March 2025 arXiv papers — page 105

V. Sazgari, J. N. Graham, S. S. Islam, 1 P. Král

The transition metal dichalcogenide 6R-TaS$_{2}$ offers a natural platform for studying the interplay among charge density wave (CDW) order, superconductivity, and transport anomalies. Recent findings reveal that, in the intermediate temperature range between charge order and superconductivity, a hidden order emerges around $T^{*}$ ${\simeq}$ 35 K-as evidenc

V. Ryzhii, C. Tang, M. Ryzhii, M. S. Shur

We investigate the carrier transport characteristics of perforated graphene layer (PGL) composed of arrays of interdigital coplanar graphene microribbons (GMRs) connected by graphene nanoribbon (GNR) bridges. We analyze their operation at room-temperature. Under an applied bias voltage, two-dimensional electron and hole systems (2DES and 2DHS) form in adjace

Bouarfa Mahi Quantiota

We introduce the Structured Knowledge Accumulation (SKA) framework, which reinterprets entropy as a dynamic, layer-wise measure of knowledge alignment in neural networks. Instead of relying on traditional gradient-based optimization, SKA defines entropy in terms of knowledge vectors and their influence on decision probabilities across multiple layers. This f

Ruike Xiang, Jiaxin Xie, Qiye Zhang

The randomized block Kaczmarz (RBK) method is a widely utilized iterative scheme for solving large-scale linear systems. However, the theoretical analysis and practical effectiveness of this method heavily rely on a good row paving of the coefficient matrix. This motivates us to introduce a novel block selection strategy to the RBK method, called volume samp

Hang Zhao, Hongru Li, Dongfang Xu, Shenghui Song

Semantic communication is emerging as a promising paradigm that focuses on the extraction and transmission of semantic meanings using deep learning techniques. While current research primarily addresses the reduction of semantic communication overhead, it often overlooks the training phase, which can incur significant communication costs in dynamic wireless

Yuxiang Lai, Jike Zhong, Ming Li, Shitian Zhao

Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annot

Qingyao Xu, Ya Zhang, Yanfeng Wang, Siheng Chen

Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such as traffic simulation. While VisionLanguage Models (VLMs) have demonstrated strong reasoning potential, their application to Bird's-Eye View (BEV) maps in traffic contexts remains limited by narrow task defini

Ram Karan Choudhary, Sunil Kumar Prajapati

In this article, we present a combinatorial formula for the Wedderburn decomposition of rational group algebras of Camina $p$-groups, where $p$ is a prime. We also provide a complete set of primitive central idempotents of rational group algebras of these groups.

Kangrui Du, Yuhang Wu, Shikuang Deng, Shi Gu

Spiking Neural Networks (SNNs), models inspired by neural mechanisms in the brain, allow for energy-efficient implementation on neuromorphic hardware. However, SNNs trained with current direct training approaches are constrained to a specific time step. This "temporal inflexibility" 1) hinders SNNs' deployment on time-step-free fully event-driven chips and 2

Yifan Su, B. Q. Lv, Alfred Zong, Aaron Müller

Understanding the origin of phase transitions and the interactions between distinct phases remains a central task in condensed matter physics. Charge density wave (CDW) systems provide an ideal platform for investigating these phenomena. While the dominant CDW phases in many materials can be explained through Fermi surface nesting or electron-phonon interact

Bowen Yuan, Yuxia Fu, Zijian Wang, Yadan Luo

Dataset Condensation (DC) aims to obtain a condensed dataset that allows models trained on the condensed dataset to achieve performance comparable to those trained on the full dataset. Recent DC approaches increasingly focus on encoding knowledge into realistic images with soft labeling, for their scalability to ImageNet-scale datasets and strong capability

Jiachen Guo, Gino Domel, Chanwook Park, Hantao Zhang

A data-free, predictive scientific AI model, Tensor-decomposition-based A Priori Surrogate (TAPS), is proposed for tackling ultra large-scale engineering simulations with significant speedup, memory savings, and storage gain. TAPS can effectively obtain surrogate models for high-dimensional parametric problems with equivalent zetta-scale ($10^{21}$) degrees

Mikhail Pomazanov

The article proposes a method of designing a statistically distinguishable rating scale that is not excessive in relation to the existing observation statistics. This allows for more stable validation with a fixed maximum number of violations of the Wald criterion compared to an excess scale, which is usually used by banks. The increased robustness of valida

Andrés Galindo-Olarte, Jennifer K. Ryan

This article establishes the usefulness of the Smoothness-Increasing Accuracy-Increasing (SIAC) filter for reducing the errors in the mean and variance for a wave equation with uncertain coefficients solved via generalized polynomial chaos (gPC) whose coefficients are approximated using discontinuous Galerkin (DG-gPC). Theoretical error estimates that utiliz

Jun Yang

We give the generalized Atiyah-Schmid formula for projective tempered representations. Then we prove the Atiyah-Schmid formula for arithmetic subgroups of real reductive groups.

