November 2025 arXiv papers — page 152

Eloi Moliner, Marco A. Martínez-Ramírez, Junghyun Koo, Wei-Hsiang Liao

Music mixing involves combining individual tracks into a cohesive mixture, a task characterized by subjectivity where multiple valid solutions exist for the same input. Existing automatic mixing systems treat this task as a deterministic regression problem, thus ignoring this multiplicity of solutions. Here we introduce MEGAMI (Multitrack Embedding Generativ

David Ellerman

Neoclassical economic theory presents marginal productivity (MP) theory using the scalar notion of marginal products, and takes pains, implicitly or explicitly, to show that competitive equilibrium satisfies the supposedly ethical principle: ``To each what he and the instruments he owns produces.'' This paper shows that MP theory can also be formulated in a

Hung Ba Tran, Toyoto Sato, Ryuhei Sato, Hiroyuki Saitoh

Hydrogen is a promising clean energy carrier, yet effective and reversible storage remains challenging. AB3-type intermetallic alloys are promising for solid-state hydrogen storage due to intermediate thermodynamic stability and rapid hydrogen uptake. Optimizing stability and gravimetric density is hindered by competing thermodynamic and magnetic effects. He

Jefferson Abrantes, Rohit Kumar, Abhishek Sarkar

This article investigates the existence, nonexistence, and multiplicity of positive solutions to the sublinear fractional elliptic problem $(P_{\lambda}^s)$. We begin by establishing several a priori estimates that provide regularity results and describe the qualitative behavior of solutions. A critical threshold level for the parameter $\lambda$ is identifi

Gongshu Wang, Zhirui Wang, Kan Yang

Monocular depth estimation (MDE) has widely applicable but remains highly challenging due to the inherently ill-posed nature of reconstructing 3D scenes from single 2D images. Modern Vision Foundation Models (VFMs), pre-trained on large-scale diverse datasets, exhibit remarkable world understanding capabilities that benefit for various vision tasks. Recent s

Nuno Garoupa, Virginia Rosales, Rok Spruk

We investigate how government-orchestrated assaults on the judiciary, disguised as modernization efforts, undermine judicial independence. Our study focuses on Venezuela's constitutional overhaul in the early 2000s, initiated by Hugo Ch\'avez and implemented through a judicial emergency committee. We employ a hybrid synthetic control and difference-in-differ

Xinyu Wang, Jinxiao Du, Yiyang Peng, Wei Ma

Decision-focused learning (DFL) has emerged as a powerful end-to-end alternative to conventional predict-then-optimize (PTO) pipelines by directly optimizing predictive models through downstream decision losses. Existing DFL frameworks are limited by their strictly sequential structure, referred to as sequential DFL (S-DFL). However, S-DFL fails to capture t

Miroslav Popovic, Marko Popovic, Pavle Vasiljevic, Ilija Basicevic

The original Python Testbed for Federated Learning Algorithms is a light FL framework, which provides the three generic algorithms: the centralized federated learning, the decentralized federated learning, and the TDM communication (i.e., peer data exchange) in the current time slot. The limitation of the latter is that it allows communication only between p

Chuanzhe Zhang, Yuke Li, Wenjun Mei

Understanding how strategic interactions and power distributions coevolve in international relations is central to explaining conflict, cooperation, and long-term inequality. We study this problem using a power-allocation game on signed networks. Departing from models that restrict strategy updates to Pareto improvements, we propose a generalized formulation

Zhaolin Wan, Yining Diao, Jingqi Xu, Hao Wang

With the rapid advancement of 3D visualization, 3D Gaussian Splatting (3DGS) has emerged as a leading technique for real-time, high-fidelity rendering. While prior research has emphasized algorithmic performance and visual fidelity, the perceptual quality of 3DGS-rendered content, especially under varying reconstruction conditions, remains largely underexplo

Chende Zheng, Ruiqi Suo, Zhoulin Ji, Jingyi Deng

The rapid advancement of generative adversarial networks (GANs) and diffusion models has enabled the creation of highly realistic deepfake content, posing significant threats to digital trust across audio-visual domains. While unimodal detection methods have shown progress in identifying synthetic media, their inability to leverage cross-modal correlations a

Aarush Sinha, Pavan Kumar S, Roshan Balaji, Nirav Pravinbhai Bhatt

Hard negatives are essential for training effective retrieval models. Hard-negative mining typically relies on ranking documents using cross-encoders or static embedding models based on similarity metrics such as cosine distance. Hard negative mining becomes challenging for biomedical and scientific domains due to the difficulty in distinguishing between sou

