October 2025 arXiv papers — page 45

Ardra A, Ameerraja Ansari, Anumol Joseph, Lakshmi Sankar

Let $B_1 ^c = \{ x\in \mathbb{R}^N: |x|>1 \}, N \geq 2$, and $\mathcal{D}^{1,N}_0(B^c_1)$, be the Beppo-Levi space. We prove that $\mathcal{D}^{1,N}_0(B^c_1)$ is compactly embedded into the weighted Lebesgue space $L^r(B_1^c;K(x))$ for all $r\in[1,\infty)$ for an appropriate class of weight functions $K$. As an application, we prove the existence of a positi

Koki Shibata, Tianheng Ling, Chao Qian, Tomokazu Matsui

The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition. While prior studies demonstrated feasibility, they often rely on complex preprocessing and large Neural Networks (NNs) requiring costly high-performance hardware, resulting in high energy usage and limited real-world

Yixuan Wu, Yifei Jiao, Wen-Yue Dai, Yukun Huang

While most near-Earth asteroids (NEAs) are thought to originate from the main belt, recent discoveries have suggested the existence of a lunar-derived NEA population, such as the asteroids Kamo'oalewa and 2024 PT5. These objects may hold key clues to the dynamical evolution of NEAs and the recent impact history of the Earth-Moon system. However, the populati

Kenji Sugisaki

The quantum-selected configuration interaction (QSCI) method is a promising approach for large-scale quantum chemical calculations on currently available quantum hardware. However, its naive implementation lacks size consistency, which is essential for accurate intermolecular interaction energy calculations using the supramolecular approach. Here, we present

Zhuang Liu, Yumei Tan, Jianhao Qian, Min Cao

Membranes selective to ions of the same charge are increasingly sought for wastewater processing and valuable element recovery. However, while narrow channels are known to be essential, other membrane parameters remain difficult to identify and control. Here we show that Zr$^{4+}$, Sn$^{4+}$, Ir$^{4+}$, and La$^{3+}$ ions intercalated into vermiculite lamina

Alexis Delplace, Samer Lahoud, Kinda Khawam

This paper presents the first comprehensive real-world measurement campaign comparing LR-FHSS and LoRa modulations within LoRaWAN networks in urban environments. Conducted in Halifax, Canada, the campaign used a LoRaWAN platform capable of operating both modulations in the FCC-regulated US915 band. Real-world measurements are crucial for capturing the effect

Sixian Liu, Chen Xu, Qiang Wang, Donghai Shi

Multimodal camera-LiDAR fusion technology has found extensive application in 3D object detection, demonstrating encouraging performance. However, existing methods exhibit significant performance degradation in challenging scenarios characterized by sensor degradation or environmental disturbances. We propose a novel Adaptive Gated Fusion (AG-Fusion) approach

Alban Etienne, Jean-Jacques Ohana, Eric Benhamou, Béatrice Guez

Recent work has emphasized the diversification benefits of combining trend signals across multiple horizons, with the medium-term window-typically six months to one year-long viewed as the "sweet spot" of trend-following. This paper revisits this conventional view by reallocating exposure dynamically across horizons using a Bayesian optimization framework de

Diego Marcondes

Physics-informed statistical learning (PISL) integrates empirical data with physical knowledge to enhance the statistical performance of estimators. While PISL methods are widely used in practice, a comprehensive theoretical understanding of how informed regularization affects statistical properties is still missing. Specifically, two fundamental questions h

Aryan Mathur, Asaduddin Ahmed

Deep reinforcement learning agents often struggle when tasks require understanding both vision and language. Conventional architectures typically isolate perception (for example, CNN-based visual encoders) from decision-making (policy networks). This separation can be inefficient, since the policy's failures do not directly help the perception module learn w

Parisa Kanani, Mohammad Javad Omidi, Mahmoud Modarres-Hashemi, Halim Yanikomeroglu

To meet the ambitious goals of next-generation 6G networks, including ultra-high data rates and ubiquitous coverage, we propose a novel high-altitude platform station (HAPS)-based integrated sensing and communication (ISAC) architecture. Operating in the stratosphere, the HAPS functions as both a powerful communication hub and an advanced environmental senso

Enrique Orduña-Malea

Scientific publishing is facing an alarming proliferation of fraudulent practices that threaten the integrity of research communication. The production and dissemination of fake research have become a profitable business, undermining trust in scientific journals and distorting the evaluation processes that depend on them. This brief piece examines the proble

