December 2020 arXiv papers — page 129

Liangjian Chen, Shih-Yao Lin, Yusheng Xie, Yen-Yu Lin

Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images. State-of-the-art estimators ad-dress this problem by regularizing3D hand pose estimationmodels during training to enforce the consistency betweenthe predicted3D

Xueting Li, Sifei Liu, Shalini De Mello, Kihwan Kim

This paper presents an algorithm to reconstruct temporally consistent 3D meshes of deformable object instances from videos in the wild. Without requiring annotations of 3D mesh, 2D keypoints, or camera pose for each video frame, we pose video-based reconstruction as a self-supervised online adaptation problem applied to any incoming test video. We first lear

Yiran Zhong, Yuchao Dai, Hongdong Li

A depth map can be represented by a set of learned bases and can be efficiently solved in a closed form solution. However, one issue with this method is that it may create artifacts when colour boundaries are inconsistent with depth boundaries. In fact, this is very common in a natural image. To address this issue, we enforce a more strict model in depth rec

Xuejiao Tang, Jiong Qiu, Ruijun Chen, Wenbin Zhang

An ideal safe workplace is described as a place where staffs fulfill responsibilities in a well-organized order, potential hazardous events are being monitored in real-time, as well as the number of accidents and relevant damages are minimized. However, occupational-related death and injury are still increasing and have been highly attended in the last decad

Roberto Reyes-Ochoa, Servando Lopez-Aguayo

This work proposes an algorithm for taking advantage of backpropagation gradients to determine feature importance at different stages of training. Additionally, we propose a way to represent the learning process qualitatively. Experiments were performed over the Wisconsin cancer dataset provided by sklearn, and results showed an interesting convergence of th

Zhao Zhang, Yanna Chen, Dehong Yu, Richard Mole

First-order magnetic transitions are of both fundamental and technological interest given that a number of emergent phases and functionalities are thereby created. Of particular interest are giant magnetocaloric effects, which are attributed to first-order magnetic transitions and have attracted broad attention for solid-state refrigeration applications. Whi

Pedro Montealegre, Diego Ramírez-Romero, Iván Rapaport

We present compact distributed interactive proofs for the recognition of two important graph classes, well-studied in the context of centralized algorithms, namely complement reducible graphs and distance-hereditary graphs. Complement reducible graphs (also called cographs) are defined as the graphs not containing a four-node path $P_4$ as an induced subgrap

Debjyoti Bhattacharya, Tarak K Patra

Machine learning (ML) and artificial intelligence (AI) have the remarkable ability to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of machine learning methods viz., deep learnings and develop a general-purpose AI tool - dPOLY for analyzing molecular dynamics trajectory and predicting phases a

Sanket S. Kalamkar, François Baccelli, Fuad M. Abinader, Andrea S. Marcano Fani

Beam management is central in the operation of beamformed wireless cellular systems such as 5G New Radio (NR) networks. Focusing the energy radiated to mobile terminals (MTs) by increasing the number of beams per cell increases signal power and decreases interference, and has hence the potential to bring major improvements on area spectral efficiency (ASE).

Deniz Turan, Ping Keng Lu, Nezih T. Yardimci, Zhaoyu Liu

Surface states generally degrade semiconductor device performance by raising the charge injection barrier height, introducing localized trap states, inducing surface leakage current, and altering the electric potential. Therefore, there has been an endless effort to use various surface passivation treatments to suppress the undesirable impacts of the surface

Akshay Dua, Yixing Li, Fengbo Ren

This paper presents Systolic-CNN, an OpenCL-defined scalable, run-time-flexible FPGA accelerator architecture, optimized for accelerating the inference of various convolutional neural networks (CNNs) in multi-tenancy cloud/edge computing. The existing OpenCL-defined FPGA accelerators for CNN inference are insufficient due to limited flexibility for supportin

Hailong Chen

Pt/Graphene and Pt/MnO2-Graphene catalysts have been prepared by microwave-assisted polyol process. Electrochemical results show that Pt/MnO2-Graphene has higher electrocatalytic activity and stability. Furthermore, carbon riveted Pt/MnO2-Graphene catalyst has been designed and synthesized on the basis of in situ carbonization of L-ascorbic acid. The stabili

