November 2025 arXiv papers — page 2

Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier, Akshay Chaudhari

Vision-language models (VLMs) have made great strides in addressing temporal understanding tasks, which involve characterizing visual changes across a sequence of images. However, recent works have suggested that when making predictions, VLMs may rely on static feature biases, such as background or object features, rather than dynamic visual changes. Static

Tanmay Ambadkar, Đorđe Žikelić, Abhinav Verma

Logical specifications have been shown to help reinforcement learning algorithms in achieving complex tasks. However, when a task is under-specified, agents might fail to learn useful policies. In this work, we explore the possibility of improving coarse-grained logical specifications via an exploration-guided strategy. We propose AutoSpec, a framework that

Tryfon Sivenas

This mixed-methods study examined secondary students' interactions with the generative AI chatbot ChatGPT-5 in a formal classroom setting, focusing on attitudes, anxiety, and responses to hallucinated outputs. Participants were 109 16-year-old students from three Greek high schools who used ChatGPT-5 during an eight-hour intervention in the course "Technolog

Hadi Nekoei, Alexandre Blondin Massé, Rachid Hassani, Sarath Chandar

Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty, an essential challenge in real-world settings, particularly in the context of the energy transition. A representative example is remote microgrids that supply power to communities disconnected from the main grid. Enabling the energy transi

Shenxi Liu, Kan Li, Mingyang Zhao, Yuhang Tian

Data-intensive artificial intelligence applications increasingly rely on large-scale, high-quality, explainable, and reproducible datasets, yet the construction of such datasets often remains labor-intensive, weakly traceable, and difficult to configure. This problem is particularly critical in multimodal medical scenarios, where each question-answer sample

Tigran Petrosyan, Arus Harutyunyan, Armen Sedrakian

We study the influence of positrons on the outer crusts of neutron stars and the interiors of white dwarfs, introducing them as a novel component in both the composition of matter and in transport processes. We solve a system of coupled Boltzmann kinetic equations for the electron and positron distribution functions in the relaxation-time approximation, taki

Thomas B. Bahder

The iconic problem of photon modes in a spherical cavity has been discussed in the literature; however, conflicting results have been reported \cite{Heitler,Davydov_QuantumMechanics}. For this reason, the solution of this problem is worked out in detail here, starting with the Maxwell equations and applying boundary conditions at the surface of the bounding

Amartya Chakrabortty, Georges Griso, Julia Orlik

In the framework of linearized elasticity, we study thin elastic composite plates with thickness $\delta$. The plates contain small, rigid rectangular plates distributed periodically along $\varepsilon$. Between two neighboring rigid plates is an elastic beam with thickness $\delta < \varepsilon/3 < 1$. Through a simultaneous process of homogenization and di

Ian Miller, Ann Hyslop, Colin Decker

Clinical trials assessing neurological treatment are challenging due to the diversity of brain function, and the difficulty in quantifying it. Traditional treatment studies in epilepsy use seizure frequency as the primary outcome measure, which may overlooking meaningful improvements in patients' quality of life. This paper introduces the Clinical Instrument

Vladislav Yu. Shishkov

Emission and absorption spectra of molecular films are impacted by low-frequency molecular vibrations. These vibrations define the linewidths of the absorption and emission spectral peaks, as well as the Stokes shift. In cavities that use a molecular film as an active medium, low-frequency molecular vibrations facilitate the thermalization of light, enabling

Hetvi Shastri, Pragya Sharma, Walid A. Hanafy, Mani Srivastava

Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs) -- FMs trained on time-series data -- have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSF

Riad Ahmed Anonto, Md Labid Al Nahiyan, Md Tanvir Hassan

Safety-aligned language models often refuse prompts that are actually harmless. Current evaluations mostly report global rates such as false rejection or compliance. These scores treat each prompt alone and miss local inconsistency, where a model accepts one phrasing of an intent but rejects a close paraphrase. This gap limits diagnosis and tuning. We introd

