June 2020 arXiv papers — page 17

Raffaele Resta

Some intensive observables of the electronic ground state in condensed matter have a geometrical or even topological nature. In this Review I present the geometrical observables whose expression is known in a full many-body framework, beyond band-structure theory. The formalism allows dealing with the general case of disordered and/or correlated many-electro

D. S. Carman, K. Joo, V. I. Mokeev

Studies of the spectrum of hadrons and their structure in experiments with electromagnetic probes offer unique insight into many facets of the strong interaction in the regime of large quark-gluon running coupling, {\it i.e.} the regime of strong QCD. The experimental program within Hall~B at Jefferson Laboratory based on data acquired with the CLAS spectrom

Tatiana Yakhno, Alexander Pakhomov, Anatoly Sanin, Vyacheslav Kazakov

An original method and a device for obtaining fingerprints of liquids are proposed. According to this method, a fingerprint is a dynamic parameter - an oscillogram of the electrical impedance of a quartz sensor on which a drop of the studied liquid dries. The design of the device and the principle of its operation are described. The method can be used for a

Eugenia Kochubinska

We study some combinatorial properties of partial wreath $k$-th power of the semigroup $\mathcal{IS}_d$. In particular, we calculate its order, the number of idempotents and the number of D-classes.

Sondre Tesdal Galtung, Katrin Grunert

We present two semidiscretizations of the Camassa-Holm equation in periodic domains based on variational formulations and energy conservation. The first is a periodic version of an existing conservative multipeakon method on the real line, for which we propose efficient computation algorithms inspired by works of Camassa and collaborators. The second method,

Yin-Dong Zheng, Zhaoyang Liu, Tong Lu, Limin Wang

The existing action recognition methods are mainly based on clip-level classifiers such as two-stream CNNs or 3D CNNs, which are trained from the randomly selected clips and applied to densely sampled clips during testing. However, this standard setting might be suboptimal for training classifiers and also requires huge computational overhead when deployed i

Eugenia Kochubinska

We study properties of eigenvalues of a matrix associated with a randomly chosen partial automorphism of a regular rooted tree. We show that asymptotically, as the numbers of levels goes to infinity, the fraction of non-zero eigenvalues converges to zero in probability.

Eugenia Kochubinska

We study asymptotics of the spectral measure of a randomly chosen partial automorphism of a rooted tree. To every partial automorphism $x$ we assign its action matrix $A_x$. It is shown that the uniform distribution on eigenvalues of $A_x$ converges weakly in probability to $δ_0$ as $n \to \infty$, where $δ_0$ is the delta measure concentrated at $0$.

Aviad Aberdam, Dror Simon, Michael Elad

Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, we define conditions that are applicable

David Macdougal, Stephanie Williams, Dharmalingam Prabhakaran, Robert I. Bewley

We explore the magnetic excitations of the spin-1/2 triangular antiferromagnet Ba3CoSb2O9 in its 120 degree ordered phase using single-crystal high-resolution inelastic neutron scattering. Sharp magnons with no decay are observed throughout reciprocal space, with a strongly renormalized dispersion and multiple soft modes compared to linear spin wave theory.

Kaide Li, Bingyan Liao, Laifeng Hu, Yaonong Wang

In this report, we describe the technical details of oursubmission to the EPIC-Kitchens Object Detection Challenge.Duck filling and mix-up techniques are firstly introduced to augment the data and significantly improve the robustness of the proposed method. Then we propose GRE-FPN and Hard IoU-imbalance Sampler methods to extract more representative global o

Matthias Weissenbacher

In this work we study type IIB Calabi-Yau orientifold compactifications in the presence of space-time filling D7-branes and O7-planes. In particular, we conclude that $α'^2 g_s$-corrections to their DBI actions lead to a modification of the four-dimensional $\mathcal{N}=1$ Kähler potential and coordinates. We focus on the one-modulus case of the geometri

