December 2025 arXiv papers — page 109

Ignas Snellen, Sebastiaan Haffert, Matthew Kenworthy, Tomas Stolker

Transmission and eclipse spectroscopy have been invaluable tools for the characterisation of extrasolar planet atmospheres. While they will continue to provide many new insights and discoveries in the decade(s) to come, these methods are running up against sources of stellar noise from stellar surface inhomogeneities and variability. In this white paper we d

Chiara Castello, Paolo Santonastaso, Martin Scotti

We investigate the maximum number \( L_{\mathrm{rk}}(n, m, k, q) \) of distinct nonzero rank weights that an \( \mathbb{F}_{q^m} \)-linear rank-metric code of dimension \( k \) in \( \mathbb{F}_{q^m}^n \) can attain. We determine the exact value of the function \( L_{\mathrm{rk}}(n, m, k, q) \) for all admissible parameters \( n, m, k, q \). In particular, w

Sven-Ake Wegner

We consider the long-standing question of whether every regular LB-space is complete. This problem has been open since the 1950s and originates in Grothendieck's early work in functional analysis. Rather than seeking a direct proof or counterexample, our approach is to study weak versions of the problem using homological methods. We consider the categories o

Quentin Rible

In this article, we introduce inhomogeneous Sobolev spaces that naturally generalise the standard Sobolev-Slobodeckij spaces. The inhomogeneity of these spaces is governed by a set function $\mu$, referred to as an environment. In the case where $\mu$ is an almost doubling set function, we relate these new spaces with inhomogeneous Besov spaces recently intr

Emre Can Acikgoz, Jinoh Oh, Jie Hao, Joo Hyuk Jeon

Effective human-agent collaboration is increasingly prevalent in real-world applications. Current trends in such collaborations are predominantly unidirectional, with users providing instructions or posing questions to agents, where agents respond directly without seeking necessary clarifications or confirmations. However, the evolving capabilities of these

Yannis Georis, Jie Sheng, Salvador Urrea, Tsutomu T. Yanagida

The QCD axion remains one of the most compelling solutions to the strong CP problem. Meanwhile, the type-I seesaw mechanism offers an elegant explanation for the lightness of the observed neutrino masses; however, its extremely heavy Majorana states place it far beyond experimental reach. Low-scale alternatives such as the inverse seesaw improve testability

Peter Kocsis, Lukas Höllein, Matthias Nießner

We introduce Intrinsic Image Fusion, a method that reconstructs high-quality physically based materials from multi-view images. Material reconstruction is highly underconstrained and typically relies on analysis-by-synthesis, which requires expensive and noisy path tracing. To better constrain the optimization, we incorporate single-view priors into the reco

Viktoria Freingruber, Rebeca Gonzalez-Cabaleiro, Havva Yoldaş

We present a mathematical model based on a system of partial differential equations (PDEs) with cross-diffusion and reaction terms to describe ecological interactions between multiple bacterial species and substrates within microaggregates, where bacteria proliferate in response to substrate availability and undergo passive dispersal driven by population pre

Sarah J Valk, Camila Caram-Deelder, Rolf. H. H. Groenwold, Johanna G van der Bom

Clinical transfusion-outcomes research faces unique methodological challenges compared with other areas of clinical research. These challenges arise because patients frequently receive multiple transfusions, each unit originates from a different donor, and the probability of receiving specific blood product characteristics is influenced by external, often un

Emre Can Acikgoz, Jinoh Oh, Joo Hyuk Jeon, Jie Hao

Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world applications favor multi-agent architectures to manage complex conversational scenarios efficiently, ambiguity resolution remains a critical and underexplored challenge--particularly due t

Ruiqi Yu, Qianshi Wang, Hongyi Li, Zheng Jun

Traversing terrains with sparse footholds like legged animals presents a promising yet challenging task for quadruped robots, as it requires precise environmental perception and agile control to secure safe foot placement while maintaining dynamic stability. Model-based hierarchical controllers excel in laboratory settings, but suffer from limited generaliza

Han Li, Xiao Yang, Kuo Ma, Hang Yang

In this paper, the design and characterization of AC-LGAD sensors at the University of Science and Technology of China is introduced. The sensors are characterized with an infrared laser Transient Current Technique (TCT) system for evaluating signal response characteristics and spatial resolution. The temporal resolution was quantified with electrons emitted

