March 2025 arXiv papers — page 70

Silvia Izquierdo-Badiola, Carlos Rizzo, Guillem Alenyà

As robots increasingly operate in dynamic human-centric environments, improving their ability to detect, explain, and recover from action-related issues becomes crucial. Traditional model-based and data-driven techniques lack adaptability, while more flexible generative AI methods struggle with grounding extracted information to real-world constraints. We in

Rainer Rehak

Artificial intelligence (AI) is currently considered a sustainability "game-changer" within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm studies, STS, transformative sustainability science, critical computer science, and public interest theory. I argue that while there are indeed

Gema M. Diaz-Toca, Henri Lombardi, Claude Quitté

This note aims to construct an ``intrinsic'' splitting field for the polynomial $Y^n-1$ over the rational field $\bf Q$, in a way that Gauss, Kummer, Kronecker and Bishop would have liked. Contrary to the usual presentations, our construction does not use any splitting field of $Y^n-1$ which would be given before demonstrating the irreducibility of the cyclo

Paul Hill, Zhiming Liu, Nantheera Anantrasirichai

Restoration and enhancement are essential for improving the quality of videos captured under atmospheric turbulence conditions, aiding visualization, object detection, classification, and tracking in surveillance systems. In this paper, we introduce a novel Mamba-based method, the 3D Mamba-Based Atmospheric Turbulence Removal (MAMAT), which employs a dual-mo

Haolin Qin, Tingfa Xu, Tianhao Li, Zhenxiang Chen

UAV tracking faces significant challenges in real-world scenarios, such as small-size targets and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this field has been hindered by the lack of relevant da

Kai Liang

This paper presents an algorithm for computing the contraction of two-dimensional tensor networks on a square lattice; and we combine it with solving congruence equations to compute the exact enumeration (including weighted enumeration) of Wang tilings. Based on this, the paper demonstrates how to transform other tiling enumeration problems (such as those of

Yanan Ma, Senkang Hu, Zhengru Fang, Yun Ji

To accommodate constantly changing road conditions, real-time vision model training is essential for autonomous driving (AD). Federated learning (FL) serves as a promising paradigm to enable autonomous vehicles to train models collaboratively with their onboard computing resources. However, existing vehicle selection schemes for FL all assume predetermined a

Lu Zhou, Zheng-Chun Li, Keye Zhang, Zhihao Lan

As a novel platform for exploring exotic quantum phenomena, the moir\'e lattice has garnered significant interest in solid-state physics, photonics, and cold atom physics. While moir\'e lattices in two- and three-dimensional systems have been proposed for neutral cold atoms, the simpler one-dimensional moir\'e effect remains largely unexplored. We present a

Yikun Ma, Yiqing Li, Jiawei Wu, Xing Luo

Generative models have made remarkable advancements and are capable of producing high-quality content. However, performing controllable editing with generative models remains challenging, due to their inherent uncertainty in outputs. This challenge is praticularly pronounced in motion editing, which involves the processing of spatial information. While some

Masoud Kishani Farahani, Morteza Kolivandi, Abbas Rajabi Ghahnavieh, Mohammad Talaei

This study investigates the reliability and robustness of data-driven Fault Detection and Diagnosis (FDD) models for CO2 refrigeration systems (CO2-RS) in supermarkets, focusing on optimal sensor selection and resilience against sensor noise. Using tree-based machine learning algorithms - Random Forest (RF), XGBoost, CatBoost, and LightGBM - we developed FDD

Kechen Meng, Sinuo Zhang, Rongpeng Li, Chan Wang

Due to network delays and scalability limitations, clustered ad hoc networks widely adopt Reinforcement Learning (RL) for on-demand resource allocation. Albeit its demonstrated agility, traditional Model-Free RL (MFRL) solutions struggle to tackle the huge action space, which generally explodes exponentially along with the number of resource allocation units

Theodosios Papazoglou, Ed Waddingham, Alastair Young

Objective: Randomised controlled trials (RCTs) are widely considered as gold standard for assessing the effectiveness of new health interventions. When treatment non-compliance is present in RCTs, the treatment effect in the subgroup of participants who complied with their original treatment allocation, the Complier Average Causal Effect (CACE), is a more re

