March 2020 arXiv papers — page 98

Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys

Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connect

Jin-Guo Liu, Taine Zhao

Reverse-mode automatic differentiation (AD) suffers from the issue of having too much space overhead to trace back intermediate computational states for back-propagation. The traditional method to trace back states is called checkpointing that stores intermediate states into a global stack and restore state through either stack pop or re-computing. The overh

Ugo Gianazza, Naian Liao

We prove an estimate on the modulus of continuity at a boundary point of a cylindrical domain for local weak solutions to singular parabolic equations of $p$-laplacian type, with $p$ in the sub-critical range $(1,\frac{2N}{N+1}]$. The estimate is given in terms of a Wiener-type integral, defined by a proper elliptic $p$-capacity.

Jorge Salazar

The aim of this paper is to show a peculiar behavior of a (hypothetical) Collatz sequence going to infinity. We study the associated Syracusa sequence (the odd elements of the former) and show that the limit set of a conveniently normalized sequence is the whole unit interval. In particular, for any positive integer there is a subsequence whose elements' exp

Jinyu Yang, Weizhi An, Sheng Wang, Xinliang Zhu

Unsupervised domain adaptation enables to alleviate the need for pixel-wise annotation in the semantic segmentation. One of the most common strategies is to translate images from the source domain to the target domain and then align their marginal distributions in the feature space using adversarial learning. However, source-to-target translation enlarges th

Antoine Fleurence, Yuto Awatani, Camille Huet, Frank B. Wiggers

In this Letter, we demonstrate that it is possible to form a two-dimensional (2D) silicene-like Si$_5$Ge compound by replacing the Si atoms occupying on-top sites in the planar-like structure of epitaxial silicene on ZrB$_2$(0001) by deposited Ge atoms. For coverages below 1/6 ML, the Ge deposition gives rise to a Si$_{6-x}$Ge$_{x}$ alloy (with $x$ between 0

Oğuzhan Bahadır

In this study, we introduce indefinite sasakian statistical manifolds and lightlike hypersurfaces of an indefinite sasakian statistical manifold. Some relations among induced geometrical objects with respect to dual connections in a lightlike hypersurface of an indefinite sasakian manifold are obtained. Some examples related to these concepts are also presen

Anthony Constantinou

Despite the massive popularity of the Asian Handicap (AH) football (soccer) betting market, its efficiency has not been adequately studied by the relevant literature. This paper combines rating systems with Bayesian networks and presents the first published model specifically developed for prediction and assessment of the efficiency of the AH betting market.

Lorenzo Gavassino, Marco Antonelli, Brynmor Haskell

The approach of extended irreversible thermodynamics consists of promoting the dissipative fluxes to non-equilibrium thermodynamic variables. In a relativistic context, this naturally leads to the formulation of the theory of Israel and Stewart (1979), which is, to date, one of the most successful theories for relativistic dissipation. Although the generalit

Julian Hölzermann

In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The

Dominique Larchey-Wendling, Yannick Forster

We formalise the undecidability of solvability of Diophantine equations, i.e. polynomial equations over natural numbers, in Coq's constructive type theory. To do so, we give the first full mechanisation of the Davis-Putnam-Robinson-Matiyasevich theorem, stating that every recursively enumerable problem -- in our case by a Minsky machine -- is Diophantine. We

Vijay Singh, Aroonkumar Beesham

An LRS Bianchi-I space-time model is studied with constant Hubble parameter in $f(R,T)=R+2\lambda T$ gravity. Although a single (primary) matter source is considered, an additional matter appears due to the coupling between matter and $f(R,T)$ gravity. The constraints are obtained for a realistic cosmological scenario, i.e., one obeying the null and weak ene

Tobias Büscher, Angel L. Diez, Gerhard Gompper, Jens Elgeti

Interfaces in tissues are ubiquitous, both between tissue and environment as well as between populations of different cell types. The propagation of an interface can be driven mechanically. % e.g. by a difference in the respective homeostatic stress of the different cell types. Computer simulations of growing tissues are employed to study the stability of th

