February 2021 arXiv papers — page 4

Shuai Yang, Hao Wang, Kui Yu, Fuyuan Cao

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Eff

Sherlock A. Licorish, Chan Won Lee, Bastin Tony Roy Savarimuthu, Priyanka Patel

It is known that user involvement and user-centered design enhance system acceptance, particularly when end-users' views are considered early in the process. However, the increasingly common method of system deployment, through frequent releases via an online application distribution platform, relies more on post-release feedback from a virtual community

Mihailo M. Martinović, Kristopher G. Klein, Jia Huang, Benjamin D. G. Chandran

Parker Solar Probe (PSP) routinely observes magnetic field deflections in the solar wind at distances less than 0.3 au from the Sun. These deflections are related to structures commonly called 'switchbacks' (SBs), whose origins and characteristic properties are currently debated. Here, we use a database of visually selected SB intervals - and regions

Xingcai Zhou, Le Chang, Pengfei Xu, Shaogao Lv

Communication efficiency and robustness are two major issues in modern distributed learning framework. This is due to the practical situations where some computing nodes may have limited communication power or may behave adversarial behaviors. To address the two issues simultaneously, this paper develops two communication-efficient and robust distributed lea

Vinod Menon, Roopak Sinha, Stephen MacDonell

Software development has steadily embraced agile software development methodology/method (ASDM) and has been moving away from the plan driven software development methodology (PDM) approaches like waterfall. Given the iterative nature of agile development, the integration of software architecture into the agile way has become challenging. This research ident

Lei Gao, Ling Guan

As an effective tool for two-dimensional data analysis, two-dimensional canonical correlation analysis (2DCCA) is not only capable of preserving the intrinsic structural information of original two-dimensional (2D) data, but also reduces the computational complexity effectively. However, due to the unsupervised nature, 2DCCA is incapable of extracting suffic

Lei Gao, Lin Qi, Ling Guan

The Fractional Fourier Transform (FRFT) has been playing a unique and increasingly important role in signal and image processing. In this letter, we investigate the property of frequency shift in two-dimensional FRFT (2D-FRFT) domain. It is shown that the magnitude of image reconstruction from phase information is frequency shift-invariant in 2D-FRFT domain,

Rohan Shad, Nicolas Quach, Robyn Fong, Patpilai Kasinpila

Non-invasive and cost effective in nature, the echocardiogram allows for a comprehensive assessment of the cardiac musculature and valves. Despite progressive improvements over the decades, the rich temporally resolved data in echocardiography videos remain underutilized. Human reads of echocardiograms reduce the complex patterns of cardiac wall motion, to a

Lei Gao, Lin Qi, Enqing Chen, Ling Guan

In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from multimodal information representations. Specifically, it finds the projected directions which simultaneously maximize the within-class correlation

Lei Gao, Rui Zhang, Lin Qi, Enqing Chen

The objective of multimodal information fusion is to mathematically analyze information carried in different sources and create a new representation which will be more effectively utilized in pattern recognition and other multimedia information processing tasks. In this paper, we introduce a new method for multimodal information fusion and representation bas

Ali I. Sandhu, Salman A. Shaukat, Abdulla Desmal, Hakan Bagci

Greedy pursuit algorithms (GPAs) are widely used to reconstruct sparse signals. Even though many electromagnetic (EM) inverse scattering problems are solved on sparse investigation domains, GPAs have rarely been used for this purpose. This is because (i) they require a priori knowledge of the sparsity level in the investigation domain, which is often not ava

Ole Peters, Alexander Adamou

Economic models often maximise expectation values of wealth or utility. In non-ergodic settings, these can differ from time-averages, so that maximising expected outcomes need not maximise -- and can systematically reduce -- long-run wealth or utility. Ergodicity economics highlights this problem and models individual agents as maximising wealth in the long

Peng Zhou

Coupling effects among different physical fields substantially reflect the conversion of energies from one form into another. For simple physical processes, their governing or constitutive equations all satisfy the law of conservation of energy. Then, analysis is extended to coupling effects. First, it is found for the linear direct and converse piezoelectri