Murray Stokely, Jim Winget, Ed Keyes, Carrie Grimes

We present a practical, market-based solution to the resource provisioning problem in a set of heterogeneous resource clusters. We focus on provisioning rather than immediate scheduling decisions to allow users to change long-term job specifications based on market feedback. Users enter bids to purchase quotas, or bundles of resources for long-term use. Thes

Eric Ngondiep, Areej A. Binsultan, Ibtisam M. Aldawish

A high-order combined interpolation/finite element technique is developed for solving the coupled groundwater-surface water system that governs flows in karst aquifers. In the proposed high-order scheme we approximate the time derivative with piecewise polynomial interpolation of second-order and use the finite element discretization of piecewise polynomials

Ziyu Yao, Xuxin Cheng, Zhiqi Huang, Lei Li

Repetitive action counting, which aims to count periodic movements in a video, is valuable for video analysis applications such as fitness monitoring. However, existing methods largely rely on regression networks with limited representational capacity, which hampers their ability to accurately capture variable periodic patterns. Additionally, their supervise

Tayyab Naseer, K. Hassan, M. Sharif

In this paper, we discuss the existence of ghost star models in the Einstein-Maxwell framework. In order to explore these objects, we put forward the idea of Zeldovich and Novikov by keeping in mind that the energy density of such models lie in the negative range in some regions of the spacetime geometry. We proceed by taking into account a static sphere and

Andy E. Williams

The trajectory of intelligence evolution is often framed around the emergence of artificial general intelligence (AGI) and its alignment with human values. This paper challenges that framing by introducing the concept of intelligence sequencing: the idea that the order in which AGI and decentralized collective intelligence (DCI) emerge determines the long-te

Nil İnce, Hasan Mermer, Ali Mostafazadeh

For a diagonalizable linear operator $H:\mathscr{H}\to\mathscr{H}$ acting in a separable Hilbert space $\mathscr{H}$, i.e., an operator with a purely point spectrum, eigenvalues with finite algebraic multiplicities, and a set of eigenvectors that form a Reisz basis of $\mathscr{H}$, the pseudo-Hermiticity of $H$ is equivalent to its generalized parity-time-r

Yan Chen, Cheng Liu

Accurate prediction of Remaining Useful Life (RUL) for complex industrial machinery is critical for the reliability and maintenance of mechatronic systems, but it is challenged by high-dimensional, noisy sensor data. We propose the Causal-Informed Data Pruning Framework (CIDPF), which pioneers the use of causal inference to identify sensor signals with robus

Yeasir Rayhan, Walid G. Aref

Modern hardware architectures, e.g., NUMA servers, chiplet processors, tiered and disaggregated memory systems have significantly improved the performance of Main-Memory Databases, and are poised to deliver further improvements in the future. However, realizing this potential depends on the database system's ability to efficiently migrate pages among differe

Dhruv Sahnan, David Corney, Irene Larraz, Giovanni Zagni

Automatic fact-checking aims to support professional fact-checkers by offering tools that can help speed up manual fact-checking. Yet, existing frameworks fail to address the key step of producing output suitable for broader dissemination to the general public: while human fact-checkers communicate their findings through fact-checking articles, automated sys

Rebecca Clain, Eduardo Fernandes Montesuma, Fred Ngolè Mboula

Decentralized Multi-Source Domain Adaptation (DMSDA) is a challenging task that aims to transfer knowledge from multiple related and heterogeneous source domains to an unlabeled target domain within a decentralized framework. Our work tackles DMSDA through a fully decentralized federated approach. In particular, we extend the Federated Dataset Dictionary Lea

Jiaming Ji, Xinyu Chen, Rui Pan, Conghui Zhang

Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of MLLMs to prevent undesired behaviors? Going further, it is critical to explore how to fine-tune MLLMs to preserve capabilities while meeting safety constraints. Fundamentally, thi

Chin-Hung Chen, Ivana Nikoloska, Wim van Houtum, Yan Wu

Impulsive noise (IN) commonly generated by power devices can severely degrade the performance of high sensitivity wireless receivers. Accurate channel state information (CSI) knowledge is essential for designing optimal maximum a posteriori detectors. This paper examines blind channel estimation methods based on the expectation-maximization (EM) algorithm ta

