October 2025 arXiv papers — page 23

Sascha Orlik

Let G be a connected split reductive p-adic Lie group. This paper can be seen as a continuation of [12] and is about the construction of locally analytic G-representations which do not lie in the principal series. Here we consider locally analytic representations which are induced by Whittaker modules of the attached Lie algebra. We prove that they are inadm

Ruobing Bai, Yajie Lian, Yifei Wu

In this work, we investigate the regularization mechanisms of the Schr\"odinger equation with a spatial potential $$ i\partial_t u+\Delta u+\eta u =0, $$ where $\eta$ denotes a given spatial potential. The regularity of solutions constitutes one of the central problems in the theory of dispersive equations. Recent works \cite{Bai-Lian-Wu-2024, M-Wu-Z24} have

Sarem Norouzi, Per Moldrup, Ben Moseley, David Robinson

Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the c

Gorka Peraza Coppola, Moritz Helias, Zohar Ringel

Self-similarity, where observables at different length scales exhibit similar behavior, is ubiquitous in natural systems. Such systems are typically characterized by power-law correlations and universality, and are studied using the powerful framework of the renormalization group (RG). Intriguingly, power laws and weak forms of universality also pervade real

K Prajwal, Tharun K, Navaneeth P, Ishwar Mandal

In the conventional cellular system, devices are not allowed to communicate directly with each other in the licensed cellular bandwidth and all communications take place through the base stations. The users requirements has led the technology to become fast and faster. Multimedia rich data exchange, fast service, high quality voice calls, newer and more dema

Dmitry A. Cheshkov, Dmitry O. Sinitsyn

A correlation between the symmetry of NMR spectra, including higher-order spectra, and the properties of the spin system has been established. It is shown that for a spectrum to be symmetric about the mid-resonance frequency ({\nu}0), two conditions must be satisfied: the resonant frequencies of the spins must be symmetrically positioned about {\nu}0, and th

Dominik Sturm, Ivo F. Sbalzarini

We propose a method for variable selection in the intensity function of spatial point processes that combines sparsity-promoting estimation with noise-robust model selection. As high-resolution spatial data becomes increasingly available through remote sensing and automated image analysis, identifying spatial covariates that influence the localization of eve

André H. A. Malavazi, Borhan Ahmadi, Paweł Horodecki, Pedro R. Dieguez

Ergotropy provides a fundamental measure of the extractable work from a quantum system and, consequently, of the maximal useful energy, or charge, stored within it. Understanding how this quantity can be manipulated and transformed efficiently is crucial for advancing quantum energy management technologies. Here, we introduce and formalize the concepts of is

Muyang Yan, Miras Mengdibayev, Ardon Floros, Weihang Guo

In task and motion planning, high-level task planning is done over an abstraction of the world to enable efficient search in long-horizon robotics problems. However, the feasibility of these task-level plans relies on the downward refinability of the abstraction into continuous motion. When a domain's refinability is poor, task-level plans that appear va

Mengqi Yang, Dong Lai, Fuyuan Wu, Jie Zhang

Recent observations suggest that planetary engulfment by a giant star may produce radiation that resembles subluminous red novae. We present three-dimensional hydrodynamical simulations of the interaction between an eccentric $5 \,M_J$ giant planet and its $1\,M_\odot$ red-giant host star. The planet's pericenter is initially $60\%$ of the stellar radius and

Tommaso Grigoletto, Lorenza Viola, Francesco Ticozzi

We consider the problem of model reduction for Markovian quantum systems whose dynamics are described by a time-dependent Lindblad generator -- notably, as arising in the presence of external control. Our approach, which builds upon Krylov operator subspaces and operator-algebraic techniques introduced for time-independent generators, returns a reduced model

King Cho Wong, Ruoming Peng, Eric Anderson, Jackson Ross

Stacking two-dimensional (2D) layered materials offers a powerful platform to engineer electronic and magnetic states. In general, the resulting states, such as Moir\'e magnetism, have a periodicity at the length scale of the Moir\'e unit cell. Here, we report a new type of magnetism -- dubbed a super-Moir\'e magnetic state -- which is characterized by long-

Hugo Lavenant, Giacomo Zanella

Recently proposed generative models for discrete data, such as Masked Diffusion Models (MDMs), exploit conditional independence approximations to reduce the computational cost of popular Auto-Regressive Models (ARMs), at the price of some bias in the sampling distribution. We study the resulting computation-vs-accuracy trade-off, providing general error boun

