Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Methods and Programs in Biomedicine

Background and Objective: COVID-19 pandemic continues unabated due to the rapid spread of new mutant strains of the virus. Decentralized cluster containment is an eﬃcient approach to manage the pandemic in the long term, without straining the healthcare system and economy. In this study, the objective is to forecast the peak and duration of COVID-19 spread in a cluster under dif- ferent conditions, using a probabilistic cellular automata conﬁguration designed to include the observed characteristics of the pandemic with appropriate neighbourhood schemes and transition rules. Methods: The cellular automata, initially conﬁgured to have only susceptible and exposed states, enlarges and evolves in discrete time steps to different inf ection states of the COVID-19 pandemic. The transition rules take into account the probability and proximity of contact between infected hosts and suscepti- ble individuals. A transmittable and transition neighbourhoods are deﬁned to identify the most probable individuals infected from a single host in a time step. Results: The model with novel neighbourhood schemes and transition rules reproduce the macroscopic behaviour of infection and recovery observed in pandemics. The temporal evolution of the pandemic trajectory is sensitive to lattice size, range, latent and recovery periods but has constraints in capturing the changes in the infectious period. A study of lockdown and migration scenarios shows strict social isolation is crucial in controlling the pandemic. The simulations also indicate that earlier vaccination with a higher capacity and rate is essential to mitigate the pandemic. A comparison of simulated and actual data shows a good match. Conclusions: The study concludes that social isolation during movement and interaction of people can limit the spread of new infections. Vaccinating a large proportion of the population reduces new cases in subsequent waves of the pandemic. The model and algorithm with real-world data as input can quickly forecast the trajectory of the pandemic, for effective response in cluster containment.
Smart health: Concepts and status of ubiquitous health with smartphone

Smart health can be defined as medical and public health practice supported by smart mobile devices. The weak points of mHealth or u-health can be made up by using smart phone. When it is connected not only to wireless network, but also to other emergencing technologies, sHealth can give more valuable value to clinicians and patients. The effectiveness and impact of sHealth must be evaluated to be wildly used.
Package Name / App Name Sample Database Fake Apps Certificate Identifier Target Apps with Official Certificates Pspt

While there have been various studies towards Android apps and their development, there is limited discussion of the broader class of apps that fall in the fake area. Fake apps and their development are distinct from official apps and belong to the mobile underground industry. Due to the lack of knowledge of the mobile underground industry, fake apps, their ecosystem and nature still remain in mystery. To fill the blank, we conduct the first systematic and comprehensive empirical study on a large-scale set of fake apps. Over 150,000 samples that fake the top 50 popular apps are collected and extensively measured. In this paper, we present discoveries from three different perspectives, namely fake sample characteristics, quantitative study on fake samples and fake authors’ developing trend. Moreover, valuable domain knowledge, like fake apps’ naming tendency and fake developers’ evasive strategies, is then presented and confirmed with case studies, demonstrating a clear vision of fake apps and their ecosystem.
Kernel-based Active Subspaces with application to CFD parametric problems using Discontinuous Galerkin method

A new method to perform a nonlinear reduction in parameter spaces is proposed. By using a kernel approach it is possible to find active subspaces in high-dimensional feature spaces. A mathematical foundation of the method is presented, with several applications to benchmark model functions, both scalar and vector-valued. We also apply the kernel-based active subspaces extension to a CFD parametric problem using the Discontinuous Galerkin method. A full comparison with respect to the linear active subspaces technique is provided for all the applications, proving the better performances of the proposed method. Moreover we show how the new kernel method overcomes the drawbacks of the active subspaces application for radial symmetric model functions.
Unsupervised Neural Machine Translation with Weight Sharing

Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve signiﬁcant improvements on English-German, English-French and Chinese-to-English translation tasks.
Automatic feature‐preserving size field for three‐dimensional mesh generation

This article presents a methodology aiming at easing considerably the generation of high‐quality meshes for complex three‐dimensional (3D) domains. To this end, a mesh size field h(x) is computed, taking surface curvatures and geometric features into account. The size field is tuned by five intuitive parameters and yields quality meshes for arbitrary geometries. Mesh size is initialized on a surface triangulation of the domain based on discrete curvatures and medial axis transform computations. It is then propagated into the volume while ensuring the size gradient ∇h is controlled so as to obtain a smoothly graded mesh. As the size field is stored in an independent octree data structure, it can be computed separately, then plugged into any mesh generator able to respect a prescribed size field. The procedure is automatic, in the sense that minimal interaction with the user is required. Applications of our methodology on CAD models taken from the very large ABC dataset are presented. In particular, all presented meshes were obtained with the same generic set of parameters, demonstrating the universality of the technique.
Blockchain-based Federated Learning for Securing Internet of Things: A Comprehensive Survey

