April 2022 arXiv papers

Yi Huang, B. I. Shklovskii, M. A. Zudov

Motivated by recent breakthrough in molecular beam epitaxy of GaAs/AlGaAs quantum wells [Y. J. Chung \textit{et al.}, Nature Materials \textbf{20}, 632 (2021)], we examine contributions to mobility and quantum mobility from various scattering mechanisms and their dependencies on the electron density. We find that at lower electron densities, $n_e \lesssim 1

Fangyu Liu, Guy Emerson, Nigel Collier

Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image

Luhao Zhang, Jincheng Yang, Rui Gao

We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution. Assuming an interchangeability principle inherent in existing duality results, our proof only uses one-dimensional convex analysis. Furthermore, we demonstrate

Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd

Influence estimation analyzes how changes to the training data can lead to different model predictions; this analysis can help us better understand these predictions, the models making those predictions, and the data sets they're trained on. However, most influence-estimation techniques are designed for deep learning models with continuous parameters. Gr

Sucheol Shin, Guang Shi, Hyun Woo Cho, D. Thirumalai

The organization of interphase chromosomes in a number of species is starting to emerge thanks to advances in a variety of experimental techniques. However, much less is known about the dynamics, especially in the functional states of chromatin. Some experiments have shown that the motility of individual loci in human interphase chromosome decreases during t

Lang Liu, Carlos Cinelli, Zaid Harchaoui

Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component. We establish non-asymptotic bounds on the excess risk of orthogonal statistical learning methods with a loss function satisfying a self-concordance property. Our bounds improve upon exist

Gaurav Chaudhary, Anton A. Burkov, Olle G. Heinonen

Twisted bilayer graphene (TBG) near "magic angles" has emerged as a rich platform for strongly correlated states of two-dimensional Dirac semimetals. Here we show that twisted bilayers of thin-film magnetic topological insulators (MTI) with large in-plane magnetization can realize flat bands near 2D Dirac nodes. Using a simple model for thin films of

Abu Hasnat Mohammad Rubaiyat, Shiying Li, Xuwang Yin, Mohammad Shifat E Rabbi

This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT). We adopt a transport-based generative model to define the classification problem. We then make use of mathematical properties of the SCDT to render the problem easier in transform domain, and solve for the class of an unknown sample us

A. A. Zheltukhin

Scalar fields in curved backgrounds are assumed to be composite objects. As an example realizing such a possibility we consider a model of the massless tensor field $l_{μν}(x)$ in a 4-dim. background $g_{μν}(x)$ with spontaneously broken Weyl and scale symmetries. It is shown that the potential of $l_{μν}$, represented by a scalar quartic polynomial, has the

Kushal Chawla, Gale M. Lucas, Jonathan May, Jonathan Gratch

Opponent modeling is the task of inferring another party's mental state within the context of social interactions. In a multi-issue negotiation, it involves inferring the relative importance that the opponent assigns to each issue under discussion, which is crucial for finding high-value deals. A practical model for this task needs to infer these priorit

Liviu Aolaritei, Nicolas Lanzetti, Hongruyu Chen, Florian Dörfler

This paper addresses the limitations of standard uncertainty models, e.g., robust (norm-bounded) and stochastic (one fixed distribution, e.g., Gaussian), and proposes to model uncertainty via Optimal Transport (OT) ambiguity sets. These constitute a very rich uncertainty model, which enjoys many desirable geometrical, statistical, and computational propertie

Peter Leviant, Qian Xu, Liang Jiang, Serge Rosenblum

Bosonic qubits encoded in continuous-variable systems provide a promising alternative to two-level qubits for quantum computation and communication. So far, photon loss has been the dominant source of errors in bosonic qubits, but the significant reduction of photon loss in recent bosonic qubit experiments suggests that dephasing errors should also be consid

Yuxi Lu, Tobias Buck, Ivan Minchev, Melissa K. Ness

Recovering the birth radii of observed stars in the Milky Way is one of the ultimate goals of Galactic Archaeology. One method to infer the birth radius and the evolution of the ISM metallicity assumes a linear relation between the ISM metallicity with radius at any given look-back time. Here we test the reliability of this assumption by using 4 zoom-in cosm