Haochen Tian, Defeng Zou, Youjian Song, Minglie Hu

Investigation on the relative phase evolution between two bounded optical solitons is essential for its potential applications in development of larger telecommunication capacity of optical fiber transmission lines, resolution improvement in advancing ultrafast characterization approaches and development of all-optical information storage. Here we characteri

Toby Thomas, David M. Bossens, Danesh Tarapore

The energy of ocean waves is the key distinguishing factor of marine environments compared to other aquatic environments such as lakes and rivers. Waves significantly affect the dynamics of marine vehicles; hence it is imperative to consider the dynamics of vehicles in waves when developing efficient control strategies for autonomous surface vehicles (ASVs).

Hisashi Noma, Kengo Nagashima, Shogo Kato, Satoshi Teramukai

In meta-analysis, the random-effects models are standard tools to address between-study heterogeneity in evidence synthesis analyses. For the random-effects distribution models, the normal distribution model has been adopted in most systematic reviews due to its computational and conceptual simplicity. However, the restrictive model assumption might have ser

Yaiza Canzani, Jeffrey Galkowski

This work concerns $L^p$ norms of high energy Laplace eigenfunctions, $(-\Delta_g-\lambda^2)\phi_\lambda=0$, $\|\phi_\lambda\|_{L^2}=1$. In 1988, Sogge gave optimal estimates on the growth of $\|\phi_\lambda\|_{L^p}$ for a general compact Riemannian manifold. The goal of this article is to give general dynamical conditions guaranteeing quantitative improveme

Leon Bungert, Ester Hait-Fraenkel, Nicolas Papadakis, Guy Gilboa

Neural networks have revolutionized the field of data science, yielding remarkable solutions in a data-driven manner. For instance, in the field of mathematical imaging, they have surpassed traditional methods based on convex regularization. However, a fundamental theory supporting the practical applications is still in the early stages of development. We ta

Oisín Creaner, Kevin Nolan, David Grennan, Niall Smith

This paper presents a catalogue of optimised pointings for differential photometry of 23,779 quasars extracted from the Sloan Digital Sky Survey (SDSS) Catalogue and a score for each indicating the quality of the Field of View (FoV) associated with that pointing. Observation of millimagnitude variability on a timescale of minutes typically requires different

A. Babichenko

We revisit the derivation of Knizhnik-Zamolodchikov equations in the case of nonsemisimple categories of modules of a superalgebra in the case of the generic affne level and representations parameters. A proof of existence of asymptotic solutions and their properties for the superalgebra $gl(1|1)$ gives a basis for the proof of existence associator which sat

D. Bresch, Cosmin Burtea

In this paper, we prove global existence of weak solutions for the stationary compressible Navier-Stokes equations with an anisotropic and nonlocal viscous term in a periodic domain. This gives an answer to an open problem important for applications in geophysics or in microuidics. The main idea is to adapt in a non-trivial way the new idea developped by the

Bibhash Paul, Sammi Kamal, Tabish Qureshi

It is well known that in classical optics, the visibility of interference, in a two-beam light interference, is related to the optical coherence of the two beams. A wave-particle duality relation can be derived using this mutual coherence. The issue of wave-particle duality in classical optics is analyzed here, in the more general context of multipath interf

Chengyuan Wu, Carol Anne Hargreaves

Topological data analysis is a relatively new branch of machine learning that excels in studying high dimensional data, and is theoretically known to be robust against noise. Meanwhile, data objects with mixed numeric and categorical attributes are ubiquitous in real-world applications. However, topological methods are usually applied to point cloud data, an

Chaitanya Patel, Zhouyingcheng Liao, Gerard Pons-Moll

In this paper, we present TailorNet, a neural model which predicts clothing deformation in 3D as a function of three factors: pose, shape and style (garment geometry), while retaining wrinkle detail. This goes beyond prior models, which are either specific to one style and shape, or generalize to different shapes producing smooth results, despite being style

Oisín Creaner, Kevin Nolan, Eugene Hickey, Niall Smith

Studies of the photometric variability of astronomical sources from ground-based telescopes must overcome atmospheric extinction effects. Differential photometry by reference to an ensemble of reference stars which closely match the target in terms of magnitude and colour can mitigate these effects. This Paper describes the design, implementation and operati

