March 2025 arXiv papers — page 67

Yara AlaaEldin, Francesca Odone

Understanding the geometric and semantic properties of the scene is crucial in autonomous navigation and particularly challenging in the case of Unmanned Aerial Vehicle (UAV) navigation. Such information may be by obtained by estimating depth and semantic segmentation maps of the surrounding environment and for their practical use in autonomous navigation, t

Minxing Zhang, Yongkui Zou, Ran Zhang, Yanzhao Cao

In this paper, we aim to study the optimal weak convergence order for the finite element approximation to a stochastic Allen-Cahn equation driven by multiplicative white noise. We first construct an auxiliary equation based on the splitting-up technique and derive prior estimates for the corresponding Kolmogorov equation and obtain the strong convergence ord

Xiran Wang, Jian Zhang, Lei Qi, Yinghuan Shi

Domain generalization is proposed to address distribution shift, arising from statistical disparities between training source and unseen target domains. The widely used first-order meta-learning algorithms demonstrate strong performance for domain generalization by leveraging the gradient matching theory, which aims to establish balanced parameters across so

Jinhui Zhou, Yongkui Zou, Shimin Chai, Boyu Wang

In this paper, we propose a novel method to approximate the mean field stochastic differential equation by means of approximating the density function via Fokker-Planck equation. We construct a well-posed truncated Fokker-Planck equation whose solution is an approximation to the density function of solution to the mean field stochastic differential equation.

Weixiang Zhao, Xingyu Sui, Jiahe Guo, Yulin Hu

Recent advancements in Large Reasoning Models (LRMs), such as OpenAI's o1/o3 and DeepSeek-R1, have demonstrated remarkable performance in specialized reasoning tasks through human-like deliberative thinking and long chain-of-thought reasoning. However, our systematic evaluation across various model families (DeepSeek, Qwen, and LLaMA) and scales (7B to 32B)

Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh, Bo Zhou

Human activity recognition (HAR) with deep learning models relies on large amounts of labeled data, often challenging to obtain due to associated cost, time, and labor. Self-supervised learning (SSL) has emerged as an effective approach to leverage unlabeled data through pretext tasks, such as masked reconstruction and multitask learning with signal processi

Camille Matar, Paola Cinnella, Xavier Gloerfelt

A cost-effective multi-objective shape optimization strategy is proposed for high-Reynolds number flows involving complex phenomena such as boundary layer transition, shock-wave interactions, and turbulent wakes. These processes are poorly captured by Reynolds-Averaged Navier--Stokes (RANS) models, necessitating higher-fidelity approaches like Large Eddy Sim

Marcelo Pereira Barbosa, Rita Suzana Pitangueira Maciel

Interpersonal trust is recognized as one of the pillars of collaboration and successful learning among students in virtual learning environments (VLEs). This systematic mapping study investigates attributes, phases, and features that support interpersonal trust among students in VLEs. Analyzing 46 articles, we identified 37 attributes that influence phases o

Yuzhi Li, Haojun Xu, Feng Tian

With the rising popularity of short video platforms, the demand for video production has increased substantially. However, high-quality video creation continues to rely heavily on professional editing skills and a nuanced understanding of visual language. To address this challenge, the Shot Sequence Ordering (SSO) task in AI-assisted video editing has emerge

Jian Ma, Xinchen Lyu, Jun Jiang, Qimei Cui

Fine-tuning large language models (LLMs) on private, on-device data can empower tailored personalized AI agents. However, fine-tuning LLMs on resource-constrained edge devices faces significant challenges, including excessive computation overhead, device heterogeneity, and data imbalance. This paper proposes SplitFrozen, a split learning framework that enabl

Narges Mehran, Zahra Najafabadi Samani, Reza Farahani, Josef Hammer

Reducing energy consumption is essential to lessen greenhouse gas emissions, conserve natural resources, and help mitigate the impacts of climate change. In this direction, edge computing, a complementary technology to cloud computing, extends computational capabilities closer to the data producers, enabling energy-efficient and latency-sensitive service del

Dušan D. Repovš, Andrei Yu. Vesnin

An $n$-component link $L$ is said to be \emph{Brunnian} if it is non-trivial but every proper sublink of $L$ is trivial. The simplest and best known example of a hyperbolic Brunnian link is the 3-component link known as "Borromean rings". For $n\geq 2,$ we introduce an infinite family of $n$-component Brunnian links with positive integer parameters $Br(k_1,

