December 2023 arXiv papers — page 10

Raphaël Côte, Camille Laurent

We consider non linear elliptic equations of the form $\Delta u = f(u,\nabla u)$ for suitable analytic nonlinearity $f$, in the vinicity of infinity in $\mathbb{R}^d$, that is on the complement of a compact set.We show that there is a \emph{one-to-one correspondence} between the non linear solution $u$ defined there, and the linear solution $u\_L$ to the Lap

Jessica Dubois

Exploring the developing brain is a major issue in understanding what enables children to acquire amazing abilities, and how early disruptions can lead to a wide range of neurodevelopmental disorders. MRI plays a key role here by providing a non-invasive way to link brain and behavioral changes. Several modalities are used in newborns and infants to characte

Xiaoqian Liu, Jianbin Jiao, Junge Zhang

Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sample efficiency and generalization, while large-scale self-supervised pretraining has enabled fast adaptation with fine-tuning or few-shot learning in language and vision. We thus

Yohann Faure, Elsa Bayart

Seismic faults release the stress accumulated during tectonic movement through rapid ruptures or slow slip events. The slow slip events play a crucial role in the seismic cycle as they impact the occurrence of earthquakes. However, the mechanisms by which a slow-slip region affects the dynamics of frictionally locked regions remain elusive. Here, building on

Jinsheng Ba, Manuel Rigger

Database systems are widely used to store and query data. Test oracles have been proposed to find logic bugs in such systems, that is, bugs that cause the database system to compute an incorrect result. To realize a fully automated testing approach, such test oracles are paired with a test case generation technique; a test case refers to a database state and

Clément Cren

Manifolds endowed with a parabolic geometry in the sense of Cartan come with natural sequences of differential operators and their analysis provide the so called (curved) BGG sequence of {\v C}ap, Slov{\'a}k and Sou{\v c}ek. The sequences involved do not form an elliptic complex in the sense of Atiyah but enjoy similar properties. The proper framework to stu

Yun Chen, Lingxiao Yang, Qi Chen, Jian-Huang Lai

Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. Existing approaches cannot well express fine-grained emotional attributes. In this paper, we propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion. We

Young-Ha Shin, Tae-Gyu Song, Gwanghyeon Ji, Hae-Won Park

This paper presents a method for achieving high-speed running of a quadruped robot by considering the actuator torque-speed operating region in reinforcement learning. The physical properties and constraints of the actuator are included in the training process to reduce state transitions that are infeasible in the real world due to motor torque-speed limitat

Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi, Anne-Mieke Vandamme

Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug to the market or due to limited use in clinical settings, resulting in clinical dataset with highly unbalanced

Tuan-Anh Vu, Duc Thanh Nguyen, Qing Guo, Nhat Chung

Text-to-image diffusion techniques have shown exceptional capabilities in producing high-quality, dense visual predictions from open-vocabulary text. This indicates a strong correlation between visual and textual domains in open concepts and that diffusion-based text-to-image models can capture rich and diverse information for computer vision tasks. However,

Xinhui Zhang, Naike Du, Jing Wang, Andrea Massa

Starting from the observation that the correlation coefficient defined by the scattered field data tested by two adjacent antennas decreases with the noise, it turns out that the imaging performance can be improved by adding non-redundant scattered field information through more measuring antennas.However, adding more measuring antennas faces practical chall

Hao Wang, Bo Tang, Chi Harold Liu, Shangqin Mao

Online display advertising platforms service numerous advertisers by providing real-time bidding (RTB) for the scale of billions of ad requests every day. The bidding strategy handles ad requests cross multiple channels to maximize the number of clicks under the set financial constraints, i.e., total budget and cost-per-click (CPC), etc. Different from exist

Mehmet Demirci, M. Fauzi Mustamin

We investigate new physics with light-neutral mediators through coherent elastic neutrino-nucleus scattering (CE$\nu$NS) at low energies. These mediators, with a mass of less than $1$ GeV, are common properties for extensions of the Standard Model (SM). We consider general scalar, vector, and tensor interactions allowed by Lorentz invariance and involve univ

Sanjay Mandal, Kazuharu Bamba

In this study, we investigate swampland conjectures within the setup of matter and non-metricity nonminimal coupling theories of gravity. We examine how the inflationary solution produced by a single scalar field can be resolved with the swampland criteria in string theory regarding the formation of de Sitter solutions. The new important findings are that th

