Miracle-dz/UniFAR-FTUs huggingface dataset:
Cosmic acceleration via space-time-matter theory

We consider the space-time-matter theory (STM) in a five-dimensional vacuum space-time with a generalized FLRW metric to investigate the late-time acceleration of the universe. For this purpose, we derive the four-dimensional induced field equations and obtain the evolution of the state parameter with respect to the redshift. Then, we show that with consideration of the extra dimension scale factor to be a linear function of redshift, this leads to a model which gives an accelerating phase in the universe. Moreover, we derive the geodesic deviation equation in the STM theory to study the relative acceleration of the parallel geodesics of this space-time, and also, obtain the observer area-distance as a measurable quantity to compare this theory with two other models.
A groupwise approach for inferring heterogeneous treatment effects in causal inference

Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of study units, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a non-parametric approach and a semi-parametric approach, with the goal of better informing practice. Specifically, we compare (a) the underlying assumptions, (b) efficiency and adaption to the underlying data generating models, and (c) a way to combine the two approaches. We also discuss how to test a key assumption concerning the semi-parametric estimator and to obtain cluster-robust standard errors if study units in the same subgroups are correlated. We demonstrate our findings by conducting simulation studies and reanalysing the Early Childhood Longitudinal Study.
Beyond Labeling: Intuitive Capability Assessment of Malware using Network Behavioral Profiles

Malicious software is still a leading threat in cybersecurity. Anti-Virus (AV) companies are pivotal in understanding and assigning labels to new malware samples. Currently, these labels are the sole source of ground truth information available to the security community to evaluate malware analysis methods. However, their adopted naming conventions are known to be inconsistent and unverifiable. The labels are also black box since they do not represent the capabilities of malware. We believe that we need a white box way to determine the capabilities of malware based on their behavior, rather than black box family labels. The current state of the art in malware capability assessment contains largely manual approaches. We propose a novel method called MalPaCA, which for a large part automates capability assessment by clustering temporal behavior observed in malware's network traces. MalPaCA uses network traces since most malware uses the Internet to carry out its objectives. In doing so, we build behavioral profiles of malware families that are significantly more descriptive than their black box family names. We also propose an intuitive, visualization-based cluster evaluation method for the obtained clusters. We evaluate MalPaCA on 1.1k malware samples collected in the wild. MalPaCA shows promising results: (i) It correctly discovers capabilities, such as port scans and reuse of Command and Control servers; (ii) It discovers a number of discrepancies between behavioral clusters and traditional malware family designations; and (iii) It demonstrates the effectiveness of clustering unlabeled network traces using temporal features by producing a false positive rate of mere 8%.
Complicated Table Structure Recognition

The task of table structure recognition aims to recognize the internal structure of a table, which is a key step to make machines understand tables. Currently, there are lots of studies on this task for different file formats such as ASCII text and HTML. It also attracts lots of attention to recognize the table structures in PDF files. However, it is hard for the existing methods to accurately recognize the structure of complicated tables in PDF files. The complicated tables contain spanning cells which occupy at least two columns or rows. To address the issue, we propose a novel graph neural network for recognizing the table structure in PDF files, named GraphTSR. Specifically, it takes table cells as input, and then recognizes the table structures by predicting relations among cells. Moreover, to evaluate the task better, we construct a large-scale table structure recognition dataset from scientific papers, named SciTSR, which contains 15,000 tables from PDF files and their corresponding structure labels. Extensive experiments demonstrate that our proposed model is highly effective for complicated tables and outperforms state-of-the-art baselines over a benchmark dataset and our new constructed dataset.
Resolving a controversy about adhesion in sliding contacts

An interesting recent paper by Menga, Carbone & Dini (MCD, 2018, [1]), suggests that in sliding adhesive contacts, the contact area should increase due to tangential shear stresses at the interface, assumed to be constant and corresponding to a material constant. This is not observed in the known experiments, and is in sharp contrast with all the classical theories about the transition from stick to sliding, both in the JKR (Griffith like) conditions which involve singular pressure and shear, as well as in full general cohesive models. We offer a rigorous thermodynamics calculation, which suggests in fact there is no qualitative contrast but a very close quantitative agreement, with previous theories. Actually, the model predicts an even stronger reduction of contact area than predicted by Savkoor and Briggs, contrary to experimental observations, so would certainly require some adjustements to consider dissipative effects.
Gravitational Radiation from Binaries: A Pedagogical Introduction