Hanxiao Jiang, Hao-Yu Hsu, Kaifeng Zhang, Hsin-Ni Yu

Creating a physical digital twin of a real-world object has immense potential in robotics, content creation, and XR. In this paper, we present PhysTwin, a novel framework that uses sparse videos of dynamic objects under interaction to produce a photo- and physically realistic, real-time interactive virtual replica. Our approach centers on two key components:

Friedrich Hegenbarth, Dušan D. Repovš

In this chapter we give a geometric representation of $H_{n}(B;\mathbb{L})$ classes, where $\mathbb{L}$ is the $4$-periodic surgery spectrum, by establishing a relationship between the normal cobordism classes ${\mathcal{N}}^{H}_{n}(B,\partial)$ and the $n$-th $\mathbb{L}$-homology of $B$, representing the elements of $H_{n}(B;\mathbb{L})$ by normal degree o

Xuan Liu, Xiaobin Chang

In continual learning (CL), catastrophic forgetting often arises due to feature drift. This challenge is particularly prominent in the exemplar-free continual learning (EFCL) setting, where samples from previous tasks cannot be retained, making it difficult to preserve prior knowledge. To address this issue, some EFCL methods aim to identify feature spaces t

Yang Luo, Shiru Wang, Jun Liu, Jiaxuan Xiao

Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morphological and molecular characteristics. This makes it difficult to extract representative features from whole slide images (WSIs) that truly re

Guido Fioretti

The Semantic Theory of Evolution (STE) takes the existence of a number of arbitrary communication codes as a fundamental feature of life, from the genetic code to human cultural communication codes. Their arbitrariness enables, at each level, the selection of one out of several possible correspondences along with the generation of meaning. STE enables more n

Dušan D. Repovš, Lyubomyr Zdomskyy

This article is devoted to the interplay between productively Menger and productively Hurewicz subspaces of the Cantor space. In particular, we show that in the Laver model for the consistency of the Borel's conjecture these two notions coincide and characterize Hurewicz spaces. On the other hand, it is consistent with CH that there are productively Hurewicz

Molin Liu, Xi Zhou, Xiangsheng Tan

In brane-world scenarios, electromagnetic waves (EMWs) are confined to the brane, while gravitational waves (GWs) can propagate through the bulk spacetime. This fundamental difference has been exploited in multiple cosmological studies to address some issues, such as the well-known horizon problem. This paper reinvestigates the problem using general bulk-bas

Zeyu Wang

We calculate the murmuration density for the family of Hecke $L$-functions of imaginary quadratic fields associated to non-trivial characters. This density exhibits a universality property like Zubrilina's density for the murmurations of holomorphic modular forms. We show all murmuration functions obtained by averaging over the family with a compactly suppor

Zeng-Hui Zhu, Wei Lu, Si-Bao Chen, Chris H. Q. Ding

Remote Sensing Image Dehazing (RSID) poses significant challenges in real-world scenarios due to the complex atmospheric conditions and severe color distortions that degrade image quality. The scarcity of real-world remote sensing hazy image pairs has compelled existing methods to rely primarily on synthetic datasets. However, these methods struggle with rea

Beining Xu, Arkaitz Zubiaga

Large Language Models (LLMs) have demonstrated exceptional performance on a range of downstream NLP tasks by generating text that closely resembles human writing. However, the ease of achieving this similarity raises concerns from potential malicious uses at scale by bad actors, as LLM-generated text becomes increasingly difficult to discern from human text.

Ryo Ishizuka

We show a certain existence of a lifting of modules under the self-$\mathrm{Ext}^2$-vanishing condition over the "derived quotient" by using the notion of higher algebra. This refines a work of Auslander-Ding-Solberg's solution of the Auslander-Reiten conjecture for complete interesctions. Together with Auslander's zero-divisor theorem, we show that the exis

Guijin Son, Hyunwoo Ko, Haneral Jung, Chami Hwang

In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight weeks, the leaderboard evaluated 1,119 submissions on a closed benchmark covering five MCQA categories: finance and accounting, stock price prediction, domestic company analysis, financial markets, and financial agent t

Tim F. Weiss, Alberto Peruzzo

The continuously growing effort towards developing real-world quantum technological applications has come to demand an increasing amount of flexibility from its respective platforms. This review presents a highly adaptable engineering technique for photonic quantum technologies based on the artificial structuring of the material nonlinearity. This technique,