Weixiao Gao, Liangliang Nan, Bas Boom, Hugo Ledoux

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understandin

Gustavo O. Heymans, Marcus Benghi Pinto

We apply the optimized perturbation theory (OPT) to resum the perturbative series describing the mass gap of the bidimensional $ϕ^4$ theory in the $\mathbb{Z}_2$ symmetric phase. Already at NLO (one loop) the method is capable of generating a quite reasonable non-perturbative result for the critical coupling. At order-$g^7$ we obtain $g_c = 2.779(25)$ which

David Eriksson, Martin Jankowiak

Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions. High-dimensional BO presents a particular challenge, in part because the curse of dimensionality makes it difficult to define -- as well as do inference over -- a suitable class of surrogate models. We argue that Gaussian process surrogate models de

Eriselda Goga, Besiana Hamzallari

In this paper, we deal with an elliptic problem with the Dirichlet boundary condition. We operate in Sobolev spaces and the main analytic tool we use is the Lax-Milgram lemma. First, we present the variational approach of the problem which allows us to apply different functional analysis techniques. Then we study thoroughly the well-posedness of the problem.

Kate Donahue, Solon Barocas

Consider a cost-sharing game with players of different contribution to the total cost: an example might be an insurance company calculating premiums for a population of mixed-risk individuals. Two natural and competing notions of fairness might be to a) charge each individual the same price or b) charge each individual according to the cost that they bring t

Cristian H. García-Duque, J. H. Muñoz, Néstor Quintero, Eduardo Rojas

Lepton flavor universality can be tested through the ratio of semileptonic $B$ meson decays and leptonic $Υ$ meson decays, with $Υ\equiv Υ(nS)$ ($n=1,2,3$). For the charged-current transitions $b \to cτ\barν_τ$, discrepancies between the experiment and the Standard Model (SM) have been observed in recent years by different flavor facilities such as BABAR, Be

Kamal Dliou, Hicham El Boujaoui, Mustapha Kchikech

In this paper, we study the $L(2, 1)$-Labeling of the Mycielski and the iterated Mycielski of graphs in general. For a graph $G$ and all $t\geq 1$, we give sharp bounds for $λ(M^t(G))$ the $L(2, 1)$-labeling number of the $t$-th iterated Mycielski in terms of the number of iterations $t$, the order $n$, the maximum degree $\bigtriangleup$, and $λ(G)$ the $L(

Marcos Solera, Julián Toledo

We study the existence and uniqueness of mild and strong solutions of nonlocal nonlinear diffusion problems of $p$-Laplacian type with nonlinear boundary conditions posed in metric random walk spaces. These spaces include, among others, weighted discrete graphs and $\mathbb{R}^N$ with a random walk induced by a nonsingular kernel. We also study the case of n

Mouad Ramil

Consider the Langevin process, described by a vector (positions and momenta) in $\mathbb{R}^{d}\times\mathbb{R}^d$. Let $\mathcal O$ be a $\mathcal{C}^2$ open bounded and connected set of $\mathbb{R}^d$. Recent works showed the existence of a unique quasi-stationary distribution (QSD) of the Langevin process on the domain $D:=\mathcal{O}\times\mathbb{R}^d$.

Ziyun Yang, Somayyeh Soltanian-Zadeh, Sina Farsiu

Salient object detection (SOD) is viewed as a pixel-wise saliency modeling task by traditional deep learning-based methods. A limitation of current SOD models is insufficient utilization of inter-pixel information, which usually results in imperfect segmentation near edge regions and low spatial coherence. As we demonstrate, using a saliency mask as the only

Ahmet M. Elbir, Kumar Vijay Mishra, Symeon Chatzinotas

Wireless communications and sensing at terahertz (THz) band are increasingly investigated as promising short-range technologies because of the availability of high operational bandwidth at THz. In order to address the extremely high attenuation at THz, ultra-massive multiple-input multiple-output (MIMO) antenna systems have been proposed for THz communicatio

