February 2022 arXiv papers — page 3

Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol

We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP) iteratively add spikes to the solution then perform a costly (when the number of spikes is large) descent on all parameters at each iteration. In 2D, it was shown that performing

Jiazhu Dai, Siwei Xiong

Capsule networks (CapsNets) are new neural networks that classify images based on the spatial relationships of features. By analyzing the pose of features and their relative positions, it is more capable to recognize images after affine transformation. The stacked capsule autoencoder (SCAE) is a state-of-the-art CapsNet, and achieved unsupervised classificat

Javier de Lucas, Xavier Gràcia, Xavier Rivas, Narciso Román-Roy

A Lie system is a non-autonomous system of first-order ordinary differential equations describing the integral curves of a non-autonomous vector field taking values in a finite-dimensional real Lie algebra of vector fields, a so-called Vessiot--Guldberg Lie algebra. In this work, multisymplectic structures are applied to the study of the reduction of Lie sys

Vasileios E. Papageorgiou, Thomas Zegkos, Georgios Efthimiadis, George Tsaklidis

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited heart muscle disease that appears between the second and forth decade of a patient's life, being responsible for 20% of sudden cardiac deaths before the age of 35. The effective and punctual diagnosis of this disease based on Electrocardiograms (ECGs) could have a vital role in reduci

Jérôme Bolte, Tam Le, Edouard Pauwels

Risk minimization for nonsmooth nonconvex problems naturally leads to first-order sampling or, by an abuse of terminology, to stochastic subgradient descent. We establish the convergence of this method in the path-differentiable case and describe more precise results under additional geometric assumptions. We recover and improve results from Ermoliev and Nor

Chengye Yu, Deyou Chen, Chuanhong Gao

In this paper, we investigate the influence of the angular momentum of a charged particle around non-extremal and extremal Einstein-Maxwell-Dilaton-Axion black holes on the Lyapunov exponent. The angular momentum's ranges and spatial regions where the bound of the exponent is violated are found for certain values of the rotation parameter and dilatonic c

Frank Wagner

A sharply 2-transitive permutation group of characteristic 0 whose point stabiliser has an abelian subgroup of finite index splits. More generally, a near-domain of characteristic 0 with a multiplicative subgroup of finite index avoiding all multipliers $d_{a,b}$ must be a near-field. In particular this answers question 12.48 b) of the Kourovka Notebook in c

Gaspard Beugnot, Julien Mairal, Alessandro Rudi

This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a phenomenon can be precisely characterized in the context of kernel methods, even though the resulting optimization pr

Manuel Ratz, Domenico Fiorini, Alessia Simonini, Christian Cierpka

We investigate the behaviour of accelerating contact lines in an unsteady quasi-capillary channel flow. The configuration consists of a liquid column that moves along a vertical 2D channel, open to the atmosphere and driven by a controlled pressure head. Both advancing and receding contact lines were analyzed to test the validity of classic models for dynami

Davide Gambino, Oleksandr I. Malyi, Zhi Wang, Björn Alling

Describing the (a) electronic and magnetic properties (EMP) of antiferromagnetic or paramagnetic phases of compounds generally requires the knowledge of (b) the spin configurations and lattice structure (SCLS) of such phases at a given temperature. Indeed, studying the coupling between (a) and (b) has been an outstanding challenge in the theory of magnetism.

Maxime Savoy

We introduce a formalism based on a combinatorial notion of cell complex subject to an inclusion-reversing duality operation. Our main goal is to open the way for a functorial definition of field theories in a context where no manifold or topological structure is assumed. This is achieved via a discrete notion of cobordism for which a composition operation i

Francesco Quinzan, Rajiv Khanna, Moshik Hershcovitch, Sarel Cohen

We study the fundamental problem of selecting optimal features for model construction. This problem is computationally challenging on large datasets, even with the use of greedy algorithm variants. To address this challenge, we extend the adaptive query model, recently proposed for the greedy forward selection for submodular functions, to the faster paradigm