Peter Koroteev

This review article discusses recent progress in understanding of various families of integrable models in terms of algebraic geometry, representation theory, and physics. In particular, we address the connections between soluble many-body systems of Calogero-Ruijsenaars type, quantum spin chains, spaces of opers, representations of double affine Hecke algeb

Junsong Cang, Yu Gao, Yin-Zhe Ma

The energy released from dark matter (DM) annihilation leads to additional ionization and heating of the intergalactic gas, impacting the hydrogen 21-cm signal during the cosmic dawn. The dark matter annihilation rate scales with its density squared and becomes inhomogeneously boosted with structure formation. This paper examines the inhomogeneity in DM anni

Yifeng Wang, Yi Zhao

As attitude and motion sensing components, inertial sensors are widely used in various portable devices. But the severe errors of inertial sensors restrain their function, especially the trajectory recovery and semantic recognition. As a mainstream signal processing method, wavelet is hailed as the mathematical microscope of signal due to the plentiful and d

Stefano Racioppi, Iren Saffarian-Deemyad, William Holle, Francesco Belli

We present theoretical and experimental studies of superconductivity and low temperature structural phase boundaries in lithium. We mapped the structural phase diagram of 6Li and 7Li under hydrostatic conditions between 5 top 55GPa and within the temperature range of 15 to 75K, observing the FCC-hR1-cI16 phase transitions. 6Li and 7Li show some differences a

Martin Bauer, Patrick Heslin, Cy Maor

We investigate the geometry of the space of immersed closed curves equipped with reparametrization-invariant Riemannian metrics; the metrics we consider are Sobolev metrics of possible fractional order $q\in [0,\infty)$. We establish the critical Sobolev index on the metric for several key geometric properties. Our first main result shows that the Riemannian

Yunfei Zhang, Chuan Qin, Dazhong Shen, Haiping Ma

During the past few decades, cognitive diagnostics modeling has attracted increasing attention in computational education communities, which is capable of quantifying the learning status and knowledge mastery levels of students. Indeed, the recent advances in neural networks have greatly enhanced the performance of traditional cognitive diagnosis models thro

Xiaozhen Ge, Lijun Liu, Yong Wang, Yu Xiang

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation $\mathcal{E}^\alpha_{i|jk}\leq \mathcal{E}^\alpha_{j|ik}+\mathcal{E}^\alpha_{k|ij}$ holds for all subadditive bipartite entanglement measure $\mathcal{E}$, all permutations under parties $i,

Xiaohua Lu, Liangxu Xie, Lei Xu, Rongzhi Mao

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, the inherent limitation of mono-modal learning arises from relying solely on one modality of molecular representation, which restricts a com

Youzhe Song, Feng Wang

The quality of a face crop in an image is decided by many factors such as camera resolution, distance, and illumination condition. This makes the discrimination of face images with different qualities a challenging problem in realistic applications. However, most existing approaches are designed specifically for high-quality (HQ) or low-quality (LQ) images,

Xiao-Yang Liu, Rongyi Zhu, Daochen Zha, Jiechao Gao

The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, especially when multiple stakeholders aim to collaboratively enhance LLMs using sensitive data. In this scenario, federat

Ubaid Ullah, Seerat Saleem, Muddassar Farooq, Basit Yameen

Despite existing for millennia, tuberculosis (TB) remains a persistent global health challenge. A significant obstacle in controlling TB spread is the need for a rapid, portable, sensitive, and accurate diagnostic test. Currently, sputum culture stands as a benchmark test for TB diagnosis. Although highly reliable, it necessitates advanced laboratory facilit

Xin Zhang, Jinheng Xie, Yuan Yuan, Michael Bi Mi

Unsupervised object discovery and localization aims to detect or segment objects in an image without any supervision. Recent efforts have demonstrated a notable potential to identify salient foreground objects by utilizing self-supervised transformer features. However, their scopes only build upon patch-level features within an image, neglecting region/image

