March 2025 arXiv papers — page 66

Tianxin Huang, Gim Hee Lee

With the growth of 3D applications and the rapid increase in sensor-collected 3D point cloud data, there is a rising demand for efficient compression algorithms. Most existing learning-based compression methods handle geometry and color attributes separately, treating them as distinct tasks, making these methods challenging to apply directly to point clouds

Nachuan Ma, Zhengfei Song, Qiang Hu, Chuang-Wei Liu

In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with

Yunfei Yang, Xiaojun Chen, Yuexin Xuan, Zhendong Zhao

Model stealing attack is increasingly threatening the confidentiality of machine learning models deployed in the cloud. Recent studies reveal that adversaries can exploit data synthesis techniques to steal machine learning models even in scenarios devoid of real data, leading to data-free model stealing attacks. Existing defenses against such attacks suffer

Tiago P. Bonetti, Williamson Silva, Thelma E. Colanzi

The discipline of Software Engineering (SE) allows students to understand specific concepts or problems while designing software. Empowering students with the necessary knowledge and skills for the software industry is challenging for universities. One key problem is that traditional methodologies often leave students as passive agents, limiting engagement a

Dimitrios Beis, Athanasios Dedes

We demonstrate the cancellation of chiral anomalies in the Standard Model Effective Field Theory (SM EFT), achieved through by a consistent choice of loop momentum routing in triangle diagrams with dimension-6 operator insertions. By enforcing gauge invariance and Bose symmetry, we show that Goldstone boson contributions cancel anomalies arising from massive

Jun Gao, Feng Yin, Wenzhong Yan, Qinglei Kong

Existing fingerprinting-based localization methods often require extensive data collection and struggle to generalize to new environments. In contrast to previous environment-unknown MetaLoc, we propose GenMetaLoc in this paper, which first introduces meta-learning to enable the generation of dense fingerprint databases from an environment-aware perspective.

Matthew Cleaveland, Pengyuan Lu, Oleg Sokolsky, Insup Lee

Verifying the behaviors of autonomous systems with learned perception components is a challenging problem due to the complexity of the perception and the uncertainty of operating environments. Probabilistic model checking is a powerful tool for providing guarantees on stochastic models of systems. However, constructing model-checkable models of black-box per

Somnath Roy, Padharthi Sreekar, Srivatsa Narasimha, Anubhav Anand

The current study introduces a novel adaptation of speculative decoding, repurposed from generation to classification tasks. We propose a multi-model framework employing up to three lightweight worker models and a single, more robust judge model analogous to draft models and target model, respectively, in speculative decoding. The worker models, tasked with

Lucas Kock, Linda S. L. Tan, Prateek Bansal, David J. Nott

Gaussian variational approximations are widely used for summarizing posterior distributions in Bayesian models, especially in high-dimensional settings. However, a drawback of such approximations is the inability to capture skewness or more complex features of the posterior. Recent work suggests applying skewness corrections to existing Gaussian or other sym

Yu Mao, Jun Wang, Nan Guan, Chun Jason Xue

Whole-Slide Images (WSIs) have revolutionized medical analysis by presenting high-resolution images of the whole tissue slide. Despite avoiding the physical storage of the slides, WSIs require considerable data volume, which makes the storage and maintenance of WSI records costly and unsustainable. To this end, this work presents the first investigation of l

Yuxuan Xie, Xuan Yu, Changjian Jiang, Sitong Mao

Open-vocabulary panoptic reconstruction is a challenging task for simultaneous scene reconstruction and understanding. Recently, methods have been proposed for 3D scene understanding based on Gaussian splatting. However, these methods are multi-staged, suffering from the accumulated errors and the dependence of hand-designed components. To streamline the pip

Germán Capdehourat, Isabel Amigo, Brian Lorenzo, Joaquín Trigo

Grading is a time-consuming and laborious task that educators must face. It is an important task since it provides feedback signals to learners, and it has been demonstrated that timely feedback improves the learning process. In recent years, the irruption of LLMs has shed light on the effectiveness of automatic grading. In this paper, we explore the perform

Zhiyu Lin, Yifei Gao, Xian Zhao, Yunfan Yang

Language models have recently advanced into the realm of reasoning, yet it is through multimodal reasoning that we can fully unlock the potential to achieve more comprehensive, human-like cognitive capabilities. This survey provides a systematic overview of the recent multimodal reasoning approaches, categorizing them into two levels: language-centric multim

