October 2024 arXiv papers — page 44

Chaitya Shah, Kashish Gandhi, Javal Shah, Kreena Shah

Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastical

Jonathan Junné, Frank Redig, Rik Versendaal

We prove that the hydrodynamic limit of the symmetric exclusion process (SEP) is a Fokker-Planck equation in the setting of Poisson random neighborhood graphs approximating a weighted Riemannian manifold with Ricci curvature bounded from below. We also consider the lift of the SEP to a principal bundle, and obtain a Fokker-Planck equation with a weighted hor

Feng Ye, Xinxi Zhang, Michael Stein, Ahmed Aziz Ezzat

To unlock access to stronger winds, the offshore wind industry is advancing towards significantly larger and taller wind turbines. This massive upscaling motivates a departure from wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly mode

Peng Huang, Bowen Guo, Shuyu Liang, Junhu Fu

Text-To-Image (TTI) generation is significant for controlled and diverse image generation with broad potential applications. Although current medical TTI methods have made some progress in report-to-Chest-Xray (CXR) generation, their generation performance may be limited due to the intrinsic characteristics of medical data. In this paper, we propose a novel

Yingjun Du, Gaowen Liu, Yuzhang Shang, Yuguang Yao

Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional shifts that negatively impact generalizability to unseen samples. This paper introduces prompt diffusion, which uses a diffusion model to gradually refine the prompts to obtain a cu

Dehai Min, Zhiyang Xu, Guilin Qi, Lifu Huang

Existing information retrieval (IR) models often assume a homogeneous structure for knowledge sources and user queries, limiting their applicability in real-world settings where retrieval is inherently heterogeneous and diverse. In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous knowledge retriever that (1) builds a unified retrie

Holger Dell, Anselm Haak, Melvin Kallmayer, Leo Wennmann

We present a randomized algorithm for solving low-degree polynomial equation systems over finite fields faster than exhaustive search. In order to do so, we follow a line of work by Lokshtanov, Paturi, Tamaki, Williams, and Yu (SODA 2017), Bj\"orklund, Kaski, and Williams (ICALP 2019), and Dinur (SODA 2021). In particular, we generalize Dinur's algorithm for

Yihao Zhang

The pursuit of interpretable artificial intelligence has led to significant advancements in the development of methods that aim to explain the decision-making processes of complex models, such as deep learning systems. Among these methods, causal abstraction stands out as a theoretical framework that provides a principled approach to understanding and explai

Gabriele Dessena, Marco Civera, Andrés Marcos, Bernardino Chiaia

The problem of damage detection and identification is of interest for many aerospace and aeronautical engineering systems. However, relevant literature mostly focuses on subsystems and parts, rather than full airframes. In structural dynamics, modal parameters, such as natural frequencies and mode shapes, from any structure are the main building blocks of vi

Xiongtao Xiao, Xiaofeng Chen, Feiyan Jiang, Songming Zhang

Single-cell spatial transcriptomics (ST) offers a unique approach to measuring gene expression profiles and spatial cell locations simultaneously. However, most existing ST methods assume that cells in closer spatial proximity exhibit more similar gene expression patterns. Such assumption typically results in graph structures that prioritize local spatial in

Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li

Diffusion models, which can be viewed as a special case of hierarchical variational autoencoders (HVAEs), have shown profound success in generating photo-realistic images. In contrast, standard HVAEs often produce images of inferior quality compared to diffusion models. In this paper, we hypothesize that the success of diffusion models can be partly attribut

Félix Antoine Goudreault, Samuel Poncé, Feliciano Giustino, Michel Côté

Using density functional theory calculations with spin-orbit coupling (SOC), we report on the temperature-dependent thermodynamical properties of Pb: electrical resistivity, thermal expansion (TE), heat capacity, bulk modulus and its pressure derivative. For the former, we employed the state-of-the-art ab initio Boltzmann Transport Equation formalism, and we

Ahmad Nemer, Ivan Katkov, Jospeh Gelfand, Changhyun Cho

Identifying the dominant ionizing sources in galaxies is essential for understanding their formation and evolution. Traditionally, spectra are classified based on their dominant ionizing source using strong emission lines and Baldwin, Phillips, \& Terlevich (BPT) diagrams. The ionizing source is traditionally determined by the emission line ratios using the

