November 2025 arXiv papers — page 41

Pascal Ruffing, Denis Petrov, Sebastian Zillien, Steffen Wendzel

Nowadays, malware increasingly uses DNS-based covert channels in order to evade detection and maintain stealthy communication with its command-and-control servers. While prior work has focused on detecting such activity, identifying specific malware families and their behaviors from captured network traffic remains challenging due to the variability of DNS.

Boris S. Maryshev, Lyudmila S. Klimenko

This paper describes the problem of drift of solid non-interacting particles in a microchannel, which can stick to its walls under the action of the van der Waals forces and break away from the wall due to thermal noise and viscous stresses arising from the flow. The pressure drop is given between the channel inlet and outlet. At the initial moment of time,

Anyang Tong, Xiang Niu, ZhiPing Liu, Chang Tian

Existing multimodal Retrieval-Augmented Generation (RAG) methods for visually rich documents (VRD) are often biased towards retrieving salient knowledge(e.g., prominent text and visual elements), while largely neglecting the critical fine-print knowledge(e.g., small text, contextual details). This limitation leads to incomplete retrieval and compromises the

Yu Sun, Yaosheng Deng, Wenjie Mei, Xiaogang Xiong

Soft robotics has advanced rapidly, yet its control methods remain fragmented: different morphologies and actuation schemes still require task-specific controllers, hindering theoretical integration and large-scale deployment. A generic control framework is therefore essential, and a key obstacle lies in the persistent use of rigid-body control logic, which

Bo Han, Zhuoming Li, Xiaoyu Wang, Yaxin Hou

Semi-supervised multi-label learning (SSMLL) aims to address the challenge of limited labeled data in multi-label learning (MLL) by leveraging unlabeled data to improve the model's performance. While pseudo-labeling has become a dominant strategy in SSMLL, most existing methods assign equal weights to all pseudo-labels regardless of their quality, which can

Rui Lin, Zhiyue Wu, Jiahe Le, Kangdi Wang

Audio tokenization bridges continuous waveforms and multi-track music language models. In dual-track modeling, tokens should preserve three properties at once: high-fidelity reconstruction, strong predictability under a language model, and cross-track correspondence. We introduce DuoTok, a source-aware dual-track tokenizer that addresses this trade-off throu

Sen Nie, Jie Zhang, Jianxin Yan, Shiguang Shan

Adversarial attacks have evolved from simply disrupting predictions on conventional task-specific models to the more complex goal of manipulating image semantics on Large Vision-Language Models (LVLMs). However, existing methods struggle with controllability and fail to precisely manipulate the semantics of specific concepts in the image. We attribute this l

Lian Shen, Zhendan Chen, Meijia Song, Yinhui jiang

In multimodal graph learning, graph structures that integrate information from multiple sources, such as vision and text, can more comprehensively model complex entity relationships. However, the continuous growth of their data scale poses a significant computational bottleneck for training. Graph condensation methods provide a feasible path forward by synth

Juexin Zhang, Qifeng Zhong, Ying Weng, Ke Chen

The significant molecular and pathological heterogeneity of glioblastoma, an aggressive brain tumor, complicates diagnosis and patient stratification. While traditional histopathological assessment remains the standard, deep learning offers a promising path toward objective and automated analysis of whole slide images. For the BraTS-Path 2025 Challenge, we d

Ruxandra-Stefania Tudose, Moritz H. W. Grüss, Grace Ra Kim, Karl H. Johansson

Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local

A. P. Schreckenberger, R. Ainsworth, M. Xiao

We describe an Xsuite simulation framework for the Fermilab Main Injector (MI) along with an evaluation of transition-crossing behaviors in the accelerator. In particular, we studied the introduction of quadrupole magnets into the lattice as part of a transition-jump system that will be implemented through the $2^{nd}$ Proton Improvement Plan (PIP-II). Simul

Yuhang Qian, Haiyan Chen, Wentong Li, Ningzhong Liu

Camouflage Images Generation (CIG) is an emerging research area that focuses on synthesizing images in which objects are harmoniously blended and exhibit high visual consistency with their surroundings. Existing methods perform CIG by either fusing objects into specific backgrounds or outpainting the surroundings via foreground object-guided diffusion. Howev

S. Kumano

In recent years, there are experimental reports on exotic-hadron candidates, which have different quark configurations from ordinary $q\bar q$ and $qqq$ constituents. However, it is not easy to confirm their exotic nature from global observables such as masses, spins, parities, and decay widths. At high energies, internal quark and gluon configurations could