Hai-Yan Wu, Yang Shen, Yan-Qing Li, Kong-Chao Shen

The low efficiency of metal halide perovskite light-emitting diodes (PeLEDs) with blue emission block their potential applications in large-area full-color displays and solid-state lighting. A delicate control over the entire electroluminescence process is indispensable to overcome the efficiency limitations of blue PeLEDs. Here, we demonstrate an efficient

Zhuangzhuang Cui, Ke Guan, Jiayi Zhang, Zhangdui Zhong

The intelligent reflective surface (IRS) technology has received many interests in recent years, thanks to its potential uses in future wireless communications, in which one of the promising use cases is to widen coverage, especially in the line-of-sight-blocked scenarios. Therefore, it is critical to analyze the corresponding coverage probability of IRS-aid

Yin Teng, Jiayu Li, Lin Liu, Guiyang Xia

In this paper, we present a novel scenario for directional modulation (DM) networks with a full-duplex (FD) malicious attacker (Mallory), where Mallory can eavesdrop the confidential message from Alice to Bob and simultaneously interfere Bob by sending a jamming signal. Considering that the jamming plus noise at Bob is colored, an enhanced receive beamformin

Linhan Yang, Xudong Han, Weijie Guo, Zixin Zhang

Over the past few decades, efforts have been made towards robust robotic grasping, and therefore dexterous manipulation. The soft gripper has shown their potential in robust grasping due to their inherent properties-low, control complexity, and high adaptability. However, the deformation of the soft gripper when interacting with objects bring inaccuracy of g

Nachuan Ma, Jiankun Wang, Max Q. -H. Meng

Path planning plays an important role in autonomous robot systems. Effective understanding of the surrounding environment and efficient generation of optimal collision-free path are both critical parts for solving path planning problem. Although conventional sampling-based algorithms, such as the rapidly-exploring random tree (RRT) and its improved optimal v

Jianbing Ni, Kuan Zhang, Athanasios V. Vasilakos

Mobile edge caching is a promising technology for the next-generation mobile networks to effectively offer service environment and cloud-storage capabilities at the edge of networks. By exploiting the storage and computing resources at the network edge, mobile edge caching can significantly reduce service latency, decrease network load, and improve user expe

Ying Wang, Hongxing Chen, Hichem Hajaiej

We establish an upper bound of the sum of the eigenvalues for the Dirichlet problem of the fractional Laplacian. Our result is obtained by a subtle computation of the Rayleigh quotient for specific functions.

Ryan T. Elliott, Payman Arabshahi, Daniel S. Kirschen

This paper presents a trajectory tracking control strategy that modulates the active power injected by geographically distributed inverter-based resources to support transient stability. Each resource is independently controlled, and its response drives the local bus voltage angle toward a trajectory that tracks the angle of the center of inertia. The center

Antoine Marchal, Marc-Antoine Miville-Deschênes

The transition from the diffuse warm neutral medium (WNM) to the dense cold neutral medium (CNM) is what set the initial conditions to the formation of molecular clouds. The properties of the turbulent cascade in the WNM, essential to describe this radiative condensation process, have remained elusive in part due to the difficulty to map out the structure an

Enesio Marinho, Pedro A. S. Autreto

Graphene-based materials (GBMs) constitute a large family of materials which has attracted great interest for potential applications. In this work, we apply first-principles calculations based on density functional theory (DFT) and fully atomistic reactive molecular dynamics (MD) simulations to study the structural and electronic effects of hydrogenation in

Ye Hu, Mingzhe Chen, Walid Saad, H. Vincent Poor

In this paper, the problem of the trajectory design for a group of energy-constrained drones operating in dynamic wireless network environments is studied. In the considered model, a team of drone base stations (DBSs) is dispatched to cooperatively serve clusters of ground users that have dynamic and unpredictable uplink access demands. In this scenario, the

Peter Schwartzman, David Schwartzman

Assuming the state-of-the-science estimates for the remaining carbon budget of carbon dioxide emissions, our modeling study supports the conclusion that it is still possible to meet the 1.5 deg C warming target even with current wind/solar energy technologies, if the creation of a global 100% renewable energy transition of sufficient capacity begins very soo