This short note serves as an introduction to gravitational radiation through reviewing the inspiral-plunge transition phase in extreme mass ratio binaries. We study the relativistic motion of a compact object (CO) of mass $m$ around a massive black hole of mass $M\gg m$. The Kerr-Newman metric, effective potential for the general case of elliptical orbits, gravitational radiation, orbital energy and angular momentum of a coalescing CO in Kerr spacetime and gravitational wave frequency and signal to noise ratio are briefly reviewed. The main focus is on the transition from inspiral to plunge for a CO assuming that a test particle approach is plausible in the regime $m\ll M$ without appealing to a perturbative analysis. The effective potential is used to obtain the properties of the Innermost Stable Circular Orbit (ISCO) near which the adiabatic inspiral phase ends abruptly and the CO enters the plunge phase. For the transition phase, the effective potential is expanded in terms of parameters such as the radial (coordinate) distance from the ISCO and the deviation of particle's angular momentum from its value at the ISCO to obtain the equation of motion. The equations of motion, during the inspiral and transition phases, are joined numerically and the gravitational wave frequency, number of wave cycles and signal to noise ratio (SN) during the transition is obtained for circular/inclined as well as elliptical/inclined orbits. The limitations and inaccuracies of the current methods used to approach this problem is discussed. A short introduction to the fundamental concepts of General Relativity, in particular Einstein Field Equations is also provided in the Appendix.
Entanglement balance of quantum 
(e,2e)
 scattering processes

The theory of quantum information constitutes the functional value of the quantum entanglement, i.e., quantum entanglement is essential for high fidelity of quantum protocols, while fundamental physical processes behind the formation of quantum entanglement are less relevant for practical purposes. In the present work, we explore physical mechanisms leading to the emergence of quantum entanglement in the initially disentangled system. In particular, we analyze spin entanglement of outgoing electrons in a nonrelativistic quantum $(e,2e)$ collision on a target with one active electron. Our description exploits the time-dependent scattering formalism for typical conditions of scattering experiments, and contrary to the customary stationary formalism operates with realistic scattering states. We quantify the spin entanglement in the final scattering channel through the pair concurrence and express it in terms of the experimentally measurable spin-resolved $(e,2e)$ triple differential cross sections. Besides, we consider Bell's inequality and inspect the regimes of its violation in the final channel. We address both the pure and the mixed initial spin state cases and uncover kinematical conditions of the maximal entanglement of the outgoing electron pair. The numerical results for the pair concurrence, entanglement of formation, and violation of Bell's inequality obtained for the $(e,2e)$ ionization process of atomic hydrogen show that the entangled electron pairs indeed can be formed in the $(e,2e)$ collisions even with spin-unpolarized projectile and target electrons in the initial channel. The positive entanglement balance---the difference between entanglements of the initial and final electron pairs---can be measured in the experiment.
Challenge of Spatial Cognition for Deep Learning

Given the success of the deep convolutional neural networks (DCNNs) in applications of visual recognition and classification, it would be tantalizing to test if DCNNs can also learn spatial concepts, such as straightness, convexity, left/right, front/back, relative size, aspect ratio, polygons, etc., from varied visual examples of these concepts that are simple and yet vital for spatial reasoning. Much to our dismay, extensive experiments of the type of cognitive psychology demonstrate that the data-driven deep learning (DL) cannot see through superficial variations in visual representations and grasp the spatial concept in abstraction. The root cause of failure turns out to be the learning methodology, not the computational model of the neural network itself. By incorporating task-specific convolutional kernels, we are able to construct DCNNs for spatial cognition tasks that can generalize to input images not drawn from the same distribution of the training set. This work raises a precaution that without manually-incorporated priors or features DCCNs may fail spatial cognitive tasks at rudimentary level.
Implementasi Rancangan Aplikasi Ekstraksi Ciri Citra Digital berbasis Client-Server