Xiaoxue Zhang, Lihua You, Xinghui Zhao

A graph $G$ is called $H$-saturated if $G$ contains no copy of $H$, but $G+e$ contains a copy of $H$ for any edge $e\in E(\overline{G})$. The saturation number of $H$ is the minimum number of edges in an $H$-saturated graph of order $n$, denoted by $sat(n,H)$. In this paper, we investigate $sat(n,K_{2}\vee P_{k})$, where $k\geq 3$. Let $a_k$ be an integer, d

Jingheng Wang, Shengminjie Chen, Xiaoming Sun, Jialin Zhang

The Quantum Approximate Optimization Algorithm (QAOA) is widely studied for combinatorial optimization and has achieved significant advances both in theoretical guarantees and practical performance, yet for general combinatorial optimization problems the expected performance and classical simulability of fixed-round QAOA remain unclear. Focusing on Max-Cut,

Hao Yu, Jinglin Wang, Jiabo Zhan, Rui Chen

Transparency-aware generation requires modeling not only RGB appearance but also alpha-based opacity and cross-layer composition, which are essential for tasks such as image matting, object removal, layer decomposition, and multi-layer content creation. However, existing RGBA-related methods remain largely fragmented, with separate pipelines designed for ind

Lenka Prouzová Procházková, Eliška MÜllerová, Jan Bárta, Estelle Homeyer

This paper deals with the photochemical preparation of nanomaterials with garnet structure. Ce3+ and Mg2+ doped Lu2.5Gd0.5Ga2Al3O12 powders were prepared by using UV irradiation of aqueous solutions with low-pressure mercury lamps and subsequent calcination of the solid products. The synthesis was optimized and gives access to a range of doping which is very

Jan Quan, Alexander Bodard, Konstantinos Oikonomidis, Panagiotis Patrinos

We introduce a generalization of the scaled relative graph (SRG) to pairs of operators, enabling the visualization of their relative incremental properties. This novel SRG framework provides the geometric counterpart for the study of nonlinear resolvents based on paired monotonicity conditions. We demonstrate that these conditions apply to linear operators c

Ben S. Ashby, Gabriel R. Barrenechea, Alex Lukyanov, Tristan Pryer

We study the discretisation of a uniaxial (rank-one) reduction of the Oldroyd-B model for dilute polymer solutions, in which the conformation tensor is represented as $\sig = \vec b \otimes \vec b$. Building on structural analogies with MHD, we formulate a finite element framework compatible with the de Rham complex, so that the discrete velocity is exactly

Haotian Wu

We extend the convergence analysis of AdaSLS and AdaSPS in [Jiang and Stich, 2024] to the nonconvex setting, presenting a unified convergence analysis of stochastic gradient descent with adaptive Armijo line-search (AdaSLS) and Polyak stepsize (AdaSPS) for nonconvex optimization. Our contributions include: (1) an $\mathcal{O}(1/\sqrt{T})$ convergence rate fo

M. Markova, A. C. Larsen, P. von Neumann-Cosel, E. Litvinova

The nuclear level density (NLD) and the $\gamma$-ray strength function (GSF) of the neutron-deficient $^{109}$In isotope were extracted for the first time with data from the $^{106}$Cd$(\alpha,p\gamma)^{109}$In reaction using a combination of the Oslo and the shape methods. Both quantities are consistent with those of neighboring Cd and Sn nuclei, but show s

Jingbo Dou, Benfeng Shi, Tian Wu, Hua Zhu

In this paper, we investigate positive solutions to a class of Laplace equations with a gradient term on a complete, connected, and noncompact Riemannian manifold \((M^n,g)\) with nonnegative Ricci curvature, namely \[-\Delta u = f(u)|\nabla u|^q\quad\text{in }~M^n,\] where \(n\geqslant 3\), \(q>0,\) and \(f\) is a positive continuous function. We prove some

Enrico Sabatini

Given a commutative noetherian ring $R$ and a finite acyclic quiver $Q$, we study the tensor triangulated category $\mathcal{D}(RQ)$ endowed with the vertexwise tensor product. We find a description of the internal hom functor and show that the category is not rigid. We compute its Balmer spectrum and, despite the non-rigidity, we get a classification of all

Junjie Ye, Zhaolin Wang, Yuanwei Liu, Peichang Zhang

A novel continuous-aperture-array (CAPA)-aided integrated sensing and communication (ISAC) framework is proposed. Specifically, an optimal continuous ISAC waveform is designed to form a directive beampattern for multi-target sensing while suppressing the multi-user interference (MUI). To achieve the goal of optimal waveform design, the directional beampatter