Joshua E. Hammond, Tyler Soderstrom, Brian A. Korgel, Michael Baldea

We present the Subset Extended Kalman Filter (SEKF) as a method to update previously trained model weights online rather than retraining or finetuning them when the system a model represents drifts away from the conditions under which it was trained. We identify the parameters to be updated using the gradient of the loss function and use the SEKF to update o

Weiming Zhao, Claudio Roncoli, Mehmet Yildirimoglu

Macroscopic traffic flow models are essential for analysing traffic dynamics in highways and urban roads. While second-order models like METANET capture non-equilibrium traffic states, they often produce unrealistic speed predictions, such as negative values or speeds above the free-flow limit, which limits their reliability in traffic management. To overcom

Jiaji Qu, Malini Rajbhandari

Enzyme kinetics has historically been described by deterministic models, with the Michaelis-Menten (MM) equation serving as a paradigm. However, recent experimental and theoretical advances have made it clear that stochastic fluctuations, particularly at low copy numbers or single-enzyme levels, can profoundly impact reaction outcomes. In this paper, we pres

Dhasarathy Parthasarathy, Yinan Yu, Earl T. Barr

Software development builds digital tools to automate processes, yet its initial phases, up to deployment, remain largely manual. There are two reasons: Development tasks are often under-specified and transitions between tasks usually require a translator. These reasons are mutually reinforcing: it makes little sense to specify tasks when you cannot connect

Hao Zhou, Yanze Zhang, Wenhao Luo

Safety is a critical concern in learning-enabled autonomous systems especially when deploying these systems in real-world scenarios. An important challenge is accurately quantifying the uncertainty of unknown models to generate provably safe control policies that facilitate the gathering of informative data, thereby achieving both safe and optimal policies.

Aaqib Ayoub Bhat, Asif Khan, M. Mursaleen

This paper presents a new approach of constructing $\alpha$-fractal interpolation functions (FIFs) using neural network operators, integrating concepts from approximation theory. Initially, we construct $\alpha$-fractals utilizing neural network-based operators, providing an approach to generating fractal functions with interpolation properties. Based on the

Huitong Chen, Yu Wang, Yan Fan, Guosong Jiang

Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use prototypes of classes during incremental updates. Despite simplicity and intuition, we find that such methods suffer from inadequate representation capability and unsatisfied feature

Jingyu Zheng, Baoyindureng Wu

A graph $G=(V,E)$ is said to be odd (or even, resp.) if $d_G(v)$ is odd (or even, resp.) for any $v\in V$. Trivially, the order of an odd graph must be even. In this paper, we show that every 4-edge connected graph of even order has a connected odd factor. A spanning tree $T$ of $G$ is called a homeomorphically irreducible spanning tree (HIST by simply) if $

Shulei Wang, Wang Lin, Hai Huang, Hanting Wang

We introduce a novel, training-free approach for enhancing alignment in Transformer-based Text-Guided Diffusion Models (TGDMs). Existing TGDMs often struggle to generate semantically aligned images, particularly when dealing with complex text prompts or multi-concept attribute binding challenges. Previous U-Net-based methods primarily optimized the latent sp

Richa Rastogi, Yuta Saito, Thorsten Joachims

The feedback that AI systems (e.g., recommender systems, chatbots) collect from user interactions is a crucial source of training data. While short-term feedback (e.g., clicks, engagement) is widely used for training, there is ample evidence that optimizing short-term feedback does not necessarily achieve the desired long-term objectives. Unfortunately, dire

Jinyuan Liu, Bowei Zhang, Qingyun Mei, Xingyuan Li

Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each modality. Existing approaches typically treat image fusion and subsequent high-level tasks as separate processes, resulting in fused images that offer only marginal gains in task per

Qing Zhong, Peng-Tao Jiang, Wen Wang, Guodong Ding

Contemporary Video Instance Segmentation (VIS) methods typically adhere to a pre-train then fine-tune regime, where a segmentation model trained on images is fine-tuned on videos. However, the lack of temporal knowledge in the pre-trained model introduces a domain gap which may adversely affect the VIS performance. To effectively bridge this gap, we present

Oucheng Huang, Yuhang Ma, Zeng Zhao, Mingrui Wu

ComfyUI is a popular workflow-based interface that allows users to customize image generation tasks through an intuitive node-based system. However, the complexity of managing node connections and diverse modules can be challenging for users. In this paper, we introduce ComfyGPT, a self-optimizing multi-agent system designed to generate ComfyUI workflows bas