Rajinda Wickrama

We develop an arbitrage-free random field LIBOR market model to price cross-currency derivatives. The uncertainty of the forward LIBOR rates of our cross-currency model is driven by a two time parameter random field instead of a finite dimensional Brownian motion. To demonstrate the applications of this model, we develop an approximate closed-form pricing fo

O. U. Salman, R. Baggio, B. Bacroix, G. Zanzotto

We study the mechanical response of a dislocation-free 2D crystal under homogenous shear using a new mesoscopic approach to crystal plasticity, a Landau-type theory, accounting for the global invariance of the energy in the space of strain tensors while operating with an infinite number of equivalent energy wells. The advantage of this approach is that it el

Gulmina Zaman Babar, Farruh Atamurotov, Abdullah Zaman Babar

In this paper we have assumed a weak-field regime to explore the gravitational lensed photons in a 4 dimensional Einstein-Gauss-Bonnet gravity, which is very much in the limelight these days. The investigation is conducted in three distinct paradigms: uniform plasma, singular isothermal sphere and a non-singular isothermal sphere. The lensing angle associate

Zahra Shomali, Robert Kovács, Peter Ván, Igor Vasilievich Kudinov

The accuracy of the classical heat conduction model, known as Fourier's law, is highly questioned, dealing with the micro and nanosystems and biological tissues. In other words, the results obtained from the classical equations deviate from the available experimental data. It means that the continuum heat diffusion equation is insufficient and inappropri

Weiqiang He, Yefeng Shen

We introduce Virasoro operators for any Landau-Ginzburg pair (W, G) where W is a non-degenerate quasi-homogeneous polynomial and G is a certain group of diagonal symmetries. We propose a conjecture that the total ancestor potential of the FJRW theory of the pair (W,G) is annihilated by these Virasoro operators. We prove the conjecture in various cases, inclu

Ram Gopal Vishwakarma

An attempt is made to uncover the physical meaning and significance of the obscure Lanczos tensor field which is regarded as a potential of the Weyl field. Despite being a fundamental building block of any metric theory of gravity, the Lanczos tensor has not been paid proper attention as it deserves. By providing an elucidation on this tensor field through i

K. V. Chelpanov

We give a proof for sharp estimate for the number of spanning trees using linear algebra and generalize this bound to multigraphs. In addition, we show that this bound is tight for complete graphs. In addition, we give estimates for number of spanning trees in specific type of graphs such as product of graphs or Cartesian product of two graphs.

Jeffrey Hatley, Jason Stack

We prove, using elementary methods, that each member of the infinite families of elliptic curves given by $E_m \colon y^2=x^3 - x + m^6$ and $E_m' \colon y^2=x^3 + x - m^6$ have rank at least $2$ and 3, respectively, under mild restrictions on $m$. We also prove stronger results for $E_m$ and $E_m'$ using more technical machinery.

Florian Hörsch

Nash-Williams proved that every graph has a well-balanced orientation. A key ingredient in his proof is admissible odd-vertex pairings. We show that for two slightly different definitions of admissible odd-vertex pairings, deciding whether a given odd-vertex pairing is admissible is co-NP-complete. This resolves a question of Frank. We also show that decidin

Marius Müller

We prove the (optimal) $W^{1,\infty}$-regularity of weak solutions to the equation $-Δu = Q \; \mathcal{H}^{n-1} \llcorner Γ$ in a domain $Ω\subset \mathbb{R}^n$ with Dirichlet boundary conditions, where $Γ\subset \subset Ω$ is a compact (Lipschitz) manifold and $Q \in L^\infty(Γ)$. We also discuss optimality and necessity of the assumptions on $Q$ and $Γ$.