Xiaoyu Chen, Haibin Liu, Jianming Cai

The radical pair mechanism (RPM) in the chemical magnetic compass model is considered to be one of the most promising candidates for the avian magnetic navigation, and quantum needle phenomenon further boosts the navigation precision to a new high level. It is well known that there are also a variety of methods in the field of magnetic field sensing in labor

Juexin Zhang, Ying Weng, Ke Chen

The ASNR-MICCAI BraTS-Inpainting Challenge was established to mitigate dataset biases that limit deep learning models in the quantitative analysis of brain tumor MRI. This paper details our submission to the 2025 challenge, a novel deep learning framework for synthesizing healthy tissue in 3D scans. The core of our method is a U-Net architecture trained to i

Dionysia Danai Brilli, Dimitrios Mallis, Vassilis Pitsikalis, Petros Maragos

We propose GHR-VQA, Graph-guided Hierarchical Relational Reasoning for Video Question Answering (Video QA), a novel human-centric framework that incorporates scene graphs to capture intricate human-object interactions within video sequences. Unlike traditional pixel-based methods, each frame is represented as a scene graph and human nodes across frames are l

Yitian Huang, Yuxuan Lei, Jianxun Lian, Hao Liao

This report presents the solution and results of our team MSRA\_SC in the Commonsense Persona-Grounded Dialogue Challenge (CPDC 2025). We propose a simple yet effective framework that unifies improvements across both GPU Track and API Track. Our method centers on two key components. First, Context Engineering applies dynamic tool pruning and persona clipping

Brigitte Schmieder, Anwesha Maharana, Jin Han Guo, Luis Linan

Eruptions of filaments are defined by different parameters, specially, sigmoid handedness and direction of the eruption, which are important parameters for forecasting the geoeffectiveness of consequent interplanetary coronal mass ejection (ICME) or magnetic cloud. Solar filaments often exhibit rotation and deflection during eruptions, which would significan

Francisco López, Lars Karlsson, Paolo Bientinesi

Generalized Matrix Chains (GMCs) are products of matrices where each matrix carries features (e.g., general, symmetric, triangular, positive-definite) and is optionally transposed and/or inverted. GMCs are commonly evaluated via sequences of calls to BLAS and LAPACK kernels. When matrix sizes are known, one can craft a sequence of kernel calls to evaluate a

Hao Wang, Dan Wang, Long Hao, Jun Li

Under high pressure, the group-VB transition metals vanadium (V) and niobium (Nb) exhibit simple crystal structures but complex physical behaviors, such as anomalous compression-induced softening and heating-induced hardening (CISHIH). Meanwhile, the impact of lattice thermal expansion-induced softening at elevated temperatures on HIH is yet to be investigat

Wei Chen, Jingxi Yu, Zichen Miao, Qiang Qiu

Transformer architectures have achieved remarkable success across language, vision, and multimodal tasks, and there is growing demand for them to address in-context compositional learning tasks. In these tasks, models solve the target problems by inferring compositional rules from context examples, which are composed of basic components structured by underly

Neta Elad, Adithya Murali, Sharon Shoham

For over two decades Separation Logic has been arguably the most popular framework for reasoning about heap-manipulating programs, as well as reasoning about shared resources and permissions. Separation Logic is often extended to include inductively-defined predicates, interpreted as least fixpoints, forming Separation Logic with Inductive Definitions (SLID)

Uri Bader, Roman Sauer

The purpose of this paper is twofold. We explore higher property T as an abstract group-theoretic property. In particular, we provide new operator-algebraic characterizations of higher property T. Then we turn to lattices in semisimple Lie groups. We relate higher property T to other cohomological, rigidity and geometric phenomena below the real rank. The se

Yi-De Lee, Hwei-Jang Yo

Gravitational wave denoising is an ongoing task for revealing the events of compact binary objects in the universe. Recently, with the aid of deep learning, gravitational waves have been efficiently and delicately extracted from the noisy data compared with the traditional match-filtering. While most of the relevant studies adopt the data in the time series

Nehal Afifi, Christoph Wittig, Lukas Paehler, Andreas Lindenmann

The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented. This fragmentation stems from uncertainty, particularly the lack of clarity on what types of DDMs to use and when to empl

Camilo Cárdenas-Hurtado, Sze Ming Lee, Yunxiao Chen, Irini Moustaki

Cognitive diagnosis models (CDMs) are restricted latent class models widely used to measure attributes of interest in diagnostic assessments across education, psychology, biomedical sciences, and related fields. Partial-mastery CDMs (PM-CDMs) are an important extension of CDMs. They model individuals' status for each attribute as continuous to measure partia

