December 2024 arXiv papers — page 161

Michael Hersche, Giacomo Camposampiero, Roger Wattenhofer, Abu Sebastian

This work compares large language models (LLMs) and neuro-symbolic approaches in solving Raven's progressive matrices (RPM), a visual abstract reasoning test that involves the understanding of mathematical rules such as progression or arithmetic addition. Providing the visual attributes directly as textual prompts, which assumes an oracle visual perception m

Saba Hesaraki, Morteza Akbari, Ramin Mousa

Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ n

Lintong Du, Huazhen Liu, Yijia Zhang, ShuXin Liu

Spatial phase unwrapping is a key technique for extracting phase information to obtain 3D morphology and other features. Modern industrial measurement scenarios demand high precision, large image sizes, and high speed. However, conventional methods struggle with noise resistance and processing speed. Current deep learning methods are limited by the receptive

Atit Pokharel, Shashank Dahal, Pratik Sapkota, Bhupendra Bimal Chhetri

The rapid advancements in Artificial Intelligence, specifically Machine Learning (ML) and Deep Learning (DL), have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (E

Yifan Wang, Chengjie Yu, Jiang Zhu, Fangyong Wang

Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions aris

Hao-Chun Yang, Sicheng Dai, Saige Rutherford, Christian Gaser

Unsupervised anomaly detection in brain imaging is challenging. In this paper, we propose self-supervised masked mesh learning for unsupervised anomaly detection on 3D cortical surfaces. Our framework leverages the intrinsic geometry of the cortical surface to learn a self-supervised representation that captures the underlying structure of the brain. We intr

Haitao Li, Qian Dong, Junjie Chen, Huixue Su

The rapid advancement of Large Language Models (LLMs) has driven their expanding application across various fields. One of the most promising applications is their role as evaluators based on natural language responses, referred to as ''LLMs-as-judges''. This framework has attracted growing attention from both academia and industry due to their excellent eff

LinLin Sun, Xiaobao Zhu

Let $(M,g)$ be a compact Riemann surface with area $1$, we shall study the Toda system $$ \begin{cases} -\Delta u_1 = 2\rho_1(h_1e^{u_1}-1) - \rho_2(h_2e^{u_2}-1),\\ -\Delta u_2 = 2\rho_2(h_2e^{u_2}-1) - \rho_1(h_1e^{u_1}-1), \end{cases} $$ on $(M,g)$ with $\rho_1=4\pi$, $\rho_2\in(0,4\pi)$, $h_1$ and $h_2$ are two smooth functions on $M$. In Jost-Lin-Wang's

Priyanka Adhikary, Sudipta Das

Barrow holographic dark energy model is an extension of holographic dark energy that incorporates modifications to entropy due to quantum gravitational effects. In this work we study the cosmological properties of interacting Barrow holographic dark energy model in the case of non-zero curvature universe. We construct the differential equations governing the

Ehsan Haghighat, Mohammad Hesan Adeli, S Mohammad Mousavi, Ruben Juanes

In this work, we introduce a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked porous media. STONet's model architecture is specifically designed for this problem and uniquely integrates an enriched DeepONet structure with a transformer-based multi-head attention mechanism, enha

Vatsal Garg, Hojin Lee, Kanghoon Lee

We present a novel framework for computing differential cross-sections in quantum field theory using the optical theorem and loop amplitudes, circumventing the traditional method of squaring scattering amplitudes. This approach addresses two major computational challenges in high-multiplicity processes: complexity from amplitude squaring and the extensive su

Zehan Wang, Yuxuan Wei, Hui Yuan, Wei Zhang

Three-dimensional (3D) point clouds are becoming more and more popular for representing 3D objects and scenes. Due to limited network bandwidth, efficient compression of 3D point clouds is crucial. To tackle this challenge, the Moving Picture Experts Group (MPEG) is actively developing the Geometry-based Point Cloud Compression (G-PCC) standard, incorporatin

Guangyuan Liu, Yu Zhang, Tianshi Ming, Chunlong Yu

Electric vehicles face significant energy supply challenges due to long charging times and congestion at charging stations. Battery swapping stations (BSSs) offer a faster alternative for energy replenishment, but their deployment costs are considerably higher than those of charging stations. As a result, selecting optimal locations for BSSs is crucial to im