Breandan Considine

We describe an exact sampler for a simply-typed, first-order functional programming language. Given an acyclic finite automaton, $\alpha_{\varnothing}$, it samples a random function uniformly without replacement from well-typed functions in $\mathcal{L}(\alpha_{\varnothing})$. This is achieved via a fixed-parameter tractable reduction from a syntax-directed

Shutong Chen, Qi Liao, Adnan Aijaz, Yansha Deng

6G services are evolving toward goal-oriented and AI-native communication, which are expected to deliver transformative societal benefits across various industries and promote energy sustainability. Yet today's networking architectures, built on complete decoupling of the applications and the network, cannot expose or exploit high-level goals, limiting their

Hristu Culetu

The relation between gravity and quantum mechanics is investigated in this work. The link is given by the wave packet expansion process, rooted from the Uncertainty Principle. The basic idea is to express the de Broglie wavelength used by Schrodinger for a massive particle in terms of the associated Compton wavelength which is replaced by the Michell-Laplace

Mansi Maheshwari, John C. Raisbeck, Bruno Castro da Silva

Artificial neural networks have shown remarkable success in supervised learning when trained on a single task using a fixed dataset. However, when neural networks are trained on a reinforcement learning task, their ability to continue learning from new experiences declines over time. This decline in learning ability is known as plasticity loss. To restore pl

Luigi Assom

This study presents an unsupervised method to infer discreteness, syntax and temporal structures of fruit-bats vocalizations, as a case study of graded vocal systems, and evaluates the complexity of communication patterns in relation with behavioral context. The method improved the baseline for unsupervised labeling of vocal units (i.e. syllables) through ma

R. Thirumalaisamy, S. Kim, H. Otomo, J. Jilesen

The phase-field-based lattice Boltzmann (LB) model has been developed to perform high fidelity multiphase flow simulations. Its ability to accurately handle high density ratio and surface tension effects is expected to be beneficial for capillary flow simulation, leading to accurate reproduction of flow patterns such as slug flow, droplet flow, and film flow

Vladimir Dragović, David Kalaj

We develop a fully explicit framework for constructing Scherk-type minimal graphs over the Pitot quadrilaterals (i.e. such that the two pairs of opposite sides have the same total length). For any Pitot quadrilateral \(Q\), we first produce a harmonic diffeomorphism of the unit disk onto \(Q\), whose dilatation is the square of a M\"obius automorphism determ

Halil Mutuk

We employ Born-Oppenheimer approximation to the $T_{c\bar s0}^a(2900)^0$ and $T_{c\bar s0}^a(2900)^{++}$ states observed by the LHCb Collaboration and study mass spectrum and root-mean-square radius values. For this purpose, we use dynamical diquark model. We assume that strange quark is a heavy for the usage of Born-Oppenheimer approximation. Our results st

H. Otomo, C. Sun, T. Inamuro, W. Li

Multiphase flows with high density ratios, such as water and air flows, have recently been simulated using the lattice Boltzmann (LB) method. This approach corresponds to solving the phase field equations, such as the Cahn-Hilliard and Allen-Cahn equations, and the hydrodynamic equations, typically the Navier-Stokes and pressure equations for pseudo-incompre

Michael Nussbaum, Arleta Szkoła

We consider a statistical model of a n-mode quantum Gaussian state which is shift invariant and also gauge invariant. Such models can be considered analogs of classical Gaussian stationary time series, parametrized by their spectral density. Defining an appropriate quantum spectral density as the parameter, we establish that the quantum Gaussian time series

Mohit Kumar, Mathias Brucker, Alexander Valentinitsch, Adnan Husakovic

Federated learning must address heterogeneity, strict communication and computation limits, and privacy while ensuring performance. We propose an operator-theoretic framework that maps the $L^2$-optimal solution into a reproducing kernel Hilbert space (RKHS) via a forward operator, approximates it using available data, and maps back with the inverse operator

Chotirawee Chatpattanasiri, Federica Ninno, Vanessa Dıaz-Zuccarini, Stavroula Balabani

