Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Southern Denmark Physical fitness is longitudinally associated with academic performance during childhood and adolescence, and waist circumference mediated the relationship Lima,

Purpose: The current investigation aimed to investigate the longitudinal association between physical fitness and academic performance over three years in adolescents. A secondary aim was to determine to what extent waist circumference (WC) mediated the association between physical fitness and academic performance. Method: For the current study, 1,020 students from first grade (mean age 7.87 ±0.34 years) to fifth grade (mean age 11.87 ±0.37 years) were monitored annually for three years (2010-2013). Physical fitness was assessed using the Andersen test, 5X5-meter shuttle run, jump height and grip strength tests and by constructing a composite score combining all four fitness tests. Academic performance was assessed by national standardized tests in Danish language and math. Generalized structural equation modeling was used to evaluate the relationships between these variables. Results: The Andersen test (standardized β= 0.15 SD), shuttle run (β= -0.18 SD), jump height (β= 0.10 SD) and the fitness composite score (β= 0.23 SD) were positively associated with academic performance over three years. In addition, WC partially mediated the association between physical fitness and academic performance. Conclusion: Thus, physical fitness abilities should be stimulated during childhood and early adolescence because of their positive association with academic performance.
HYDROCARBON PLAY ASSESSMENT OF “OSWIL” FIELD, ONSHORE NIGER DELTA REGION

Article history Received: 8 January 2021 Accepted: 21 January 2021 Published Online: 31 January 2021 Hydrocarbon play assessment of any field involves the evaluation of the production capacity of hydrocarbon reservoir unit in the field. This involves detail study of the reservoir petrophysical properties and geological interpretation of structures suitable for hydrocarbon accumulation in the field as observed from seismic reflection images. This study details the assessment of hydrocarbon play in OSWIL field onshore in Niger Delta, with the intent of appraising its productivity using a combination of seismic, well logs, petrophysical parameters and volumetric estimation using proven techniques which involves an integrated methodology. Two reservoir windows “R1” and “R2” were defined from five wells OSWIL-02, 04, 06, 07 and 12. The top and base of each reservoir window was delineated from the wells. Structural interpretation for inline 6975 revealed two horizons (X and Y) and eight faults labelled (F1, F2, F6, F8, F10, F16, F17 and F18). Five faults (F1, F6, F10, F17 and F18) were identified as synthetic faults and dip basin wards while three faults (F2, F8 and F16) were identified as antithetic faults and dips landwards. Time-depth structural map at top of reservoirs R1 and R2 revealed structural highs and closures. These observations are characteristics of growth structures (faults) which depicts the tectonic style of the Niger Delta. Results of petrophysical evaluation for reservoirs “R1” and “R2” across the five wells were analysed. For reservoir “R1” effective porosity values of 27%, 26%, 23%, 20% and 22% were obtained for wells OSWIL-04, 12, 07, 06 and 02 respectively with an average of 23.6%, while for reservoir “R2” effective porosity values of 26%, 22%, 21%, 24% and 23% for wells OSWIL-04, 12, 07, 06 and 02 were obtained respectively with an average of 23.2%. This porosity values correspond with the already established porosity range of 28-32% within the Agbada formation of the Niger Delta. Permeability index of the order (K > 100mD) were obtained for both reservoirs across the five wells and is rated very good. Hydrocarbon saturation (Shc) across the five wells averages at 61.6% for reservoir “R1” and 67.4% for reservoir “R2”. Result of petrophysical model for porosity, permeability and water saturation reveal that the reservoir system in R1 and R2 is fault assisted and fluid flow within both reservoirs is aided by presence of effective porosity and faulting. Volumetric estimation for both reservoirs showed that reservoir R1 contains an estimate of 455 × 106 STB of hydrocarbon in place, while reservoir R2 contains an estimate of 683 × 106 STB of hydrocarbon in place. These findings impact positively on hydrocarbon production in the field and affirm that the two reservoirs R1 and R2 are highly prospective.
‘Polymorphism’ in a novel anti-viral agent: Lamivudine

Two modifications of Lamivudine have been studied. One has a highly symmetrical crystal lattice and the other, unusually, an asymmetric unit containing five non-equivalent molecules (with some disorder). The latter contains one molecule of water for every five of Lamivudine. Solid-state NMR spectra reflect these dramatic differences and the technique has been used to predict the extent of the asymmetry in the latter form. X-Ray diffraction studies confirm the differences in symmetry between the two polymorphs, which have also been characterised by IR spectroscopy and differential scanning calorimetry.
Double-Step deep learning framework to improve wildfire severity classification