Eugenia Kochubinska

We classify $\mathcal{R}$- and $\mathcal{L}$-cross-sections of partial wreath product of inverse semigroups. As a corollary, we get the description of $\mathcal{R}$- and $\mathcal{L}$-cross-sections of the semigroupof partial automorphisms of finite regular rooted tree and compute also the number of different $\mathcal{R}$- ($\mathcal{L}$-) cross-sections in

Qiuyue Duan, Qi Yan, Yuqi Huang, Wenxiu Zhang

We proposed a dual-template multi-cycled DNA nanomachine driven by polymerase nicking enzyme with high efficiency. The reaction system simply consists of two templates (T1, T2) and two enzymes (KF polymerase, Nb.BbvCI). The two templates are similar in structure (X-X-Y, Y-Y-C): primer recognition region, primer analogue generation region, output region (3 to

Eugenia Kochubinska

We classify $\mathcal{R}$- and $\mathcal{L}$-cross-sections of wreath products of finite inverse symmetric semigroups $\mathcal{IS}_m \wr_p \mathcal{IS}_n$ up to isomorphism. We show that every isomorphism of $\mathcal{R}$ ($\mathcal{L}$-) cross-sections of $\mathcal{IS}_m \wr_p \mathcal{IS}_n$ is a conjugacy. As an auxiliary result, we get that every isomor

Hee-Seung Moon, Jiwon Seo

Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches b

Xuzhou Zhan

Given a block triangular matrix $M$ over a noncommutative ring with invertible diagonal blocks, this work gives two new representations of its inverse $M^{-1}$. Each block element of $M^{-1}$ is explicitly expressed via a quasideterminant of a submatrix of $M$ with the block Hessenberg type. Accordingly another representation for each inverse block is attain

Denis V. Sosnovsky, Konstantin L. Ivanov

A theoretical approach is proposed to treat the spin dynamics in defect color centers. The method explicitly takes into account the spin dynamics in the ground state and excited state of the defect center as well as spin state dependent transitions involving the ground state and excited state, as well as an additional intermediate state. The proposed theory

A. I. Morosov, A. S. Sigov

We introduce below the concept of defecton, and describe briefly the electron polaron effect, the clustering of defectons, and also the defecton mechanism of superconductivity. It is shown that in the case of high-temperature superconducting metal hydrides, which acquire superconducting properties under pressure of hundreds of gigapascals, this mechanism can

Adam Kortylewski, Qing Liu, Angtian Wang, Yihong Sun

Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neural networks (DCNNs) have only limited robustness to partial occlusion. We overcome these limitations by unifying DCNNs with part-based models into Compositional Convolutional Neura

Mengyue Xu, Mingbo He, Hongguang Zhang, Jian Jian

The coherent transmission technology using digital signal processing and advanced modulation formats, is bringing networks closer to the theoretical capacity limit of optical fibres, the Shannon limit. The in-phase quadrature electro-optic modulator that encodes information on both the amplitude and the phase of light, is one of the underpinning devices for

Huan Ma, Guofa Cai, Yi Fang, Pingping Chen

LoRa is a modulation technology for low power wide area networks (LPWAN) with enormous potential in 5G era. However, the performance of LoRa system deteriorates seriously in fading-channel environments. To tackle this problem, in this paper we introduce multiple-input-multiple-output (MIMO) configuration employing space-time block coding (STBC) schemes into

Lu Shaochuan

By attaching auxiliary event times to the chronologically ordered observations, we formulate the Bayesian multiple changepoint problem of discrete-time observations into that of continuous-time ones. A version of forward-filtering backward-sampling (FFBS) algorithm is proposed for the simulation of changepoints within a collapsed Gibbs sampling scheme. Ideal

J. Fausty, B. Murgas, S. Florez, N. Bozzolo

Grain growth in polycrystals is one of the principal mechanisms that take place during heat treatment of metallic components. This work treats an aspect of the anisotropic grain growth problem. By applying the first principles of thermodynamics and mechanics, an expression for the velocity field of a migrating grain boundary with an inclination dependent ene