Yaoyan Zheng, Huiqun Wang, Nan Zhou, Di Huang

Transferability estimation identifies the best pre-trained models for downstream tasks without incurring the high computational cost of full fine-tuning. This capability facilitates deployment and advances the pre-training and fine-tuning paradigm. However, existing methods often struggle to accurately assess transferability for emerging pre-trained models w

Ziyu Wang, Wenhao Li, Ji Wu

3D object detection from multi-view images in traffic scenarios has garnered significant attention in recent years. Many existing approaches rely on object queries that are generated from 3D reference points to localize objects. However, a limitation of these methods is that some reference points are often far from the target object, which can lead to false

Victor Przyjalkowski

Mirror symmetry predicts that bounded derived category of a smooth Fano variety is equivalent to Fukaya-Seidel category of its Landau-Ginzburg model. It is expected that fibers of Landau-Ginzburg model with ordinary double points correspond to an exceptional collection of a Fano variety. We verify this expectation on a numerical level for Fano complete inter

Chi Liu

We provide a new perspective on GSPO's length-normalized importance ratios by establishing their connection to information-theoretic quantities. We show that GSPO's sequence-level weight $s(\theta) = (\pi_\theta/\pi_{\theta_{\text{old}}})^{1/|y|}$ can be equivalently expressed as the inverse perplexity ratio $\text{PPL}_{\theta_{\text{old}}}/\text{PPL}_\thet

Sarabeth S. Mullins, Georg Götz, Eric Bezzam, Steven Zheng

Accurate far-field speech datasets are critical for tasks such as automatic speech recognition (ASR), dereverberation, speech enhancement, and source separation. However, current datasets are limited by the trade-off between acoustic realism and scalability. Measured corpora provide faithful physics but are expensive, low-coverage, and rarely include paired

Christian Salomonsen, Samuel Kuttner, Michael Kampffmeyer, Robert Jenssen

Tracer kinetic modeling serves a vital role in diagnosis, treatment planning, tracer development and oncology, but burdens practitioners with complex and invasive arterial input function estimation (AIF). We adopt a physics-informed CycleGAN showing promise in DCE-MRI quantification to dynamic PET quantification. Our experiments demonstrate sound AIF predict

Yanyan Geng, Chang Li, Shuo Mi, Manyu Wang

The delicate interfacial conditions and behaviors play critical roles in determining the valuable physical properties of two-dimensional materials and their heterostructures on substrates. However, directly probing these complex interface conditions remains challenging. Here, we reveal the coupled in-plane strain and out-of-plane bonding conditions in strain

Alessandro Pacco

High-dimensional random landscapes underlie phenomena as diverse as glassy physics and optimization in machine learning, and even their simplest toy models already display extraordinarily rich behavior. This thesis aims to deepen our understanding of that behavior, by combining landscape-based approaches, via the Kac-Rice formalism, with dynamical approaches

Josef Bigun, Fernado Alonso-Fernandez

This note presents a theoretical discussion of two structure tensor constructions: one proposed by Bigun and Granlund 1987, and the other by Granlund and Knutsson 1995. At first glance, these approaches may appear quite different--the former is implemented by averaging outer products of gradient filter responses, while the latter constructs the tensor from w

Juan A. Lara, David Lizcano, Víctor Rampérez, Javier Soriano

Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper,

Ning Sun, Lei Feng, Pengfei Zhang

Understanding the emergence of novel collective behaviors in strongly interacting systems lies at the heart of quantum many-body physics. Valuable insight comes from examining how few-body correlations manifest in many-body systems, embodying the ``from few to many'' philosophy. An intriguing example is the set of universal relations in ultracold atomic gase

Núria Alcalde-Herraiz, Alessia Garibaldi, Karn Rongrueangkul, Alexei Kalaboukhov

Superconducting materials are a key for technologies enabling a large number of devices including THz wave mixers and single photon detectors, though limited at very low temperatures for conventional superconductors. High temperature operation could in principle be offered using cuprate superconductors. However, the complexity of the material in thin film fo

Farida Lombarkia, Assia Bezai, Néstor Thome

This paper provides new necessary and sufficient conditions for the solvability to the operator equations $ AX-XB=C$ and $AX-YB=C,$ where $A $ and $B $ are group invertible operators defined on an infinite dimensional Hilbert space. In addition, the general solutions to the equation $AX-YB=C,$ are derived in terms of group inverse of $ A $ and $ B $. As a co