Sandra Hayes

Hyperbolic numbers are a variation of complex numbers, but their dynamics is quite different. The hyperbolic Mandelbrot set for quadratic functions over hyperbolic numbers is simply a filled square, and the filled Julia set for hyperbolic parameters inside the hyperbolic Mandelbrot set is a filled rectangle. For hyperbolic parameters outside the hyperbolic M

Max Aires

The saturation number $\text{sat}(n,\mathcal{F})$ is the minimum number of edges in any graph which does not contain a member of $\mathcal{F}$ as a subgraph, but will if any edge is added. We give a few upper and lower bounds for saturation numbers for minors. In particular, we shall show that certain Generalized Petersen Graphs are $K^r$-minor saturated for

Yuanjun Zhao, Roberto Togneri, Victor Sreeram

Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types of attacks from synthetic speech to replay presentations. While there are numerous effective defenses reported on standalone anti-spoofing sol

Thanh Vu, Marc Eder, True Price, Jan-Michael Frahm

Despite remarkable improvements in speed and accuracy, convolutional neural networks (CNNs) still typically operate as monolithic entities at inference time. This poses a challenge for resource-constrained practical applications, where both computational budgets and performance needs can vary with the situation. To address these constraints, we propose the A

Zichu Liu, Qing Zhang, Pei Wang, Yaxin Li

Terrestrial laser scanning technology provides an efficient and accuracy solution for acquiring three-dimensional information of plants. The leaf-wood classification of plant point cloud data is a fundamental step for some forestry and biological research. An automatic sampling and training method for classification was proposed based on tree point cloud dat

C. Royon

We analyze the differences between the $pp$ and $p\bar{p}$ differentia l elastic cross section measurements by the D0 and TOTEM Collaborations at the Tevatron, Fermilab, and the LHC, CERN that lead to a significance larger than 3$σ$ of the existence of the colourless $C$-odd three-gluon state, the odderon.

Vasily Zadorozhnyy, Qiang Cheng, Qiang Ye

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data resp

James H. Adler, Casey Cavanaugh, Xiaozhe Hu, Ludmil T. Zikatanov

Maxwell's equations are a system of partial differential equations that govern the laws of electromagnetic induction. We study a mimetic finite-difference (MFD) discretization of the equations which preserves important underlying physical properties. We show that, after mass-lumping and appropriate scaling, the MFD discretization is equivalent to a struc

Ethan A. Klein, Farheen Naqvi, Jacob E. Bickus, Hin Y. Lee

Neutron Resonance Transmission Analysis (NRTA) is a spectroscopic technique which uses the resonant absorption of neutrons in the epithermal range to infer the isotopic composition of an object. This spectroscopic technique has relevance in many traditional fields of science and nuclear security. NRTA in the past made use of large, expensive accelerator faci

Kristjan Haule, Kun Chen

We calculate the single-particle excitation spectrum and the Landau Fermi liquid parameters for the archetypal model of solids, the three-dimensional uniform electron gas, with the numerically exact variational diagrammatic Monte Carlo method. In the metallic range of density, we establish benchmark values for the wave-function renormalization factor $Z$, th

Thomas Vojta, Alex Warhover

Fractional Brownian motion and the fractional Langevin equation are models of anomalous diffusion processes characterized by long-range power-law correlations in time. We employ large-scale computer simulations to study these models in two geometries, (i) the spreading of particles on a semi-infinite domain with an absorbing wall at one end and (ii) the stat

Marino Miculan, Nicola Vitacolonna

MTProto 2.0 is a suite of cryptographic protocols for instant messaging at the core of the popular Telegram messenger application. In this paper we analyse MTProto 2.0 using the symbolic verifier ProVerif. We provide fully automated proofs of the soundness of MTProto 2.0's authentication, normal chat, end-to-end encrypted chat, and rekeying mechanisms wi

Prabhanjan Ananth, Kai-Min Chung, Rolando L. La Placa

We study the notion of zero-knowledge secure against quantum polynomial-time verifiers (referred to as quantum zero-knowledge) in the concurrent composition setting. Despite being extensively studied in the classical setting, concurrent composition in the quantum setting has hardly been studied. We initiate a formal study of concurrent quantum zero-knowledge

Masatoshi Noumi, Ayako Sano

We introduce a new infinite family of higher-order difference operators that commute with the elliptic Ruijsenaars difference operators of type $A$. These operators are related with Ruijsenaars' operators through a formula of Wronski type.