Quoc Van Tran, Minh Hoang Trinh, Hyo-Sung Ahn

This article investigates discrete-time matrix-weighted consensus of multi-agent networks over undirected and connected graphs. We first present consensus protocols for the agents in common networks of symmetric matrix weights with possibly different update rates and switching network topologies. A special type of matrix-weighted consensus with non-symmetric

Vetrivel Chelian Thirumavalavan, PGS Velmurugan, Thiruvengadam S J

In this paper, Recofigurable Intelligent Surface (RIS) assisted dual-hop multicast wireless communication network is proposed with two source nodes and two destination nodes. RIS boosts received signal strength through an intelligent software-controlled array of discrete phase-shifting metamaterials. The multicast communication from the source nodes is enabl

Lorenzo Travaglini, Adam Micolich, Claudio Cazorla, Erica Zeglio

The organic electrochemical transistor (OECT) with a conjugated polymer as the active material is the elementary unit of organic bioelectronic devices. Increased functionalities, such as low power consumption, can be achieved by building complementary circuits featuring two or more OECTs. Complementary circuits commonly combine both p- and n-type transistors

Dong-Hee Kim, Changwoo Lee, Ki-Seok Chung

In this paper, we propose a confidence-calibration method for predicting the winner of a famous multiplayer online battle arena (MOBA) game, League of Legends. In MOBA games, the dataset may contain a large amount of input-dependent noise; not all of such noise is observable. Hence, it is desirable to attempt a confidence-calibrated prediction. Unfortunately

Ruizhen Hu, Zihao Yan, Jingwen Zhang, Oliver van Kaick

Humans can predict the functionality of an object even without any surroundings, since their knowledge and experience would allow them to "hallucinate" the interaction or usage scenarios involving the object. We develop predictive and generative deep convolutional neural networks to replicate this feat. Specifically, our work focuses on functionaliti

Tao Zhang, Gennian Ge

Let $\mathcal{H}$ be an $r$-uniform hypergraph. The Turán number $\text{ex}(n,\mathcal{H})$ is the maximum number of edges in an $n$-vertex $\mathcal{H}$-free $r$-uniform hypergraph. The Turán density of $\mathcal{H}$ is defined by \[π(\mathcal{H})=\lim_{n\rightarrow\infty}\frac{\text{ex}(n,\mathcal{H})}{\binom{n}{r}}.\] In this paper, we consider the Turán

Rui Zhao, Kin-Man Lam, Daniel P. K. Lun

Convolutional neural network (CNN)-based image denoising methods have been widely studied recently, because of their high-speed processing capability and good visual quality. However, most of the existing CNN-based denoisers learn the image prior from the spatial domain, and suffer from the problem of spatially variant noise, which limits their performance i

Jaykumar Sheth, Cyrus Miremadi, Amir Dezfouli, Behnam Dezfouli

The broad deployment of 802.11 (a.k.a., WiFi) access points and significant enhancement of the energy efficiency of these wireless transceivers has resulted in increasing interest in building 802.11-based IoT systems. Unfortunately, the main energy efficiency mechanisms of 802.11, namely PSM and APSD, fall short when used in IoT applications. PSM increases l

Sarma Abinash

A partition of $n$ is $l$-regular if none of its parts is divisible by $l$. Let $b_l(n)$ denote the number of $l$-regular partitions of $n$. In this paper, using the theory of Hecke eigenforms explored by J.-P. Serre, we establish exact criteria for the $3$-divisibility of $b_9(n)$ and $b_{27}(n)$.

Xianzhi Li, Ruihui Li, Lei Zhu, Chi-Wing Fu

This paper presents a deep normal filtering network, called DNF-Net, for mesh denoising. To better capture local geometry, our network processes the mesh in terms of local patches extracted from the mesh. Overall, DNF-Net is an end-to-end network that takes patches of facet normals as inputs and directly outputs the corresponding denoised facet normals of th

Chen Liang, Yue Yu, Haoming Jiang, Siawpeng Er

We study the open-domain named entity recognition (NER) problem under distant supervision. The distant supervision, though does not require large amounts of manual annotations, yields highly incomplete and noisy distant labels via external knowledge bases. To address this challenge, we propose a new computational framework -- BOND, which leverages the power