Aditya Bhattacharya

With Artificial Intelligence (AI) becoming ubiquitous in every application domain, the need for explanations is paramount to enhance transparency and trust among non-technical users. Despite the potential shown by Explainable AI (XAI) for enhancing understanding of complex AI systems, most XAI methods are designed for technical AI experts rather than non-tec

Mashniah Gazwani, James McCoy

We study families of smooth immersed regular planar curves $ \alpha : \left [-1,1 \right ]\times \left [0,T \right )\to \mathbb{R}^{2}$ satisfying the fourth order nonlinear curve diffusion flow with generalised Neumann boundary conditions inside cones. We show that if the initial curve has sufficiently small oscillation of curvature then this remains so und

Christopher Wang, Alex Townsend

We construct the first rigorously justified probabilistic algorithm for recovering the solution operator of a hyperbolic partial differential equation (PDE) in two variables from input-output training pairs. The primary challenge of recovering the solution operator of hyperbolic PDEs is the presence of characteristics, along which the associated Green's func

Jiadong Xie, Fan Zhang, Kai Wang, Jialu Liu

We study the influence minimization problem: given a graph $G$ and a seed set $S$, blocking at most $b$ nodes or $b$ edges such that the influence spread of the seed set is minimized. This is a pivotal yet underexplored aspect of network analytics, which can limit the spread of undesirable phenomena in networks, such as misinformation and epidemics. Given th

Dongjae Lee, Minwoo Jung, Wooseong Yang, Ayoung Kim

Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system (GPS) are unavailable. The main goal of odometry is to predict the robot's motion and accurately determine its current location. Various sensors, such as wheel encoder, inertial measurement unit (IMU), camera, radar, and Light

Xiongfei Wang

The BESIII detector on the BEPCII collider collected the world's largest dataset at the peaks of $J/\psi$, $\psi(3686)$ and $\psi(3770)$. The use of polarization and entanglement states in multidimensional angular distribution analysis can provide new probes to the production and decay characteristics of hyperon anti hyperon pairs. In a recent series of stud

Zelin Zhao, Zhaogui Xu, Jialong Zhu, Peng Di

Automatic program repair (APR) techniques have the potential to reduce manual efforts in uncovering and repairing program defects during the code review (CR) process. However, the limited accuracy and considerable time costs associated with existing APR approaches hinder their adoption in industrial practice. One key factor is the under-utilization of review

Zhongzhi Chen, Xingwu Sun, Xianfeng Jiao, Fengzong Lian

Despite the great success of large language models (LLMs) in various tasks, they suffer from generating hallucinations. We introduce Truth Forest, a method that enhances truthfulness in LLMs by uncovering hidden truth representations using multi-dimensional orthogonal probes. Specifically, it creates multiple orthogonal bases for modeling truth by incorporat

Dongmin Kim, Sengthai Heng, Sanghyeon Lee, Youngsun Han

Quantum Random Access Memory (qRAM) is an essential computing element for running oracle-based quantum algorithms. qRAM exploits quantum superposition to access all data stored in the memory cells simultaneously and guarantees the superior performance of quantum algorithms. A qRAM memory cell comprises logical qubits encoded through quantum error correction

Jacob Portes, Alex Trott, Sam Havens, Daniel King

Although BERT-style encoder models are heavily used in NLP research, many researchers do not pretrain their own BERTs from scratch due to the high cost of training. In the past half-decade since BERT first rose to prominence, many advances have been made with other transformer architectures and training configurations that have yet to be systematically incor

Weitao Chen, Olivier Giraud, Jiangbin Gong, Gabriel Lemarié

Through a combination of rigorous analytical derivations and extensive numerical simulations, this work reports an exotic multifractal behavior, dubbed "logarithmic multifractality", in effectively infinite-dimensional systems undergoing the Anderson transition. In marked contrast to conventional multifractal critical properties observed at finite-dimensiona

Wensha Zhang, Lam Si Tung Ho, Toby Kenney

Sudden changes in environmental conditions can lead to evolutionary shifts not only in the optimal trait value, but also in the diffusion variance under the Ornstein-Uhlenbeck (OU) model. While several methods have been developed to detect shifts in optimal values, few explicitly account for concurrent shifts in both evolutionary variance and diffusion varia

