December 2020 arXiv papers — page 30

Jayanta K. Bhattacharjee, Arnab K. Ray

We impose a linearized Eulerian perturbation on a steady, shallow, radial outflow of a liquid (water), whose local pressure function includes both the hydrostatic and the Laplace pressure terms. The resulting wave equation bears the form of a hydrodynamic metric. A dispersion relation, extracted from the wave equation, gives an instability due to surface ten

Naeha Sharif, Mohammed Bennamoun, Wei Liu, Syed Afaq Ali Shah

Existing Image Captioning (IC) systems model words as atomic units in captions and are unable to exploit the structural information in the words. This makes representation of rare words very difficult and out-of-vocabulary words impossible. Moreover, to avoid computational complexity, existing IC models operate over a modest sized vocabulary of frequent word

Yaquan Zhang, Qi Wu, Nanbo Peng, Min Dai

The essence of multivariate sequential learning is all about how to extract dependencies in data. These data sets, such as hourly medical records in intensive care units and multi-frequency phonetic time series, often time exhibit not only strong serial dependencies in the individual components (the "marginal" memory) but also non-negligible memories in the

Minghao Du, Shaolan Bi, Xianfei Zhang, Yaguang Li

Identifying the angular degrees $l$ of oscillation modes is essential for asteroseismology and depends on visual tagging before fitting power spectra in a so-called peakbagging analysis. In oscillating subgiants, radial ($l$= 0) mode frequencies distributed linearly in frequency, while non-radial ($l$ >= 1) modes are p-g mixed modes that having a complex dis

Henry Setiyanto, Ferizal Ferizal, Vienna Saraswaty, Ria Sri Rahayu

Rhodamine B is a synthetic dye used for coloring textiles, paper and ceramics. In addition, Rhodamine B is also often used for coloring ingredients in food ingredients such as crackers, syrups, candy, cakes, and is often used for coloring lipsticks. The accumulation of Rhodamine B in the body can cause liver, kidney and lymph damage. In this study, a modifie

Pengdi Huang, Liqiang Lin, Fuyou Xue, Kai Xu

Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdorff distance as a shape-aware distance measure for calculating point convolutional responses. The technique we present, coined Hausdorff Point Convolution (HPC), is shape-aware. We

Task Force on Best Practices for Software Registries, :, Alain Monteil, Alejandra Gonzalez-Beltran

Scientific software registries and repositories serve various roles in their respective disciplines. These resources improve software discoverability and research transparency, provide information for software citations, and foster preservation of computational methods that might otherwise be lost over time, thereby supporting research reproducibility and re

Peter Y. H. Pang, Yifu Wang, Jingxue Yin

The chemotaxis--Navier--Stokes system \begin{equation*}\label{0.1} \left\{\begin{array}{ll} n_t+u\cdot \nabla n=\triangle n-\chi\nabla\cdotp \left(\displaystyle\frac n {c}\nabla c\right)+n(r-\mu n), c_t+u\cdot \nabla c=\triangle c-nc, u_t+ (u\cdot \nabla) u=\Delta u+\nabla P+n\nabla\phi, \nabla\cdot u=0, \end{array}\right. \end{equation*} is considered in a

Ashok Cutkosky, Abhimanyu Das, Manish Purohit

We provide a simple method to combine stochastic bandit algorithms. Our approach is based on a "meta-UCB" procedure that treats each of $N$ individual bandit algorithms as arms in a higher-level $N$-armed bandit problem that we solve with a variant of the classic UCB algorithm. Our final regret depends only on the regret of the base algorithm with the best r

Abhilash Singh, Sandeep Sharma, Jitenda Singh

In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the cons

Daniela Vianna, Amélie Marian

Personal digital traces are constantly produced by connected devices, internet services and interactions. These digital traces are typically small, heterogeneous and stored in various locations in the cloud or on local devices, making it a challenge for users to interact with and search their own data. By adopting a multidimensional data model based on the s

Yonggan Fu, Haoran You, Yang Zhao, Yue Wang

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such systems remains a challenge due to the limited resources available at the edge and the required massive training costs for state-of-the-art (SOTA) DNNs. As reducing precision is o