Haibin He, Qihuang Zhong, Juhua Liu, Bo Du

Video text-based visual question answering (Video TextVQA) task aims to answer questions about videos by leveraging the visual text appearing within the videos. This task poses significant challenges, requiring models to accurately perceive and comprehend scene text that varies in scale, orientation, and clarity across frames, while effectively integrating t

Ilya Kuleshov, Alexey Zaytsev

Neural Controlled Differential Equations (Neural CDEs, NCDEs) are a unique branch of methods, specifically tailored for analysing temporal sequences. However, they come with drawbacks, the main one being the number of parameters, required for the method's operation. In this paper, we propose an alternative, parameter-efficient look at Neural CDEs. It require

Lincen Yang, Zhong Li, Matthijs van Leeuwen, Saber Salehkaleybar

Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While most prior work is formulated in the potential outcome framework, the corresponding structural causal model (SCM) for this task has been largely overlooked. In practice, two approac

Stuart Marongwe, Stuart Kauffman

The Baryonic Tully-Fisher relation (BTFR) links the baryonic mass of galaxies to their characteristic rotational velocity and has been shown to hold with remarkable precision across a wide mass range. Recent studies, however, indicate that galaxy clusters occupy a parallel but offset relation, raising questions about the universality of the BTFR. Here, we de

Matteo Rosellini, Filippo Fruzza, Alessandro Mariotti, Maria Vittoria Salvetti

Sparse grids based on Lagrange polynomials have become one of the staple methods for approximating functions that are high-dimensional and expensive to evaluate, in the context e.g. of PDE-based parametric design exploration. They are however known to be inefficient for problems requiring local refinement, such as when the target function exhibits localized

Mohammad Mahdi, Yuqian Fu, Nedko Savov, Jiancheng Pan

Foundation video generation models such as WAN 2.2 exhibit strong text- and image-conditioned synthesis abilities but remain constrained to the same-view generation setting. In this work, we introduce Exo2EgoSyn, an adaptation of WAN 2.2 that unlocks Exocentric-to-Egocentric(Exo2Ego) cross-view video synthesis. Our framework consists of three key modules. Eg

O. Sergijenko., I. B. Vavilova, I. O. Izviekova, D. R. Karakuts

Identification of electromagnetic emission in coincidence with high-energy neutrinos is fundamentally important for multimessenger astronomy. Such observations are essential for constraining source localization, determining the source type, and understanding emission mechanisms. Typically, they require following up a neutrino alert (IceCube issues two alert

Adilet Metinov, Gulida M. Kudakeeva, Gulnara D. Kabaeva

Kyrgyz remains a low-resource language with limited foundational NLP tools. To address this gap, we introduce KyrgyzBERT, the first publicly available monolingual BERT-based language model for Kyrgyz. The model has 35.9M parameters and uses a custom tokenizer designed for the language's morphological structure. To evaluate performance, we create kyrgyz-sst2,

David Lee, Kieran Ricardo, Tamara Tambyah

A high order discontinuous Galerkin method for the material transport of thermodynamic tracers is coupled to a low order mixed finite element solver in the context of the thermal shallow water equations. The coupling preserves the energy conserving structure of the low order dynamics solver, while the high order material transport scheme is provably tracer v

Ryohei Kobayashi, Kosei Isomoto, Kosei Yamao, Soma Fumoto

This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team developed a dataset generator for training a robot vision system and an open-source development environment running on a Human Support Robot simulator. The large-language-model-powered task plann

Veith Weilnhammer, Jefferson Ortega, David Whitney

Scalable assessments of mental illness remain a critical roadblock toward accessible and equitable care. Here, we show that everyday human-computer interactions encode high-dimensional information about self-reported psychological distress and wellbeing. We introduce MAILA, a MAchine-learning framework for Inferring Latent mental states from digital Activity

Tianjie Dai, Xu Chen, Yunmeng Shu, Jinsong Lan

Sequential Recommendation System~(SRS) has become pivotal in modern society, which predicts subsequent actions based on the user's historical behavior. However, traditional collaborative filtering-based sequential recommendation models often lead to suboptimal performance due to the limited information of their collaborative signals. With the rapid developme

Carlos Valero

We consider on a spin manifold with boundary a Dirac operator $D_A$ with chiral boundary conditions, twisted by a unitary connection $A$. When $m$ is not in the chiral spectrum of $D_A$, we define an analogue of the Dirichlet-to-Neumann map for the Dirac equation $D_A - m$, which we call the boundary conjugation map, and show that it is a pseudodifferential