Tongyao Li, Andrea Schirato, Taku Suwabe, Remo Proietti Zaccaria

The localized surface plasmon resonance of metallic nanostructures produces strongly localized and enhanced near-field light, significantly contributing to nanophotonics research and applications. Plasmon nanofocusing represents another method for generating near-field light through the propagation and condensation of plasmons on tapered plasmonic structures

I-Hsuan Li, Tian-Sheuan Chang

Training on edge devices enables personalized model fine-tuning to enhance real-world performance and maintain data privacy. However, the gradient computation for backpropagation in the training requires significant memory buffers to store intermediate features and compute losses. This is unacceptable for memory-constrained edge devices such as microcontroll

Kowei Shih, Yi Han, Li Tan

Sequential recommendation is an extensively explored approach to capturing users' evolving preferences based on past interactions, aimed at predicting their next likely choice. Despite significant advancements in this domain, including methods based on RNNs and self-attention, challenges like limited supervised signals and noisy data caused by unintentional

Yugo Takanashi

In this short note, we address a gap in the proof of Sauvageot's density principle, which was pointed out in a paper by Nelson-Venkatesh.

Long Zeng, Xing-Gang Wu, Dan-Dan Hu, Yu-Jie Zhang

In this paper, we investigate the semileptonic decays $B_c \to \eta_c\ell\bar\nu_\ell$ using the quantum chromodynamics(QCD) sum rules within the framework of Standard Model (SM). We further explore the potential to probe signatures of new Physics (NP) beyond the SM through these decays. First, we derive the $\xi$-moments $\langle\xi_{2;\eta_c}^{n}\rangle$ o

Bryan Druzin, Anatole Boute, Michael Ramsden

While artificial intelligence (AI) holds enormous promise, many experts in the field are warning that there is a non-trivial chance that the development of AI poses an existential threat to humanity. Existing regulatory initiative do not address this threat but merely instead focus on discrete AI-related risks such as consumer safety, cybersecurity, data pro

Sami Zhioua, Ruta Binkyte, Ayoub Ouni, Farah Barika Ktata

Accurately measuring discrimination is crucial to faithfully assessing fairness of trained machine learning (ML) models. Any bias in measuring discrimination leads to either amplification or underestimation of the existing disparity. Several sources of bias exist and it is assumed that bias resulting from machine learning is born equally by different groups

Stefan Pasch, Sun-Young Ha

Human-AI Interaction (HAI) guidelines and design principles have become increasingly important in both industry and academia to guide the development of AI systems that align with user needs and expectations. However, large-scale empirical evidence on how HAI principles shape user satisfaction in practice remains limited. This study addresses that gap by ana

Liang Zhang, Jionghao Lin, John Sabatini, Diego Zapata-Rivera

Learner performance data collected by Intelligent Tutoring Systems (ITSs), such as responses to questions, is essential for modeling and predicting learners' knowledge states. However, missing responses due to skips or incomplete attempts create data sparsity, challenging accurate assessment and personalized instruction. To address this, we propose a generat

Yong-Kun Huo, Hui-Feng Feng, Chao Yao, Zhong-Xiao Song

While nanoscale electronic logic circuits are well-established, the development of na-noscale thermal logic circuits has been slow, mainly due to the absence of efficient and controllable nonvolatile field-effect thermal transistors. In this study, we introduce a novel approach that leverages ferroelectric orthorhombic hafnium dioxide (o-HfO2) thin films to

Sheng Ouyang, Yihao Qin, Bo Lin, Liqian Chen

The proliferation of Large Language Models (LLMs) has revolutionized natural language processing and significantly impacted code generation tasks, enhancing software development efficiency and productivity. Notably, LLMs like GPT-4 have demonstrated remarkable proficiency in text-to-code generation tasks. However, the growing reliance on LLMs for code genera

Divyansh Singh, Manuel Nunez Martinez, Bonnie J. Dorr, Sonja Schmer Galunder

Constructing accurate knowledge graphs from long texts and low-resource languages is challenging, as large language models (LLMs) experience degraded performance with longer input chunks. This problem is amplified in low-resource settings where data scarcity hinders accurate entity and relationship extraction. Contextual retrieval methods, while improving re