Seong-Sik Kim, Hyun Min Lee, Bin Zhu

We consider a new mechanism for enhancing the self-scattering and annihilation cross sections for dark matter with multiple components but without a light mediator. The lighter dark matter component plays a role of the $u$-channel pole in the elastic co-scattering for dark matter, leading to a large self-scattering cross section and a Sommerfeld enhancement

Lukas Schmid, Chao Ni, Yuliang Zhong, Roland Siegwart

Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to directly learn the underlying distribution of informative views based on the spatial context in the robot's map. We further explore a variety of method

Yuhan Huang, Qingyu Li, Xiaokai Hou, Rebing Wu

Variational quantum algorithms (VQAs) are promising methods to demonstrate quantum advantage on near-term devices as the required resources are divided between a quantum simulator and a classical optimizer. As such, designing a VQA which is resource-efficient and robust against noise is a key factor to achieve potential advantage with the existing noisy quan

Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer

Adaptive defenses, which optimize at test time, promise to improve adversarial robustness. We categorize such adaptive test-time defenses, explain their potential benefits and drawbacks, and evaluate a representative variety of the latest adaptive defenses for image classification. Unfortunately, none significantly improve upon static defenses when subjected

Andrea Celli, Matteo Castiglioni, Christian Kroer

We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints. By casting the learning process over a suitably defined space of strategy mixtures, we recover strong duality on a Lagrangian relaxation of the underlying optimization problem, even for general settin

Guohang Yan, Feiyu He, Chunlei Shi, Xinyu Cai

Sensor-based environmental perception is a crucial step for autonomous driving systems, for which an accurate calibration between multiple sensors plays a critical role. For the calibration of LiDAR and camera, the existing method is generally to calibrate the intrinsic of the camera first and then calibrate the extrinsic of the LiDAR and camera. If the came

Jarle Sogn, Stefan Takacs

We are interested in a fast solver for the Stokes equations, discretized with multi-patch Isogeometric Analysis. In the last years, several inf-sup stable discretizations for the Stokes problem have been proposed, often the analysis was restricted to single-patch domains. We focus on one of the simplest approaches, the isogeometric Taylor--Hood element. We s

Franco Giovenzana, Luca Giovenzana, Claudio Onorati

We extend classical results of Perego and Rapagnetta on moduli spaces of sheaves of type OG10 to moduli spaces of Bridgeland semistable objects on the Kuznetsov component of a cubic fourfold. In particular, we determine the period of this class of varieties and use it to understand when they become birational to moduli spaces of sheaves on a K3 surface.

Fei Gao, Masatoshi Yamada

We show a possible way to build the AdS/CFT correspondence starting from the quantum field theory side based on renormalization group approach. An extra dimension is naturally introduced in our scheme as the renomalization scale. The holographic wave equations are derived, with the potential term being determined by the QFT properties. We discover that only

Wanying Ge, Massimiliano Todisco, Nicholas Evans

Despite several years of research in deepfake and spoofing detection for automatic speaker verification, little is known about the artefacts that classifiers use to distinguish between bona fide and spoofed utterances. An understanding of these is crucial to the design of trustworthy, explainable solutions. In this paper we report an extension of our previou

Congpei An, Hao-Ning Wu

This paper investigates the role of quadrature exactness in the approximation scheme of hyperinterpolation. Constructing a hyperinterpolant of degree $n$ requires a positive-weight quadrature rule with exactness degree $2n$. We examine the behavior of such approximation when the required exactness degree $2n$ is relaxed to $n+k$ with $0<k\leq n$. Aided by th

Paolo Onorati, Brunero Liseo

We discuss a Bayesian hierarchical copula model for clusters of financial time series. A similar approach has been developed in recent paper. However, the prior distributions proposed there do not always provide a proper posterior. In order to circumvent the problem, we adopt a proper global-local shrinkage prior, which is also able to account for potential

Xiuquan Du, Kunpeng Ma, Yuhui Song

Automatic and accurate lesion segmentation is critical for clinically estimating the lesion statuses of stroke diseases and developing appropriate diagnostic systems. Although existing methods have achieved remarkable results, further adoption of the models is hindered by: (1) inter-class indistinction, the normal brain tissue resembles the lesion in appeara

Marian Petrica, Ionel Popescu

In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between the real total number of infected and the number of infected that were documented in the official statistics.

