Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Intermittent Pneumatic Compression on Blood Flow in Patient with Varicose Veins

Background and Objective: Although compression therapy is applied extensively in the treatment of varicose veins, we still don't have enough evidence on its effectiveness. Varicose veins with or without active venous ulceration can lead to serious complications resulting in more governmental financial loads. The aim of this study was to investigate the effect of intermittent pneumatic compression on blood flow in patients with varicose veins, the mean flow volume and pulsality index in both femoral and popliteal veins, pain level, and ankle girth measurement. Methodology: forty women with varicose veins were selected and randomly assigned into two equal groups. Group A, which included 20 patients who were treated with pneumatic compression therapy five times per week for 8 weeks and Group (B), which included 20 patients who were instructed to continue receiving their medical treatment based on their medical prescription. Selected parameters related to Venous blood flow were assessed using Doppler ultra-sound device, in addition to evaluating pain by a visual analogue scale and ankle girth measurements as edema assessment. Results: After 8 weeks of treatment with intermittent pneumatic compression, all selected parameters showed significant improvements in subjects enrolled in group A in comparison to subjects enrolled in group B. Conclusion: The results of this study revealed that intermittent pneumatic compression therapy with the applied parameters can be considered as valuable method for increasing venous blood flow, as well as decreasing ankle girth measurement and pain in patient with varicose veins.
QUESTIONS IN PARLIAMENTARY DISCOURSE DURING DIFFERENT RULING PERIODS IN TAIWAN: A NEO-POLITENESS-THEORETICAL ANALYSIS

Kuomintang (KMT) is the largest party in Taiwan and it has long held the reins of government since 1949, except the period between 2000 and 2008, in which the Democratic Progressive Party (DPP) came into power. Previous studies on parliamentary discourse in Taiwan focused more on question and answer sessions in the legislature when the KMT was in power. The current research, therefore, aims to investigate questions in parliamentary discourse when the DPP was in power. The purpose of this study is twofold: to examine legislators’ use of politeness strategies for questioning government officials during parliamentary question and answer sessions on the one hand and also to find out possible factors that influence their choice of questioning strategies on the other. Based on video recorded data, the present study adopts Lee-Wong’s framework of the classification of politeness strategy types for conversational interaction, which has been greatly revised from Brown and Levinson’s classical politeness theory. Initial findings show that the bald on record strategy is in wide use, which has been found in more than half of the total questions. Other strategies are used for either modification or intensification or both. Besides, the change of political roles as a government supporter or an opposition member does have some influence on legislators’ use of politeness strategies for questions.
A summary study of data-driven photometric stereo

Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light directions. It is an ill-posed problem due to the unknown of general reflectance properties of the surface. Methods This paper reviews existing data-driven methods and focuses on their technical insights for the problem of photometric stereo. We divide these methods into two categories according to how they process an image, i.e. , in a per-pixel and all-pixel manner. We discuss the differences and relations of methods between the two categories. The discussions are from aspects of inputs, networks, and data, which are key factors of designing a deep learning approach. Results We demonstrate their performance using a popular benchmark dataset. Conclusions Data-driven photometric stereo methods have shown superior performance advantage over traditional methods. However, these methods suffer from various limitations, such as limited generalization capability. Finally, this study suggests research directions for future work.
Value creation, redistribution and integration

In recent years corporate social responsibility (CSR) has become increasingly important as the concept which frames the business contribution to sustainable development. CSR has potential to become a strategic activity adding value on different dimensions—business, society and ecosystems—if two conditions are met. The first condition for strategic CSR is that CSR needs to become integrated with the strategy of the firm. The second condition for strategic CSR is to measure and monitor these new or additional values. We distinguish three ways in which CSR may provide value to a firm, society and ecosystems: value creation, value integration and value redistribution. Building on this distinction, we explore the consequences for measuring the impact of CSR activities. We conclude with a discussion on the implications, limitations and suggestions for further research.
Fostering Trust and Acceptance of a Collision Avoidance System through Retrospective Feedback