Yuetong Zhao, Abbas Askar, Youjun Lu, Zhoujian Cao

Stellar-mass binary black holes (sBBHs) formed in globular clusters (GCs) are promising sources for multiband gravitational wave (GW) observations, particularly with low- and middle-frequency detectors. These sBBHs can retain detectable eccentricities when they enter the sensitivity bands of low-frequency GW observatories. We study multiband GW observations

Paul Weinmann, Ferdinand Schenck, Martin Šiklar

Change detection for linear infrastructure monitoring requires reliable high-resolution data and regular acquisition cadence. Optical very-high-resolution (VHR) imagery is interpretable and straightforward to label, but clouds break this cadence. Synthetic Aperture Radar (SAR) enables all-weather acquisitions, yet is difficult to annotate. We introduce SkyCa

Mikael Escobar-Bach, Alexandre Popier, Malo Sahin

We consider a renewal process which models a cumulative shock model that fails when the accumulation of shocks up-crosses a certain threshold. The ratio limit properties of the probabilities of non-failure after n cumulative shocks are studied. We establish that the ratio of survival probabilities converges to the probability that the renewal epoch equals ze

Xinwei Tai, Dongmian Zou, Hongfei Wang

Recent years have witnessed significant advancements in machine learning methods on graphs. However, transferring knowledge effectively from one graph to another remains a critical challenge. This highlights the need for algorithms capable of applying information extracted from a source graph to an unlabeled target graph, a task known as unsupervised graph d

Sangmin Hong, Suyoung Lee, Kyoung Mu Lee

The problem of depth completion involves predicting a dense depth image from a single sparse depth map and an RGB image. Unsupervised depth completion methods have been proposed for various datasets where ground truth depth data is unavailable and supervised methods cannot be applied. However, these models require auxiliary data to estimate depth values, whi

Ruyu Yang, Xiaoming Sun, Hongyi Zhou

Shadow estimation provides an efficient framework for estimating observable expectation values using randomized measurements. While originally developed for discrete-variable systems, its recent extensions to continuous-variable (CV) quantum systems face practical limitations due to idealized assumptions of continuous phase modulation and infinite measuremen

Carlos Viscasillas Vázquez, Giada Casali, Laura Magrini, Gabriele Cescutti

Chemical clocks, based on age-sensitive stellar abundance ratios, offer a powerful and scalable approach to reconstruct the formation history of the Milky Way. This white paper outlines how wide-field, high-resolution spectroscopy can transform chemical clocks into precise and broadly applicable stellar age estimators when combined with astrometry and astero

Chun Kit Wong, Paraskevas Pegios, Nina Weng, Emilie Pi Fogtmann Sejer

Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The crucial question is whether the model actively utilizes this encoded information for its final prediction. We introduce Weight Space Correlation Analysis, an interpretable methodolo

Brendan Rhyno, Swarnadeep Majumder, Smitha Vishveshwara, Khadijeh Najafi

Understanding how noise influences nonequilibrium quantum critical dynamics is essential for both fundamental physics and the development of practical quantum technologies. While the quantum Kibble-Zurek (QKZ) mechanism predicts universal scaling during quenches across a critical point, real quantum systems exhibit complex decoherence that can substantially

Anika Sharma, Malavika Mampally, Chidaksh Ravuru, Kandyce Brennan

As Large Language Models (LLMs) increasingly mediate stigmatized health decisions, their capacity to understand complex psychological phenomena remains inadequately assessed. Can LLMs understand what we cannot say? We investigate whether LLMs coherently represent abortion stigma across cognitive, interpersonal, and structural levels. We systematically tested

Wallace Jaffray, Sven Stengel, Domenico de Ceglia, Neset Akozbek

Epsilon near zero (ENZ) materials can dramatically enhance local optical fields, enabling nonlinear interactions at relatively low intensities. Yet, near their plasma frequency, conventional isotropic ENZ media remain highly absorptive, limiting nonlinear operations that require good transparency. Longitudinal epsilon near zero metamaterials (LENZ), characte

Asger Weeth, Lars Bojer Madsen

High-harmonic spectroscopy in solids gives insight into the inner workings of solids, such as reconstructing band structures or probing the topological phase of materials. High-harmonic generation (HHG) is a highly non-linear phenomena and simulations guide interpretation of experimental results. These simulations often rely on the electric dipole approximat

Will Hide, Bram Petri, Anna Roig-Sanchis, Joe Thomas

We study the spectrum of the Laplacian on two models of random hyperbolic 3-orbifolds, related to the Apollonian group and the super Apollonian group. We determine explicit spectral gaps for these random orbifolds. Moreover, we use our model to investigate the bass note spectrum of the set of hyperbolic 3-orbifolds.