Huichen Will Wang, Kylie Lin, Andrew Cohen, Ryan Kennedy

Trust plays a critical role in visual data communication and decision-making, yet existing visualization research employs varied trust measures, making it challenging to compare and synthesize findings across studies. In this work, we first took a bottom-up, data-driven approach to understand what visualization readers mean when they say they "trust" a visua

Yuheng Feng, Jianhui Wang, Kun Li, Sida Li

Although text-to-image generation technologies have made significant advancements, they still face challenges when dealing with ambiguous prompts and aligning outputs with user intent.Our proposed framework, TDRI (Two-Phase Dialogue Refinement and Co-Adaptation), addresses these issues by enhancing image generation through iterative user interaction. It cons

Usha Kumari, Shuvendu Rana

Creating accurate and efficient 3D models poses significant challenges, particularly in addressing large viewpoint variations, computational complexity, and alignment discrepancies. Efficient camera path generation can help resolve these issues. In this context, a modified version of the Affine Scale-Invariant Feature Transform (ASIFT) is proposed to extract

Austin Deng-Yao Yang, Shih-Jen Tsai, Hsin-Jung Tsai

This study explores the influence of environmental colors on human behavior, specifically focusing on aggressiveness and passiveness. Color is widely regarded as an influential environmental factor shaping human behavior, yet existing studies present conflicting evidence regarding its impact on aggressiveness and passiveness. This study employed Minecraft as

Junshuo Liu, Xin Shi, Yunchuan Zhang, Yinhao Ge

Radio-frequency (RF)-based human activity recognition (HAR) provides a contactless and privacy-preserving solution for monitoring human behavior in applications such as astronaut extravehicular activity monitoring, human-autonomy collaborative cockpit, and unmanned aerial vehicle surveillance. However, real-world deployments usually face the challenge of dom

Peijin Guo, Minghui Li, Hewen Pan, Ruixiang Huang

While deep learning models play a crucial role in predicting antibody-antigen interactions (AAI), the scarcity of publicly available sequence-structure pairings constrains their generalization. Current AAI methods often focus on residue-level static details, overlooking fine-grained structural representations of antibodies and their inter-antibody similariti

J. S. Vorotyntseva, S. A. Levshakov

Near (~100 pc) and far (~8.7 kpc) relative to the Galactic center, the molecular clouds SgrB2(N) and Orion-KL exhibit different values of the fundamental physical constant mu=m_e/m_p - the electron-to-proton mass ratio. Measured frequency difference between the emission lines of methanol (CH3OH), - J_K_u - J_K_l = 6_3 - 5_2 A+ 542000.981 MHz, 6_3 - 5_2 A- 54

Md. Shaheenur Islam Sumon, Md. Sakib Bin Islam, Md. Sohanur Rahman, Md. Sakib Abrar Hossain

The early detection and prediction of cardiovascular diseases are crucial for reducing the severe morbidity and mortality associated with these conditions worldwide. A multi-headed self-attention mechanism, widely used in natural language processing (NLP), is operated by Transformers to understand feature interactions in feature spaces. However, the relation

Guanghui Li, Xu Li, Bin Yan

The breaking of the Lam-Tung relation in the Drell-Yan process at the LHC exhibits a long-standing tension with the Standard Model (SM) prediction at $\mathcal{O}(\alpha_s^3)$ accuracy. This tension could be explained by weak dipole interactions of leptons and quarks, associated with the $Z$-boson within the framework of the Standard Model Effective Field Th

Swastik Bhandari

Detecting maximal square submatrices of ones in binary matrices is a fundamental problem with applications in computer vision and pattern recognition. While the standard dynamic programming (DP) solution achieves optimal asymptotic complexity, its practical performance suffers from repeated minimum operations and inefficient memory access patterns that degra

Solomon Bekele, Aurelio Vivas, Thomas Applencourt, Servesh Muralidharan

As we reach exascale, production High Performance Computing (HPC) systems are increasing in complexity. These systems now comprise multiple heterogeneous computing components (CPUs and GPUs) utilized through diverse, often vendor-specific programming models. As application developers and programming models experts develop higher-level, portable programming m