Tomáš Sourada, Jana Straková

The traditional approach to morphological inflection (the task of modifying a base word (lemma) to express grammatical categories) has been, for decades, to consider lexical entries of lemma-tag-form triples uniformly, lacking any information about their frequency distribution. However, in production deployment, one might expect the user inputs to reflect a

Ewa Damek, Sebastian Mentemeier

We consider random vectors $X$ that satisfy the equation in law $X=AX+B$, where $A$ is a given random diagonal matrix and $B$ a given random vector, both independent of $X$. It is well known by the works of Kesten and Goldie that the marginals of $X$ may exhibit heavy tails, with possibly different tail indices. In recent works (Damek 2025, Mentemeier and Wi

Sabino Francesco Roselli, Ze Zhang, Knut Åkesson

The deployment of mobile robots for material handling in industrial environments requires scalable coordination of large fleets in dynamic settings. This paper presents a two-layer framework that combines high-level scheduling with low-level control. Tasks are assigned and scheduled using the compositional algorithm ComSat, which generates time-parameterized

Xin Liao, Juntao Lv

We investigate the limiting behavior of solutions with infinitely many peaks to nonlinear Schr\"odinger equations [-epsilon^2 Delta u_epsilon + u_epsilon = u_epsilon^p, u_epsilon > 0 in R^n,] as epsilon -> 0, where p is Sobolev subcritical. We derive the interaction law among the limiting peak points and complete the analysis for the previously unresolved ra

Kai Zhuang, Jiawei Zhang, Yumou Liu, Hanqun Cao

Scientific Large Language Models (Sci-LLMs) have emerged as a promising frontier for accelerating biological discovery. However, these models face a fundamental challenge when processing raw biomolecular sequences: the tokenization dilemma. Whether treating sequences as a specialized language, risking the loss of functional motif information, or as a separat

Claudio Galelli, Lorenzo Caccianiga, Lorenzo Apollonio, Paolo Magnani

We present a phenomenological study demonstrating the feasibility of using olivine xenoliths from the Cha\^ine des Puys as a time-resolved paleo-detector array to probe the cosmic-ray flux over the last 40,000 years. This volcanic region provides a unique chronosequence of samples brought to the surface by well-dated eruptions. By modeling the expected densi

David E. Ruiz-Guirola, Prasoon Raghuwanshi, Gabriel M. de Jesus, Mateen Ashraf

Dependability is the ability to consistently deliver trusted and uninterrupted service in the face of operational uncertainties. Ensuring dependable operation in large-scale, energy-constrained Internet of Things (IoT) deployments is as crucial as challenging, and calls for context-aware protocols where context refers to situational or state information. In

Rupasree Dey, Abdul Matin, Everett Lewark, Tanjim Bin Faruk

Soil salinization poses a significant threat to both ecosystems and agriculture because it limits plants' ability to absorb water and, in doing so, reduces crop productivity. This phenomenon alters the soil's spectral properties, creating a measurable relationship between salinity and light reflectance that enables remote monitoring. While laboratory spectro

Shiwei Li, Xiandi Luo, Haozhao Wang, Xing Tang

Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). LoRA essentially describes the projection of an input space into a low-dimensional output space, with the dimensionality determined by the LoRA rank. In standard LoRA, all input tokens share the same weights and undergo an identical inpu

Ningxiao Tao, Liru Zhang, Xingyu Ni, Mengyu Chu

We present FlowCapX, a physics-enhanced framework for flow reconstruction from sparse video inputs, addressing the challenge of jointly optimizing complex physical constraints and sparse observational data over long time horizons. Existing methods often struggle to capture turbulent motion while maintaining physical consistency, limiting reconstruction quali

Bharath Santhanam, Alex Mitrevski, Santosh Thoduka, Sebastian Houben

Learned robot policies have consistently been shown to be versatile, but they typically have no built-in mechanism for handling the complexity of open environments, making them prone to execution failures; this implies that deploying policies without the ability to recognise and react to failures may lead to unreliable and unsafe robot behaviour. In this pap

Hironobu Kimura

For positive integers $r,n,N:=rn$, we consider the Radon hypergeometric function (Radon HGF) associated with a partition $\lambda$ of $n$ defined on the Grassmannian $Gr(m,N)$ for $r<m<N$, which is obtained as the Radon transform of a character of the group $H_{\lambda}\subset G:=GL(N)$. We study its symmetry described by the Weyl group analogue $N_{G}(H_{\l