Liulei Li, Wenguan Wang, Yi Yang

Prevalent human-object interaction (HOI) detection approaches typically leverage large-scale visual-linguistic models to help recognize events involving humans and objects. Though promising, models trained via contrastive learning on text-image pairs often neglect mid/low-level visual cues and struggle at compositional reasoning. In response, we introduce DI

Adittya Chaudhuri

We investigate an interplay between some ideas in traditional gauge theory and certain concepts in fibered categories. We accomplish this by introducing a notion of a principal Lie 2-group bundle over a Lie groupoid and studying its connection structures, gauge transformations, and parallel transport. We obtain a Lie 2-group torsor version of the one-one cor

Jiasen Zhang, Mingrui Yang, Weihong Guo, Brian A. Xavier

Lung cancer ranks as one of the leading causes of cancer diagnosis and is the foremost cause of cancer-related mortality worldwide. The early detection of lung nodules plays a pivotal role in improving outcomes for patients, as it enables timely and effective treatment interventions. The segmentation of lung nodules plays a critical role in aiding physicians

Alexander Bodard, Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos

This work introduces an unconventional inexact augmented Lagrangian method where the augmenting term is a Euclidean norm raised to a power between one and two. The proposed algorithm is applicable to a broad class of constrained nonconvex minimization problems that involve nonlinear equality constraints. In a first part of this work, we conduct a full comple

Ivan Latella, Philippe Ben-Abdallah

When a magnetic field is applied in the perpendicular direction to a metallic disk under the action of a radial bias voltage, a tangential electric current superimposes to the radial current due to the presence of the Lorentz force which acts on electrons. Here we introduce a thermal analog of this Corbino effect in many-body systems made of nonreciprocal bo

Shanshan Song, Bingwen Huangfu, Jiani Guo, Jun Liu

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT)

Jafar Vafaei, Omid Akbari

For critical applications that require a higher level of reliability, the Triple Modular Redundancy (TMR) scheme is usually employed to implement fault-tolerant arithmetic units. However, this method imposes a significant area and power/energy overhead. Also, the majority-based voter in the typical TMR designs is highly sensitive to soft errors and the desig

Yabin Zhang, Lei Zhang

Recent research has shown that pre-trained vision-language models are effective at identifying out-of-distribution (OOD) samples by using negative labels as guidance. However, employing consistent negative labels across different OOD datasets often results in semantic misalignments, as these text labels may not accurately reflect the actual space of OOD imag

Jialin Luo, Yuanzhi Wang, Ziqi Gu, Yide Qiu

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidab

Foad Parsaei, Sara Rastgoo

This study explores asymptotically flat wormhole solutions within the framework of $f(R,T)$ gravity. We analyze $f(R,T)$ expressed as $f(R,T)=R+\lambda T+\lambda_1 T^2$. A linear equation of state is employed for both radial and lateral pressures, resulting in a power-law shape function. The investigation encompasses solutions characterized by both negative

Ryoichi Takase, Masaya Tsunokake, Yuta Tsuchiya, Shota Inuzuka

Mathematical reasoning problems are among the most challenging, as they typically require an understanding of fundamental laws to solve. The laws are universal, but the derivation of the final answer changes depending on how a problem is approached. When training large language models (LLMs), learning the capability of generating such multiple solutions is e

Xin Qian, Chuang Zhang, Te-Huan Liu, Ronggui Yang

Hydrodynamic second sound can be generated by heat pulses when the phonon-phonon interaction is dominantly momentum conserving, and the propagation of the temperature field becomes wavelike rather than diffusive. While the Boltzmann transport equation (BTE) has been widely applied to study phonon dynamics and thermal transport at the nanoscale, modeling the

Ruhan Conceição, Marcelo Porto, Wen-Hsiao Peng, Luciano Agostini

In this paper, we first show that current learning-based video codecs, specifically the SSF codec, are not suitable for real-world applications due to the mismatch between the encoder and decoder caused by floating-point round-off errors. To address this issue, we propose the static quantization of the hyper prior decoding path. The quantization parameters a

Changsoo Shin

In this paper, we define a novel recursive Heaviside step sequence function and demonstrate its applicability to modeling human mental states such as thought processes, memory recall, and forgetfulness. By extending the traditional Heaviside step function, which typically represents binary transitions, into a recursive sequence framework, we introduce a dyna

Haozhe Gou, Liangxun Li

Assuming the Ramanujan conjecture, the zero density estimate and some subconvexity type bound, we describe a general method to obtain the log-saving upper bound for the second moment of standard twisted higher degree $L$-function in the $q$-aspect. Specifically, let $L(s, F)$ be a standard $L$-function of degree $d\geq3$. Under these foundational hypotheses.