Ke Ji, Yixin Lian, Linxu Li, Jingsheng Gao

In recent years, large language models (LLMs) have achieved breakthrough progress in many dialogue generation tasks. However, their lack of emotion and fine-grained role awareness limits the model's ability to provide personalized and diverse interactions further. Current methods face high costs in collecting high-quality annotated data for scenarios such as

Muneera Bano, Didar Zowghi, Jon Whittle, Liming Zhu

Adopting AI copilots in professional workflows presents opportunities for enhanced productivity, efficiency, and decision making. In this paper, we present results from a six month trial of M365 Copilot conducted at our organisation in 2024. A qualitative interview study was carried out with 27 participants. The study explored user perceptions of M365 Copilo

Kun Li, Jianhui Wang, Miao Zhang, Xueqian Wang

Generative AI has significantly advanced text-driven image generation, but it still faces challenges in producing outputs that consistently align with evolving user preferences and intents, particularly in multi-turn dialogue scenarios. In this research, We present a Visual Co-Adaptation (VCA) framework that incorporates human-in-the-loop feedback, utilizing

Robert Brandenberger

The recent DESI results provide increasing evidence that the density of dark energy is time-dependent. I will recall why, from the point of view of fundamental theory,, this result should not be surprising.

Davide Villaboni, Alberto Castellini, Ivan Luciano Danesi, Alessandro Farinelli

Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architectures exploit either temporal or inter-channel dependencies, limiting their effectiveness in multivariate time-series forecasting where both types of dependencies are crucial. We propo

Yuheng Ding, Bo Qiang, Shaoning Li, Yiran Zhou

Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug discovery. Nowadays, existing deep learning methods for natural products research primarily rely on supervised learning approaches designed for specific downstream tasks. However, such one-model-for-a-task paradigm oft

Nouédyn Baspin

The surface code is a two-dimensional stabiliser code with parameters $[[n,1,\Theta(\sqrt{n})]]$. To this day, no stabiliser code with growing distance is know to live in less than two dimensions. In this note we show that no such code can exist.

Connor Ding, Abhiram Gorle, Sagnik Bhattacharya, Divija Hasteer

Recent advances in symbolic music generation primarily rely on deep learning models such as Transformers, GANs, and diffusion models. While these approaches achieve high-quality results, they require substantial computational resources, limiting their scalability. We introduce LZMidi, a lightweight symbolic music generation framework based on a Lempel-Ziv (L

Manoj Mandal, Sabyasachi Pal, G. K. Jaisawal, Anne Lohfink

We report the broadband timing and spectral properties of the neutron star low-mass X-ray binary Aql X-1 during the 2024 outburst with NICER, NuSTAR, and Swift observatories. We detected six thermonuclear X-ray bursts during the NICER and NuSTAR observations, with the observed X-ray burst profiles exhibiting a strong energy dependence. The time-resolved burs

Haruki Kanaya, Ryota Eguchi, Taisho Sasada, Fukuhito Ooshita

We address the self-stabilizing exact majority problem in the population protocol model, introduced by Angluin, Aspnes, Diamadi, Fischer, and Peralta (2004). In this model, there are $n$ state machines, called agents, which form a network. At each time step, only two agents interact with each other, and update their states. In the self-stabilizing exact majo

Zhuo Tao, Liang Li, Qi Chen, Yunbin Tu

Natural language video localization (NLVL) is a crucial task in video understanding that aims to localize the target moment in videos specified by a given language description. Recently, a point-supervised paradigm has been presented to address this task, requiring only a single annotated frame within the target moment rather than complete temporal boundarie

Xi Xiao, Yunbei Zhang, Yanshuh Li, Xingjian Li

Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of full fine-tuning. While existing visual prompt tuning (VPT) methods have made significant strides, they predominantly rely on static, domain-specific prompts that fail to capture th

Jiacheng Yao, Wei Xu, Guangxu Zhu, Zhaohui Yang

Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though AirComp leverages the superposition property of multiple access channels for computation, it inherently limits its ability to manage inter-task interference in multi-task computin