A simulator study explored the effects of providing retrospective feedback on drivers’ acceptance of a collision avoidance system (CAS) following a false activation. Sixteen experienced drivers undertook two drives, each lasting approximately 20 minutes. During both drives, the CAS identified a rogue pedestrian and intervened by providing an audible warning closely following by emergency braking. Feedback was provided following one of the activations (with the order counterbalanced between participants), in the form of a detailed storyboard ‘playback’ depicting the system’s analysis of the situation. Subjective ratings of trust, confidence, annoyance and desirability, revealed no differences overall between conditions (i.e. with and without post-event feedback). However, there was a tendency for drivers to trust the system more if feedback was provided during their first drive, whereas drivers who were provided feedback during their second drive indicated higher levels of confidence in the system and found it less annoying. There was also a significant rise in the number of drivers who detected the potential pedestrian hazard prior to system activation during their second drive. Results suggests benefits associated with the provision of retrospective feedback, but effects may have been influenced by the experimental design, which exposed participants to similar pedestrian hazard events in consecutive drives. Future investigations, which should continue to explore techniques to enhance trust and acceptance of active safety systems, should therefore adopt a between-subjects design to isolate effects.
Real-time Light Transport in Analytically Integrable Quasi-heterogeneous Media

Our focus is on the real-time rendering of large-scale volumetric participating media, such as fog. Since a physically correct simulation of light transport in such media is inherently difficult, the existing real-time approaches are typically based on low-order scattering approximations or only consider homogeneous media. We present an improved image-space method for computing light transport within quasi-heterogeneous, optically thin media. Our approach is based on a physically plausible formulation of the image-space scattering kernel and analytically integrable medium density functions. In particular, we propose a novel, hierarchical anisotropic filtering technique tailored to the target environments with inhomogeneous media. Our parallelizable solution enables us to render visually convincing, temporally coherent animations with fog-like media in real time, in a bounded time of only milliseconds per frame.
A Cross-linguistic Study into the Contribution of Affective Connotation in the Lexico-semantic Representation of Concrete and Abstract Concepts

Simon De Deyne (simon.dedeyne@unimelb.edu.au)a Álvaro Cabana (acabana@psico.edu.uy)b Bing Li (52163200020@stu.ecnu.edu.cn)c Qing Cai (qcai@psy.ecnu.edu.cn)c Meredith McKague (mckaguem@unimelb.edu.au)a a School of Psychological Sciences, University of Melbourne, Melbourne, VIC, Australia b Facultad de Psicología, Universidad de la República, Montevideo, Uruguay c Speech, Language and Neuroscience Group, NYU Shanghai, Shanghai, China
HOW DOES ZAKAT INSTITUTION RESPOND TO FINTECH? EVIDENCE FROM BAZNAS, INDONESIA

: The notion of Zakat and financial technology or fintech has been gaining significant attention from academics, industry professionals and other related stakeholders. However, the existing scholarly studies on this topic have massively explained its related theoretical issues and have not discussed technical understanding in what way a Zakat institution has been responding to the emergence of fintech. From this gap, the present paper sets out to study the degree to which a Zakat institution has been adapting the unstoppable advent of fintech. In doing so, this work selects an Indonesian Zakat institution which is BAZNAS for further studying the topic. This paper employs a qualitative method of overviewing the recent management of Zakat in BAZNAS. With performing so, this work finds that BAZNAS has been developing its online platform along with its mobile-based application. In addition, the institution also has been collaborating with the existing fintech firms for payment services and issuing quick respond (QR) code, in which these efforts aim at collecting Zakat funds. These findings suggest the way the reviewed institution has been adapting to the rise of fintech. This is the first study to make clear the understanding of the means a Zakat institution responds to the recent technological changes. The discussion of this work is relevant to academics, industry professionals, and other Zakat stakeholders.
Antidiabetic Drugs Influences of the Activity of Acetylcholinesterase in Type 2 Diabetic Mellitus

Many studies showed that several diseases including diabetes mellitus DM are directly implicated in the elevating of acetylcholinesterase ACHE activity in most biological fluids and considered as a risk factor for neuronal death causing many degenerative disorders involving dementia. The most common dementia lesions related to DM are Alzheimer's disease AD. Metformin is currently as a first-line oral therapy for T2DM and considered a prophylactic factor to prevent of diabetic development. It lowers the AChE efficacy , that directs for the neurotransmitter ( Ach) dissociation. The current investigation involves a study the effect of metformin on the activity of ACHE and some clinical variables for T2DM patients taken metformin monotherapy and combined to insulin of Iraqi population. Methods : study population was 65 patients with type 2 diabetes and 22 healthy control subjects were age (42-80) year. The patients groups were distributed in three groups. The first had newly disease diagnosis without therapy , second had orally metformin monotherapy and the third had injected insulin with orally metformin as a combination therapy. The activity of acetylcholinesterase AChE and clinical biochemical tests ( FBG, HbA1c% , BMI , and lipid profile ) were assessed . Duration of disease and therapy and body mass index had been obtained from patients directly during their attending to the National Center of Diabetes in AL-Mustansyria University in Baghdad / Iraq. Results: No significant difference in the AChE enzyme, BMI, TG, therapy duration and significant difference in age, FPG, HbA1c%, disease duration, T.CH, HDL, and LDL. Conclusion: it was concluded that metformin drug lowers the activity of enzyme to the extent that had been appeared close to the normal activity values enzyme for healthy people in either monotherapy or combined therapy. As well as, it was more efficacy in male than female at all the studied variables for this investigation.
Critical Cultural Awareness and Identity Development: Insights from a Short-Term Thai Language Immersion