Sean Kauffman, Martin Zimmermann

Nfer is a rule-based language for abstracting event streams into a hierarchy of intervals with data. Nfer has multiple implementations and has been applied in the analysis of spacecraft telemetry and autonomous vehicle logs. This work provides the first complexity analysis of nfer evaluation, i.e., the problem of deciding whether a given interval is generate

Hannaneh Akrami, Rojin Rezvan, Masoud Seddighin

We study the problem of fairly allocating a set of $m$ indivisible goods to a set of $n$ agents. Envy-freeness up to any good (EFX) criteria -- which requires that no agent prefers the bundle of another agent after removal of any single good -- is known to be a remarkable analogous of envy-freeness when the resource is a set of indivisible goods. In this pap

Wai-Chung Kwan, Hongru Wang, Huimin Wang, Kam-Fai Wong

Dialogue Policy Learning is a key component in a task-oriented dialogue system (TDS) that decides the next action of the system given the dialogue state at each turn. Reinforcement Learning (RL) is commonly chosen to learn the dialogue policy, regarding the user as the environment and the system as the agent. Many benchmark datasets and algorithms have been

Martin Speight, Thomas Winyard, Egor Babaev

Nematic systems are two component superconductors that break rotational symmetry, but exhibit a mixed symmetry that couples spatial rotations and phase difference rotations. We show that a consequence of this induced spatial anisotropy is mixed normal modes, that is the linear response to a small perturbation of the system about its ground state, generally c

Firoozeh Babayekhorasani, Maryam Hosseini, Patrick T. Spicer

Bacterial cellulose biofilms are complex networks of strong interwoven nanofibers that control transport and protect bacterial colonies in the film. Design of diverse applications of bacterial cellulose films also relies on understanding and controlling transport through the fiber mesh, and transport simulations of the films are most accurate when guided by

Yan-Qi Wang, Roman Rausch, Christoph Karrasch, Joel E. Moore

A strongly interacting plasma of linearly dispersing electron and hole excitations in two spatial dimensions (2D), also known as a Dirac fluid, can be captured by relativistic hydrodynamics and shares many universal features with other quantum critical systems. We propose a one-dimensional (1D) model to capture key aspects of the 2D Dirac fluid while includi

Lidia Fantauzzo, Eros Fanì, Debora Caldarola, Antonio Tavera

Semantic Segmentation is essential to make self-driving vehicles autonomous, enabling them to understand their surroundings by assigning individual pixels to known categories. However, it operates on sensible data collected from the users&#39; cars; thus, protecting the clients&#39; privacy becomes a primary concern. For similar reasons, Federated Learning h

Tomer Gafni, Michal Yemini, Kobi Cohen

We consider an extension to the restless multi-armed bandit (RMAB) problem with unknown arm dynamics, where an unknown exogenous global Markov process governs the rewards distribution of each arm. Under each global state, the rewards process of each arm evolves according to an unknown Markovian rule, which is non-identical among different arms. At each time,

Rui Li, Darius Rückert, Yuanhao Wang, Ramzi Idoughi

Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost. While the most recent generation of such methods has made progress on the rendering (inference) times, very little progress has been made on improving the reconstruction (tra

Chulun Zhou, Fandong Meng, Jie Zhou, Min Zhang

Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although many previous studies try to incorporate global information into NMT models, there still exist limitations on how to effectively exploit bidirectional global context. In this paper

Niles Johnson, Donald Yau

We show that the free construction from multicategories to permutative categories is a categorically-enriched non-symmetric multifunctor. Our main result then shows that the induced functor between categories of algebras is an equivalence of homotopy theories. We describe an application to ring categories.