Marco Fenucci, Giovanni F. Gronchi

We consider a $(1+N)$-body problem in which one particle has mass $m_0 \gg 1$ and the remaining $N$ have unitary mass. We can assume that the body with larger mass (central body) is at rest at the origin, coinciding with the center of mass of the $N$ bodies with smaller masses (satellites). The interaction force between two particles is defined through a pot

Zohar Komargodski, Shlomo S. Razamat, Orr Sela, Adar Sharon

We show that exactly marginal operators of Supersymmetric Conformal Field Theories (SCFTs) with four supercharges cannot obtain a vacuum expectation value at a generic point on the conformal manifold. Exactly marginal operators are therefore nilpotent in the chiral ring. This allows us to associate an integer to the conformal manifold, which we call the nilp

René van Bevern, Pavel V. Smirnov

The known linear-time kernelizations for $d$-Hitting Set guarantee linear worst-case running times using a quadratic-size data structure (that is not fully initialized). Getting rid of this data structure, we show that problem kernels of asymptotically optimal size $O(k^d)$ for $d$-Hitting Set are computable in linear time and space. Additionally, we experim

Jiyang Xie, Dongliang Chang, Zhanyu Ma, Guoqiang Zhang

Channel attention mechanisms have been commonly applied in many visual tasks for effective performance improvement. It is able to reinforce the informative channels as well as to suppress the useless channels. Recently, different channel attention modules have been proposed and implemented in various ways. Generally speaking, they are mainly based on convolu

Kevin Nolan, Eugene Hickey, Oisín Creaner

We present the software system developed to implement the Locus Algorithm, a novel algorithm designed to maximise the performance of differential photometry systems by optimising the number and quality of reference stars in the Field of View with the target. Firstly, we state the design requirements, constraints and ambitions for the software system required

Bence Horváth, Zsigmond Tarcsay

A remarkable result of Moln\'ar [Proc. Amer. Math. Soc., 126 (1998), 853-861] states that automorphisms of the algebra of operators acting on a separable Hilbert space is stable under "small" perturbations. More precisely, if $\phi,\psi$ are endomorphisms of $\mathcal{B}(\mathcal{H})$ such that $\|\phi(A)-\psi(A)\|<\|A\|$ and $\psi$ is surjective then so is

Stoicho D. Stoichev

In this article we present the largest set of unitals (totally 553) in projective planes of order 16. An open question is what is the number of the known unitals that are non-isomorphic to the reported ones. The results are obtained with a program that implements author&#39;s algorithm.

Oisín Creaner, John Walsh, Kevin Nolan, Eugene Hickey

This paper discusses the requirements for and performance metrics of the the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,779 quasars. Initial operational tests indicated a need for a software system to analyse the data and a High Performance Computing system

Sudan Hansraj, Megandhren Govender, Lushen Moodly, Ksh. Newton Singh

We study the influence of higher curvature effects on stellar structure and conclude that the properties of stars are greatly impacted when such terms are dynamic. In particular the surface gravitational redshift which is connected to the equation of state and also the mass-radius ratio differs greatly from the corresponding values in general relativity as e

Ronen Tamari, Gabriel Stanovsky, Dafna Shahaf, Reut Tsarfaty

Large-scale natural language understanding (NLU) systems have made impressive progress: they can be applied flexibly across a variety of tasks, and employ minimal structural assumptions. However, extensive empirical research has shown this to be a double-edged sword, coming at the cost of shallow understanding: inferior generalization, grounding and explaina

Yun Ye, Ganmei You, Jong-Kae Fwu, Xia Zhu

Structured pruning, especially channel pruning is widely used for the reduced computational cost and the compatibility with off-the-shelf hardware devices. Among existing works, weights are typically removed using a predefined global threshold, or a threshold computed from a predefined metric. The predefined global threshold based designs ignore the variatio