Ekstraksi ciri merupakan salah satu langkah yang terdapat pada pemrosesan Content Based Image Retrieval (CBIR). Secara standalone proses ekstraksi ciri satu berkas citra berukuran MxN akan memakan waktu T. Sehingga untuk mempersingkat waktu pemrosesan ekstraksi ciri dengan dataset sekitar 10.800 berkas citra maka dibangun aplikasi model client-server. Model ini memanfaatkan lebih dari satu unit komputer melakukan ekstrasi ciri dengan media penyimpanan berbentuk database SQL. Hasil ekstraksi pada model client-server dapat membantu mengurangi durasi waktu pada proses ekstrasi ciri citra koleksi. Kata Kunci—. CBIR, Citra, Client-Server, database SQL
Deep Hashing for Signed Social Network Embedding

Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification. Recently, feature hashing has been adopted in several existing embedding algorithms to improve the efficiency, which has obtained a great success. However, the existing feature hashing based embedding algorithms only consider the positive links in signed social networks. Intuitively, negative links can also help improve the performance. Thus, in this paper, we propose a novel deep hashing method for signed social network embedding by considering simultaneously positive and negative links. Extensive experiments show that the proposed method performs better than several state-of-the-art baselines through link prediction task over two real-world signed social networks.
Einconv: Exploring Unexplored Tensor Decompositions for Convolutional Neural Networks

Tensor decomposition methods are widely used for model compression and fast inference in convolutional neural networks (CNNs). Although many decompositions are conceivable, only CP decomposition and a few others have been applied in practice, and no extensive comparisons have been made between available methods. Previous studies have not determined how many decompositions are available, nor which of them is optimal. In this study, we first characterize a decomposition class specific to CNNs by adopting a flexible graphical notation. The class includes such well-known CNN modules as depthwise separable convolution layers and bottleneck layers, but also previously unknown modules with nonlinear activations. We also experimentally compare the tradeoff between prediction accuracy and time/space complexity for modules found by enumerating all possible decompositions, or by using a neural architecture search. We find some nonlinear decompositions outperform existing ones.
Optimal Estimation of Generalized Average Treatment Effects using Kernel Optimal Matching

In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad-hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regression modeling, but these may be sensitive to model misspecification, practical violations of positivity, or both. The contribution of this paper is twofold. First, we formulate the generalized average treatment effect (GATE) to unify these causal estimands as well as their IPW estimates. Second, we develop a method based on Kernel Optimal Matching (KOM) to optimally estimate GATE and to find the GATE most easily estimable by KOM, which we term the Kernel Optimal Weighted Average Treatment Effect. KOM provides uniform control on the conditional mean squared error of a weighted estimator over a class of models while simultaneously controlling for precision. We study its theoretical properties and evaluate its comparative performance in a simulation study. We illustrate the use of KOM for GATE estimation in two case studies: comparing spine surgical interventions and studying the effect of peer support on people living with HIV.
Playing log(N)-Questions over Sentences

We propose a two-agent game wherein a questioner must be able to conjure discerning questions between sentences, incorporate responses from an answerer, and keep track of a hypothesis state. The questioner must be able to understand the information required to make its final guess, while also being able to reason over the game's text environment based on the answerer's responses. We experiment with an end-to-end model where both agents can learn simultaneously to play the game, showing that simultaneously achieving high game accuracy and producing meaningful questions can be a difficult trade-off.
The Noise Collector for sparse recovery in high dimensions

Significance The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. For optimal results, current approaches need to tune parameters that depend on the level of noise, which is often difficult to estimate. We develop a parameter-free, computationally efficient, ℓ1-norm minimization approach that has a zero false discovery rate (no false positives) with high probability for any level of noise while it detects the exact location of sparse signals when the noise is not too large. The ability to detect sparse signals from noisy, high-dimensional data is a top priority in modern science and engineering. It is well known that a sparse solution of the linear system Aρ=b0 can be found efficiently with an ℓ1-norm minimization approach if the data are noiseless. However, detection of the signal from data corrupted by noise is still a challenging problem as the solution depends, in general, on a regularization parameter with optimal value that is not easy to choose. We propose an efficient approach that does not require any parameter estimation. We introduce a no-phantom weight τ and the Noise Collector matrix C and solve an augmented system Aρ+Cη=b0+e, where e is the noise. We show that the ℓ1-norm minimal solution of this system has zero false discovery rate for any level of noise, with probability that tends to one as the dimension of b0 increases to infinity. We obtain exact support recovery if the noise is not too large and develop a fast Noise Collector algorithm, which makes the computational cost of solving the augmented system comparable with that of the original one. We demonstrate the effectiveness of the method in applications to passive array imaging.
August 2012 Critical Care Journal Club