Debidutta Pradhan, Jagadish Kumar

Perovskite material such as BCZT (Ba$_{0.875}$Ca$_{0.125}$(Zr$_{0.125}$Ti$_{0.875}$)O$_{3}$) is well known for its high value of piezoceramic properties and Curie temperature which has potential applications in sensors, actuators, optoelectronic and thermoelectric devices. Based on its composition and physical parameters such as pressure and temperature, exp

Santanu Roy, Ashvath Suresh, Archit Gupta, Shubhi Tiwari

This research proposes a very lightweight model "Fibonacci-Net" along with a novel pooling technique, for automatic brain tumor classification from imbalanced Magnetic Resonance Imaging (MRI) datasets. Automatic brain tumor detection from MRI dataset has garnered significant attention in the research community, since the inception of Convolutional Neural Net

Ziwei Fan, Taeseung Hwang, Yixin Chen, Zi Jing Wong

Mid-infrared photodetectors are susceptible to background noise since every object in the surroundings emits thermal radiation from different directions. To reduce this background noise and enhance signal-to-noise ratio of mid-infrared sensing, different strategies to achieve angular-selective filtering have been proposed. However, these methods are either w

Huan Ren, Wenfei Yang, Xiang Liu, Shifeng Zhang

Category-level object pose estimation aims to determine the pose and size of novel objects in specific categories. Existing correspondence-based approaches typically adopt point-based representations to establish the correspondences between primitive observed points and normalized object coordinates. However, due to the inherent shape-dependence of canonical

Da Kuang, Guanwen Qiu, Junhyong Kim

How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow, divide, and acquire differentiated characteristics through poorly understood molecular processes. A key approach to studying developmental processes is to infer the tree graph of cell lineage division and diffe

Hyuntai Kim, Seong-Yong Cho

Top-emission quantum dot light-emitting diodes (QLEDs) have been extensively studied due to their potential application in augmented/virtual reality. Particularly, the impact of Fabry-P\'erot resonance on top-emission QLEDs has been investigated through both experimental and theoretical studies. Additionally, multi-beam interference effects in QLED emission

Alexey Karev, Dong Xu

Large language models (LLMs) have been one of the most important discoveries in machine learning in recent years. LLM-based artificial intelligence (AI) assistants, such as ChatGPT, have consistently attracted the attention from researchers, investors, and the general public, driving the rapid growth of this industry. With the frequent introduction of new LL

Cheng Zhen, Prayoga, Nischal Aryal, Arash Termehchy

Missing data often exists in real-world datasets, requiring significant time and effort for data repair to learn accurate models. In this paper, we show that imputing all missing values is not always necessary to achieve an accurate ML model. We introduce concepts of minimal and almost minimal repair, which are subsets of missing data items in training data

Roberta Di Gennaro, Rosa Maria Miró-Roig

In this paper, we characterize all Artinian complete intersection $K$-algebras $A_F$ whose Macaulay dual generator $F$ is a binomial. In addition, we prove that such complete intersection Artinian $K$-algebras $A_F$ satisfy the Strong Lefschetz property.

Xingjue Liao, Wenhao Liu, Hao Wu, Feifei Qian

The capability of effectively moving on complex terrains such as sand and gravel can empower our robots to robustly operate in outdoor environments, and assist with critical tasks such as environment monitoring, search-and-rescue, and supply delivery. Inspired by the Mount Lyell salamander's ability to curl its body into a loop and effectively roll down {\Re

Chetana Badala Viswanatha, Johannes Stöckl, Benito Arnoldi, Johannes Knippertz

Spin crossover (SCO) complexes are highly flexible bistable molecular switches with two distinct spin states that can be switched into each other by external stimuli such as temperature, pressure, or light. In the condensed phase, this spin switching phenomenon is determined not only by the chemical and structural properties of the SCO compound but also by i