Promoting and cultivating intercultural competence is an important goal of foreign language education today. In-country language immersion, an out-of-class component of the foreign language curriculum, has the potential to contribute in this respect, as it gives learners first-hand experience in the target language country and allows them to discover and partake in the local community’s sociocultural practices. This article focuses on a study of the impact of a short-term in-country language immersion in Chiangrai, Thailand, on the development of critical cultural awareness and identity in Singapore university students of Thai as a foreign language. The study adopted a case study approach and collected qualitative data on two subjects through journals, interviews, activity observation and document inspection. The data were analysed based on Byram’s (1997, 2008) model of intercultural competence, Lave and Wenger’s (1991) Situated Learning Theory and their notion of legitimate peripheral participation, and Weinreich’s (2003) Identity Structure Analysis. The findings suggest that the immersion programme in Chiangrai provided the learners with access to legitimate peripheral participation, which helped them gain insights into the target language culture, negotiate and (co-)construct new cultural meanings and knowledge, and critically appraise both the target language and the native cultures. These experiences and a growing identification with the Thai culture prompted them to re-examine and question their own culture and identity, but led eventually to the re-affirmation and re-construction of their own identity, as they sought to extend their value systems by adopting and aspiring towards Thai traits and practices that they found desirable.
GPU-Accelerated SVM Learning for Extremely Fast Massive-Scale Proteomics Classiﬁcation

In proteomic analysis pipelines, semi-supervised support vector machine (SVM) learning (Kall et al., 2007) is a critical step towards accurately identifying the generating peptides of tandem mass spectra. Called Percolator, this algorithm iteratively learns the linear decision boundary between correct and incorrect peptide-spectrum matches (PSMs) and uses the converged decision boundary to rerank the input PSMs. While this reranking greatly improves PSM identiﬁcation accuracy, Percolator requires substantial analysis time in practice. Recent work (Halloran & Rocke, 2018b) has reduced such lengthy runtimes by updating Percolator’s SVM solver to state-of-the-art, multithreaded solvers. In this work, we present linear SVM primal solvers novelly designed to take advantage of graphical processing units (GPUs) which signiﬁcantly outperform previous multithreaded speedups. Most importantly, we show how GPU optimizations may be mixed with multithreading to enable such speedups for commonly produced large-scale proteomics datasets which do not ﬁt in GPU memory alone. On a massive proteomics dataset of nearly a quarter-billion data instances, we show that such mixed-architecture speedups reduce SVM analysis time from over half a week down to less than a single day while efﬁciently using limited GPU memory.
Conditional Independence and Markov Properties Lecture 1

Markov Properties Lecture 1 Saint Flour Summerschool, July 5, 2006 Steffen L. Lauritzen, University of Oxford 1 Overview of lectures 1. Conditional independence and Markov properties 2. More on Markov properties 3. Graph decompositions and junction trees 4. Probability propagation and similar algorithms 5. Log-linear and Gaussian graphical models 6. Conjugate prior families for graphical models 7. Hyper Markov laws 8. Structure learning and Bayes factors 9. More on structure learning. 2 Conditional independence The notion of conditional independence is fundamental for graphical models. For three random variables X, Y and Z we denote this as X ⊥⊥Y |Z and graphically as u u u X Z Y If the random variables have density w.r. t. a product measure μ, the conditional independence is reflected in the relation f(x, y, z)f(z) = f(x, z)f(y, z), where f is a generic symbol for the densities involved. 3 Graphical models 3 6 1 5 7 2 4 u u u u u u u @ @ @ @ @ @ @ @ For several variables, complex systems of conditional independence can be described by undirected graphs. Then a set of variables A is conditionally independent of set B, given the values of a set of variables C if C separates A from B. 4 A directed graphical model Directed model showing relations between risk factors, diseases, and symptoms. 5 A pedigree Graphical model for a pedigree from study of Werner’s syndrome. Each node is itself a graphical model. 6 A highly complex pedigree
Prediction of solvation free energy of neutral solutes in methanol using molecular dynamic simulation