Oisń Creaner, Kevin Nolan, John Walsh, Eugene Hickey

This paper discusses the hardware and software components of the Grid Computing system used to implement the Locus Algorithm to identify optimum pointings for differential photometry of 61,662,376 stars and 23,799 quasars. The scale of the data, together with initial operational assessments demanded a High Performance Computing (HPC) system to complete the d

Andrea Natale, Gabriele Todeschi

In this paper we introduce a new class of finite element discretizations of the quadratic optimal transport problem based on its dynamical formulation. These generalize to the finite element setting the finite difference scheme proposed by Papadakis et al. [SIAM J Imaging Sci, 7(1):212--238,2014]. We solve the discrete problem using a proximal splitting appr

J. Košata, O. Zilberberg, Christian L. Degen, R. Chitra

Recent demonstrations of ultracoherent nanomechanical resonators introduce the prospect of new protocols for solid state sensing applications. Here, we propose to use two coupled ultracoherent resonator modes on a Si$_3$N$_4$ membrane for the detection of small nuclear spin ensembles. To this end, we employ parametric frequency conversion between nondegenera

Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao

Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-sc

Dipendra Prasad, Vinay Wagh

In this paper, using computations done through the LiE software, we compare the tensor product of irreducible selfdual representations of the special linear group with those of classical groups to formulate some conjectures relating the two. In the process a few other phenomenon present themselves which we record as questions. More precisely, under the natur

Jehanzeb Hameed Chaudhry, Luke N. Olson, Peter Sentz

We present a reduced basis (RB) method for parametrized linear elliptic partial differential equations (PDEs) in a least-squares finite element framework. A rigorous and reliable error estimate is developed, and is shown to bound the error with respect to the exact solution of the PDE, in contrast to estimates that measure error with respect to a finite-dime

Robert Denk

In this note, we give an introduction to the concept of maximal $L^p$-regularity as a method to solve nonlinear partial differential equations. We first define maximal regularity for autonomous and non-autonomous problems and describe the connection to Fourier multipliers and $\mathcal R$-boundedness. The abstract results are applied to a large class of para

Z. Yousaf, M. Z. Bhatti, H. Asad

This work is devoted to study the analytical and regular solutions of a particular self-gravitating object (i.e., gravastar) in a particular theory of gravity. We derive the corresponding field equations in the presence of effective energy momentum tensor associated with the perfect fluid configuration of a spherical system. We then describe the mathematical

Marius A. Oancea, Jérémie Joudioux, I. Y. Dodin, D. E. Ruiz

The propagation of electromagnetic waves in vacuum is often described within the geometrical optics approximation, which predicts that wave rays follow null geodesics. However, this model is valid only in the limit of infinitely high frequencies. At large but finite frequencies, diffraction can still be negligible, but the ray dynamics becomes affected by th

Bing-Sui Lu, Khatee Zathul Arifa, Xing Ru Hong

We consider the generic problem of a two-level quantum emitter near a two-dimensional Chern insulator in the dipole approximation, and study how the frequency-dependent response and electronic density of states of the insulator modifies the transition rate of the emitter between the ground and excited levels. To this end, we obtain the full real-frequency be

Kazuaki Toyoura, Takeo Fujii, Kenta Kanamori, Ichiro Takeuchi

We propose a machine-learning-based (ML-based) method for efficiently predicting atomic diffusivity in crystals, in which the potential energy surface (PES) of a diffusion carrier is partially evaluated by first-principles calculations. To preferentially evaluate the region of interest governing the atomic diffusivity, a statistical PES model based on a Gaus

Ki Hyun Tae, Steven Euijong Whang

As machine learning becomes democratized in the era of Software 2.0, a serious bottleneck is acquiring enough data to ensure accurate and fair models. Recent techniques including crowdsourcing provide cost-effective ways to gather such data. However, simply acquiring data as much as possible is not necessarily an effective strategy for optimizing accuracy an

Francis Froborg, Alan R Duffy

The measurement of an annual modulation in the event rate of direct dark matter detection experiments is a powerful tool for dark matter discovery. Indeed, several experiments have already claimed such a discovery in the past decade. While most of them have later revoked their conclusions, and others have found potentially contradictory results, one still st