Gogulapati Sreedurga

Participatory budgeting (PB) is a voting paradigm for distributing a divisible resource, usually called a budget, among a set of projects by aggregating the preferences of individuals over these projects. It is implemented quite extensively for purposes such as government allocating funds to public projects and funding agencies selecting research proposals t

Mingrui Liu, Sixiao Zhang, Cheng Long

Retrieval-Augmented Generation (RAG) has been an effective approach to mitigate hallucinations in large language models (LLMs) by incorporating up-to-date and domain-specific knowledge. Recently, there has been a trend of storing up-to-date or copyrighted data in RAG knowledge databases instead of using it for LLM training. This practice has raised concerns

Zhichao Wang, Lin Wang, Yongxin Guo, Ying-Jun Angela Zhang

The increasing concern for data privacy has driven the rapid development of federated learning (FL), a privacy-preserving collaborative paradigm. However, the statistical heterogeneity among clients in FL results in inconsistent performance of the server model across various clients. Server model may show favoritism towards certain clients while performing p

Kumud Lakara, Georgia Channing, Christian Rupprecht, Juil Sock

One of the most challenging forms of misinformation involves pairing images with misleading text to create false narratives. Existing AI-driven detection systems often require domain-specific finetuning, limiting generalizability, and offer little insight into their decisions, hindering trust and adoption. We introduce MAD-Sherlock, a multi-agent debate syst

Daniel Peck, Gaspare Da Fies, Ivan Virshylo, Gennady Mishuris

This paper investigates hydraulic fracture in a media with periodic heterogeneous toughness. Results for the plane-strain (KGD) model are analysed. The energy distribution as the fracture propagates is examined, along with the evolution of the crack geometry. It is shown that the solid layer acts as an elastic battery, discharging to promote rapid propagatio

Christian Capezza, Fabio Centofanti, Davide Forcina, Antonio Lepore

Industrial applications often exhibit multiple in-control patterns due to varying operating conditions, which makes a single functional linear model (FLM) inadequate to capture the complexity of the true relationship between a functional quality characteristic and covariates, which gives rise to the multimode profile monitoring problem. This issue is clearly

Dariusz Dereniowski, Janusz Dybizbański, Przemysław Karpiński, Michał Zakrzewski

We present a simple linear-time algorithm that finds a spanning tree $T$ of a given $2$-edge-connected graph $G$ such that each vertex $v$ of $T$ has degree at most $\lceil \frac{\deg_G(v)}{2}\rceil + 1$.

Fangwen Mu, Junjie Wang, Zhuohao Yu, Lin Shi

Neural code models have found widespread success in tasks pertaining to code intelligence, yet they are vulnerable to backdoor attacks, where an adversary can manipulate the victim model's behavior by inserting triggers into the source code. Recent studies indicate that advanced backdoor attacks can achieve nearly 100% attack success rates on many software e

Aniket Das, Dheeraj Nagaraj, Soumyabrata Pal, Arun Suggala

We consider the problem of high-dimensional heavy-tailed statistical estimation in the streaming setting, which is much harder than the traditional batch setting due to memory constraints. We cast this problem as stochastic convex optimization with heavy tailed stochastic gradients, and prove that the widely used Clipped-SGD algorithm attains near-optimal su

J. Scheffel, J. Jäderberg, K. Bendtz, R. Holmberg

The Novatron magnetic mirror fusion reactor concept features significant advantages. These include stability against MHD interchange and kinetic DCLC modes, axisymmetry, and minimized radial particle drifts and neoclassical losses. For achieving a ratio Q > 30 of fusion power to heating power, axial particle confinement is uniquely designed to rely on the si