Manuel Rieger, Nori N. Chavira Leal, Rubek Poudel, Tobias Waldmann

Negatively charged silicon vacancy centers (SiV$^-$) in diamond exhibit excellent spin coherence and optical properties, making them promising candidates for quantum technologies. However, the strain-induced inhomogeneous distribution of optical transition frequencies poses a challenge for scalability. We demonstrate electrical tuning of the SiV$^-$ center z

Yuan Cheng, Yu Huang, Zhe Xiong, Yingbin Liang

Uncovering hidden graph structures underlying real-world data is a critical challenge with broad applications across scientific domains. Recently, transformer-based models leveraging the attention mechanism have demonstrated strong empirical success in capturing complex dependencies within graphs. However, the theoretical understanding of their training dyna

Rafael Chiclana, Mark Iwen

The Johnson-Lindenstrauss (JL) lemma is a fundamental result in dimensionality reduction, ensuring that any finite set $X \subseteq \mathbb{R}^d$ can be embedded into a lower-dimensional space $\mathbb{R}^k$ while approximately preserving all pairwise Euclidean distances. In recent years, embeddings that preserve Euclidean distances when measured via the $\e

Janosz W. Dewberry

Tidal interactions influence the orbital motions of binary star systems and extrasolar planets alike. Tides also affect stellar and planetary rotation rates. We demonstrate that in addition to altering spin synchronization and pseudosynchronization, tidally driven inertial waves in the convective envelopes of low-mass stars and gas giant planets can enhance

Ruobing Bai, Yajie Lian, Yifei Wu

In this work, we investigate the following Schr\"odinger equation with a spatial potential \begin{align*} i\partial_t u+\partial_x^2 u+\eta u=0, \end{align*} where $\eta$ is a given spatial potential (including the delta potential and $|x|^{-\gamma}$-potential). Our goal is to provide the regularization mechanism of this model when the potential $\eta\in L_x

Marcus V. Marinho, Eric C. Andrade

We investigate the emergence of quantum spin liquid phases in pyrochlore oxides with non-Kramers ions, in which structural randomness effectively acts as a transverse field, introducing quantum fluctuations on top of the spin ice manifold. This is contrary to the naive expectation that disorder favors phases with short-range entanglement by adjusting the spi

J. -E. Schneider, H. Beuther, C. Gieser, S. Jiao

We quantify the gas flows from a scale of up to several parsecs down to the sub-parsec scale along filamentary structures in the three high-mass star-forming regions G75.78, IRAS21078+5211 and NGC7538 with data obtained from the IRAM 30 m telescope. The analysis is carried out using the surface density derived from 1.2 mm continuum emission and velocity diff

Camen A. Royse, J. E. Thomas

We observe and model spin currents arising from chirality and effective spin-exchange interactions in a weakly interacting $^6$Li Fermi gas. Chirality is introduced by a static displacement between the center of the trapped atoms and the center of an applied magnetic bowl, which produces left- or right-handed spatially varying spin rotation. Spin current is

Bangde Du, Minghao Guo, Songming He, Ziyi Ye

Large Language Models (LLMs) are exhibiting emergent human-like abilities and are increasingly envisioned as the foundation for simulating an individual's communication style, behavioral tendencies, and personality traits. However, current evaluations of LLM-based persona simulation remain limited: most rely on synthetic dialogues, lack systematic frameworks

Christopher R. Schwarze, Anthony D. Manni, David S. Simon, Abdoulaye Ndao

Measurement technology employing optical interference phenomena such as a fringe pattern or frequency shift has been evolving for more than a century. The systems are being designed better, and their components are being built better. But the major components themselves hardly change. Most modern interferometers rely on the same conventional set of component

Rita P. Santos, Krinio Marouda, David Hilditch

Solutions to the Einstein equations near the threshold of black hole formation exhibit remarkable behavior known as critical phenomena gravitational collapse. In this work we perform characteristic evolution in compactified Bondi coordinates in order to study the critical collapse of a Yang-Mills field, allowing for the extraction of global quantities such a

Bastian Castorene, Martin HvE Groves, Francisco J. Peña, Eugenio E. Vogel

We present a general analytical framework for {critical two-isothermal engines that emerge operationally from quasi-static quantum Stirling cycles across ground-state level crossings (GLCs) in the low-temperature regime, where reversible heat exchange is governed by the structural entropy change while the internal energy remains constant.} We derive the Prim