David Walsh, Alejandro Schuler, Diana Hall, Jon Walsh

Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates via covariate adjustment. Here we go further, utilizing a Bayesian framework that combines prognostic covariate

Ruqi Bai, Saurabh Bagchi, David I. Inouye

Adversarial examples (AEs) are images that can mislead deep neural network (DNN) classifiers via introducing slight perturbations into original images. This security vulnerability has led to vast research in recent years because it can introduce real-world threats into systems that rely on neural networks. Yet, a deep understanding of the characteristics of

Muhammad Waseem, Saeed D. Manshadi

This paper presents a reformulation for the automatic generation control (AGC) in a decomposed convex relaxation algorithm. It finds an optimal solution to the AC optimal power flow (ACOPF) problem that is secure against a large set of contingencies. The original ACOPF problem which represents the system without contingency constraints, is convexified by app

Moustafa Ahmed, Ahmed Bakry, Ahmed Alshahrie, Hamed Dalir

We report on converting the multimode hopping oscillation (MHO) in long-wavelength semiconductor laser into single-mode oscillation (SMO) by applying external optical feedback (OFB). We characterize and compare the noise performance of the laser when supporting SMO and multimode oscillations. The study is based on a modified time-delay multimode rate-equatio

Ariessa Davaindran Lingham, Nelson Tang Kwong Kin, Chen Wan Jing, Chong Heng Loong

Security holds an important role in a software. Most people are not aware of the significance of security in software system and tend to assume that they will be fine without security in their software systems. However, the lack of security features causes to expose all the vulnerabilities possible to the public. This provides opportunities for the attackers

Moustafa Ahmed, Ahmed Bakry, Ahmed Alshahrie, Hamed Dalir

Enhancing the modulation bandwidth (MBW) of semiconductor lasers has been the challenge of research and technology to meet the need of high-speed photonic applications. In this paper, we propose the design of vertical-cavity surface-emitting laser integrated with multiple transverse coupled cavities (MTCCs) as a promising device with ultra-high 3-dB bandwidt

Akifumi Okuno, Keisuke Yano

This paper discusses a design-dependent nature of variance in nonparametric link regression aiming at predicting a mean outcome at a link, i.e., a pair of nodes, based on currently observed data comprising covariates at nodes and outcomes at links.

Dong An, Di Fang, Lin Lin

The accuracy of quantum dynamics simulation is usually measured by the error of the unitary evolution operator in the operator norm, which in turn depends on certain norm of the Hamiltonian. For unbounded operators, after suitable discretization, the norm of the Hamiltonian can be very large, which significantly increases the simulation cost. However, the op

Nikolai V. Ivanov

The present paper is a continuation of author's paper arXiv:1909.00940 [math.AT] devoted to the lemmas of Alexander and Sperner, but is independent from it. We begin by a step back from Alexander and Sperner to Lebesgue work on the invariance of the dimension. In contrast with almost everybody else, Lebesgue worked with cubes rather than with simplices. His

Mengting Xu, Tao Zhang, Zhongnian Li, Daoqiang Zhang

A range of defense methods have been proposed to improve the robustness of neural networks on adversarial examples, among which provable defense methods have been demonstrated to be effective to train neural networks that are certifiably robust to the attacker. However, most of these provable defense methods treat all examples equally during training process

Yunqiu Shao, Bulou Liu, Jiaxin Mao, Yiqun Liu

In this paper, we present our methodologies for tackling the challenges of legal case retrieval and entailment in the Competition on Legal Information Extraction / Entailment 2020 (COLIEE-2020). We participated in the two case law tasks, i.e., the legal case retrieval task and the legal case entailment task. Task 1 (the retrieval task) aims to automatically

Plamen G. Krastev, Kiranjyot Gill, V. Ashley Villar, Edo Berger

One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or rea

Peng Yue

In the past hundred years, chaos has always been a mystery to human beings, including the butterfly effect discovered in 1963 and the dissipative structure theory which won the chemistry Nobel Prize in 1977. So far, there is no quantitative mathematical-physical method to solve and analyze these problems. In this paper, the idea of using field theory to stud

Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni

Multi-agent reinforcement learning (MARL) has been increasingly explored to learn the cooperative policy towards maximizing a certain global reward. Many existing studies take advantage of graph neural networks (GNN) in MARL to propagate critical collaborative information over the interaction graph, built upon inter-connected agents. Nevertheless, the vanill

Christian Nwachioma, Martins Ezuma, Olusiji . O. Medaiyese

We propose a design that uses the principle of chaos for UAV secure communication. A UAV identified as an aerial base station communicates with a ground base station over an RF channel. The communication units have dynamics based on the logistic map. The map is chaotic in the appropriate parameter space. Its states are non-periodic, broadband, and noise-like

Xiang Deng, Zhongfei Zhang

Deep neural networks have been successfully deployed in various domains of artificial intelligence, including computer vision and natural language processing. We observe that the current standard procedure for training DNNs discards all the learned information in the past epochs except the current learned weights. An interesting question is: is this discarde

Friederike Metz, Juan Polo, Natalya Weber, Thomas Busch

Quantum vortices naturally emerge in rotating Bose-Einstein condensates (BECs) and, similarly to their classical counterparts, allow the study of a range of interesting out-of-equilibrium phenomena like turbulence and chaos. However, the study of such phenomena requires to determine the precise location of each vortex within a BEC, which becomes challenging

Sundar Shrestha, Anand Koirala, Maksym Spiryagin, Qing Wu

The surface roughness between the wheel and rail has a huge influence on rolling noise level. The presence of the third body such as frost or grease at wheel-rail interface contributes towards change in adhesion coefficient resulting in the generation of noise at various levels. Therefore, it is possible to estimate adhesion conditions between the wheel and

Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian

The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this paper introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile networks are less powerful in feature re

Gyo Taek Jin, Hwa Jeong Lee

In this article, we give a list of minimal grid diagrams of the 12 crossing prime alternating knots. This is a continuation of the work in https://doi.org/10.1142/S0218216520500765

Song He, Zhenjie Li, Yichao Tang, Qinglin Yang

We introduce and study the Wilson-loop ${\rm d}\log$ representation of certain Feynman integrals for scattering amplitudes in ${\cal N}=4$ SYM and beyond, which makes their evaluation completely straightforward. Such a representation was motivated by the dual Wilson loop picture, and it can also be derived by partial Feynman parametrization of loop integrals

Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng

Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features collaborate in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. High-

Yuri D. Glinka, Junzi Li, Tingchao He, Xiao Wei Sun

Ultrafast carrier dynamics in the topological insulator Bi2Se3 have recently been intensively studied using a variety of techniques. However, we are not aware of any successful experiments exploiting transient absorption (TA) spectroscopy for these purposes. Here we demonstrate that if the ~730 nm wavelength pumping (~1.7 eV photon energy) is applied to ultr

Yonggan Fu, Zhongzhi Yu, Yongan Zhang, Yingyan Celine Lin

AlphaGo's astonishing performance has ignited an explosive interest in developing deep reinforcement learning (DRL) for numerous real-world applications, such as intelligent robotics. However, the often prohibitive complexity of DRL stands at the odds with the required real-time control and constrained resources in many DRL applications, limiting the great p

Hidetsugu Sakaguchi, Yuta Nakao

The SIR model with spatially inhomogeneous infection rate is studied with numerical simulations in one, two, and three dimensions, considering the case that the infection spreads inhomogeneously in densely populated regions or hot spots. We find that the total population of infection decays very slowly in the inhomogeneous systems in some cases, in contrast

Lili Yan

We study inverse boundary problems for first order perturbations of the biharmonic operator on a conformally transversally anisotropic Riemannian manifold of dimension $n \ge 3$. We show that a continuous first order perturbation can be determined uniquely from the knowledge of the set of the Cauchy data on the boundary of the manifold provided that the geod

Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan

Self-supervised representation learning is a critical problem in computer vision, as it provides a way to pretrain feature extractors on large unlabeled datasets that can be used as an initialization for more efficient and effective training on downstream tasks. A promising approach is to use contrastive learning to learn a latent space where features are cl

Eric Han, Ishank Arora, Jonathan Scarlett

Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping

Md Sarowar Morshed, Sabbir Ahmad, Md Noor-E-Alam

Recently proposed adaptive Sketch & Project (SP) methods connect several well-known projection methods such as Randomized Kaczmarz (RK), Randomized Block Kaczmarz (RBK), Motzkin Relaxation (MR), Randomized Coordinate Descent (RCD), Capped Coordinate Descent (CCD), etc. into one framework for solving linear systems. In this work, we first propose a Stochastic

Moustafa Ahmed, Ahmed Bakry, Essam R. Shaaban, Hamed Dalir

In terms of mixing graded TiO2 and SnO2 powders by solid-state reaction method, ITO was prepared. Using electron beam gun technology, ITO films with different thicknesses were prepared. The influence of film thickness on structure, electrical and optical properties was studied. The XRD patterns were utilized to determine the structural parameters (lattice st

Binghui Wang, Jinyuan Jia, Neil Zhenqiang Gong

Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classifica

Anthony D Stephens, David R Walwyn

Sales of new petrol and diesel passenger vehicles may not be permitted in the United Kingdom (UK) post-2030. Should this happen, it is likely that vehicles presently powered by hydrocarbons will be progressively replaced by Battery Electric Vehicles (BEVs). This paper describes the use of mathematical modelling, drawing on real time records of the UK electri

Federico Marocco, Peter R. M. Eisenhardt, John W. Fowler, J. Davy Kirkpatrick

The CatWISE2020 Catalog consists of 1,890,715,640 sources over the entire sky selected from WISE and NEOWISE survey data at 3.4 and 4.6 $\mu$m (W1 and W2) collected from 2010 Jan. 7 to 2018 Dec. 13. This dataset adds two years to that used for the CatWISE Preliminary Catalog (Eisenhardt et al., 2020), bringing the total to six times as many exposures spannin

Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be

Min Qiu, Xiaowei Wu, Alexandre Graell i Amat, Jinhong Yuan

In this paper, we study a class of spatially coupled turbo codes, namely partially information- and partially parity-coupled turbo codes. This class of codes enjoy several advantages such as flexible code rate adjustment by varying the coupling ratio and the encoding and decoding architectures of the underlying component codes can remain unchanged. For this

C. Wu, D. Currie, D. Wellnitz, B. Behr

Lunar laser ranging (LLR) to the Apollo retro-reflectors, which features the most long-lasting experiment in testing General Relativity theories, has remained operational over the past four decades. To date, with significant improvement of ground observatory conditions, the bottleneck of LLR accuracy lies in the retro-reflectors. A new generation of large ap

Yao Li, Yonggang Yang, Chengbing Qin, Yunrui Song

The interaction between light and metal nanoparticles enables investigations of microscopic phenomena on nanometer length and ultrashort time scales, benefiting from strong confinement and enhancement of the optical field. However, the ultrafast dynamics of these nanoparticles are primarily investigated by multiphoton photoluminescence on picoseconds or phot

Jianfeng Wang, Jing Wang, Maurizio Brunetti

The Hoffman program with respect to any real or complex square matrix $M$ associated to a graph $G$ stems from A. J. Hoffman's pioneering work on the limit points for the spectral radius of adjacency matrices of graphs less than $\sqrt{2+\sqrt{5}}$. The program consists of two aspects: finding all the possible limit points of $M$-spectral radii of graphs and

Deepak K. Gupta, Devanshu Arya, Efstratios Gavves

Rotation is among the long prevailing, yet still unresolved, hard challenges encountered in visual object tracking. The existing deep learning-based tracking algorithms use regular CNNs that are inherently translation equivariant, but not designed to tackle rotations. In this paper, we first demonstrate that in the presence of rotation instances in videos, t