Komal Singh, Jyoti Sindkar, Mrunal Ramdasi, Vrishali Jadhav

Three distinct samples were proceed through synthesis utilizing the hydrothermal method to develop tin dioxide (SnO2) nanoparticles. Consistency in all other parameters was ensured by maintaining a constant temperature and time throughout the synthesis. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to examine how the surfactant aff

Jingdong Zhang, Bo Zhang, Shuangjing Xu, Xiaofeng Mai

The alignment between the radio-based International Celestial Reference Frame (ICRF) and the optical Gaia Celestial Reference Frame (Gaia-CRF) is critical for multi-waveband astronomy, yet systematic offsets at the optical bright end (G<13) limit their consistency. While radio stars offer a potential link between these frames, their utility has been restrict

Anshu Agarwal, Biplab Basak, Debolina Ghosh

A \emph{semi-equivelar gem} of a PL $d$-manifold is a regular colored graph that represents the manifold and admits a regular embedding on a surface, such that the cyclic sequence of face degrees around each vertex is identical. In [1,4], semi-equivelar gems of PL $d$-manifolds embedded on surfaces with Euler characteristic $\chi \geq -1$ were classified. In

Nelson de Gaay Fortman, Radoslaw Kolkowski, Nick Feldman, Peter Schall

Metasurface lasers offer unprecedented control over light emission, yet their spatial and modal characteristics are typically fixed post-fabrication. Here, we introduce a reconfigurable plasmonic metasurface laser platform in which the lasing area geometry, and thus the emission properties, are dynamically programmed via spatially structured optical pumping.

Shuo Liu

With the aggressive scaling of VLSI technology, the explosion of layout patterns creates a critical bottleneck for DFM applications like OPC. Pattern clustering is essential to reduce data complexity, yet existing methods struggle with computational prohibitiveness ($O(N^2)$ comparisons), sub-optimal discrete sampling for center alignment, and difficult spee

Yusuke Tampo

WZ Sge-type dwarf novae form one of the most intriguing classes of compact accreting binaries. They are recognized as the most evolved population of hydrogen-rich cataclysmic variables. Yet they exhibit energetic disk-powered outbursts with an amplitude of 6-9 mag, duration of a month, and decade-long outburst cycles. Despite the dramatic increase in the num

Xin Guo, Yifan Zhao, Jia Li

Generating 3D-based body movements from speech shows great potential in extensive downstream applications, while it still suffers challenges in imitating realistic human movements. Predominant research efforts focus on end-to-end generation schemes to generate co-speech gestures, spanning GANs, VQ-VAE, and recent diffusion models. As an ill-posed problem, in

Shanghua Liu, Majharulislam Babor, Christoph Verduyn, Breght Vandenberghe

High resolution phenotyping at the level of individual leaves offers fine-grained insights into plant development and stress responses. However, the full potential of accurate leaf tracking over time remains largely unexplored due to the absence of robust tracking methods-particularly for structurally complex crops such as canola. Existing plant-specific tra

Yingqiu Mao, Han-Yu Ren, Zi-Yi Liu, Yi-Zheng Zhen

We demonstrate genuine tripartite strong coupling in a solid-state hybrid quantum system comprising a superconducting transmon qubit, a fixed-frequency coplanar-waveguide resonator, and an ensemble of NV$^-$ centers in diamond. Frequency-domain spectroscopy reveals a characteristic three-mode avoided crossing, indicating that single excitations are coherentl

Linh Han Thanh, Julien Dörner, Horst Fichtner, Julia Becker Tjus

A complete understanding of the cosmic-ray energy spectrum remains a challenge to theory that must be met by comprehensive modeling efforts. One of these is the subject of the present study, namely, an explanation of the recently discovered spectral hardening at $\sim 300$ GeV with self-consistently treated cosmic-ray diffusion, where self-generated waves re

Maximilian Spellauge, Ramon Auer, Meike Tack, Florentine Limani

Microparticle laser fragmentation in liquids has emerged as a promising approach to generate nanoparticles with high efficiency. Despite its advantages, the underlying fragmentation mechanisms, their connection to the nanoparticle size distribution, and the energy efficiency of the process remain poorly understood. In this study for the first time, micropart

Xia Liao, Xiping Zhang

In this paper, we study how global index formulas arise in the theory of one-dimensional holomorphic foliation from the microlocal point of view. We give short proofs and generalizations to a few exisiting index formulas concerning Schwartz, GSV and logarithmic indices.