Alexandre Sainterme, Fatima Ebrahimi

Non-axisymmetric, flow-driven instabilities in the incompressible Hall-MHD model are studied in a differentially rotating cylindrical plasma. It is found that in the Hall-MHD regime, both whistler waves and ion-cyclotron waves can extract energy from the flow shear, resulting in two distinct branches of global instability. The non-axisymmetric whistler modes

Clemens Schächter, Maren Hackenberg, Michelle Pfaffenlehner, Félix B. Tambe-Ndonfack

Many rare diseases offer limited established treatment options, leading patients to switch therapies when new medications emerge. To analyze the impact of such treatment switches within the low sample size limitations of rare disease trials, it is important to use all available data sources. This, however, is complicated when usage of measurement instruments

Anjali A R, Syamsundar De, Pranabendu Ganguly

We compare modulator designs on thick-film and thin-film silicon-on-insulator (SOI) substrates based on the carrier-depletion effect. This effect exhibits a lower junction capacitance as compared to its injection counterparts, leading to a higher electro-optic bandwidth, although the effective refractive index change is low. In this work, commercially availa

Likun Wang, Xiangteng Zhang, Yinuo Wang, Guojian Zhan

Exploration is fundamental to reinforcement learning (RL), as it determines how effectively an agent discovers and exploits the underlying structure of its environment to achieve optimal performance. Existing exploration methods generally fall into two categories: active exploration and passive exploration. The former introduces stochasticity into the policy

Eugenius Mario Situmorang, Adila Alfa Krisnadhi, Ari Wibisono

TextGrad is a novel approach to text-based automatic differentiation that enables composite AI systems to perform optimization without explicit numerical equations. However, it currently lacks self-verification mechanisms that ensure reasoning validity in text-based decision making. This research introduces TextualVerifier, a verification framework that leve

Yuyuan Zeng, Yufei Huang, Can Xu, Qingfeng Sun

Zero Reinforcement Learning (Zero-RL) has proven to be an effective approach for enhancing the reasoning capabilities of large language models (LLMs) by directly applying reinforcement learning with verifiable rewards on pretrained models, without the need for a supervised fine-tuning phase. However, current research on zero-RL primarily focuses on domains w

Anna Miriam Benini, Tom Potthink, Jasmin Raissy

In this paper we show that Baker domains of transcendental skew products can either bulge or not, depending on the higher order terms. This is in contrast to polynomial skew products where all Fatou components with bounded orbits of an invariant attracting fiber do bulge.

Olfa Draouil, Rahma Yasmina Moulay Hachemi, Bernt Øksendal

We construct a white noise theory and white noise calculus for the (multi-parameter) L\' evy sheet and its compensated Poisson random measures. The theory applies to stochastic partial differential equations subject to L\' evy noise.

Michael Tang

A permutation statistic is substring-compatible if its value on a permutation determines its value on every substring of that permutation. We construct the substring coalgebra of such a statistic, an analog of the shuffle algebra of a shuffle-compatible statistic introduced by Gessel and Zhuang. Furthermore, we show that for substring-compatible statistics t

Aleksandra V. Mishakina, Sergei I. Blinnikov

We present a fast method for obtaining fully analytical approximations for gravitational waveforms produced by merging of neutron stars and/or black holes for the earliest stages of the merger process. The obtained analytical formula is compared with numerical calculations, its accuracy and limits of applicability are evaluated. Our results may be useful not

Doan-Van-Anh Ly, Thanh-Hai Le, Thi-Thu-Hien Pham

Segmentation of liver structures in multi-phase contrast-enhanced computed tomography (CECT) plays a crucial role in computer-aided diagnosis and treatment planning. In this study, we investigate the performance of UNet-based architectures for liver tumor segmentation, evaluating ResNet, Transformer-based, and State-space (Mamba) backbones initialized with p

Jaroslav I. Borodavka, Max Hirsch, Sebastian Krumscheid, Andrea Zanoni

We consider the problem of density estimation in the context of multiscale Langevin diffusion processes, where a single-scale homogenized surrogate model can be derived. In particular, our aim is to learn the density of the invariant measure of the homogenized dynamics from a continuous-time trajectory generated by the full multiscale system. We propose a sp