Denis Baručić, Jan Kybic, Olga Teplá, Zinovij Topurko

Infertility is becoming an issue for an increasing number of couples. The most common solution, in vitro fertilization, requires embryologists to carefully examine light microscopy images of human oocytes to determine their developmental potential. We propose an automatic system to improve the speed, repeatability, and accuracy of this process. We first loca

Andhika Kiswandhi, Toshihito Osada

We report the observation of nonlinear anomalous Hall effect (NLAHE) in the multilayered organic conductor $α$-(BEDT-TTF)$_2$I$_3$ in the charge order (CO) insulating phase just under the critical pressure for transition into two-dimensional (2D) massless Dirac fermion (DF) phase. We successfully extracted the finite nonlinear Hall voltage proportional to sq

Daniil Tiapkin, Alexander Gasnikov

We consider the problem of learning the optimal policy for infinite-horizon Markov decision processes (MDPs). For this purpose, some variant of Stochastic Mirror Descent is proposed for convex programming problems with Lipschitz-continuous functionals. An important detail is the ability to use inexact values of functional constraints and compute the value of

Colin Benjamin, Arjun Krishnan U M

How cooperation evolves and manifests itself in the thermodynamic or infinite player limit of social dilemma games is a matter of intense speculation. Various analytical methods have been proposed to analyze the thermodynamic limit of social dilemmas. In this work, we compare two analytical methods, i.e., Darwinian evolution and Nash equilibrium mapping, wit

Steven Hoehner

For a convex body $K$ in $\mathbb{R}^n$, we introduce and study the extremal general affine surface areas, defined by \[ {\rm IS}_φ(K):=\sup_{K^\prime\subset K}{\rm as}_φ(K),\quad {\rm os}_ψ(K):=\inf_{K^\prime\supset K}{\rm as}_ψ(K) \] where ${\rm as}_φ(K)$ and ${\rm as}_ψ(K)$ are the $L_φ$ and $L_ψ$ affine surface area of $K$, respectively. We prove that th

Taha Aksu, Zhengyuan Liu, Min-Yen Kan, Nancy F. Chen

Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure. In this work, we introduce an augmentation framework that utilizes belief state annotations to match turns from various dialogues and form new synthetic dialogues

Jin Liu

Longitudinal data analysis has been widely employed to examine between-individual differences in within-individual changes. One challenge of such analyses is that the rate-of-change is only available indirectly when change patterns are nonlinear with respect to time. Latent change score models (LCSMs), which can be employed to investigate the change in rate-

Mingrui Liu, Francesco Orabona

Convex-concave min-max problems are ubiquitous in machine learning, and people usually utilize first-order methods (e.g., gradient descent ascent) to find the optimal solution. One feature which separates convex-concave min-max problems from convex minimization problems is that the best known convergence rates for min-max problems have an explicit dependence

Paolo Lipparini

The class of linearly ordered sets with one order preserving unary operation has the Strong Amalgamation Property (SAP). The class of linearly ordered sets with one strict order preserving unary operation has AP but not SAP. The class of linearly ordered sets with two order preserving unary operations has not AP. For every set $F$, the class of linearly orde

Raf Cluckers, Itay Glazer, Yotam I. Hendel

We provide uniform estimates on the number of $\mathbb{Z}/p^{k}\mathbb{Z}$-points lying on fibers of flat morphisms between smooth varieties whose fibers have rational singularities, termed (FRS) morphisms. For each individual fiber, the estimates were known by work of Avni and Aizenbud, but we render them uniform over all fibers. The proof technique for ind

William H. Kinney

I discuss constraints on the power spectrum of primordial tensor perturbations from a combination of Cosmic Microwave Background (CMB) measurements and the gravitational wave direct detection experiments LIGO/Virgo and DECIGO. There are two main points: (1) Inflation predicts an approximately power-law form for the primordial tensor spectrum, but makes no pr

Daniel Z. Huang, Jiaoyang Huang

The unscented Kalman inversion (UKI) method presented in [1] is a general derivative-free approach for the inverse problem. UKI is particularly suitable for inverse problems where the forward model is given as a black box and may not be differentiable. The regularization strategies, convergence property, and speed-up strategies [1,2] of the UKI are thoroughl

Joao A. Silva

We compute spinning four point functions in the quasi-fermionic three dimensional conformal field theory with slightly broken higher spin symmetry at finite t'Hooft coupling. More concretely, we obtain a formula for $\langle j_s j_{\tilde{0}} j_{\tilde{0}} j_{\tilde{0}} \rangle$, where $j_s$ is a higher spin current and $j_{\tilde{0}}$ is the scalar sing