Yi-Lin Wei, Zhexi Luo, Yuhao Lin, Mu Lin

Enabling robots to dexterously grasp and manipulate objects based on human commands is a promising direction in robotics. However, existing approaches are challenging to generalize across diverse objects or tasks due to the limited scale of semantic dexterous grasp datasets. Foundation models offer a new way to enhance generalization, yet directly leveraging

Gianfranco Basile, Johannes Jakubik, Benedikt Blumenstiel, Thomas Brunschwiler

We propose a task-agnostic framework for multimodal fusion of time series and single timestamp images, enabling cross-modal generation and robust downstream performance. Our approach explores deterministic and learned strategies for time series quantization and then leverages a masked correlation learning objective, aligning discrete image and time series to

Omer Jauhar Khan, Sudais Khan, Hafeez Anwar, Shahzeb Khan

Physics Informed Neural Networks (PINNs) are gaining attention for their ability to embed physical laws into deep learning models, which is particularly useful in structural engineering tasks with limited data. This paper aims to explore the use of PINNs to predict the weight of small scale spaghetti bridges, a task relevant to understanding load limits and

Hebaixu Wang, Jing Zhang, Haoyang Chen, Haonan Guo

Diffusion bridge models establish probabilistic paths between arbitrary paired distributions and exhibit great potential for universal image restoration. Most existing methods merely treat them as simple variants of stochastic interpolants, lacking a unified analytical perspective. Besides, they indiscriminately reconstruct images through global noise inject

Zhiwei Chen, Youjun Lu, Hao Ma, Qingbo Chu

With the rapid advancements in next-generation ground-based gravitational wave (GW) detectors, it is anticipated that $10^3$-$10^5$ binary neutron star (BNS) mergers per year will be detected, with a significant fraction accompanied by observable merger-nova signals through future sky surveys. Merger-novae are typically powered by the radioactive decay of he

Tomáš Sourada, Jana Straková

We present a compact, single-model approach to multilingual inflection, the task of generating inflected word forms from base lemmas to express grammatical categories. Our model, trained jointly on data from 73 languages, is lightweight, robust to unseen words, and outperforms monolingual baselines in most languages. This demonstrates the effectiveness of mu

Longhua Qin, Jiancheng Wang, Chuyuan Yang, Huaizhen Li

Very-high-energy (VHE; $E \gtrsim 100$ GeV) gamma rays are expected to experience strong attenuation during cosmological propagation due to electron-positron pair production on the extragalactic background light (EBL). Recent observations of GRB 221009A (z = 0.151), including photons up to $\sim 18$ detected by LHAASO and a $\sim 300\ \mathrm{TeV}$ event rep

Minjoo Kim, Jinwoong Kim, Sangjin Park

Recent advances in finance-specific language models such as FinBERT have enabled the quantification of public sentiment into index-based measures, yet compressing diverse linguistic signals into single metrics overlooks contextual nuances and limits interpretability. To address this limitation, explainable AI techniques, particularly SHAP (SHapley Additive E

Felix Koehler, Nils Thuerey

Neural operators or emulators for PDEs trained on data from numerical solvers are conventionally assumed to be limited by their training data&#39;s fidelity. We challenge this assumption by identifying &#34;emulator superiority,&#34; where neural networks trained purely on low-fidelity solver data can achieve higher accuracy than those solvers when evaluated

Kang Shen, Xiangming Hu, Fei Wang

Multiensemble superradiance extends Dicke superradiance to multiple ensembles and supports dark states whose properties depend on the initial state. In the large-\(N\) limit, we derive analytical covariance matrices for these dark states, revealing inter-ensemble entanglement that enhances quantum metrology. The minimum eigenvalue, determined by the curvatur

Bernhard Rameder, Hubert Gattringer, Ronald Naderer, Andreas Mueller

This paper deals with the design of a cost effective automated tape laying system (ATL system) with integrated uniaxial force control to ensure the necessary compaction forces as well as with an accurate temperature control to guarantee the used tape being melted appropriate. It is crucial to control the substrate and the oncoming tape onto a specific temper

Marlene J E Mayer, Nicolas B Garnier, Clara Becker, Marta C Antonelli

Prenatal maternal stress (PS) is a risk factor for adverse offspring neurodevelopment. Heart rate variability (HRV) complexity provides a non-invasive marker of maternal autonomic regulation and may be influenced by mind--body interventions such as Yoga. In this quasi-randomized controlled trial, 28 chronically stressed pregnant women were followed from the