Shengyao Zhang, Yiyuan Zhang, Chenrui Zhang, Yiming Li

The stereotypical reaching motion of the octopus arm has drawn growing attention for its efficient control of a highly deformable body. Previous studies suggest that its characteristic bend propagation may share underlying principles with the dynamics of a whip. This work investigates whether whip-like passive dynamics in water can reproduce the kinematic fe

Angelo Russotto, Filiberto Ares, Pasquale Calabrese, Vincenzo Alba

We study the out-of-equilibrium dynamics of entanglement fluctuations in the $\nu=1$ Quantum Symmetric Simple Exclusion Process, a free-fermion chain with hopping amplitudes that are stochastic in time but homogeneous in space. Previous work showed that the average entanglement growth after a quantum quench can be explained in terms of pairs of entangled qua

Thomas Cook, Richard Osuagwu, Liman Tsatiashvili, Vrynsia Vrynsia

Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology, and acronyms complicate effective retrieval and synthesis. This paper introduces an agentic RAG architecture designed to address these challenges through a modular pipeline of specialized agents. The

Elisabetta Gentili, Tony Ribeiro, Fabrizio Riguzzi, Katsumi Inoue

In this paper, we address the problem of giving names to predicates in logic rules using Large Language Models (LLMs). In the context of Inductive Logic Programming, various rule generation methods produce rules containing unnamed predicates, with Predicate Invention being a key example. This hinders the readability, interpretability, and reusability of the

Wen-Long Sang, Feng Feng, Yu Jia

We present for the first time the complete three-loop mixed electroweak-QCD ($\mathcal{O}(\alpha\alpha_s)$) corrections for the decay channel $H \to \gamma\gamma$, by implementing three different on-shell $\alpha$ schemes in computing the electroweak correction. Our studies indicate that the $\mathcal{O}(\alpha_s)$ correction amounts to approximately $1.7\%$

D. Calchetti, K. Albert, F. J. Bailén, J. Blanco Rodríguez

The Polarimetric and Helioseismic Imager (SO/PHI) onboard Solar Orbiter is a spectropolarimeter scanning the Fe I line at 617.3 nm, providing data of the solar photosphere. The same line is sampled by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) and many other on-ground instruments. In this paper, we aim at assessi

Maik Overmars, Jasper Goseling, Richard Boucherie

We study the convergence of off-policy TD(0) with linear function approximation when used to approximate the expected discounted reward in a Markov chain. It is well known that the combination of off-policy learning and function approximation can lead to divergence of the algorithm. Existing results for this setting modify the algorithm, for instance by rewe

Arash Bahari Kordabad, Rupak Majumdar, Sadegh Soudjani

In this paper, we present a computational approach to certify almost sure reachability for discrete-time polynomial stochastic systems by turning drift--variant criteria into sum-of-squares (SOS) programs solved with standard semidefinite solvers. Specifically, we provide an SOS method based on two complementary certificates: (i) a drift certificate that enf

Amin Parchami-Araghi, Sukrut Rao, Jonas Fischer, Bernt Schiele

Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge. Many post-hoc concept-based approaches have been introduced to understand their workings, yet they are not always faithful to the model. Further, they make restrictive assumptions on the

Zekun Xu, Siyu Xia, Chuhuai Yue, Jiajun Chai

As large language models (LLMs) are increasingly used in Text-to-SQL tasks, Reinforcement Learning (RL) has become a common method for improving performance. Existing methods primarily rely on static execution feedback, which restricts real-time error correction. However, integrating multi-turn tool invocation along with dynamic feedback could significantly

Bruno W. G. Teodosio, Mário J. O. T. Lira, Pedro H. M. Araújo, Lucas R. C. Farias

Burnout is a psychological syndrome marked by emotional exhaustion, depersonalization, and reduced personal accomplishment, with a significant impact on individual well-being and organizational performance. This study proposes a machine learning approach to predict burnout risk using the HackerEarth Employee Burnout Challenge dataset. Three supervised algori

Yuliang Xu, Yun Wei, Li Ma

We propose a function-valued evaluation metric for generative models based on the relative density ratio (RDR) designed to characterize distributional differences between real and generated samples. As an evaluation metric, the RDR function preserves $\phi$-divergence between two distributions, enables sample-level evaluation that facilitates downstream inve

Florian Angermeir, Maximilian Amougou, Mark Kreitz, Andreas Bauer

Large Language Models have gained remarkable interest in industry and academia. The increasing interest in LLMs in academia is also reflected in the number of publications on this topic over the last years. For instance, alone 78 of the around 425 publications at ICSE 2024 performed experiments with LLMs. Conducting empirical studies with LLMs remains challe