Alton Barbehenn, Sihai Dave Zhao

The simultaneous estimation of multiple unknown parameters lies at heart of a broad class of important problems across science and technology. Currently, the state-of-the-art performance in the such problems is achieved by nonparametric empirical Bayes methods. However, these approaches still suffer from two major issues. First, they solve a frequentist prob

Guruprasad Raghavan, Bahey Tharwat, Surya Narayanan Hari, Dhruvil Satani

Transformers have emerged as the state of the art neural network architecture for natural language processing and computer vision. In the foundation model paradigm, large transformer models (BERT, GPT3/4, Bloom, ViT) are pre-trained on self-supervised tasks such as word or image masking, and then, adapted through fine-tuning for downstream user applications

Oleksiy Ostapenko, Timothee Lesort, Pau Rodríguez, Md Rifat Arefin

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the

Mohammad Javad Pour Razzaghi, Seyed Mojtaba Rezaei Sani, Yasin Masoumi, Guoping Huan

The present work studied various models for predicting turbulence in the problem of injecting a fluid microjet into the boundary layer of a turbulent flow. For this purpose, the one-equation Spalart-Allmaras (SA), two-equation k-$ε$ and k-$ω$, multi-equation transition k-kL-$ω$, transition shear stress transport (SST), and Reynolds stress models were used fo

Yi-Chun Hung, Ta-Hsuan Chao, Pojen Yu, Shang-Hua Yang

Terahertz computed tomography (THz CT) has drawn significant attention because of its unique capability to bring multi-dimensional object information from invisible to visible. However, current physics-model-based THz CT modalities present low data use efficiency on time-resolved THz signals and low model fusion extensibility, limiting their application fiel

Chottiwatt Jittprasong

In practically every industry today, artificial intelligence is one of the most effective ways for machines to assist humans. Since its inception, a large number of researchers throughout the globe have been pioneering the application of artificial intelligence in medicine. Although artificial intelligence may seem to be a 21st-century concept, Alan Turing p

Salman Ahamad Khan, Binoy Krishna Patra

We have studied the transport coefficients as a tool to probe the collision integral appeared in the Boltzmann equation. For this purpose, we have estimated the transport coefficients (momentum: \{$η$,$ζ$\}, heat: \{$κ$\}, and charge: \{$σ_{\rm el}$\}) in the kinetic theory with Bhatnagar-Gross-Krook (BGK) and the collision integral in RTA and ask whether we

Daehan Kim, Minseok Seo, Jinsun Park, Dong-Geol Choi

In this paper, we introduce source domain subset sampling (SDSS) as a new perspective of semi-supervised domain adaptation. We propose domain adaptation by sampling and exploiting only a meaningful subset from source data for training. Our key assumption is that the entire source domain data may contain samples that are unhelpful for the adaptation. Therefor

Santiago Capriotti, Viviana Alejandra Díaz, Eduardo García-Toraño Andrés, Tom Mestdag

We discuss Lagrangian and Hamiltonian field theories that are invariant under a symmetry group. We apply the polysymplectic reduction theorem for both types of field equations and we investigate aspects of the corresponding reconstruction process. We identify the polysymplectic structures that lie at the basis of cotangent bundle reduction and Routh reductio

Tianxin Tao, Matthew Wilson, Ruiyu Gou, Michiel van de Panne

Getting up from an arbitrary fallen state is a basic human skill. Existing methods for learning this skill often generate highly dynamic and erratic get-up motions, which do not resemble human get-up strategies, or are based on tracking recorded human get-up motions. In this paper, we present a staged approach using reinforcement learning, without recourse t

Frido Rolloos

Two novel closed-form formulas for the price of barrier options in stochastic volatility models with zero interest rate and dividend yield but nonzero correlation between the asset and its instantaneous volatility are derived. The first is a Hull and White type formula, and the second is a decomposition formula similar in form to the Alòs decomposition for v