M. Girardi, W. Boschin, A. Mercurio, N. Nocerino

We aim to gain new insights into the controversial dynamical status of MACS J0329-0211 (MACS0329), a massive cluster at z=0.4503, with a new analysis using a large sample of member galaxies as kinematic tracers. Our analysis is based on extensive spectroscopic data for more than 1700 galaxies obtained with the VIMOS and MUSE spectrographs at the Very Large T

Jörn Fischer, Thomas Ihme

Traditional AI algorithms, such as Genetic Programming and Reinforcement Learning, often require extensive computational resources to simulate real-world physical scenarios effectively. While advancements in multi-core processing have been made, the inherent limitations of parallelizing rigid body dynamics lead to significant communication overheads, hinderi

Amar Halilovic, Senka Krivic

In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framework for automated planning of explanation

Wenwen Zhang, Zhouzhuo Tang, Yingmei Feng, Xia Yu

During the early stages of respiratory virus outbreaks, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the efficient utilize of limited nasopharyngeal swabs for rapid and accurate screening is crucial for public health. In this study, we present a methodology that integrates attenuated total reflection-Fourier transform infrared spectr

S. M. Kukhtaruk, V. A. Kochelap

Dispersion equations are a common paradigm of collective excitation physics. However, in some systems, dispersion equations contain multivalued functions and their solutions are ambiguous. As an example, we consider graphene on a polar substrate where Dirac plasmons are coupled with surface optical phonons. The dispersion equation for this system contains sq

Renwen Zhang, Han Li, Han Meng, Jinyuan Zhan

As conversational AI systems increasingly permeate the socio-emotional realms of human life, they bring both benefits and risks to individuals and society. Despite extensive research on detecting and categorizing harms in AI systems, less is known about the harms that arise from social interactions with AI chatbots. Through a mixed-methods analysis of 35,390

Yu-Xin Wang, Xiaotong Wei, Chun-Yue Li, Tian-Yang Sun

The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high signal-to-noise ratio events from the observational runs suggest that standard waveform templates, which assume strict adhe

Wenyuan Li, Pengyu Wei

This paper investigates the optimal consumption, investment, and life insurance/annuity decisions for a family in an inflationary economy under money illusion. The family can invest in a financial market that consists of nominal bonds, inflation-linked bonds, and a stock index. The breadwinner can also purchase life insurance or annuities that are available

Xiangli Lei, Qingwen Wu, Hui Li, Ya-Ping Li

The geometrically thick dusty torus structure is believed to exist in the nuclear region of galaxies (especially in active galactic nuclei, AGNs). The debris stream from a tidal disruption event (TDE) will possibly collide with the dusty torus and produce a transient flare. We perform three-dimensional hydrodynamic simulations to model the dynamical evolutio

Senran Fan, Zhicheng Bao, Chen Dong, Haotai Liang

The end-to-end image communication system has been widely studied in the academic community. The escalating demands on image communication systems in terms of data volume, environmental complexity, and task precision require enhanced communication efficiency, anti-noise ability and semantic fidelity. Therefore, we proposed a novel paradigm based on Semantic

Joachim Cuntz

We develop important properties of the KK-functor on the basis of split exactness. In particular we discuss two slightly different short proofs for the existence of the Kasparov product and its associativity. We use the approach with quasihomomorphisms to obtain a short proof of the fact that operator homotopy implies homotopy . Using an idea of Gabe-Szabo w

Zijun Wan, Jiawei Tang, Linghang Cai, Xin Tong

In spatial design, Artificial Intelligence (AI) tools often generate the entire spatial design outcome in a single automated step, rather than engaging users in a deepening and iterative process. This significantly reduces users' involvement, learning, and creative capabilities, leading to a superficial understanding of spatial design. We conducted a Wizard-

Weijun Cheng, Zhihai Wang, Yu-Xi Liu

Nonlocal interactions between photonic resonator array and giant atoms have attracted extensive attentions. Optimization and control of quantum states via giant atoms have been shown. We here study the dynamical scattering of a single-photon wave packet by a giant atom coupled to a two-dimensional photonic resonator array via multiple spatial points. Using s

Jules Mercadier, Stefan Bittner, Damien Rontani, Marc Sciamanna

We experimentally report on the detection of chaos from a free-running commercial broad-area VCSEL without the need for external perturbation such as optical feedback, injection or current modulation. The evolution of nonlinear dynamics leading to chaotic behavior is studied, and the system's complexity is characterized using chaos titration and correlation