Wildfires are dangerous events which cause huge losses under natural, humanitarian and economical perspectives. To contrast their impact, a fast and accurate restoration can be improved through the automatic census of the event in terms of (i) delineation of the affected areas and (ii) estimation of damage severity, using satellite images. This work proposes to extend the state-of-the-art approach, named Double-Step U-Net (DS-UNet), able to automatically detect wildfires in satellite acquisitions and to associate a damage index from a defined scale. As a deep learning network, the DS-UNet model performance is strongly dependent on many factors. We propose to focus on alternatives in its main architecture by designing a configurable Double-Step Framework, which allows inspecting the prediction quality with different loss-functions and convolutional neural networks used as backbones. Experimental results show that the proposed framework yields better performance with up to 6.1% lower RMSE than current state of the art.
The relationship between premorbid intelligence and symptoms of severe anorexia nervosa restricting type

The purposes of this study were as follows: to compare premorbid IQ with present IQ in patients with more severe anorexia nervosa restricting type (AN-R) and to investigate the relationship between decreasing IQ and symptoms in patients with severe AN-R. Twenty-two participants were recruited (12 were AN-R patients; 10 were healthy controls). The average BMI in AN-R patients and healthy controls was 12.65 and 19.82, respectively. We assessed the outcomes using the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III), the Japanese Adult Reading Test, The Eating Disorders Inventory-2 (EDI-2), Beck Depression Scale-2 (BDI-2) and State-Trait Anxiety Index. In two-way ANOVA, there were significant interactions for the FIQ and PIQ. Only in the AN-R group, a significant single main effect of time was evidenced for the FIQ and PIQ. In the AN-R group, a significantly high positive correlation was found between changes in the PIQ and the body dissatisfaction subscale of the EDI-2. These findings raise the possibility that in patients with severe AN-R, an excessive decrease in body weight induces decreased PIQ; as a result, they have worse dissatisfaction with their body shape.
Novel Nanoporous Au-Pd Alloy with High Catalytic Activity and Excellent Electrochemical Stability

A novel nanoporous gold−palladium alloy has been successfully fabricated by electrochemically dealloying a multicomponent metallic glass. In comparison with conventional nanoporous gold prepared by dealloying Au−Ag alloys, the nanoporous gold−palladium alloy shows much higher catalytic activity for electrooxidation of methanol as a free-standing electrode. Moreover, a small amount of palladium in the nanoporous alloy is found to dramatically improve the electrochemical stability of nanoporous gold.
Predicting Hospitals Hygiene Rate during COVID-19 Pandemic

COVID-19 pandemic has reached global attention with the increasing cases in the whole world Increasing awareness for the hygiene procedures between the hospital’s staff, and the society became the main concern of the World Health Organization (WHO) However, the situation of COVID-19 Pandemic has encouraged many researchers in different fields to investigate to support the efforts offered by the hospitals and their health practitioners The main aim of this research is to predict the hospital’s hygiene rate during COVID-19 using COVID-19 Nursing Home Dataset We have proposed a feature extraction, and comparing the results estimating from K-means clustering algorithm, and three classification algorithms: random forest, decision tree, and Naive Bayes, for predicting the hospital’s hygiene rate during COVID-19 However, the results show that classification algorithms have addressed better performance than K-means clustering, in which Naive Bayes considered the best algorithm for achieving the research goal with accuracy value equal to 98 1% AS a result the research has discovered that the hospitals that offered weekly amounts of personal protective equipment (PPE) have passed the personal quality test, which lead to a decrease in the number of COVID-19 cases between the hospital’s staff © 2020 All Rights Reserved
Optical microscopic study of helical superstructures of chiral bilayer membranes

Donnees sur l'observation directe de surstructures helicoidales, formees a partir de membranes en couches bimoleculaires d'amphiphiles d'ammonium chiraux. Cas, par exemple, d'amphiphiles d'ammonium a double chaine du type ester dialkyle d'acide (N-[trimethylammonio-alkyl]) glutamique
Cognitive Behavioral Treatment as a Digital Therapeutic for Insomnia

Insomnia is a disorder defined by dissatisfaction with the quantity or quality of sleep that occurs more than three nights per week for at least 3 months, and includes clinically significant distress in important areas of functioning. Sleep dissatisfaction is characterized by difficulty in initiating or maintaining sleep, or early-morning awakening with an inability to return to sleep despite adequate opportunity. Overall, 45–75% of insomnia patients are chronic sufferers [1] and there is a relatively high relapse rate after remission (12–33%) [2]. Increasing insomnia prevalence is a global problem [3,4]. In South Korea, a statistical analysis of Korea Health Insurance Claims data showed a 34% increase in the number of patients with insomnia between 2012 and 2016 [5]. As the prevalence of insomnia increases, so does the demand for treatment. The report estimated that the global market for sleep-related drugs will grow at an average annual rate of 5.2% by 2026 [6]. Cognitive behavioral therapy, the first-line treatment for inCognitive Behavioral Treatment as a Digital Therapeutic for Insomnia
E(n) Equivariant Graph Neural Networks