Ekaterina Dmitrieva, Petr Satunin

We investigate the solution of the Klein-Gordon equation for a charged scalar particle in an electromagnetic plane wave background with $k^2>0$, which can be realized in a medium with a refractive index $n<1$. We reduce the equation of motion to the Mathieu equation, which has resonant solutions that grow exponentially for certain parameter ranges. We apply

Oren Salzman, Carlos Hernández Ulloa, Ariel Felner, Sven Koenig

Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, reflecti

Maya Grace Torii, Takahito Murakami, Shuka Koseki, Yoichi Ochiai

Access to expert knowledge often requires real-time human communication. Digital tools improve access to information but rarely create the sense of connection needed for deep understanding. This study addresses this issue using Social Presence Theory, which explains how a feeling of "being together" enhances communication. An "Embodied Information Hub" is pr

Nataliia Yilmaz, Pavlo Kozub, Svitlana Kozub

We present a vector-based method to balance chemical reactions. The algorithm builds candidates in a deterministic way, removes duplicates, and always prints coefficients in the lowest whole-number form. For redox cases, electrons and protons/hydroxide are treated explicitly, so both mass and charge are balanced. We also outline the basic principles of the v

Vladyslav Moroshan, Julien Siems, Arber Zela, Timur Carstensen

Foundation models for zero-shot time series forecasting face challenges in efficient long-horizon prediction and reproducibility, with existing synthetic-only approaches underperforming on challenging benchmarks. This paper presents TempoPFN, a univariate time series foundation model based on linear Recurrent Neural Networks (RNNs) pre-trained exclusively on

Tong Han, Yan Xu, Rui Zhang

This paper presents a new neural network (NN) paradigm for scalable and generalizable stability analysis of power systems. The paradigm consists of two parts: the neural stability descriptor and the sample-augmented iterative training scheme. The first part, based on system decomposition, constructs the object (such as a stability function or condition) for

Mila Racca, Terese T. Hansen, Ian U. Roederer, Vinicius M. Placco

The astrophysical origin of the rapid neutron-capture process (r-process), which produces about half of the elements heavier than iron, remains uncertain. The oldest, most metal-poor stars preserve the chemical signatures of early nucleosynthesis events and can reveal the nature of the r-process sites. We present a homogeneous chemical abundance analysis of

Usama Syed Aqeel, Clare Burrage, Oliver Gould, Paul M. Saffin

We investigate how matter density distributions affect thin-wall bubble formation in the asymmetron mechanism, a scalar-tensor theory with a universal coupling to matter and explicit symmetry-breaking, and analyse the stability of its metastable state. We show that the screening mechanism of the asymmetron inside dense objects induces a surface tension assoc

Mihai Mazilu, Aiden Valentine, George Parisis

Low Earth Orbit (LEO) satellite networks introduce unique congestion control (CC) challenges due to frequent handovers, rapidly changing round-trip times (RTTs), and non-congestive loss. This paper presents the first comprehensive, emulation-driven evaluation of CC schemes in LEO networks, combining realistic orbital dynamics via the LeoEM framework with tar

Luca Andolfi, Eleonora Giunchiglia

Neurosymbolic AI is growing in popularity thanks to its ability to combine neural perception and symbolic reasoning in end-to-end trainable models. However, recent findings reveal these are prone to shortcut reasoning, i.e., to learning unindented concepts--or neural predicates--which exploit spurious correlations to satisfy the symbolic constraints. In this

Kunxi Li, Yufan Xiong, Zhonghua Jiang, Yiyun Zhou

Traditional KV cache eviction strategies, which discard less critical KV-pairs based on attention scores, often degrade generation quality, causing context loss or hallucinations. Recent efforts shift toward KV merging, merging eviction tokens with retention tokens based on similarity. However, in multimodal scenarios, distributional biases across modality t

Duc Nguyen Dao, André B. J. Kokkeler, Haibin Zhang, Yang Miao

Integrated Sensing and Communication (ISAC) is a key enabler in 6G networks, where sensing and communication capabilities are designed to complement and enhance each other. One of the main challenges in ISAC lies in resource allocation, which becomes computationally demanding in dynamic environments requiring real-time adaptation. In this paper, we propose a

A. Giarnetti, D. Meloni

This short review discusses recent applications of Reinforcement Learning (RL) techniques to the flavor problem in particle physics. Traditional approaches to fermion masses and mixing often rely on extensions of the Standard Model based on horizontal symmetries, but the vast landscape of possible models makes systematic exploration infeasible. Recent works

Kira Dudziak, Hanspeter Schmidli

We consider a diffusion risk model where dividends are paid at rate $U(t) \in [0, u_0]$. We are interested in maximising the dividend payments under a drawdown constraint, that is, we penalise a drawdown size larger than a level $d > 0$. We show that the optimal dividend rate $U(t)$ is either zero or the maximal rate $u_0$ and determine the optimal strategy.