Lukas Bischof, Rudolf M. Füchslin, Kurt Stockinger, Pavel Sulimov

The analysis of spectra, such as Nuclear Magnetic Resonance (NMR) spectra, for the comprehensive characterization of peaks is a challenging task for both experts and machines, especially with complex molecules. This process, also known as deconvolution, involves identifying and quantifying the peaks in the spectrum. Machine learning techniques have shown pro

Xinlei Wang, Zhen Yan, Fu-Guo Xie, Jun-Feng Wang

Based on broadband timing analysis of Insight-HXMT and NICER data from the 2019 outburst of the black hole X-ray binary (BHXRB) MAXI J1348-630, we report the detection of the coexistence and competitive interplay between type-C and type-B quasi-periodic oscillations (QPOs). Specifically, the two QPO types were detected simultaneously but exhibited distinct e

Liviu Aolaritei, Michael I. Jordan

The problem of stopping stochastic gradient descent (SGD) in an online manner, based solely on the observed trajectory, is a challenging theoretical problem with significant consequences for applications. While SGD is routinely monitored as it runs, the classical theory of SGD provides guarantees only at pre-specified iteration horizons and offers no valid w

Guilin Zhang, Wulan Guo, Ziqi Tan

Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms presents significant resource allocation challenges due to heterogeneous agent workloads, varying computational demands, and

Vivek Alumootil, Tuan-Anh Vu

Current methods for dense 3D point tracking in dynamic scenes typically rely on pairwise processing, require known camera poses, or assume temporal ordering of input frames, thereby constraining their flexibility and applicability. Additionally, recent advances have successfully enabled efficient 3D reconstruction from large-scale, unposed image collections,

Mabiao Long, Jiaxi Liu, Yufeng Li, Hao Xiong

Deploying dynamic heterogeneous graph embeddings in production faces key challenges of scalability, data freshness, and cold-start. This paper introduces a practical, two-stage solution that balances deep graph representation with low-latency incremental updates. Our framework combines HetSGFormer, a scalable graph transformer for static learning, with Incre

Keke Tang, Tianyu Hao, Xiaofei Wang, Weilong Peng

Most adversarial attacks on point clouds perturb a large number of points, causing widespread geometric changes and limiting applicability in real-world scenarios. While recent works explore sparse attacks by modifying only a few points, such approaches often struggle to maintain effectiveness due to the limited influence of individual perturbations. In this

K. Bekki, K. Furuya, T. Shimonishi

Recent observations have revealed significant variations in the abundances of gas- and ice-phase molecules in galaxies with different luminosities and types. In order to discuss the physical origins of these variations, we incorporate gas- and dust-phase interstellar chemistry into galaxy-scale simulations with various baryonic physics including dust formati

Sulaimaan Lim, Julien Vermot, Chiu Fan Lee

Epithelia are confluent cell layers that self-organize into polygonal networks whose geometry encodes their mechanical state. A principal driver is the tunable contractility of the actomyosin cortex, which links cell-junction tension to tissue architecture. Notably, epithelial tilings frequently resemble centroidal Voronoi tessellations (CVTs), yet the physi

Junlin Qin, Hong-Er Gong, Yusen Wang, Zhan-Feng Mai

This paper investigates photon motion in black hole of Einstein-Maxwell-dilaton theory, exploring black hole shadows and observational characteristics under various accretion models. We first give the relation of the event horizon, photon sphere, and critical impact parameter in terms of the magnetic charge $q$. We then use the Event Horizon Telescope data t

Leonardo Tolomeo, Nicola Visciglia

We study the transport of Gaussian measures under the flow of the 2-dimensional defocusing Schr\"odinger equation $i \partial_t u + \Delta u = |u|^{2k} u$ posed on $\mathbb T^2$. In particular, we show that the Gaussian measures with inverse covariance $\|u\|_{H^s}^2$, are quasi-invariant under the flow for $s>2$. Moreover, we show that the Radon-Nykodim den

Kosuke Yamazaki, Takuma Shiga, Kumpei Shiraishi, Emi Minamitani

Understanding and predicting thermal transport in disordered materials remains a significant challenge due to the absence of periodicity and the complex nature of medium-range structural motifs. In this work, we investigate amorphous graphene and demonstrate that persistent homology, a topological data analysis technique, can serve as a physically interpreta