Yashvardhan Singh

Adders are fundamental components in digital circuits, playing a crucial role in arithmetic operations within computing systems and many other applications. This paper focuses on the design and simulation of a 32-bit Brent-Kung parallel prefix adder, which is recognized for its efficient carry propagation and logarithmic delay characteristics. The Brent-Kung

Si Shen, Fei Huang, Zhixiao Zhao, Chang Liu

Difficult problems, which often result in long reasoning traces, are widely recognized as key factors for enhancing the performance of reasoning models. However, such high-challenge problems are scarce, limiting the size of available datasets. In this paper, we propose a simple method to decouple the reliance on problem difficulty. First, we empirically demo

Ismail El Korde, Dóra Bárdfalvy, Jason M. Lewis, Alexander Morozov

Giant number fluctuations (GNFs), whereby the standard deviation $\Delta N$ in the local number of particles $\langle N \rangle$ grows faster than $\sqrt{\langle N \rangle}$, are a hallmark property of dry active matter systems with orientational order, such as a collection of granular particles on a vibrated plate. This contrasts with momentum-conserving ("

Shahaf Bassan, Shlomit Gur, Sergey Zeltyn, Konstantinos Mavrogiorgos

Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model t

Zeyuan Ma, Hongqiao Lian, Wenjie Qiu, Yue-Jiao Gong

Detecting potential optimal peak areas and locating the accurate peaks in these areas are two major challenges in Multimodal Optimization problems (MMOPs). To address them, much efforts have been spent on developing novel searching operators, niching strategies and multi-objective problem transformation pipelines. Though promising, existing approaches more o

Wenjie Qiu, Hongshu Guo, Zeyuan Ma, Yue-Jiao Gong

Cooperative Co-evolution, through the decomposition of the problem space, is a primary approach for solving large-scale global optimization problems. Typically, when the subspaces are disjoint, the algorithms demonstrate significantly both effectiveness and efficiency compared to non-decomposition algorithms. However, the presence of overlapping variables co

Ziming Wei, Bingqian Lin, Yunshuang Nie, Jiaqi Chen

Data scarcity is a long-standing challenge in the Vision-Language Navigation (VLN) field, which extremely hinders the generalization of agents to unseen environments. Previous works primarily rely on additional simulator data or web-collected images/videos to improve the generalization. However, the simulator environments still face limited diversity, and th

Xiaoming Qi, Jingyang Zhang, Huazhu Fu, Guanyu Yang

Federated continual learning (FCL) offers an emerging pattern to facilitate the applicability of federated learning (FL) in real-world scenarios, where tasks evolve dynamically and asynchronously across clients, especially in medical scenario. Existing server-side FCL methods in nature domain construct a continually learnable server model by client aggregati

Pieyi Zhang, Richong Zhang, Zhijie Nie

Multi-task prompt tuning utilizes multiple high-resource source tasks to improve performance on low-source target tasks. Existing approaches transfer the soft prompt trained by combining all source tasks or a single ``high-similar'' source task one-time-only. However, we find that the optimal transfer performance often comes from a combination of source task

B. Pritychenko

We present calculations of neutron thermal cross sections, Westcott factors, resonance integrals, Maxwellian-averaged cross sections, astrophysical reaction rates, and solar system $r$-process abundances using the latest data from the major evaluated nuclear libraries for 849 ENDF target materials. The recent release of ENDF/B-VIII.1 library, progress in $^{

Anh Duc Nguyen, Hieu Minh Phi, Anh Viet Ngo, Long Hai Trieu

Large Language Models (LLMs) have shown remarkable proficiency in Machine Reading Comprehension (MRC) tasks; however, their effectiveness for low-resource languages like Vietnamese remains largely unexplored. In this paper, we fine-tune and evaluate two state-of-the-art LLMs: Llama 3 (8B parameters) and Gemma (7B parameters), on ViMMRC, a Vietnamese MRC data

Hongshu Guo, Sijie Ma, Zechuan Huang, Yuzhi Hu

Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable meta-level policies that excel in dynamic algorithm configuration (DAC) tasks within the low-level optimization, reducing the expertise required to adapt optimizers for novel optimi

Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao

Recent Meta-Black-Box Optimization (MetaBBO) approaches have shown possibility of enhancing the optimization performance through learning meta-level policies to dynamically configure low-level optimizers. However, existing MetaBBO approaches potentially consume massive function evaluations to train their meta-level policies. Inspired by the recent trend of u

Zhongyin Xu, Chengming Bai, Yanyong Hong

We introduce the notion of Leibniz conformal bialgebras, presenting a bialgebra theory for Leibniz conformal algebras as well as the conformal analogues of Leibniz bialgebras. They are equivalently characterized in terms of matched pairs and conformal Manin triples of Leibniz conformal algebras. In the coboundary case, the classical Leibniz conformal Yang-Ba

Minghao Li, Ling Yang

Establishing detailed relationships between transnormal systems of different types and their behaviors under covering maps, this paper presents a classification of transnormal systems on compact 3-manifolds in the sense of equivalence. For CPC transnormal systems, we show that the ambient manifolds must be locally isometric to one of six standard geometries

Eric Rains, Hjalmar Rosengren

We prove that the Ruijsenaars model admits a one-parameter commuting family of Q-operators. The commutativity is equivalent to an elliptic hypergeometric integral transformation that was conjectured by Gadde et al., and has an alternative interpretation in terms of S-duality for quiver gauge theories. We present two proofs of this conjecture, one using the e

Alex A. Gorodetsky, Patrick D. Mullen, Aditya Deshpande, Joshua C. Dolence

We present a novel tensor network algorithm to solve the time-dependent, gray thermal radiation transport equation. The method invokes a tensor train (TT) decomposition for the specific intensity. The efficiency of this approach is dictated by the rank of the decomposition. When the solution is "low-rank," the memory footprint of the specific intensity solut

Mingde Yao, Menglu Wang, King-Man Tam, Lingen Li

Reflection removal is challenging due to complex light interactions, where reflections obscure important details and hinder scene understanding. Polarization naturally provides a powerful cue to distinguish between reflected and transmitted light, enabling more accurate reflection removal. However, existing methods often rely on small-scale or synthetic data

Ludovico T. Giorgini, Tobias Bischoff, Andre N. Souza

We propose a hybrid method for accurately estimating the score function, i.e., the gradient of the log steady-state density, using a Gaussian Mixture Model (GMM) in conjunction with a bisecting K-means clustering step. Our approach, which we call KGMM, offers a systematic way to combine statistical density estimation with a neural-network-based interpolation

Yue Li, Qi Ma, Runyi Yang, Huapeng Li

Recognizing arbitrary or previously unseen categories is essential for comprehensive real-world 3D scene understanding. Currently, all existing methods rely on 2D or textual modalities during training or together at inference. This highlights the clear absence of a model capable of processing 3D data alone for learning semantics end-to-end, along with the ne

Yuepeng Qian, Jingfeng Xiong, Haoyong Yu, Chenglong Fu

Gait asymmetry is a significant clinical characteristic of hemiplegic gait that most stroke survivors suffer, leading to limited mobility and long-term negative impacts on their quality of life. Although a variety of exoskeleton controls have been developed for robot-assisted gait rehabilitation, little attention has been paid to correcting the gait asymmetr

Hanwool Lee

We propose (G)I-DLE, a new approach to constrained decoding that leverages KL divergence minimization to preserve the intrinsic conditional probability distribution of autoregressive language models while excluding undesirable tokens. Unlike conventional methods that naively set banned tokens' logits to $-\infty$, which can distort the conversion from raw lo

Matthäus Schulik, James E. Owen, Richard A. Booth, Shun Fai Ling

The bolometric radiation from a central body is potentially a powerful driver of atmospheric escape from planets or satellites. When heated above their equilibrium temperatures those satellites, due to their low surface gravity, are be prone to significant atmospheric erosion. Such high temperatures can be reached through a known mechanism: a large ratio of

Xiaochen Zhang, Haoyi Xiong

In high-dimensional and high-stakes contexts, ensuring both rigorous statistical guarantees and interpretability in feature extraction from complex tabular data remains a formidable challenge. Traditional methods such as Principal Component Analysis (PCA) reduce dimensionality and identify key features that explain the most variance, but are constrained by t