Ye Wang, Yaxiong Wang, Guoshuai Zhao, Xueming Qian

Continuous Generalized Category Discovery (C-GCD) aims to continually discover novel classes from unlabelled image sets while maintaining performance on old classes. In this paper, we propose a novel learning framework, dubbed Neighborhood Commonality-aware Evolution Network (NCENet) that conquers this task from the perspective of representation learning. Co

Yuheng Xu, Taiping Zhang

Traditional domain generalization methods often rely on domain alignment to reduce inter-domain distribution differences and learn domain-invariant representations. However, domain shifts are inherently difficult to eliminate, which limits model generalization. To address this, we propose an innovative framework that enhances data representation quality thro

Pablo Diego-Simón, Stéphane D'Ascoli, Emmanuel Chemla, Yair Lakretz

Originally formalized with symbolic representations, syntactic trees may also be effectively represented in the activations of large language models (LLMs). Indeed, a 'Structural Probe' can find a subspace of neural activations, where syntactically related words are relatively close to one-another. However, this syntactic code remains incomplete: the distanc

Diwen Wan, Yuxiang Wang, Ruijie Lu, Gang Zeng

While novel view synthesis for dynamic scenes has made significant progress, capturing skeleton models of objects and re-posing them remains a challenging task. To tackle this problem, in this paper, we propose a novel approach to automatically discover the associated skeleton model for dynamic objects from videos without the need for object-specific templat

Kangjie Zheng, Siyue Liang, Junwei Yang, Bin Feng

SMILES, a crucial textual representation of molecular structures, has garnered significant attention as a foundation for pre-trained language models (LMs). However, most existing pre-trained SMILES LMs focus solely on the single-token level supervision during pre-training, failing to fully leverage the substructural information of molecules. This limitation

Jordan Pertile, Valeriia V. Starichkova

In 1977, Lenstra provided a criterion for norm-Euclideanity of number fields and noted that this criterion becomes ineffective for number fields of large enough degrees under the Generalised Riemann Hypothesis (GRH) for the Dedekind zeta-functions. In the first part of the paper we make Lenstra's observation explicit by proving that, under GRH, the criterion

Haoyang Ji, Qihan Wang

We prove that infinitely renormalizable contracting Lorenz maps with bounded geometry or the so-called {\it a priori bounds} satisfies the slow recurrence condition to the singular point $c$ at its two critical values $c_1^-$ and $c_1^+$. As the first application, we show that the pointwise Lyapunov exponent at $c_1^-$ and $c_1^+$ equals 0. As the second app

Andrea Avogaro, Luigi Capogrosso, Franco Fummi, Marco Cristani

The fast fashion industry suffers from significant environmental impacts due to overproduction and unsold inventory. Accurately predicting sales volumes for unreleased products could significantly improve efficiency and resource utilization. However, predicting performance for entirely new items is challenging due to the lack of historical data and rapidly c

Andrea Avogaro, Luigi Capogrosso, Franco Fummi, Marco Cristani

In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve the efficiency and profits of industries. However, predicting the success of entirely new styles is difficult due to the absence of past data and ever-changing trends. Specifically

Jiahao Qin, Feng Liu

Electrocardiogram (ECG) analysis plays a crucial role in diagnosing cardiovascular diseases, but accurate interpretation of these complex signals remains challenging. This paper introduces a novel multimodal framework(GAF-FusionNet) for ECG classification that integrates time-series analysis with image-based representation using Gramian Angular Fields (GAF).

Hiroaki Matsueda, Yusuke Masaki, Kanji Itoh, Atsushi Ono

Motivated by anomalous nonlocal correlation in the Kitaev spin liquids, we propose a quantum energy teleportation protocol between remote partners Alice and Bob on a quantum spin model, and examine how its performance is characterized by Majorana fermions that clearly depict nonlocal correlations inherent in the model. In our model, Bob's energy extraction i

Jia-Ming Chen, Ke-Rui Zhu, Zhao-Yang Peng, Li Zhang

The classification of Gamma-Ray Bursts has long been an unresolved problem. Early long and short burst classification based on duration is not convincing due to the significant overlap in duration plot, which leads to different views on the classification results. We propose a new classification method based on Convolutional Neural Networks and adopt a sampl