Federico Paredes-Valles, Yoshitaka Miyatani, Kirk Y. W. Scheper

Eye tracking is fundamental to numerous applications, yet achieving robust, high-frequency tracking with ultra-low power consumption remains challenging for wearable platforms. While event-based vision sensors offer microsecond resolution and sparse data streams, they have lacked fully integrated, low-power processing solutions capable of real-time inference

Eden Grossman, Alon Herman, Keren Shushan Alshochat, Dafna Amichay

The selective separation of same-charge ions is a longstanding challenge in resource recovery, battery recycling, and water treatment. Theoretical studies have shown that ratchet-based ion pumps (RBIPs) can separate ions with the same charge and valance by driving them in opposite directions according to their diffusion coefficients. This process relies on f

Xinjun Yang, Qingda Hu, Junru Li, Feifei Li

The rapid increase in LLM model sizes and the growing demand for long-context inference have made memory a critical bottleneck in GPU-accelerated serving systems. Although high-bandwidth memory (HBM) on GPUs offers fast access, its limited capacity necessitates reliance on host memory (CPU DRAM) to support larger working sets such as the KVCache. However, th

Jiakuan Lu, hangyang Meng

Let $G$ be a finite group and \( M \) be a maximal subgroup of \( G \). We call every irreducible constituent \( \chi \) of \( (1_M)^G \) a \( \mathcal{P} \)-character of \( G \) with respect to \( M \). In this paper, we prove that all $\mathcal{P}$-characters of $G$ are monomial if and only if $G$ is solvable, which solves a question posed by Qian and Yang

Bruno Belucci, Karim Lounici, Katia Meziani

Neural networks struggle on small tabular datasets, where tree-based models remain dominant. We introduce Adaptive Contrastive Approach (AdaCap), a training scheme that combines a permutation-based contrastive loss with a Tikhonov-based closed-form output mapping. Across 85 real-world regression datasets and multiple architectures, AdaCap yields consistent a

Hai Ling, Jia Guo, Zhulin Tao, Yunkang Cao

Anomaly detection (AD) aims to identify defects using normal-only training data. Existing anomaly detection benchmarks (e.g., MVTec-AD with 15 categories) cover only a narrow range of categories, limiting the evaluation of cross-context generalization and scalability. We introduce ADNet, a large-scale, multi-domain benchmark comprising 380 categories aggrega

Riccardo Zaccone, Sai Praneeth Karimireddy, Carlo Masone

Recent works have explored the use of momentum in local methods to enhance distributed SGD. This is particularly appealing in Federated Learning (FL), where momentum intuitively appears as a solution to mitigate the effects of statistical heterogeneity. Despite recent progress in this direction, it is still unclear if momentum can guarantee convergence under

Sean Bin Yang, Ying Sun, Yunyao Cheng, Yan Lin

Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have recently begun to explore spatio-temporal foundation models (STFMs) to improve adaptability and generalization acro

Yadong Liu, Shangfei Wang

Multimodal sentiment analysis remains a challenging task due to the inherent heterogeneity across modalities. Such heterogeneity often manifests as asynchronous signals, imbalanced information between modalities, and interference from task-irrelevant noise, hindering the learning of robust and accurate sentiment representations. To address these issues, we p

Kai Wang, Joop Schaye, Alejandro Benítez-Llambay, Evgenii Chaikin

We investigate the origin of the scatter in the stellar mass-halo mass (SMHM) relation using the \colibre cosmological hydrodynamical simulations. At fixed halo mass, we find a clear positive correlation between stellar mass and halo concentration, particularly in low-mass haloes between $10^{11}$ and $10^{12}\,\rm M_\odot$, where all halo properties are com

Karl Svozil

We unify two complementary viewpoints on relativistic spacetime and the counting of fundamental constants. Operationally, Matsas, Pleitez, Saa, and Vanzella (MPSV) have recently argued that relativistic spacetime requires only a single fundamental dimensional constant. Mathematically, theorems due to Alexandrov and Zeeman demonstrate that the light-cone stru

Tzu-Yang Chou

Let $X \subset \mathbb{P}^4$ be a quadric threefold with a single ordinary double point, and let $\mathcal{K}u(X)$ be its Kuznetsov component. In this paper, we construct a weak stability condition on Kuznetsov's categorical resolution $\widetilde{D} \subset \mathrm{D^b}(\widetilde{X})$, compatible with the Verdier localization $\mathbf{R}\pi_* \colon \widet

Gesualdo Delfino

We recently showed that the two-dimensional Ising spin glass allows for a line of renormalization group fixed points which explains properties observed in numerical studies. We observe that this exact result corresponds to enhancement to a one-generator continuous internal symmetry. This finally explains why no finite temperature transition to a spin glass p