Minati De, Satyam Singh, Csaba D. Tóth

We are given a set $P$ of $n$ points in the plane, and a sequence of axis-aligned squares that arrive in an online fashion. The online hitting set problem consists of maintaining, by adding new points if necessary, a set $H\subseteq P$ that contains at least one point in each input square. We present an $O(\log n)$-competitive deterministic algorithm for thi

Sergei Kholkin, Francisco Vargas, Alexander Korotin

We introduce the Target Concrete Score Identity Sampler (TCSIS), a method for sampling from unnormalized densities on discrete state spaces by learning the reverse dynamics of a Continuous-Time Markov Chain (CTMC). Our approach builds on a forward in time CTMC with a uniform noising kernel and relies on the proposed Target Concrete Score Identity, which rela

Shuai Feng, Yan Gong, Xiaohui Liu, Jun-Hui Yan

We constrain and compare the $w_{0}w_{a}$CDM dynamical dark energy model and three $f(R)$ modified gravity models using the current cosmological distance measurements, including 112 high-quality localized FRBs, BAO measurements from the DESI-DR2 and the BOSS-DR12, Type~Ia supernovae (SNe~Ia) from the PantheonPlus compilation and the DESY5 sample, cosmic chro

Yi-Li Hsu, Katelyn X. Mei, Lucy Lu Wang

Medical multi-document summarization (MDS) is a complex task that requires effectively managing cross-document relationships. This paper investigates whether incorporating hierarchical structures in the inputs of MDS can improve a model's ability to organize and contextualize information across documents compared to traditional flat summarization methods. We

B. Molina, P. Mróz, P. V. De la Parra, A. C. S. Readhead

The combined University of Michigan Radio Astronomy Observatory (UMRAO) and Owens Valley Radio Observatory (OVRO) blazar monitoring programs at 14.5/15 GHz provide uninterrupted light curves of $\sim~46-50$ yr duration for 83 blazars, selected from amongst the brightest and most rapidly flaring blazars north of declination $-20^\circ$. In a search for superm

Yifan Zhang, Xin Zhang

Directed greybox fuzzing (DGF) aims to efficiently trigger bugs at specific target locations by prioritizing seeds whose execution paths are more likely to reach the targets. However, existing DGF approaches suffer from imprecise potential estimation due to their reliance on static-analysis-based distance metrics. The over-approximation inherent in static an

Xiao-Yun Wang, Liu-Lin Wang, Xiao-Hai Liu, Qiang Zhao

The BESIII collaboration has recently observed a resonant structure $Y(4500)$ in $e^{+}e^{-}\to J/\psi K^{+}K^{-}$, whose origin remains unresolved. In this study, we analyze the cross section line shape of $e^{+}e^{-}\to J/\psi K^{+}K^{-}$ by taking into account the $\psi(4415)$ state and intermediate charmed meson loops. By treating $\psi(4415)$ as both a

Changming Ke, Yudi Yang, Minjun Wang, Jianhui Wang

Developing high-energy-density batteries is essential for advancing sustainable energy technologies. However, leading cathode materials such as Li-rich oxides, including Li$_2$MnO$_3$, suffer from capacity loss due to irreversible oxygen release and structural degradation, both consequences of the oxygen redox activity that also enables their high capacity.

Jiyoung Hong, Yoonseo Chung, Seungyeon Oh, Juntae Kim

Audio deepfakes pose a growing threat, already exploited in fraud and misinformation. A key challenge is ensuring detectors remain robust to unseen synthesis methods and diverse speakers, since generation techniques evolve quickly. Despite strong benchmark results, current systems struggle to generalize to new conditions limiting real-world reliability. To a

Jie Huang, Xuejing Liu, Sibo Song, Ruibing Hou

Multimodal position encoding is essential for vision-language models, yet there has been little systematic investigation into multimodal position encoding. We conduct a comprehensive analysis of multimodal Rotary Positional Embedding (RoPE) by examining its two core components: position design and frequency allocation. Through extensive experiments, we ident

Xiaohao Liu, Heyan Wang, Wenjuan Chen

Quasi-Boolean algebras were introduced as the generalization of Boolean algebras in the setting of quantum computation logic. In this paper, we investigate the completeness and congruences of quasi-Boolean algebras. First, we discuss the number of finite quasi-Boolean algebras and characterize the finite irreducible quasi-Boolean algebras. Second, we show th