Ola Shorinwa, Zhiting Mei, Justin Lidard, Allen Z. Ren

The remarkable performance of large language models (LLMs) in content generation, coding, and common-sense reasoning has spurred widespread integration into many facets of society. However, integration of LLMs raises valid questions on their reliability and trustworthiness, given their propensity to generate hallucinations: plausible, factually-incorrect res

Yang Cao, Xiaoyu Li, Yuanpeng Li, Yingyu Liang

Modern Hopfield Networks (MHNs) have emerged as powerful components in deep learning, serving as effective replacements for pooling layers, LSTMs, and attention mechanisms. While recent advancements have significantly improved their storage capacity and retrieval efficiency, their fundamental theoretical boundaries remain underexplored. In this paper, we rig

Rajat Singh, Srikanta Bedathur

Equivalence checking of SQL queries is an intractable problem often encountered in settings ranging from grading SQL submissions to debugging query optimizers. Despite recent work toward developing practical solutions, only simple queries written using a small subset of SQL are supported, leaving the equivalence checking of sophisticated SQL queries at the m

Wenqing Wang, Yun Fu

Text-to-3D generation is a valuable technology in virtual reality and digital content creation. While recent works have pushed the boundaries of text-to-3D generation, producing high-fidelity 3D objects with inefficient prompts and simulating their physics-grounded motion accurately still remain unsolved challenges. To address these challenges, we present an

Yunnong Chen, Yishu Shen, Ruiyi Liu, Xinyu Yu

Online programming communities provide a space for novices to engage with computing concepts, allowing them to learn and develop computing skills using user-generated projects. However, the lack of structured guidance in the informal learning environment often makes it difficult for novices to experience progressively challenging learning opportunities. Lear

Feng Li, Jiusong Luo, Wanjun Xia

Speech emotion recognition (SER) remains a challenging yet crucial task due to the inherent complexity and diversity of human emotions. To address this problem, researchers attempt to fuse information from other modalities via multimodal learning. However, existing multimodal fusion techniques often overlook the intricacies of cross-modal interactions, resul

Huajian Zeng, Maolin Gao, Daniel Cremers

The interest in matching non-rigidly deformed shapes represented as raw point clouds is rising due to the proliferation of low-cost 3D sensors. Yet, the task is challenging since point clouds are irregular and there is a lack of intrinsic shape information. We propose to tackle these challenges by learning a new shape representation -- a per-point high dimen

Naoya Arakawa

This report proposes a neural cognitive model for discovering regularities in event sequences. In a fluid intelligence task, the subject is required to discover regularities from relatively short-term memory of the first-seen task. Some fluid intelligence tasks require discovering regularities in event sequences. Thus, a neural network model was constructed

Joao Morais, Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad

This paper introduces a task-specific, model-agnostic framework for evaluating dataset similarity, providing a means to assess and compare dataset realism and quality. Such a framework is crucial for augmenting real-world data, improving benchmarking, and making informed retraining decisions when adapting to new deployment settings, such as different sites o

Yunnong Chen, Shuhong Xiao, Jiazhi Li, Tingting Zhou

Automatically constructing GUI groups of different granularities constitutes a critical intelligent step towards automating GUI design and implementation tasks. Specifically, in the industrial GUI-to-code process, fragmented layers may decrease the readability and maintainability of generated code, which can be alleviated by grouping semantically consistent

Tierui Gong, Jiaming Sun, Chau Yuen, Guangwei Hu

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting c

Arturo Miguel Russell Bernal, Jane Cleland-Huang, Walter Scheirer

The success of Emergency Response (ER) scenarios, such as search and rescue, is often dependent upon the prompt location of a lost or injured person. With the increasing use of small Unmanned Aerial Systems (sUAS) as "eyes in the sky" during ER scenarios, efficient detection of persons from aerial views plays a crucial role in achieving a successful mission

Gengze Zhou, Yicong Hong, Zun Wang, Chongyang Zhao

The academic field of learning instruction-guided visual navigation can be generally categorized into high-level category-specific search and low-level language-guided navigation, depending on the granularity of language instruction, in which the former emphasizes the exploration process, while the latter concentrates on following detailed textual commands.