Sergey Avvakumov, Alfredo Hubard

For each $d\geq 3$ we construct cube complexes homeomorphic to the $d$-sphere with $n$ vertices in which the number of facets (assuming $d$ constant) is $\Omega(n^{5/4})$. This disproves a conjecture of Kalai's stating that the number of faces (of all dimensions) of cubical spheres is maximized by the boundaries of neighbourly cubical polytopes. The conjectu

Selim F. Yilmaz, Can Karamanli, Deniz Gunduz

We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or bandwidth) among users, which limits their capacity. We introduce a machine learning (ML)-aided wireless image transmission

Ling-Di Wang, Yu Chen, Yu-Hui Zhang

For discrete-time Markov chains on general state spaces, we establish criteria for non-ergodicity and non-strong ergodicity, and derive sufficient conditions for non-geometric ergodicity via the theory of minimal nonnegative solutions. Our criteria are formulated based on the existence of solutions to inequalities involving the chain's one-step transition ke

Jiachang Ye, Jianguo Qian, Zoran Stanić

A graph is determined by its signless Laplacian spectrum if there is no other non-isomorphic graph sharing the same signless Laplacian spectrum. Let $C_l$, $P_l$, $K_l$ and $K_{s,l-s}$ be the cycle, the path, the complete graph and the complete bipartite graph with $l$ vertices, respectively. We prove that $$G\cong K_1\vee (C_{l_1}\cup C_{l_2}\cup\cdots \cup

Nishavi Ranaweera, Jiarui Xu, Suranga Seneviratne, Aruna Seneviratne

Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market. This widespread usage makes Android a prime target for malicious attacks. Despite efforts to combat malicious attacks through tools like Google Play Protect and antivirus software, new and evolved malware continues to infil

Qiang Wang, Yuhang He, SongLin Dong, Xiang Song

Domain-Incremental Learning (DIL) enables vision models to adapt to changing conditions in real-world environments while maintaining the knowledge acquired from previous domains. Given privacy concerns and training time, Rehearsal-Free DIL (RFDIL) is more practical. Inspired by the incremental cognitive process of the human brain, we design Dual-level Concep

Weiquan Chen, Zhao Dong, Yang Zheng

We show non-uniqueness of local strong solutions to stochastic fractional Navier-Stokes equations with linear multiplicative noise and some certain deterministic force. Such non-uniqueness holds true even if we perturb such deterministic force in appropriate sense.This is closely related to a critical condition on force under which Leray-Hopf solution to the

Tao Fang, Majid Zamani

There is a need for fast adaptation in spike sorting algorithms to implement brain-machine interface (BMIs) in different applications. Learning and adapting the functionality of the sorting process in real-time can significantly improve the performance. However, deep neural networks (DNNs) depend on large amounts of data for training models and their perform

Z. Haba

We consider a path integral representation of the time evolution $\exp(-\frac{i}{\hbar}tH)$ for Lagrangians of the variable $A$ which can be represented in the form (quadratic in $Q$) ${\cal L}(A)=\frac{1}{2}Q(A){\cal M}Q(A)+\partial_{\mu}L^{\mu}$. We show that $\exp(-\frac{i}{\hbar}tH)\exp(\frac{i}{\hbar}\int d{\bf x}L^{0}) =\exp(\frac{i}{\hbar}\int d{\bf x

Wei-Na Li, Yi Zhou, Jiatai Chen, Hongjie Ou

We propose an autofocusing algorithm to obtain, relatively accurately, the 3D position of each particle, particularly its axial location, and particle number of a dense transparent particle solution via its hologram. First, morphological analyses and constrained intensity are used on raw reconstructed images to obtain information on candidate focused particl

Dohyeon Lee, Juyeon Park, Juheon Lee, Chungha Lee

Holotomography (HT) is a label-free, three-dimensional imaging technique that captures refractive index distributions of biological samples at sub-micron resolution. As modern HT systems enable high-throughput and large-scale acquisition, they produce terabyte-scale datasets that require efficient data management. This study presents a systematic benchmarkin

Ian Koot

Let $K_1 \subset H$ and $K_2 \subset H$ be half-sided modular inclusions in a common standard subspace $H$. We prove that the inclusion $K_1 \subset K_2$ holds if and only if we have an inclusion of spectral subspaces of the generators of the positive one-parameter groups associated to the half-sided modular inclusions $K_1 \subset H$ and $K_2 \subset H$. Fr