Timo Stoll, Luis Müller, Christopher Morris

Graph Transformers (GTs) have shown strong empirical performance, yet current architectures vary widely in their use of attention mechanisms, positional embeddings (PEs), and expressivity. Existing expressivity results are often tied to specific design choices and lack comprehensive empirical validation on large-scale data. This leaves a gap between theory a

Iasson Karafyllis, Dionysis Theodosis, Markos Papageorgiou

Zhong-Ping Jiang devoted a large part of his work to the study of the stability properties of interconnected systems. In this short paper we celebrate Zhong-Ping Jiang's 60th birthday by studying a special class of families of interconnected systems: the so-called strings. We develop trajectory-based and Lyapunov-based tools that allow the verification of st

Yonghua Zhan

Search for new physics with charm rare decays at BESIII. The BESIII experiment has collected 2.6 billion $\psi(3686)$ events, 10 billion $J/\psi$ events, $20 fb^{-1}$ D meson pairs at 3.773 GeV, and $7.33 fb^{-1}~D_{s}D_{s}^{*}$ events from 4.128 to 4.226 GeV. The huge data samples allow us to search for rare processes in charm hadron decays. In this paper,

Xiujie Deng

To describe longitudinal fine structure like microbunching within a particle beam, a classical approach is to define a bunching factor which is the Fourier transform of the particle longitudinal density distribution. Such a 1D definition of bunching factor can be generalized to a 6D spectral form function (SFF) to describe more complicated structure in phase

Tianwen Lyu, Xiang Zhuang, Keyan Ding, Xinzhe Cao

Understanding complex biomolecular mechanisms requires multi-step reasoning across molecular interactions, signaling cascades, and metabolic pathways. While large language models(LLMs) show promise in such tasks, their application to biomolecular problems is hindered by logical inconsistencies and the lack of grounding in domain knowledge. Existing approache

Oscar Ivan Agudelo Rico, Matteo Rizzi

In this paper we study non-degeneracy properties of $\Sigma$ via the Jacobi operator $J_\Sigma:=\Delta_\Sigma+|A_\Sigma|^2$ of a given minimal hypersurface $\Sigma$ asymptotic to a cone $C\subset \mathbb{R}^{N+1}$ of co-dimension one. Here $\Delta_{\Sigma}$ is the Laplace Beltrami operator of $\Sigma$ and $|A_{\Sigma}|$ is the norm of the second fundamental

Nicola Menadeo, Serena Giardino, Miguel Zumalacárregui

Gravitational waves (GWs) are direct probes of cosmological gravity, sensitive to space-time inhomogeneities along their propagation. The presence of massive objects breaks homogeneity and isotropy, allowing for new interactions between different GW polarizations, and opening up the intriguing opportunity to test modified gravity theories. This setup general

Zhishen Sun, Guang Dai, Ivor Tsang, Haishan Ye

LLMs have made significant progress in the field of mathematical reasoning, but whether they have true the mathematical understanding ability is still controversial. To explore this issue, we propose a new perturbation framework to evaluate LLMs' reasoning ability in complex environments by injecting additional semantically irrelevant perturbation sentences

Josselin Garnier, Laurent Mertz

We present a novel control variate technique for enhancing the efficiency of Monte Carlo (MC) estimation of expectations involving solutions to stochastic differential equations (SDEs). Our method integrates a primary fine-time-step discretization of the SDE with a control variate derived from a secondary coarse-time-step discretization driven by a piecewise

Patrick Kopper, Anna Schwarz, Jens Keim, Andrea Beck

High-order accurate discontinuous Galerkin (DG) methods have emerged as powerful tools for solving partial differential equations such as the compressible Navier-Stokes equations due to their excellent dispersion-dissipation properties and scalability on modern hardware. The open-source DG framework FLEXI has recently been extended to support DG schemes on g

Kohei Honda

This paper presents a tutorial and survey on Probabilistic Inference-based Model Predictive Control (PI-MPC). PI-MPC reformulates finite-horizon optimal control as inference over an optimal control distribution expressed as a Boltzmann distribution weighted by a control prior, and generates actions through variational inference. In the tutorial part, we deri

Zhiyuan Chen, Yuelin Guo, Zitong Huang, Haoyu He

Object detection models demand large-scale annotated datasets, which are costly and labor-intensive to create. This motivated Imaginary Supervised Object Detection (ISOD), where models train on synthetic images and test on real images. However, existing methods face three limitations: (1) synthetic datasets suffer from simplistic prompts, poor image quality,