Lekai Song, Pengyu Liu, Yang Liu, Jingfang Pei

Brains perform decision-making by Bayes theorem. The theorem quantifies events as probabilities and, based on probability rules, renders the decisions. Learning from this, Bayes theorem can be applied to enable efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem in hardware using conventional deterministic c

Jiacheng Jiang, Yuan Meng, Chen Tang, Han Yu

Research on loss surface geometry, such as Sharpness-Aware Minimization (SAM), shows that flatter minima improve generalization. Recent studies further reveal that flatter minima can also reduce the domain generalization (DG) gap. However, existing flatness-based DG techniques predominantly operate within a full-precision training process, which is impractic

Ao Xu, Rui-Qi Li, Heng-Dong Xi

We studied flow organization and heat transfer properties in mixed turbulent convection within Poiseuille-Rayleigh-B\'enard channels subjected to temporally modulated sinusoidal wall temperatures. Three-dimensional direct numerical simulations were performed for Rayleigh numbers in the range $10^6 \leq Ra \leq 10^8$, a Prandtl number $Pr = 0.71$ and a bulk R

Behnam Esmayli, Ryan Schardine, Nageswari Shanmugalingam

The goal of this paper is to provide an expository description of a result of Carrasco Piaggio connecting the Ahlfors regular conformal dimension of a compact uniformly perfect doubling metric space with the combinatorial $p$-moduli of the metric space. We give detailed construction of a metric associated with the $p$-modulus of the space when the $p$-modulu

Ruida Zhang, Chengxi Li, Chenyangguang Zhang, Xingyu Liu

Realistic scene reconstruction in driving scenarios poses significant challenges due to fast-moving objects. Most existing methods rely on labor-intensive manual labeling of object poses to reconstruct dynamic objects in canonical space and move them based on these poses during rendering. While some approaches attempt to use 3D object trackers to replace man

Weijie Chen, Ting Bai, Jinbo Su, Jian Luan

Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model to navigate across multiple documents and generate comprehensive responses based on fragmented information. To tackle this challenge, we introduce a novel Knowledge Graph-based RAG

Haosong Peng, Tianyu Qi, Yufeng Zhan, Hao Li

With the advancement of computer vision, the recently emerged 3D Gaussian Splatting (3DGS) has increasingly become a popular scene reconstruction algorithm due to its outstanding performance. Distributed 3DGS can efficiently utilize edge devices to directly train on the collected images, thereby offloading computational demands and enhancing efficiency. Howe

Shaofei Cai, Bowei Zhang, Zihao Wang, Haowei Lin

Developing agents that can follow multimodal instructions remains a fundamental challenge in robotics and AI. Although large-scale pre-training on unlabeled datasets (no language instruction) has enabled agents to learn diverse behaviors, these agents often struggle with following instructions. While augmenting the dataset with instruction labels can mitigat

Xianliang Xu, Ye Li, Zhongyi Huang

Neural operators are aiming at approximating operators mapping between Banach spaces of functions, achieving much success in the field of scientific computing. Compared to certain deep learning-based solvers, such as Physics-Informed Neural Networks (PINNs), Deep Ritz Method (DRM), neural operators can solve a class of Partial Differential Equations (PDEs).

Kerun Shao

We study the existence of global solutions to semilinear wave equations on exterior domains $\mathbb{R}^n\setminus\mathcal{K}$, $n\geq2$, with small initial data and nonlinear terms $F(\partial u)$ where $F\in C^\kappa$ and $\partial^{\leq\kappa}F(0)=0$. If $n\geq2$ and $\kappa>n/2$, criteria of the existence of a global solution for general initial data are

Minglai Shao, Hua Huang, Qiyao Peng, Hongtao Liu

Recent advances in Large Language Models (LLMs) have demonstrated promising performance in sequential recommendation tasks, leveraging their superior language understanding capabilities. However, existing LLM-based recommendation approaches predominantly focus on modeling item-level co-occurrence patterns while failing to adequately capture user-level person

Yan-Wei Chen, Meng-Zhe Lian, Jin-Jian Han, Ting Zeng

Accurate long-distance ranging is crucial for diverse applications, including satellite formation flying, very-long-baseline interferometry, gravitational-wave observatory, geographical research, etc. The integration of the time-of-flight mesurement with phase interference in dual-comb method enables high-precision ranging with a rapid update rate and an ext