Zhizhao Wang, Shuju Yang, Kaihang Wu, Xiaojie Wang

Tilt-to-length (TTL) coupling is expected to be one of the major noise sources in the interferometric phase readouts in TianQin mission. Arising from the angular motion of spacecraft (SC) and the onboard movable optical subassemblies (MOSAs), TTL noise needs to be removed in postprocessing after suppressing the laser phase noise with time-delay interferometr

Gergő Batta, Lajos Hajdu, András Pongrácz

Diophantine tuples are of ancient and modern interest, with a huge literature. In this paper we study Diophantine graphs, i.e., finite graphs whose vertices are distinct positive integers, and two vertices are linked by an edge if and only if their product increased by one is a square. We provide various results for Diophantine graphs, including extendabilit

Zheng-An Chen, Tao Luo, GuiHong Wang

The multi-stage phenomenon in the training loss curves of neural networks has been widely observed, reflecting the non-linearity and complexity inherent in the training process. In this work, we investigate the training dynamics of neural networks (NNs), with particular emphasis on the small initialization regime, identifying three distinct stages observed i

Su Jiang, Chuyang Liu, Dipankar Dwivedi

Seawater intrusion into coastal aquifers poses a significant threat to groundwater resources, especially with rising sea levels due to climate change. Accurate modeling and uncertainty quantification of this process are crucial but are often hindered by the high computational costs of traditional numerical simulations. In this work, we develop GeoFUSE, a nov

Kai Liu, Deguang Han

Distinguishable and non-distinguishable quantum states are fundamental resources in quantum mechanics and quantum technologies. Interactions with the environment often induce decoherence, impacting both the distinguishability and non-distinguishability between quantum states. In this paper, we investigate mixed unitary quantum channels and the conditions und

Spencer Lin, Basem Rizk, Miru Jun, Andy Artze

The rise in capability and ubiquity of generative artificial intelligence (AI) technologies has enabled its application to the field of Socially Interactive Agents (SIAs). Despite rising interest in modern AI-powered components used for real-time SIA research, substantial friction remains due to the absence of a standardized and universal SIA framework. To t

Eito Nagao, Yuta Hyodo, Teruyuki Kitabayashi

The scaling relations in the two-zero textures of the neutrino mass matrices in the minimal extended seesaw mechanism were discovered by Kumar and Patgiri. We demonstrate that some of these scaling relations can be satisfied without requiring two zero elements in the texture.

Bowen Jiang, Xiaoshan Luo, Toshiaki Iitaka, Ying Sun

Recently, ternary clathrate hydrides are promising candidates for high-temperature superconductor. However, it is a formidable challenge to effectively hunt high-temperature superconductivity among multinary hydrides due to the expensive computational cost associated with large unit cells and huge stoichiometric choices. Here we present an efficiently data-d

Eric Carlen, Mathieu Lewin, Elliott H. Lieb, Robert Seiringer

The Lane-Emden inequality controls $\iint_{\mathbb{R}^{2d}}\rho(x)\rho(y)|x-y|^{-\lambda}\,dx\,dy$ in terms of the $L^1$ and $L^p$ norms of $\rho$. We provide a remainder estimate for this inequality in terms of a suitable distance of $\rho$ to the manifold of optimizers.

Erik Christensen

The Schur product of two complex m x n matrices is their entry wise product. We show that an extremal element X in the convex set of m x n complex matrices of Schur multiplier norm at most 1 satisfies the inequality rank(X) =< (m +n)^(1/2) . For positive n x n matrices with unit diagonal, we give a characterization of the extremal elements, and show that suc

Jun Zhang, Hai-Qian Zhang, T. M. Shneidman, R. V. Jolos

The $K^{\pi}=8^{-}$ isomeric states and rotational bands in the even-even $N = 150$ isotones with $94 \leqslant Z \leqslant 104$ are investigated by the cranked shell model (CSM) with pairing correlations treated by the particle-number-conserving (PNC) method. The experimental bandhead energies and kinematic moments of inertia (MOIs) are reproduced quite wel

Do Hai Son, Vu Tung Lam, Tran Thi Thuy Quynh

Massive Multiple-Input Multiple-Output (massive MIMO) technology stands as a cornerstone in 5G and beyonds. Despite the remarkable advancements offered by massive MIMO technology, the extreme number of antennas introduces challenges during the channel estimation (CE) phase. In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iter