Maximilian Kurjahn, Antaran Deka, Antoine Girot, Leila Abbaspour

Filamentous cyanobacteria are one of the oldest and today still most abundant lifeforms on earth, with manifold implications in ecology and economics. Their flexible filaments, often several hundred cells long, exhibit gliding motility in contact with solid surfaces. The underlying force generating mechanism is not yet understood. Here, we demonstrate that p

Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco, Davide Bacciu

Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is ba

Pipi Hu, Peng Xie, Yi Zhu

Topologically protected wave motion has attracted considerable interest due to its novel properties and potential applications in many different fields. In this work, we study edge modes and traveling edge states via the linear Dirac equations with so-called domain wall masses. The unidirectional edge state provides a heuristic approach to more general trave

Aïssatou Diallo, Johannes Fürnkranz

Entity Set Expansion is an important NLP task that aims at expanding a small set of entities into a larger one with items from a large pool of candidates. In this paper, we propose GausSetExpander, an unsupervised approach based on optimal transport techniques. We propose to re-frame the problem as choosing the entity that best completes the seed set. For th

Ji Xu, Xi-Ruo Zhang, Shuai Zhao

We perform a study on the structure of inverse moment (IM) of quasi distributions, by taking $B$-meson quasi distribution amplitude (quasi-DA) as an example. Based on a one-loop calculation, we derive the renormalization group equation and velocity evolution equation for the first IM of quasi-DA. We find that, in the large velocity limit, the first IM of $B$

David Zwicker

Phase separation is the thermodynamic process that explains how droplets form in multicomponent fluids. These droplets can provide controlled compartments to localize chemical reactions, and reactions can also affect the droplets&#39; dynamics. This review focuses on the tight interplay between phase separation and chemical reactions originating from thermod

Anthony Leverrier, Gilles Zémor

Tanner codes are long error correcting codes obtained from short codes and a graph, with bits on the edges and parity-check constraints from the short codes enforced at the vertices of the graph. Combining good short codes together with a spectral expander graph yields the celebrated expander codes of Sipser and Spielman, which are asymptotically good classi

Matthijs Borst, Martijn Caspers, Mario Klisse, Mateusz Wasilewski

For a real Hilbert space $H_{\mathbb{R}}$ and $-1 < q < 1$ Bozejko and Speicher introduced the C$^\ast$-algebra $A_q(H_{\mathbb{R}})$ and von Neumann algebra $M_q(H_{\mathbb{R}})$ of $q$-Gaussian variables. We prove that if $\dim(H_{\mathbb{R}}) = \infty$ and $-1 < q < 1, q \not = 0$ then $M_q(H_{\mathbb{R}})$ does not have the Akemann-Ostrand property with

Zhixuan Yang, Nicolas Wu

Structured recursion schemes have been widely used in constructing, optimising, and reasoning about programs over inductive and coinductive datatypes. Their plain forms, catamorphisms and anamorphisms, are restricted in expressiveness. Thus many generalisations have been proposed, which further lead to several unifying frameworks of structured recursion sche

Purnaprajna Bangere, Jayan Mukherjee, Debaditya Raychaudhury

Let $\mathcal{E}$ be a vector bundle on a smooth projective variety $X\subseteq\mathbb{P}^N$ that is Ulrich with respect to the hyperplane section $H$. In this article, we study the Koszul property of $\mathcal{E}$, the slope-semistability of the $k$-th iterated syzygy bundle $\mathcal{S}_k(\mathcal{E})$ for all $k\geq 0$ and rationality of moduli spaces of

Siqi Yang, Yao Fu, Minghui Liu, Liang Han

The forward-backward charge asymmetry (AFB) at hadron colliders is sensitive to both the electroweak (EW) symmetry breaking represented by the effective weak mixing angle, and the proton structure information in the initial state modeled by the parton distribution functions (PDFs). Due to their strong correlation, the precisions of the determination on the w

B. F. Zhu, Q. Wang, D. Leykam, H. R. Xue

Single-mode operation is a desirable but elusive property for lasers operating at high pump powers. Typically, single-mode lasing is attainable close to threshold, but increasing the pump power gives rise to multiple lasing peaks due to inter-modal gain competition. We propose a laser with the opposite behavior: multi-mode lasing occurs at low output powers,

Dhanyamol Antony, Sagartanu Pal, R. B. Sandeep, R. Subashini

For a graph property $Π$, Subgraph Complementation to $Π$ is the problem to find whether there is a subset $S$ of vertices of the input graph $G$ such that modifying $G$ by complementing the subgraph induced by $S$ results in a graph satisfying the property $Π$. We prove that the problem of Subgraph Complementation to $T$-free graphs is NP-Complete, for $T$