Zhengyi Zhou

We show that the minimal symplectic area of Lagrangian submanifolds are universally bounded in symplectically aspherical domains with vanishing symplectic cohomology. If an exact domain admits a $k$-semi-dilation, then the minimal symplectic area is universally bounded for $K(\pi,1)$-Lagrangians. As a corollary, we show that Arnol'd chord conjecture holds fo

Saeed Khaki, Hieu Pham, Lizhi Wang

Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote

Ernest Ma

In the conventional left-right gauge model, if the Higgs scalar sector consists only of an $SU(2)_L$ doublet and an $SU(2)_R$ doublet, fermion masses are zero at tree level. There have been many studies on how they would become massive. With the help of a dark sector with $U(1)_D$ gauge symmetry, it is shown how all standard-model fermions may acquire realis

Partha Pratim Ghosh

The notion of an internal preneighbourhood space on a finitely complete category with finite coproducts and a proper $(\mathsf{E}, \mathsf{M})$ system such that for each object $X$ the set of $\mathsf{M}$-subobjects of $X$ is a complete lattice was initiated in \cite{2020}. The notion of a closure operator, closed morphism and its near allies investigated in

Elena K. Brusyanskaya, Anton A. Klyachko

It is known that the number of homomorphisms from a group $F$ to a group $G$ is divisible by the greatest common divisor of the order of $G$ and the exponent of $F/[F,F]$. We investigate the number of homomorphisms satisfying some natural conditions such as injectivity or surjectivity. The simplest nontrivial corollary of our results is the following fact: {

Yu Jiang, B. Theodore Zhang, Kohta Murase

Energetics of the ultrahigh-energy cosmic rays (UHECRs) generated in the universe is crucial for pinning down their candidate sources. Using the recent Auger data on UHECR spectra, we calculate the UHECR energy generation rate density for different species of nuclei at the injection, considering intermediate and heavy nuclei as well as protons, through scann

Mark Schmidt, Christin Grill, Simon Lotz, Tom Pfeiffer

We report on the formation of various intensity pattern types in detuned Fourier domain mode-locked (FDML) lasers and identify the corresponding operating conditions. Such patterns are a result of the complex laser dynamics and serve as an ideal tool for the study of the underlying physical processes as well as for model verification. By numerical simulation

Yifan Li, Chao Li, Stephen Price, Carola-Bibiane Schönlieb

Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track c

F. Bakharev, A. Enin, K. Kalinin, Yu. Petrova

The graded viscosity banks technology (tapering) for polymer flooding is studied for several different models of mixing zones behavior. Depending on the viscosity function the limiting polymer injection profile is rigorously derived for the transverse flow equilibrium model, for the Koval model and for the Todd-Longstaff model. The potential gain in the poly

Onur Mutlu, Saugata Ghose, Juan Gómez-Luna, Rachata Ausavarungnirun

This paper discusses recent research that aims to enable computation close to data, an approach we broadly call processing-in-memory (PIM). PIM places computation mechanisms in or near where the data is stored (i.e., inside memory chips or modules, in the logic layer of 3D-stacked memory, in the memory controllers, in storage devices or chips), so that data

Steffen Jung, Margret Keuper

Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actual

Xiaoxia Wu, Ethan Dyer, Behnam Neyshabur

Inspired by human learning, researchers have proposed ordering examples during training based on their difficulty. Both curriculum learning, exposing a network to easier examples early in training, and anti-curriculum learning, showing the most difficult examples first, have been suggested as improvements to the standard i.i.d. training. In this work, we set

Stephan Ramon Garcia, Ethan Simpson Lee, Josh Suh, Jiahui Yu

We obtain an effective analytic formula, with explicit constants, for the number of distinct irreducible factors of a polynomial $f \in \mathbb{Z}[x]$. We use an explicit version of Mertens' theorem for number fields to estimate a related sum over rational primes. For a given $f \in \mathbb{Z}[x]$, our result yields a finite list of primes that certifies