Olamilekan Shobayo, Sidikat Adeyemi-Longe, Olusogo Popoola, Bayode Ogunleye

This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced n

Tim Zolkin, Sergei Nagaitsev, Ivan Morozov, Sergei Kladov

Symplectic mappings of the plane serve as key models for exploring the fundamental nature of complex behavior in nonlinear systems. Central to this exploration is the effective visualization of stability regimes, which enables the interpretation of how systems evolve under varying conditions. While the area-preserving quadratic H\'enon map has received signi

Boxun Xu, Junyoung Hwang, Pruek Vanna-iampikul, Yuxuan Yin

Spiking Neural Networks(SNNs) provide a brain-inspired and event-driven mechanism that is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts approach mirrors the parallel distributed processing of nervous systems, introducing conditional computation policies and expanding model capacity without scaling up the number of c

Chuchu Chen, Tonghe Dang, Jialin Hong, Ziyi Lei

It is well known that for a stochastic differential equation driven by L\'evy noise, the temporal H\"older continuity in $L^p$ sense of the exact solution does not exceed $1/p$. This leads to that the $L^p$-strong convergence order of a numerical scheme will vanish as $p$ increases to infinity if the temporal H\"older continuity of the solution process is di

Jiahao Qin, Feng Liu

Electroencephalogram (EEG) signals play a pivotal role in biomedical research and clinical applications, including epilepsy diagnosis, sleep disorder analysis, and brain-computer interfaces. However, the effective analysis and interpretation of these complex signals often present significant challenges. This paper presents a novel approach that integrates co

Akhila Mamillapalli, Bayode Ogunleye, Sonia Timoteo Inacio, Olamilekan Shobayo

Stock price prediction is challenging due to global economic instability, high volatility, and the complexity of financial markets. Hence, this study compared several machine learning algorithms for stock market prediction and further examined the influence of a sentiment analysis indicator on the prediction of stock prices. Our results were two-fold. Firstl

Hao Cheng, Erjia Xiao, Jiayan Yang, Jiahang Cao

Current image generation models can effortlessly produce high-quality, highly realistic images, but this also increases the risk of misuse. In various Text-to-Image or Image-to-Image tasks, attackers can generate a series of images containing inappropriate content by simply editing the language modality input. To mitigate this security concern, numerous guar

Syed Abubacker Siddique, Md. Manirul Ali, Arijit Sen

We explore the charging advantages of a many-body quantum battery driven by a Landau-Zener field. Such a system may be modeled as a Heisenberg XY spin chain with $\textit{N}$ interacting spin-$\frac{1}{2}$ particles under an external magnetic field. Here we consider both nearest-neighbor and long-range spin interactions. The charging performance of this many

Cailian Ruan, Chengyue Huang, Yahe Yang

This study introduces an evaluation framework for multimodal models in medical imaging diagnostics. We developed a pipeline incorporating data preprocessing, model inference, and preference-based evaluation, expanding an initial set of 500 clinical cases to 3,000 through controlled augmentation. Our method combined medical images with clinical observations t

Feng Long, Ilaria Pascucci, Adrien Houge, Andrea Banzatti

We present a JWST MIRI/MRS spectrum of the inner disk of WISE J044634.16$-$262756.1B (hereafter J0446B), an old ($\sim$34 Myr) M4.5 star but with hints of ongoing accretion. The spectrum is molecule-rich and dominated by hydrocarbons. We detect 14 molecular species (H$_2$, CH$_3$, CH$_4$, C$_2$H$_2$, $^{13}$CCH$_2$, C$_2$H$_4$, C$_2$H$_6$, C$_3$H$_4$, C$_4$H

Haiyang Jiang, Tong Chen, Wentao Zhang, Nguyen Quoc Viet Hung

Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Temporal Graph Neural Networks (STGNNs) have established themselves as capable predictors, they tend to suffer from distribution shifts that are common with the urban flow data due to

Ali Dadsetan, Dorsa Soleymani, Xijie Zeng, Frank Rudzicz

Protecting patient data privacy is a critical concern when deploying machine learning algorithms in healthcare. Differential privacy (DP) is a common method for preserving privacy in such settings and, in this work, we examine two key trade-offs in applying DP to the NLP task of medical coding (ICD classification). Regarding the privacy-utility trade-off, we

Ha L. Viet, On V. Phung, Hoa N. Nguyen

Webshell attacks are becoming more common, requiring robust detection mechanisms to protect web applications. The dissertation clearly states two research directions: scanning web application source code and analyzing HTTP traffic to detect webshells. First, the dissertation proposes ASAF, an advanced DL-Powered Source-Code Scanning Framework that uses signa