Maksim Radionov, Daria Popova-Gorelova

Improving our understanding of electron dynamics is essential for advancing energy transfer, optoelectronics, light harvesting systems and quantum computing. Recent developments in attosecond x-ray sources provide the fundamental possibility of observing these dynamics with atomic-scale resolution. However, connecting a time-resolved signal to dynamics is ch

Francisco Díaz-Ruiz, Francisco J. Martín-Vega, Jose A. Cortés, Gerardo Gómez

Accurate and timely channel state information (CSI) is fundamental for efficient link adaptation. However, challenges such as channel aging, user mobility, and feedback delays significantly impact the performance of adaptive modulation and coding (AMC). This paper proposes and evaluates two CSI prediction frameworks applicable to both time division duplexing

C. Grimani, M. Fabi, M. Menichelli, F. Sabbatini M. Villani

Galactic cosmic rays and solar energetic particles (SEPs) affect the performance of instruments carried on board space missions and are the source of the absorbed dose to astronauts. Particles above 100 MeV are the most penetrating. The overall particle flux impacting spacecraft increases by several orders of magnitude during the most intense SEP events and

Bin Hu, Zijian Lu, Haicheng Liao, Chengran Yuan

Motion planning for autonomous driving must handle multiple plausible futures while remaining computationally efficient. Recent end-to-end systems and world-model-based planners predict rich multi-modal trajectories, but typically rely on handcrafted anchors or reinforcement learning to select a single best mode for training and control. This selection disca

Marc Karnat, Gautham Hari Narayana, Sudheer Kumar Peneti, Victoria Guglielmotti

Quantifying the in-plane rheology of epithelial monolayers remains challenging due to the difficulty of imposing controlled shear. We introduce a self-driven, rheometer-like assay in which collective migration generates stationary shear flows, allowing rheological parameters to be inferred directly from image sequences. The assay relies on two sets of ring-s

Xin Hong, Ying Shi, Yinhao Li, Yen-Wei Chen

The uncertainty of clinical examinations frequently leads to irregular observation intervals in longitudinal imaging data, posing challenges for modeling disease progression.Most existing imaging-based disease prediction models operate in Euclidean space, which assumes a flat representation of data and fails to fully capture the intrinsic continuity and nonl

Tancredi Salamone, Biel Martinez Diaz, Jing Li, Lukas Cvitkovich

We discuss the choice and implementation of inter-valley potentials in the so-called two bands $\mathbf{k}\cdot\mathbf{p}$ model for the opposite $X$, $Y$ or $Z$ valleys of silicon. We focus on the description of valley splittings in Si/SiGe heterostructures for spin qubits, with a particular attention to alloy disorder. We demonstrate that the two bands $\m

Arnela Hadzic, Franz Thaler, Lea Bogensperger, Simon Johannes Joham

Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible trajectory design, while maintaining high-quality image generation. This capability makes it suitable as a generative prior for image restoration tasks. Although current methods l

Haodong Pan, Hao Wei, Yusong Wang, Nanning Zheng

Learned image compression (LIC) has recently benefited from Transformer- and state space models (SSM)- based backbones for modeling long-range dependencies. However, the former typically incurs quadratic complexity, whereas the latter often disrupts neighborhood continuity by flattening 2D features into 1D sequences. To address these issues, we propose a com

Margot Celerie, Andrew Oldfield, William Ritchie

Motivation: Modern genomics laboratories generate massive volumes of sequencing data, often resulting in significant storage costs. Genomics storage consists of duplicate files, temporary processing files, and redundant intermediate data. Results: We developed SeqManager, a web-based application that provides automated identification, classification, and man

Matthias Ehrhardt, Michael Günther, Daniel Ševčovič

The port-Hamiltonian framework is a structure-preserving modeling approach that preserves key physical properties such as energy conservation and dissipation. When subsystems are modeled as port-Hamiltonian systems (pHS) with linearly related inputs and outputs, their interconnection remains port-Hamiltonian. This paper introduces a systematic method for tra

Yuan Jia, Guoqin Zhao, Hao Ma, Xin Li

Accurate prediction of hypersonic flow fields over a compression ramp is critical for aerodynamic design but remains challenging due to the scarcity of experimental measurements such as velocity. This study systematically develops a data fusion framework to address this issue. In the first phase, a model trained solely on Computational Fluid Dynamics (CFD) d

