-Ching Chen, Florida International University IEEE Pervasive Computing Marc Langheinrich, Università della Svizzera italiana IEEE Security & Privacy Sean Peisert, Lawrence Berkeley National Laboratory and University of California, Davis IEEE Software Ipek Ozkaya, Software Engineering Institute IT Professional Irena Bojanova, NIST 11 Saliency Computation for Virtual Cinematography in 360° Videos 38 Attacking Machine Learning Systems 42 Preparing America’s Cyber Intelligence Workforce FEBRUARY 2022 � VOLUME 8 � NUMBER 2
Analysing the e ↵ ectiveness of a generative model for semi-supervised medical image segmentation

Image segmentation is important in medical imaging, providing valuable, quantitative information for clinical decision-making in diagnosis, therapy, and intervention. The state-of-the-art in automated segmentation remains supervised learning, employing discriminative models such as U-Net. However, training these models requires access to large amounts of manually labelled data which is often di � cult to obtain in real medical applications. In such settings, semi-supervised learning (SSL) attempts to leverage the abundance of unlabelled data to obtain more robust and reliable models. Recently, generative models have been proposed for semantic segmentation, as they make an attractive choice for SSL. Their ability to capture the joint distribution over input images and output label maps provides a natural way to incorporate information from unlabelled images. This paper analyses whether deep generative models such as the SemanticGAN are truly viable alternatives to tackle challenging medical image segmentation problems. To that end, we thoroughly evaluate the segmentation performance, robustness, and potential subgroup disparities of discriminative and generative segmentation methods when applied to large-scale, publicly available chest X-ray datasets
From intensional properties to universal support

An optimality-theoretic (OT) system is specified by defining its constraints and the structures they evaluate. These give rise to a set of grammars, the typology of the system, which emerges from the often complex interactions among constraints and structures. Every typology is determined by a finite collection of candidate sets (csets). How do we know that we have assembled a universal support, a collection of csets sufficient to distinguish all grammars of the system? Lacking a universal support, we do not have the typology and we cannot deal systematically with its structure and consequences.This concrete question can be answered in terms of an enhanced abstract understanding of typological structure. Under property theory (Alber & Prince 2015a,b), a typology is resolved into a set of properties: ranking conditions that have mutually exclusive values. When the structural correlates of each value are determined, the ranking values defining a grammar also determine the extensional traits exhibited in its optima. Suppose we have the property analysis of a typology derived from a proposed support for an OT system. If every consistent choice of values ensures that a single optimum is chosen in every cset admitted by the system, then no grammar derived from the proposed support can be split by consideration of further csets, and that support must be universal for the system. This method of proof is applicable to any OT system. Here we use it to analyze the prosodic system nGX (Alber & Prince 2015b), determining its universal supports and the shape of the forms made optimal by its grammars.
Spatial analysis techniques applied to mineral prospecting: an evaluation in the Poços de Caldas plateau

Boolean Logic, Weighted Means, Fuzzy/AHP (Analytical Hierarchic Process), Fuzzy Gamma, and Bayesian Weight of Evidence (WofE) spatial analysis techniques were evaluated to address potential areas for radioactive mineral prospecting in the Pocos de Caldas Plateau, Brazil. These techniques were applied according to a prospecting model based on diagnostic criteria, such as favorable lithologies, structural features and gamma ray (total count) data. Resulting maps were organized into four classes of potentiality (null, low, middle and high potential), which express different levels of favorability for radioactive mineral occurrences in the study area. To compare the performance of the resulting scenarios, some 48 known radioactive mineral occurrences were used. Cross-tabulation technique was applied to measure the confidence grade (conditional probability) of each potential class. According to the results, the scenarios showed different performances. The Boolean Logic-based scenario proved to be limited, whereas result of the Fuzzy Gamma technique was unsatisfactory. However, very good results were obtained with the scenarios derived from the Bayesian WofE, Fuzzy/AHP, and Weighted Means techniques, indicating that spatial inference techniques can be useful guides in mineral prospecting campaigns.
Hydromagnetic planetary waves