Haemodynamic indices derived from Computational Fluid Dynamics (CFD), such as Time-averaged Wall Shear Stress (TAWSS) and Oscillatory Shear Index (OSI), are closely associated with restenosis risk in Peripheral Arterial Disease (PAD). However, translating these insights into clinical practice may require computationally efficient approaches such as Reduced O

Orestis Kaparounakis, Yunqi Zhang, Phillip Stanley-Marbell

Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, with further applications in speech processing and medicine (patient monitoring). Yet, their inherent weakness at representing the likelihood of the observation (which often leads to

Yi-Lin Wei, Haoran Liao, Yuhao Lin, Pengyue Wang

In this paper, we explore an important yet underexplored task in robot manipulation: cycle-based manipulation, where robots need to perform cyclic or repetitive actions with an expected terminal time. These tasks are crucial in daily life, such as shaking a bottle or knocking a nail. However, few prior works have explored this task, leading to two main chall

Aditya Aradhye, David Lagziel, Eilon Solan

We study a mechanism-design problem in which spiteful agents strive to not only maximize their rewards but also, contingent upon their own payoff levels, seek to lower the opponents' rewards. We characterize all individually rational (IR) and incentive-compatible (IC) mechanisms that are immune to such spiteful behavior, showing that they take the form of th

Jinu Lee, Kyoung-Woon On, Simeng Han, Arman Cohan

Evaluating the quality of LLM-generated reasoning traces in expert domains (e.g., law) is essential for ensuring credibility and explainability, yet remains challenging due to the inherent complexity of such reasoning tasks. We introduce LEGIT (LEGal Issue Trees), a novel large-scale (24K instances) expert-level legal reasoning dataset with an emphasis on re

Maitri Mandal, Pappu Acharya, Rituparno Mandal, Sayantan Majumdar

Living organisms can demonstrate highly adaptable and sophisticated responses using memory resulting from repeated exposure to external conditions or training. However, realizing similar adaptability in mechanical responses in inanimate, physical materials presents an outstanding challenge in several fields, including soft matter, materials science, and in t

Adelina Giurea, Stijn Luchie, Dieter Coppens, Jeroen Hoebeke

This paper presents an infrastructure-free approach for obstacle detection and environmental mapping using ultra-wideband (UWB) radar mounted on a mobile robotic platform. Traditional sensing modalities such as visual cameras and Light Detection and Ranging (LiDAR) fail in environments with poor visibility due to darkness, smoke, or reflective surfaces. In t

Xinhang Li, Jingbo Zhou, Pengfei Luo, Yixiong Xiao

Recent advances in multimodal large language models (MLLMs) highlight the need for benchmarks that rigorously evaluate structured chart comprehension. Chart grounding refers to the bidirectional alignment between a chart's visual appearance and its structured semantics. This task requires models to produce a symbolic specification that faithfully captures th

Zifeng Huang, Konstantin M. Zuev, Yong Xia, Michael Beer

Neural oscillators, originating from second-order ordinary differential equations (ODEs), have demonstrated strong performance in stably learning causal mappings between long-term sequences or continuous temporal functions, as well as in accurately approximating physical systems. However, theoretically quantifying the capacities of their neural network archi

Ajneet Dhillon, Nicole Lemire, Jonathan Martin, Yidi Wang

We study the strong approximation for classifying stacks $BG$, where $G$ is a linear algebraic group over a number field $k$. More specifically, we prove that the \'etale Brauer-Manin obstruction is the only obstruction to strong approximation for $BG$. To prove the result, we formulate the theory of torsors and Galois twists for algebraic stacks.