Shin Inoue, Kouji Ohta, Fumiya Maeda

Cloud-cloud collision (CCC) has been proposed as a mechanism for triggering massive star formation. Observations in the Milky Way and nearby galaxies have revealed the presence of CCCs with collision velocity ($v_{\rm col}$) of 1-40 km/s, and the connection between star formation activity and the properties of colliding clouds has been investigated. In this

Ziqiao Weng, Weidong Cai, Bo Zhou

Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) extends this paradigm by allowing clients to train personalized models with heterogeneous architectures tailored to their computational resources and application-specific needs. However, existing MHFL methods predominantl

Suparno Ghoshal, Arijit Jana

Let $R(q)$ denote the Rogers-Ramanujan continued fraction. Define $$ \frac{1}{R^5(q)}=\displaystyle \sum_{n=0}^{\infty}A(n)q^{n} \quad \text{and} \quad R^5(q)=\displaystyle\sum_{n=0}^{\infty}B(n)q^{n}.$$ Baruah and Sarma recently posed conjectures regarding the sign patterns of $A(5n), B(5n)$ for $n\geq 0.$ In this paper, we show that these conjectures do no

Moin Uddin Maruf, Sungmin Kim, Zeeshan Ahmad

Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approach

Jinou Dong, Xueqin Zhao, Lingfeng Xie, Xun Pan

Due to the dominant Kitaev exchange interactions, the cobaltate, Na$_{3}$Co$_{2}$SbO$_{6}$, has been considered to be approximate to the Kitaev quantum spin liquid (QSL). Here, we investigate both magnetic dilution and chemical pressure effects of Na$_{3}$Co$_{2}$SbO$_{6}$ by the substitutions of Mg$^{2+}$ for Co$^{2+}$ through the structural, optical, magne

Oliver J. Clark, Anugrah Azhar, Ben A. Chambers, Daniel McEwen

Van der Waals materials enable the construction of atomically sharp interfaces between compounds with distinct crystal and electronic properties. This is dramatically exploited in moir\'e systems, where a lattice mismatch or twist between monolayers generates an emergent in-plane periodicity, giving rise to electronic properties absent in the constituent mat

S. D. Odintsov, V. K. Oikonomou, German S. Sharov

Several models within the framework of Einstein-Gauss-Bonnet gravities are considered with regard their late-time phenomenological viability. The models contain a non-minimally coupled scalar field and satisfy a constraint on the scalar field Gauss-Bonnet coupling, that guarantees that the speed of the tensor perturbations is equal to the speed of light. The

Owais Ahmad, Albert Linda, Saumya Ranjan Jha, Somnath Bhowmick

Microstructure imaging is crucial in materials science, but experimental images often introduce noise that obscures critical structural details. This study presents a novel deep learning approach for robust microstructure image denoising, combining phase-field simulations, Fourier transform techniques, and an attention-based neural network. The innovative fr

Koustav Mondal

In this paper, we analyze the theta series associated to the quadratic form $Q(\mathbf{x}) := x_1^2 + x_2^2 + x_3^2 + x_4^2$ with congruence conditions on $x_i$ modulo $2, 3, 4$, and $6$. By employing special operators on modular, non-holomorphic Eisenstein series of weight $2$, we construct a basis for the Eisenstein space for levels $2^k$ (with $k \le 7$),

Shuli Zeng, Mengjie Zhou, Sijia Zhang, Yixiang Hu

Constraint ordering plays a critical role in the efficiency of Mixed-Integer Linear Programming (MILP) solvers, particularly for large-scale problems where poorly ordered constraints trigger increased LP iterations and suboptimal search trajectories. This paper introduces CLCR (Contrastive Learning-based Constraint Reordering), a novel framework that systema

Ruoxu Tan, Yiming Zang

Classification is a core topic in functional data analysis. A large number of functional classifiers have been proposed in the literature, most of which are based on functional principal component analysis or functional regression. In contrast, we investigate this topic from the perspective of manifold learning. It is assumed that functional data lie on an u

Yexin Li

Exploration remains a fundamental challenge in reinforcement learning, as many existing methods either lack theoretical guarantees or fall short in practical effectiveness. In this paper, we propose CAE, i.e., the Critic as an Explorer, a lightweight approach that repurposes the value networks in standard deep RL algorithms to drive exploration, without intr