Nigini Oliveira, Jasmine Li, Koosha Khalvati, Rodolfo Cortes Barragan

Constructing a universal moral code for artificial intelligence (AI) is difficult or even impossible, given that different human cultures have different definitions of morality and different societal norms. We therefore argue that the value system of an AI should be culturally attuned: just as a child raised in a particular culture learns the specific values

Li Xu, Haoxuan Qu, Yujun Cai, Jun Liu

Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds. Meanwhile, diffusion models have shown appealing performance in generating high-quality images from random noise with high indeterminacy through step-by-step denoising. Inspired by their denoising capabi

Zhengjie Sun, Leevan Ling, Meng Chen

In this paper, we propose a general meshless structure-preserving Galerkin method for solving dissipative PDEs on surfaces. By posing the PDE in the variational formulation and simulating the solution in the finite-dimensional approximation space spanned by (local) Lagrange functions generated with positive definite kernels, we obtain a semi-discrete Galerki

P. A. Krachkov

In the present paper, we consider processes involving the emission of soft photons in the presence of a strong laser field. We demonstrate that the matrix element $S$ for a process $\text{i} \rightarrow \text{f} + \gamma$, with a soft photon $\gamma$, can be expressed in terms of the matrix element $S_0$ for the process $\text{i} \rightarrow \text{f}$ throug

Manikanta Loya, Divya Anand Sinha, Richard Futrell

The advancement of Large Language Models (LLMs) has led to their widespread use across a broad spectrum of tasks including decision making. Prior studies have compared the decision making abilities of LLMs with those of humans from a psychological perspective. However, these studies have not always properly accounted for the sensitivity of LLMs' behavior to

Xiaocheng Zhang, Zonghai Yao, Hong Yu

With the rapid advancement of Large Language Models (LLMs) and their outstanding performance in semantic and contextual comprehension, the potential of LLMs in specialized domains warrants exploration. This paper introduces the NoteAid EHR Interaction Pipeline, an innovative approach developed using generative LLMs to assist in patient education, a task stem

Monika E. Pietrzyk, Cécile Barbachoux, Joseph Kouneiher

The aim of this paper is to understand the relation between the canonical Hamilton-Jacobi equation for Maxwell's electrodynamics, which is an equation with variational derivatives for a functional of field configurations, and the covariant (De Donder-Weyl) Hamilton-Jacobi equation, which is a partial derivative equation on a finite dimensional space of vecto

Zhiqiang Shen

We present FerKD, a novel efficient knowledge distillation framework that incorporates partial soft-hard label adaptation coupled with a region-calibration mechanism. Our approach stems from the observation and intuition that standard data augmentations, such as RandomResizedCrop, tend to transform inputs into diverse conditions: easy positives, hard positiv

Haibei Zhu, Svitlana Vyetrenko, Serafin Grundl, David Byrd

We study how experience with asset price bubbles changes the trading strategies of reinforcement learning (RL) traders and ask whether the change in trading strategies helps to prevent future bubbles. We train the RL traders in a multi-agent market simulation platform, ABIDES, and compare the strategies of traders trained with and without bubble experience.

Killian Wood, Ahmed Zamzam, Emiliano Dall'Anese

This paper tackles the problem of solving stochastic optimization problems with a decision-dependent distribution in the setting of stochastic strongly-monotone games and when the distributional dependence is unknown. A two-stage approach is proposed, which initially involves estimating the distributional dependence on decision variables, and subsequently op

Bohdan Senyuk, Jin-Sheng Wua, Ivan I. Smalyukh

Many living and artificial systems show a similar emergent behavior and collective motions on different scales, starting from swarms of bacteria to synthetic active particles, herds of mammals and crowds of people. What all these systems often have in common is that new collective properties like flocking emerge from interactions between individual self-prop

Sylvie Corteel, Olya Mandelshtam, Lauren Williams

In this article we give a combinatorial formula for a certain class of Koornwinder polynomials, also known as Macdonald polynomials of type $\tilde{C}$. In particular, we give a combinatorial formula for the Koornwinder polynomials $K_{\lambda} = K_{\lambda}(z_1,\dots,z_N; a,b,c,d; q,t)$, where $\lambda = (1,\dots,1,0,\dots,0)$. We also give combinatorial fo