Shihao Yang, Zhicong Lu, Yong Yang, Bo Lv

Multi-character role-playing aims to equip models with the capability to simulate diverse roles. Existing methods either use one shared parameterized module across all roles or assign a separate parameterized module to each role. However, the role-shared module may ignore distinct traits of each role, weakening personality learning, while the role-specific m

Adrian Schönnagel, Michael Dubé, Christoph Steup, Felix Keppler

This paper presents a novel approach to avoiding jackknifing and mutual collisions in Heavy Articulated Vehicles (HAVs) by leveraging decentralized swarm intelligence. In contrast to typical swarm robotics research, our robots are elongated and exhibit complex kinematics, introducing unique challenges. Despite its relevance to real-world applications such as

Jian Wang, Lijun He, Yixing Yong, Haixia Bi

Modern autonomous driving (AD) systems leverage 3D object detection to perceive foreground objects in 3D environments for subsequent prediction and planning. Visual 3D detection based on RGB cameras provides a cost-effective solution compared to the LiDAR paradigm. While achieving promising detection accuracy, current deep neural network-based models remain

Haowen Li, Zhengding Luo, Dongyuan Shi, Boxiang Wang

Direction-of-Arrival (DOA) estimation is critical in spatial audio and acoustic signal processing, with wide-ranging applications in real-world. Most existing DOA models are trained on synthetic data by convolving clean speech with room impulse responses (RIRs), which limits their generalizability due to constrained acoustic diversity. In this paper, we revi

Konrad K. Dabrowski, Vadim V. Lozin, Martin Milanič, Andrea Munaro

A graph class is monotone if it is closed under taking subgraphs. It is known that a monotone class defined by finitely many obstructions has bounded treewidth if and only if one of the obstructions is a so-called tripod, that is, a disjoint union of trees with exactly one vertex of degree 3 and paths. This dichotomy also characterizes exactly those monotone

Aknur Sakan, Nurzhan Ussipov, Ernazar Abdikamalov, Almat Akhmetali

We study an entropy-based framework to analyze gravitational-wave signals from core-collapse supernovae. We use waveforms generated by numerical simulations and analyze them in both the time domain and the time-frequency domain using short-time Fourier and continuous wavelet transforms. From each representation, we compute four entropy measures -- Shannon, e

Chaoyi Lin, Yaojun Wu, Yue Li, Junru Li

Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain inferior to end-to-end (E2E) compression approaches due to the absence of expressive latent representations. On the other hand, E2E methods rely on transmitting latent codes and requir

Zhiqi Chen, Yuzhou Liu, Jiarui Liu, Wanfu Gao

Multi-view multi-label feature selection aims to identify informative features from heterogeneous views, where each sample is associated with multiple interdependent labels. This problem is particularly important in machine learning involving high-dimensional, multimodal data such as social media, bioinformatics or recommendation systems. Existing Multi-View

Rui Zhu, Yuexing Peng, George C. Alexandropoulos, Wenbo Wang

The Method of Moments (MoM) is constrained by the usage of static, geometry-defined basis functions, such as the Rao-Wilton-Glisson (RWG) basis. This letter reframes electromagnetic modeling around a learnable basis representation rather than solving for the coefficients over a fixed basis. We first show that the RWG basis is essentially a static and piecewi

Yifei Cao, Yu Liu, Guolong Wang, Zhu Liu

Egocentric visual query localization is vital for embodied AI and VR/AR, yet remains challenging due to camera motion, viewpoint changes, and appearance variations. We present EAGLE, a novel framework that leverages episodic appearance- and geometry-aware memory to achieve unified 2D-3D visual query localization in egocentric vision. Inspired by avian memory

Peiyu Hu, Wayne Lu, Jia Wang

Cross-domain recommendation (CDR) is crucial for improving recommendation accuracy and generalization, yet traditional methods are often hindered by the reliance on shared user/item IDs, which are unavailable in most real-world scenarios. Consequently, many efforts have focused on learning disentangled representations through multi-domain joint training to b

Huacen Wang, Hongqiang Wang

Robotic systems driven by conventional motors often suffer from challenges such as large mass, complex control algorithms, and the need for additional braking mechanisms, which limit their applications in lightweight and compact robotic platforms. Electrostatic film actuators offer several advantages, including thinness, flexibility, lightweight construction