Junjie Li, Jianghong Ma, Xiaofeng Zhang, Yuhang Li

Multimodal Large Language Models have advanced AI in applications like text-to-video generation and visual question answering. These models rely on visual encoders to convert non-text data into vectors, but current encoders either lack semantic alignment or overlook non-salient objects. We propose the Guiding Visual Encoder to Perceive Overlooked Information

Long-Ze Tan, Ming-Yu Deng, Jia-Li Qiu, Xue-Ping Guo

In this work, we first present an adaptive deterministic block coordinate descent method with momentum (mADBCD) to solve the linear least-squares problem, which is based on Polyak's heavy ball method and a new column selection criterion for a set of block-controlled indices defined by the Euclidean norm of the residual vector of the normal equation. The mADB

Amir Joudaki, Thomas Hofmann

Understanding how neural networks transform input data across layers is fundamental to unraveling their learning and generalization capabilities. Although prior work has used insights from kernel methods to study neural networks, a global analysis of how the similarity between hidden representations evolves across layers remains underexplored. In this paper,

William T Taitano, Joshua W Burby, Alex Alekseenko

We propose a novel reformulation of the Vlasov-Amp{\`e}re equations for plasmas. This reformulation exposes discrete symmetries to achieve simultaneous conservation of mass, momentum, and energy; preservation of Gauss's law involution; positivity of the distribution function; and quasi-neutral asymptotics. Our approach relies on transforming variables and co

Zihan Tan, Guancheng Wan, Wenke Huang, Mang Ye

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of Graph Neural Networks (GNNs) without compromising privacy while accommodating personalized requirements for non-IID participants. In cross-domain scenarios, structural heterogeneity poses significant challenges for pFGL. Nevertheless, previous pFGL methods incorrectly shar

Muhammad Amien Ibrahim, Alif Tri Handoyo, Maria Susan Anggreainy

Limited public understanding of legal processes and inconsistent verdicts in the Indonesian court system led to widespread dissatisfaction and increased stress on judges. This study addresses these issues by developing a deep learning-based predictive system for court sentence lengths. Our hybrid model, combining CNN and BiLSTM with attention mechanism, achi

Bui Duc Son, Ngo Nam Khanh, Trinh Van Chien, Dong In Kim

Autoencoder permits the end-to-end optimization and design of wireless communication systems to be more beneficial than traditional signal processing. However, this emerging learning-based framework has weaknesses, especially sensitivity to physical attacks. This paper explores adversarial attacks against a double reconfigurable intelligent surface (RIS)-ass

Yuto Shibata, Yasunori Kudo, Yohei Sugawara

In this study, we propose a novel federated learning (FL) approach that utilizes 3D style transfer for the multi-organ segmentation task. The multi-organ dataset, obtained by integrating multiple datasets, has high scalability and can improve generalization performance as the data volume increases. However, the heterogeneity of data owing to different client

Ruijin Liu, Xiaoling Cui

Polaron, a typical quasi-particle that describes a single impurity dressed with surrounding environment, serves as an ideal platform for bridging few- and many-body physics. In particular, different few-body correlations can compete with each other and lead to many intriguing phenomena. In this work, we review the recent progresses made in understanding few-

Tian Wang, Chuang Wang

Pretraining methods gain increasing attraction recently for solving PDEs with neural operators. It alleviates the data scarcity problem encountered by neural operator learning when solving single PDE via training on large-scale datasets consisting of various PDEs and utilizing shared patterns among different PDEs to improve the solution precision. In this wo

Binbin Liu, Carlo Silvestri, Kang Zhou, Xuhong Ma

Chaos characterized by its irregularity and high sensitivity to initial conditions finds various applications in secure optical communications, random number generations, light detection and ranging systems, etc. Semiconductor lasers serve as ideal light platforms for chaos generations owing to the advantages in on-chip integration and complex nonlinear effe

Vivek F. Farias, Adam D. Jozefiak

Plasticity Loss is an increasingly important phenomenon that refers to the empirical observation that as a neural network is continually trained on a sequence of changing tasks, its ability to adapt to a new task diminishes over time. We introduce Self-Normalized Resets (SNR), a simple adaptive algorithm that mitigates plasticity loss by resetting a neuron's