Hiroshi Otomo, Rafael Salazar-Tio, Jingjing Yang, Hongli Fan

In this study, we develop computational models and methodology for accurate multi-component-flow simulation in under-resolved multi-scale porous structures. It is generally impractical to fully resolve the flow in porous structures with large length-scale difference due to tremendously high computational expense. The flow contributions from under-resolved sc

Osama M. Raisuddin, Haimeng Zhang, Mario Motta, Fabian M. Faulstich

We provide a systematic evaluation of the sample-based quantum diagonalization (SQD) method for electronic structure based on the W4-11 thermochemistry dataset, comprising 124 total atomization, 83 bond dissociation, 20 isomerization, 505 heavy-atom transfer, and 13 nucleophilic substitution processes, covering diverse bonding situations and reaction mechani

Meraj Hassanzadeh, Ehsan Ghaderi, Fatemeh Fatahi, Mohamad Ali Bijarchi

Thermal Energy Storage (TES) using Phase Change Materials (PCMs) represents a critical technology for sustainable energy management and grid stability. This study presents a novel Physics-Driven Deep Learning (PDDL) framework for modeling the complex solid-liquid phase transition in a two-dimensional PCM-based TES system integrated with finned heat exchanger

Vansh Sharma, Venkat Raman

Agentic large language models are proposed as autonomous code generators for scientific computing, yet their reliability in high-stakes problems remains unclear. Developing computational scientific software from natural-language queries remains challenging broadly due to (a) sparse representation of domain codes during training and (b) the limited feasibilit

Thomas Chabal, Shizhe Chen, Jean Ponce, Cordelia Schmid

This paper addresses the Object Goal Navigation problem, where a robot must efficiently find a target object in an unknown environment. Existing implicit memory-based methods struggle with long-term memory retention and planning, while explicit map-based approaches lack rich semantic information. To address these challenges, we propose FOM-Nav, a modular fra

Olivier Rivoire, Guy Bunin

A fundamental challenge in both biology and engineering is understanding how adaptation can emerge from simple physical or chemical building blocks. Biological adaptation appears to rely on two key capabilities: information processing, to enable learning of complex tasks, and reproduction, to give such learning its value through reproductive success. Creatin

Wilten Nicola

In reservoir computing, the coupling strength of the initial untrained recurrent neural network (the reservoir) is an important hyperparameter that can be varied for accurate training. A common heuristic is to set this parameter near the ``edge of chaos", where the untrained reservoir is near the transition to chaotic dynamics, and the chaos can be ``tamed".

Voalaza Mahavily Romuald Aubert, Benjamin Randrianirina

In this work, the Hao grammar $G=\{\, u\rightarrow u^{b_1+b_2+1} v^{a_1+a_2},\quad v\rightarrow u^{b_2}v^{a_2+1} \,\},$ together with the correspondence between grammars and combinatorial differential equations, is employed to obtain an interpretation of any triangular array of the form \[ T(n,k)=(a_2 n + a_1 k + a_0)\,T(n-1,k) + (b_2 n + b_1 k + b_0)\,T(n-1

Andreas Bernig, Dmitry Faifman, Jan Kotrbatý

Convolution of valuations was introduced by the first named author and Fu for linear spaces, and later by Alesker and the first named author for compact Lie groups. In this paper we study the convolution of invariant valuations on Lie groups. First, we obtain an explicit formula for the convolution of left-invariant valuations on compact groups in terms of d

William H. Press

Results in epidemiology and social science often require the removal of confounding effects from measurements of the pairwise correlation of variables in survey data. This is typically accomplished by some variant of linear regression (e.g., ``logistic" or ``Cox proportional"). But, knowing whether all possible confounders have been identified, or are even v

Emile Parolin, Frédéric Nataf

An abstract construction of coarse spaces for non-Hermitian problems and non-Hermitian domain decomposition preconditioners based on extended generalized eigenproblems was proposed in [Nataf and Parolin, arXiv:2404.02758] and analyzed on the matrix formulation. Building upon this work, we consider instead here the specific case of heterogeneous Helmholtz pro

Galit Ashkenazi-Golan, János Flesch, Eilon Solan

We study multi-player games with perfect information and general payoff function, where the set of stages is the set of non-positive integers $\{\ldots,-2,-1,0\}$. We define two related equilibrium concepts: one considering only deviations at finitely many stages and another considering all deviations. We show that (i) The sets of equilibrium plays coincide

Maxwell H Wang, Till Hoffmann, Jukka-Pekka Onnela

Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may be designed to facilitate the appearance of certain network motifs. In the formation of real-world networks, multiple me