Huiyuan Chen, Vivian Lai, Hongye Jin, Zhimeng Jiang

Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Information between different augmented views of the same instance. However, we empirically observe that existing CL models suffer from the \textsl{dimensional collapse} issue, where user

Haoyu Han, Heng Yang

We revisit the inverted pendulum problem with the goal of understanding and computing the true optimal value function. We start with an observation that the true optimal value function must be nonsmooth ($i.e.$, not globally $C^1$) due to the symmetry of the problem. We then give a result that can certify the optimality of a candidate $\textit{piece-wise}$ $

Yuan Chang, Liqin Zhao, Qiuyi Wang

In this paper, we obtain the upper bound of the number of zeros of Abelian integral for a class of cubic Hamiltonian systems with nesting period annuli under perturbations of polynomials of degree n. Furthermore, we consider the Hopf and homoclinic bifurcation when a=-1,b=-2,c=1 and n=3, and obtain 18 distributions in which system has at least 3 limit cycles

Brett Scheiner

A theory for presheath-like structures near probes biased at and above the plasma potential is developed for collisionless plasmas with an electron-neutral mean free path on the order of the chamber scale. The theory predicts presheath-like perturbations to the plasma that result from the free streaming of electrons and an effusion loss process from the cham

Jinyi Du, George F. R. Chen, Hongwei Gao, James A. Grieve

We report a stable, low loss method for coupling light from silicon-on-insulator (SOI) photonic chips into optical fibers. The technique is realized using an on-chip tapered waveguide and a cleaved small core optical fiber. The on-chip taper is monolithic and does not require a patterned cladding, thus simplifying the chip fabrication process. The optical fi

Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan

Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However, out-of-distribution generalization for regression-the analogous problem for modeling continuous targets-remains relatively unexplored. To tackle this problem, we return to first p

Andrew Murdza, Khai T. Nguyen, Etienne Phillips

The paper provides an elementary proof establishing a sharp universal bound on the $(d-1)$-Hausdorff measure of the zeros of any nontrivial multivariable polynomial $p:\mathbb{R}^d\to\mathbb{R}$ within a $d$-dimensional cube of size $r$. This bound depends solely on the parameter $r$, the dimension $d$, and the degrees of $p$.

Xiaochuan Tian, Yixuan Wu, Yanzhi Zhang

The paper introduces a new meshfree pseudospectral method based on Gaussian radial basis functions (RBFs) collocation to solve fractional Poisson equations. Hypergeometric functions are used to represent the fractional Laplacian of Gaussian RBFs, enabling an efficient computation of stiffness matrix entries. Unlike existing RBF-based methods, our approach en

Zhan Gao, Lin Chen, Peng-Hui Hu, Man Xie

We carry out a detailed study of medium modifications on momentum and angular correlations between a large transverse momentum hadron and a $Z/\gamma$ trigger in relativistic heavy-ion collisions within a perturbative QCD parton model improved by the Sudakov resummation technique. The total energy loss of a hard parton propagating inside the medium is employ

Yirong Wang, Xiaohu Yang, Yizhou Gu, Xiaoju Xu

In this investigation, we leverage the combination of Dark Energy Spectroscopic Instrument Legacy imaging Surveys Data Release 9 (DESI LS DR9), Survey Validation 3 (SV3), and Year 1 (Y1) data sets to estimate the conditional luminosity and stellar mass functions (CLFs & CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our anal

Jian Li, Mengdan Tian, Yi Li, Wenwen Si

The synchronisation between rotating turbulent flows in periodic boxes is investigated numerically. The flows are coupled via a master-slave coupling, taking the Fourier modes with wavenumber below a given value $k_m$ as the master modes. It is found that synchronisation happens when $k_m$ exceeds a threshold value $k_c$, and $k_c$ depends strongly on the fo

Kosuke Nomura

The low-energy structure and $\beta$ decay properties of the neutron-rich even-mass nuclei near the neutron number $N=60$ that are experimentally of much interest are investigated within the framework of the nuclear density functional theory and the interacting boson-fermion-fermion model. By using the results of the constrained self-consistent mean-field ca

Xing Pan, Keping Qiu, Kai Yang, Yue Cao

To investigate whether disk-mediated accretion is the primary mechanism in high-mass star formation, we have established a survey of a large sample of massive dense cores within a giant molecular cloud. We used high angular resolution ($\sim 1.8''$) observations with SMA to study the dust emission and molecular line emission of about 50 massive dense cores i