Jeremy VanderDoes, Shojaeddin Chenouri

Modeling functions that are sequentially observed as functional time series is becoming increasingly common. In such models, it is often crucial to ensure data homogeneity. We investigate the sensitivity of graph-based change point detection for changes in the distribution of functional data that demarcate homogeneous regions. Related test statistics and thr

Muhammad Ahmad, Sardar Usman, Ildar Batyrshin, Muhammad Muzammil

Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of

Dalia Saha, Abhik Kumar Sanyal

Both the generalized teleparallel theories of gravity suffer from some serious problems. The strong coupling issue appearing as a consequence of extra degrees of freedom in the `generalized metric teleparallel gravity' theory, prompted to consider `generalized symmetric teleparallel gravity' theory (GSTG). Unfortunately, recent perturbative analysis in the b

Phil Pollett

In a recent paper, Shah [arXiv:2502.03073] derived an explicit expression for the distribution of occupancy times for a two-state Markov chain, using a method based on enumerating sample paths. We consider here the more general problem of finding the distribution of occupancy times for countable-state Markov chains in discrete time. Our approach, which emplo

Yen Cheng Chang, Jesse Codling, Yiwen Dong, Jiale Zhang

Crowd monitoring in sports stadiums is important to enhance public safety and improve the audience experience. Existing approaches mainly rely on cameras and microphones, which can cause significant disturbances and often raise privacy concerns. In this paper, we sense floor vibration, which provides a less disruptive and more non-intrusive way of crowd sens

Adam Atanas, Kai Liu

Large language models (LLMs) exhibit impressive capabilities but struggle with reasoning errors due to hallucinations and flawed logic. To investigate their internal representations of reasoning, we introduce ArrangementPuzzle, a novel puzzle dataset with structured solutions and automated stepwise correctness verification. We trained a classifier model on L

Mudit Gaur, Utsav Singh, Amrit Singh Bedi, Raghu Pasupathu

Bilevel reinforcement learning (BRL) has emerged as a powerful framework for aligning generative models, yet its theoretical foundations, especially sample complexity bounds, remain underexplored. In this work, we present the first sample complexity bound for BRL, establishing a rate of $\mathcal{O}(\epsilon^{-3})$ in continuous state-action spaces. Traditio

Belle, Belle II Collaborations, :, I. Adachi

Using 983.0 $\rm{fb}^{-1}$ and 427.9 $\rm{fb}^{-1}$ data samples collected with the Belle and Belle II detectors at the KEKB and SuperKEKB asymmetric energy $e^+e^-$ colliders, respectively, we present studies of the Cabibbo-favored $\Xi_c^+$ decays ${\Xi_{c}^{+}\to \Sigma^{+}K_{S}^{0}}$ and $\Xi_{c}^{+}\to \Xi^{0}\pi^{+}$, and the singly Cabibbo-suppressed

Fei Sun, Xun Chen, Shuang Li, Akira Watanabe

The precise determination of critical exponents is crucial for understanding the properties of strongly interacting matter under extreme conditions. These exponents are fundamentally linked to the system's behavior near the critical end point (CEP), making precise localization of the CEP essential. However, precisely identifying the CEP within the framework

Chi Zhang, Chengjian Feng, Feng Yan, Qiming Zhang

Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data. This scarcity not only limits the availability of training data but also hinders the systematic exploration of model architectures and training strategies. While prior work has improved specific aspects of

Alexis Teter, Abhishek Halder

The purpose of this note is to clarify the importance of the relation $\boldsymbol{gg}^{\top}\propto \boldsymbol{\sigma\sigma}^{\top}$ in solving control-affine Schr\"{o}dinger bridge problems via the Hopf-Cole transform, where $\boldsymbol{g},\boldsymbol{\sigma}$ are the control and noise coefficients, respectively. We show that the Hopf-Cole transform appl

Casey Randazzo, Tawfiq Ammari

Trauma can disrupt one's sense of self and mental well-being, leading survivors to reconstruct their identities in online communities. Drawing from 30 in-depth interviews, we present a sociotechnical process model that illustrates the mechanisms of online identity reconstruction and the pathways to integration. We introduce the concept of fractured identitie

Xiangyu Cui, Nicholas G. Hall, Yun Shi, Tianyuan Su

We propose a Policy Averaging Approach (PAA) that synthesizes the strengths of existing approaches to create more reliable, flexible and justifiable policies for stochastic optimization problems. An important component of the PAA is risk diversification to reduce the randomness of policies. A second component emulates model averaging from statistics. A third