Yujia Tong, Yuze Wang, Jingling Yuan, Chuang Hu

Model quantization enables efficient deployment of deep neural networks on edge devices through low-bit parameter representation, yet raises critical challenges for implementing machine unlearning (MU) under data privacy regulations. Existing MU methods designed for full-precision models fail to address two fundamental limitations in quantized networks: 1) N

Tomohiro Motoda, Ryo Hanai, Ryoichi Nakajo, Masaki Murooka

Robots that can operate autonomously in a human living environment are necessary to have the ability to handle various tasks flexibly. One crucial element is coordinated bimanual movements that enable functions that are difficult to perform with one hand alone. In recent years, learning-based models that focus on the possibilities of bimanual movements have

Dongkwan Lee, Kyomin Hwang, Nojun Kwak

We address the problem of semi-supervised domain generalization (SSDG), where the distributions of train and test data differ, and only a small amount of labeled data along with a larger amount of unlabeled data are available during training. Existing SSDG methods that leverage only the unlabeled samples for which the model's predictions are highly confident

Junfeng Zhang, Jintao Wang

Two kinds of novel generalizations of Nesbitt's inequality are explored in various cases regarding dimensions and parameters in this article. Some other cases are also discussed elaborately by using the semiconcave-semiconvex theorem. The general inequalities are then employed to deduce some alternate inequalities and mathematical competition questions. At l

Barza Nisar, Steven L. Waslander

Self-supervised learning (SSL) on 3D point clouds has the potential to learn feature representations that can transfer to diverse sensors and multiple downstream perception tasks. However, recent SSL approaches fail to define pretext tasks that retain geometric information such as object pose and scale, which can be detrimental to the performance of downstre

Berke Gur

With this paper, the design of a biomimetic robotic squid (dubbed URSULA) developed for dexterous underwater manipulation is presented. The robot serves as a test bed for several novel underwater technologies such as soft manipulators, propeller-less propulsion, model mediated tele-operation with video and haptic feedback, sonar-based underwater mapping, loc

Eshan Mehendale, Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar

We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference. By utilizing KAN's unique abilities to learn univariate activation functions as opposed to learning linear weights by Multi-Layer Perceptrons (MLPs), we improve the estimates of

Ruobing Jiang, Yacong Li, Haobing Liu, Yanwei Yu

Heterogeneous graphs (HGs) are composed of multiple types of nodes and edges, making it more effective in capturing the complex relational structures inherent in the real world. However, in real-world scenarios, labeled data is often difficult to obtain, which limits the applicability of semi-supervised approaches. Self-supervised learning aims to enable mod

Osama F. Abdel Aal, Necdet Sinan Ozbek, Jairo Viola, YangQuan Chen

From the perspective of control theory, the gradient descent optimization methods can be regarded as a dynamic system where various control techniques can be designed to enhance the performance of the optimization method. In this paper, we propose a prescribed finite-time convergent gradient flow that uses time-varying gain nonlinear feedback that can drive

Pavia Bera, Sanjukta Bhanja

Modern neural network architectures have achieved remarkable accuracies but remain highly dependent on their training data, often lacking interpretability in their learned mappings. While effective on large datasets, they tend to overfit on smaller ones. Probabilistic neural networks, such as those utilizing variational inference, address this limitation by

Kyle DeBry, Nadine Meister, Agustin Valdes Martinez, Colin D. Bruzewicz

Quantum error correction (QEC) is essential for quantum computers to perform useful algorithms, but large-scale fault-tolerant computation remains out of reach due to demanding requirements on operation fidelity and the number of controllable quantum bits (qubits). Traditional QEC schemes involve encoding each logical qubit into multiple physical qubits, req

Chao Dong, Yiyang Liao, Ziye Jia, Qihui Wu

Unmanned aerial vehicles (UAVs) play significant roles in multiple fields, which brings great challenges for the airspace safety. In order to achieve efficient surveillance and break the limitation of application scenarios caused by single communication, we propose the collaborative surveillance model for hierarchical UAVs based on the cooperation of automat

Yuhao Qiu, Shuyan Bai, Tingfa Xu, Peifu Liu

Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant spectral information, while HSOD methods are hindered by the

Alan Scheidegger, Jiří J. L. Vaníček

Mixed quantum-classical methods, such as surface hopping and Ehrenfest dynamics, have proven useful for describing molecular processes involving multiple electronic states. These methods require propagating many independent trajectories, which is computationally demanding. Therefore, we propose the single potential evaluation Ehrenfest dynamics (SPEED), a va