Zichao Xiao, Rang Liu, Ming Li, Wei Wang

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo p

Siqing Fu, Tiejun Li, Chunyuan Zhang, Hanqing Li

High-quality random numbers are very critical to many fields such as cryptography, finance, and scientific simulation, which calls for the design of reliable true random number generators (TRNGs). Limited by entropy source, throughput, reliability, and system integration, existing TRNG designs are difficult to be deployed in real computing systems to greatly

Nayuta Takemori, Yusuke Teranishi, Wataru Mizukami, Nobuyuki Yoshioka

The reduced density matrix (RDM) is crucial in quantum many-body systems for understanding physical properties, including all local physical quantity information. This study aims to minimize various error constraints that causes challenges in higher-order RDMs estimation in quantum computing. We identify the optimal balance between statistical and systematic

Jie Shen, Shusen Yang, Cong Zhao, Xuebin Ren

Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial agents with limited samples to construct a fault diagnosis model without jeopardizing their raw data privacy. Existing approaches, however, can neither address the intense sample het

Nicholas Laracuente, Graeme Smith

Quantum states naturally decay under noise. Many earlier works have quantified and demonstrated lower bounds on the decay rate, showing exponential decay in a wide variety of contexts. Here we study the converse question: are there uniform upper bounds on the ratio of post-noise to initial information quantities when noise is sufficiently weak? In several sc

Siqiao Xue, Caigao Jiang, Wenhui Shi, Fangyin Cheng

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with tradi

Jiawen Zhu, Zhi-Qi Cheng, Jun-Yan He, Chenyang Li

Advances in perception modeling have significantly improved the performance of object tracking. However, the current methods for specifying the target object in the initial frame are either by 1) using a box or mask template, or by 2) providing an explicit language description. These manners are cumbersome and do not allow the tracker to have self-reasoning

Muneera Bano, Zahid Chaudhri, Didar Zowghi

As Artificial Intelligence (AI) transforms the domain of diplomacy in the 21st century, this research addresses the pressing need to evaluate the dualistic nature of these advancements, unpacking both the challenges they pose and the opportunities they offer. It has been almost a year since the launch of ChatGPT by OpenAI that revolutionised various work dom

Elena Gomes

Let $f$ be a polynomial-like mapping of the sphere of degree $d \geq 2$. We show that the Julia set $J(f)$ of $f$ cannot be the union of a finite number of proper indecomposable subcontinua. As a corollary, we prove that $J(f)$ is an indecomposable continuum if and only if there exists a prime end of some complementary region of $J(f)$ whose impression is th

Miao Rang, Zhenni Bi, Chuanjian Liu, Yunhe Wang

The laws of model size, data volume, computation and model performance have been extensively studied in the field of Natural Language Processing (NLP). However, the scaling laws in Optical Character Recognition (OCR) have not yet been investigated. To address this, we conducted comprehensive studies that involved examining the correlation between performance

Dongbin Hou, Lixin Li, Wensheng Lin, Junli Liang

With the rapid development of deep learning (DL) in recent years, automatic modulation recognition (AMR) with DL has achieved high accuracy. However, insufficient training signal data in complicated channel environments and large-scale DL models are critical factors that make DL methods difficult to deploy in practice. Aiming to these problems, we propose a

Jia Liu, Jie Shuai, Xiyao Li

Current Large Language Model-based agents reason within an exploration-evaluation framework, navigating problem-solving processes in a tree-like manner. However, these methods often neglect successful reasoning trajectories once a problem is resolved, leading to inefficient use of these trajectories for future analogous problems. To address this inefficiency

Xin Gao, Tianheng Qiu, Xinyu Zhang, Hanlin Bai

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however, in the context of deep learning, existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics, but also manually generate low-resolution pairs of images that do not have sufficient confidence.