Rodrigo Jaeschke-Ubiergo, Eric Suarez Morell, Alvaro Sebastian Nunez

We study the microscopical origin of anisotropic ferromagnetism in the van der Waals magnet CrI3. We conclude that the nearest neighbors exchange is well described by the Heisenberg-Kitaev-Gamma (HKGamma) model, and we also found a nonzero Dzyaloshinskii-Moriya interaction (DMI) on next nearest neighbors. Both Kitaev and DMI are known to generate a non-trivi

Dainius Kilda, Alberto Biella, Marco Schiró, Rosario Fazio

We present calculations of the time-evolution of the driven-dissipative XYZ model using the infinite Projected Entangled Pair Operator (iPEPO) method, introduced by [A. Kshetrimayum, H. Weimer and R. Orús, Nat. Commun. 8, 1291 (2017)]. We explore the conditions under which this approach reaches a steady state. In particular, we study the conditions where app

Siddhant Gangapurwala, Mathieu Geisert, Romeo Orsolino, Maurice Fallon

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy. This RL policy is trained

Xianpeng Mao, Yuning Yang

Sparse tensor best rank-1 approximation (BR1Approx), which is a sparsity generalization of the dense tensor BR1Approx, and is a higher-order extension of the sparse matrix BR1Approx, is one of the most important problems in sparse tensor decomposition and related problems arising from statistics and machine learning. By exploiting the multilinearity as well

Keith Cortis, Brian Davis

Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarc

Fabrice Baudoin, Li Chen

We prove on some nested fractals scale invariant $L^p$-Poincar\'e inequalities on metric balls in the range $1 \le p \le 2$. Our proof is based on the development of the local $L^p$-theory of Korevaar-Schoen-Sobolev spaces on fractals using heat kernel methods. Applications to scale invariant Sobolev inequalities and to the study of maximal functions and Haj

Adit Agarwal, K. K. Shukla, Arjan Kuijper, Anirban Mukhopadhyay

The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretation strategies focus on human understanding of the underlying decision mechanisms of the complex ML models. However, these strategies are restricted by the subjective biases of human

Andrei Yu. Bazhenov, Dmitriy V. Tsarev, Alexander P. Alodjants

In this work we consider a superradiant phase transition problem for the Dicke-Ising model, which generalizes the Dicke and Ising models for annealed complex networks presuming spin-spin interaction. The model accounts the interaction between a spin (two-level) system and external classical (magnetic) and quantized (transverse) fields. We examine regular, ra

T. Garaev

We present an accurate detailed exposition of the proof of existence of the Alexander-Conway polynomial (of links in 3-dimensional space). Other proofs were given by J. Alexander, J. Conway, V. Mantourov and L. Kauffman.

Federico Errica, Davide Bacciu, Alessio Micheli

We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology. By combining ideas from mixture models and graph representation learning, we address a broader class of challenging conditional density estimation problems that rely on structured d

Stefanos Leonardos, Georgios Piliouras

Exploration-exploitation is a powerful and practical tool in multi-agent learning (MAL), however, its effects are far from understood. To make progress in this direction, we study a smooth analogue of Q-learning. We start by showing that our learning model has strong theoretical justification as an optimal model for studying exploration-exploitation. Specifi

Janis Postels, Hermann Blum, Yannick Strümpler, Cesar Cadena

The distribution of a neural network's latent representations has been successfully used to detect out-of-distribution (OOD) data. This work investigates whether this distribution moreover correlates with a model's epistemic uncertainty, thus indicates its ability to generalise to novel inputs. We first empirically verify that epistemic uncertainty c

Philip Dittmann, Borys Kadets

Suppose $f \in K[x]$ is a polynomial. The absolute Galois group of $K$ acts on the preimage tree $\mathrm{T}$ of $0$ under $f$. The resulting homomorphism $\phi_f: \mathrm{Gal}_K \to \mathrm{Aut} \mathrm{T}$ is called the arboreal Galois representation. Odoni conjectured that for all Hilbertian fields $K$ there exists a polynomial $f$ for which $\phi_f$ is s