Jun-Yong Lee, Asha Kumari, Ye Yuan, Mykola Tasinkevych

Topology can manifest itself in colloids when quantified by invariants like Euler characteristics of nonzero-genus colloidal surfaces, albeit spherical colloidal particles are most often studied, and colloidal particles with complex topology are rarely considered. On the other hand, singular defects and topological solitons often define the physical behavior

Sunwoong Yang, Youngkyu Lee, Namwoo Kang

This study presents an enhanced multi-fidelity Deep Operator Network (DeepONet) framework for efficient spatio-temporal flow field prediction when high-fidelity data is scarce. Key innovations include: a merge network replacing traditional dot-product operations, achieving 50.4% reduction in prediction error and 7.57% accuracy improvement while reducing trai

Dong Zhao, Jinlong Li, Shuang Wang, Mengyao Wu

Vision Foundation Models (VFMs) excel in generalization due to large-scale pretraining, but fine-tuning them for Domain Generalized Semantic Segmentation (DGSS) while maintaining this ability remains challenging. Existing approaches either selectively fine-tune parameters or freeze the VFMs and update only the adapters, both of which may underutilize the VFM

Tomoya Monomi, Wataru Setoyama, Yoshihiko Hasegawa

Quantum reservoir computing (QRC) leverages the natural dynamics of quantum systems to process time-series data efficiently, offering a promising approach for near-term quantum devices. Unlike classical reservoir computing, the efficacy of feedback in QRC has not yet been thoroughly explored. Here, we develop a feedback-enhanced QRC framework with weak measu

Xiang Fang, Shihua Zhang, Hao Zhang, Tao Lu

Two-view correspondence learning aims to discern true and false correspondences between image pairs by recognizing their underlying different information. Previous methods either treat the information equally or require the explicit storage of the entire context, tending to be laborious in real-world scenarios. Inspired by Mamba's inherent selectivity, we pr

Zhi Zhang, Minfu Li, Lu Li, Daoyi Chen

Single underwater image enhancement (UIE) is a challenging ill-posed problem, but its development is hindered by two major issues: (1) The labels in underwater reference datasets are pseudo labels, relying on these pseudo ground truths in supervised learning leads to domain discrepancy. (2) Underwater reference datasets are scarce, making training on such sm

Riya Naik, Ashwin Srinivasan, Estrid He, Swati Agarwal

Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startling capacities for processing and generating language. Many of us now treat LLMs as modern-day oracles, asking it almost any kind of question. Unlike its Delphic predecessor, consul

Koustubh Phalak, Junde Li, Swaroop Ghosh

Training Quantum Neural Networks (QNNs) on large amount of classical data can be both time consuming as well as expensive. Higher amount of training data would require higher number of gradient descent steps to reach convergence. This, in turn would imply that the QNN will require higher number of quantum executions, thereby driving up its overall execution

Xuewei Chen, Zhimin Chen, Yiren Song

Text-to-video generative models have made remarkable advancements in recent years. However, generating RGBA videos with alpha channels for transparency and visual effects remains a significant challenge due to the scarcity of suitable datasets and the complexity of adapting existing models for this purpose. To address these limitations, we present TransAnima

Justice Ou, Tinglin Huang, Yilun Zhao, Ziyang Yu

To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context ground

Xunguang Wang, Wenxuan Wang, Zhenlan Ji, Zongjie Li

Large Language Models (LLMs) have become increasingly vulnerable to jailbreak attacks that circumvent their safety mechanisms. While existing defense methods either suffer from adaptive attacks or require computationally expensive auxiliary models, we present STShield, a lightweight framework for real-time jailbroken judgement. STShield introduces a novel si

Hyeongjae Lee, Inho Hong

Assessing the potential influence of Vocational Education and Training (VET) courses on creating job opportunities and nurturing work skills has been considered challenging due to the ambiguity in defining their complex relationships and connections with the local economy. Here, we quantify the potential influence of VET courses and explain it with future ec

William Terrell, Mark Muzi, Bijoy Kundu

Objectives: Many existing techniques for the non-invasive quantification of the blood input function in dynamic FDG-PET imaging require strong historical information or user input. The technique proposed in this work utilizes the assumption that a dynamic PET scan can be modeled by the Patlak plot to determine an unscaled blood input function. Materials and