Linshuai Zhang, Huihui Li

Magic (non-stabilizerness) is a key resource for achieving universal fault-tolerant quantum computation beyond classical computation. While previous studies have primarily focused on magic in single systems, its interactions and distribution in multipartite settings remain largely unexplored. In this work, we introduce mutual mana as a measure of magic corre

Jialong Qin, Xin Zou, Di Lu, Yibo Yan

Current Video Large Language Models (VideoLLMs) suffer from quadratic computational complexity and key-value cache scaling, due to their reliance on processing excessive redundant visual tokens. To address this problem, we propose SharpV, a minimalist and efficient method for adaptive pruning of visual tokens and KV cache. Different from most uniform compres

Wejdan Beida, Gustav Bihlmayer, Christoph Friedrich, Gregor Michalicek

We present an implementation of the density-functional theory DFT$+U$$+V$ formalism within the all-electron full-potential linearized augmented-plane-wave (FLAPW) method as implemented in the FLEUR code. The DFT$+U$$+V$ formalism extends DFT, supplemented by the onsite Coulomb interaction $U$, to address local correlation effects in localized states by incor

Avishav Engle, Andrey Zhitnikov, Oren Salzman, Omer Ben-Porat

To facilitate effective, safe deployment in the real world, individual robots must reason about interactions with other agents, which often occur without explicit communication. Recent work has identified game theory, particularly the concept of Nash Equilibrium (NE), as a key enabler for behavior-aware decision-making. Yet, existing work falls short of full

Catherine Bénéteau, Raymond Cheng, Christopher Felder, Dmitry Khavinson

The well-known proof of Beurling's Theorem in the Hardy space $H^2$, which describes all shift-invariant subspaces, rests on calculating the orthogonal projection of the unit constant function onto the subspace in question. Extensions to other Hardy spaces $H^p$ for $0 < p < \infty$ are usually obtained by reduction to the $H^2$ case via inner-outer factoriz

Riccardo De Santis, Anna Vesely, Angela Andreella

This paper tackles the challenge of performing multiple quantile regressions across different quantile levels and the associated problem of controlling the familywise error rate, an issue that is generally overlooked in practice. We propose a multivariate extension of the rank-score test and embed it within a closed-testing procedure to efficiently account f

Yushan Zhu, Wen Zhang, Long Jin, Mengshu Sun

Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have driven significant progress in unified structural QA frameworks like TrustUQA. However, these frameworks face challenges when applied to small-scale LLMs since small-scale LLMs are

Ke Jia, Yuheng Ma, Yang Li, Feifei Wang

We revisit the problem of generating synthetic data under differential privacy. To address the core limitations of marginal-based methods, we propose the Private Adaptive Generative Adversarial Network with Bayes Network Structure (PrAda-GAN), which integrates the strengths of both GAN-based and marginal-based approaches. Our method adopts a sequential gener

Jinbo Li, Witold Pedrycz, Iqbal Jamal

In this study, we develop an approach to multivariate time series anomaly detection focused on the transformation of multivariate time series to univariate time series. Several transformation techniques involving Fuzzy C-Means (FCM) clustering and fuzzy integral are studied. In the sequel, a Hidden Markov Model (HMM), one of the commonly encountered statisti

Han Yu, Xiaojuan Zhao, Aiping Li, Kai Chen

Graph neural networks (GNNs) can effectively model structural information of graphs, making them widely used in knowledge graph (KG) reasoning. However, existing studies on the expressive power of GNNs mainly focuses on simple single-relation graphs, and there is still insufficient discussion on the power of GNN to express logical rules in KGs. How to enhanc

Jiarui Chen, Xinwei Loo, Yien Hong, Anand Bhojan

With the proliferation of Virtual Reality (VR) technologies and the emergence of the Metaverse, social VR applications have become increasingly prevalent and accessible to the general user base. Serving as a novel form of social media, these platforms give users a unique opportunity to engage in social activities. However, there remains a significant limitat

M. V. Pugachev, A. E. Borisov, A. V. Shupletsov, V. O. Sakhin

By applying the conventional two-liquid model to the magnetoresistivity tensor, we reveal a record-high carrier mobility for surface states in tetradymite topological insulators ($\sim$ 20000 cm$^2$/Vs) in both bulk crystals and thin flakes of Sn-Bi$_{1.1}$Sb$_{0.9}$Te$_2$S. Bulk crystals of this 3D topological insulator exhibit a transition from bulk to sur

Hyojun Choi, Seokju Hwang, Kyong-Ho Lee

Competency Questions (CQs) play a crucial role in validating ontology design. While manually crafting CQs can be highly time-consuming and costly for ontology engineers, recent studies have explored the use of large language models (LLMs) to automate this process. However, prior approaches have largely evaluated generated CQs based on their similarity to exi