Yanbing Mao, Naira Hovakimyan, Tarek Abdelzaher, Evangelos Theodorou

This paper studies social system inference from a single trajectory of public evolving opinions, wherein observation noise leads to the statistical dependence of samples on time and coordinates. We first propose a cyber-social system that comprises individuals in a social network and a set of information sources in a cyber layer, whose opinion dynamics expli

Angela Meyer

The trend towards larger wind turbines and remote locations of wind farms fuels the demand for automated condition monitoring strategies that can reduce the operating cost and avoid unplanned downtime. Normal behaviour modelling has been introduced to detect anomalous deviations from normal operation based on the turbine's SCADA data. A growing number of

Raoni Wohnrath Arroyo, Jonas R. B. Arenhart

According to scientific realism, science gives us an approximately true description of what the world is like. But what does it mean? In this chapter, we focus on the ontological and metaphysical aspects of this discussion. That is, we are concerned with the following questions: what there is, according to our best scientific theories? And how are these thin

Valeriya Korol, Mohammadtaher Safarzadeh

The nature of GW190425, a presumed binary neutron star (BNS) merger detected by the LIGO/Virgo Scientific Collaboration (LVC) with a total mass of $3.4^{+0.3}_{-0.1}$ M$_{\odot}$, remains a mystery. With such a large total mass, GW190425 stands at five standard deviations away from the total mass distribution of Galactic BNSs of $2.66\pm 0.12$ M$_{\odot}$. L

Waad Subber, Piyush Pandita, Sayan Ghosh, Genghis Khan

Most common mechanistic models are traditionally presented in mathematical forms to explain a given physical phenomenon. Machine learning algorithms, on the other hand, provide a mechanism to map the input data to output without explicitly describing the underlying physical process that generated the data. We propose a Data-based Physics Discovery (DPD) fram

Tairan Liu, Marcio de Queiroz

In this paper, we propose a novel approach to the problem of augmenting distance-based formation controllers with a secondary constraint for the purpose of preventing 3D formation ambiguities. Specifically, we introduce three controlled variables that form an orthogonal space and uniquely characterize a tetrahedron formation in 3D. This orthogonal space inco

Shubhranshu Shekhar, Neil Shah, Leman Akoglu

Fairness and Outlier Detection (OD) are closely related, as it is exactly the goal of OD to spot rare, minority samples in a given population. However, when being a minority (as defined by protected variables, such as race/ethnicity/sex/age) does not reflect positive-class membership (such as criminal/fraud), OD produces unjust outcomes. Surprisingly, fairne

Yutao Chen, Yu Zhang, Fei Yang

Railway systems require regular manual maintenance, a large part of which is dedicated to inspecting track deformation. Such deformation might severely impact trains' runtime security, whereas such inspections remain costly for both finance and human resources. Therefore, a more precise and efficient approach to detect railway track deformation is in urg

Luc Guyot

Let $R$ be a commutative ring with identity and let $M$ be an $R$-module which is generated by $μ$ elements but not fewer. We denote by $\operatorname{SL}_n(R)$ the group of the $n \times n$ matrices over $R$ with determinant $1$. We denote by $\operatorname{E}_n(R)$ the subgroup of $\operatorname{SL}_n(R)$ generated by the the matrices which differ from the

Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu

Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research. In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI -- which we call BayLIME. Compared to LIME, BayLIME exploits prior knowledge and Bayesian reasoning to

Luc Guyot

Let $R$ be a commutative ring with identity and let $I$ be a two-generated ideal of $R$. We denote by $\operatorname{SL}_2(R)$ the group of $2 \times 2$ matrices over $R$ with determinant $1$. We study the action of $\operatorname{SL}_2(R)$ by matrix right-multiplication on $\operatorname{V}_2(I)$, the set of generating pairs of $I$. Let $\operatorname{Fitt}

Ahmed Diab

We will derive a function that eliminates any sequence of equidistant numbers from the integer numbers, then we will derive its inverse. Then we will use the Sequence elimination function to eliminate the multiples of the prime numbers from the integer numbers to produce formulas that generate all prime numbers in some interval.