Surya N Reddy, Vaibhav Kurrey, Mayank Nagar, Gagan Raj Gupta

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The

Xinjie Sun, Boxiong Wei, Yalong Jiang, Liquan Mao

Thyroid nodule segmentation in ultrasound images is crucial for accurate diagnosis and treatment planning. However, existing methods face challenges in segmentation accuracy, interpretability, and generalization, which hinder their performance. This letter proposes a novel framework, CLIP-TNseg, to address these issues by integrating a multimodal large model

Jianan Chen, Qin Hu, Fangtian Zhong, Yan Zhuang

In Federated Learning (FL), multiple clients collaboratively train a model without sharing raw data. This paradigm can be further enhanced by Differential Privacy (DP) to protect local data from information inference attacks and is thus termed DPFL. An emerging privacy requirement, ``the right to be forgotten'' for clients, poses new challenges to DPFL but r

Dillon Z. Chen, Pulkit Verma, Siddharth Srivastava, Michael Katz

Automated decision-making is a fundamental topic that spans multiple sub-disciplines in AI: reinforcement learning (RL), AI planning (AP), foundation models, and operations research, among others. Despite recent efforts to ``bridge the gaps'' between these communities, there remain many insights that have not yet transcended the boundaries. Our goal in this

Georgios Diamantopoulos, Nikos Tziritas, Rami Bahsoon, Nan Zhang

Blockchain adoption is reaching an all-time high, with a plethora of blockchain architectures being developed to cover the needs of applications eager to integrate blockchain into their operations. However, blockchain systems suffer from the trilemma trade-off problem, which limits their ability to scale without sacrificing essential metrics such as decentra

Elena Grigorescu, Nithish Kumar Kumar, Young-San Lin

Designing sparse directed spanners, which are subgraphs that approximately maintain distance constraints, has attracted sustained interest in TCS, especially due to their wide applicability, as well as the difficulty to obtain tight results. However, a significant drawback of the notion of spanners is that it does not capture a natural setting where demand p

H. Sababha, A. Elmaradny, H. Taha, M. Daqaq

Advancements in computational fluid mechanics have largely relied on Newtonian frameworks, particularly through the direct simulation of Navier-Stokes equations. In this work, we propose an alternative computational framework that employs variational methods, specifically by leveraging the principle of minimum pressure gradient, which turns the fluid mechani

Zhi-Qiang You, Xingjiang Zhu, Xiaojin Liu, Bernhard Müller

The birth mass function of neutron stars encodes rich information about supernova explosions, double star evolution, and properties of matter under extreme conditions. To date, it has remained poorly constrained by observations, however. Applying probabilistic corrections to account for mass accreted by recycled pulsars in binary systems to mass measurements

Hugo A. Akitaya, Sándor P. Fekete, Peter Kramer, Saba Molaei

We consider algorithmic problems motivated by modular robotic reconfiguration in the sliding square model, in which we are given $n$ square-shaped modules in a (labeled or unlabeled) start configuration and need to find a schedule of sliding moves to transform it into a desired goal configuration, maintaining connectivity of the configuration at all times. R

József Balogh, Jialin He, Robert A. Krueger, The Nguyen

In 1966, Erd\H{o}s, Goodman, and P\'{o}sa showed that if $G$ is an $n$-vertex graph, then at most $\lfloor n^2/4 \rfloor$ cliques of $G$ are needed to cover the edges of $G$, and the bound is best possible as witnessed by the balanced complete bipartite graph. This was generalized independently by Gy\H{o}ri--Kostochka, Kahn, and Chung, who showed that every

Yang-yang Wu, Gao-cheng Yue

In this paper, we consider the 2D periodic stochastic Nernst-Planck-Navier-Stokes equations with body forces perturbed by multiplicative white noise. We first transform the stochastic Nernst-Planck-Navier-Stokes system into the deterministic system and address the problem of global well-posedness of the solution. Then, we generate a corresponding random dyna

Amelia Hardy, Anka Reuel, Kiana Jafari Meimandi, Lisa Soder

Public AI benchmark results are widely broadcast by model developers as indicators of model quality within a growing and competitive market. However, these advertised scores do not necessarily reflect the traits of interest to those who will ultimately apply AI models. In this paper, we seek to understand if and how AI benchmarks are used to inform decision-