Hayk Amirkhanian, Marco F. Huber

In recent years, neural networks have revolutionized various domains, yet challenges such as hyperparameter tuning and overfitting remain significant hurdles. Bayesian neural networks offer a framework to address these challenges by incorporating uncertainty directly into the model, yielding more reliable predictions, particularly for out-of-distribution dat

Zhen-Yu Zheng, Shu Chen

We investigate the entanglement properties in a generalized quantum cluster model under periodic boundary condition. By evaluating the quantum conditional mutual information entropy under four subsystem partitions, we identify clear signatures of long-range entanglement. Specifically, when both the system size $N$ and the interaction range $m$ are odd, the s

Zewen Qiang, Sendong Zhao, Haochun Wang, Bing Qin

Large language models (LLMs) have demonstrated strong performance on a variety of natural language processing (NLP) tasks. However, they often struggle with long-text sequences due to the ``lost in the middle'' phenomenon. This issue has been shown to arise from a U-shaped attention bias, where attention is disproportionately focused on the beginning and end

Zhijian He, Feifei Liu, Yuwei Li, Zhanpeng Luo

Multi-modal 3D object detection is important for reliable perception in robotics and autonomous driving. However, its effectiveness remains limited under adverse weather conditions due to weather-induced distortions and misalignment between different data modalities. In this work, we propose DiffFusion, a novel framework designed to enhance robustness in cha

Shenzhi Yang, Guangcheng Zhu, Xing Zheng, Yingfan MA

Reinforcement learning with verifiable rewards (RLVR) has proven effective in training large reasoning models (LRMs) by leveraging answer-verifiable signals to guide policy optimization, which, however, suffers from high annotation costs. To alleviate this problem, recent work has explored unsupervised RLVR methods that derive rewards solely from the model's

Antoine El-Hayek, Monika Henzinger, Jason Li

We present an exact fully-dynamic minimum cut algorithm that runs in $n^{o(1)}$ deterministic update time when the minimum cut size is at most $2^{\Theta(\log^{3/4-c}n)}$ for any $c>0$, improving on the previous algorithm of Jin, Sun, and Thorup (SODA 2024) whose minimum cut size limit is $(\log n)^{o(1)}$. Combined with graph sparsification, we obtain the f

Yan Zhang, Baoxin Li, Han Sun, Yuhang Gao

Forest pests threaten ecosystem stability, requiring efficient monitoring. To overcome the limitations of traditional methods in large-scale, fine-grained detection, this study focuses on accurately identifying infected trees and analyzing infestation patterns. We propose FID-Net, a deep learning model that detects pest-affected trees from UAV visible-light

Hubert Pugzlys, Shreyas Varude, Sam Dillon, Huy Tran

Understanding the dynamical response of quantum materials is central to revealing their microscopic properties, yet access to long-time and large-scale dynamics remains severely limited by rapidly growing computational costs and entanglement, particularly in gapless systems. Here we introduce an autoregressive machine-learning framework that enables the extr

Rajeev Bhatt Ambati, Tianyi Niu, Aashu Singh, Shlok Mishra

Large language Models (LLMs) are usually used to answer questions, but many high-stakes applications (e.g., tutoring, clinical support) require the complementary skill of asking questions: detecting missing information, requesting clarifications, and using them to solve tasks. We study this skill in reasoning-heavy domains where progress depends on inquiry r

Wenjing Lu, Yi Hong, Yang Yang

Vision foundation models have demonstrated strong generalization in medical image segmentation by leveraging large-scale, heterogeneous pretraining. However, they often struggle to generalize to specialized clinical tasks under limited annotations or rare pathological variations, due to a mismatch between general priors and task-specific requirements. To add

Guanhua Ji, Harsha Polavaram, Lawrence Yunliang Chen, Sandeep Bajamahal

Large and diverse datasets are needed for training generalist robot policies that have potential to control a variety of robot embodiments -- robot arm and gripper combinations -- across diverse tasks and environments. As re-collecting demonstrations and retraining for each new hardware platform are prohibitively costly, we show that existing robot data can

Harbir Antil, Alex Kaltenbach, Keegan L. A. Kirk

In this paper, we study an insulation problem that seeks to determine the optimal distribution of a given amount $m>0$ of insulating material coating an insulated boundary part $\Gamma_I\subseteq \partial\Omega$ of a thermally conducting body $\Omega\subseteq \mathbb{R}^d$, $d\in \mathbb{N}$, subject to convective heat transfer. The `$\textit{thickness}$' of

Jianchun Chu, Man-Chun Lee, Jintian Zhu

In this paper, we establish some diameter rigidity for Kähler manifolds with positive holomorphic sectional curvature.