Sijia Chen, Yu Wang, Li Huang, Runzhou Ge

A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons. In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios. Specifically, we integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection. Furthe

Yu Wang, Sijia Chen, Li Huang, Runzhou Ge

This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges. An efficient and pragmatic online tracking-by-detection framework named HorizonMOT is proposed for camera-based 2D tracking in the image space and LiDAR-based 3

Zhuangzhuang Ding, Yihan Hu, Runzhou Ge, Li Huang

In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capabil

Oscar E. González

We give a substantial improvement for the error term in the asymptotic formula for the smallest parts function $\mathrm{spt}(n)$ of Andrews. Our methods depend on an explicit bound for sums of Kloosterman sums of half integral weight on the full modular group.

Chaitanya Mitash, Rahul Shome, Bowen Wen, Abdeslam Boularias

Recent progress in robotic manipulation has dealt with the case of previously unknown objects in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape information to achieve safe execution. This work deals with pick-

Hanjun Dai, Azade Nazi, Yujia Li, Bo Dai

Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However current deep neural methods suffer from limited scalability: for a graph with $n$ nodes and $m$ edges, existing deep neural methods require $Ω(n^2)$ complexity by building up the adjacen

Qingyi Zhan, Jinqiao Duan, Xiaofan Li

This paper proposes a general symplectic Euler scheme for a class of Hamiltonian stochastic differential equations driven by L$\acute{e}$vy noise in the sense of Marcus form. The convergence of the symplectic Euler scheme for this Hamiltonian stochastic differential equations is investigated. Realizable numerical implementation of this scheme is also provide

Abhinaba Sinha, Abhishek Sharma, Pankaj Priyadarshi, Ashwin Tulapurkar

Motivated by the recent prediction of anisotropy in piezoresistance of ballistic graphene along longitudinal and transverse directions, we investigate the angular gauge factor of graphene in the ballistic and diffusive regimes using highly efficient quantum transport models. It is shown that the angular guage factor in both ballistic and diffusive graphene b

Hengtao He, Mengjiao Zhang, Shi Jin, Chao-Kai Wen

Massive multiuser multiple-input multiple-output (MU-MIMO) has been the mainstream technology in fifth-generation wireless systems. To reduce high hardware costs and power consumption in massive MU-MIMO, low-resolution digital-to-analog converters (DAC) for each antenna and radio frequency (RF) chain in downlink transmission is used, which brings challenges

Pankaj Priyadarshi, Bhaskaran Muralidharan

Utilizing the non-coherent quantum transport formalism, we investigate thermoelectric performance across dissipative superlattice configurations in the linear regime of operation. Using the {\it{dissipative}} non-equilibrium Green's function formalism coupled self-consistently with the Poisson's equation, we report an enhanced figure of merit $zT$ in

Wenbo Lin, Yasutomo Ota, Yasuhiko Arakawa, Satoshi Iwamoto

A full Poincaré (FP) beam possesses all possible optical spin states in its cross-section, which constitutes an optical analogue of a skyrmion. Until now, FP beams have been exclusively generated using bulk optics. Here, we propose a generation scheme of an FP beam based on an optical microring cavity. We position two different angular gratings along with ch

Shouhang Li, Ao Wang, Yue Hu, Xiaokun Gu

Metallic transition-metal nitrides (TMNs) are promising conductive ceramics for many applications, whose thermal transport is of great importance in device design. It is found metallic TiN and HfN hold anomalous thermal transport behaviors compared to common metals and nonmetallic TMNs. They have extremely large intrinsic phonon thermal conductivity mainly d

Wei Wang, Huifu Xu, Tiejun Ma

When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise. Kratscher et al. [1] propose a new framework to examine t

Zhenyu Han

A single-column model (SCM) is constructed in the regional climate model RegCM4. The evolution of a dry convection boundary layer (DCBL) is used to evaluate this SCM and compare four planetary boundary layer (PBL) schemes, the Holtslag-Boville scheme (HB), Yonsei University scheme (YSU), and two University of Washington schemes (UW01, Grenier-Bretherton-McCa