Tianyu Zhang, Xiangjia Chen, Guoxin Fang, Yingjun Tian

Multi-axis additive manufacturing enables high flexibility of material deposition along dynamically varied directions. The Cartesian motion platforms of these machines include three parallel axes and two rotational axes. Singularity on rotational axes is a critical issue to be tackled in motion planning for ensuring high quality of manufacturing results. The

Kang Lai, Sailong Ju, Hongen Zhu, Hanwen Wang

Recently, intriguing physical properties have been unraveled in anisotropic layered semiconductors, in which the in-plane electronic band structure anisotropy often originates from the low crystallographic symmetry and thus a thickness-independent character emerges. Here, we apply high-resolution angle-resolved photoemission spectroscopy to directly image th

Naoki Wake, Daichi Saito, Kazuhiro Sasabuchi, Hideki Koike

This study investigates how text-driven object affordance, which provides prior knowledge about grasp types for each object, affects image-based grasp-type recognition in robot teaching. The researchers created labeled datasets of first-person hand images to examine the impact of object affordance on recognition performance. They evaluated scenarios with rea

Monika Moscibrodzka, Agnieszka Janiuk, Mariefelicia De Laurentis

Magnetically arrested accretion flows are thought to fuel some of the supermassive black holes and to power their relativistic jets. We calculate and study a time sequence of linear and circular polarimetric images of numerical, high resolution and long duration simulations of magnetically dominated flows to investigate observational signatures of strong mag

P. A. Gazca-Orozco

We propose a semismooth Newton method for non-Newtonian models of incompressible flow where the constitutive relation between the shear stress and the symmetric velocity gradient is given implicitly; this class of constitutive relations captures for instance the models of Bingham and Herschel-Bulkley. The proposed method avoids the use of variational inequal

Mamuka Jibladze, Victor G. Kac

We find the normal form of nilpotent elements in semisimple Lie algebras that generalizes the Jordan normal form in $\mathfrak{sl}_N$, using the theory of cyclic elements.

Jiebin Yan, Yu Zhong, Yuming Fang, Zhangyang Wang

Semantic segmentation is an extensively studied task in computer vision, with numerous methods proposed every year. Thanks to the advent of deep learning in semantic segmentation, the performance on existing benchmarks is close to saturation. A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transf

Chen-Hao Wu, Ya-Peng Hu, Hao Xu

Einstein-Gauss-Bonnet theory is a string-generated gravity theory when approaching the low energy limit. By introducing the higher order curvature terms, this theory is supposed to help to solve the black hole singularity problem. In this work, we investigate the evaporation of the static spherically symmetric neutral AdS black holes in Einstein-Gauss-Bonnet

Armin Goudarzi, Carsten Spehr, Steffen Herbold

This paper presents an Expert Decision Support System for the identification of time-invariant, aeroacoustic source types. The system comprises two steps: first, acoustic properties are calculated based on spectral and spatial information. Second, clustering is performed based on these properties. The clustering aims at helping and guiding an expert for quic

Andrew James Bruce, Janusz Grabowski

Roughly speaking, $\mathbb{Z}_2^n$-manifolds are `manifolds' equipped with $\mathbb{Z}_2^n$-graded commutative coordinates with the sign rule being determined by the scalar product of their $\mathbb{Z}_2^n$-degrees. We examine the notion of a symplectic $\mathbb{Z}_2^n$-manifold, i.e., a $\mathbb{Z}_2^n$-manifold equipped with a symplectic two-form that

Silvia Noschese, Lothar Reichel

The capability of a network to cope with threats and survive attacks is referred to as its robustness. This paper discusses one kind of robustness, commonly denoted structural robustness, which increases when the spectral radius of the adjacency matrix associated with the network decreases. We discuss computational techniques for identifying edges, whose rem