Dandan Sun, Qikai Wang, Haiyan Zhu

Let $\mathcal{I}$ and $\mathcal{J}$ be object ideals in an exact category $(\mathcal{A}; \mathcal{E})$. It is proved that $(\mathcal{I},\mathcal{J})$ is a perfect ideal cotorsion pair if and only if $({\rm Ob}(\mathcal{I}),{\rm Ob}(\mathcal{J}))$ is a perfect cotorsion pair, where ${\rm Ob}(\mathcal{I})$ and ${\rm Ob}(\mathcal{J})$ is the objects of $\mathca

Kewen Wang, Yu Xin

Given a reductive group $G$ and a reductive subgroup $H$, both defined over a number field $F$, we introduce the notion of the $H$-distinguished automorphic spectrum of $G$ and analyze it for the pair $(\mathrm{U}_{2n},\mathrm{Sp}_{2n})$. We derived a formula for period integrals of pseudo-Eisenstein series of $\mathrm{U}_{2n}$ in analogy with the main resul

Kazutoshi Akita, Norimichi Ukita

Super-resolution (SR) with arbitrary scale factor and cost-and-quality controllability at test time is essential for various applications. While several arbitrary-scale SR methods have been proposed, these methods require us to modify the model structure and retrain it to control the computational cost and SR quality. To address this limitation, we propose a

Ali Goli, Jason Huang, David Reiley, Nickolai M. Riabov

A randomized experiment with almost 35 million Pandora listeners enables us to measure the sensitivity of consumers to advertising, an important topic of study in the era of ad-supported digital content provision. The experiment randomized listeners into nine treatment groups, each of which received a different level of audio advertising interrupting their m

Runhao Zeng, Dingjie Zhou, Qiwei Liang, Junlin Liu

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on t

Carlos Saji, Eduardo Saavedra, Roberto E. Troncoso, Mario A. Castro

We demonstrate that domain walls built from bimeron chains (bc-DW) in two-dimensional systems constitute a spontaneously assembled medium that holds magnonic excitations along its direction. We prove that such magnons are topological, leading to protected edge states. We also verify the stability of the domain walls and its edge modes' resilience against dis

Jun Feng, Xueyi Liu, Jiamin Lu, Pingping Shao

Accurate flood prediction is crucial for disaster prevention and mitigation. Hydrological data exhibit highly nonlinear temporal patterns and encompass complex spatial relationships between rainfall and flow. Existing flood prediction models struggle to capture these intricate temporal features and spatial dependencies. This paper presents an adaptive period

Ubong Sam Idiong

In this work, the Heun operator is written as an element in the universal enveloping algebra of the Lie algebra $\mathscr{G}=\mathscr{L}(G)$ of the Lie group $G=SL(2,\mathbb{C})$. The Green function and the spectral shift function of the exactly solvable Heun operator from the resulting Lie algebraic equation are obtained via Fourier transform over $G$.

Shuo Liu, Minghui Xu, Yuezhou Zheng, Yifei Zou

Consensus is becoming increasingly important in wireless networks. Partially synchronous BFT consensus, a significant branch of consensus, has made considerable progress in wired networks. However, its implementation in wireless networks, especially in dynamic ad hoc wireless networks, remains challenging. Existing wireless synchronous consensus protocols, d

Lulu Xiong, Jin Cao, Fan Yang, Xiaoxin Yang

Few-layer CrPS$_{4}$ is a two-dimensional (2D) magnetic material with excellent stability in ambient environment, which attracted significant interest in recent research. Here, via first-principles calculations, we show that 2D CrPS$_{4}$ hosts a variety of anomalous Hall transport phenomena, owing to its layer-dependent magnetism and symmetry character. Mon

Diogo Kendy Matsumoto

A travel groupoid is an algebraic system satisfying two suitable conditions, which has a relation to graphs. In this article, we characterize travel groupoids on finite complete multipartite graphs, and we give the numbers of travel groupoids on the complete multipartite graphs.