Ting Yang

Suppose $\{X_{t}:t\ge 0\}$ is a supercritical superprocess on a Luzin space $E$, with a non-local branching mechanism and probabilities $\mathbb{P}_{\delta_{x}}$, when initiated from a unit mass at $x\in E$. By ``supercritical", we mean that the first moment semigroup of $X_{t}$ exhibits a Perron-Frobenius type behaviour characterized by an eigentriplet $(\l

Zefeng Zhang, Hengzhu Tang, Jiawei Sheng, Zhenyu Zhang

Multimodal Large Language Models excel in various tasks, yet often struggle with modality bias, where the model tends to rely heavily on a single modality and overlook critical information in other modalities, which leads to incorrect focus and generating irrelevant responses. In this paper, we propose using the paradigm of preference optimization to solve t

Levon Hakobyan, Sergey Lototsky

While the Kelly portfolio has many desirable properties, including optimal long-term growth rate, the resulting investment strategy is rather aggressive. In this paper, we suggest a unified approach to the risk assessment of the Kelly criterion in both discrete and continuous time by introducing and analyzing the asymptotic variance that describes fluctuatio

Yu Chen, Hui Kou, Zhenchao Lyu, Weiyu Yang

We present several equivalent conditions of the continuity of the supremum function from the square of the Scott space of $C(X)$ to itself under mild assumptions, where $C(X)$ denotes the lattice of closed subsets of a $\mathbf{T_0}$ topological space. We also show that a $\mathbf{T_0}$ space is quasicontinuous (quasialgebraic) iff the lattice of its closed

Seonjong Ryu, Jinpyo Jeong, Mintae Kang, Taemin Son

We present a theoretical study of a spectral filter, which consists of a diffraction grating, a coupling lens, and an optical fiber. As the diffracted beam is highly dispersed spatially, coupling into an optical fiber naturally creates a Gaussian spectral filtering effect. Using ray transfer matrices, we derive simple equations to calculate the spectral filt

Zheng Wang, Anna Cai, Xinfeng Xie, Zaifeng Pan

In this work, we present WLB-LLM, a workLoad-balanced 4D parallelism for large language model training. We first thoroughly analyze the workload imbalance issue in LLM training and identify two primary sources of imbalance at the pipeline parallelism and context parallelism levels. Then, to address the imbalance issue, at the pipeline parallelism level, WLB-

Shu-Min Wu, Xiao-Wei Teng, Wen-Mei Li, Yu-Xuan Wang

We investigate the nonseparability of N-partite quantum systems by employing the Abe-Rajagopal (AR) $q$-conditional entropy for both free bosonic and fermionic fields in the background of a Garfinkle-Horowitz-Strominger (GHS) dilaton black hole. An intriguing finding is that the Hawking effect of the black hole can generate a net nonseparability of W state f

Youhui Zuo, Sibo Wei, Chen Zhang, Zhuorui Liu

With the advancements in long-context inference capabilities of large language models (LLMs), the KV cache has become one of the foundational components. However, its substantial GPU memory consumption makes KV cache compression a key technique for enabling efficient LLM inference in industrial scenarios. While recent studies have focused on optimizing the m

Bradley C. Whitmore, Rupali Chandar, Janice C. Lee, Kiana F. Henny

JWST observations, when combined with HST data, promise to improve age estimates of star clusters in nearby spiral galaxies. However, feedback from young cluster stars pushes out the natal gas and dust, making cluster formation and evolution a challenge to model. Here, we use JWST + HST observations of the nearby spiral galaxy NGC 628 to produce spectral ene

Shuzhe Zeng, Xue-Ning Bai, Xiaochen Sun

Cosmic-ray (CR) streaming instability (CRSI) is believed to play an important role in CR transport and CR feedback to galaxies. It drives the growth of magnetohydrodynamic (MHD) waves that scatter CRs, and leads to energy/momentum exchange between CRs and interstellar medium. Despite extensive research on CRSI, its dependence on the thermodynamic state of th

Peilin Cao, Ying Geng, Nan Feng, Xiang Zhang

As current group contribution (GC) methods are mostly proposed for a wide size-range of molecules, applying them to property prediction of small refrigerant molecules could lead to unacceptable errors. In this sense, for the design of novel refrigerants and refrigeration systems, tailoring GC-based models specifically fitted to refrigerant molecules is of gr

Saikat Mahapatra, Anirban Sen, Riddhick Birbonshi, Kallol Paul

In this article, we establish the Berezin number and Berezin norm inequalities for bounded linear operators on a reproducing kernel Hilbert space using the Moore-Penrose inverse. The inequalities obtained here refine and generalize the earlier inequalities.