Remco Tuinier, Alvaro Gonzalez Garcia

We investigate liquid-liquid phase separation (LLPS) and interfacial properties of two LLPS modes: associative (ALLPS) and segregative (SLLPS). Analytical expressions for the critical point (CP) and binodal boundaries are derived and show excellent agreement with self-consistent field (SCF) lattice computations. Distinct thermodynamic features differentiate

Yoav Koral, Shilo Avraham, Manimuthu Peryasamy, Shmuel E. Schacham

Decoherence of quantum bits arises primarily from the parasitic resistance within the qubit. This study presents the analysis of the decoherence process due to physical interactions between the qubit photons and parasitic resistance atoms, utilizing exclusively the Caldeira-Leggett electrical model, without relying on external Hamiltonians. The analysis show

Elena Fernández, Nicolás Zerega

We introduce a new formulation for the multiple allocation hub location problem that exploits supermodular properties and uses 1- and 2-index variables only. We show that the new formulation produces the same Linear Programming bound as the tightest existing formulations for the studied problem, which use 4-index variables, outperforming existing supermodula

Lu Yin, Shuhang Xiong, Joby Kochappan, Bum-Hoon Lee

The Early Dark Energy (EDE) model has been proposed as a candidate mechanism to generate cosmic birefringence through a Chern-Simons coupling between a dynamical scalar field and the cosmic microwave background (CMB) photon. Such birefringence induces a nonzero cross-correlation between the CMB $E$- and $B$-modes, providing a direct observational signature o

Yiteng Tu, Weihang Su, Yujia Zhou, Yiqun Liu

Query rewriting is a fundamental technique in information retrieval (IR). It typically employs the retrieval result as relevance feedback to refine the query and thereby addresses the vocabulary mismatch between user queries and relevant documents. Traditional pseudo-relevance feedback (PRF) and its vector-based extension (VPRF) improve retrieval performance

Peter Krack

The LHC generates an intense beam of high-energy neutrinos in the forward direction, whose scientific potential has been left unexploited for many years. The FASER and SND@LHC experiments, operating since 2023, have recently measured LHC neutrinos for the first time. In this contribution we discuss how to produce accurate predictions, including NLO QCD corre

K. Tan, X. G. Lan

In this paper, we utilize the ray-tracing method to conduct an in-depth study of the observational images of asymmetric thin-shell wormholes in the Kalb-Ramond field. Initially, we calculate the null geodesics and effective potential energy of the asymmetric thin-shell wormhole, and investigate the variations in the photon sphere radius and critical impact p

Ben Bakary Junior Siriki, Adama Coulibaly

This work investigates the global exponential stabilization of a degenerate Euler-Bernoulli beam subjected to a non uniform axial force and a delayed feedback control. First, we address the well-posedness of the system by constructing an appropriate energy space in weighted Sobolev settings. Using L\"umer-Phillips theorem, we prove that the linear operator a

Krzysztof Hryniewicz, Agata Różańska, Tek Prasad Adhikari, Matteo Guainazzi

We present the analysis of 900 ks high-resolution RGS XMM-Newton observations of the nearby Seyfert galaxy Mrk 509 with the use of a self-consistent warm absorber (WA) model. We perform a physically motivated approach to the modeling of WA as a stratified medium in constant total pressure (CTP) regime. Powerful outflows are fundamental ingredients of any act

Sweta Das, Andrii Dmytryshyn

Complete eigenstructure, e.g., eigenvalues with multiplicities and minimal indices, of a skew-symmetric matrix pencil may change drastically if the matrix coefficients of the pencil are subjected to (even small) perturbations. These changes can be investigated qualitatively by constructing the stratification (closure hierarchy) graphs of the congruence orbit