Janek Meyer, Kai Graf, Thomas Slawig

This paper presents a method for the simulation of scour around arbitrary offshore structures. It is based on the solution of the Reynolds-Averaged-Navier-Stokes equations implemented in the OpenFOAM framework. The sediment is simulated with the help of a Bingham model, which basically models a solid sediment behavior by introducing a very high viscosity. Th

Jian Feng, Yanguang Chen

Urban population density provides a good perspective for understanding urban growth and socio-spatial dynamics. Based on sub-district data of the five times of national population censuses in 1964, 1982, 1990, 2000, and 2010, this paper is devoted to making analyses of urban growth and the spatial restructuring of population in the city of Hangzhou, China. R

Rahul Kumar Jinger, Heike Fliegl, Radovan Bast, Maria Dimitrova

We develop a methodology for calculating, analyzing and visualizing nuclear magnetic shielding densities, which are calculated from the current density via the Biot-Savart relation. Atomic contributions to nuclear magnetic shielding constants can be estimated within our framework with a Becke partitioning scheme. The new features have been implemented in the

Krzysztof Stempak

Let $W$ be a finite reflection group associated with a root system $R$ in $\mathbb R^d$. Let $C_+$ denote a positive Weyl chamber. Consider an open subset $\Omega$ of $\mathbb R^d$, symmetric with respect to reflections from $W$. Let $\Omega_+=\Omega\cap C_+$ be the positive part of $\Omega$. We define a family $\{-\Delta_{\eta}^+\}$ of self-adjoint extensio

J. Nättilä, A. M. Beloborodov

We perform 2D and 3D kinetic simulations of reconnection-mediated turbulent flares in a magnetized electron-positron plasma, with weak and strong radiative cooling. Such flares can be generated around neutron stars and accreting black holes. We focus on the magnetically-dominated regime where tension of the background magnetic field lines exceeds the plasma

Margaret E. Carrington, Alina Czajka, Stanislaw Mrowczynski

Nuclear collisions at high energies produce a gluon field that can be described using the Colour Glass Condensate (CGC) effective theory at proper times $\tau \lesssim 1$ fm/c. The theory can be used to calculate the gluon energy-momentum tensor, which provides information about the early time evolution of the chromo-electric and chromo-magnetic fields, ener

Yigit Baran Can, Alexander Liniger, Ozan Unal, Danda Paudel

Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, this translates to scene understanding in the bird's-eye view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surr

Liang Li, Felix Ryde, Asaf Pe'er, Hoi-Fung Yu

Gamma-ray bursts (GRBs) are highly variable and exhibit strong spectral evolution. In particular, the emission properties vary from pulse to pulse in multipulse bursts. Here we present a time-resolved Bayesian spectral analysis of a compilation of GRB pulses observed by the {\it Fermi}/Gamma-ray Burst Monitor. The pulses are selected to have at least four ti

Caterina Czibula, August Brandberg, Megan J. Cordill, Aleksandar Matković

Atomic force microscopy-based nanoindentation (AFM-NI) enables characterization of the basic mechanical properties of wood pulp fibers in conditions representative of the state inside a paper sheet. Determination of the mechanical properties under different loads is critical for the success of increasingly advanced computational models to understand, predict

Alberto Saldana-Lopez, Alberto Domínguez, Pablo G. Pérez-González, Justin Finke

The diffuse extragalactic background light (EBL) is formed by ultraviolet (UV), optical, and infrared (IR) photons mainly produced by star formation processes over the history of the Universe, and contains essential information about galaxy evolution and cosmology. Here, we present a new determination of the evolving EBL spectral energy distribution using a

Chunyi Huang, Chengmin Wang, Mingzhi Zhang, Ning Xie

The burgeoning integration of distributed energy resources (DER) poses new challenges for the economic and safe operation of the electricity system. The current distribution-side policy is largely based on mandatory regulations and incentives, rather than the design of a competitive market mechanism to arouse DERs to freely compete in the retail market. To a