WAEL ISSA, School of Engineering and Information Technology, UNSW Canberra, Australia NOUR MOUSTAFA, School of Engineering and Information Technology, UNSW Canberra, Australia BENJAMIN TURNBULL, School of Engineering and Information Technology, UNSW Canberra, Australia NASRIN SOHRABI, Centre of Cyber Security Research & Innovation (CCSRI), School of Computing Technologies, RMIT, Australia ZAHIR TARI, Centre of Cyber Security Research & Innovation (CCSRI), School of Computing Technologies, RMIT, Australia
Importance Weighted Policy Learning and Adaptation

The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones. In the meta reinforcement learning literature much recent work has focused on the problem of optimizing the learning process itself. In this paper we study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning. The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior, or default behavior that constrains the space of solutions and serves as a bias for exploration; as well as a representation for the value function, both of which are easily learned from a number of training tasks in a multi-task scenario. Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
Extinction efficiency of “elastic–sheet” beams by a cylindrical (viscous) fluid inclusion embedded in an elastic medium and mode conversion—Examples of nonparaxial Gaussian and Airy beams

Stemming from the law of the conservation of energy in an elastic medium, this work extends the scope of the previous analysis for a scatterer immersed in a nonviscous liquid [F. G. Mitri, Ultrasonics 62, 20–26 (2015)] to the case of a (viscous) fluid circular cylinder cross-section encased in a homogeneous, isotropic, elastic matrix. Analytical expressions for the absorption, scattering, and extinction efficiencies (or cross-sections) are derived for “elastic-sheets” (i.e., finite beams in 2D propagating in elastic media) of arbitrary wavefront, in contrast to the ideal case of plane waves of infinite extent. The mathematical expressions are formulated in generalized partial-wave series expansions in cylindrical coordinates involving the beam-shape coefficients of finite elastic-sheet beams with arbitrary wavefront, and the scattering coefficients of the fluid cylinder encased in the elastic matrix. The analysis shows that in elastodynamic scattering, both the scattered L-wave as well as the scattered T-.
Visual Analysis of Multi-Parameter Distributions across Ensembles

For an ensemble of data points in a multi-parameter space, we present a visual analytics technique to select a representative distribution of parameter values, and analyse how representative this distribution is in all ensemble members. A multi-parameter cluster in a representative ensemble member is visualized via a parallel coordinates plot, to provide initial distributions and let domain experts interactively select relevant parameters and value ranges. Since unions of value ranges select hyper-cubes in parameter space, data points in these unions are not necessarily contained in the cluster. By using a multi-parameter kD-tree to further refine the selected parameter ranges, in combination with a covariance analysis of refined sets of data points, a tight partition in multi-parameter space with reduced number of falsely selected points is obtained. To assess the representativeness of the selected multi-parameter distribution across the ensemble, a linked side-by-side view of per-member violin plots is provided. We propose modifications of violin plots to show multi-parameter distributions simultaneously, and investigate the visual design that effectively conveys (dis-)similarities in multi-parameter distributions. In a linked spatial view, users can analyse and compare the spatial distribution of selected points in different ensemble members via interval-based isosurface raycasting. In two real-world application cases we show how our approach is used to analyse the multi-parameter distributions across an ensemble of 3D fields.
Simulation Methods for Particulate Flows and Concentrated Suspensions

Numerical simulations are extensively used to investigate the motion of suspended particles in a fluid and their influence on the dynamics of the overall flow. Contexts range from the rheology of concentrated suspensions in a viscous fluid to the dynamics of particle-laden turbulent flows. This review summarizes several current approaches to the numerical simulation of rigid particles suspended in a flow, pointing out both common features and differences, along with their primary range of application. The focus is on non-Brownian systems for which thermal fluctuations do not play a role, whereas interparticle forces may result in particle self-assembly. Applications may include the motion of a few isolated particles with complex shape or the collective dynamics of many suspended particles.
Back to square one : identification issues in DSGE models