Charalampos S. Kouzinopoulos, Yuri Manna

Weeds significantly reduce crop yields worldwide and pose major challenges to sustainable agriculture. Traditional weed management methods, primarily relying on chemical herbicides, risk environmental contamination and lead to the emergence of herbicide-resistant species. Precision weeding, leveraging computer vision and machine learning methods, offers a pr

Taja Kuzman Pungeršek, Peter Rupnik, Ivan Porupski, Vuk Dinić

Until recently, fine-tuned BERT-like models provided state-of-the-art performance on text classification tasks. With the rise of instruction-tuned decoder-only models, commonly known as large language models (LLMs), the field has increasingly moved toward zero-shot and few-shot prompting. However, the performance of LLMs on text classification, particularly

Nico Policzer, Cameron Braunstein, Mariya Toneva

Recent studies on audio models show brain-tuning - fine-tuning models to better predict corresponding fMRI activity - improves brain alignment and increases performance on downstream semantic and audio tasks. We extend this approach to a multimodal audio-video model to enhance social cognition, targeting the Superior Temporal Sulcus (STS), a key region for s

Chae-Yeon Heo, Yeong-Jun Cho

In this paper, we propose a semantic-guided framework to address the challenging problem of large-mask image inpainting, where essential visual content is missing and contextual cues are limited. To compensate for the limited context, we leverage a pretrained Amodal Completion (AC) model to generate structure-aware candidates that serve as semantic priors fo

Yoichi Ochiai, Takashi Shimizu

This paper critically re-examines "Digital Nature," a concept that has proliferated across various domains over the last ten years. By "Digital Nature," we refer to an evolving view of nature as a dynamic process of circulating computation and matter, one that extends into the realms of AI, XR, indigenous perspectives, and post-human theory. Despite its popu

Simei Yang, Xinyu Shi, Lu Zhao, Yunyu Ling

Near-bank Processing-in-Memory (PIM) architectures integrate processing cores (PIMcores) close to DRAM banks to mitigate the high cost of off-chip memory accesses. When accelerating convolutional neural network (CNN) on DRAM-PIM, performance is often constrained by cross-bank (or cross-PIMcore) data transfers, which are induced by the conventional layer-by-l

Ying Wang, Jiaqian Li, Tianze Wei, Hau Chan

We study the fair allocation of indivisible items for groups of agents from the perspectives of the agents and a centralized allocator. In our setting, the centralized allocator is interested in ensuring the allocation is fair among the groups and between agents. This setting applies to many real-world scenarios, including when a school administrator wants t

Nikolay Nechaev, Evgeniia Przhezdzetskaia, Dmitry Umerenkov, Dmitry V. Dylov

Generating accurate and clinically meaningful radiology reports from chest X-ray images remains a significant challenge in medical AI. While recent vision-language models achieve strong results in general radiology report generation, they often fail to adequately describe rare but clinically important pathologies like fractures. This work addresses this gap

Maoqi Liu, Quan Fang, Yuhao Wu, Can Zhao

Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable intelligence needed for operational decisions. We formalize

Yunlong Liu, Yongbin Du

In this work, we study the constraints on the characteristic parameters $(\zeta,b)$ of the Deser-Woodard nonlocal gravity model in a static and spherically symmetric background, using four classes of high-precision Solar-System experiments: stellar light deflection, Shapiro time delay, perihelion advance, and geodetic precession. From geodesic equations, we

Zheng Chenghong, Zongyin Deng, Liu Cheng, Xiong Simin

We study the problem of traffic forecasting, aiming to predict the inflow and outflow of a region in the subsequent time slot. The problem is complex due to the intricate spatial and temporal interdependence among regions. Prior works study the spatial and temporal dependency in a decouple manner, failing to capture their joint effect. In this work, we propo

Wenhan Yu, Xinbo Lin, Lanxin Ni, Jinhua Cheng

Large language models (LLMs) have demonstrated strong reasoning abilities across specialized domains, motivating research into their application to legal reasoning. However, existing legal benchmarks often conflate factual recall with genuine inference, fragment the reasoning process, and overlook the quality of reasoning. To address these limitations, we in

Da-Yeong Kim, Yeong-Jun Cho

Point cloud completion aims to recover missing geometric structures from incomplete 3D scans, which often suffer from occlusions or limited sensor viewpoints. Existing methods typically assume fixed input/output densities or rely on image-based representations, making them less suitable for real-world scenarios with variable sparsity and limited supervision.