Abhinandan Ravi, T. R. Govindarajan, Surajit Kalita

Type Ia supernovae (SNe\,Ia) serve as crucial cosmological distance indicators because of their empirical consistency in peak luminosity and characteristic light curve decline rates. These properties facilitate them to be standardized candles for the determination of the Hubble constant ($H_0$) within late-time universe cosmology. Nevertheless, a statistical

Qiang Qi, Xiao Wang

Video object detection has made significant progress in recent years thanks to convolutional neural networks (CNNs) and vision transformers (ViTs). Typically, CNNs excel at capturing local features but struggle to model global representations. Conversely, ViTs are adept at capturing long-range global features but face challenges in representing local feature

Md Firoz Ali, Md Nurezzaman

Let $\mathcal{S}_u^*$ denote the class of all analytic functions $f$ in the unit disk $\mathbb{D}:=\{z\in\mathbb{C}:|z|<1\}$, normalized by $f(0)=f'(0)-1=0$ that satisfies the inequality $\left|zf'(z)/f(z)-1\right|<1$ in $\mathbb{D}$. In the present article, we obtain the sharp estimate of Hankel determinants whose entries are coefficients of $f\in\mathcal{S

Romuald Meango

Stated choice probabilities are increasingly used in conjunction with the random-coefficient model (RCM) to describe individual preferences. They allow survey respondents to express uncertainty about the future or the incompleteness of a hypothetical scenario: the resolvable uncertainty. Parametric assumptions such as a Type I extreme value (EV1) distributio

Addison D. S. Richards, Erik S. Sørensen

The infinite projected entangled-pair state (iPEPS) ansatz is a powerful tensor-network approximation of an infinite two-dimensional quantum many-body state. Tensor-based calculations are particularly well-suited to utilize the high parallel efficiency of modern GPUs. We present Ace-TN, a modular and easily extendable open-source library developed to address

Sarah Liaw, Rebecca Morrison, Youssef Marzouk, Ricardo Baptista

Identifying the Markov properties or conditional independencies of a collection of random variables is a fundamental task in statistics for modeling and inference. Existing approaches often learn the structure of a probabilistic graphical model, which encodes these dependencies, by assuming that the variables follow a distribution with a simple parametric fo

Z. -B. Cui, Z. -Q. Wang, P. -C. Lai, Y. Wang

Quantum network and quantum repeater are promising ways to scale up a quantum information system to enable various applications with unprecedented performance. As a current bottleneck of building a long-distance quantum network, the distribution rate of heralded entanglement between remote network nodes is typically much lower than the decoherence rate of ea

Tianran Chen, D. Jonathan P. Morris, Isaac C. Ownby, Anjana Samarakoon

Frustrated systems can host hidden order, in which weak interactions select correlated structure from a highly degenerate manifold. Water ice Ih is the canonical example of such a manifold, yet whether its hydrogen disorder conceals local structure beyond the Bernal-Fowler ice rules has remained controversial. Here, using high-resolution inelastic neutron sc

Yi Yang, Xuran Zhao, H. Charles Zhao, Shumin Yuan

Recently there has been growing interest in the use of aerial and satellite map data for autonomous vehicles, primarily due to its potential for significant cost reduction and enhanced scalability. Despite the advantages, aerial data also comes with challenges such as a sensor-modality gap and a viewpoint difference gap. Learned localization methods have sho

Xinliang Zhang, Lei Zhu, Shuang Zeng, Hangzhou He

Scribble-based weakly supervised semantic segmentation leverages only a few annotated pixels as labels to train a segmentation model, presenting significant potential for reducing the human labor involved in the annotation process. This approach faces two primary challenges: first, the sparsity of scribble annotations can lead to inconsistent predictions due

Yuzi Zhang, Donghai Liang, Youran Tan, Anne L. Dunlop

With advances in high-resolution mass spectrometry technologies, metabolomics data are increasingly used to investigate biological mechanisms underlying associations between exposures and health outcomes in clinical and epidemiological studies. Mediation analysis is a powerful framework for investigating a hypothesized causal chain and when applied to metabo

Keyu Chen, Zetian Wang, Yunxin Zhang

Recently, a Wasserstein-type distance for Gaussian mixture models has been proposed. However, that framework can only be generalized to identifiable mixtures of general elliptically contoured distributions whose components come from the same family and satisfy marginal consistency. In this paper, we propose a simple relaxed Wasserstein distance for identifia