Tianyi Shang, Zhenyu Li, Pengjie Xu, Zhaojun Deng

Environment description-based localization in large-scale point cloud maps constructed through remote sensing is critically significant for the advancement of large-scale autonomous systems, such as delivery robots operating in the last mile. However, current approaches encounter challenges due to the inability of point cloud encoders to effectively capture

Qiao Liang, Yanjiang Liu, Weixiang Zhou, Ben He

Does the prior knowledge of the vision encoder constrain the capability boundary of Multi-modal Large Language Models (MLLMs)? While most existing research treats MLLMs as unified systems optimized through end-to-end training, the impact of vision encoder's prior knowledge is seldom investigated. In this work, we introduce a novel metric, $Rank_e$, to quanti

Dvir Samuel, Matan Levy, Nir Darshan, Gal Chechik

In Omnimatte, one aims to decompose a given video into semantically meaningful layers, including the background and individual objects along with their associated effects, such as shadows and reflections. Existing methods often require extensive training or costly self-supervised optimization. In this paper, we present OmnimatteZero, a training-free approach

Emma Coletta, Davide Salvi, Viola Negroni, Daniele Ugo Leonzio

The rise of AI-driven generative models has enabled the creation of highly realistic speech deepfakes - synthetic audio signals that can imitate target speakers' voices - raising critical security concerns. Existing methods for detecting speech deepfakes primarily rely on supervised learning, which suffers from two critical limitations: limited generalizatio

Vladimir Rovenski

Weak almost contact metric manifolds (i.e., the complex structure is replaced by a nonsingular skew-symmetric tensor), defined by the author and R. Wolak, allow a new look at the classical theory and find novel applications. An important case of these manifolds, which is locally a twisted product, is a weak $\beta$-Kenmotsu manifold defined by the author and

Jiaqi Xiu, Yongjian Li

Parameterized systems play a crucial role in the computer field, and their security is of great significance. Formal verification of parameterized protocols is especially challenging due to its "parameterized" feature, which brings complexity and undecidability. Existing automated parameterized verification methods have limitations, such as facing difficulti

Zongxiao Wu, Yizhe Dong, Yaoyiran Li, Baofeng Shi

This study explores the integration of a representative large language model, ChatGPT, into lending decision-making with a focus on credit default prediction. Specifically, we use ChatGPT to analyse and interpret loan assessments written by loan officers and generate refined versions of these texts. Our comparative analysis reveals significant differences be

Yergali Kurmanov, Kuantay Boshkayev, Talgar Konysbayev, Marco Muccino

We explore circular geodesics of neutral test particles in the gravitational field of a rotating deformed mass. The geometry around this source is described by the Quevedo-Mashhoon solution, which corresponds to a naked singularity. To this end, we compute the orbital parameters of test particles in the equatorial plane, such as the angular velocity $\Omega$

Ahmad Al-Badawi, Faizuddin Ahmed, Tursunali Xamidov, Sanjar Shaymatov

In this study, we investigate black holes (BHs) surrounded by a quintessence field (QF) within the framework of effective quantum gravity (EQG). We analyze the spacetime metric characterized by quantum correction parameter $\xi$ and quintessence parameters $(c,w)$, revealing a rich three-horizon structure whose properties depend on both quantum effects and d

Vardaan Mongia, Abhishek Kumar, Shashi Prabhakar, R. P. Singh

Quantum random numbers are essential for security against quantum algorithms. Randomness as a beacon is a service being provided for companies and governments to upgrade their security standards from RSA to PQC-QKD or PQC-RSA protocols. Both security mechanisms assume trust in the service provider unless one aims for device-independent protocols. How does an

Zuan Xie, Yang Xu, Hongli Xu, Yunming Liao

Recent advancements in large language models (LLMs) have catalyzed a substantial surge in demand for LLM services. While traditional cloud-based LLM services satisfy high-accuracy requirements, they fall short in meeting critical demands for low delay and enhanced privacy. To address these limitations, we propose HAT, a novel device-cloud collaborative infer

Alexandre Perez-Lebel, Gael Varoquaux, Sanmi Koyejo, Matthieu Doutreligne

Probabilistic classifiers are central for making informed decisions under uncertainty. Based on the maximum expected utility principle, optimal decision rules can be derived using the posterior class probabilities and misclassification costs. Yet, in practice only learned approximations of the oracle posterior probabilities are available. In this work, we qu

Sebastian Bervoets, Mathieu Faure, Ludovic Renou

Deviations from Bayesian updating are traditionally categorized as biases, errors, or fallacies, thus implying their inherent ``sub-optimality.'' We offer a more nuanced view. We demonstrate that, in learning problems with misspecified models, non-Bayesian updating can outperform Bayesian updating.