We investigate identifiability issues in DSGE models and their consequences for parameter estimation and model evaluation when the objective function measures the distance between estimated and model impulse responses. We show that observational equivalence, partial and weak identification problems are widespread, that they lead to biased estimates, unreliable t-statistics and may induce investigators to select false models. We examine whether different objective functions affect identification and study how small samples interact with parameters and shock identification. We provide diagnostics and tests to detect identification failures and apply them to a state-of-the-art model.
A Temporal-to-Spatial Neural Network for Classification of Hand Movements from Electromyography Data

Deep convolutional neural networks (CNNs) are appealing for the purpose of classification of hand movements from surface electromyography (sEMG) data because they have the ability to perform automated person-specific feature extraction from raw data. In this paper, we make the novel contribution of proposing and evaluating a design for the early processing layers in the deep CNN for multichannel sEMG. Specifically, we propose a novel temporal-to-spatial (TtS) CNN architecture, where the first layer performs convolution separately on each sEMG channel to extract temporal features. This is motivated by the idea that sEMG signals in each channel are mediated by one or a small subset of muscles, whose temporal activation patterns are associated with the signature features of a gesture. The temporal layer captures these signature features for each channel separately, which are then spatially mixed in successive layers to recognise a specific gesture. A practical advantage is that this approach also makes the CNN simple to design for different sample rates. We use NinaPro database 1 (27 subjects and 52 movements + rest), sampled at 100 Hz, and database 2 (40 subjects and 40 movements + rest), sampled at 2 kHz, to evaluate our proposed CNN design. We benchmark against a feature-based support vector machine (SVM) classifier, two CNNs from the literature, and an additional standard design of CNN. We find that our novel TtS CNN design achieves 66.6% per-class accuracy on database 1, and 67.8% on database 2, and that the TtS CNN outperforms all other compared classifiers using a statistical hypothesis test at the 2% significance level.
MLModelScope: Evaluate and Measure ML Models within AI Pipelines

The current landscape of Machine Learning (ML) and Deep Learning (DL) is rife with non-uniform frameworks, models, and system stacks but lacks standard tools to facilitate the evaluation and measurement of model. Due to the absence of such tools, the current practice for evaluating and comparing the benefits of proposed AI innovations (be it hardware or software) on end-to-end AI pipelines is both arduous and error prone -- stifling the adoption of the innovations. We propose MLModelScope -- a hardware/software agnostic platform to facilitate the evaluation, measurement, and introspection of ML models within AI pipelines. MLModelScope aids application developers in discovering and experimenting with models, data scientists developers in replicating and evaluating for publishing models, and system architects in understanding the performance of AI workloads. We describe the design and implementation of MLModelScope and show how it is able to give users a holistic view into the execution of models within AI pipelines. Using AlexNet as a case study, we demonstrate how MLModelScope aids in identifying deviation in accuracy, helps in pin pointing the source of system bottlenecks, and automates the evaluation and performance aggregation of models across frameworks and systems.
Using Modules in Teaching Complex Analysis

Abstract Educational modules can play an important part in revitalizing the teaching and learning of complex analysis. At the Westmont College workshop on the subject in June 2014, time was spent generating ideas and creating structures for module proposals. Sharing some of those ideas and giving a few example modules is the main purpose of this paper. The author has started to construct Mathematica-based educational modules for complex analysis, one of which is available in the online repository of supplemental materials for this article. The author has also begun work on a website called The Complex Moduli Project to make modules in complex analysis available worldwide, and invites others to join him in this endeavor.
GENERALIZED MORREY SPACES OVER NONHOMOGENEOUS METRIC MEASURE SPACES

Let $({\mathcal{X}},d,\unicode[STIX]{x1D707})$ be a nonhomogeneous metric measure space satisfying the so-called upper doubling and the geometric doubling conditions. In this paper, the authors give the natural definition of the generalized Morrey spaces on $({\mathcal{X}},d,\unicode[STIX]{x1D707})$ , and then investigate some properties of the maximal operator, the fractional integral operator and its commutator, and the Marcinkiewicz integral operator.
Logics of Organization Theory: Audiences, Codes, and Ecologies

negative effects brought on by violence. We seek an affective turn that celebrates feeling, and free bodies, bodies seeking their day in a sun that shines on life after the apocalypse. These are the promises and the hopes contained in the brilliant chapters in this important new book. A call to arms, The Affective Turn, as Michael Hardt argues in his urgent foreword, is an invitation to critical social scientists and informed citizens alike to interrupt history. Clough and her co-authors show us how to use our interpretive methods and models of truth, power, and knowledge to disrupt history as it operates behind our backs. Such interventions call for an interruption of history itself. This is what critical theory is supposed to do.
Isogeometric Analysis for Navier-Stokes equations