Mikołaj Frączyk, Sebastian Hurtado, Jean Raimbault

We prove that any arithmetic locally symmetric space is homotopy equivalent to a simplicial complex where the number of simplices is bounded linearly in the volume of the space. This settles a well-known conjecture of Gelander. The main technical ingredient, which is of independent interest, is a strengthened version of the Margulis&#39; collar lemma for ari

Faizuddin Ahmed

In this paper, We analyze a spin-zero relativistic quantum oscillator in the presence of the Aharonov-Bohm (AB) magnetic flux in a space-time background produced by a point-like global monopole (PGM). Afterward, we introduce a static Coulomb-type scalar potential and subsequently with the same type of vector potential in the quantum system. We solve the gene

Rahul Balasubramanian, Lydia Brenner, Carsten Burgard, Wouter Verkerke

With the LHC entering the precision era, focus on interpreting the measurements performed in an effective field theory holds key to testing the Standard Model. An effective field theory provides a well-defined theoretical formalism which extends the Standard Model by introduce new terms with free coefficients that can be measured with experimental data. Cons

Dominik Beese, Begüm Altunbaş, Görkem Güzeler, Steffen Eger

In this paper, we classify scientific articles in the domain of natural language processing (NLP) and machine learning (ML), as core subfields of artificial intelligence (AI), into whether (i) they extend the current state-of-the-art by the introduction of novel techniques which beat existing models or whether (ii) they mainly criticize the existing state-of

Canh Hao Nguyen

We explain Poisson learning on graph-based semi-supervised learning to see if it could avoid the problem of global information loss problem as Laplace-based learning methods on large graphs. From our analysis, Poisson learning is simply Laplace regularization with thresholding, cannot overcome the problem.

Heyang Zhao, Dongruo Zhou, Jiafan He, Quanquan Gu

We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additive noise. We provide a sharp analysis of the classical follow-the-regularized-leader (FTRL) algorithm to cope with the label noise. More specifically, for $σ$-sub-Gaussian label noi

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We report the first measurements of the absolute branching fractions of $D^0\to K_L^0ϕ$, $D^0\to K_L^0η$, $D^0\to K_L^0ω$, and $D^0\to K_L^0η^{\prime}$, obtained by analyzing $2.93\,\rm fb^{-1}$ of $e^+e^-$ collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. Taking the world averages of the branching fractions of $D^0\to K_

Shihang Shen, Serdar Elhatisari, Timo A. Lähde, Dean Lee

The carbon atom provides the backbone for the complex organic chemistry composing the building blocks of life. The physics of the carbon nucleus in its predominant isotope, $^{12}$C, is similarly full of multifaceted complexity. Some nuclear states of $^{12}$C can be preferentially treated as a collection of independent particles held by the mean field of th

Louis H. Kauffman

This paper is dedicated to the memory of Zbigniew Oziewicz, to his generosity, intelligence and intensity in the search that is science and mathematics. The paper begins with a basic construction that produces Clifford algebras inductively, starting with a base algebra A that is associative and has an involution. This construction is an analog of the Cayley-

Tooru Akagi, Kouta Okabe, Takuya Mieno, Yuto Nakashima

A string $w$ is called a minimal absent word (MAW) for another string $T$ if $w$ does not occur (as a substring) in $T$ and any proper substring of $w$ occurs in $T$. State-of-the-art data structures for reporting the set $\mathsf{MAW}(T)$ of MAWs from a given string $T$ of length $n$ require $O(n)$ space, can be built in $O(n)$ time, and can report all MAWs

Keita Nonaka, Kazutaka Yamanouchi, Tomohiro I, Tsuyoshi Okita

In this study, we propose a simple and effective preprocessing method for subword segmentation based on a data compression algorithm. Compression-based subword segmentation has recently attracted significant attention as a preprocessing method for training data in Neural Machine Translation. Among them, BPE/BPE-dropout is one of the fastest and most effectiv