Yue Su, Hao Li, Maoguo Gong

Visible-infrared pedestrian Re-identification (VI-ReID) aims to match pedestrian images captured by infrared cameras and visible cameras. However, VI-ReID, like other traditional cross-modal image matching tasks, poses significant challenges due to its human-centered nature. This is evidenced by the shortcomings of existing methods, which struggle to extract

Tim Lukas Faust, Habib Maraqten, Erfan Aghadavoodi, Boris Belousov

The ``AI Olympics with RealAIGym'' competition challenges participants to stabilize chaotic underactuated dynamical systems with advanced control algorithms. In this paper, we present a novel solution submitted to IROS'24 competition, which builds upon Soft Actor-Critic (SAC), a popular model-free entropy-regularized Reinforcement Learning (RL) algorithm. We

Parismita Gogoi, Priyankoo Sarmah, S. R. M. Prasanna

The current work explores long-term speech rhythm variations to classify Mising and Assamese, two low-resourced languages from Assam, Northeast India. We study the temporal information of speech rhythm embedded in low-frequency (LF) spectrograms derived from amplitude (AM) and frequency modulation (FM) envelopes. This quantitative frequency domain analysis o

Alexander A. Sherstov, Andrey A. Storozhenko

We fully determine the communication complexity of approximating matrix rank, over any finite field $\mathbb{F}$. We study the most general version of this problem, where $0\leq r<R\leq n$ are given integers, Alice and Bob's inputs are matrices $A,B\in\mathbb{F}^{n\times n}$, respectively, and they need to distinguish between the cases $\mathrm{rk}(A+B)=r$ a

Maxim N. Demchenko

We consider the ultrahyperbolic equation in the Euclidean space. The behavior at the infinity of a certain class of solutions is studied. We examine the issue of existence of solutions to the scattering problem: for a given asymptotics at the infinity, the corresponding solution to the equation is constructed.

Seohong Park, Kevin Frans, Benjamin Eysenbach, Sergey Levine

Offline goal-conditioned reinforcement learning (GCRL) is a major problem in reinforcement learning (RL) because it provides a simple, unsupervised, and domain-agnostic way to acquire diverse behaviors and representations from unlabeled data without rewards. Despite the importance of this setting, we lack a standard benchmark that can systematically evaluate

Zhenxin Hu, Zhenhua Yu

Trapped ions are an ideal platform to implement quantum simulation. Previously the parity-time reversal ($\mathcal{PT}$) symmetry-breaking transition in the paradigmatic non-Hermitian Hamiltonian $h_{PT}=J\sigma_x-i\Gamma\sigma_z$ has been observed in a single ion experiment in a passive way. In this work, we propose to study the interaction effects on the $

Tishuo Wang, Zhihuang Luo, Shizhong Zhang, Zhenhua Yu

Nonlinear spin dynamics are essential in exploring nonequilibrium quantum phenomena and have broad applications in precision measurement. Among these systems, the combination of a bias magnetic field and feedback mechanisms can induce self-sustained oscillations at the base Larmor frequency due to nonlinearity. These features have driven the development of s

Zihan Zhou, Muhammad Qasim Elahi, Murat Kocaoglu

Causal discovery is a fundamental problem with applications spanning various areas in science and engineering. It is well understood that solely using observational data, one can only orient the causal graph up to its Markov equivalence class, necessitating interventional data to learn the complete causal graph. Most works in the literature design causal dis

Atula Tejaswi, Yoonsang Lee, Sujay Sanghavi, Eunsol Choi

While in-context learning is well-studied with decoder-only language models (LLMs), its utility for encoder-only models remains underexplored. We study in-context learning for encoder-only models for text retrieval tasks. Can incorporating in-context examples (query-document pairs) to the target query enhance retriever performance? Our approach, RARe, finetu

Mohammad Zia Ur Rehman, Somya Mehta, Kuldeep Singh, Kunal Kaushik

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observatio

Tishuo Wang, Zhenhua Yu

Spin masers are a prototype nonlinear dynamic system. They undergo a bifurcation at a critical amplification factor, transiting into a limit cycle phase characterized by a Larmor precession around the external bias magnetic field, thereby serving as a key frequency reference for precision measurements. Recently, a system of coupled twin spin masers placed in