Chin-Lun Fu, Zih-Ching Chen, Yun-Ru Lee, Hung-yi Lee

Transformer-based pre-trained models with millions of parameters require large storage. Recent approaches tackle this shortcoming by training adapters, but these approaches still require a relatively large number of parameters. In this study, AdapterBias, a surprisingly simple yet effective adapter architecture, is proposed. AdapterBias adds a token-dependen

Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge

In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic

Fan Yan, Ming Nie, Xinyue Cai, Jianhua Han

We present ONCE-3DLanes, a real-world autonomous driving dataset with lane layout annotation in 3D space. Conventional 2D lane detection from a monocular image yields poor performance of following planning and control tasks in autonomous driving due to the case of uneven road. Predicting the 3D lane layout is thus necessary and enables effective and safe dri

Konstantin Sozykin, Andrei Chertkov, Roman Schutski, Anh-Huy Phan

We present a novel procedure for optimization based on the combination of efficient quantized tensor train representation and a generalized maximum matrix volume principle. We demonstrate the applicability of the new Tensor Train Optimizer (TTOpt) method for various tasks, ranging from minimization of multidimensional functions to reinforcement learning. Our

Lasse Peters, David Fridovich-Keil, Laura Ferranti, Cyrill Stachniss

In multi-agent settings, game theory is a natural framework for describing the strategic interactions of agents whose objectives depend upon one another's behavior. Trajectory games capture these complex effects by design. In competitive settings, this makes them a more faithful interaction model than traditional "predict then plan" approaches. H

Alexey Sholokhov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests). While many commercial products exist,

Ashish Jaiswal, Mohammad Zaki Zadeh, Aref Hebri, Fillia Makedon

Fatigue is a loss in cognitive or physical performance due to physiological factors such as insufficient sleep, long work hours, stress, and physical exertion. It adversely affects the human body and can slow reaction times, reduce attention, and limit short-term memory. Hence, there is a need to monitor a person's state to avoid extreme fatigue conditio

Nikolaos Evangelou, Felix Dietrich, Juan M. Bello-Rivas, Alex Yeh

We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian Dynamics Simulations. We use Diffusion Maps (a manifold learning algorithm) to identify a set of useful latent observables. In this latent space we identify an eSDE

Rafael Gonzalez-Hernandez, Carlos Pinilla, Bernardo Uribe

Band inversion is a known feature in a wide range of topological insulators characterized by a change of orbital type around a high-symmetry point close to the Fermi level. In some cases of band inversion in topological insulators, the existence of quasinodal spheres has been detected, and the change of orbital type is shown to be concentrated along these sp

Shaojie Li, Sheng Ouyang, Yong Liu

The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: \emph{relaxed} RatioCut and \emph{relaxed} NCut. Firstly, we show that their excess risk bounds between the em

Toshiyuki Nakayama, Stefan Tappe

The goal of this paper is to clarify when the solutions to stochastic partial differential equations stay close to a given subset of the state space for starting points which are close as well. This includes results for deterministic partial differential equations. As an example, we will consider the situation where the subset is a finite dimensional submani

Srinivas Arigapudi, Omer Edhan, Yuval Heller, Ziv Hellman

We study games in which the set of strategies is multi-dimensional, and new agents might learn various strategic dimensions from different mentors. We introduce a new family of dynamics, the recombinator dynamics, which is characterised by a single parameter, the recombination rate r in [0,1]. The case of r = 0 coincides with the standard replicator dynamics

Xinyang Wang, Igor A. Shovkovy

Using the Landau-level representation for the imaginary part of the photon polarization tensor, we derive an explicit expression for the dilepton production rate from a hot quark-gluon plasma in a quantizing background magnetic field. We study in detail the dependence of the production rate on the dilepton invariant mass and the transverse momentum at mid-ra

Xinyu Yang, Tilo Burghardt, Majid Mirmehdi

We propose a novel end-to-end curriculum learning approach for sparsely labelled animal datasets leveraging large volumes of unlabelled data to improve supervised species detectors. We exemplify the method in detail on the task of finding great apes in camera trap footage taken in challenging real-world jungle environments. In contrast to previous semi-super