Aoran Xiao, Jiaxing Huang, Dayan Guan, Xiaoqin Zhang

Point cloud data have been widely explored due to its superior accuracy and robustness under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved very impressive success in various applications such as surveillance and autonomous driving. The convergence of point cloud and DNNs has led to many deep point cloud models, largely trai

Chia-Yen Lee, Hsiang-Chin Chien, Ching-Ping Wang, Hong Yen

Colorectal cancer is one of the most common cancers worldwide, so early pathological examination is very important. However, it is time-consuming and labor-intensive to identify the number and type of cells on H&E images in clinical. Therefore, automatic segmentation and classification task and counting the cellular composition of H&E images from pathologica

Siyang Liu, Sahand Sabour, Yinhe Zheng, Pei Ke

Distinct-$n$ score\cite{Li2016} is a widely used automatic metric for evaluating diversity in language generation tasks. However, we observed that the original approach for calculating distinct scores has evident biases that tend to assign higher penalties to longer sequences. We refine the calculation of distinct scores by scaling the number of distinct tok

Zipeng Wang

We study a family of convolution operators whose symbols and kernels have singularity on the light-cone in $\mathbb{R}^{n+1}$. First, we prove a desired ${\bf L}^p\to {\bf L}^q$ norm inequality which has been left open. Moreover, we obtain certain sharp regularity results for the cone multiplier problem of negative order.

Xing Yang, Jianjun Xu

Our research proposes a novel method for reducing the dimensionality of functional data, specifically for the case where the response is a scalar and the predictor is a random function. Our method utilizes distance covariance, and has several advantages over existing methods. Unlike current techniques which require restrictive assumptions such as linear cond

Hiroshi Noguchi, Caterina Tozzi, Marino Arroyo

Bin/Amphiphysin/Rvs superfamily proteins and other curvature-inducing proteins have anisotropic shapes and anisotropically bend biomembrane. Here, we report how the anisotropic proteins bind the membrane tube and are orientationally ordered using mean-field theory including an orientation-dependent excluded volume. The proteins exhibit a second-order or firs

Cheng-Rong Deng

We make an exhaustive investigation on the pentaquark states $qqqc\bar{c}$ ($q=u, d$ and $s$) and discuss the effect of color structures in a multiquark color flux-tube model. We exhibit a novel picture of the structure and properties of the states $P_c$ and $P_{cs}$ observed by the LHCb Collaboration. We can describe the states as the compact pentaquark sta

Yanshi Sun, Zhiguo Ding, Xuchu Dai, Momiao Zhou

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the pr

Ijaz Ahmed, Mashal Shakar, M. U. Ashraf, Jamil Muhammad

Different production processes involving the Higgs boson, such as annihilation and W/Z boson fusion, will be observed in the International Linear Collider (ILC). The ILC operates at a center-of-mass (CM) energy of $\sqrt{s}$ = 200-1000 GeV. The study reveals that the production cross-section can either be enhanced or reduced depending on the CM energy and th

Zhi-Song Liu, Li-Wen Wang, Wan-Chi Siu, Vicky Kalogeiton

Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way to describe the style. More importantly, text can describe implicit abstract styles, like styles of specific ar

Jiachen Li, Chixin Wang, Banban Huang, Zekun Zhou

This manuscript gives a brief description of the algorithm used to participate in CoNIC Challenge 2022. After the baseline was made available, we follow the method in it and replace the ResNet baseline with ConvNeXt one. Moreover, we propose to first convert RGB space to Haematoxylin-Eosin-DAB(HED) space, then use Haematoxylin composition of origin image to

Feng-Li Lin, Avani Patel, Hung-Yi Pu

Light bending by the strong gravity around the black hole will form the so-called black hole shadow, the shape of which can shed light on the structure of the near-horizon geometry to possibly reveal novel physics of strong gravity and black hole. In this work, we adopt both analytical and ray-tracing methods to study the black hole shadow in the presence of

Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang

Multilingual pre-trained language models have shown impressive performance on cross-lingual tasks. It greatly facilitates the applications of natural language processing on low-resource languages. However, there are still some languages that the current multilingual models do not perform well on. In this paper, we propose CINO (Chinese Minority Pre-trained L