Timothy Sudijono, Simon Ejdemyr, Apoorva Lal, Martin Tingley

Experimentation in online digital platforms is used to inform decision making. Specifically, the goal of many experiments is to optimize a metric of interest. Null hypothesis statistical testing can be ill-suited to this task, as it is indifferent to the magnitude of effect sizes and opportunity costs. Given access to a pool of related past experiments, we d

Jiong Lin, Lechen Zhang, Kwansoo Lee, Jialong Ning

Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files for unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model t

Zebin Wang, Yi Han, Ethan X. Fang, Lan Wang

We consider the inference for the ranking of large language models (LLMs). Alignment arises as a significant challenge to mitigate hallucinations in the use of LLMs. Ranking LLMs has proven to be an effective tool to improve alignment based on the best-of-$N$ policy. In this paper, we propose a new inferential framework for hypothesis testing among the ranki

Boxun Xu, Yufei Song, Peng Li

Spiking Neural Networks (SNNs) are amenable to deployment on edge devices and neuromorphic hardware due to their lower dissipation. Recently, SNN-based transformers have garnered significant interest, incorporating attention mechanisms akin to their counterparts in Artificial Neural Networks (ANNs) while demonstrating excellent performance. However, deployin

Harvey McGuinness, Tianyu Wang, Carey E. Priebe, Hayden Helm

Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of the effect of mirroring on alignment have been limited due to the scalability of traditional experimental des

Malbor Asllani, Alex Arenas

Chimera states, marked by the coexistence of order and disorder in systems of coupled oscillators, have captivated researchers with their existence and intricate patterns. Despite ongoing advances, a fully understanding of the genesis of chimera states remains challenging. This work formalizes a systematic method by evoking pattern formation theory to explai

Minghui Xu, Hechuan Guo, Ye Cheng, Chunchi Liu

Permissionless blockchains face considerable challenges due to increasing storage demands, driven by the proliferation of Decentralized Applications (DApps). This paper introduces EC-Chain, a cost-effective storage solution for permissionless blockchains. EC-Chain reduces storage overheads of ledger and state data, which comprise blockchain data. For ledger

Yidong Chai, Kangwei Shi, Jiaheng Xie, Chunli Liu

Fake news on social media platforms poses a significant threat to societal systems, underscoring the urgent need for advanced detection methods. The existing detection methods can be divided into machine intelligence-based, crowd intelligence-based, and hybrid intelligence-based methods. Among them, hybrid intelligence-based methods achieve the best performa

Danil Tyulmankov

Many mathematical models of synaptic plasticity have been proposed to explain the diversity of plasticity phenomena observed in biological organisms. These models range from simple interpretations of Hebb's postulate, which suggests that correlated neural activity leads to increases in synaptic strength, to more complex rules that allow bidirectional synapti

Anand Deopurkar, Jayan Mukherjee

We study the syzygies of the canonical embedding of a ribbon $\widetilde{C}$ on a curve $C$ of genus $g \geq 1$. We show that the linear series Clifford index and the resolution Clifford index are equal for a general ribbon of arithmetic genus $p_a$ on a general curve of genus $g$ with $p_{a} \geq \operatorname{max}\{3g+7, 6g-4\}$. Among non-general ribbons,

Zhu Yufan, Hao Zeyu, Li Siqi, Niu Boqian

SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, it attains an Exact Match of 85.95% and an F1 Score of 92.97%, outperforming traditional BERT-based models in both accuracy and resource efficiency.

Pengyu Li, Zhijie Zhong, Tong Zhang, Zhiwen Yu

Time series anomaly detection (TSAD) has been a research hotspot in both academia and industry in recent years. Deep learning methods have become the mainstream research direction due to their excellent performance. However, new viewpoints have emerged in recent TSAD research. Deep learning is not required for TSAD due to limitations such as slow deep learni

Jinho Bok, Jason M. Altschuler

Surprisingly, recent work has shown that gradient descent can be accelerated without using momentum -- just by judiciously choosing stepsizes. An open question raised by several papers is whether this phenomenon of stepsize-based acceleration holds more generally for constrained and/or composite convex optimization via projected and/or proximal versions of g

Juechu Dong, Boyuan Feng, Driss Guessous, Yanbo Liang

Over the past 7 years, attention has become one of the most important primitives in deep learning. The primary approach to optimize attention is FlashAttention, which fuses the operation together, drastically improving both the runtime and the memory consumption. However, the importance of FlashAttention combined with its monolithic nature poses a problem fo