Zedong Tang, Fenlong Jiang, Junke Song, Maoguo Gong

Optimizers that further adjust the scale of gradient, such as Adam, Natural Gradient (NG), etc., despite widely concerned and used by the community, are often found poor generalization performance, compared with Stochastic Gradient Descent (SGD). They tend to converge excellently at the beginning of training but are weak at the end. An immediate idea is to c

Clifford Cheung, Zander Moss

We argue that symmetry and unification can emerge as byproducts of certain physical constraints on dynamical scattering. To accomplish this we parameterize a general Lorentz invariant, four-dimensional theory of massless and massive scalar fields coupled via arbitrary local interactions. Assuming perturbative unitarity and an Adler zero condition, we prove t

Zhou Lan

Positive-definite matrix-variate data is becoming popular in computer vision. The computer vision data descriptors in the form of Region Covariance Descriptors (RCD) are positive definite matrices, which extract the key features of the images. The RCDs are extensively used in image set classification. Some classification methods treating RCDs as Wishart dist

Min zhao, Xiuheng Wang, Jie Chen, Wei Chen

Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials and their corresponding abundances. Early solutions to spectral unmixing are performed independently on each pixel. Nowadays, investigating proper priors into the unmixing problem has been popular as it ca

Eva Agapaki, Ioannis Brilakis

The challenge that this paper addresses is how to efficiently minimize the cost and manual labour for automatically generating object oriented geometric Digital Twins (gDTs) of industrial facilities, so that the benefits provide even more value compared to the initial investment to generate these models. Our previous work achieved the current state-of-the-ar

Shiyuan Huang, Jiawei Ma, Guangxing Han, Shih-Fu Chang

We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown ineffective for FSOR problem due to data limitation. Current FSOR methods typically cal

Kanae Hatano, Yoshimichi Ueda

This note is a complement to Pusz--Woronowicz's works on functional calculus for two positive forms from the viewpoint of operator theory. Based on an elementary, self-contained and purely Hilbert space operator explanation of their functional calculus, we show that any operator connection type operations (including any operator perspectives) are captured by

Li Ma

In this paper, we study the sign-changing Kazdan-Warner's problem on two dimensional closed Riemannian manifold with negative Euler number $\chi(M)<0$. We show that once, the direct method on convex sets is used to find a minimizer of the corresponding functional, then there is another solution via a use of the variational method of mountain pass. In conclus

Nathan Bowler, Christian Elbracht, Joshua Erde, J. Pascal Gollin

A graph $G$ is said to be ubiquitous, if every graph $\Gamma$ that contains arbitrarily many disjoint $G$-minors automatically contains infinitely many disjoint $G$-minors. The well-known Ubiquity conjecture of Andreae says that every locally finite graph is ubiquitous. In this paper we show that locally finite graphs admitting a certain type of tree-decompo

Li Ma

In this paper, we study the existence of complete Yamabe metric with zero scalar curvature on an n-dimensional complete Riemannian manifold $(M,g_0)$, $n\geq 3$. Under suitable conditions about the initial metric, we show that there is a global fine solution to the Yamabe flow. The interesting point here is that we have no curvature assumption about the init

Katsuki Kobayashi, Satoshi Tsujimoto

The discrete Toda lattice preserves the eigenvalues of tridiagonal matrices, and convergence of dependent variables to the eigenvalues can be proved under appropriate conditions. We show that the ultradiscrete Toda lattice preserves invariant factors of a certain bidiagonal matrix over a principal ideal domain, and prove convergence of dependent variables to

Li Ma, Vicente Miquel

In this paper, we prove that the infimum of the mean curvature is zero for a translating solitons of hypersurface in $\re^{n+k}$. We give some conditions under which a complete hypersurface translating soliton is stable. We show that if the norm of its mean curvature is less than one, then the weighted volume may have exponent growth. We also study the Diric

Yu-Wen Li, Xinyuan Wu

In the last few decades, numerical simulation for nonlinear oscillators has received a great deal of attention, and many researchers have been concerned with the design and analysis of numerical methods for solving oscillatory problems. In this paper, from the perspective of the continuous finite element method, we propose and analyze new energy-preserving f