Mohsin Rasheed, Abdullah Al-Mamun

Medical imaging is essential for clinical diagnosis, yet real-world data frequently suffers from corruption, noise, and potential tampering, challenging the reliability of AI-assisted interpretation. Conventional reconstruction techniques prioritize pixel-level recovery and may produce visually plausible outputs while compromising anatomical fidelity, an iss

Christof Röhrig, Benz Cramer

Continuous energy monitoring is essential for identifying potential savings and predicting the energy requirements of buildings. Energy meters are often located in underground spaces that are difficult to reach with wireless technology. This paper presents an experimental study comparing different Low Power Wide Area Networks (LPWAN) technologies in terms of

Param Biyani, Shashank Kirtania, Yasharth Bajpai, Sumit Gulwani

Reliable autoformalization remains challenging even in the era of large language models (LLMs). The scarcity of high-quality training data is a major bottleneck. Expert annotation requires substantial time and deep expertise in both mathematics and theorem proving. We introduce IndiMathBench, a human-verified benchmark designed to evaluate mathematical theor

Raymond J. Hinton,, Pepa Ramírez Cobo, Brani Vidakovic

The wavelet spectra is a common starting point for estimating the Hurst exponent of a self-similar signal using wavelet-based techniques. The decay of the $\log_2$ average energy of the detail wavelet coefficients as a function of the level of signal decomposition can be used to construct estimators for this parameter. In this paper, we expand on previous wo

Han Su, Tianyu Huang, Zichen Wan, Xiaohe Wu

Part-level point cloud segmentation has recently attracted significant attention in 3D computer vision. Nevertheless, existing research is constrained by two major challenges: native 3D models lack generalization due to data scarcity, while introducing 2D pre-trained knowledge often leads to inconsistent segmentation results across different views. To addres

Hang Wu, Qin Wu, Yue Liu, Mengmeng Shi

The rapid expansion of digital commerce platforms has amplified the strategic importance of coordinated pricing and inventory management decisions among competing retailers. Motivated by practices on leading e-commerce platforms, we analyze a sequential duopolistic newsvendor game where retailers first publicly set prices and subsequently make private invent

Zhiyuan You, Ke Wang, He Zhang, Xin Cai

Composition matters during the photo-taking process, yet many casual users struggle to frame well-composed images. To provide composition guidance, we introduce PhotoFramer, a multi-modal composition instruction framework. Given a poorly composed image, PhotoFramer first describes how to improve the composition in natural language and then generates a well-c

Shideh Tayebnaimi, Kamran Kiasaleh

We investigate the scintillation index of multi-wavelength Gaussian optical beams propagating through a turbulent optical channel. We consider a turbulent environment ranging from weak to strong, with a specific focus on the weak turbulent regime. Furthermore, the impacts of absorption and scattering effects are taken into account. It is shown here that the

Nicole Favero, Francesca Salute, Daniel Hardt

Most evaluations of large language models focus on standard tasks such as factual question answering or short summarization. This research expands that scope in two directions: first, by comparing two retrieval strategies, Graph RAG, structured knowledge-graph based, and Advanced RAG, hybrid keyword-semantic search, for QA; and second, by evaluating whether

Mojtaba Fanoodi, Farzaneh Abdollahi, Mahdi Aliyari Shoorehdeli, Mohsen Maboodi

Faults and operational disturbances in Heat Recovery Steam Generators (HRSGs), such as valve leakage, present significant challenges, disrupting steam temperature regulation and potentially causing efficiency losses, safety risks, and unit shutdowns. Traditional PI controllers often struggle due to inherent system delays, nonlinear dynamics, and static gain

Dominik Luszczynski, Richard Fei Yin, Nicholas Afonin, Andrew S. P. Lim

Objectives: We present and evaluate a Mamba-based deep-learning model for diagnosis and event-level characterization of sleep disordered breathing based on signals from the ANNE One, a non-intrusive dual-module wireless wearable system measuring chest electrocardiography, triaxial accelerometry, chest and finger temperature, and finger phototplethysmography.