A study is made of hydromagnetic oscillations in a rotating fluid sphere. The basic state is chosen as a uniform current parallel to the axis of rotation. It is found that the non-dissipative normal modes are described by a modified form of the Poincaré eigenvalue problem. For small rotation rates, the lowest non-axisymmetric modes are unstable. For rotation rates of geophysical interest all normal modes are stable. The introduction of ohmic dissipation leads to a hydro-magnetic boundary-layer problem. Solutions for the boundary layer are outlined indicating its role in altering the free periods, damping the oscillations and producing external magnetic fields. Dispersion relations are derived which establish that the zonal phase velocities of both ‘fast’ hydrodynamic and ‘slow’ hydro-magnetic waves can be of either sign. Observations of the secular variations of the earth's magnetic field indicate motion primarily towards the west. A mechanism for the selective excitation of the observed motion is discussed.
PARALLEL SOLUTION OF AN ELECTROSTATIC FIELD PROBLEM - CASE STUDY

The paper presents a parallel approach for the efficient solution of a simple 1D Laplace-Poisson equation problem by parallel finite element method. This problem is a case study. The non-overlapping domain decomposition method has been used to cut the problem into sub-regions or also called sub-domains, and it reduces the large mass matrix into smaller parts. The independent subdomains, and the assembling of these equation systems can be handled by the independent processors of a supercomputer, i.e. in a parallel way. The results of parallel finite element method have been compared to the results of serial finite element method as well as the analytical results.
Planejamento Otimizado da Infra-estrutura de Rede Celular

Nos últimos anos, o crescimento da rede móvel celular no Brasil e no resto do mundo tem sido explosivo. Além deste crescimento, tem-se verificado uma demanda cada vez maior por novos serviços na rede móvel, notadamente acesso à Internet com a necessidade de taxas de acesso mais altas. Gerações mais avançadas têm sido propostas, como por exemplo, os sistemas 2,5 G como uma evolução dos atuais sistemas digitais de segunda geração e uma nova terceira geração (3G). Para suportar esta evolução serão necessários novos equipamentos e infraestrutura tanto na interface aérea quanto na interligação BTS (Base Transceiver Station) com BSC/MSC (Base Station Controller/Mobile Switching Center). Neste contexto, no presente trabalho é introduzido um modelo de alocação ótima de recursos de rede fixa para a interligação das BTS ́s ao conjunto BSC/MSC. Adicionalmente, é mostrada uma aplicação deste modelo em um estudo de caso para sistemas de segunda geração (2G), podendo posteriormente ser evoluído para as novas gerações com a inclusão de novos parâmetros.
Changing Preprogrammed Selective Harmonic Eliminated PWM for Three-Level NPC AFE Rectifier

Power converters efficiency depends largely on pulse-width modulation (PWM) methods. The currently existing preprogrammed selective harmonic eliminated PWM (PPWM SHE) methods have been created to improve the power quality and optimize electric losses. PPWM SHE uses pre-calculated switching tables of semiconductor modules, it can be changed during the operation modes to define the best variant. This paper focuses on designing the PPWM SHE changing algorithm for three-phase three-level neural point clamped (NPC) active front-end (AFE) rectifier. The obtained experimental results have been presented. The proposed algorithm has prospects, in particular, in the field of wind power, metallurgical rolling mills and powerful ship electric drives, where high-power three-level NPC AFE rectifiers are used.
Providing a Solution for Optimal Management of Resources using the Multi-objective Crow Search Algorithm in Cloud Data Centers

Cloud data centers are a model of distributed systems that provide users with services to share data and information through the Internet. These centers also face challenges due to their popularity among users. With the increase in the number of hosts to respond to users' needs, challenges such as increasing power consumption, service level agreement violation, and time are seen. As a result, it is very important to address these challenges in these centers in order to reduce costs and increase profits. task scheduling for hosts is one of the most effective methods to improve productivity and optimal use of hosts' resources. In this process, with proper allocation, we can prevent hosts from becoming overloaded and increasing energy consumption due to inefficient use of hosts' resources. The proposed solution in this paper is to use multiple goals for the allocation process using the Crow search optimization algorithm. The Crow search optimization algorithm is new, fast, and powerful. As a result, in the proposed method by modeling this algorithm and considering the multi-criteria fitness function based on the requested resources of the tasks and the available resources of the host, we tend to manage resources properly. The simulation results show that the proposed method has a 9% reduction in service quality parameters such as power consumption compared to paper [21] and 15% compared to article [16], 11% execution time compared to paper [21], and 14% compared to the paper [16] and the service level agreement violation has improved by 16% compared to the paper [21] and 8% compared to the paper [16] and has been able to reduce the mentioned parameters.
A relação entre as emoções do professor e a autoeficácia docente: uma revisão sistemática