Li Yang, Yan Xu, Chunfeng Yuan, Wei Liu

Visual grounding is a task to locate the target indicated by a natural language expression. Existing methods extend the generic object detection framework to this problem. They base the visual grounding on the features from pre-generated proposals or anchors, and fuse these features with the text embeddings to locate the target mentioned by the text. However

Hongwei Zhang, Shuo Shao, Meixia Tao, Xiaoyan Bi

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observabl

Sanjar Shaymatov, Mubasher Jamil, Kimet Jusufi, Kazuharu Bamba

We study the dynamics of test particles around a magnetized Ernst black hole considering its magnetic field in the environment surrounding the black hole. We show how its magnetic field can influence the dynamics of particles and epicyclic motion around the black hole. Based on the analysis, we find that the radius of the innermost stable circular orbit (ISC

Hung Viet Chu

The goal of this paper is to study the performance of the Thresholding Greedy Algorithm (TGA) when we increase the size of greedy sums by a constant factor $λ\geqslant 1$. We introduce the so-called $λ$-almost greedy and $λ$-partially greedy bases. The case when $λ= 1$ gives us the classical definitions of almost greedy and (strong) partially greedy bases. W

Ivan Vulić, Goran Glavaš, Fangyu Liu, Nigel Collier

Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., LaBSE, xMPNet) via additional fine-tuning or model distillation with parallel data. However, it remains unclear how to best leverage them to represent sub-sentence lexical items (i.e., words and phrases) in cross-lingual lexical tasks

Tan H. Cao, Nilson Chapagain, Haejoon Lee, Phung Ngoc Thi

This paper is devoted to the study of the dynamic optimization of several controlled crowd motion models in the general planar settings, which is an application of a class of optimal control problems involving a general nonconvex sweeping process with perturbations. A set of necessary optimality conditions for such optimal control problems involving the crow

H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené

Multivariate spatio-temporal data refers to multiple measurements taken across space and time. For many analyses, spatial and time components can be separately studied: for example, to explore the temporal trend of one variable for a single spatial location, or to model the spatial distribution of one variable at a given time. However for some studies, it is

Chengyu Wang, Minghui Qiu, Chen Shi, Taolin Zhang

The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP). Yet, it is not easy to obtain high-performing models and deploy them online for industrial practitioners. To bridge this gap, EasyNLP is designed to make it easy to build NLP applications, which supports a comprehensive suite of NLP algorithms. It furt

Cuiying Huo, Dongxiao He, Yawen Li, Di Jin

Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem

Letian Peng, Zuchao Li, Hai Zhao

We report the performance of DeBERTaV3 on CommonsenseQA in this report. We simply formalize the answer selection as a text classification for DeBERTaV3. The strong natural language inference ability of DeBERTaV3 helps its single and ensemble model set the new (w/o external knowledge) state-of-the-art on CommonsenseQA.

William Banks, Igor E. Shparlinski

Motivated by a question of V. Bergelson and F. K. Richter (2017), we obtain asymptotic formulas for the number of relatively prime tuples composed of positive integers $n\le N$ and integer parts of polynomials evaluated at $n$. The error terms in our formulas are of various strengths depending on the Diophantine properties of the leading coefficients of thes

V. V. Mikhailenko, Hae June Lee, V. S. Mikhailenko, M. O. Azarenkov

The kinetic theory of the microinstabilities of a cylindrical plasma, produced by the cylindrical azimuthally symmetric (azimuthal mode number $m_{0}=0$) helicon wave, is developed. This theory is based on the derived linear integral equation for the Fourier-Bessel transform of the electrostatic potential, which accounts for the plasma response on the macros

Kristin Kirchner, Joshua Willems

A new class of fractional-order stochastic evolution equations of the form $(\partial_t + A)^γX(t) = \dot{W}^Q(t)$, $t\in[0,T]$, $γ\in (0,\infty)$, is introduced, where $-A$ generates a $C_0$-semigroup on a separable Hilbert space $H$ and the spatiotemporal driving noise $\dot{W}^Q$ is the formal time derivative of an $H$-valued cylindrical $Q$-Wiener proces