Zhuoming Lan

In this paper, we establish equivariant mirror symmetry for footballs $\mathcal{F}(m,r)$. This extends the results by B. Fang, C.C. Liu and Z. Zong, where the projective line was considered [{\it Geometry \& Topology} 24:2049-2092, 2017], and the results by D. Tang of weighted projective lines, on [arXiv:1712.04836]. More precisely, we prove the equivalence

Seyedehanita Madani, Vishal M. Patel

Remote sensing change detection is often challenged by spatial misalignment between bi-temporal images, especially when acquisitions are separated by long seasonal or multi-year gaps. While modern convolutional and transformer-based models perform well on aligned data, their reliance on precise co-registration limits their robustness in real-world conditions

Li Peng, Jiayao Zhang, Yihang Wu, Weiran Liu

The explosive growth of data has highlighted its critical role in driving economic growth through data marketplaces, which enable extensive data sharing and access to high-quality datasets. To support effective trading, signaling mechanisms provide participants with information about data products before transactions, enabling informed decisions and facilita

Lintong Zhang, Kang Yin, Seong-Whan Lee

Attribution-based explanation techniques capture key patterns to enhance visual interpretability; however, these patterns often lack the granularity needed for insight in fine-grained tasks, particularly in cases of model misclassification, where explanations may be insufficiently detailed. To address this limitation, we propose a fine-grained counterfactual

Yue Wang, Yuyang Xu, Renjun Hu, Fanqi Shen

Despite the rapid advancements of electrocardiogram (ECG) signal diagnosis and analysis methods through deep learning, two major hurdles still limit their clinical adoption: the lack of versatility in processing ECG signals with diverse configurations, and the inadequate detection of risk signals due to sample imbalances. Addressing these challenges, we intr

Gradimir V. Milovanovic, Federico Nudo

In this paper, we investigate the reconstruction of a bivariate function from weighted edge integrals on a triangular mesh, a problem of central importance in tomography, computer vision, and numerical approximation. Our approach is based on local histopolation methods defined through unisolvent triples, where the edge weights are induced by probability dens

Selime Beyza Özçevik, Abdullah Dertli

Number sequences with wide-ranging applications in mathematics, physics, medicine, and engineering remain an active research topic. This study examines these sequences through the general framework of Horadam numbers and their special cases associated with Cartan numbers. By defining spinor transformations on the resulting structures, new types of spinors ar

Justin Lee, Zheda Mai, Jinsu Yoo, Chongyu Fan

Machine unlearning--the ability to remove designated concepts from a pre-trained model--has advanced rapidly, particularly for text-to-image diffusion models. However, existing methods typically assume that unlearning requests arrive all at once, whereas in practice they often arrive sequentially. We present the first systematic study of continual unlearning

Matthias De Lange, Jens-Joris Decorte, Jeroen Van Hautte

Applications in labor market intelligence demand specialized NLP systems for a wide range of tasks, characterized by extreme multi-label target spaces, strict latency constraints, and multiple text modalities such as skills and job titles. These constraints have led to isolated, task-specific developments in the field, with models and benchmarks focused on s

He Panjing, Cheng Mingyue, Li Li, Zhang XiaoHan

Generating high-quality time series data has emerged as a critical research topic due to its broad utility in supporting downstream time series mining tasks. A major challenge lies in modeling the intrinsic stochasticity of temporal dynamics, as real-world sequences often exhibit random fluctuations and localized variations. While diffusion models have achie

Pabitra Gayen, Ratna Koley

We investigate the shadow properties in a recently proposed geometry of a rotating wormhole under realistic astrophysical conditions, particularly in the presence of a cold and non magnetized plasma environment surrounding the wormhole throat. Using the Hamilton Jacobi formalism, we derive the orbit equation under specific plasma density profiles, where we c

Shenao Zhao, Pengpeng Liang, Zhoufan Yang

Unsupervised domain adaptation for LiDAR-based 3D object detection (3D UDA) based on the teacher-student architecture with pseudo labels has achieved notable improvements in recent years. Although it is quite popular to collect point clouds and images simultaneously, little attention has been paid to the usefulness of image data in 3D UDA when training the m

Lukas Schilcher, Peter Waldert, Benedikt Kantz, Tobias Schreck

Exploring tabular datasets to understand how different feature pairs partition data into meaningful cohorts is crucial in domains such as biomarker discovery, yet comparing clusters across multiple feature pair projections is challenging. We introduce Clusters in Focus, an interactive visual analytics dashboard designed to address this gap. Clusters in Focus