Nils Melsom Kristensen, Kristian Mogensen, Sarah-Jane Lock, Øyvind Breivik

The global ocean model NEMO is run in a series of stand-alone configurations (2015-2022) to investigate the potential for improving global medium-range storm surge forecasts by including the inverse barometer effect. The analysis focus on the residual water level, i.e. the water level variations not due to tides. Here, we compare a control experiment, where

Florian A. Hölzl, Daniel Rueckert, Georgios Kaissis

Robust validation metrics remain essential in contemporary deep learning, not only to detect overfitting and poor generalization, but also to monitor training dynamics. In the supervised classification setting, we investigate whether interactions between training data and model weights can yield such a metric that both tracks generalization during training a

Tsung-Wei Pan, Chang-Hong Wu, Jung-Hua Wang, Ming-Jer Chen

Medical image datasets frequently exhibit significant class imbalance, a challenge that is further amplified by the inherently limited sample sizes that characterize clinical imaging data. Using human embryo time-lapse imaging (TLI) as a case study, this work investigates how generative adversarial networks (GANs) can be optimized for small datasets to gener

Alexander B. Rambech, Ivar B. Saksvik, Vahid Hassani

In this paper, we present a Newton-Euler formulation of the equations of motion for underwater vehicles with an interntal moving mass actuator. Furthermore, the moving mass dynamics are expressed as an extension to the manoeuvring model for underwater vehicles, originally introduced by Fossen (1991). The influence of the moving mass is described in body-fram

Yaozheng Zhang, Wei Wang, Jie Kong, Jiehan Zhou

The increasing adoption of large language models (LLMs) on heterogeneous computing platforms poses significant challenges to achieving high inference efficiency. To address these efficiency bottlenecks across diverse platforms, this paper proposes Opt4GPTQ, a practical optimization method designed for 4-bit GPTQ quantized LLMs inference on heterogeneous AI a

Yi Chen, Bin Chen, Peichang Zhang, Da Che

Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholars

Ketaki Deshpande, Jasmina Magdalenic, Immanuel Christopher Jebaraj, Senthamizh Pavai Valliappan

The distribution of the coronal electron density at different distances from the Sun strongly influences the physical processes in the solar corona and is therefore a very important topic in solar physics. Most methods, including radio observations, used for estimating coronal electron density were not fully validated due to the absence of in situ observatio

Timothy W. H. Yiu, Harish K. Vedantham, Joseph R. Callingham, Timothy W. Shimwell

Brown dwarfs display Jupiter-like auroral phenomena, such as rotationally modulated electron cyclotron maser radio emission. Radio observations of cyclotron maser emission can be used to measure their magnetic field strength, topology, and to deduce the presence of magnetically interacting exoplanets. Observations of the coldest brown dwarfs (spectral types

Stephane Dartois, Parham Radpay

We study the injective norm of random skew-symmetric tensors and the associated fermionic quantum states, a natural measure of multipartite entanglement for systems of indistinguishable particles. Extending recent advances on random quantum states, we analyze both real and complex skew-symmetric Gaussian ensembles in two asymptotic regimes: fixed particle nu

Sem Saada Khelkhal, Louis Barcikowsky

We address the realization of Maximum Weighted Independent Set (MWIS) and quantum Maximum Independent Set (MIS) problems under Pasqal's neutral-atom QPU constraints: limited qubit number, bounds on {\Omega} and {\Delta}, sequence duration, confinement space, minimum distances, and parasitic interactions. To obtain results directly compatible with current har

Zheng Zhang, Guanlong Wu, Sen Deng, Shuai Wang

In the rapidly expanding landscape of Large Language Model (LLM) applications, real-time output streaming has become the dominant interaction paradigm. While this enhances user experience, recent research reveals that it exposes a non-trivial attack surface through network side-channels. Adversaries can exploit patterns in encrypted traffic to infer sensitiv

Willem Fourie

Instrumental goals such as resource acquisition, power-seeking, and self-preservation are key to contemporary AI alignment research, yet the phenomenon's ontology remains under-theorised. This article develops an ontological account of instrumental goals and draws out governance-relevant distinctions for advanced AI systems. After systematising the dominant

Parick Ozoh, John K Omoniyi, Bukola Ibitoye

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and posing significant privacy threats. This study surveys commonly

D. Fernández-Martínez, E. J. Vega, J. M. Montanero, U. J. Gutiérrez-Hernández

We experimentally investigate the influence of surfactant adsorption kinetics on cavity dynamics during the rapid formation of interfaces. For this purpose, we use a submillimeter jet impacting onto a surfactant-laden liquid pool much larger than the jet dimensions. Cavity retraction and closure occur on a submillisecond timescale, posing a stringent test of

Emanuele De Angelis, Florian Frohn

This volume contains the post-proceedings of the 12th Workshop on Horn Clauses for Verification and Synthesis (HCVS 2025), which took place in Zagreb, Croatia, on July 22, 2025, as affiliated workshop of the 37th International Conference on Computer Aided Verification (CAV 2025).