Dingzhu Wen, Mehdi Bennis, Kaibin Huang

To leverage data and computation capabilities of mobile devices, machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models, resulting in the new paradigm of edge learning. In this paper, we consider the framework of partitioned edge learning for iteratively training a large-scale model using many resource-c

Zhenheng Tang, Shaohuai Shi, Wei Wang, Bo Li

Distributed deep learning (DL) has become prevalent in recent years to reduce training time by leveraging multiple computing devices (e.g., GPUs/TPUs) due to larger models and datasets. However, system scalability is limited by communication becoming the performance bottleneck. Addressing this communication issue has become a prominent research topic. In thi

M. A. Yurischev

The anisotropic Heisenberg two-spin-1/2 model in an inhomogeneous magnetic field with both antisymmetric Dzyaloshinsky-Moriya and symmetric Kaplan-Shekhtman-Entin-Wohlman-Aharony cross interactions is considered at thermal equilibrium. Using a group-theoretical approach, we find fifteen spin Hamiltonians and as many corresponding Gibbs density matrices (quan

Zequn Sun, Qingheng Zhang, Wei Hu, Chengming Wang

Entity alignment seeks to find entities in different knowledge graphs (KGs) that refer to the same real-world object. Recent advancement in KG embedding impels the advent of embedding-based entity alignment, which encodes entities in a continuous embedding space and measures entity similarities based on the learned embeddings. In this paper, we conduct a com

Peter Burton, Kate Juschenko

This paper studies certain aspects of harmonic analysis on nonabelian free groups. We focus on the concept of a positive definite function on the free group and our primary goal is to understand how such functions can be extended from balls of finite radius to the entire group. Previous work showed that such extensions always exist and we study the problem o

Amirhassan Shams-Ansari, Mengjie Yu, Zaijun Chen, Christian Reimer

A high-resolution broad-spectral-bandwidth spectrometer on a chip would create new opportunities for gas-phase molecular fingerprinting, especially in environmental sensing. A resolution high enough to observe transitions at atmospheric pressure and the simultaneous sensitive detection of multiple atoms or molecules are the key challenges. Here, an electro-o

Ahmad Borzou

The stability of isothermal dark matter halos has been widely studied before. In this paper, we investigate the stability of non-isothermal fermionic dark matter halos. We show that in the presence of temperature gradient, the force due to the pressure has both inward and outward components. In some regions of halos, the inward force that provides stability

Liangyun Chen, Tianqi Feng, Yao Ma, Ripan Saha

A Hom-group is the non-associative generalization of a group, whose associativity and unitality are twisted by a compatible bijective map. In this paper, we give some new examples of Hom-groups, and show the first and the second isomorphism fundamental theorems of homomorphisms on Hom-groups. We also introduce the notion of Hom-group action, and as an applic

Thanh-Nhan Nguyen, Minh-Phuong Tran

The aim of this paper is to develop the regularity theory for a weak solution to a class of quasilinear nonhomogeneous elliptic equations, whose prototype is the following mixed Dirichlet $p$-Laplace equation of type \begin{align*} \begin{cases} \mathrm{div}(|\nabla u|^{p-2}\nabla u) &= f+ \ \mathrm{div}(|\mathbf{F}|^{p-2}\mathbf{F}) \qquad \text{in} \ \ Ω,

Arpita Sanyal, Amit Saha, Debasri Saha, Banani Saha

Finding cliques in a graph has several applications for its pattern matching ability. $k$-clique problem, a special case of clique problem, determines whether an arbitrary graph contains a clique of size $k$, has already been addressed in quantum domain. A variant of $k$-clique problem that lists all cliques of size $k$, has also popular modern-day applicati

Xudong Chen

We address an open problem in ensemble control: Whether there exist controllable linear ensemble systems over multi-dimensional parameterization spaces? We provide a negative result: Any real-analytic linear ensemble system is not $\mathrm{L}^p$-controllable, for $2\leq p \leq \infty$, if its parameterization space contains an open set in $\mathbb{R}^d$ for

Joontae Kim

We prove the Hamiltonian unknottedness of real Lagrangian tori in the monotone $S^2\times S^2$, namely any real Lagrangian torus in $S^2\times S^2$ is Hamiltonian isotopic to the Clifford torus $\mathbb{T}_{\text{Clif}}$. The proof is based on a neck-stretching argument, Gromov&#39;s foliation theorem, and the Cieliebak-Schwingenheuer criterion.