Guimei Liu, Yu Zhang, Jing Zhong, Li Chen

The formation mechanisms of open cluster (OCs) groups remain unclear due to limited sample sizes and data precision. Recent advancements in Gaia astrometric data provide an unprecedented opportunity to study OC groups in greater detail. This study aims to extend the sample of OC groups and investigate their formation and evolution mechanisms, with a focus on

Xiaoying Xing, Chia-Wen Kuo, Li Fuxin, Yulei Niu

Large Vision-Language Models (LVLMs) have shown promising performance in vision-language understanding and reasoning tasks. However, their visual understanding behaviors remain underexplored. A fundamental question arises: to what extent do LVLMs rely on visual input, and which image regions contribute to their responses? It is non-trivial to interpret the f

Sota Uchimura, Josep Miquel Jornet, Koji Ishibashi

In this paper, we consider near-field beams that can mitigate signal attenuation and blockage effects using a uniform linear array (ULA). In particular, closed-form expressions for phase distributions in a ULA are derived to generate Bessel beams and curving beams based on the desired propagation directions and trajectories. Based on the phase distributions,

Asim Ullah, Jameel-Un Nabi, Muhammad Tahir

Pairing of nucleons plays a key role in solving various nuclear physics problems. We investigate the probable effects of pairing correlations on the calculated Gamow-Teller (GT) strength distributions and the associated $\beta$-decay half-lives. Computations are performed for a total of 35 fp-shell nuclei using the proton-neutron quasiparticle random phase a

Jieming Sheng, Jiahang Hu, Lei Xu, Le Wang

Discovery of new states of matter is a key objective in modern condensed matter physics, which often leads to revolutionary technological advancements such as superconductivity. Quantum spin nematic, a ``hidden order'' that evades conventional magnetic probes, is one such state. Na$_2$BaNi(PO$_4$)$_2$ is a potential spin nematic material, suggested by the ob

Lei Cai, Wenjuan Chen

In this paper, we introduce the subvariety of quasi-MV* algebras in order to characterize the logic which is related to complex fuzzy logic. First, we give the definitions of strong quasi-MV* algebra and strong quasi-Wajsberg* algebra and show that they are term equivalence. Second, we present the representation theorem and the standard completeness of stron

Sanchit Srivastava, Shohini Ghose

Classical chaos is marked by an extreme sensitivity to initial conditions, where infinitesimally close trajectories separate exponentially over time. In quantum mechanics, however, unitary evolution and the uncertainty principle preclude such behavior, necessitating alternative approaches to identifying chaos in quantum systems. One must therefore seek quant

Zhichao Duan, Tengyu Pan, Zhenyu Li, Xiuxing Li

Document-level relation extraction (DocRE) is the process of identifying and extracting relations between entities that span multiple sentences within a document. Due to its realistic settings, DocRE has garnered increasing research attention in recent years. Previous research has mostly focused on developing sophisticated encoding models to better capture t

Ashna Gulati, Tara Murphy, Dougal Dobie, Adam Deller

We present results from a search for radio afterglows of compact object mergers conducted with the Australian SKA Pathfinder. We used data from four epochs of the Rapid ASKAP Continuum Survey to search compact binary merger localization regions observed during the LIGO/Virgo O2, and O3 observing runs. Our investigation focused on eleven events (published in

Ziyu Lin, Yunfan Wu, Yuhang Ma, Junzhou Chen

Traffic sign detection is essential for autonomous driving and Advanced Driver Assistance Systems (ADAS). However, existing methods struggle to address the challenges of poor image quality and insufficient information under low-light conditions, leading to a decline in detection accuracy and affecting driving safety. To address this issue, we propose YOLO-LL

Zhengsheng Guo, Linwei Zheng, Xinyang Chen, Xuefeng Bai

While human cognition inherently retrieves information from diverse and specialized knowledge sources during decision-making processes, current Retrieval-Augmented Generation (RAG) systems typically operate through single-source knowledge retrieval, leading to a cognitive-algorithmic discrepancy. To bridge this gap, we introduce MoK-RAG, a novel multi-source

Donggon Jang, Yucheol Cho, Suin Lee, Taehyeon Kim

The fusion of Large Language Models with vision models is pioneering new possibilities in user-interactive vision-language tasks. A notable application is reasoning segmentation, where models generate pixel-level segmentation masks by comprehending implicit meanings in human instructions. However, seamless human-AI interaction demands more than just object-l