Paul-Emmanuel Sornette, Didier Sornette

We report the single-case trajectory of a 75-year-old retired occupational female therapist with idiopathic Parkinson's disease, Hoehn and Yahr stage 2 at diagnosis. Following progressive impairment despite standard care, she initiated training with Logic Workout (LW) in July 2025 under supervision. Within weeks, she reported meaningful improvements spanning

Christoforus Dimas Satrya, Aleksandr S. Strelnikov, Luca Magazzù, Yu-Cheng Chang

Coherence is a key property of quantum systems, and it plays a central role in the operation and performance of quantum heat engines and refrigerators. Despite its importance for the fundamental understanding in quantum thermodynamics and its technological implications, coherence effects in heat transport have not been observed previously. Here, we measure q

Wansheng Wang, Jiangtao Pan, Jie Wang, Zaijun Ye

We propose a new deep learning algorithm for solving high-dimensional parabolic integro-differential equations (PIDEs) and forward-backward stochastic differential equations with jumps (FBSDEJs). This novel algorithm can be viewed as an extension and generalization of the DBDP2 scheme and a dynamic programming version of the forward-backward algorithm propos

Nicolas Allegra

Tensor network methods strike a middle ground between fully-fledged quantum computing and classical computing, as they take inspiration from quantum systems to significantly speed up certain classical operations. Their strength lies in their compressive power and the wide variety of efficient algorithms that operate within this compressed space. In this work

M. V. Umansky, G. J. Parker, R. D. Smirnov

A propagator-based approach is investigated for Monte-Carlo (MC) modeling of neutral particles transport in fusion boundary plasmas. The propagator is essentially a Green function for the neutral kinetic equation, which depends on the plasma profiles. A Neural Network (NN) based model for the propagator provides a fast and accurate solution for the neutral d

Taoyu Wu, Yiyi Miao, Jiaxin Guo, Ziyan Chen

In robot-assisted minimally invasive surgery, accurate 3D reconstruction from endoscopic video is vital for downstream tasks and improved outcomes. However, endoscopic scenarios present unique challenges, including photometric inconsistencies, non-rigid tissue motion, and view-dependent highlights. Most 3DGS-based methods that rely solely on appearance const

Jin Li, Kexun Wu, Qi Hao, Yan Chen

We demonstrate a fully integrable and reconfigurable platform for controlling quantum emission by harnessing chiral exceptional bound states in the continuum (BICs) as a higher-order non-Hermitian singularity. Our architecture employs dual-microring resonators evanescently coupled to two waveguides, supporting symmetry-protected BICs. By integrating {a waveg

Julian Lang, Mirian Tsulaia

We give a brief introduction into the gauge invariant formulation of irreducible massive bosonic higher spin fields. We discuss both free Lagrangians and the ones which include cubic interactions. We demonstrate an application of these Lagrangians to a description of the interactions between Kerr black holes in a Post Minkowskian approximation.

Sunyou Hwang, Christophe De Wagter, Bart Remes, Guido de Croon

Orographic soaring can significantly extend the endurance of micro aerial vehicles (MAVs), but circling behavior, arising from control conflicts between the longitudinal and vertical axes, increases energy consumption and the risk of divergence. We propose a control switching method, named SAOS: Switched Control for Autonomous Orographic Soaring, which mitig

Jan Niklas Groeneveld, Xi Qin, Alexander Schaefer, Yaad Oren

Generating high-quality code remains a challenge for Large Language Models (LLMs). For the evolution of reasoning models on this task, reward models are a necessary intermediate step. These models judge outcomes or intermediate steps. Decoder-only transformer models can be turned into reward models by introducing a regression layer and supervised fine-tuning

A. Compagnucci, A. Formicola, M. Campostrini, J. Cruz

While the 14N(p,g)15O reaction plays a key role in the hydrogen-burning processes in various stellar conditions, its reaction rate is not known with sufficient precision. Therefore, the first scientific project at the recently launched Bellotti Ion Beam Facility of the Laboratori Nazionali del Gran Sasso was the measurement of the 14N(p,g)15O reaction cross

Yehao Zhang, Yuncheng Xu, Chenyi Tan, Yangfeng Su

Accurate and efficient computation of Floquet multipliers and subspaces is essential for analyzing limit cycle in dynamical systems and periodic steady state in Radio Frequency simulation. This problem is typically addressed by solving a periodic linear eigenvalue problem, which is discretized from the linear time-periodic system using one-step collocation m