Kanta Ogawa

Older male workers exhibit diverse retirement behaviors across occupations and respond differently to policy changes, influenced significantly by the part-time penalty-wage reduction faced by part-time workers compared to their full-time counterparts. Many older individuals reduce their working hours, and in occupations with high part-time penalties, they te

Wancheng Zhang, Mingkun Zheng, Yong Liu, Zhenhua Zhang

Recent studies reveal that $\mathcal{T}$-odd spin currents generated via the nonrelativistic altermagnetic spin splitting effect (ASSE) exhibit significant potential for spintronics applications, with both computational and experimental validations. Addressing the scarcity of conductive altermagnets, we propose strain engineering as a reliable method for ind

Jiachen Jiang, Tianyu Ding, Ke Zhang, Jinxin Zhou

All-in-one image restoration seeks to recover high-quality images from various types of degradation using a single model, without prior knowledge of the corruption source. However, existing methods often struggle to effectively and efficiently handle multiple degradation types. We present Cat-AIR, a novel \textbf{C}ontent \textbf{A}nd \textbf{T}ask-aware fra

Jianjian Yin, Tao Chen, Gensheng Pei, Yazhou Yao

Consistency regularization has prevailed in semi-supervised semantic segmentation and achieved promising performance. However, existing methods typically concentrate on enhancing the Image-augmentation based Prediction consistency and optimizing the segmentation network as a whole, resulting in insufficient utilization of potential supervisory information. I

Shuo Yuan, Yaohua Sun, Mugen Peng

With the burgeoning demand for data-intensive services, satellite-terrestrial networks (STNs) face increasing backhaul link congestion, deteriorating user quality of service (QoS), and escalating power consumption. Cache-aided STNs are acknowledged as a promising paradigm for accelerating content delivery to users and alleviating the load of backhaul links.

Shuo Yuan, Mugen Peng, Yaohua Sun

In the evolution of sixth-generation (6G) mobile communication networks, satellite-terrestrial integrated networks emerge as a promising paradigm, characterized by their wide coverage and reliable transmission capabilities. By integrating with cloud-based terrestrial mobile communication networks, the limitations of low Earth orbit (LEO) satellites, such as

Xiaoyao Zhong, Haotian Li, Jiabao Jin, Mingyu Yang

Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based se

Md. Waliullah, Md Zahin Hossain George, Md Tarek Hasan, Md Khorshed Alam

The rapid digitalization of banking services has significantly transformed financial transactions, offering enhanced convenience and efficiency for consumers. However, the increasing reliance on digital banking has also exposed financial institutions and users to a wide range of cybersecurity threats, including phishing, malware, ransomware, data breaches, a

Hisaichi Shibata, Soya Shimizu, Takahiro Nozaki

Solid-state electro-aerodynamic propulsion systems are devices that utilize atmospheric pressure corona discharge and have been actively researched in recent years as a means of achieving silent drones. However, these systems contain multiple, widely disparate time and spatial scales. Therefore, the governing equations of the systems, a three-component plasm

Bokai Cao, Xueyuan Lin, Yiyan Qi, Chengjin Xu

Market simulator tries to create high-quality synthetic financial data that mimics real-world market dynamics, which is crucial for model development and robust assessment. Despite continuous advancements in simulation methodologies, market fluctuations vary in terms of scale and sources, but existing frameworks often excel in only specific tasks. To address

Yongqi Huang, Jitao Zhao, Dongxiao He, Di Jin

Graph Contrastive Learning (GCL) aims to self-supervised learn low-dimensional graph representations, primarily through instance discrimination, which involves manually mining positive and negative pairs from graphs, increasing the similarity of positive pairs while decreasing negative pairs. Drawing from the success of Contrastive Learning (CL) in other dom

Takahiro Shindo, Yui Tatsumi, Taiju Watanabe, Hiroshi Watanabe

Image compression technology eliminates redundant information to enable efficient transmission and storage of images, serving both machine vision and human visual perception. For years, image coding focused on human perception has been well-studied, leading to the development of various image compression standards. On the other hand, with the rapid advanceme