Haotian Xu, Jianyi Yang, Cheng Zhuo, Thomas Kämpfe

Frequency multipliers, a class of essential electronic components, play a pivotal role in contemporary signal processing and communication systems. They serve as crucial building blocks for generating high-frequency signals by multiplying the frequency of an input signal. However, traditional frequency multipliers that rely on nonlinear devices often require

Yongsu Ahn, Yu-Ru Lin

Despite the benefits of personalizing items and information tailored to users' needs, it has been found that recommender systems tend to introduce biases that favor popular items or certain categories of items, and dominant user groups. In this study, we aim to characterize the systematic errors of a recommendation system and how they manifest in various acc

Yifei Zhou, Xuchu Huang, Jianyi Yang, Kai Ni

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as 'memory wall' issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neur

Youseung Cho, Hyunjin In, Minsuk Yang

In this paper, we study the Liouville-type property for smooth solutions to the steady 3D tropical climate model. We prove that if a smooth solution $(u,v,\theta)$ satisfies $u \in L^3 (\mathbb{R}^3)$, $v \in L^2 (\mathbb{R}^3)$, and $\nabla \theta \in L^2 (\mathbb{R}^3)$, then $u=v=0$ and $\theta$ is constant, which improves the previous result, Theorem 1.3

Jiayu Fan, Nikolce Murgovski, Jun Liang

This research addresses the increasing demand for advanced navigation systems capable of operating within confined surroundings. A significant challenge in this field is developing an efficient planning framework that can generalize across various types of collision avoidance missions. Utilizing numerical optimal control techniques, this study proposes a uni

R. Flores-Calderón, Leonardo Medel, A. Martín-Ruiz

Nodal-line semimetals are topological phases where the conduction and the valence bands cross each other along one-dimensional lines in the Brillouin zone, which are symmetry protected by either spatial symmetries or time-reversal symmetry. In particular, nodal lines protected by the combined $\mathcal{PT}$ symmetry exhibits the parity anomaly of 2D Dirac fe

Asutosh Kumar

We reaffirm the claim of Lee et al. [preceding Comment, Phys. Rev. A 108, 066401 (2023)] that the expression of quantum dual total correlation of a multipartite system in terms of quantum relative entropy as proposed in previous work [A. Kumar, Phys. Rev. A 96, 012332 (2017)] is not correct. We provide alternate expression(s) of quantum dual total correlatio

Nuno Costa Dias, Franz Luef, João Nuno Prata

We revisit the uncertainty principle from the point of view suggested by A. Wigderson and Y. Wigderson. This approach is based on a primary uncertainty principle from which one can derive several inequalities expressing the impossibility of a simultaneous sharp localization in time and frequency. Moreover, it requires no specific properties of the Fourier tr

Ryu Ikeda, Kei-Ichi Kondo, Akihiro Shibata, Seikou Kato

The lattice gauge-scalar model with the scalar field in the fundamental representation of the gauge group has a single confinement-Higgs phase which is well-known as the Fradkin-Shenker-Osterwalder-Seiler analytic continuity theorem: Confinement and Higgs regions are subregions of an analytically continued single phase and there are no thermodynamics phase t

Achintya Dutta, Ashok P, Amit Verma

Despite widespread interest in the phase-change applications of vanadium dioxide (VO$_2$), the fabrication of high-quality VO$_2$ thin films with elevated transition temperatures (TIMT) and high Insulator-Metal-Transition resistance switching still remains a challenge. This study introduces a two-step atmospheric oxidation approach to fabricate bilayer VO$_{

R. Flores-Calderón, Md Mursalin Islam, Michele Pini, Francesco Piazza

Coupling a system to two different baths can lead to novel phenomena escaping the constraints of thermal equilibrium. In quantum materials inside optical cavities, this feature can be exploited as electrons and cavity-photons are easily pulled away from their mutual equilibrium, even in the steady state. This offers new routes for a non-invasive control of m

Debmalya Basak, Nicolas Robles, Alexandru Zaharescu

We consider partitions $p_{w}(n)$ of a positive integer $n$ arising from the generating functions \[ \sum_{n=1}^\infty p_{w}(n) z^n = \prod_{m \in \mathbb{N}} (1-z^m)^{-w(m)}, \] where the weights $w(m)$ are M\"{o}bius convolutions. We establish an upper bound for $p_w(n)$ and, as a consequence, we obtain an asymptotic formula involving the number of odd and