Han Xu, Xiao Li, Ming Li, Zhe Ren

The evolution from 3rd to 4th generation synchrotron radiation (SR) sources provide promising potential improvements in X-ray techniques, particularly in spatial resolution for imaging, temporal resolution for dynamic studies, and beam size control for nanoprobes. Achieving these enhancements demands effective vibration suppression in beamline systems. This

Wei Chen, Han Ding, Meng Yuan, Zhao Zhang

The rapid growth of scholarly submissions has overwhelmed traditional peer review systems, driving the need for intelligent automation to preserve scientific rigor. While large language models (LLMs) show promise in automating manuscript critiques, their ability to synthesize high-stakes meta-reviews, which require conflict-aware reasoning and consensus deri

Jung-Woo Lee, Jieun Kim, Anthony L. Edgeton, Tula R. Paudel

Point defects in complex oxide thin films play a critical role in determining material properties but remain challenging to control with precision. This study introduces metal-organic pulsed laser deposition (MOPLD) as a novel synthesis technique for the precise manipulation of these defects, using LaAlO3/SrTiO3 (LAO/STO) as a model system. By employing tita

Jonathan Gorard, Ammar Hakim

First-order systems of hyperbolic partial differential equations (PDEs) occur ubiquitously throughout computational physics, commonly used in simulations of fluid turbulence, shock waves, electromagnetic interactions, and even general relativistic phenomena. Such equations are often challenging to solve numerically in the non-linear case, due to their tenden

Weixiong Lin, Chen Ju, Haicheng Wang, Shengchao Hu

Widely observed data scaling laws, in which error falls off as a power of the training size, demonstrate the diminishing returns of unselective data expansion. Hence, data governance is proposed to downsize datasets through pruning non-informative samples. Yet, isolating the impact of a specific sample on overall model performance is challenging, due to the

Puja Majee, Devojyoti Kansabanik, Divya Oberoi

Solar type-II radio bursts are coherent plasma emissions arising from magnetohydrodynamic shocks produced by either coronal mass ejections (CMEs) or flares. Type-II bursts sometimes show split-band emissions in the dynamic spectrum. When these split-band emissions come from regions just upstream and downstream of the shock, type-II band-splitting can be used

Art Waeterschoot

The geometry of a toroidal scheme over a DVR is encoded in a $\mathbb{Z}$-PL space known as the dual polyhedral complex. Any such dual complex is a skeleton, i.e. a nonarchimedean analytic retract, and admits a combinatorial divisor theory via specialisation. These structures on the dual complex interact via a Poincar\'e-Lelong slope formula, which interpret

Cong Guo, Changqin Huang, Wenhua Zhou, Xiaodi Huang

Multi-label learning poses significant challenges in extracting reliable supervisory signals from the label space. Existing approaches often employ continuous pseudo-labels to replace binary labels, improving supervisory information representation. However, these methods can introduce noise from irrelevant labels and lead to unreliable graph structures. To o

Kunpeng Liu, Shaohua Wu, Aimin Li, Qinyu Zhang

Goal-oriented communication shifts the focus from merely delivering timely information to maximizing decision-making effectiveness by prioritizing the transmission of high-value information. In this context, we introduce the Goal-oriented Tensor (GoT), a novel closed-loop metric designed to directly quantify the ultimate utility in Goal-oriented systems, cap

Pedro Faustini, Natasha Fernandes, Annabelle McIver, Mark Dras

NLP models trained with differential privacy (DP) usually adopt the DP-SGD framework, and privacy guarantees are often reported in terms of the privacy budget $\epsilon$. However, $\epsilon$ does not have any intrinsic meaning, and it is generally not possible to compare across variants of the framework. Work in image processing has therefore explored how to

Jin Guanghui, Huali Zhang

In this paper, we study the Cauchy problem for the Chern-Simons gauged $O(3)$ sigma model under the Lorenz gauge condition. We prove the local well-posedness of solutions if the initial matter field and gauge field satisfy $(\bm{\phi}_0, \bA_0) \in H^s(\R^2)\times H^{s-\frac12}(\R^2)$, $s>1$, where the critical regularity for $\bm{\phi}_0$ is $s_c=1$. Our pr

Rang Liu, Ming Li, A. Lee Swindlehurst

Integrated sensing and communication (ISAC) has emerged as a promising paradigm for next-generation (6G) wireless networks, unifying radar sensing and communication on a shared hardware platform. This paper proposes a dynamic array partitioning framework for monostatic ISAC systems to fully exploit available spatial degrees of freedom (DoFs) and reconfigurab