Oya Kawashima, Naho Yanase, Yoshihisa Okitsu, Masafumi Hirahara

Ion sources using electron impact ionization (EI) methods have been widely accepted in mass spectrometry for planetary exploration missions because of their simplicity. Previous space-borne mass spectrometers were primarily designed with the EI method using rhenium tungsten alloy filaments, enabling up to 200 uA emission in typical cases. The emission level

Grigory K. Olkhovikov, Guillermo Badia

In this article we show that bi-intuitionistic predicate logic lacks the Craig Interpolation Property. We proceed by adapting the counterexample given by Mints, Olkhovikov and Urquhart for intuitionistic predicate logic with constant domains (G. Mints, G. K. Olkhovikov and A. Urquhart. Failure of Interpolation in Constant Domain Intuitionistic Logic. Journal

Motoharu Nowada, Yukinaga Miyashita, Noora Partamies, Alexander William Degeling

The ultraviolet imager (UVI) of the Polar spacecraft and an all-sky camera at Longyearbyen contemporaneously detected an auroral vortex structure (so-called "auroral spiral") on 10 January 1997. From space, the auroral spiral was observed as a "small spot" (one of an azimuthally-aligned chain of similar spots) in the poleward region of the ma

Sue Sin Chong

Most of the current inference techniques rely upon Bayesian inference on Probabilistic Graphical Models of observations and do predictions and classification on observations. However, there is very little literature on the mining of relationships between observations and building models of the relationship between sets of observations or the generating conte

Taisuke Shibata

As a refinement of the Weinstein conjecture, it is a natural question whether a Reeb orbit of particular types exists. D. Cristofaro-Gardiner, M. Hutchings and D. Pomerleano showed that every nondegenerate closed contact three manifold with $b_{1}>0$ has at least one positive hyperbolic orbit by directly using the isomorphism between ECH and Seiberg-Witten F

Behnam Pourhassan, Ruben Campos Delgado, Sudhaker Upadhyay, Hoda Farahani

We study the quantum gravitational corrections to the geometry of a four-dimensionalcharged (Reissner-Nordström) Anti de Sitter black hole starting from an effective field theory approach to quantum gravity. We use the expression of the modified horizon radius to compute the quantum corrected Wald entropy, whose expression reproduces the logarithmic behaviou

Yusuke Kobayashi, Ryoga Mahara

We study the problem of fairly allocating a set of indivisible goods to multiple agents and focus on the proportionality, which is one of the classical fairness notions. Since proportional allocations do not always exist when goods are indivisible, approximate concepts of proportionality have been considered in the previous work. Among them, proportionality

Yun Kong, Youlong Wu, Minquan Cheng

This paper considers a hierarchical caching system where a server connects with multiple mirror sites, each connecting with a distinct set of users, and both the mirror sites and users are equipped with caching memories. Although there already exist works studying this setup and proposing coded caching scheme to reduce transmission loads, two main problems a

Yasuhiro Oishi, Hirotsugu Ogi, Satoshi Hagiwara, Minoru Otani

The adsorbed structure of 1-pyrenebutanoic acid succinimidyl ester (PASE) on graphene was investigated based on density functional theory. We found two locally stable structures: a straight structure with the chainlike part of butanoic acid succinimidyl ester (BSE) lying down and a bent structure with the BSE part directed away from graphene, keeping the pyr

Li Duan, Gerardo Argon-Camarasa

Robotic deformable-object manipulation is a challenge in the robotic industry because deformable objects have complicated and various object states. Predicting those object states and updating manipulation planning is time-consuming and computationally expensive. In this paper, we propose learning known configurations of garments to allow a robot to recognis

Sue Sin Chong

Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without ground-truth annotation? This paper will modify the contrastive learning objectives to automatically train a self-complementing ensemble to produce a state-of-the-art prediction on the CIFAR10 and CIFAR100-20 tasks. This paper wil

Alexander Fominyh

The paper considers the problem of constructing program control for an object described by a system with a quasidifferentiable right-hand side. The control aim is to bring the system from a given initial position to a given final state in given finite time. The admissible controls are piecewise continuous vector-functions with values from a parallelepiped. T