Clarissa Astuto

In this paper, we investigate the correlated diffusion of two ion species governed by a Poisson-Nernst-Planck (PNP) system. Here we further validate the numerical scheme recently proposed in \cite{astuto2025asymptotic}, where a time discretization method was shown to be Asymptotic-Preserving (AP) with respect to the Debye length. For vanishingly Debye length

Yu. E. Geints

Quantum dot superparticles are a specific class of metamaterials created through the self-assembly of nanometer semiconductor quantum dots into organized micro-scale structures, such as microspheres. Superparticles exhibit unique optical, chemical, and electronic properties. These properties are not merely the sum of the constituent quantum dots but rather b

Liping Tang, Michiko Ogaku

We study linguistic indirectness when speakers attend to social ties. Social ties are modeled by a graph, and conferences are the sets of nodes that hear a message. Conference worth is a distance polynomial on the graph; allocations are given by the Myerson value of the conference-restricted worth, which yields the bargaining-power components for each partic

Kaleem Ullah Qasim, Jiashu Zhang

Background: Recursive reasoning models achieve strong performance through iterative refinement, allowing small networks to match large language models. However, training is computationally expensive, often requiring 36 GPU-hours for Sudoku extreme. Existing models use fixed recursion depth and uniform supervision weighting, leading to inefficient training. O

Ken Shirakawa, Yoshihiro Nagano, Misato Tanaka, Fan L. Cheng

A recent high-profile study by Koide-Majima et al. (2024) claimed a major advance in reconstructing visual imagery from brain activity using a novel variant of a generative AI-based method. However, our independent reanalysis reveals multiple methodological concerns that raise questions about the validity of their conclusions. Specifically, our evaluation de

Pulong Ma

This work is focused on constructing space-time covariance functions through a hierarchical mixture approach that can serve as building blocks for capturing complex dependency structures. This hierarchical mixture approach provides a unified modeling framework that not only constructs a new class of asymmetric space-time covariance functions with closed-form

Xiaoye Liang, Lai Jiang, Minglang Qiao, Yichen Guo

In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, the redundancy in burst images leads to the increased storage and transmission demands, as well as reduced efficiency of downstream tasks. To address this, we propose a new task of B

Cunshi Wang, Zuke Feng, Difan Yi, Yuyang Li

To validate key technologies for wide field-of-view (FOV) X-ray polarization measurements, the Cosmic X-ray Polarization Detector (CXPD) CubeSat series has been developed as a prototype platform for the Low-Energy X-ray Polarization Detector (LPD) onboard the POLAR-2 mission. The wide-FOV design significantly increases the complexity of the background enviro

Qiuyue Liang, Kimihiro Nomura, Hidetoshi Omiya

Pulsar timing arrays probe isotropic stochastic gravitational wave (GW) backgrounds in the nanohertz band but are insensitive to its parity-violating component. Motivated by recent progress in pulsar polarization arrays, we study the response of pulsar polarimetry to GWs and evaluate its potential to detect circular polarization in isotropic stochastic GW ba

Ziqian Zhang, Min Huang, Zhongzhe Xiao

Speech emotion recognition (SER) has advanced significantly for the sake of deep-learning methods, while textual information further enhances its performance. However, few studies have focused on the physiological information during speech production, which also encompasses speaker traits, including emotional states. To bridge this gap, we conducted a series

Nimish Kumar Mahapatra

We investigate a new family of cyclic septic fields $\{K_t\}_{t\in\mathbb{Z}}$ arising from the Hashimoto--Hoshi construction. For this family, we compute the discriminant explicitly and characterize their P\'olya property under the condition that the polynomial $E(t) = t^{6} + 2t^{5} + 11t^{4} + t^{3} + 16t^{2} + 4t + 8$ takes fifth-power free values. We sh

Francesco Battistoni, Aris Daniilidis, Carlo Alberto De Bernardi, Enrico Miglierina

The notion of regular pair $(A,B)$ for two nonempty closed convex subsets $A$ and~$B$ of a Hilbert space $\H$ was introduced by Borwein and Bauschke in 1993 to ensure convergence (in norm) of the alternating projection method to some point of the best approximation set. In 2022, De Bernardi and Miglierina showed that regularity of the pair $(A,B)$ guarantees