Mohammad Walid Charrwi, Zaid Hussain

We investigate adaptive minimal routing in 2D torus networks on chip NoCs under node fault conditions comparing a reinforcement learning RL based strategy to an adaptive routing baseline A torus topology is used for its low diameter high connectivity properties The RL approach models each router as an agent that learns to forward packets based on network sta

Feng Zhang, Zezhong Tan, Xinhong Ma, Ziqiang Dong

To address the limited capability expansion and low sample efficiency of Reinforcement Learning (RL), recent methods have integrated &#39;&#39;hints&#39;&#39; into post-training, which are prefix segments of complete reasoning trajectories, aiming for powerful knowledge expansion and reasoning generalization. However, existing hint-based RL methods often neg

Xiang Li, Gang Liu, Weitao Zhou, Hongyi Zhu

Imitation learning (IL) has emerged as a central paradigm in autonomous driving. While IL excels in matching expert behavior in open-loop settings by minimizing per-step prediction errors, its performance degrades unexpectedly in closed-loop due to the gradual accumulation of small, often imperceptible errors over time.Over successive planning cycles, these

Mingqi Yuan, Tao Yu, Haolin Song, Bo Li

Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of humanoid robots. To address th

Rommin Adl

While founder backgrounds account for less than 4% of funding variation among Y Combinator startups, this suggests that other factors, such as industry trends and product innovation, may play a more significant role in funding outcomes. Using data on 4,323 YC companies from 2005-2024 merged with S&P Global funding data, I estimate OLS regressions with batch

Zhizhong Huang

We prove asymptotic formulas for counting (primitive) integral points with local conditions on the (punctured) affine cone defined by a non-singular integral ternary quadratic form, and we relate our results to the Brauer--Manin obstruction. Our approach is based on the $\delta$-variant of the Hardy--Littlewood circle method developed by Heath-Brown.

Ziqiang Zhu, Bowei Yang

Change detection (CD) identifies scene changes from multi-temporal observations and is widely used in urban development and environmental monitoring. Most existing CD methods rely on supervised learning, making performance strongly dataset-dependent and incurring high annotation costs; they typically focus on a few predefined categories and generalize poorly

Chenmin Sun, Nikolay Tzvetkov

In this article, we prove an almost-sure global in time nonlinear smoothing effect for NLS on the two-dimensional torus. For deterministic data, this phenomenon was proved for the NLS on the circle by Erdo\u{g}an--Tzirakis, which remains unknown on multidimensional torus. Our argument is based on a quantitative quasi-invariance of Gaussian measures with cova

Bienvenido Barraza Martínez, Robert Denk, Jonathan González Ospino, Jairo Hernández Monzón

In this work, we consider a transmission problem describing a thermoelastic plate surrounding a membrane without any mechanical damping. The main results consist of the lack of exponential stability for this problem and the polynomial stability without the usual geometric condition.

Yu-Shuai Li

In this work, we investigate the two-body nonleptonic decays of double-charmed baryon $\Omega_{cc}^{+}$ within a nonrelativistic quark model, in which the nonfactorizable amplitudes contributed by $W$-exchange diagrams are evaluated under pole model assumption. To reduce sensitivity of decay amplitudes to arbitrary choice of baryon wave functions, we adopted

Ilja Gogić, Mateo Tomašević

Let $M_n(\mathbb{F})$ denote the algebra of $n \times n$ matrices over an algebraically closed field $\mathbb{F}$ of characteristic different from $2$. For $n \ge 2$, we classify all maps $ϕ: M_n(\mathbb{F}) \to M_n(\mathbb{F})$ satisfying the mixed Jordan-power identity $$ ϕ(A^{k} \circ B) = ϕ(A)^{k} \circ ϕ(B), \quad \text{for all } A,B \in M_n(\mathbb{F})

Michael P. H. Stumpf

FlowClass.jl is a Julia package for classifying continuous-time dynamical systems into a hierarchy of structural classes: Gradient, Gradient-like, Morse-Smale, Structurally Stable, and General. Given a vector field \(\mathbf{F}(\mathbf{x})\) defining the system \(\mathrm{d}\mathbf{x}/\mathrm{d}t = \mathbf{F}(\mathbf{x})\), the package performs a battery of c

Saumyaranjan Mohanty, Aravind Reddy, Konda Reddy Mopuri

In Dataset Condensation, the goal is to synthesize a small dataset that replicates the training utility of a large original dataset. Existing condensation methods synthesize datasets with significant redundancy, so there is a dire need to reduce redundancy and improve the diversity of the synthesized datasets. To tackle this, we propose an intuitive Diversit