Abdelkader Tizaoui

This study investigates the asymptotic dynamics of solutions to the Fokker-Planck-Kolmogorov (FPK) equation, with a specific focus on ship roll stability in dynamic sea conditions. Utilizing a fourth-order filter, we conduct a thorough analysis of the time evolution of the probability distributions for roll angles, roll speeds, and roll excitations. Our theo

Van Hao Can, Remco van der Hofstad

On locally tree-like random graphs, we relate the random cluster model with external magnetic fields and $q\geq 2$ to Ising models with vertex-dependent external fields. The fact that one can formulate general random cluster models in terms of two-spin ferromagnetic Ising models is quite interesting in its own right. However, in the general setting, the exte

Sayantan Choudhury

We provide a study of the effects of the Effective Field Theory (EFT) generalisation of stochastic inflation on the production of primordial black holes (PBHs) in a model-independent single-field context. We demonstrate how the scalar perturbations' Infra-Red (IR) contributions and the emerging Fokker-Planck equation driving the probability distribution char

Junwei Ji, Dongyuan Shi, Woon-Seng Gan

Distributed multichannel active noise control (DMCANC), which utilizes multiple individual processors to achieve a global noise reduction performance comparable to conventional centralized multichannel active noise control (MCANC), has become increasingly attractive due to its high computational efficiency. However, the majority of current DMCANC algorithms

Tejas Panambur, Mario Parente

Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous feature embeddings from Martian rover imagery, the natural variations in intensity, scale, and rotation pose significant challenges for accur

Jiali Cheng, Hadi Amiri

Language models are prone to dataset biases, known as shortcuts and spurious correlations in data, which often result in performance drop on new data. We present a new debiasing framework called ``FairFlow'' that mitigates dataset biases by learning to be undecided in its predictions for data samples or representations associated with known or unknown biases

Marcus Tantakoun, Xiaodan Zhu, Christian Muise

Large Language Models (LLMs) excel in various natural language tasks but often struggle with long-horizon planning problems requiring structured reasoning. This limitation has drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. However, identifying optimal AP deployment

Jiali Cheng, Hadi Amiri

This study finds that existing information retrieval (IR) models show significant biases based on the linguistic complexity of input queries, performing well on linguistically simpler (or more complex) queries while underperforming on linguistically more complex (or simpler) queries. To address this issue, we propose EqualizeIR, a framework to mitigate lingu

Breanna Camden, Jörg Frauendiener, Joseph Galinski, Kaushal Pillay

In this contribution we present an overview of our work on the numerical simulation of the perturbation of a black hole space-time by incoming gravitational waves. The formulation we use is based on Friedrich's general conformal equations which have the unique property that they allow access to the asymptotic region of an asymptotically regular space-time. I

Bin Duan, Matthew B. Dwyer, Guowei Yang

Deep Neural Networks (DNNs) have been widely employed across various domains, including safety-critical systems, necessitating comprehensive testing to ensure their reliability. Although numerous DNN model testing methods have been proposed to generate adversarial samples that are capable of revealing faults, existing methods typically perturb samples in the

Robin Dietrich, Tobias Fischer, Nicolai Waniek, Nico Reeb

Efficient spatial navigation is a hallmark of the mammalian brain, inspiring the development of neuromorphic systems that mimic biological principles. Despite progress, implementing key operations like back-tracing and handling ambiguity in bio-inspired spiking neural networks remains an open challenge. This work proposes a mechanism for activity back-tracin

Weikai Wang, Erick Delage

For continuing tasks, average cost Markov decision processes have well-documented value and can be solved using efficient algorithms. However, it explicitly assumes that the agent is risk-neutral. In this work, we extend risk-neutral algorithms to accommodate the more general class of dynamic risk measures. Specifically, we propose a relative value iteration

Kunal Mozumdar, Herbert F. Fotso, Jong E. Han

Transport in disordered systems often occurs via the variable range hopping (VRH) in the dilute carrier density limit, where electrons hop between randomly distributed localized levels. We study the nonequilibrium transport by a uniform DC electric field on a one-dimensional insulating tight-binding chain with the on-site disorder, using a disordered-lattice