Jinge Ma, Jiangpeng He, Fengqing Zhu

3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception models must continuously adapt to new data and emerging object categories, but retraining from scratch incurs prohibitive costs. Therefore, adopting class-incremental learning (CIL) becomes particularly essentia

Wentao Cao, Jonas Hirsch, Dominik Inauen

Given any short immersion from an $n$-dimensional bounded and simply connected domain into $\mathbb{R}^{n+1}$ and any H\"older exponent $\alpha<(1+n^2-n)^{-1}$, we construct a $C^{1, \alpha}$ isometric immersion arbitrarily close in the $C^0$ topology. This extends the classical Nash--Kuiper theorem and shows the flexibility of $C^{1, \alpha}$ isometric imme

Sirin Chakraborty, Yin Sun

In this paper, we study the optimal timing for pilot and data transmissions to maximize effective throughput, also known as goodput, over a wireless fading channel. The receiver utilizes the received pilot signal and its Age of Information (AoI), termed the Age of Channel State Information (AoCSI), to estimate the channel state. Based on this estimation, the

Junhyung Shim, Quazi Ishtiaque Mahmud, Ali Jannesari

Detection of data races is one of the most important tasks for verifying the correctness of OpenMP parallel codes. Two main models of analysis tools have been proposed for detecting data races: dynamic analysis and static analysis. Dynamic analysis tools such as Intel Inspector, ThreadSanitizer, and Helgrind+ can detect data races through the execution of th

Shitao Liu, Antonio Pierrottet

We consider an inverse problem of recovering all spatial dependent coefficients in the time dependent Schr\"odinger equation defined on an open bounded domain in $\mathbb{R}^n$, $n\geq 2$, with smooth enough boundary. We show that by appropriately selecting a finite number of initial conditions and a fixed Dirichlet boundary condition, we may recover all the

Jiaqi Yang, Wenting Chen, Xiaohan Xing, Sean He

Multimodal learning that integrates histopathology images and genomic data holds great promise for cancer survival prediction. However, existing methods face key limitations: 1) They rely on multimodal mapping and metrics in Euclidean space, which cannot fully capture the hierarchical structures in histopathology (among patches from different resolutions) an

Yujin Wang, Quanfeng Liu, Zhengxin Jiang, Tianyi Wang

Accurately understanding and deciding high-level meta-actions is essential for ensuring reliable and safe autonomous driving systems. While vision-language models (VLMs) have shown significant potential in various autonomous driving tasks, they often suffer from limitations such as inadequate spatial perception and hallucination, reducing their effectiveness

Xin Liu, Jianjun Chen, Yu Lei, Ruichao Liu

Artificial reefs (ARs) are man-made structures deployed on the seabed to support benthic marine ecosystems. Their presence significantly damps the local flow and therefore can be used for scour protection of offshore wind monopiles. Although the concept appears feasible, the underlying flow-sediment process is very complex and has yet been systematically inv

Yi Du, Zhipeng Zhao, Shaoshu Su, Sharath Golluri

Point cloud (PC) processing tasks-such as completion, upsampling, denoising, and colorization-are crucial in applications like autonomous driving and 3D reconstruction. Despite substantial advancements, prior approaches often address each of these tasks independently, with separate models focused on individual issues. However, this isolated approach fails to

Jinseok Bae, Inwoo Hwang, Young Yoon Lee, Ziyu Guo

Recent advances in motion diffusion models have led to remarkable progress in diverse motion generation tasks, including text-to-motion synthesis. However, existing approaches represent motions as dense frame sequences, requiring the model to process redundant or less informative frames. The processing of dense animation frames imposes significant training c

Hongyu Ke, Jack Morris, Kentaro Oguchi, Xiaofei Cao

3D visual perception tasks, such as 3D detection from multi-camera images, are essential components of autonomous driving and assistance systems. However, designing computationally efficient methods remains a significant challenge. In this paper, we propose a Mamba-based framework called MamBEV, which learns unified Bird's Eye View (BEV) representations usin

Rui Yang, Jiayi Tong, Haoyuan Wang, Hui Huang

Background. Systematic reviews in comparative effectiveness research require timely evidence synthesis. Preprints accelerate knowledge dissemination but vary in quality, posing challenges for systematic reviews. Methods. We propose AutoConfidence (automated confidence assessment), an advanced framework for predicting preprint publication, which reduces relia