Nihar B. Darbhamulla, Rajeev K. Jaiman

We present a finite element framework for the numerical prediction of cavitating turbulent flows interacting with flexible structures. The vapor-fluid phases are captured through a homogeneous mixture model, with a scalar transport equation governing the spatio-temporal evolution of cavitation dynamics. High-density gradients in the two-phase cavitating flow

E. M. Wilson, J. T. Haraldsen

This investigation covers the effects of variable exchange interactions on the spin dynamics of the zig-zag honeycomb lattice. Using a Holstein-Primakoff expansion of the Heisenberg Hamiltonian with easy-axis anisotropy, we characterize the effects of multiple nearest-neighbor and next-nearest-neighbor interactions with asymmetry within the context of a frus

Yolo Y. Tang, Jing Bi, Siting Xu, Luchuan Song

With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly. Given the remarkable capabilities of large language models (LLMs) in language and multimodal tasks, this survey provides a detailed overview of recent advancements in video understandin

Zheng Zhou, Hongbo Zhao, Ju Liu, Qiaosheng Zhang

Recent studies have shown that Adversarial Patches (APs) can effectively manipulate object detection models. However, the conspicuous patterns often associated with these patches tend to attract human attention, posing a significant challenge. Existing research has primarily focused on enhancing attack efficacy in the physical domain while often neglecting t

Waqwoya Abebe, Pablo Munoz, Ali Jannesari

Federated learning (FL) is a machine learning paradigm where multiple clients collaborate to optimize a single global model using their private data. The global model is maintained by a central server that orchestrates the FL training process through a series of training rounds. In each round, the server samples clients from a client pool before sending them

Meghana Holla, Ismini Lourentzou

Zero-shot Natural Language-Video Localization (NLVL) methods have exhibited promising results in training NLVL models exclusively with raw video data by dynamically generating video segments and pseudo-query annotations. However, existing pseudo-queries often lack grounding in the source video, resulting in unstructured and disjointed content. In this paper,

Deyi Ji, Siqi Gao, Mingyuan Tao, Hongtao Lu

Change Detection (CD) has been attracting extensive interests with the availability of bi-temporal datasets. However, due to the huge cost of multi-temporal images acquisition and labeling, existing change detection datasets are small in quantity, short in temporal, and low in practicability. Therefore, a large-scale practical-oriented dataset covering wide

Zongyang Lu, Honglei Li, Zhi-Long Han, Zong-Guo Si

Heavy neutral gauge boson $Z^\prime$ is proposed in many new physics models. It has rich phenomena at the future muon collider. We study the properties of $Z^\prime$ boson with the process of $\mu^+ \mu^- \rightarrow q \bar{q}$, $\mu^+ \mu^- \rightarrow l^+ l^-$, $\mu^+ \mu^- \rightarrow Z H$ and $\mu^+ \mu^- \rightarrow W^+ W^-$. The discrepancy of $Z^\prim

Wanting Qi, Xingyong Zhang

We are interested in looking for two nontrivial solutions for a class of nonhomogeneous quasilinear elliptic system with sign-changing weight functions and concave-convex nonlinearities on the bounded domain. This kind of quasilinear elliptic system arises from nonlinear optics, whose feature is that its differential operator depends on not only $\nabla u$ b

Qishen Chen, Jianzhi Liu, Xinyu Lyu, Lianli Gao

Scene Graph Generation (SGG) endeavors to predict the relationships between subjects and objects in a given image. Nevertheless, the long-tail distribution of relations often leads to biased prediction on coarse labels, presenting a substantial hurdle in SGG. To address this issue, researchers focus on unbiased SGG and introduce data transfer methods to tran

David Hathcock, Qiwei Yu, Yuhai Tu

The Escherichia coli chemoreceptors form an extensive array that achieves cooperative and adaptive sensing of extracellular signals. The receptors control the activity of histidine kinase CheA, which drives a nonequilibrium phosphorylation-dephosphorylation reaction cycle for response regulator CheY. Cooperativity and dissipation are both important aspects o

Kai-Cheng Yang, Onur Varol, Alexander C. Nwala, Mohsen Sayyadiharikandeh

While social media are a key source of data for computational social science, their ease of manipulation by malicious actors threatens the integrity of online information exchanges and their analysis. In this Chapter, we focus on malicious social bots, a prominent vehicle for such manipulation. We start by discussing recent studies about the presence and act