Carlos Allende Prieto

As the multiplexing power of spectroscopic instruments increases, so does the need for automated analysis. In practice, the bottleneck for speed is the calculation of model spectra to evaluate the likelihood of candidate parameters. This presentation gives an overview of the steps required for automating spectroscopic analyses, focusing on the speedups achie

Ziang Zheng, Guojian Zhan, Bin Shuai, Shengtao Qin

Reinforcement learning (RL) has demonstrated remarkable capability in acquiring robot skills, but learning each new skill still requires substantial data collection for training. The pretrain-and-finetune paradigm offers a promising approach for efficiently adapting to new robot entities and tasks. Inspired by the idea that acquired knowledge can accelerate

Karol Chlasta, Katarzyna Wisiecka, Krzysztof Krejtz, Izabela Krejtz

Well-being is a dynamic construct that evolves over time and fluctuates within individuals, presenting challenges for accurate quantification. Reduced well-being is often linked to depression or anxiety disorders, which are characterised by biases in visual attention towards specific stimuli, such as human faces. This paper introduces a novel approach to AI-

MohammadAmin Zaheri, Michalis Famelis, Eugene Syriani

Workarounds enable users to achieve goals despite system limitations but expose design flaws, reduce productivity, risk compromising data quality, and cause inconsistencies. This study investigates how users employ workarounds when the data they want to enter does not align with software form constraints. Through a descriptive user study, we analyzed how wor

Methusela Sulle, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs explainable artificial intelligence (XAI) to identify key factors contributing to pedestrian fatalities across the five

Hongwei Mei, Rui Wang, Qingmeng Wei, Jiongmin Yong

This paper is concerned with an infinite horizon stochastic linear quadratic (LQ, for short) optimal control problems with conditional mean-field terms in a switching environment. Different from [17], the cost functionals do not have positive-definite weights here. When the problems are merely finite, we construct a sequence of asymptotic optimal controls an

Sarif Khan, Jongkuk Kim, Hyun Min Lee

In this study, we explore vector dark matter (DM) production in the early Universe focusing on a scenario with a low reheating temperature. One can achieve low reheat temperature in many ways, for example, by considering a longer lifetime of the inflaton field. We analyze the impact of various model parameters on DM production, including gauge coupling and r

I. P-Castro, J. L. Díaz-Cruz, A. Pérez-Lorenzana

In this paper, we present a revision of the discrete symmetries (C, P, T, CP, and CPT) within an approach that treats 2-component Weyl spinors as the fundamental building blocks. In particular, we show that we can define transformations for CP, T, and CPT without exchanging the right-handed and left-handed representative components that form a Dirac spinor,

Mingyue Yuan, Jieshan Chen, Zhenchang Xing, Gelareh Mohammadi

Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation accuracy. We present MCHR (Multi-LLM Consensus with Human Review), a novel semi-automated framework that enhances annotati

Alexander Smith

Given an elliptic curve E/Q, we show that 50% of the quadratic twists of E have $2^{\infty}$-Selmer corank 0 and 50% have $2^{\infty}$-Selmer corank 1. As one consequence, we prove that the Birch and Swinnerton-Dyer conjecture implies Goldfeld's conjecture. Previously, this result was known by work of the author for elliptic curves over Q satisfying certain

Shingyu Leung

We propose implicit integrators for solving stiff differential equations on unit spheres. Our approach extends the standard backward Euler and Crank-Nicolson methods in Cartesian space by incorporating the geometric constraint inherent to the unit sphere without additional projection steps to enforce the unit length constraint on the solution. We construct t

Eloise Lardet, Letian Chen, Thibault Bertrand

A hallmark in natural systems, self-organization often stems from very simple interaction rules between individual agents. While single-species self-propelled particle (SPP) systems are well understood, the behavior of binary mixtures with general alignment interactions remains largely unexplored with a few scattered results hinting at the existence of a ric

Chenbo Shi, Shichen Liang, Jin Pan, Xin Gu

This paper presents a unified formulation for synthesizing the generalized scattering matrix (GS-matrix) of hybrid electromagnetic systems comprising arbitrary numbers of antennas and scatterers. The proposed method provides a modular region decomposition framework that enables efficient analysis of electromagnetic interactions between distinct structures, u