Achiya Elyasaf, Tom Yaacov, Gera Weiss

We identify and demonstrate a weakness of Petri Nets (PN) in specifying composite behavior of reactive systems. Specifically, we show how, when specifying multiple requirements in one PN model, modelers are obliged to specify mechanisms for combining these requirements. This yields, in many cases, over-specification and incorrect models. We demonstrate how s

Weirong Xiu, Baozhu Li, Xusheng Du, Zheng Chu

With the rapid growth in the number of devices of the Internet of Things (IoT), the volume and types of stream data are rapidly increasing in the real world. Unfortunately, the stream data has the characteristics of infinite and periodic volatility in the real world, which cause problems with the inefficient stream processing tasks. In this study, we report

Tao Liu, Jie Huang

Recently, the distributed state estimation problem for continuous-time linear systems over jointly connected switching networks was solved. It was shown that the estimation errors will asymptotically converge to the origin by using the generalized Barbalat's Lemma. This paper further studies the same problem with two new features. First, the asymptotic c

Wulian Yun, Mengshi Qi, Chuanming Wang, Huiyuan Fu

Video denoising aims to recover high-quality frames from the noisy video. While most existing approaches adopt convolutional neural networks~(CNNs) to separate the noise from the original visual content, however, CNNs focus on local information and ignore the interactions between long-range regions in the frame. Furthermore, most related works directly take

Alberto Giaretta

Virtual reality (VR) is a multibillionaire market that keeps growing, year after year. As VR is becoming prevalent in households and small businesses, it is critical to address the effects that this technology might have on the privacy and security of its users. In this paper, we explore the state-of-the-art in VR privacy and security, we categorise potentia

Mingshang Hu, Lianzi Jiang, Gechun Liang, Shige Peng

This article establishes a universal robust limit theorem under a sublinear expectation framework. Under moment and consistency conditions, we show that, for $α\in(1,2)$, the i.i.d. sequence \[ \left \{ \left( \frac{1}{\sqrt{n}}\sum_{i=1}^{n}X_{i},\frac{1}{n}\sum _{i=1}^{n}Y_{i},\frac{1}{\sqrt[α]{n}}\sum_{i=1}^{n}Z_{i}\right) \right \} _{n=1}^{\infty} \] con

Souvik Bandyopadhyay, Anatoli Polkovnikov, Amit Dutta

In recent times it has been observed that signatures of equilibrium quantum criticality surprisingly show up in many-body systems which are manifestly far from equilibrium. We explore such scenarios in interacting spin systems subject to a quench and develop a robust method to systematically probe ground state critical physics through nonequilibrium post-que

Mythra Varun Nemallapudi, Atiq Ur Rahman, Vanny Marantha Sihotang, Thien Cong Tran

The distribution of the prompt gamma emissions induced by proton interactions in a target material carries information concerning the proton range and target composition. In order to image prompt gamma we present different approaches. The first approach is based on knife-edge collimator utilized in tandem with a compact PET type detector in order to verify p

Yifan Yan, Xudong Pan, Yining Wang, Mi Zhang

Recently, how to protect the Intellectual Property (IP) of deep neural networks (DNN) becomes a major concern for the AI industry. To combat potential model piracy, recent works explore various watermarking strategies to embed secret identity messages into the prediction behaviors or the internals (e.g., weights and neuron activation) of the target model. Sa

Giuseppe Di Pietra, Chiara Marletto

A general entanglement-based witness of non-classicality has recently been proposed, which can be applied to testing quantum effects in gravity. This witness is based on generating entanglement between two quantum probes via a mediator. In this paper we provide a "temporal" variant of this witness, using a single quantum probe to assess the non-class

Koichi Nagasaki

In this work we consider a spacial kind of spacetime called AdS accelerating black holes. This is a kind of black holes which contain a stringlike singularity along polar axises attached to the black hole and it accelerates the black hole. In these kind of spacetimes the growth of Einstein-Hilbert action is independent of the acceleration as found in the pre

Yanyan Bu, Xiyang Sun, Biye Zhang

We construct effective field theory for SU(2) isospin charge diffusion, based on holographic Schwinger-Keldysh contour arXiv:2008.01269. The holographic model consists of a probe SU(2) gauge field in a doubled Schwarzschild-AdS$_5$ geometry. Accurate to first order in derivative expansion, we analytically compute the effective action up to quartic order in h