Michael Dine, Nicolas Fernandez, Akshay Ghalsasi, Hiren H. Patel

Axions have for some time been considered a plausible candidate for dark matter. They can be produced through misalignment, but it has been argued that when inflation occurs before a Peccei-Quinn transition, appreciable production can result from cosmic strings. This has been the subject of extensive simulations. But there are reasons to be skeptical about t

Yu-Wen Li, Xinyuan Wu

In this paper, combining the ideas of exponential integrators and discrete gradients, we propose and analyze a new structure-preserving exponential scheme for the conservative or dissipative system $\dot{y} = Q(M y + \nabla U (y))$, where $Q$ is a $d\times d$ skew-symmetric or negative semidefinite real matrix, $M$ is a $d\times d$ symmetric real matrix, and

Chengxi Li, Gang Li, Pramod K. Varshney

In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server. Most of the existing DFL schemes are composed of two alternating steps, i.e., model u

Olivier Mailhot, Rafael Najmanovich

Summary: Coarse-grained normal mode analysis (NMA) is a fast computational technique to study the dynamics of biomolecules. Here we present the Najmanovich Research Group Toolkit for Elastic Networks (NRGTEN). NRGTEN is a Python toolkit that implements four different NMA models in addition to popular and novel metrics to benchmark and measure properties from

Patrick Ocheja, Yang Cao, Shiyao Ding, Masatoshi Yoshikawa

How to contain the spread of the COVID-19 virus is a major concern for most countries. As the situation continues to change, various countries are making efforts to reopen their economies by lifting some restrictions and enforcing new measures to prevent the spread. In this work, we review some approaches that have been adopted to contain the COVID-19 virus

D. Arzoumanian, R. Furuya, T. Hasegawa, M. Tahani

[Abridged] Filaments and hubs have received special attention recently thanks to studies showing their role in star formation. While the column density and velocity structures of both filaments and hubs have been studied, their magnetic fields (B-field) are not yet characterized. We aim to understand the role of the B-field in the dynamical evolution of the

Lavanya Umapathy, Gloria Guzman Perez-Carillo, Blair Winegar, Srinivasan Vedantham

The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from

Arthur Blanc-Renaudie

We introduce a new stick-breaking construction for inhomogeneous continuum random trees (ICRT). This new construction allows us to prove the necessary and sufficient condition for compactness conjectured by Aldous, Miermont and Pitman arXiv:math/0401115 by comparison with L\'evy trees. We also compute the fractal dimensions (Minkowski, Packing, Hausdorff).

Jaein Lim, Panagiotis Tsiotras

Both geometric and semantic information of the search space is imperative for a good plan. We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color), and propose a generalized A* to find the shortest path among the set of paths with minimal inclusion of lo

Jinpeng Yang, Haruki Sato, Hibiki Orio, Xianjie Liu

The conduction band structure in methylammonium lead iodide (CH3NH3PbI3) was studied both by angle-resolved two-photon photoemission spectroscopy (AR-2PPE) with low-photon intensity and angle-resolved low-energy inverse photoelectron spectroscopy (AR-LEIPS). Clear energy dispersion of the conduction band along the {\Gamma}M direction was observed by these in

Zachary J. Grant

In this work we consider a mixed precision approach to accelerate the implemetation of multi-stage methods. We show that Runge-Kutta methods can be designed so that certain costly intermediate computations can be performed as a lower-precision computation without adversely impacting the accuracy of the overall solution. In particular, a properly designed Run

Shu-Yu Liu, Shuang-Xing Zhu, Qi-Yi Wu, Chen Zhang

Dirac semimetal PdTe2 single-crystal temperature-dependent ultrafast carrier and phonon dynamics were studied using ultrafast optical pump-probe spectroscopy. Two distinct carrier and coherent phonons relaxation processes were identified in the 5 K - 300 K range. Quantitative analysis revealed a fast relaxation process ({\tau}_f) occurring on a subpicosecond