Shruti Prasad, Pabitra Kumar Nayak, Dibyajyoti Ghosh

Lead-free vacancy-ordered perovskites (VOHPs) such as Cs2SnCl6 have emerged as promising materials for optoelectronic applications but typically suffer from wide band gaps and low photoluminescence quantum yields (PLQY). In this work, the electronic origins of broadband blue emission in Bi3+-doped Cs2SnCl6 are elucidated by combining density functional theor

Adem Utku Atasayar, Aimin Li, Çağrı Arı, Elif Uysal

Links in practical systems, such as satellite--terrestrial integrated networks, exhibit distinct delay distributions, intermittent availability, and heterogeneous energy costs. These characteristics pose significant challenges to maintaining timely and energy-efficient status updates. While link availability restricts feasible transmission routes, routing de

Fernando E. Rosas

Many systems of interest exhibit nested emergent layers with their own rules and regularities, and our knowledge about them seems naturally organised around these levels. This paper proposes that this type of hierarchical emergence arises as a result of underlying symmetries. By combining principles from information theory, group theory, and statistical mech

Rohit Chhiber, Yanwen Wang, Manuel E. Cuesta, Jiaming Wang

The connection between turbulence and solar-wind acceleration, long known in space physics, is further developed in this Letter by establishing a robust empirical law that relates the bulk-flow speed to the magnetohydrodynamic-scale fluctuation energy in the plasma. The model is based on analysis of twenty-five years of near-Earth observations by NASA's Adva

Christoph Pütz

We completely classify Laurent series converging on the unit circle over a non-Archimedean local field (of any characteristic) that map infinitely many roots of unity to roots of unity. For a given Laurent series $f$ over a field of positive characteristic with residue field $\mathbb{F}_q$, we prove effective bounds for the number of possible roots of unity

Mateusz Oleszko, Ralf Hambach, Herbert Gross

The increasing use of freeform optical surfaces raises the demand for optical design tools developed for generalized systems. In the design process surface-by-surface aberration contributions are of special interest. The expansion of the wave aberration function into field and pupil dependent coefficients is an analytical method used for that purpose. An alt

Giacomo Prando, Benjamin Costarella, Matthew S. Dickson, Ryan A. Murphy

Owing to their exceptional chemical and electronic tunability, metal-organic frameworks can be designed to develop magnetic ground states making a range of applications feasible, from magnetic gas separation to the implementation of lightweight, rare-earth free permanent magnets. However, the typically weak exchange interactions mediated by the diamagnetic o

Victor Saquicela, Kenneth Palacio-Baus, Mario Chifla

Principal Component Analysis (PCA) and K-means constitute fundamental techniques in multivariate analysis. Although they are frequently applied independently or sequentially to cluster observations, the relationship between them, especially when K-means is used to cluster variables rather than observations, has been scarcely explored. This study seeks to add

Mircea Merca

Motivated by earlier work of P.~A.~MacMahon and recent contributions of T.~Amdeberhan, G.~E.~Andrews, K.~Ono, A.~Singh, and R.~Tauraso on higher-order partition enumerants, we study a class of $q$-series arising from nested divisor structures. In particular, we consider the $q$-series \[ V_k(q) = \sum_{1 \le n_1 \le n_2 \le \cdots \le n_k} \frac{q^{\,n_1+n_2

Ryan Nduma, Hyunsoo Park, Aron Walsh

We present Crystalyse, an open, provenance-enforced scientific agent for computational materials design of inorganic crystals that orchestrates tools for compositional screening, crystal structure generation, and machine-learning force-field evaluation. Crystalyse offers three operating modes to trade exploration speed against validation depth: creative (rap

Cynthia Feeney, Shane Williams, Benjamin S. Wessler, Michael C. Hughes

Echocardiogram datasets enable training deep learning models to automate interpretation of cardiac ultrasound, thereby expanding access to accurate readings of diagnostically-useful images. However, the gender, sex, race, and ethnicity of the patients in these datasets are underreported and subgroup-specific predictive performance is unevaluated. These repor