Andrés Carnero Bravo, Shuchita Goyal, Sofía Martínez Alberga, Cherry Ng

The key information of a model category structure on a poset is encoded in a transfer system, which is a combinatorial gadget, originally introduced to investigate homotopy coherence structures in equivariant homotopy theory. We describe how a transfer system associated with in a model structure on a lattice is affected by left and right Bousfield localizati

Vinay Vaibhav, Tamoghna Das, Suman Dutta

Particles undergoing Fickian diffusion within smooth energy landscapes exhibit Gaussian statistics. However, this Gaussian behavior is often elusive in complex liquids, where particle dynamics within spontaneously fluctuating or spatio-temporally heterogeneous environments lead to a breakdown of ergodicity and time-reversal symmetry. This is usually caused b

Yara AlaaEldin, Enrico Simetti, Francesca Odone

Developing a robust and effective obstacle detection and tracking system for Unmanned Surface Vehicle (USV) at marine environments is a challenging task. Research efforts have been made in this area during the past years by GRAAL lab at the university of Genova that resulted in a methodology for detecting and tracking obstacles on the image plane and, then,

Weichao Liang, Daoyi Dong

In practical applications, quantum systems are inevitably subject to significant uncertainties, including unknown initial states, imprecise physical parameters, and unmodeled environmental noise, all of which pose major challenges to robust quantum feedback control. This paper proposes a feedback stabilization strategy based on a reduced quantum filter that

Hongyang Gu, Qisong Yang, Lei Pu, Siming Han

Extracting robust discriminative features is a critical challenge in person re-identification (ReID). While Transformer-based methods have successfully addressed some limitations of convolutional neural networks (CNNs), such as their local processing nature and information loss resulting from convolution and downsampling operations, they still face the scala

Yaxin Xiao, Qingqing Ye, Zi Liang, Haoyang Li

Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through black-box queries. Model watermarking counters MEAs by embedding forensic markers for ownership verification. Current black-box watermarks prioritize MEA survival through represent

Lorenzo Gavassino, Henry Hirvonen, Jean-Francois Paquet, Mayank Singh

We summarize our recent investigations on how causality violations in Israel-Stewart-type relativistic viscous hydrodynamic simulations can give rise to both analytical and numerical instabilities. The classification of spacetime regions into causal and stable ("good"), acausal but stable ("bad"), and acausal and unstable ("ugly") is reviewed. We compare the

Rongjie Yin

Let $(\lambda_f(n))_{n\geqslant1}$ be the Hecke eigenvalues of a holomorphic cusp form $f$. We prove that the exponent of distribution of $\lambda_f*1$ in arithmetic progressions is as large as $\frac{1}{2}+\frac{1}{46}$ when the modulus $q$ is square-free.

Jiaxuan Wang, Zhen Wang, Borong Peng, Renjun Wang

The Very Large Area Gamma-ray Space Telescope Pathfinder (VLAST-P), as the technology validation satellite for the VLAST mission, is designed to observe high-energy solar bursts on orbit. The CsI electromagnetic calorimeter (ECAL) is one of the key sub-detectors of VLAST-P. To investigate the on-orbit energy calibration method of the ECAL, a Geant4-based sim

Ziyuan Gao, Di Liang, Xianjie Wu, Philippe Morel

Existing reinforcement learning methods for Chain-of-Thought reasoning suffer from two critical limitations. First, they operate as monolithic black boxes that provide undifferentiated reward signals, obscuring individual step contributions and hindering error diagnosis. Second, sequential decoding has O(n) time complexity. This makes real-time deployment im

Jun Xu, Xinkai Du, Yu Ao, Peilong Zhao

Efficient retrieval of external knowledge bases and web pages is crucial for enhancing the reasoning abilities of LLMs. Previous works on training LLMs to leverage external retrievers for solving complex problems have predominantly employed end-to-end reinforcement learning. However, these approaches neglect supervision over the reasoning process, making it

Rishabh Agrawal, Yusuf Alvi, Rahul Jain, Ashutosh Nayyar

Imitation Learning (IL) has proven highly effective for robotic and control tasks where manually designing reward functions or explicit controllers is infeasible. However, standard IL methods implicitly assume that the environment dynamics remain fixed between training and deployment. In practice, this assumption rarely holds where modeling inaccuracies, rea

Zhenfeng Zhuang, Fangyu Zhou, Liansheng Wang

While Large Language Models (LLMs) are emerging as a promising direction in computational pathology, the substantial computational cost of giga-pixel Whole Slide Images (WSIs) necessitates the use of Multi-Instance Learning (MIL) to enable effective modeling. A key challenge is that pathological tasks typically provide only bag-level labels, while instance-l