Samuel Dittmer, Yuval Ishai, Steve Lu, Rafail Ostrovsky

In this work we describe a token-based solution to Contact Tracing via Distributed Point Functions (DPF) and, more generally, Function Secret Sharing (FSS). The key idea behind the solution is that FSS natively supports secure keyword search on raw sets of keywords without a need for processing the keyword sets via a data structure for set membership. Furthe

Zhendong Chu, Jing Ma, Hongning Wang

Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new challenges in learning a high-quality model from the crowdsourced annotations. In this work, we provide a new perspective to decompose annotation noise into common noise and individ

Meng Xin, Benny Davidovitch

We address the fully-developed wrinkle pattern formed upon stretching a Hookean, rectangular-shaped sheet, when the longitudinal tensile load induces transverse compression that far exceeds the stability threshold of a purely planar deformation. At this "far from threshold" parameter regime, which has been the subject of the celebrated Cerda-Mahadevan (CM) m

Jiadong Zhang, Tengbo Lyu, Changchun Yang, Yimeng Yang

Photoacoustic imaging develops very fast in recent years due to its superior performance in many preclinical and clinical applications. However, it is still in a developing stage, and a lot of experiments have to be performed in a simulation setting. To simulate photoacoustic imaging in a computer, k-Wave is currently the most popular MATLAB toolbox. Lots of

Takaaki Nomura, Takashi Shimomura

We consider a model with gauged $L_\mu - L_\tau$ symmetry in which the symmetry is spontaneously broken by a scalar field. The decay of the scalar boson into two new gauge bosons is studied as a direct consequence of the spontaneous symmetry breaking. Then, a possibility of searches for the gauge and scalar bosons through such a decay at the LHC experiment i

Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark

Transformers have been shown to emulate logical deduction over natural language theories (logical rules expressed in natural language), reliably assigning true/false labels to candidate implications. However, their ability to generate implications of a theory has not yet been demonstrated, and methods for reconstructing proofs of answers are imperfect. In th

Samuel Boury, Thomas Peacock, Philippe Odier

In this paper, we present an experimental study of weakly non-linear interaction of axisymmetric internal gravity waves in a resonant cavity, supported by theoretical considerations. Contrary to plane waves in Cartesian coordinates, for which self-interacting terms are null in a linear stratifiation, the non-linear self-interaction of an internal wave mode i

Meng Xin, Benny Davidovitch

The wrinkle pattern exhibited upon stretching a rectangular sheet has attracted considerable interest in the "extreme mechanics" community. Nevertheless, key aspects of this notable phenomenon remain elusive. Specifically -- what is the origin of the compressive stress underlying the instability of the planar state? what is the nature of the ensuing bifurcat

Aldo Pacchiano, Christoph Dann, Claudio Gentile, Peter Bartlett

We propose a simple model selection approach for algorithms in stochastic bandit and reinforcement learning problems. As opposed to prior work that (implicitly) assumes knowledge of the optimal regret, we only require that each base algorithm comes with a candidate regret bound that may or may not hold during all rounds. In each round, our approach plays a b

Jingyi Zhou, Qingfang He, Zhiying Lin

In real applications, generally small data sets can be obtained. At present, most of the practical applications of machine learning use classic models based on big data to solve the problem of small data sets. However, the deep neural network model has complex structure, huge model parameters, and training requires more advanced equipment, which brings certa

Qianzhi Zhang, Zhaoyu Wang, Shanshan Ma, Anmar Arif

This paper proposes a stochastic optimal preparation and resource allocation method for upcoming extreme weather events in distribution systems, which can assist utilities to achieve faster and more efficient post-event restoration. With the objective of maximizing served load and minimizing operation cost, this paper develops a two-stage stochastic mixed-in

Xiaobo Guo, Soroush Vosoughi

The prevalence of state-sponsored propaganda on the Internet has become a cause for concern in the recent years. While much effort has been made to identify state-sponsored Internet propaganda, the problem remains far from being solved because the ambiguous definition of propaganda leads to unreliable data labelling, and the huge amount of potential predicti