Chengying Tu, Xuemiao Zhang, Rongxiang Weng, Rumei Li

Recent advances in foundation models have highlighted the significant benefits of multi-stage training, with a particular emphasis on the emergence of mid-training as a vital stage that bridges pre-training and post-training. Mid-training is distinguished by its use of intermediate data and computational resources, systematically enhancing specified capabili

Joon-Hwi Kim

We derive first-order and second-order field equations from ambitwistor spaces as phase spaces of massless particles. In particular, the second-order field equations of Yang-Mills theory and general relativity are formulated in a unified form $\{\{H,H\}\}_\nabla = 0$, whose left-hand side describes a doubling of Poisson bracket in a covariant sense. This str

Joel Honkamaa, Pekka Marttinen

Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, significantly hindering their usability. LUMIR brain registration challenge for Learn2Reg 2025 aims to advance the field by evaluating the performance of the registration on contrasts and

Phonepaserth Sisaykeo, Shogo Muramatsu

A numerical framework is proposed for identifying partial differential equations (PDEs) governing dynamical systems directly from their observation data using Chebyshev polynomial approximation. In contrast to data-driven approaches such as dynamic mode decomposition (DMD), which approximate the Koopman operator without a clear connection to differential ope

Xiaoyu Kong, Junguang Jiang, Bin Liu, Ziru Xu

The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent research has explored leveraging the reasoning capabilities of LLMs to enhance rating prediction tasks. However, existing distillation-based methods suffer from limitations such as the te

Xuetao Yang, Ruilu An, Quanxin Zhu

In this paper, we focus on a periodic event-triggered impulsive control (PETIC) for fully heterogeneous stochastic multi-agent systems (MASs) with a time-varying topology. Firstly, a novel time-varying topology is established by incorporating the energy consumption of each agent. This topology enables active adjustment of the information interaction intensit

Yongming Li

Possibilistic computation tree Logic (PoCTL) is one kind of branching temporal logic combined with uncertain information in possibility theory, which was introduced in order to cope with the systematic verification on systems with uncertain information in possibility theory. There are two decision problems related to PoCTL: the model checking problem and the

Hiromu Takahashi, Shotaro Ishihara

We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs). MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. However, computational demands have grown substantially: recent methods rely on rep

Zishang Li, Changqing Ye, Eric T. Chung

In this paper, we propose a novel iterative multiscale framework for solving high-contrast contact problems of Signorini type. The method integrates the constrained energy minimizing generalized multiscale finite element method (CEM-GMsFEM) with a primal-dual active set strategy derived from semismooth Newton methods. First, local spectral problems are emplo

Shuai Liu, Jianrong Shi, Huigang Wei, Wenxian Li

Context. The interplay between surface magnetic topology and chromospheric heating in active M dwarfs remains poorly constrained, limiting our understanding of their magnetic cycles and high-energy environments. Aims. We aim to test whether detailed Zeeman-Doppler imaging (ZDI) maps of AD Leo can be used to spatially anchor a multi-component chromospheric mo

Limin Cao, Yanmao Chen, Li Wang, Loic Salles

Computing accurate periodic responses in strongly nonlinear or even non-smooth vibration systems remains a fundamental challenge in nonlinear dynamics. Existing numerical methods, such as the Harmonic Balance Method (HBM) and the Shooting Method (SM), have achieved notable success but face intrinsic limitations when applied to complex, high-dimensional, or n

Hoyeon Moon, Byeolhee Kim, Nikhil Verma

Multilingual Retrieval-Augmented Generation (mRAG) often retrieves English documents and translates them into the query language for low-resource settings. However, poor translation quality degrades response generation performance. Existing approaches either assume sufficient translation quality or utilize the rewriting method, which introduces factual disto

Laura Rinaldi, Alvise Sommariva, Marco Vianello

We discuss a cheap and stable approach to polynomial moment-based compression of multivariate measures by discrete signed measures. The method is based on the availability of an orthonormal basis and a low-cardinality algebraic quadrature formula for an auxiliary measure in a bounding set. Differently from other approaches, no conditioning issue arises since

Ella Dodor, Cristina V. Lopes

Good code style improves program readability, maintainability, and collaboration, and is an integral component of software quality. Developers, however, often cut corners when following style rules, leading to the wide adoption of tools such as linters in professional software development projects. Traditional linters like Checkstyle operate using rigid, rul

Yuchong Xie, Zesen Liu, Mingyu Luo, Zhixiang Zhang

Modern coding agents integrated into IDEs orchestrate powerful tools and high-privilege system access, creating a high-stakes attack surface. Prior work on Indirect Prompt Injection (IPI) is mainly query-specific, requiring particular user queries as triggers and leading to poor generalizability. We propose query-agnostic IPI, a new attack paradigm that reli