This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs). In contrast with existing methods, our work does not require computationally expensive higher-order representations in intermediate layers while it still achieves competitive or better performance. In addition, whereas existing methods are limited to equivariance on 3 dimensional spaces, our model is easily scaled to higher-dimensional spaces. We demonstrate the effectiveness of our method on dynamical systems modelling, representation learning in graph autoencoders and predicting molecular properties.
Covariant Ergodic Quantum Markov Semigroups via Systems of Imprimitivity

We construct relativistic quantumMarkov semigroups from covariant completely positive maps. We proceed by generalizing a step in Stinespring’s dilation to a general system of imprimitivity and basing it on Poincarè group. The resulting noise channels are relativistically consistent and the method is applicable to any fundamental particle, though we demonstrate it for the case of light-like particles. The Krauss decomposition of the relativistically consistent completely positive identity preserving maps (our set up is in Heisenberg picture) enables us to construct the covariant quantum Markov semigroups that are uniformly continuous. We induce representations from the little groups to ensure the quantum Markov semigroups that are ergodic due to transitive systems imprimitivity.
Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions

Abstract The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regression. In low-dimensional regimes, where the number of observations far exceeds the number of variables, the optimality of the sample covariance matrix as an estimator of this parameter is well-established. High-dimensional regimes do not admit such a convenience. Thus, a variety of estimators have been derived to overcome the shortcomings of the canonical estimator in such settings. Yet, selecting an optimal estimator from among the plethora available remains an open challenge. Using the framework of cross-validated loss-based estimation, we develop the theoretical underpinnings of just such an estimator selection procedure. We propose a general class of loss functions for covariance matrix estimation and establish accompanying finite-sample risk bounds and conditions for the asymptotic optimality of the cross-validation selector. In simulation studies, we demonstrate the optimality of our proposed selector in moderate sample sizes and across diverse data-generating processes. The practical benefits of our procedure are highlighted in a dimensionality reduction application to single-cell transcriptome sequencing data. Supplementary materials for this article are available online.
Network Coding with Myopic Adversaries

We consider the problem of reliable communication over a network containing a hidden myopic adversary who can eavesdrop on some Zrolinks, jam some Zwo links, and do both on some Zrw links. We provide the first information-theoretically tight characterization of the optimal rate of reliable communication possible under all possible settings of the tuple (Zro, Zwo, Zrw) by providing a novel coding scheme/analysis for a subset of parameter regimes. In particular, by leveraging the adversary's uncertainty on unobserved links our vanishing-error schemes bypass the Network Singleton Bound (which requires a zero-error recovery criteria) in a certain parameter regime where the capacity had been heretofore open. As a direct corollary we also obtain the capacity of the corresponding problem where information-theoretic secrecy against eavesdropping is required in addition to reliable communication.
Efficient Distributed Auto-Differentiation

Although distributed machine learning has opened up numerous frontiers of research, the separation of large models across different devices, nodes, and sites can invite signiﬁcant communication overhead, making reliable training difﬁcult. The focus on gradients as the primary shared statistic during training has led to a number of intuitive algorithms for distributed deep learning; however, gradient-based algorithms for training large deep neural networks (DNNs) are communication-heavy, often requiring additional modiﬁcations via sparsity constraints, compression, quantization, and other similar approaches, to lower bandwidth. We introduce a surprisingly simple statistic for training distributed DNNs that is more communication-friendly than the gradient. The error backpropagation process can be modiﬁed to share these smaller intermediate values instead of the gradient, reducing communication overhead with no impact on accuracy. The process provides the ﬂexibility of averaging gradients during backpropagation, enabling novel ﬂexible training schemas while leaving room for further bandwidth reduction via existing gradient compression methods. Finally, consideration of the matrices used to compute the gradient inspires a new approach to compression via structured power iterations, which can not only reduce bandwidth but also enable introspection into distributed training dynamics, without signiﬁcant performance loss.
Two-Sided Weak Submodularity for Matroid Constrained Optimization and Regression

We study the following problem: Given a variable of interest, we would like to find a best linear predictor for it by choosing a subset of $k$ relevant variables obeying a matroid constraint. This problem is a natural generalization of subset selection problems where it is necessary to spread observations amongst multiple different classes. We derive new, strengthened guarantees for this problem by improving the analysis of the residual random greedy algorithm and by developing a novel distorted local-search algorithm. To quantify our approximation guarantees, we refine the definition of weak submodularity by Das and Kempe and introduce the notion of an upper submodularity ratio, which we connect to the minimum $k$-sparse eigenvalue of the covariance matrix. More generally, we look at the problem of maximizing a set function $f$ with lower and upper submodularity ratio $\gamma$ and $\beta$ under a matroid constraint. For this problem, our algorithms have asymptotic approximation guarantee $1/2$ and $1-e^{-1}$ as the function is closer to being submodular. As a second application, we show that the Bayesian A-optimal design objective falls into our framework, leading to new guarantees for this problem as well.
On the Similarity Between von Neumann Graph Entropy and Structural Information: Interpretation, Computation, and Applications