Ferdaus Kawsar, Mohammad Adibuzzaman

The development of an algorithm with related mathematical concepts and supporting hypothesis for detecting changes in system dynamics from time series along with empirical analysis and theoretical justification is presented. For the method, changes in the second largest eigenvalue of Markov Chain (SLEM) or mixing rate, is observed as an indicator of the chan

Jiawei Fan, Lei Xing, Peng Dong, Jiazhou Wang

In this study we performed a feasibility investigation on implementing a fast and accurate dose calculation based on a deep learning technique. A two dimensional (2D) fluence map was first converted into a three dimensional (3D) volume using ray traversal algorithm. A 3D U-Net like deep residual network was then established to learn a mapping between this co

Eugene Gorsky, Tye Lidman, Beibei Liu, Allison H. Moore

We establish some new relationships between Milnor invariants and Heegaard Floer homology. This includes a formula for the Milnor triple linking number from the link Floer complex, detection results for the Whitehead link and Borromean rings, and a structural property of the $d$-invariants of surgeries on certain algebraically split links.

Vanderson Martins Do Rosario, Thais A. Silva Camacho, Otávio O. Napoli, Edson Borin

The use of cloud computational resources has become increasingly important for companies and researchers to access on-demand and at any moment high-performance resources. However, given the wide variety of virtual machine types, network configurations, number of instances, among others, finding the best configuration that reduces costs and resource waste whi

Ke Sun, Zigang Geng, Depu Meng, Bin Xiao

The typical bottom-up human pose estimation framework includes two stages, keypoint detection and grouping. Most existing works focus on developing grouping algorithms, e.g., associative embedding, and pixel-wise keypoint regression that we adopt in our approach. We present several schemes that are rarely or unthoroughly studied before for improving keypoint

Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang

We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problems, the MCFS problem commonly occurs in practical applications but has been rarely studied in previous literature. It brings new challenges of distinguishing between many classes gi

Riza Rae Pineda, Kristofer delas Peñas, Dana Manogan

Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness, reproductive patterns, group decision-

Hyungjin Choi, Umesh Vaidya, Yongxin Chen

We consider a class of nonlinear control synthesis problems where the underlying mathematical models are not explicitly known. We propose a data-driven approach to stabilize the systems when only sample trajectories of the dynamics are accessible. Our method is founded on the density function based almost everywhere stability certificate that is dual to the

José Augusto Stuchi, Levy Boccato, Romis Attux

Machine learning applied to computer vision and signal processing is achieving results comparable to the human brain on specific tasks due to the great improvements brought by the deep neural networks (DNN). The majority of state-of-the-art architectures nowadays are DNN related, but only a few explore the frequency domain to extract useful information and i

Mao-Hua Du, Jiaqiang Yan, Valentino R. Cooper, Markus Eisenbach

MnBi2Te4 and MnBi4Te7 are intrinsic antiferromagnetic topological insulators, offering a promising materials platform for realizing exotic topological quantum states. However, high densities of intrinsic defects in these materials not only cause bulk metallic conductivity, preventing the measurement of quantum transport in surface states, but may also affect

Ahmad Mustafa, Ghassan AlRegib

Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural network to learn a mapping from seismic data to rock properties using well log data as the labels. However, well logs are often very limited in number due to the high cost of drilling

Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib

Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion

Abdelkader Nasreddine Belkacem, Sofia Ouhbi, Abderrahmane Lakas, Elhadj Benkhelifa

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of

Alban Pothérat, Joël Sommeria, René Moreau

This paper presents a model for quasi two-dimensional MHD flows between two planes with small magnetic Reynolds number and constant transverse magnetic field orthogonal to the planes. A method is presented that allows to take 3D effects into account in a 2D equation of motion thanks to a model for the transverse velocity profile. The latter is obtained by us