Abdul Kabir, Jameel-Un Nabi, Muhammad Tahir, Abdul Muneem

The neutron capture rates and temperature dependent stellar beta decay rates of Mo isotopes are investigated within the framework of the statistical code (Talys v1.96) and proton neutron quasi particle random phase approximation (pnQRPA) models. The Maxwellian average cross section (MACS) and neutron capture rates for 95 98Mo(n,{\gamma}) 96 99Mo radiative ca

N. Nakatsuyama, K. Saji, R. Shimada, M. Takahashi

We investigate helicoidal (screw) surfaces generated not only by regular curves but also by curves with singular points. For curves with singular points, it is useful to use frontals in the Euclidean plane. The helicoidal surface of a frontal can naturally be considered as a generalised framed base surface. Moreover, we show that it is also a framed base sur

Quanjian Song, Mingbao Lin, Wengyi Zhan, Shuicheng Yan

This paper presents UniVST, a unified framework for localized video style transfer based on diffusion models. It operates without the need for training, offering a distinct advantage over existing diffusion methods that transfer style across entire videos. The endeavors of this paper comprise: (1) A point-matching mask propagation strategy that leverages the

Mohammad Zia Ur Rehman, Devraj Raghuvanshi, Nagendra Kumar

Delivering prompt information and guidance to farmers is critical in agricultural decision-making. Farmers helpline centres are heavily reliant on the expertise and availability of call centre agents, leading to inconsistent quality and delayed responses. To this end, this article presents Kisan Query Response System (KisanQRS), a Deep Learning-based robust

Da-Chuan Tian, Yang Yang, Zhong-Lue Wen, Jun-Qing Xia

Building a comprehensive catalog of galaxy clusters is a fundamental task for the studies on the structure formation and galaxy evolution. In this paper, we present COSMIC (Cluster Optical Search using Machine Intelligence in Catalogs), an algorithm utilizing machine learning techniques to efficiently detect galaxy clusters. COSMIC involves two steps, includ

Zhijie Fan, Xiaofeng Wang, Zhicheng Zeng

In this paper, we use the non-increasing rearrangement of ${\rm IDA}$ function with respect to a suitable measure to characterize the asymptotic behavior of the singular values sequence $\{s_n(H_f)\}_n$ of Hankel operators $H_f$ acting on a large class of weighted Bergman spaces, including standard Bergman spaces on the unit disc, standard Fock spaces and we

Viswanath Sivakumar, Jeffrey Seely, Alan Du, Sean R Bittner

Surface electromyography (sEMG) non-invasively measures signals generated by muscle activity with sufficient sensitivity to detect individual spinal neurons and richness to identify dozens of gestures and their nuances. Wearable wrist-based sEMG sensors have the potential to offer low friction, subtle, information rich, always available human-computer inputs

Muhammad Umair, Syed Jalaluddin Hashmi, Young-Koo Lee

Keyphrase ranking plays a crucial role in information retrieval and summarization by indexing and retrieving relevant information efficiently. Advances in natural language processing, especially large language models (LLMs), have improved keyphrase extraction and ranking. However, traditional methods often overlook diversity, resulting in redundant keyphrase

InPyo Song, Jangwon Lee

This paper addresses the problem of multi-object tracking in Unmanned Aerial Vehicle (UAV) footage. It plays a critical role in various UAV applications, including traffic monitoring systems and real-time suspect tracking by the police. However, this task is highly challenging due to the fast motion of UAVs, as well as the small size of target objects in the

Sohail Khan

These lecture notes provide a comprehensive guide on Grid Modeling of Renewable Energy, offering a foundational overview of power system network modeling, power flow, and load flow algorithms critical for electrical and renewable energy engineering. Key topics include steady-state, dynamic, and frequency domain models, with a particular focus on renewable en

Aiden V. Harbick, Mark K. Transtrum

Modern superconducting radio frequency (SRF) applications demand precise control over material properties across multiple length scales - from microscopic composition, to mesoscopic defect structures, to macroscopic cavity geometry. We present a time-dependent Ginzburg-Landau (TDGL) framework that incorporates spatially varying parameters derived from experi

A. F. Krenn, M. Lendl, S. Sulis, M. Deleuil

Context. The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. Aims. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's a