Momchil Hardalov, Arnav Arora, Preslav Nakov, Isabelle Augenstein

Understanding attitudes expressed in texts, also known as stance detection, plays an important role in systems for detecting false information online, be it misinformation (unintentionally false) or disinformation (intentionally false information). Stance detection has been framed in different ways, including (a) as a component of fact-checking, rumour detec

Cat P. Le, Mohammadreza Soltani, Robert Ravier, Vahid Tarokh

In this paper, we propose a neural architecture search framework based on a similarity measure between some baseline tasks and a target task. We first define the notion of the task similarity based on the log-determinant of the Fisher Information matrix. Next, we compute the task similarity from each of the baseline tasks to the target task. By utilizing the

Floor Verhoeven, Amir Vaxman, Tim Hoffmann, Olga Sorkine-Hornung

We introduce an algorithm to remesh triangle meshes representing developable surfaces to planar quad dominant meshes. The output of our algorithm consists of planar quadrilateral (PQ) strips that are aligned to principal curvature directions and closely approximate the curved parts of the input developable, and planar polygons representing the flat parts of

Arata Yamamoto

This is an introductory review of lattice QCD with external fields. The study of external magnetic fields is one of the greatest achievements in modern lattice QCD. Large-scale simulations and detailed analyses have revealed intriguing properties of QCD in the magnetic fields. The study of external electric fields is more challenging because of a technical d

Rajeev Singh, Masoud Shokri, Radoslaw Ryblewski

Evolution of spin polarization in the presence of external electric field is studied for collision energies $\sqrt{s_{\rm NN}}=27\,{\rm GeV}$ and $\sqrt{s_{\rm NN}}=200\,{\rm GeV}$. The numerical analysis is done in the perfect-fluid Bjorken-expanding resistive magnetohydrodynamic background and novel results are reported. In particular, we show that the ele

Dayan Guan, Jiaxing Huang, Aoran Xiao, Shijian Lu

Unsupervised domain adaptive object detection aims to adapt detectors from a labelled source domain to an unlabelled target domain. Most existing works take a two-stage strategy that first generates region proposals and then detects objects of interest, where adversarial learning is widely adopted to mitigate the inter-domain discrepancy in both stages. Howe

JunRu Luo, Hong Qiao, Bo Zhang

Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms. In this paper, we introduce two smooth Hinge losses $ψ_G(α;σ)$ and $ψ_M(α;σ)$ which are infinitely differentiable and converge to the Hinge loss uniformly in $α$ as $σ$ tends to $0$. By replacing the Hinge loss with the

Tim Wyssling, Andreas Adelmann

The Particle-Particle-Particle-Mesh algorithm elegantly extends the standard Particle-In-Cell scheme by direct summation of interaction that happens over distances below or around mesh size. Generally, this allows for a more accurate description of Coulomb interactions and improves precision in the prediction of key observables. Nevertheless, most implementa

R. Laker, T. S. Horbury, S. D. Bale, L. Matteini

The recent launches of Parker Solar Probe (PSP), Solar Orbiter (SO) and BepiColombo, along with several older spacecraft, have provided the opportunity to study the solar wind at multiple latitudes and distances from the Sun simultaneously. We take advantage of this unique spacecraft constellation, along with low solar activity across two solar rotations bet

Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of

Sergey Goryainov, Leonid V. Shalaginov

In this paper we survey existing results on Deza graphs and give some new results. We present an introduction to Deza graphs for the reader who is unfamiliar with the subject, and then give an overview of some developments in the area of Deza graphs since the initial paper by five authors [M. Erickson, S. Fernando, W. H. Haemers, D. Hardy, J. Hemmeter, Deza

Richard D. Gill

I point out critical errors in the paper "Bell's Theorem Versus Local Realism in a Quaternionic Model of Physical Space" by J. Christian, published in IEEE Access. Christian's paper in fact contains several conflicting models. None of them form counterexamples to Bell's theorem. Most of Christian's paper is devoted to a model based on

M. Dajczer, C. -R. Onti, Th. Vlachos

In this paper we give local and global parametric classifications of a class of Einstein submanifolds of Euclidean space. The highlight is for submanifolds of codimension two since in this case our assumptions are only of intrinsic nature.