Michela Ignoti, Claudia Frugiuele, Matteo G. A. Paris, Marco G. Genoni

The precise measurement of the leptonic CP-violating phase $\delta_{CP}$ remains one of the major open challenges in neutrino physics, as current experiments achieve only very limited accuracy. We address this issue through the lens of quantum estimation theory. A distinctive feature of neutrino oscillation experiments is that they cannot freely optimize the

Jian Mou, Hai-Liang Chen, Dengkai Jiang, Hongwei Ge

Helium white dwarfs (He WDs) are end products of low-mass red giant donors in close binary systems via stable mass transfer or common envelope evolution. At the end of stable mass transfer, there is a well-known relation between the He WD mass and orbital period. Although this relation has been widely investigated, the influence of different types of opacity

Carson Collins, William M Feldman

We study a rate independent energetic model of the Wilhelmy plate experiment in capillarity. The evolution is driven by vertical motions of the plate. We show stability of energy solutions to the evolution, in the sense used in the rate-independent systems literature, as the ratio between container width and plate width goes to infinity. In particular, we sh

Wenpei Jiao, Kun Shang, Hui Li, Ke Yan

Positron emission tomography/computed tomography (PET/CT) is essential in oncology, yet the rapid expansion of scanners has outpaced the availability of trained specialists, making automated PET/CT report generation (PETRG) increasingly important for reducing clinical workload. Compared with structural imaging (e.g., X-ray, CT, and MRI), functional PET poses

Céline Crépisson, Mila Fitzgerald, Domenic Peake, Patrick Heighway

Oxygen and other light elements comprise up to 5 wt% of the Earth's outer-core, and may significantly influence its physical properties and the operation of the geodynamo. Here we report in situ x-ray diffraction measurements of Fe, Fe + 4.5 FeO (atomic proportion), and Fe2O3 melts at 177-438 GPa, achieved using laser-driven shock compression at an x-ray fre

Wen-Fang Su, Hsiao-Wei Chou, Wen-Yang Lin

Named Entity Recognition (NER) is a critical task in natural language processing, yet it remains particularly challenging for discontinuous entities. The primary difficulty lies in text segmentation, as traditional methods often missegment or entirely miss cross-sentence discontinuous entities, significantly affecting recognition accuracy. Therefore, we aim

Alexandre Epalle, Isabelle Ramière, Guillaume Latu, Frédéric Lebon

Parallel implementation of numerical adaptive mesh refinement (AMR)strategies for solving 3D elastostatic contact mechanics problems is an essential step toward complex simulations that exceed current performance levels. This paper introduces a scalable, robust, and efficient algorithm to deal with 2D and 3D elastostatics contact problems between deformable

Aleksei Samarin, Artem Nazarenko, Egor Kotenko, Valentin Malykh

This paper presents a novel approach to neural network compression that addresses redundancy at both the filter and architectural levels through a unified framework grounded in information flow analysis. Building on the concept of tensor flow divergence, which quantifies how information is transformed across network layers, we develop a two-stage optimizatio

Anagha Gayathri, Aryan Bhardwaj, Nilesh Sharma, Tarun Goel

We present the experimental implementation of a three-time-bin phase-encoded Twin-Field Quantum Key Distribution (TF-QKD) protocol using a Sagnac-based star-topology plug-and-play architecture. The proposed encoding method leverages the relative phases of three consecutive time bins to encode two bits per signal. The Sagnac loop configuration enables self-co

Mariana M Garcez Duarte, Mahmoud Sakr

Outlier detection and cleaning are essential steps in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within individual trajectories, i.e., points that deviate significantly inside a single trajectory. We experiment with ten open-source libraries to comprehensively evaluate available tools, compa

Jason Lo, Mohammadnima Jafari

A wiring diagram is a labeled directed graph that represents an abstract concept such as a temporal process. In this article, we introduce the notion of a quasi-skeleton wiring diagram graph, and prove that quasi-skeleton wiring diagram graphs correspond to Hasse diagrams. Using this result, we designed algorithms that extract wiring diagrams from sequential

George Dan Chita

Historically, solar flare detection has been dependent on methods that require the presence of expensive satellites or other Earth based costly equipment. In this paper, we propose a cost effective, terrestrial alternative that enables reliable solar flare detection. We will discuss the design, practical implementation, and demonstration of a monitoring syst

Silvia Cingolani, Minbo Yang, Shunneng Zhao

In this paper, we study that the nearly critical nonlocal problem \begin{equation*} \left\lbrace \begin{aligned} &-\Delta u=(|x|^{-{(n-2)}}\ast u^{p-\epsilon})u^{p-1-\epsilon} \quad \mbox{in}\quad \Omega, &u>0\quad \mbox{in}\quad\hspace{1mm} \Omega, &u=0\quad \mbox{on}\hspace{2.5mm}\partial\Omega, \end{aligned} \right. \end{equation*} where $\Omega$ is a smo