In this project, we present some applications of Isogeometric Analysis in stationary fluid dynamics problems. Specially, we focus on the numerical solution of the Stokes and NavierStokes equations and the fulfillment of the inf-sup conditions, specifically by computing the Brezzi and Babuska inf-sup constants. The Isogeometric Taylor-Hood spaces are presented and the stability of the Stokes and Navier-Stokes equations has been numerically tested in a variety of settings, including smooth NURBS basis functions and the multiple patches case.
Mathematical analysis for a multi-group SEIR epidemic model with age-dependent relapse

Abstract We consider a multi-group SEIR epidemic model in which recovered population relapse back to infectives depending on the time elapsed since the recovery. This leads to a hybrid system for which we can determine the basic reproduction number by the spectral radius of the next generation matrix and prove the threshold behaviors. The key idea to prove the global asymptotic stability of each equilibrium is the usage of the graph-theoretic approach to construct suitable Lyapunov functionals. The necessary arguments, including the existence of an endemic equilibrium, the asymptotic smoothness of the semiflow, the uniform persistence of the system, and the existence of a global attractor are also addressed.
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

The recently proposed distributional approach to reinforcement learning (DiRL) is centered on learning the distribution of the reward-to-go, often referred to as the value distribution. In this work, we show that the distributional Bellman equation, which drives DiRL methods, is equivalent to a generative adversarial network (GAN) model. In this formulation, DiRL can be seen as learning a deep generative model of the value distribution, driven by the discrepancy between the distribution of the current value, and the distribution of the sum of current reward and next value. We use this insight to propose a GAN-based approach to DiRL, which leverages the strengths of GANs in learning distributions of high-dimensional data. In particular, we show that our GAN approach can be used for DiRL with multivariate rewards, an important setting which cannot be tackled with prior methods. The multivariate setting also allows us to unify learning the distribution of values and state transitions, allowing us to devise a novel exploration method that is driven by the discrepancy in estimating both values and states.
WEATHERGOV+: A Table Recognition and Summarization Dataset to Bridge the Gap Between Document Image Analysis and Natural Language Generation

Tables, ubiquitous in data-oriented documents like scientific papers and financial statements, organize and convey relational information. Automatic table recognition from document images, which involves detection within the page, structural segmentation into rows, columns, and cells, and information extraction from cells, has been a popular research topic in document image analysis (DIA). With recent advances in natural language generation (NLG) based on deep neural networks, data-to-text generation, in particular for table summarization, offers interesting solutions to time-intensive data analysis. In this paper, we aim to bridge the gap between efforts in DIA and NLG regarding tabular data: we propose WEATHERGOV+, a dataset building upon the WEATHERGOV dataset, the standard for tabular data summarization techniques, that allows for the training and testing of end-to-end methods working from input document images to generate text summaries as output. WEATHERGOV+ contains images of tables created from the tabular data of WEATHERGOV using visual variations that cover various levels of difficulty, along with the corresponding human-generated table summaries of WEATHERGOV. We also propose an end-to-end pipeline that compares state-of-the-art table recognition methods for summarization purposes. We analyse the results of the proposed pipeline by evaluating WEATHERGOV+ at each stage of the pipeline to identify the effects of error propagation and the weaknesses of the current methods, such as OCR errors. With this research (dataset and code available here1), we hope to encourage new research for the processing and management of inter- and intra-document collections.
fastMI : a fast and consistent copula-based estimator of mutual information

As a fundamental concept in information theory, mutual information (MI) has been commonly applied to quantify association between random variables. Most existing estimators of MI have unstable statistical performance since they involve parameter tuning. We develop a consistent and powerful estimator, called fastMI, that does not incur any parameter tuning. Using a copula formulation, fastMI estimates MI by leveraging Fast Fourier transformation-based estimation of the underlying density. Extensive simulation studies reveal that fastMI outperforms state-of-the-art estimators with improved estimation accuracy and reduced run time for large data sets. fastMI not only provides a powerful test for independence that controls type I error, it may be used for further inference purposes. We establish asymptotic normality of fastMI for dependent random variables using a new data-splitting analytic argument. Antici-pating that fastMI will be a powerful tool in estimating mutual information in a broad range of data, we develop an R package fastMI for broader dissemination.