The von Neumann graph entropy is a measure of graph complexity based on the Laplacian spectrum. It has recently found applications in various learning tasks driven by the networked data. However, it is computationally demanding and hard to interpret using simple structural patterns. Due to the close relation between the Laplacian spectrum and the degree sequence, we conjecture that the structural information, defined as the Shannon entropy of the normalized degree sequence, might be a good approximation of the von Neumann graph entropy that is both scalable and interpretable. In this work, we thereby study the difference between the structural information and the von Neumann graph entropy named as entropy gap. Based on the knowledge that the degree sequence is majorized by the Laplacian spectrum, we for the first time prove that the entropy gap is between 0 and $\log _{2} e$ in any undirected unweighted graphs. Consequently we certify that the structural information is a good approximation of the von Neumann graph entropy that achieves provable accuracy, scalability, and interpretability simultaneously. This approximation is further applied to two entropy-related tasks: network design and graph similarity measure, where a novel graph similarity measure and the corresponding fast algorithms are proposed. Meanwhile, we show empirically and theoretically that maximizing the von Neumann graph entropy can effectively hide the community structure, and then propose an alternative metric called spectral polarization to guide the community obfuscation. Our experimental results on graphs of various scales and types show that the very small entropy gap readily applies to a wide range of simple/weighted graphs. As an approximation of the von Neumann graph entropy, the structural information is the only one that achieves both high efficiency and high accuracy among the prominent methods. It is at least two orders of magnitude faster than SLaQ (Tsitsulin et al., 2020) with comparable accuracy. Our structural information based methods also exhibit superior performance in downstream tasks such as entropy-driven network design, graph comparison, and community obfuscation.
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces an $\ell_\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\tau_{\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\tau_{\min}$ only increases logarithmically with the number of workers and the intrinsic dimensionality, while nearly invariant to the nominal dimensionality. We test our theory by extensive simulation studies, and a variable screening task on a semi-synthetic dataset based on the US Airline On-Time Performance dataset. The code to reproduce the numerical results is available at GitHub: https://github.com/skchao74/Distributed-bootstrap.
To Trust or to Think

People supported by AI-powered decision support tools frequently overrely on the AI: they accept an AI's suggestion even when that suggestion is wrong. Adding explanations to the AI decisions does not appear to reduce the overreliance and some studies suggest that it might even increase it. Informed by the dual-process theory of cognition, we posit that people rarely engage analytically with each individual AI recommendation and explanation, and instead develop general heuristics about whether and when to follow the AI suggestions. Building on prior research on medical decision-making, we designed three cognitive forcing interventions to compel people to engage more thoughtfully with the AI-generated explanations. We conducted an experiment (N=199), in which we compared our three cognitive forcing designs to two simple explainable AI approaches and to a no-AI baseline. The results demonstrate that cognitive forcing significantly reduced overreliance compared to the simple explainable AI approaches. However, there was a trade-off: people assigned the least favorable subjective ratings to the designs that reduced the overreliance the most. To audit our work for intervention-generated inequalities, we investigated whether our interventions benefited equally people with different levels of Need for Cognition (i.e., motivation to engage in effortful mental activities). Our results show that, on average, cognitive forcing interventions benefited participants higher in Need for Cognition more. Our research suggests that human cognitive motivation moderates the effectiveness of explainable AI solutions.
Role of molecular interactions and end chain length on the photosensitivity of liquid crystalline alkyl cyanobiphenyl dimers – UV absorption-based approach through DFT calculations

In this paper, the photosensitivity of liquid crystalline alkyl cyanobiphenyl (nCB: n = 6, 7; n is the number of carbon atoms in the alkyl chain) dimers has been presented through density functional theory (DFT) calculations. The nCB structures have been optimized using the Becke, three-parameter, Lee–Yang–Parr hybrid functional with the 6-31+G (d) basis set using the crystallographic geometry as input. The electronic structures of the dimer molecules have been computed using the optimized geometries. The spectra of the dimer molecules have been calculated by employing the DFT method. The excited states have been calculated via configuration interaction singles with the semi-empirical Hamiltonian Zerner intermediate neglect of differential overlap. The influence of molecular interactions and the end chain length on ultraviolet absorption spectral characteristics and the photosensitivity of the compounds has been discussed. These results offer a hint for the protection of various optical devices from the intense light induced damages, and to model photosensitivity.