Aditya Akella

Recent advances in learnable reward shaping have shown promise in single-agent reinforcement learning by automatically discovering effective feedback signals. However, the effectiveness of decentralized learnable reward shaping in cooperative multi-agent settings remains poorly understood. We propose DMARL-RSA, a fully decentralized system where each agent l

Sangmin Kim, Taehun Kim, Guntae Kim, Chang Mook Kang

This paper proposes NeuroDOB, a deep neural network based observer controller for vehicle lateral dynamics, which replaces the conventional disturbance observer (DOB) with a deep neural network (DNN) to enhance personalized lateral control. Unlike conventional DOBs that compensate for general disturbances such as road friction variation and crosswind, NeuroD

Yichao Jin, Yushuo Wang, Qishuai Zhong, Kent Chiu Jin-Chun

Financial documents are essential sources of information for regulators, auditors, and financial institutions, particularly for assessing the wealth and compliance of Small and Medium-sized Businesses. However, SMB documents are often difficult to parse. They are rarely born digital and instead are distributed as scanned images that are none machine readable

Gokul Ganesan

Watermarking has been proposed as a lightweight mechanism to identify AI-generated text, with schemes typically relying on perturbations to token distributions. While prior work shows that paraphrasing can weaken such signals, these attacks remain partially detectable or degrade text quality. We demonstrate that cross-lingual summarization attacks (CLSA) --

Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang, Ryuhei Okuno

Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theory (DFT), and subsequently, to machine learning interatomic potentials (MLIPs). While universal MLIPs (u-MLIPs) offer broad transferability, their computational overhead limits lar

Si Hyeok Yang, Lim Kyung Oh, Na Young Lee, Dong Ho Lee

Molecular zero-dimensional (0D) halide perovskite-inspired cesium copper iodide (Cs3Cu2I5) is a highly promising candidate for optoelectronic applications due to their low toxicity, high stability, and intense blue emission. However, their intrinsically poor electrical conductivity, stemming from isolated conductive copper iodide tetrahedra by cesium atoms,

Zhifeng Wang, Meixin Su, Yang Yang, Chunyan Zeng

Driven by the dual principles of smart education and artificial intelligence technology, the online education model has rapidly emerged as an important component of the education industry. Cognitive diagnostic technology can utilize students' learning data and feedback information in educational evaluation to accurately assess their ability level at the know

Junru Wu, Zongpu Wang, Lin Sun, Kaichao Zhang

Proper treatment of the many-body interactions is of paramount importance in our understanding of strongly correlated systems. Here we investigate the effects of particle-hole fluctuations on the Berezinskii-Kosterlitz-Thouless (BKT) transition in two-dimensional Fermi gases throughout the entire BCS-BEC crossover. We include self-consistently in the self en

Yongtong Zhu, Lei Li, Iggy Qian, WenBin Zhou

The facial expression generation capability of humanoid social robots is critical for achieving natural and human-like interactions, playing a vital role in enhancing the fluidity of human-robot interactions and the accuracy of emotional expression. Currently, facial expression generation in humanoid social robots still relies on pre-programmed behavioral pa

Rutvij Bhavsar, N. D. Hari Dass

In this paper we examine the issue of quantum trajectories generated by QND-POVM's on {\it single} copies of unknown states. After an introduction to various aspects of quantum measurements, we discuss an earlier approach by one of us(NDH) based on Gaussian QND measurement operators that addressed the asymptotic behaviour of such trajectories showing the imp

Panneer Selvam Santhalingam, Swann Thantsin, Ahmad Kamari, Parth Pathak

In recent years, video conferencing applications have become increasingly prevalent, relying heavily on high-speed internet connectivity. When such connectivity is lacking, users often default to audio-only communication, a mode that significantly disadvantages American Sign Language (ASL) users, whose communication relies on hand gestures, body movement, an

Garima Jain, Anand Bodade, Sanghamitra Pati

Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled

Manjeshwar Aniruddh Mallya, Alessio Ferrari, Mohammad Amin Zadenoori, Jacek Dąbrowski

[Context and Motivation] Online user feedback provides valuable information to support requirements engineering (RE). However, analyzing online user feedback is challenging due to its large volume and noise. Large language models (LLMs) show strong potential to automate this process and outperform previous techniques. They can also enable new tasks, such as