Samuel Darwisman

The science of pipeline transport is currently governed by a collection of fragmented, discipline-specific theories that are inadequate for addressing the systemic challenges of 21st-century infrastructure. This paper introduces and formalizes a new, unified theory: the General Theory of Piping Transportation (GTPT), formulated by Darwisman. The GTPT posits

Yicheng Feng, Wanpeng Zhang, Ye Wang, Hao Luo

Vision-Language-Action (VLA) models provide a promising paradigm for robot learning by integrating visual perception with language-guided policy learning. However, most existing approaches rely on 2D visual inputs to perform actions in 3D physical environments, creating a significant gap between perception and action grounding. To bridge this gap, we propose

Daniel E. Rivas, Lorenzo Paoloni, Rebecca Boll, Alberto De Fanis

Traditional x-ray photoelectron spectroscopy (XPS) relies upon a direct mapping between the photoelectron binding energies and the local chemical environment, which is well-characterized by an electrostatic partial charges model for systems in equilibrium. However, the extension of this technique to out-of-equilibrium systems has been hampered by the lack of

Sakshi Ahuja, Subhankar Mishra

Research funding allocation remains a critical bottleneck in scientific advancement, yet the review process for funding proposals lacks the transparency that has revolutionized academic paper peer review. Traditional funding agencies operate with closed review systems, limiting accountability and preventing systematic improvements. We present OpenProposal, a

Mohaiminul Islam Bhuiyan, Chan Hue Wah, Nur Shazwani Kamarudin, Nur Hafieza Ismail

This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods relying solely on a doctor's experience often lack precision. To address this limitation, intelligent systems are applied as an alternative to traditional approaches. While various int

Mika Sipilä, Sabrina Maggio, Sandra De Iaco, Klaus Nordhausen

Satellite data of atmospheric pollutants are often available only at coarse spatial resolution, limiting their applicability in local-scale environmental analysis and decision-making. Spatial downscaling methods aim to transform the coarse satellite data into high-resolution fields. In this work, two widely used deep learning architectures, the super-resolut

Md Awsafur Rahman, Adam Gabrys, Doug Kang, Jingjing Sun

A personalized LLM should remember user facts, apply them correctly, and adapt over time to provide responses that the user prefers. Existing LLM personalization benchmarks are largely centered on two axes: accurately recalling user information and accurately applying remembered information in downstream tasks. We argue that a third axis, likability, is both

Zhou-Run Zhu, Sheng Wang, Man-Li Tian, Defu Hou

In this work, we study the effective running coupling constant of heavy quark pair and jet quenching parameter in the spinning background. Ultra-locally, the boosted fluid is described by the boosted parameter and dual to a globally rotating system. Our results show that the angular momentum suppresses the effective running coupling constant and reduces its

Zhumin Ding, Rui Yang, Xiaoyao Zhou

We aim to investigate the dimension theory of $\alpha$-pressure-like quantities. By means of the Carath$\acute{\rm e}$odory-Pesin structure, we define $\alpha$-BS dimension and $\alpha$-Pesin topological pressure on subsets using $\alpha$-Bowen metric $$d_{n}^{\alpha}(x,y)=\max_{0\leq i\leq n-1}e^{\alpha i}d(f^{i}x,f^{i}y),$$ where $\alpha \geq 0$. Specifica

Zaber Al Hassan Ayon, Nur Hafieza Ismail, Nur Shazwani Kamarudin

Post-Traumatic Stress Disorder (PTSD) is a multifaceted mental health condition, particularly challenging for individuals with pre-existing medical conditions. This review critically examines the intersection of PTSD and chronic illnesses as expressed on social media platforms. By systematically analyzing literature from 2008 to 2024, the study explores how

Huimu Wang, Yiming Qiu, Xingzhi Yao, Zhiguo Chen

Dense retrieval has become the industry standard in large-scale information retrieval systems due to its high efficiency and competitive accuracy. Its core relies on a coarse-to-fine hierarchical architecture that enables rapid candidate selection and precise semantic matching, achieving millisecond-level response over billion-scale corpora. This capability

Jocelyn Nembé

We develop a unified geometric framework for nonparametric estimation based on the notion of Twin Kernel Spaces, defined as orbits of a reproducing kernel under a group action. This structure induces a family of transported RKHS geometries in which classical orthogonal polynomial estimators, kernel estimators, and spectral smoothing methods arise as projecti