Ze-Fang Jiang, Shanshan Cao, Wen-Jing Xing, Xiang-Yu Wu

Heavy quarks are valuable probes of the electromagnetic field and the initial condition of the quark-gluon plasma (QGP) matter produced in high-energy nuclear collisions. Within an improved Langevin model that is coupled to a (3+1)-dimensional viscous hydrodynamic model, we explore the origin of the directed flow coefficient ($v_1$) of heavy mesons and their

Ryuichi Okamoto

We study the transport properties of dilute electrolyte solutions on the basis of the fluctuating hydrodynamic equation, which is a set of nonlinear Langevin equations for the ion densities and flow velocity. The nonlinearity of the Langevin equations generally leads to effective kinetic coefficients for the deterministic dynamics of the average ion densitie

Hongming Nie

Let $K$ be an algebraically closed and complete nonarchimedean field with characteristic $0$ and let $f\in K[z]$ be a polynomial of degree $d\ge 2$. We study the Lyapunov exponent $L(f,μ)$ of $f$ with respect to an $f$-invariant and ergodic Radon probability measure $μ$ on the Berkovich Julia set of $f$ and the lower Lyapunov exponent $L_f^{-}(f(c))$ of $f$

Bo Li, Ting Wang, Peng Yang, Mingsong Chen

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To address these challenges, both academia and industry turn to artificial intelligence technology to realize network intelligenc

Michel Dekking, Mike Keane

We consider in general two-block substitutions and their fixed points. We prove that some of them have a simple structure: their fixed points are morphic sequences. Others are intrinsically more complex, such as the Kolakoski sequence. We prove this for the Thue-Morse sequence in base 3/2.

Yu-Chang Chen, Haitian Xie

Subsidies are commonly used to encourage behaviors that can lead to short- or long-term benefits. Typical examples include subsidized job training programs and provisions of preventive health products, in which both behavioral responses and associated gains can exhibit heterogeneity. This study uses the marginal treatment effect (MTE) framework to study pers

Kevin Han

When estimating treatment effects, the golden standard is to conduct a randomized experiment and then contrast outcomes associated with the treatment group and the control group. However, in many cases, randomized experiments are either conducted with a much smaller scale compared to the size of the target population or accompanied with certain ethical issue

Sandro Donadi, Angelo Bassi

We review seven models, which consistently couple quantum matter and (Newtonian) gravity in a non standard way. For each of them we present the underlying motivations, the main equations and, when available, a comparison with experimental data.

Jiztom Kavalakkatt Francis, Chandan Kumar, Jansel Herrera-Gerena, Kundan Kumar

We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data. We used a conversion of sensors-to-image which enables us to take advantage of Computer Vision architectures and training processes. In addition to this data preparation methodology, we explore the use of state

Lei Chen, Steven M. Anlage

We identify the poles and zeros of the scattering matrix of a simple quantum graph by means of systematic measurement and analysis of Wigner, transmission, and reflection complex time delays. We examine the ring graph because it displays both shape and Feshbach resonances, the latter of which arises from an embedded eigenstate on the real frequency axis. Our

Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang

Subgraph-based graph representation learning (SGRL) has been recently proposed to deal with some fundamental challenges encountered by canonical graph neural networks (GNNs), and has demonstrated advantages in many important data science applications such as link, relation and motif prediction. However, current SGRL approaches suffer from scalability issues

Xingyu Guo, Jiaxing Zhao, Pengfei Zhuang

We study Casimir effect in equilibrium and non-equilibrium photon gas in the frame of quantum kinetic theory for $U(1)$ gauge field. We derive first the transport, constraint and gauge fixing equations for the photon number distribution from Maxwell&#39;s equations, and then calculate the energy variation and Casimir force for a finite system by considering

Geon-Hyeong Kim, Jongmin Lee, Youngsoo Jang, Hongseok Yang

We consider the problem of learning from observation (LfO), in which the agent aims to mimic the expert&#39;s behavior from the state-only demonstrations by experts. We additionally assume that the agent cannot interact with the environment but has access to the action-labeled transition data collected by some agents with unknown qualities. This offline sett