Kun Zhou, Xiaoguang Han, Nianjuan Jiang, Kui Jia

Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state-Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps

Chen Cai, Woojin Kim, Facundo Memoli, Yusu Wang

An augmented metric space is a metric space $(X, d_X)$ equipped with a function $f_X: X \to \mathbb{R}$. This type of data arises commonly in practice, e.g, a point cloud $X$ in $\mathbb{R}^d$ where each point $x\in X$ has a density function value $f_X(x)$ associated to it. An augmented metric space $(X, d_X, f_X)$ naturally gives rise to a 2-parameter filtr

Yongtao Li, Lihua Feng

We first give an Oppenheim type determinantal inequality for the Khatri-Rao product of two block positive semidefinite matrices, and then we extend our result to multiple block matrices. As products, the extensions of Oppenheim type inequalities for the Hadamard product are also included.

Yongtao Li

We first present a determinant inequality related to partial traces for positive semidefinite block matrices. Our result extends a result of Lin [Czech. Math. J. 66 (2016)] and improves a result of Kuai [Linear Multilinear Algebra 66 (2018)]. Moreover, we provide a unified treatment of a result of Ando [ILAS Conference (2014)] and a recent result of Li, Liu

Yan Song, Yingfeng Chen, Yujing Hu, Changjie Fan

Exploration is a key problem in reinforcement learning. Recently bonus-based methods have achieved considerable successes in environments where exploration is difficult such as Montezuma&#39;s Revenge, which assign additional bonuses (e.g., intrinsic rewards) to guide the agent to rarely visited states. Since the bonus is calculated according to the novelty

Shi-Zheng Yang, Jian-Hua Gao, Zuo-Tang Liang, Qun Wang

We solve the Wigner equation for massless spin-1/2 charged fermions near global equilibrium. The Wigner function can be obtained order by order in the power expansion of the vorticity and electromagnetic field. The Wigner function has been derived up to the second order from which the non-dissipative charge currents and the stress tensor can be obtained. The

Cheng-Ju Lin, Vladimir Calvera, Timothy H. Hsieh

We find exponentially many exact quantum many-body scar states in a two-dimensional PXP model -- an effective model for a two-dimensional Rydberg atom array in the nearest-neighbor blockade regime. Such scar states are remarkably simple valence bond solids despite being at effectively infinite temperature, and thus strongly violate the eigenstate thermalizat

Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle

Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research. Bayesian approaches including Bayesian Neural Nets (BNNs) do not scale well to modern computer vision tasks, as they are

Feng Qi

In the paper, the author presents a double inequality for completely monotonic degree of a remainder for an asymptotic expansion of the trigamma function. This result partially confirms one in a series of conjectures on completely monotonic degrees of remainders of asymptotic expansions for the logarithm of the gamma function and for polygamma functions.

Vamsi Vegamoor, Shaojie Yan, Sivakumar Rathinam, Swaroop Darbha

In this paper, we re-examine the notion of string stability as it relates to safety by providing an upper bound on the maximum spacing error of any vehicle in a homogeneous platoon in terms of the input of the leading vehicle. We reinforce our previous work on lossy CACC platoons by accommodating for burst-noise behavior in the V2V link. Further, through Mon

M. Sharif, Faisal Javed

This paper examines the stability of thin-shell gravastars in the context of regular spacetimes (Bardeen and Bardeen-de Sitter black holes). We apply cut and paste approach to construct gravastars through the matching of interior non-singular de Sitter geometry with exterior regular black hole. This model contains three regions, i.e., interior, thin-shell an