Zizhi Chen, Yizhen Gao, Minghao Han, Yizhou Liu

Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-grained intra-modality features while bridging the significant domain gap across different modalities. To address this challenge, we propose a comprehensive framework. Leveraging ou

Bizhe Bai, Hongming Wu, Peng Ye, Tao Chen

Self-supervised reinforcement learning (RL) presents a promising approach for enhancing the reasoning capabilities of Large Language Models (LLMs) without reliance on expensive human-annotated data. However, we find that existing methods suffer from a critical failure mode under long-horizon training: a "policy collapse" where performance precipitously degra

Michael C. H. Choi, Ryan J. Y. Lim, Youjia Wang

We study group-averaged Markov chains obtained by augmenting a $π$-stationary kernel $P$ with orbit kernels induced by a group action. We analyse the Gibbs ($G$), Metropolis--Hastings ($M$), and Barker ($B$) kernels, their sandwiches $QPQ$, and mixtures $\tfrac{1}{2}(P+Q)$, where $Q\in\{G,M,B\}$. Under suitable conditions, $M^t$ and $B^t$ converge blockwise

Taoran Sheng, Manfred Huber

Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labe

Hoai-Minh Nguyen

We investigate the local boundary controllability of the Korteweg-de Vries (KdV) equation with right Neumann boundary controls at critical lengths. We show that the KdV system is not locally null-controllable in small time for all critical lengths for which the unreachable subspace of the linearized system has dimension at least two. This result extends the

Szu-Chi Chung, Chien-Hong Cho, Strong Wen

While short-term models such as the Short-Term Earthquake Probability (STEP) and Epidemic-Type Aftershock Sequence (ETAS) are well established and supported by open-source software, medium- to long-term models, notably the Every Earthquake a Precursor According to Scale (EEPAS) and Proximity to Past Earthquakes (PPE), remain under-documented and largely inac

Cheril Shah, Akshit Agarwal, Kanak Garg, Mourad Heddaya

Bilateral negotiation is a complex, context-sensitive task in which human negotiators dynamically adjust anchors, pacing, and flexibility to exploit power asymmetries and informal cues. We introduce a unified mathematical framework for modeling concession dynamics based on a hyperbolic tangent curve, and propose two metrics burstiness tau and the Concession-

Willy Hereman, Rehana Naz

Conservation laws are computed for various nonlinear partial differential equations that arise in elasticity and acoustics. Using a scaling homogeneity approach, conservation laws are established for two models describing shear wave propagation in a circular cylinder and a cylindrical annulus. Next, using the multiplier method, conservation laws are derived

Jianjun Xiao, Cixiao Wang, Wenmei Zhang

Measuring collaborative problem solving (CPS) synergy remains challenging in learning analytics, as classical manual coding cannot capture emergent system-level dynamics. This study introduces a computational framework that integrates automated discourse analysis with the Synergy Degree Model (SDM) to quantify CPS synergy from group communication. Data were

Jin Sob Kim, Hyun Joon Park, Wooseok Shin, Sung Won Han

Recent speaker verification studies have achieved notable success by leveraging layer-wise output from pre-trained Transformer models. However, few have explored the advancements in aggregating these multi-level features beyond the static weighted average. We present Layer Attentive Pooling (LAP), a novel strategy for aggregating inter-layer representations

Kangning Gao, Yi Hu, Cong Nie, Wei Li

This paper addresses the challenges of low scheduling efficiency, unbalanced resource allocation, and poor adaptability in ETL (Extract-Transform-Load) processes under heterogeneous data environments by proposing an intelligent scheduling optimization framework based on deep Q-learning. The framework formalizes the ETL scheduling process as a Markov Decision

Ikuya Yamada, Wataru Ikeda, Ko Yoshida, Mengyu Ye

We present an open deep research system for long-form question answering, selected as a winning system in the text-to-text track of the MMU-RAG competition at NeurIPS 2025. The system combines an open-source large language model (LLM) with an open web search API to perform iterative retrieval, reasoning, and synthesis in real-world open-domain settings. To e

Marek Černý, Tim Seppelt

Two graphs $G$ and $H$ are homomorphism indistinguishable over a graph class $\mathcal{F}$ if they admit the same number of homomorphisms from every graph $F \in \mathcal{F}$. Many graph isomorphism relaxations such as (quantum) isomorphism and cospectrality can be characterised as homomorphism indistinguishability over specific graph classes. Thereby, the p