Hugo Flores-Arguedas, Marcos A. Capistrán

In this paper, we propose a model of the dynamics of the blood glucose level during an Oral Glucose Tolerance Test (OGTT). This dynamic includes the action of insulin and glucagon in the glucose homeostasis process as the reaction of an oral stimulus. We propose a Bayesian approach in the inference of five parameters related to insulin secretion, glucagon se

Marius E. Yamakou, Tat Dat Tran

Self-induced stochastic resonance (SISR) is a subtle resonance mechanism requiring a nontrivial scaling limit between the stochastic and the deterministic timescales of an excitable system, leading to the emergence of a limit cycle behavior which is absent without noise. All previous studies on SISR in neural systems have only considered the idealized Gaussi

Tadamune Kaneko, Macoto Kikuchi

The aim of this paper is two-fold. First, we propose a new computational method to investigate the particularities of evolution. Second, we apply this method to a model of gene regulatory networks (GRNs) and explore the evolution of mutational robustness and bistability. Living systems have developed their functions through evolutionary processes. To underst

Andrea Giacobbe

We give three algebraic equations which allow a geometric classification of all spectral types of equilibria of a given $m$-dimensional dynamical system, and we analyse them thoroughly in dimension 3 and 4. The loci defined by these equations correspond to definite types of bifurcations. The complement of such loci give a geometric decomposition of the space

Marija Popovic, Florian Thomas, Sotiris Papatheodorou, Nils Funk

In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstacle-free space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used for sensing fail to register valid depth values on shiny, glossy, bright, or distant surfaces, leading to missing data in the map. To address th

Takahiro Kinoshita, Satoshi Ono

Depth (disparity) estimation from 4D Light Field (LF) images has been a research topic for the last couple of years. Most studies have focused on depth estimation from static 4D LF images while not considering temporal information, i.e., LF videos. This paper proposes an end-to-end neural network architecture for depth estimation from 4D LF videos. This stud

Dan Reznik, Ronaldo Garcia, Mark Helman

Inverting the vertices of elliptic billiard N-periodics with respect to a circle centered on one focus yields a new "focus-inversive" family inscribed in Pascal's Lima\c{c}on. The following are some of its surprising invariants: (i) perimeter, (ii) sum of cosines, and (iii) sum of distances from inversion center (the focus) to vertices. We prove these for th

Xinran Ma, Z. C. Tu, Shi-Ju Ran

Human experts cannot efficiently access the physical information of quantum many-body states by simply "reading" the coefficients, but have to reply on the previous knowledge such as order parameters and quantum measurements. In this work, we demonstrate that convolutional neural network (CNN) can learn from the coefficients of local reduced density matrices

Ilya Goldsheid, Sasha Sodin

It is known that the eigenfunctions of a random Schr\"odinger operator on a strip decay exponentially, and that the rate of decay is not slower than prescribed by the slowest Lyapunov exponent. A variery of heuristic arguments suggest that no eigenfunction can decay faster than at this rate. We make a step towards this conjecture (in the case when the distri

Vugar Ismailov

In 1987, Hecht-Nielsen showed that any continuous multivariate function can be implemented by a certain type three-layer neural network. This result was very much discussed in neural network literature. In this paper we prove that not only continuous functions but also all discontinuous functions can be implemented by such neural networks.

Matthieu Martin, Bruno Sciolla, Michaël Sdika, Philippe Quétin

Preterm neonates are highly likely to suffer from ventriculomegaly, a dilation of the Cerebral Ventricular System (CVS). This condition can develop into life-threatening hydrocephalus and is correlated with future neuro-developmental impairments. Consequently, it must be detected and monitored by physicians. In clinical routing, manual 2D measurements are pe

Anish Rai, Ajit Mahata, Md Nurujjaman, Om Prakash

During any unique crisis, panic sell-off leads to a massive stock market crash that may continue for more than a day, termed as mainshock. The effect of a mainshock in the form of aftershocks can be felt throughout the recovery phase of stock price. As the market remains in stress during recovery, any small perturbation leads to a relatively smaller aftersho

Yang Li, Yu Shen, Jiawei Jiang, Jinyang Gao

Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep learning models or training models on large datasets), vanilla Bayesian optimization (BO) is typically computationally infeasible. To alleviate this issue, Hyperband (HB) utilizes the