Raghul Parthipan, Mohit Anand, Hannah M Christensen, Frederic Vitart

Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inputs map to different outputs. This improves generalization by preventing ML models from becoming overconfident in their predictions. This paper shows how using longer timesteps when

Hannes Rosseel, Toon van Waterschoot

Historical Worship Spaces (HWS) are significant architectural landmarks which hold both cultural and spiritual value. The acoustic properties of these spaces play a crucial role in historical and contemporary religious liturgies, rituals, and ceremonies, as well as in the performance of sacred music. However, the original acoustic characteristics of these sp

Florian Kogelbauer, Ilya Karlin

We derive the dynamically optimal projection onto the linear slow manifold from a temporal variational principle. We demonstrate that the projection captures transient dynamics of the overall dissipative system and leads to a considerably improved fit of reduced trajectories compared to full trajectories. We illustrate these optimal model reduction propertie

Anjali Anjali, Akhil Prakash, Amita, Prabhat Kumar

This paper deals with eigenvalues and eigenvectors of bicomplex linear operators defined on bicomplex space. We investigate the properties of these operators in the context of eigenvalues and eigenvectors, along with some relevant theorems. Several theorems are explored to establish conditions for bicomplex eigenvalues and eigenvectors. Additionally, we exam

Jorge Torres Gómez, Joana Angjo, Moritz Garkisch, Vahid Jamali

Intelligent reflective surface (IRS) technologies help mitigate undesirable effects in wireless links by steering the communication signal between transmitters and receivers. IRS elements are configured to adjust the phase of the reflected signal for a user's location and enhance the perceived signal-to-noise ratio (SNR). In this way, an IRS improves the com

Nicholas Sukiennik, Haoyu Wang, Zailin Zeng, Chen Gao

An increasing reliance on recommender systems has led to concerns about the creation of filter bubbles on social media, especially on short video platforms like TikTok. However, their formation is still not entirely understood due to the complex dynamics between recommendation algorithms and user feedback. In this paper, we aim to shed light on these dynamic

Aabid Karim, Abdul Karim, Bhoomika Lohana, Matt Keon

We demonstrate that large language models' (LLMs) mathematical reasoning is culturally sensitive: testing 14 models from Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and Microsoft across six culturally adapted variants of the GSM8K benchmark, we find accuracy drops ranging from 0.3% (Claude 3.5 Sonnet) to 5.9% (LLaMA 3.1-8B) when math problems are emb

Chengxin Wu, Teng Wang, Bing-Nan Lu, Ning Li

The nuclear lattice effective field theory (NLEFT) is an efficient tool for solving nuclear many-body problems, which takes high-fidelity lattice chiral interactions as input and computes nuclear low-energy observables via quantum Monte Carlo techniques. In this work, we present the first next-to-next-to-next-to-leading order (N$^3$LO) chiral forces on the l

Xu Zheng, Ziqiao Weng, Yuanhuiyi Lyu, Lutao Jiang

Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs) by enabling access to external, reliable, and up-to-date knowledge sources. In the context of AI-Generated Content (AIGC), RAG has proven invaluable by augmenting model outputs wit

Manuel Aguiar Ferreira, Carlos Navas Rodríguez, Gunnar Jacobi, Daniele Fiscaletti

The present study experimentally investigates the onset of ventilation of surface-piercing hydrofoils. Under steady-state conditions, the depth-based Froude number $Fr$ and the angle of attack $\alpha$ define regions where distinct flow regimes are either locally or globally stable. To map the boundary between these stability regions, the parameter space $(\