András Kovács

In type theories, universe hierarchies are commonly used to increase the expressive power of the theory while avoiding inconsistencies arising from size issues. There are numerous ways to specify universe hierarchies, and theories may differ in details of cumulativity, choice of universe levels, specification of type formers and eliminators, and available in

Ali Unlu, Laurence Aitchison

We develop variational Laplace for Bayesian neural networks (BNNs) which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational Laplace objective is simple to evaluate, as it is (in essence) the log-likelihood, plus weight-decay, plus a squ

Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah, Hamid R. Rabiee

Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a preval

Zexi Hu, Chengfeng Cai, Yi-Lei Tang, Zhao-Huan Yu

We propose a vector dark matter model with an exotic dark SU(2) gauge group. Two Higgs triplets are introduced to spontaneously break the symmetry. All of the dark gauge bosons become massive, and the lightest one is a viable vector DM candidate. Its stability is guaranteed by a remaining Z_2 symmetry. We study the parameter space constrained by the Higgs me

Felix Juefei-Xu, Run Wang, Yihao Huang, Qing Guo

The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications, e.g., visual effect assistance in movie and misinformation generation by faking famous persons. The evil side of this new technique poses another popular st

Hyunseung Kim, Jonggeol Na, Won Bo Lee

Discovering new materials better suited to specific purposes is an important issue in improving the quality of human life. Here, a neural network that creates molecules that meet some desired conditions based on a deep understanding of chemical language is proposed (Generative Chemical Transformer, GCT). The attention mechanism in GCT allows a deeper underst

Mohammad Javad Kazemi, Shehu AbdusSalam

We study the dynamics of electroweak phase transition in a simple extension of the Standard Model where the Higgs sector is extended by adding an $SU(2)_L$-triplet with hypercharge Y=2. By making random scans over the parameters of the model, we show that there are regions consistent with constraints from collider experiments and the requirement for a strong

Steven X. Ding, Linlin Li, Dong Zhao, Chris Louen

This draft addresses issues of detecting stealthy integrity cyber-attacks on automatic control systems in the unified control and detection framework. A general form of integrity cyber-attacks that cannot be detected using the well-established observer-based technique is first introduced as kernel attacks. The well-known replay, zero dynamics and covert atta

Yan Liu, Masanobu Taniguchi, Hernando Ombao

Granger causality has been employed to investigate causality relations between components of stationary multiple time series. We generalize this concept by developing statistical inference for local Granger causality for multivariate locally stationary processes. Our proposed local Granger causality approach captures time-evolving causality relationships in

Hao Chen, Zipeng Qi, Zhenwei Shi

Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral characteristics at different times and spatial locations. Most

Nazir Khan, Danil Prishchenko, Mary H. Upton, Vladimir G. Mazurenko

Crystal structure, electronic state of Ir$^{4+}$, and magnetic properties of the antifluorite compound K$_2$IrBr$_6$ are studied using high-resolution synchrotron x-ray diffraction, resonant inelastic x-ray scattering (RIXS), thermodynamic and transport measurements, and ab initio calculations. The crystal symmetry is reduced from cubic at room temperature t

Laia Domingo, Florentino Borondo

In this paper we design and use two Deep Learning models to generate the ground and excited wavefunctions of different Hamiltonians suitable for the study the vibrations of molecular systems. The generated neural networks are trained with Hamiltonians that have analytical solutions, and ask the network to generalize these solutions to more complex Hamiltonia

Wenrui Gan, Zhulin Liu, C. L. Philip Chen, Tong Zhang

In deep learning, auxiliary training has been widely used to assist the training of models. During the training phase, using auxiliary modules to assist training can improve the performance of the model. During the testing phase, auxiliary modules can be removed, so the test parameters are not increased. In this paper, we propose a novel auxiliary training m

J. Ambjørn, Z. Drogosz, J. Gizbert-Studnicki, A. Görlich

Using Monte-Carlo computer simulations, we study the impact of matter fields on the geometry of a typical quantum universe in the CDT model of lattice quantum gravity. The quantum universe has the size of a few Planck lengths and the spatial topology of a three-torus. The matter fields are multi-component scalar fields taking values in a torus with circumfer