Shuixin Xiao, Yuanlong Wang, Zhibo Hou, Aritra Das

Quantum tomography is a fundamental technique for characterizing, benchmarking, and verifying quantum states and devices. It plays a crucial role in advancing quantum technologies and deepening our understanding of quantum mechanics. Collective quantum state tomography, which estimates an unknown state \r{ho} through joint measurements on multiple copies $\r

Dongze Sun, Sizheng Ma, Mark A. Scheel, Saul A. Teukolsky

Long-term numerical relativity (NR) simulations of binary black hole (BBH) systems in the Spectral Einstein Code (SpEC) code exhibit an unexpected exponential drift of the center-of-mass (CoM) away from the simulation's origin. In our work, we analyze this phenomenon and demonstrate that it is not a physical effect but rather a manifestation of a gauge artif

Amirhossein Azarbahram, Onel L. A. López

In this paper, we consider a dynamic radio frequency sensing system aiming to spatially track multiple targets over time. We develop a conditional denoising diffusion probabilistic model (C-DDPM)-assisted framework that learns the temporal evolution of target parameters by leveraging the noisy echo observations as conditioning features. The proposed framewor

Hongjie Zhang, Gideon Billings, Stefan B. Williams

Underwater infrastructure requires frequent inspection and maintenance due to harsh marine conditions. Current reliance on human divers or remotely operated vehicles is limited by perceptual and operational challenges, especially around complex structures or in turbid water. Enhancing the spatial awareness of underwater vehicles is key to reducing piloting r

Manuel Garzón, Salvador López-Martínez

We establish the existence of non-constant periodic solutions to the Lorentz force equation, where no scalar potential is needed to induce the electromagnetic field. Our results extend to cases where a possibly singular scalar potential is present, although the vector potential assumes a leading role. The approach is based on minimizing the action functional

Belle, Belle II Collaborations, :, I. Adachi

We study the processes $\chi_{bJ}\,\omega$ and $\chi_{bJ}\,(\pi^+\pi^-\pi^0)_{\rm non-\omega}$ ($J$ = 0, 1, 2) at center-of-mass energies $\sqrt{s}$ from 10.73 to 11.02 GeV using a $142.5\,\mathrm{fb}^{-1}$ data sample collected with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider; and at $\sqrt{s}\sim10.75$ GeV using a $19.8\,\mathrm{fb}

Jun Wang, Fuming Lin, Yuyu Chen

This paper presents a pipeline integrating fine-tuned large language models (LLMs) with named entity recognition (NER) for efficient domain-specific text summarization and tagging. The authors address the challenge posed by rapidly evolving sub-cultural languages and slang, which complicate automated information extraction and law enforcement monitoring. By

Geraint W. Evans, Naoki Yamamoto, Di-Lun Yang

We study vorticity-induced effects arising from the Wess-Zumino-Witten terms for Nambu-Goldstone modes in chiral perturbation theory. We first provide an alternative derivation of the Wess-Zumino-Witten terms in the presence of external vector, axial-vector, and pseudoscalar fields using a derivative expansion of the fermion determinant. We then employ the p

Harald Kristen, Daniel Kulmer, Manuela Hirschmugl

Rapid climate change and other disturbances in alpine ecosystems demand frequent habitat monitoring, yet manual mapping remains prohibitively expensive for the required temporal resolution. We employ deep learning for change detection using long-term alpine habitat data from Gesaeuse National Park, Austria, addressing a major gap in applying geospatial found

Mahmoud Hegazy, Michael I. Jordan, Aymeric Dieuleveut

Ensuring that classifiers are well-calibrated, i.e., their predictions align with observed frequencies, is a minimal and fundamental requirement for classifiers to be viewed as trustworthy. Existing methods for assessing multiclass calibration often focus on specific aspects associated with prediction (e.g., top-class confidence, class-wise calibration) or u

William Réveillard, Richard Combes

We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn enables the implementation of asymptotically optimal algorithms for this