Hiroaki Nishikawa

In this paper, we resolve the ever-present confusion over the QUICK scheme: it is a second-order scheme or a third-order scheme. The QUICK scheme, as proposed in the original reference [B. P. Leonard, Comput. Methods. Appl. Mech. Eng., 19, (1979), 59-98], is a third-order (not second-order) finite-volume scheme for the integral form of a general nonlinear co

Ganzhao Yuan, Li Shen, Wei-Shi Zheng

Sparse optimization is a central problem in machine learning and computer vision. However, this problem is inherently NP-hard and thus difficult to solve in general. Combinatorial search methods find the global optimal solution but are confined to small-sized problems, while coordinate descent methods are efficient but often suffer from poor local minima. Th

K. Aditya Mohan, Robert M. Panas, Jefferson A. Cuadra

Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast. The effective blur in a radiograph is the combined effect of blur from multiple sources such as the detector panel, X-ray source spot, and system motion. In this paper, we use a systems approach to model the point spread function (PSF) of the effect

César Hernández-Aguayo, Christian Arnold, Baojiu Li, Carlton M. Baugh

We carry out ``full-physics'' hydrodynamical simulations of galaxy formation in the normal-branch Dvali-Gabadadze-Porrati (nDGP) braneworld model using a new modified version of the {\sc Arepo} code and the IllustrisTNG galaxy formation model. We simulate two nDGP models (N5 and N1) which represent, respectively, weak and moderate departures from GR,

Yijiang Pang, Rui Liu

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a heterogeneous team, is widely used for emergent scenarios such as criminal tracking in social security and victim assistance in a natural disaster. These emergent scenarios require a cooperative team to quickly terminate the current task and transit the system to a

Maxime Dupont, Nicolas Laflorencie, Gabriel Lemarié

Building on large-scale quantum Monte Carlo simulations, we investigate the zero-temperature phase diagram of hard-core bosons in a random potential on site-centered Cayley trees with branching number $K=2$. In order to follow how the Bose-Einstein condensate (BEC) is affected by the disorder, we focus on both the zero-momentum density, probing the quantum c

Andrés Collinucci, Roberto Valandro

We explore the Higgs branches of five-dimensional $\mathcal{N}=1$ quiver gauge theories at finite coupling from the paradigm of M-theory on local Calabi-Yau threefolds described as $\mathbb{C}^\ast$-fibrations over local K3's. By properly counting local deformations of singularities, we find results compatible with unitary as opposed to special unitary g

Vincent Guigues

We provide three new proofs of the strong concavity of the dual function of some convex optimization problems. For problems with nonlinear constraints, we show that the the assumption of strong convexity of the objective cannot be weakened to convexity and that the assumption that the gradients of all constraints at the optimal solution are linearly independ

Terry Adams

The notion of slow entropy, both upper and lower slow entropy, was defined by Katok and Thouvenot as a more refined measure of complexity for dynamical systems, than the classical Kolmogorov-Sinai entropy. For any subexponential rate function $a_n(t)$, we prove there exists a generic class of invertible measure preserving systems such that the lower slow ent

Stefano Sarao Mannelli, Eric Vanden-Eijnden, Lenka Zdeborová

We study the dynamics of optimization and the generalization properties of one-hidden layer neural networks with quadratic activation function in the over-parametrized regime where the layer width $m$ is larger than the input dimension $d$. We consider a teacher-student scenario where the teacher has the same structure as the student with a hidden layer of s

ATLAS Collaboration

Differential cross-section measurements are presented for the electroweak production of two jets in association with a $Z$ boson. These measurements are sensitive to the vector-boson fusion production mechanism and provide a fundamental test of the gauge structure of the Standard Model. The analysis is performed using proton-proton collision data collected b

Nicholas Landry, Juan G. Restrepo

The dynamics of network social contagion processes such as opinion formation and epidemic spreading are often mediated by interactions between multiple nodes. Previous results have shown that these higher-order interactions can profoundly modify the dynamics of contagion processes, resulting in bistability, hysteresis, and explosive transitions. In this pape