Nerea Sebastián, Martin Čopič, Alenka Mertelj

Recent experimental realization of ferroelectric nematic liquid crystalline phases stimulated material development and numerous experimental studies of these new phases, guided by their fundamental and applicative interest. In this Perspective, we give an overview of this emerging field by linking history and theoretical predictions to a general outlook of t

Timur V. Tscherbul, Jun Ye, Ana Maria Rey

We propose a general protocol for on-demand generation of robust entangled states of nuclear and/or electron spins of ultracold $^1Σ$ and $^2Σ$ polar molecules using electric dipolar interactions. By encoding a spin-1/2 degree of freedom in a combined set of spin and rotational molecular levels, we theoretically demonstrate the emergence of effective spin-sp

Michael Christ, Ben Pineau, Mitchell A. Taylor

Examples are constructed of infinite-dimensional subspaces $V\subset L^2(μ)$ with the property that for any $f,g\in V$, if $|f|$ is approximately equal to $|g|$ with respect to the $L^2$ norm, then there exists a unimodular scalar $z$ such that $f$ is approximately equal to $zg$.

Kai Wang, Xiangyu Peng, Shuo Yang, Jianfei Yang

Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and noisy sets. This paradigm, however, is prone to significant degeneration under heavy label noise, as

Xiaosong Jia, Penghao Wu, Li Chen, Yu Liu

Encoding a driving scene into vector representations has been an essential task for autonomous driving that can benefit downstream tasks e.g. trajectory prediction. The driving scene often involves heterogeneous elements such as the different types of objects (agents, lanes, traffic signs) and the semantic relations between objects are rich and diverse. Mean

Huihui Zhu, Liyun Wu, Jianlong Chen

Let $S$ be a $*$-semigroup and let $a,w,v\in S$. The initial goal of this work is to introduce two new classes of generalized inverses, called the $w$-core inverse and the dual $v$-core inverse in $S$. An element $a\in S$ is $w$-core invertible if there exists some $x\in S$ such that $awx^2=x$, $xawa=a$ and $(awx)^*=awx$. Such an $x$ is called a $w$-core inv

Mifta Sintaha, Noor Nashid, Ali Mesbah

Contextual information plays a vital role for software developers when understanding and fixing a bug. Consequently, deep learning-based program repair techniques leverage context for bug fixes. However, existing techniques treat context in an arbitrary manner, by extracting code in close proximity of the buggy statement within the enclosing file, class, or

Yangcheng Gao, Zhao Zhang, Richang Hong, Haijun Zhang

To obtain lower inference latency and less memory footprint of deep neural networks, model quantization has been widely employed in deep model deployment, by converting the floating points to low-precision integers. However, previous methods (such as quantization aware training and post training quantization) require original data for the fine-tuning or cali

Jiao Suo, Yifan Liu, Clio Cheng, Keer Wang

Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc. Lightweight wearable sensors, including inertial sensors, surface electromyography sensors, and flexible strain/pressure, are promising to become the next-ge

Hajime Otsuka, Yutaka Sakamura

We investigate the time evolution of the background spacetime during the moduli stabilization process, which is assumed to occur in the radiation dominated era. The setup is basically the Salam-Sezgin model, but we add a potential term for the dilaton in order to stabilize the moduli completely. We numerically solve the higher-dimensional background field eq

Ya. S. Greenberg, A. G. Moiseev, A. A. Shtygashev

We study the space-time structure of the scattered field induced by the scattering of a narrow single-photon Gaussian pulse on a qubit embedded in 1D open waveguide. For a weak excitation power we obtain explicit analytical expressions for space and time dependence of reflected and transmitted fields which are, in general, are different from plain travelling

Surabhi Gupta, Sudhir Kumar Gupta

Flavour-violating interactions of the stop-quarks are expected to provide an additional few GeV contributions to the Higgs-Boson mass, particularly when mix with scharm-quarks, thereby allowing reduced supersymmetry (SUSY) breaking scale compared to flavour-conserving constrained minimal supersymmetric Standard Model (CMSSM). Inspired by this, we analyse the