Yao Cui, Yuheng Wu, Xinmei Zhu, Hongxia Huang

We perform a systematical investigation of the doubly charmed dibaryon system with quantum numbers $IJ=01$, and strangeness numbers $S=0$, $-2$ and $-4$ in the framework of the chiral quark model. Two resonance states with strangeness numbers $S=-2$ is obtained in the $\Lambda\Omega_{cc}$ scattering channel, which are $\Xi_{cc}^{\ast}\Xi$ with resonance mass

Yufei Zhan, Yousong Zhu, Shurong Zheng, Hongyin Zhao

Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the language domain, has emerged as an effective post-training reinforcement strategy to enhance capabilities of LVLMs. However, constructing high-quality human-annotated preference data a

Shaoqian Zhou, Wen You, Ling Guo, Xuhui Meng

Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs with high-dimensional spatial space. In the present study, we propose a scalable physics-informed deep gene

Priyanshu Chakraborty, Yuhui shen, Bin Shu

In [Kac77, Section 5.4] and [Kac 98], V. G. Kac tried to raise, and finished a classification of infinite-dimensional primitive Lie superalgebras. The series $\mathbf{W}(m,n)$ with $m,n$ being positive integers are the fundamental ones. In this article, we introduce the BGG category $\mathcal{O}$ of modules over $\textbf{W}(m,n)$, and try to systematically i

Thomas Dagès, Simon Weber, Ya-Wei Eileen Lin, Ronen Talmon

Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications in data analysis and visualisation. To preserve the underlying data structure, multi-dimensional scaling (MDS) methods focus on preserving pairwise dissimilarities, such as distan

Stephan Baier

We revisit the large sieve for square moduli and obtain conditional improvements under hypotheses on higher additive energies of modular square roots.

Kyuyoung Kim, Jinwoo Shin, Jaehyung Kim

Personalization in language models aims to tailor model behavior to individual users or user groups. Prompt-based methods incorporate user preferences into queries, while training-based methods encode them into model parameters. Model merging has also been explored for personalization under limited data. However, existing methods often fail to directly optim

Hongyu Yan, Zijun Li, Kunming Luo, Li Lu

Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we i

Alexander Zuyev, Victoria Grushkovskaya

We consider the stabilization problem for driftless control-affine systems under the bracket-generating condition. In our previous works, a class of time-varying feedback laws has been constructed to stabilize the equilibrium of a nonholonomic system under rather general controllability assumptions. This stabilization scheme is based on the sampling concept,

Álvaro Cartea, Leandro Sánchez-Betancourt

We model the trading activity between a broker and her clients (informed and uninformed traders) as an infinite-horizon stochastic control problem. We derive the broker's optimal dealing strategy in closed form and use this to introduce an algorithm that bypasses the need to calibrate individual parameters, so the dealing strategy can be executed in real-wor

Si-Yao Wei, Wei-Xing Zhou

Establishing a resilient food trade system is an international consensus on safeguarding food security amid growing disruptions. However, a unified resilience framework has yet to be established, leading to the proliferation of diverse measures. Here, we conceptualize resilience as a trade-off between efficiency and redundancy and employ an entropy-based app

Jerzy Marcinkowski, Mateusz Orda

Query Containment Problem (QCP) is one of the most fundamental decision problems in database query processing and optimization. Complexity of QCP for conjunctive queries (QCP-CQ) has been fully understood since 1970s. But, as Chaudhuri and Vardi noticed in their classical 1993 paper [1], this understanding is based on the assumption that query answers are se

Srijan Kumar

We consider the supersymmetric Wess-Zumino model at large $N$ in $(2+1)$ dimension. We introduce a chemical potential($\mu$) at finite temperature($T$). The non-trivial fixed point of this model is described by a pair of coupled gap equations. This fixed point behaves as a thermal CFT for all values of the coupling. We find that at large chemical potential t

Navid Bin Hasan, Md. Ashraful Islam, Junaed Younus Khan, Sanjida Senjik

We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructe

Haoliang Shang, Hanyu Wu, Guangyao Zhai, Boyang Sun

Scene graphs capture complex relationships among objects, serving as strong priors for content generation and manipulation. Yet, reasonably manipulating scene graphs -- whether by adding nodes or modifying edges -- remains a challenging and untouched task. Tasks such as adding a node to the graph or reasoning about a node's relationships with all others are