In this research, molecular dynamics simulation was applied for calculating solvation free energy of ten solute molecules in methanol solvent. Thermodynamic integration method was used because it was possible to calculate the difference in free energy in any thermodynamic path. After comparing results for solvation free energy in different force fields, COMPASS force field was selected due to its minimum error compared to experimental result. Group based summation method was used to compute interaction between electrostatic and van der Waals forces at temperature of 298.15 K and pressure of 1 atm. Results of solvation free energy were obtained from molecular dynamics simulation and were compared to the results from Solvation Model Density (SMD) and Universal Continuum Solvation Model (denoted as SM8) , which was obtained from other researchs. Average square root error for molecular dynamics simulation, SMD and SM8 models were 0.048011, 0.403051, 0.556749. Moreover, R 2 for molecular dynamics simulation was 0.9849, which shows higher accuracy of molecular dynamics simulation for calculating solvation free energy than two other models.
An auto-parameter denoising method for nuclear magnetic resonance spectroscopy based on low-rank Hankel matrix.

Nuclear Magnetic Resonance (NMR) spectroscopy, which is modeled as the sum of damped exponential signals, has become an indispensable tool in various scenarios, such as the structure and function determination, chemical analysis, and disease diagnosis. NMR spectroscopy signals, however, are usually corrupted by Gaussian noise in practice, raising difficulties in sequential analysis and quantification of the signals. The low-rank Hankel property plays an important role in the denoising issue, but selecting an appropriate parameter still remains a problem. In this work, we explore the effect of the regularization parameter of a convex optimization denoising method based on low-rank Hankel matrices for exponential signals corrupted by Gaussian noise. An accurate estimate on the spectral norm of weighted Hankel matrices is provided as a guidance to set the regularization parameter. The bound can be efficiently calculated since it only depends on the standard deviation of the noise and a constant. Aided by the bound, one can easily obtain an auto-setting regularization parameter to produce promising denoised results. Our experiments on synthetic and realistic NMR spectroscopy data demonstrate a superior denoising performance of our proposed approach in comparison with the typical Cadzow and the state-of-the-art QR decomposition methods, especially in the low signal-to-noise ratio regime.
Vis-CRF, A Classical Receptive Field Model for VISION

Over the last decade, a variety of new neurophysiological experiments have led to new insights as to how, when and where retinal processing takes place, and the nature of the retinal representation encoding sent to the cortex for further processing. Based on these neurobiological discoveries, in our previous work, we provided computer simulation evidence to suggest that Geometrical illusions are explained in part, by the interaction of multiscale visual processing performed in the retina. The output of our retinal stage model, named Vis-CRF, is presented here for a sample of natural image and for several types of Tilt Illusion, in which the final tilt percept arises from multiple scale processing of Difference of Gaussians (DoG) and the perceptual interaction of foreground and background elements (Nematzadeh and Powers, 2019; Nematzadeh, 2018; Nematzadeh, Powers and Lewis, 2017; Nematzadeh, Lewis and Powers, 2015).
FTK: A High-Dimensional Simplicial Meshing Framework for Robust and Scalable Feature Tracking

We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our high-dimensional simplicial meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including Tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open-sourced under the MIT license: this https URL
Simple and optimal methods for stochastic variational inequalities, II: Markovian noise and policy evaluation in reinforcement learning

The focus of this paper is on stochastic variational inequalities (VI) under Markovian noise. A prominent application of our algorithmic developments is the stochastic policy evaluation problem in reinforcement learning. Prior investigations in the literature focused on temporal difference (TD) learning by employing nonsmooth finite time analysis motivated by stochastic subgradient descent leading to certain limitations. These encompass the requirement of analyzing a modified TD algorithm that involves projection to an a-priori defined Euclidean ball, achieving a non-optimal convergence rate and no clear way of deriving the beneficial effects of parallel implementation. Our approach remedies these shortcomings in the broader context of stochastic VIs and in particular when it comes to stochastic policy evaluation. We developed a variety of simple TD learning type algorithms motivated by its original version that maintain its simplicity, while offering distinct advantages from a non-asymptotic analysis point of view. We first provide an improved analysis of the standard TD algorithm that can benefit from parallel implementation. Then we present versions of a conditional TD algorithm (CTD), that involves periodic updates of the stochastic iterates, which reduce the bias and therefore exhibit improved iteration complexity. This bring us to the fast TD (FTD) algorithm which combines elements of CTD and the stochastic operator extrapolation method of the companion paper. For a novel index resetting policy FTD exhibits the best known convergence rate. We also devised a robust version of the algorithm that is particularly suitable for discounting factors close to 1.