Abhiraj Sharma, Phanish Suryanarayana

We present an accurate and efficient formulation for the calculation of phonons in real-space Kohn-Sham density functional theory. Specifically, employing a local exchange-correlation functional, norm-conserving pseudopotential in the Kleinman-Bylander representation, and local form for the electrostatics, we derive expressions for the dynamical matrix and a

Minoru Matsumoto, Yu Nakayama

We show that the Wess-Zumino action for the spontaneously broken Weyl (or conformal) symmetry, a.k.a dilaton effective action, in even $D$ dimensions can be obtained from the Kaluza-Klein dimensional reduction of the Lovelock action in $D+ε$ dimensions by taking the $ε\to 0$ limit, where the dilaton is identified with the metric in the extra dimension. The c

Jin Zeng, Yang Liu, Gene Cheung, Wei Hu

A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performance; instead, the output converges to an uninformative low-dimensional subspace, where the convergence rate is characterized by the graph spectrum -- this is the known over-smoothi

Shengjing Tian, Jun Liu, Xiuping Liu

Single object tracking in point clouds has been attracting more and more attention owing to the presence of LiDAR sensors in 3D vision. However, the existing methods based on deep neural networks focus mainly on training different models for different categories, which makes them unable to perform well in real-world applications when encountering classes uns

Mehmet Dogan, Marvin L. Cohen

When single-layer h-BN is subjected to a high-energy electron beam, triangular pores with nitrogen edges are formed. Because of the broken sp2 bonds, these pores are known to possess magnetic states. We report on the magnetism and electronic structure of triangular pores as a function of their size. Moreover, in the Bernal-stacked h-BN (AB-h-BN), multilayer

Jong-Ping Hsu, Leonardo Hsu

Yang-Mills gravity is a quantum theory of gravity with translational gauge symmetry that is based on a flat space-time. The universal coupling of all quantum fields to quantum Yang-Mills gravity is based on the replacement of $\partial_μ$ by the translational gauge covariant derivative $(\partial_μ+gϕ_μ^ν\partial_ν)$ in the Lagrangians of non-gravitational f

Yuyao Chen, Yilin Zhu, Wesley A. Britton, Luca Dal Negro

We propose an efficient inverse design approach for multifunctional optical elements based on adaptive deep diffractive neural networks (a-D$^2$NNs). Specifically, we introduce a-D$^2$NNs and design two-layer diffractive devices that can selectively focus incident radiation over two well-separated spectral bands at desired distances. We investigate focusing

Abraham Loeb

I show that the quadrupole of the Cosmic Microwave Background (CMB) evolves more rapidly than previously expected, as a result of the acceleration of the Sun towards the Galactic center. The acceleration, measured most recently by Gaia EDR3, implies a fractional change in the quadrupole of ~10^{-9} per year, an order of magnitude larger than expected from th

Yunhao Du, Zhicheng Zhao, Yang Song, Yanyun Zhao

Recently, Multi-Object Tracking (MOT) has attracted rising attention, and accordingly, remarkable progresses have been achieved. However, the existing methods tend to use various basic models (e.g, detector and embedding model), and different training or inference tricks, etc. As a result, the construction of a good baseline for a fair comparison is essentia

Riccardo Mancini, Srinivas Karthik, Bikash Chandra, Vasilis Mageirakos

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of

Jiajun Li, Hao Luo, Guang Yang, Yiqiang Zhang

Basal-plane dislocations (BPDs) pose a great challenge to the reliability of bipolar power devices based on the 4H silicon carbide (4H-SiC). It is well established that heavy nitrogen (N) doping promotes the conversion of BPDs to threading edge dislocations (TEDs) and improves the reliability of 4H-SiC-based bipolar power devices. However, the interaction be

Zhengwei Bai, Saswat Priyadarshi Nayak, Xuanpeng Zhao, Guoyuan Wu

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobilit

Lin Qiu, Lynn Lin, Vernon M. Chinchilli

The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational canonical correlation analysis (DICCA) for multi-view learning. The developed model extends the existing latent variable model for linear CCA to nonlinear models through the use of de