Qingliang Fan, Zijian Guo, Ziwei Mei

This paper proposes an overidentifying restriction test for high-dimensional linear instrumental variable models. The novelty of the proposed test is that it allows the number of covariates and instruments to be larger than the sample size. The test is scale-invariant and is robust to heteroskedastic errors. To construct the final test statistic, we first in

Alex Sheng, Shankar Padmanabhan

Recent progress in large-scale language models has enabled breakthroughs in previously intractable computer programming tasks. Prior work in meta-learning and neural architecture search has led to substantial successes across various task domains, spawning myriad approaches for algorithmically optimizing the design and learning dynamics of deep learning mode

Zhijie Deng, Jiaxin Shi, Jun Zhu

Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems. Traditional nonparametric solutions based on the Nystr{ö}m formula suffer from scalability issues. Recent work has resorted to a parametric approach, i.e., training neural networks to approximate the eige

Yelai Feng, Huaixi Wang

Betweenness centrality is essential in complex network analysis; it characterizes the importance of nodes and edges in networks. It is a crucial problem that exactly computes the betweenness centrality in large networks faster, which urgently needs to be solved. We propose a novel algorithm for betweenness centrality based on the parallel computing of adjace

Yuki Adachi, C. S. Lim, Nobuhito Maru

In this paper, we discuss the vacuum structure of the gauge-Higgs unification theory, which is one of the attractive candidates of physics beyond the standard model. This scenario has a remarkable feature, namely it has infinitely degenerate vacua due to the characteristic periodic potential of the Higgs filed, to be identified with the extra space component

Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang

Supervised training of deep neural networks (DNNs) by noisy labels has been studied extensively in image classification but much less in image segmentation. Our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. We address this deficiency in both binary segmentation of biological microscopy images and multi-c

Yongkun Du, Zhineng Chen, Caiyan Jia, Xiaoting Yin

Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient. In this study, we propose a Single Visual model for Scene Text recognition within the patch-wise image tokenization framewor

Libo Zhang, Junyuan Gao, Zhen Xiao, Heng Fan

Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is crucial for animal motion and behavior analysis and has many crucial applications such as biology, ecology and animal conservation. Despite its importance, MAT is largely under-explored compared to other MOT problems such as multi-human tracking due to the scarcity of dedicated benchmarks

Nirmalendu Acharyya, Akash Sinha

In a $(2+1)$-dimensional Maxwell-Chern-Simons theory coupled with a fermion and a scalar, which has $\mathcal{N}=2$ SUSY in absence of the boundary, the insertion of a spatial boundary breaks the supersymmetry. We show that only a subset of the boundary conditions allowed by the self-adjointness of the Hamiltonian can preserve partial $\mathcal{N}=1$ supersy

Roberto Leo Medrano, Gray Cortright Thomas, Connor G. Keais, Elliott J. Rouse

Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven k

Yutaka Hosotani

Universality in anomaly flow by an Aharonov-Bohm (AB) phase $θ_H$ is shown in the flat $M^4 \times (S^1/Z_2)$ spacetime and in the Randall-Sundrum (RS) warped space. We analyze $SU(2)$ gauge theory with doublet fermions. With orbifold boundary conditions the $U(1)$ part of gauge symmetry remains unbroken at $θ_H = 0$ and $π$. Chiral anomalies smoothly vary w

Abanoub Ghobrial, Xuan Zheng, Darryl Hond, Hamid Asgari

Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to allow them to operate in complex, high-dimensional, non-linear, and dynamically changing environments. Due to the complexity of these environments, DNN classifiers may output misclassifications during operation when they face domains not identified during development. Removing a syste

Yuxin Kong, Canjie Luo, Weihong Ma, Qiyuan Zhu

Automatic font generation remains a challenging research issue due to the large amounts of characters with complicated structures. Typically, only a few samples can serve as the style/content reference (termed few-shot learning), which further increases the difficulty to preserve local style patterns or detailed glyph structures. We investigate the drawbacks