Yu He, Wei Zhang, Qingyang Hu, Shichuan Sun

The properties and stability of hydrous phases are key to unraveling the mysteries of the water cycle in Earth's interior. Under the deep lower mantle conditions, hydrous phases transition into a superionic state. However, the influence of the superionic effect on their stability and dehydration processes remains poorly understood. Using ab initio calculatio

Yongcheol Kim, Chanjae Lee, Young Yoon

Intrusion Detection Systems (IDS) are crucial for identifying malicious traffic, yet traditional signature-based methods struggle with zero-day attacks and high false positive rates. AI-driven packet-capture analysis offers a promising alternative. However, existing approaches rely heavily on flow-based or statistical features, limiting their ability to dete

Luke McDermott, Rahul Parhi

Recent works have shown that Dataset Distillation, the process for summarizing the training data, can be leveraged to accelerate the training of deep learning models. However, its impact on training dynamics, particularly in neural network pruning, remains largely unexplored. In our work, we use distilled data in the inner loop of iterative magnitude pruning

Yizhu Wang, Zhou Zhang, Saman Atapattu, Marco Di Renzo

In Reconfigurable Intelligent Surfaces (RIS), reflective elements (REs) are typically configured as a single array, but as RE numbers increase, this approach incurs high overhead for optimal configuration. Subarray grouping provides an effective tradeoff between performance and overhead. This paper studies RIS-aided massive random access (RA) at the Medium A

Yali Fu, Jindong Li, Qi Wang, Qianli Xing

Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture long-range dependencies efficiently and neglect the spectral information. Recently, selective state sp

Muhammad Haroon, Magdalena Wojcieszak, Anshuman Chhabra

The rapid growth of social media platforms has led to concerns about radicalization, filter bubbles, and content bias. Existing approaches to classifying ideology are limited in that they require extensive human effort, the labeling of large datasets, and are not able to adapt to evolving ideological contexts. This paper explores the potential of Large Langu

Adriano del Río, Christoph Stoeffler

Approximating nonlinear systems as linear ones is a common workaround to apply control tools tailored for linear systems. This motivates our present work where we developed a data-driven model predictive controller (MPC) based on the Koopman operator framework, allowing the embedding of nonlinear dynamics in a higher dimensional, but linear function space. T

Wenqian Tu, Run Lv, Dingfu Shao, Yuping Sun

Monolayer vanadium ditelluride (VTe2) exhibits a 2\sqrt{3}*2\sqrt{3} charge density wave (CDW) order intertwined with a Mott-insulating state. However, the physical mechanisms driving the emergence of CDW order and Mott-insulating state are still not well understood. In this study, we systematically investigate the electronic band structure, phonon dispersio

Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai

Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; t

Elija Perrier

We analyse circumstances in which bifurcation-driven jumps in AI systems are associated with emergent heavy-tailed outcome distributions. By analysing how a control parameter's random fluctuations near a catastrophic threshold generate extreme outcomes, we demonstrate in what circumstances the probability of a sudden, large-scale, transition aligns closely w

Tobias Timofeyev, Alice Patania

Almost equitable partitions (AEPs) have been linked to cluster synchronization in oscillatory systems, highlighting the importance of structure in collective network dynamics. We provide a general spectral framework that formalizes this connection, showing how eigenvectors associated with AEPs span a subspace of the Laplacian spectrum that governs partition-

Xudong Huai, Luke Pritchard Cairns, Bridget Delles, Michal J. Winiarski

Diamond lattice magnets, formed by a framework of corner-sharing tetrahedra of magnetic cations, offer unique opportunities to realize novel states of matter for potential utility in information technology. However, research has mostly focused on AB2X4 spinels with Td magnetic ions. This hinders the atomically enabled tunability of competing interactions at

Dongheng Lin, Han Hu, Jianbo Jiao

Time becomes visible through illumination changes in what we see. Inspired by this, in this paper we explore the potential to learn time awareness from static images, trying to answer: *what time tells us?* To this end, we first introduce a Time-Oriented Collection (TOC) dataset, which contains 130,906 images with reliable timestamps. Leveraging this dataset

Shaohua Guan

A universal and rigorous ensemble framework for nonequilibrium system remains lacking. Here, we provide a concise framework for the generalized ensemble theory of nonequilibrium discrete systems using matrix-based approach. By introducing an observation matrix, we show that any discrete probability distribution can be formulated as a generalized Boltzmann di