Yuki Amano, Leo Hirata, Moto Togawa, Hiromasa Suzuki

We present the basic performance and experimental results of an electron beam ion trap (JAXA-EBIT), newly introduced to the Japanese astronomical community. Accurate atomic data are indispensable for the reliable interpretation of high-resolution X-ray spectra of astrophysical plasmas. The JAXA-EBIT generates highly charged ions under well-controlled laborat

Mukunda P. Das, Frederick Green

We give a short overview of the role of microscopic conservation in charge transport at small scales, and at driving fields beyond the linear-response limit. As a practical example we recall the measurement and theory of interband coupling effects in a quantum point contact driven far from equilibrium.

A. R. Mukhamedyanov, E. S. Andrianov, A. A. Zyablovsky

We propose a model of binary random number generator (RNG) based on a Brillouin optomechanical system. The device uses a hard excitation mode in a Brillouin optomechanical system, where thermal noise induces spontaneous transitions between two stable states in the hard excitation mode. We demonstrate the existence of an amplitude criterion for observing thes

Yuhang Wang, Heye Huang, Zhenhua Xu, Kailai Sun

Autonomous driving faces critical challenges in rare long-tail events and complex multi-agent interactions, which are scarce in real-world data yet essential for robust safety validation. This paper presents a high-fidelity scenario generation framework that integrates a conditional variational autoencoder (CVAE) with a large language model (LLM). The CVAE e

Jamie Bell, Shirly Geffen, David Kerr

We initiate an investigation into the local structure of simple nonnuclear C$^*$-crossed products by showing that stable rank one is generic within two natural classes of minimal actions of free groups on the Cantor set. The arguments also apply to some other free product groups. Our approach is inspired by Li and Niu's stable rank one theorem in the amenabl

Richard Boadi, Dominic Breit, Thamsanqa Castern Moyo

We study the isentropic compressible Euler equations in multi-dimensions with stochastic perturbation of transport type. On the one hand, this is motivated by the physical modelling in turbulence theory. On the other hand, it has been shown recently that this type of noise can have regularising effects. In this paper, we prove the existence of dissipative me

H. R. Paz

This study provides a causal validation of the dual-stressor hypothesis in a long-cycle engineering programme in Argentina, testing whether academic staff strikes (proximal shocks) and inflation (distal shocks) jointly shape student dropout. Using a leak-aware longitudinal panel of 1,343 students and a manually implemented LinearDML estimator, we estimate la

Pablo Montero de Hijes, Kaihang Shi, Carlos Vega, Christoph Dellago

Crystal nucleation studies using hard-sphere and Lennard-Jones models have shown that the pressure within the nucleus is lower than that in the surrounding liquid. Here, we use the mechanical route to obtain it for an ice nucleus in supercooled water (TIP4P/Ice) at 1 bar and 247 K. From this (mechanical) pressure, we obtain the interfacial stress using a the

Bensaid Mohamed

We study the Cauchy problem in the space $H^1(\Sigma)$ for a nonlinear damped Schr\"odinger equation of the form \begin{equation}\tag{NLS-$\zeta$}\label{nls} i u_t + \Delta u + i \lambda u \, \zeta(|u|+1) = 0, \quad u(0,x) = u_0, \end{equation} where $\zeta$ denotes the Riemann zeta function. We first establish the uniqueness of solutions in the sense of dis

Ali Khalesi

This paper studies multi-user distributed computation over shared real-valued subfunctions under computation and communication constraints. We consider a \emph{General Multi-User Distributed Computing (GMUDC)} model in which different users request heterogeneous target functions represented in the reproducing-kernel Hilbert space of a shift-invariant kernel,

Yihua Hu, Hao Ding, Wei Dong

Differential privacy (DP) has been widely adopted to protect sensitive information in graph analytics. While edge-DP, which protects privacy at the edge level, has been extensively studied, node-DP, offering stronger protection for entire nodes and their incident edges, remains largely underexplored due to its technical challenges. A natural way to bridge th

Lorenzo Sillari, Adriano Tomassini

In this paper we investigate the Kodaira dimension of almost complex $4$-manifolds with torsion first Chern class. First, we prove that, if the almost complex structure is also tamed, the only possible values for the Kodaira dimension are $0$ or $-\infty$. This is done by developing the theory of pseudoholomorphic structures on vector bundles. In arbitrary d