Wonkyung Jung, Eojin Lee, Sangpyo Kim, Keewoo Lee

Homomorphic Encryption (HE) draws a significant attention as a privacy-preserving way for cloud computing because it allows computation on encrypted messages called ciphertexts. Among numerous HE schemes proposed, HE for Arithmetic of Approximate Numbers (HEAAN) is rapidly gaining popularity across a wide range of applications because it supports messages th

Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer

Let~$\cH$ be a class of boolean functions and consider a {\it composed class} $\cH&#39;$ that is derived from~$\cH$ using some arbitrary aggregation rule (for example, $\cH&#39;$ may be the class of all 3-wise majority-votes of functions in $\cH$). We upper bound the Littlestone dimension of~$\cH&#39;$ in terms of that of~$\cH$. As a corollary, we derive clo

Rui Zhang, Yunxing Zhang, Xuelong Li

Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this paper, two novel unsupervised graph emb

Poliane de Morais Teixeira, Ilya L. Shapiro, Tiago G. Ribeiro

We review and present full detail of the Feynman diagram - based and heat-kernel method - based calculations of the simplest nonlocal form factors in the one-loop contributions of a massive scalar field. The paper has a pedagogical and introductory purposes and is intended to help the reader in better understanding the existing literature on the subject. The

Ryan Shaffer, Eli Megidish, Joseph Broz, Wei-Ting Chen

Analog quantum simulation is expected to be a significant application of near-term quantum devices. Verification of these devices without comparison to known simulation results will be an important task as the system size grows beyond the regime that can be simulated classically. We introduce a set of experimentally-motivated verification protocols for analo

Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie J. Su

Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall privacy bound degrades under composition. To address this question, we introduce a family of analytical and sharp privacy bounds under composition using the Edgeworth expansion in t

Hanchao Yu, Shanhui Sun, Haichao Yu, Xiao Chen

Motion estimation of cardiac MRI videos is crucial for the evaluation of human heart anatomy and function. Recent researches show promising results with deep learning-based methods. In clinical deployment, however, they suffer dramatic performance drops due to mismatched distributions between training and testing datasets, commonly encountered in the clinica

Rei Inoue

We consider an infinite quiver $Q(\mathfrak{g})$ and a family of periodic quivers $Q_m(\mathfrak{g})$ for a finite dimensional simple Lie algebra $\mathfrak{g}$ and $m \in \mathbb{Z}_{>1}$. The quiver $Q(\mathfrak{g})$ is essentially same as what introduced by Hernandez and Leclerc for the quantum affine algebra. We construct the Weyl group $W(\mathfrak{g})$

Adam Kortylewski, Ju He, Qing Liu, Alan Yuille

Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion. We term this architecture

Ignacio Brevis, Ignacio Muga, Kristoffer G. van der Zee

We introduce the concept of data-driven finite element methods. These are finite-element discretizations of partial differential equations (PDEs) that resolve quantities of interest with striking accuracy, regardless of the underlying mesh size. The methods are obtained within a machine-learning framework during which the parameters defining the method are t

Keqin Liu

Based on a class of associative algebras with zero-divisors which are called real-like algebras by us, we introduce a way of defining automatic differentiation and present different ways of doing automatic differentiation to compute the first, the second and the third derivatives of a function exactly and simultaneously.

Remi Tachet, Han Zhao, Yu-Xiang Wang, Geoff Gordon

Adversarial learning has demonstrated good performance in the unsupervised domain adaptation setting, by learning domain-invariant representations. However, recent work has shown limitations of this approach when label distributions differ between the source and target domains. In this paper, we propose a new assumption, generalized label shift ($GLS$), to i

Lucas A. Souza, Mariana Dutra, César H. Lenzi, Odilon Lourenço

In the present work, we investigate the effects of short-range correlations (SRC) on the dimensionless deformability of the binary neutron system related to the GW170817 event. We implemented phenomenological SRC in a relativistic mean-field model in which the bulk parameters, namely, incompressibility ($K_0$), effective nucleon mass ratio ($m^*$), symmetry

V. M. Ngo, N. A. Le-Khac, M. T. Kechadi

In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting data about everything; from information