Pedro Pereyra

Recently, an approach for metallic superlattices based on the finite periodic systems theory was introduced \cite{Pereyra2020}. Unlike most, if not all, of the published approaches that are valid in the $n \rightarrow \infty $ limit, the finite periodic approach is valid for any natural number $n$ and allows one to determine analytical expressions for scatte

Yang Zhang, Ling-Fang Lin, Adriana Moreo, Gonzalo Alvarez

Using {\it ab initio} density functional theory and single-orbital Hubbard model calculations via the density matrix renormalization group method, we systematically studied the monolayer VOI$_2$ with a $3d^1$ electronic configuration. Our phonon calculations indicate that the orthorhombic $Pmm2$ FE-II phase is the most likely ground state, involving a ferroe

Sean T. Vittadello, Michael P. H. Stumpf

In many scientific and technological contexts we have only a poor understanding of the structure and details of appropriate mathematical models. We often, therefore, need to compare different models. With available data we can use formal statistical model selection to compare and contrast the ability of different mathematical models to describe such data. Th

Zhao Ma, Zhou Zhang, Yue Dai, Yuli Dong

Quantum coherence has wide-ranging applications from quantum thermodynamics to quantum metrology, quantum channel discrimination and even quantum biology. Thus, detecting and quantifying coherence are two fundamental problems in quantum resource theory. Here, we introduce feasible methods to detect and estimate the coherence by constructing coherence witness

Vasanth Sarathy, Daniel Kasenberg, Shivam Goel, Jivko Sinapov

Symbolic planning models allow decision-making agents to sequence actions in arbitrary ways to achieve a variety of goals in dynamic domains. However, they are typically handcrafted and tend to require precise formulations that are not robust to human error. Reinforcement learning (RL) approaches do not require such models, and instead learn domain dynamics

Abhishek Shende, Deepanshu Vasal, Sriram Vishwanath

In this paper, we consider the problem of network design on network games. We study the conditions on the adjacency matrix of the underlying network to design a game such that the Nash equilibrium coincides with the social optimum. We provide the examples for linear quadratic games that satisfy this condition. Furthermore, we identify conditions on propertie

Agustin Castellano, Juan Bazerque, Enrique Mallada

We consider the problem of finding optimal policies for a Markov Decision Process with almost sure constraints on state transitions and action triplets. We define value and action-value functions that satisfy a barrier-based decomposition which allows for the identification of feasible policies independently of the reward process. We prove that, given a poli

Fan Wang, Yongsheng Han, Ziyi He, Dachun Yang

In this article, the authors introduce Besov and Triebel-Lizorkin spaces on spaces of homogeneous type in the sense of Coifman and Weiss, prove that these (in)homogeneous Besov and Triebel-Lizorkin spaces are independent of the choices of both exp-ATIs (or exp-IATIs) and underlying spaces of distributions, and give some basic properties of these spaces. As a

Matthew D. Blair

The Van Vleck formula is a semiclassical approximation to the integral kernel of the propagator associated to a time-dependent Schr\"odinger equation. Under suitable hypotheses, we present a rigorous treatment of this approximation which is valid on "Ehrenfest time scales", i.e. $\hbar$-dependent time intervals which most commonly take the form $|t| \leq c|\

Dario Fuoli, Zhiwu Huang, Danda Pani Paudel, Luc Van Gool

Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain. In practice, these challenges are often coupled with the lack of example pairs, which inhibits the application of supervised learning strategies. To address

Sean Gillen, Katie Byl

In previous work, using a process we call meshing, the reachable state spaces for various continuous and hybrid systems were approximated as a discrete set of states which can then be synthesized into a Markov chain. One of the applications for this approach has been to analyze locomotion policies obtained by reinforcement learning, in a step towards making

Ghazi Felhi, Joseph Le Roux, Djamé Seddah

We present an unsupervised method to obtain disentangled representations of sentences that single out semantic content. Using modified Transformers as building blocks, we train a Variational Autoencoder to translate the sentence to a fixed number of hierarchically structured latent variables. We study the influence of each latent variable in generation on th