Haotian Liang, Xinyi Chen, Bin Wang, Mingkang Chen

A generalist robotic policy needs both semantic understanding for task planning and the ability to interact with the environment through predictive capabilities. To tackle this, we present MM-ACT, a unified Vision-Language-Action (VLA) model that integrates text, image, and action in shared token space and performs generation across all three modalities. MM-

Aradhya Chakrabarti

Soft-core processors on resource-constrained FPGAs often suffer from low code density and reliance on proprietary toolchains. This paper details the design, implementation, and evaluation of a 32-bit dual-stack microprocessor architecture optimized for low-cost, resource-constrained Field-Programmable Gate Arrays (FPGAs). Implemented on the Gowin GW1NR-9 (Ta

John Douglas Moore

We use the generalized Gauss-Bonnet formula for Riemannian polyhedra discovered by Allendoerfer, Weil and Chern to show that hyperbolic space of dimension $n$ has no isometric immersion into Euclidean space of dimension $2n-1$.

Guowei Yang, Ruihan Chen, Changchao Liu, Jing Li

Altermagnets are characterized by anisotropic band/spin splittings in momentum space, dictated by their spin-space group symmetries. However, the real-space modulations of altermagnetism are often neglected and have not been explored experimentally. Here we combine neutron diffraction, angle-resolved photoemission spectroscopy (ARPES), spin-resolved ARPES an

Yunfeng Lin, Minghuan Liu, Yufei Xue, Ming Zhou

The rapid advancement of humanoid robotics has intensified the need for robust and adaptable controllers to enable stable and efficient locomotion across diverse platforms. However, developing such controllers remains a significant challenge because existing solutions are tailored to specific robot designs, requiring extensive tuning of reward functions, phy

Kiran Adhikari

Quantum information scrambling has emerged as a powerful tool for studying the dynamics of chaotic quantum many-body systems, assessing benchmarking protocols, and even investigating exotic black hole models. During quantum information scrambling, localized quantum information disperses across the entire system, hiding from the observers who can only access

Felix Saretzky, Lucas Andersen, Thomas Engel, Fazel Ansari

The transition to prescriptive maintenance (PsM) in manufacturing is critically constrained by a dependence on predictive models. Such purely predictive models tend to capture statistical associations in the data without identifying the underlying causal drivers of failure, which can lead to costly misdiagnoses and ineffective measures. This fundamental limi

Ziyang Zeng, Heming Jing, Jindong Chen, Xiangli Li

Ranking relevance is a fundamental task in search engines, aiming to identify the items most relevant to a given user query. Traditional relevance models typically produce scalar scores or directly predict relevance labels, limiting both interpretability and the modeling of complex relevance signals. Inspired by recent advances in Chain-of-Thought (CoT) reas

Sarah Huang, Chen Jiang

The high-precision photometry from NASA's Kepler and TESS missions has revolutionized exoplanet detection, enabling the discovery of over 5500 confirmed exoplanets via the transit method and around 10000 additional candidates awaiting validation. However, confirming these candidates as true planets demands meticulous vetting and follow-up observations, which

Mintong Kang, Chong Xiang, Sanjay Kariyappa, Chaowei Xiao

Indirect prompt injection attacks (IPIAs), where large language models (LLMs) follow malicious instructions hidden in input data, pose a critical threat to LLM-powered agents. In this paper, we present IntentGuard, a general defense framework based on instruction-following intent analysis. The key insight of IntentGuard is that the decisive factor in IPIAs i

Siyi Xiao, Changjun Liu, Haoming He, Yuhao Feng

This letter proposes a high-efficiency dual-band class-F rectifier for wireless power transmission (WPT). The rectifier comprises a dual-band harmonic termination network, a dual-band matching network, a single Schottky diode, and a dc pass filter. A theoretical analysis of the harmonic termination network is performed to improve the rectifying efficiency. T

Chang Liu, Tianjiao Jing, Chengcheng Ma, Xuanqi Zhou

Recent photo-realistic 3D talking head via 3D Gaussian Splatting still has significant shortcoming in emotional expression manipulation, especially for fine-grained and expansive dynamics emotional editing using multi-modal control. This paper introduces a new editable 3D Gaussian talking head, i.e. EmoDiffTalk. Our key idea is a novel Emotion-aware Gaussian