Xin Xue, Haoyi Zhou, Tianyu Chen, Shuai Zhang

Spatial-temporal sequence forecasting (STSF) is a long-standing research problem with widespread real-world applications. Neural architecture search (NAS), which automates the neural network design, has been shown effective in tackling the STSF problem. However, the existing NAS methods for STSF focus on generating architectures in a time-consuming data-driv

Jussi Jokinen, Patrick Ebel, Tuomo Kujala

Modern driving involves interactive technologies that can divert attention, increasing the risk of accidents. This paper presents a computational cognitive model that simulates human multitasking while driving. Based on optimal supervisory control theory, the model predicts how multitasking adapts to variations in driving demands, interactive tasks, and auto

Xueying Liu, Lianfang Wang, Jun Liu, Yong Wang

Normal reconstruction is crucial in non-line-of-sight (NLOS) imaging, as it provides key geometric and lighting information about hidden objects, which significantly improves reconstruction accuracy and scene understanding. However, jointly estimating normals and albedo expands the problem from matrix-valued functions to tensor-valued functions that substant

Valentina Beorchia, Rosa Maria Miró-Roig

In this paper, we prove that any Artinian complete intersection homogeneous ideal $I$ in $K[x_0,\cdots,x_n]$ generated by $n+1$ forms of degree $d\ge 2$ satisfies the weak Lefschetz property (WLP) in degree $t< d+\lceil \frac{d}{n} \rceil$. As a consequence, we get that the Jacobian ideal of a smooth 3-fold of degree $d\ge 7$ in ${\mathbb P}^4$ satisfies the

Masud Mansuripur

In 1924, Einstein received a short manuscript in the mail from the Indian physicist S.N. Bose. He quickly translated Bose's manuscript to German and submitted it to Zeitschrift f\"ur Physik. Within a few weeks, Einstein presented his own findings (using a generalization of Bose's counting method) to a session of the Prussian Academy of Sciences. Whereas Bose

V Venktesh, Mandeep Rathee, Avishek Anand

Complex question-answering (QA) systems face significant challenges in retrieving and reasoning over information that addresses multi-faceted queries. While large language models (LLMs) have advanced the reasoning capabilities of these systems, the bounded-recall problem persists, where procuring all relevant documents in first-stage retrieval remains a chal

Yuan Zhou, Kai Qi, Marco De Corato, Kevin Stratford

Microorganisms thrive in complex environments and their behavior in fluids holds significant importance for various medical and industrial applications. By conducting Lattice Boltzmann simulations, the transport and rotational properties of a generic squirmer are investigated in solutions embedded with flexible polymers at different Reynolds numbers. The int

Tomofumi Shimoda, Wataru Kokuyama, Hideaki Nozato

The calibration technique for accelerometers has been internationally developed for up to 20 kHz to ensure the reliability of vibration measurement. However, it has been established that the calibrated sensitivity changes at over 10 kHz depending on the mounting conditions, and this makes it difficult to accurately measure the characteristics of acceleromete

Chenyu Zhang, Lanjun Wang, Yiwen Ma, Wenhui Li

Text-to-Image(T2I) models typically deploy safety filters to prevent the generation of sensitive images. Unfortunately, recent jailbreaking attack methods manually design instructions for the LLM to generate adversarial prompts, which effectively bypass safety filters while producing sensitive images, exposing safety vulnerabilities of T2I models. However, d

Go Soma, Koto Ariu, Seidai Karakida, Yusuke Tsubai

Active metasurfaces incorporating electro-optic (EO) materials enable high-speed free-space optical modulators that show great promise for a wide range of emerging applications, including free-space optical communication, light detection and ranging, and optical computing. However, the limited light-matter interaction lengths in ultrathin metasurfaces typica

Mahsa Khosravi, Zhanhong Jiang, Joshua R Waite, Sarah Jonesc

This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture. The framework employs a hierarchical decision-making structure with conditional action masking, where high-level actions direct the robot's exploration, while low-level actions optimize its navigation and

Maochen Yang, Zekun Li, Jian Zhang, Lei Qi

Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes b

Baizhi Wang, Rui Yan, Wenxin Ma, Xu Zhang

Histomorphology is crucial in cancer diagnosis. However, existing whole slide image (WSI) classification methods struggle to effectively incorporate histomorphology information, limiting their ability to capture key pathological features. Particularly when the number of instances within a bag is large and their features are complex, it becomes challenging to