Mohammed Khalil, Jan Steinhoff, Justin Vines, Alessandra Buonanno

In a compact binary coalescence, the spins of the compact objects can have a significant effect on the orbital motion and gravitational-wave (GW) emission. For generic spin orientations, the orbital plane precesses, leading to characteristic modulations of the GW signal. The observation of precession effects is crucial to discriminate among different binary

Robert J. Colvin, Kirsten Winter

Reasoning about correctness and security of software is increasingly difficult due to the complexity of modern microarchitectural features such as out-of-order execution. A class of security vulnerabilities termed Spectre that exploits side effects of speculative, out-of-order execution was announced in 2018 and has since drawn much attention. In this paper

Ghassan Samara, Munir Al-okour

The Wireless Sensor Network (WSN) has been one of the leading research fields of wireless networks, particularly in recent year. Sensors are randomly positioned in the region, every node senses the surroundings and sends the data collected to the cluster head (CH), which aggregates and transmits obtained information to the Base Station (BS). A non-rechargeab

Umesh Kumar Sharma, Vipin Chandra Dubey

In this paper, we have examined the Sharma-Mittal holographic dark energy model (SMHDE) in the framework of an isotropic and spatially homogeneous flat Friedmann-Robertson-Walker(FRW) Universe by considering different values of parameter $δ$ and $R$, where the infrared cut-off is taken care by the Hubble horizon. We examined the SMHDE model through the analy

Yankun Ren, Jianbin Lin, Siliang Tang, Jun Zhou

Today text classification models have been widely used. However, these classifiers are found to be easily fooled by adversarial examples. Fortunately, standard attacking methods generate adversarial texts in a pair-wise way, that is, an adversarial text can only be created from a real-world text by replacing a few words. In many applications, these texts are

Alessandro Berlati, Oliver Scheel, Luigi Di Stefano, Federico Tombari

Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of special importance, as often multiple futur

Thong Nguyen, Duy Nguyen, Pramod Rao

For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc. In this report,

Skylar Sible, Rodrigo Iza-Teran, Jochen Garcke, Nikola Aulig

Modern product design in the engineering domain is increasingly driven by computational analysis including finite-element based simulation, computational optimization, and modern data analysis techniques such as machine learning. To apply these methods, suitable data representations for components under development as well as for related design criteria have

Vijaya Kumar Sundar, Shreyas Ramakrishna, Zahra Rahiminasab, Arvind Easwaran

Learning Enabled Components (LECs) are widely being used in a variety of perception based autonomy tasks like image segmentation, object detection, end-to-end driving, etc. These components are trained with large image datasets with multimodal factors like weather conditions, time-of-day, traffic-density, etc. The LECs learn from these factors during trainin

A. Nicolaidis

We suggest that the unusual events observed by the ANITA experiment originate from axion particles traversing the Earth. Under the influence of the geomagnetic field, the axion may oscillate into a photon and vice-versa. To amplify the axion transition into photon, we consider that the phenomenon takes place at resonance, where the effective photon mass is e

Shaode Yu, Zhicheng Zhang, Xiaokun Liang, Junjie Wu

More attention is being paid for feature importance ranking (FIR), in particular when thousands of features can be extracted for intelligent diagnosis and personalized medicine. A large number of FIR approaches have been proposed, while few are integrated for comparison and real-life applications. In this study, a matlab toolbox is presented and a total of 3

Alejandro Romanelli

The paper develops the dynamics and thermodynamics of Stirling engines that run with temperature differences below 100 0C. The working gas pressure is analytically expressed using an alternative thermodynamic cycle. The shaft dynamics is studied using its rotational equation of motion. It is found that the initial volumes of the cold and hot working gas play

Jun Zhang, Lihong Wang, Ji Wang

After the sudden outbreak of Coronavirus in Wuhan, continuous and rich data of the epidemic has been made public as the vital fact for decision support in control measures and aggressive implementation of containment strategies and plans. With the further growth and spreading of the virus, future resource allocation and planning under updated strategies and

Shaode Yu, Erlei Zhang, Junjie Wu, Hang Yu

Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of noisy annotation on deep learning-based medical image segmentation. We studied the effects of noisy annotation