Ang Li, Haozhe Chen, Hongseok Namkoong, Tianyi Peng

The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics. Persona-based simulations hold promise for transforming disciplines that rely on population-level feedback, including social science, economic analysis, marketing research, and busines

Kai Chen, Xinfeng Li, Tianpei Yang, Hewei Wang

Large Language Models (LLMs) have made significant progress in various fields. However, challenges remain in Multi-Disciplinary Team (MDT) medical consultations. Current research enhances reasoning through role assignment, task decomposition, and accumulation of medical experience. Multi-role collaboration in MDT consultations often results in excessively lo

Tomomi Tateishi, Akihito Kato, Jun-ichiro Kishine

We explicitly derive the wavefunctions of chiral phonons propagating along the helical axis in chiral crystals and clarify the characteristics of electron-phonon interactions in chiral helical crystals. In particular, we elucidate how the conservation of not only the crystal momentum (CM) but also the crystal angular momentum (CAM) manifests in the interacti

Lingyan Lian, Chuandong Lin, Demei Li, Huilin Lai

The Richtmyer-Meshkov (RM) instability plays an important role in various natural and engineering fields, such as inertial confinement fusion. In this work, the effect of relaxation time on the RM instability under reshock impact is investigated by using a two-component discrete Boltzmann method. The hydrodynamic and thermodynamic characteristics of the flui

Shaolin Chen, Hidetaka Hamada

Let $\varphi$, $\psi\in C(\mathbb{T})$, $g\in C(\overline{\mathbb{D}})$, where $\mathbb{D}$ and $\mathbb{T}$ denote the unit disk and the unit circle, respectively. Suppose that $f\in C^{4}(\mathbb{D})$ satisfies the following: (1) the inhomogeneous biharmonic equation $ \Delta(\Delta f(z))=g(z)$ for $z\in\mathbb{D}$, (2) the Dirichlet boundary conditions $\

Yubo Zhang, Akilan Ramasamy, Kanun Pokharel, James W. Furness

Transition-metal compounds (TMCs) with open-shell d-electrons are characterized by a complex interplay of lattice, charge, orbital, and spin degrees of freedom, giving rise to a diverse range of fascinating applications. Often exhibiting exotic properties, these compounds are commonly classified as correlated systems due to strong inter-electronic interactio

Yanchao Zhang, Xinzhi Liu, Xiaolong Lü, Shanhe Su

We investigate the thermodynamic uncertainty relations (TURs) in steady-state transport for a multi-terminal system consisting of two conducting terminals and N-2 probe terminals, within the linear response regime under broken time-reversal symmetry. We independently derive exact bounds on the TURs for the steady-state particle and heat currents under a stro

Long Zhang, Guangxin Ni, Junjie He, Guoying Gao

Altermagnets with nonrelativistic momentum-dependent spin splitting and compensated net magnetic moments have recently garnered significant interest in spintronics, particularly as pinning layers in magnetic tunnel junctions (MTJs). However, room temperature (RT) altermagnet-based MTJs with tunable tunneling magnetoresistance (TMR) or electroresistance (TER)

Anmol Harshana, Mohamed-Ali Belabbas

We prove that the super-linearizability of polynomial systems is preserved by all currently known classes of polynomial automorphisms of $\R^n$. We then establish connections between such automorphisms and a sufficient condition for super-linearizability.

Tinglue Wang, Yiming Li, Wei Tang, Jiapeng Guan

Reliability and real-time responsiveness in safety-critical systems have traditionally been achieved using error detection mechanisms, such as LockStep, which require pre-configured checker cores,strict synchronisation between main and checker cores, static error detection regions, or limited preemption capabilities. However, these core-bound hardware mechan

Monika Shah, Somdeb Sarkhel, Deepak Venugopal

Multimodal systems have highly complex processing pipelines and are pretrained over large datasets before being fine-tuned for specific tasks such as visual captioning. However, it becomes hard to disentangle what the model learns during the fine-tuning process from what it already knows due to its pretraining. In this work, we learn a probabilistic model us

Shiji Lyu

We study two important numerical invariants, Hilbert--Kunz multiplicity and $F$-signature, on the spectrum of a Noetherian $\mathbf{F}_p$-algebra $R$ that is not necessarily $F$-finite. When $R$ is excellent, we show that the limits defining the invariants are uniform. As a consequence, we show that the $F$-signature is lower semi-continuous, and the Hilbert