Francesco Cianfrani

A manifestly Lorentz-covariant formulation of Loop Quantum Gravity (LQG) is given in terms of finite-dimensional representations of the Lorentz group. The formulation accounts for discrete symmetries, such as parity and time-reversal, and it establishes a link with Wigner classification of particles. The resulting quantum model can be seen as LQG with the in

Anna Konrad, Ciarán Eising, Ganesh Sistu, John McDonald

Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and automated parking. In this paper, we prop

Zhepei Wang, Xin Zhou, Chao Xu, Fei Gao

We present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations o

JiaQian Liu

In this paper, we extend the article that Minkowski problem in Gaussian probability space of Huang et al. to $L_p$-Gaussian Minkowski problem, and obtain the existence and uniqueness of $o$-symmetry weak solution in case of $p\geq1$.

Michael Walton, Viliam Lisy

In this report, we present results reproductions for several core algorithms implemented in the OpenSpiel framework for learning in games. The primary contribution of this work is a validation of OpenSpiel's re-implemented search and Reinforcement Learning algorithms against the results reported in their respective originating works. Additionally, we pro

Miel Sharf, Iliya Romm, Michael Palman, Daniel Zelazo

This work considers the economic dispatch problem for a single micro-gas turbine, governed by a discrete state-space model, under combined heat and power (CHP) operation and coupled with a utility. If the exact power and heat demands are given, existing algorithms can be used to give a quick optimal solution to the economic dispatch problem. However, in prac

Ewe-Wei Saw, Xiaohui Meng

Considering turbulent clouds containing small inertial particles, we investigate the effect of particle collision, in particular collision-coagulation, on particle clustering and particle relative motion. We perform direct numerical simulation (DNS) of coagulating particles in isotropic turbulent flow in the regime of small Stokes number ($St=0.001-0.54$) an

Roberto Casadio

We present a quantum description of black holes given by coherent states of gravitons sourced by a matter core. The expected behaviour in the weak-field region outside the horizon is recovered, with arbitrarily good approximation, but the classical central singularity is not resolved because the coherent states may not contain modes of arbitrarily short wave

Yulin Liu, Luyao Zhang, Yinhong Zhao

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It

Abubakr Awad, Wei Pang, David Lusseau, George M. Coghill

In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze. Subsequent researches have confirmed the ability of Physarum-inspired algorithms to solve a wide range of NP-hard problems. In contrast to previous

Sanjay Mandal, Abhishek Parida, P. K. Sahoo

The standard formulation of general relativity fails to describe some recent interests in the universe. It impels us to go beyond the standard formulation of gravity. The $f(Q)$ gravity theory is an interesting modified theory of gravity, where the gravitational interaction is driven by the nonmetricity $Q$. This study aims to examine the cosmological models

Rodrigo Laigner, Yongluan Zhou, Marcos Antonio Vaz Salles, Yijian Liu

Microservices have become a popular architectural style for data-driven applications, given their ability to functionally decompose an application into small and autonomous services to achieve scalability, strong isolation, and specialization of database systems to the workloads and data formats of each service. Despite the accelerating industrial adoption o

Dirk Fahland, Vadim Denisov, Wil. M. P. van der Aalst

To identify the causes of performance problems or to predict process behavior, it is essential to have correct and complete event data. This is particularly important for distributed systems with shared resources, e.g., one case can block another case competing for the same machine, leading to inter-case dependencies in performance. However, due to a variety

Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks. What is worse, the trained diagnosis system would be fixed once deployed but collecting training data that covers enough diseas

Menglin Yang, Ziqiao Meng, Irwin King

Dynamic graphs arise in a plethora of practical scenarios such as social networks, communication networks, and financial transaction networks. Given a dynamic graph, it is fundamental and essential to learn a graph representation that is expected not only to preserve structural proximity but also jointly capture the time-evolving patterns. Recently, graph co