This was a randomized control trial, which enrolled 181 patients from multiple medical-surgical ICUs to receive either nasojejunal or nasogastric nutrition. The number of patients selected for this study provided an 80% power to detect a 12% difference in mean energy delivery. Inclusion criteria for the study were patient that were admitted to the ICU, needing mechanically ventilated, narcotic drips for sedation as well as elevated gastric residuals (>150ml). Patients were excluded if patient had abnormal anatomy or imminent death.
Pose Synchronization for Quadrotor Networks under Fixed General Interconnection Topology: A Passivity Approach

: In this paper, a distributed 3-D pose synchronization law is proposed for a group of networked quadrotors. The quadrotor network consisting of multiple quadrotors with dynamics and interconnection topology between them is ﬁrst deﬁned, and then, the deﬁnition of pose synchronization is provided. A novel pose synchronization law is next proposed, where linear approximation and passivation approaches are employed in the controller design. The former allows us to independently deal with the yaw, vertical, and horizontal motion dynamics of the quadrotor network, and the latter enables us to apply a passivity-based output synchronization technique. Convergence is next analyzed, and ﬁnally, the present pose synchronization law is demonstrated via simulation and an experiment to show its validity.
Cahier 17-2002 TESTING MEAN-VARIANCE EFFICIENCY IN CAPM WITH POSSIBLY NON-GAUSSIAN ERRORS : AN EXACT SIMULATION-BASED APPROACH

In this paper, we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the framework of multivariate linear regressions (MLR). It is well known, however, that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gibbons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance, 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken's mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and multivariate generalization of the well-known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i. d. assumption, and (iii) mean-variance efficiency tests of the market portfolio are not rejected as frequently once they are allowed for the possibility of non-normal errors.
WooKey : USB Devices Strike Back

The USB bus has been a growing subject of research in recent years. In particular, securing the USB stack (and hence the USB hosts and devices) started to draw interest from the academic community since major exploitable flaws have been revealed by the BadUSB threat [41]. The work presented in this paper takes place in the design initiatives that have emerged to thwart such attacks. While some proposals have focused on the host side by enhancing the Operating System’s USB sub-module robustness [53,54], or by adding a proxy between the host and the device [12, 37], we have chosen to focus our efforts on the device side. More specifically, our work presents the WooKey platform: a custom STM32-based USB thumb drive with mass storage capabilities designed for user data encryption and protection, with a full-fledged set of in-depth security defenses. The device embeds a firmware with a secure DFU (Device Firmware Update) implementation featuring up-to-date cryptography, and uses an extractable authentication token. The runtime software security is built upon EwoK: a custom microkernel implementation designed with advanced security paradigms in mind, such as memory confinement using the MPU (Memory Protection Unit) and the integration of safe languages and formal methods for very sensitive modules. This microkernel comes along with MosEslie: a versatile and modular SDK that has been developed to easily integrate user applications in C, Ada and Rust. Another strength of this project is its core guiding principle: provide an open source and open hardware platform using off-the-shelf components for the PCB design to ease its manufacturing and reproducibility.
Experimental Investigations on the Combustion of Gaseous Fuels

The basic objective of the present work is to evaluate the potential of using a clean burning fuel such as CNG in the gasoline internal combustion engine. A comparison is carried out between the exhaust emissions for gasoline and CNG fuels. At idling condition, exhaust emissions concentrations are measured at different engine speeds (5001800 rpm) and fuelair equivalence ratios. At different speeds, the reduction of CO and CO2 for CNG than that gasoline by about 5% and 17%, respectively and nitrogen oxide emission increases for CNG fuel than that for gasoline by about 2.5%. At different equivalence ratios, the reduction of CO and CO2 for CNG than that gasoline by about 40% and 25%, respectively and nitrogen oxide emission increases for CNG fuel than that for gasoline by about 4%.
ON MULTIDIMENSIONAL FREDHOLM INTEGRAL EQUATIONS OF THE FIRST KIND

We introduce a new algorithm for solving multidimensional Fredholm integral equations of the first kind. The regularization and the weighted mean-value methods constitute the algorithm. The former is used to transform the Fredholm integral equations of the first kind to the Fredholm integral equations of the second kind and the latter is employed to solve the resulting Fredhom integral equations. Examples will be provided to demonstrate the reliability and applicability of the proposed approach.