Nora Brambilla, Hee Sok Chung, Vladyslav Shtabovenko, Antonio Vairo

We present new results on FeynOnium, an ongoing project to develop a general purpose software toolkit for semi-automatic symbolic calculations in nonrelativistic Effective Field Theories (EFTs). Building upon FeynCalc, an existing Mathematica package for symbolic evaluation of Feynman diagrams, we have created a powerful framework for automatizing calculatio

David Gabai

We show how self-referential discs in 4-manifolds lead to the construction of pairs of discs with a common geometrically dual sphere which are homotopic rel $\partial$, concordant and coincide near their boundaries, yet are not properly isotopic. This occurs in manifolds without 2-torsion in their fundamental group, e.g. the boundary connect sum of $S^2\time

Magdalene Ng, Kovila P. L. Coopamootoo, Ehsan Toreini, Mhairi Aitken

FinBots are chatbots built on automated decision technology, aimed to facilitate accessible banking and to support customers in making financial decisions. Chatbots are increasing in prevalence, sometimes even equipped to mimic human social rules, expectations and norms, decreasing the necessity for human-to-human interaction. As banks and financial advisory

Michał Lesiuk, Michał Przybytek, Bogumił Jeziorski

We report theoretical determination of the dipole polarizability of the neon atom, including its frequency dependence. Corrections for the relativistic, quantum electrodynamics, finite nuclear mass, and finite nuclear size effects are taken into account. We obtain the value $α_0=2.66080(36)$ for the static polarizability, and $α_2=2.850(7)$ and $α_4=4.932(14

Oleksiy Roslyak, Godfrey Gumbs, Antonios Balassis, Heba Elsayed

The interaction energy for the indirect-exchange or Ruderman-Kittel-Kasuva-Yosida (RKKY) interaction between magnetic spins localized on lattice sites of the $α$-${\cal T}_3$ model is calculated using linear response theory. In this model, the $\texttt{AB}$-honeycomb lattice structure is supplemented with $\texttt{C}$ atoms at the centers of the hexagonal la

Shreya Singal, Tanishq Goel, Shivang Chopra, Sonika Dahiya

Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attempt to overcome these challenges, we propo

Andreas Bender, David Rügamer, Fabian Scheipl, Bernd Bischl

The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, and that extend to settings with multiple competing events. However, many machine learning methods for survival analysis only consider the standard setting with right-censored data a

Nicholas Edelen, Chao Li

We prove an Allard-type regularity theorem for free-boundary minimal surfaces in Lipschitz domains locally modelled on convex polyhedra. We show that if such a minimal surface is sufficiently close to an appropriate free-boundary plane, then the surface is $C^{1,α}$ graphical over this plane. We apply our theorem to prove partial regularity results for free-

Q. Zhong, A. Hashemi, S. K. Ozdemir, R. El-Ganainy

We investigate spontaneous emission from a quantum emitter located within the mode volume of a microring resonator that features chiral exceptional points. We show that this configuration offers enough degrees of freedom to exhibit a full control to either enhance or suppress the emission process. Particularly, we demonstrate that the Purcell factor can be e

Gustavo Plensack, Paulo Finardi

Documents of any kind present a wide variety of date and address formats, in some cases dates can be written entirely in full or even have different types of separators. The pattern disorder in addresses is even greater due to the greater possibility of interchanging between streets, neighborhoods, cities and states. In the context of natural language proces

Lara Du, Jeffrey C. Lagarias

Let $G_n= \prod_{k=0}^n \binom{n}{k},$ the product of the elements of the $n$-th row of Pascal's triangle. This paper studies the partial factorizations of $G_n$ given by the product $G(n,x)$ of all prime factors $p$ of $G_n$ having $p \le x$, counted with multiplicity. It shows $\log G(n, αn) \sim f_G(α)n^2$ as $n \to \infty$ for a limit function $f_{G}

Ajinkya Borle, Vincent E. Elfving, Samuel J. Lomonaco

The Quantum Approximate Optimization Algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks. Here, we explore using QAOA for Binary Linear Least Squares (BLLS); a problem that can serve as a building block of several other hard problems in linear algebra, such as the Non-negative Binary Matri