O objetivo deste estudo é compreender a relação entre as emoções discretas do professor vivenciados no contexto escolar e a autoeficácia docente. Para tal, realizou-se uma revisão sistemática de literatura. As buscas dos estudos foram realizadas nas bases de dados ERIC EBSCOhost, Scopus e Web of Science desde o ano 1980 até fevereiro de 2022. Foram incluídos artigos revisados por pares, publicados nos idiomas português, inglês e espanhol, que investigaram a relação entre as emoções discretas de professores, do ensino fundamental e médio, com a autoeficácia docente. A qualidade dos estudos foi mensurada pela ferramenta Appraisal Tool for Cross-Sectional Studies (DOWNES; BRENNAN; WILLIAMS; DEAN, 2016). No geral, sete artigos foram incluídos com um total de participantes variando de 68 a 3010 professores. Os estudos incluídos revelaram uma qualidade de moderada a alta. Os resultados suportam, de uma forma moderada, a relação entre algumas emoções discretas dos professores e as crenças de autoeficácia docente. A presente revisão sugere a necessidade de mais estudos investigarem as emoções do professor em sua abordagem discreta e em diferentes culturas com a finalidade de aprofundar o conhecimento sobre como diferentes emoções se relacionam com a autoeficácia docente.
Development of an Adaptive Technology for Distance Learning of Microprocessor Technology Based on Immunological Homeostasis

At present, with the development of modern IT technologies and artificial intelligence methods, it is relevant to develop adaptive distance education systems for students of technical specialties capable of providing a personalized cognitive approach to the process of obtaining knowledge and timely adjustment of the learning trajectory. Particular difficulties arise when teaching the principles of operation of complex industrial equipment based on microprocessor technology, the basics of programming and the development of automatic control systems based on them. The problems associated with the complexity of adapting technical training courses for specific students and the peculiarities of information perception in the absence of direct contact with the teacher can be successfully solved with the introduction of algorithms based on a unified artificial immune system (UAIS). An innovative e-learning technology is proposed for students of engineering specialties in order to obtain high-quality technical professional education in a short time on real industrial equipment. When creating the technology and predicting the learning outcomes of students, immunological homeostasis was used to quickly correct the learning process. UAIS was developed on the basis of modified clonal selection, negative selection and immune network algorithms in order to identify the most effective of them in predicting multivariate dynamic data about students and forming an adequate immune response to maintain immunological homeostasis. The main stages and advantages of the proposed technology are given. The simulation results were obtained with the participation of students of the Faculty of Information Technology on the basis of the Kazakh-British Technical University.
Gittins procedures for bandits with delayed responses

This paper introduces the multi-armed delayed response bandit with geometric discounting. The existence of dynamic allocation indices is shown when the discount factor is less than 1/2 or when the information bank size is zero. For the multi-armed delayed response bandit, the arm indicated by the dynamic allocation procedure or Gittins procedure is optimal when all information bank sizes are zero. A computational method for calculating indices is presented. The idea is to approximate the optimal strategy using a class of strategies whose worths are easy to calculate.
WATER SECTOR INFRASTRUCTURE SYSTEMS RESILIENCE A Social-Ecological-Technical System-of-Systems and Whole-Life Approach

Water is often referred to as our most precious resource, and for a good reason— drinking water and wastewater services sustain core functions of the critical infrastructure, communities, and human life itself. Our water systems are threatened by aging infrastructure, floods, drought, storms, earthquakes, sea level rise, population growth, cyber-security breaches, and pollution, often in combination. Marginalized communities inevitably feel the worst impacts, and our response continues to be hampered by fragmented and antiquated governance and management practices. This paper focuses on the resilience of water sector (drinking water, wastewater, and stormwater (DWS)) to three major hazards (Sea-Level Rise, Earthquake, and Cyberattack). The purpose of this paper is to provide information useful for creating and maintaining resilient water system services. The term resilience describes the ability to adapt to changing conditions and to withstand and recover from disruptions. The resilience of DWS