Onuttom Narayan, B Sriram Shastry

We present an explicit solution of the eigen-spectrum Toeplitz matrix $C_{ij}= e^{- κ|i-j|}$ with $0\leq i,j \leq N$ and apply it to find analytically the plasma modes of a layered assembly of 2-dimensional electron gas. The solution is found by elementary means that bypass the Wiener-Hopf technique usually used for this class of problems. It rests on the ob

Xiangyi Chen, Zhiwei Steven Wu, Mingyi Hong

Deep learning models are increasingly popular in many machine learning applications where the training data may contain sensitive information. To provide formal and rigorous privacy guarantee, many learning systems now incorporate differential privacy by training their models with (differentially) private SGD. A key step in each private SGD update is gradien

Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Dąbrowski-Tumański

The central challenge in automated synthesis planning is to be able to generate and predict outcomes of a diverse set of chemical reactions. In particular, in many cases, the most likely synthesis pathway cannot be applied due to additional constraints, which requires proposing alternative chemical reactions. With this in mind, we present Molecule Edit Graph

H. W. Diehl

Interacting field theories for systems with a free surface frequently exhibit distinct universality classes of boundary critical behaviors depending on gross surface properties. The boundary condition satisfied by the continuum field theory on some scale may or may not be decisive for the universality class that applies. In many recent papers on boundary fie

Chengcheng Li, Chenchen Ma, Gongjun Xu

Estimation of the large $Q$-matrix in Cognitive Diagnosis Models (CDMs) with many items and latent attributes from observational data has been a huge challenge due to its high computational cost. Borrowing ideas from deep learning literature, we propose to learn the large $Q$-matrix by Restricted Boltzmann Machines (RBMs) to overcome the computational diffic

Siran Li

We obtain a dynamical--topological obstruction for the existence of isometric embedding of a Riemannian manifold-with-boundary $(M,g)$: if the first real homology of $M$ is nontrivial, if the centre of the fundamental group is trivial, and if $M$ is isometrically embedded into a Euclidean space of dimension at least $3$, then the isometric embedding must vio

Takao Inoué

In this paper, we shall give another proof of the faithfulness of Blass translation (for short, $B$-translation) of the propositional fragment $\bf L_1$ of Leśniewski's ontology in the modal logic $\bf K$ \it by means of Hintikka formula\rm . And we extend the result to von Wright-type deontic logics, i.e., ten Smiley-Hanson systems of monadic deontic lo

Ruihan Zhang, Prashan Madumal, Tim Miller, Krista A. Ehinger

Convolutional neural network (CNN) models for computer vision are powerful but lack explainability in their most basic form. This deficiency remains a key challenge when applying CNNs in important domains. Recent work on explanations through feature importance of approximate linear models has moved from input-level features (pixels or segments) to features f

A. B. Boyd, J. P. Crutchfield, M. Gu

Adaptive systems -- such as a biological organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients -- must model the regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machi

Daniel Freeman, Thomas Schlumprecht, Andras Zsak

We formulate general conditions which imply that $L(X,Y)$, the space of operators from a Banach space $X$ to a Banach space $Y$, has $2^{\mathfrak c}$ closed ideals where $\mathfrak c$ is the cardinality of the continuum. These results are applied to classical sequence spaces and Tsirelson type spaces. In particular, we prove that the cardinality of the set

Rishabh Iyer, Ninad Khargonkar, Jeff Bilmes, Himanshu Asnani

Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize indepen

Chenchen Ma, Jimmy de la Torre, Gongjun Xu

A number of parametric and nonparametric methods for estimating cognitive diagnosis models (CDMs) have been developed and applied in a wide range of contexts. However, in the literature, a wide chasm exists between these two families of methods, and their relationship to each other is not well understood. In this paper, we propose a unified estimation framew

Sergi Perez-Castanos, Javier Naranjo-Alcazar, Pedro Zuccarello, Maximo Cobos

Automated audio captioning is machine listening task whose goal is to describe an audio using free text. An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal. This task can be useful in many applications such as automatic content description or machin