Muhammad Israr Aslam, Rabia Saleem, Chen-Yu Yang, Xiao-Xiong Zeng

Based on two distinct thick accretion flow disk models, such as a phenomenological RIAF-like model and an analytical Hou disk model, we investigate the impact of relevant parameters on the visual characteristics of the Schwarzschild black hole (BH) surrounded by perfect fluid dark matter (PFDM). We impose a general relativistic radiative transfer equation to

Emmanuel Fonseca

A future measurement of Lense-Thirring (LT) precession using a binary radio pulsar is expected to yield the pulsar's moment of inertia ($I_{\rm p}$). However, most of the known pulsar-binary systems expected to provide this opportunity will exhibit linear variations in the orbital elements due to LT precession that are difficult to separate from variations i

Sandro Fonseca de Souza, Gilvan Augusto Alves, Mapse Barroso, Helio Nogima

This document presents an overview of the activities of Brazilian groups on gaseous detectors, with emphasis on resistive plate chambers (RPCs) for the CMS experiment at the CERN LHC and related applications. It summarises the design, performance, maintenance and upgrade of the CMS RPC system, including the installation of improved RPCs for the HL-LHC era an

Qi Wang, Mian Wu, Yuyang Zhang, Mingqi Yuan

Reinforcement Learning (RL) has achieved remarkable success in various domains, yet it often relies on carefully designed programmatic reward functions to guide agent behavior. Designing such reward functions can be challenging and may not generalize well across different tasks. To address this limitation, we leverage the rich world knowledge contained in pr

Boran Wen, Ye Lu, Sirui Wang, Keyan Wan

Generalized robots must learn from diverse, large-scale human-object interactions (HOI) to operate robustly in the real world. Monocular internet videos offer a nearly limitless and readily available source of data, capturing an unparalleled diversity of human activities, objects, and environments. However, accurately and scalably extracting 4D interaction d

Alex Mwololo Kimuya, Dickson Mwenda Kinyua

Conventional electrical fault models, which rely on static thresholds and instantaneous trip mechanisms, fail to capture the time-evolving dynamics of real faults, creating vulnerabilities in modern power systems. This paper introduces a diode-clamp circuit architecture that reconceives short-circuits as governed, sustained processes and establishes a physic

Vladimir Dzhunushaliev, Vladimir Folomeev

Within Einstein-Dirac-Maxwell theory, we consider a wormhole solution supported by a complex non-phantom spinor field with a bare mass of the order of the Planck mass (which provides a nontrivial spacetime topology and an intrinsic angular momentum), an electric field (which provides a charge of the system), and a magnetic field. This solution describes an a

Yukun Du, Sa'ar Hersonsky

We give a complete criterion for when two hyperbolic automorphisms of a tree generate a free, discrete subgroup. The decision depends only on three geometric invariants: the translation lengths of the generators and the length of overlap of their axes. This data is organized using the continued-fraction expansion of the translation-length ratio. We extend th

Alistair Pattison

This paper provides a novel summary measure of ideological polarization in the American public based on the joint distribution of survey responses. Intuitively, polarization is maximized when views are concentrated at opposing extremes with little mass in between and when opinions are highly correlated across many issues. Using this measure, I show that publ

C Yu, F Tan, C Liu

A rectenna (rectifying antenna) is usually applied to a microwave power transmission (MPT) system as a terminal, which receives microwave (MW) power and converts them into DC power. A 5.8 GHz aperture-coupled patch antenna array is developed with a gain of 19.5 dBi, which is a part of the rectenna. A novel Class-F rectifier with a series diode is proposed to

Haojian Huang, Kaijing Ma, Jin Chen, Haodong Chen

In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pre-trained models that struggle with fine-grained information and deterministic reasoning, leading to difficulties in aligning with complex or ambiguous moments. To overcome these l

Audrius Alkauskas, Chris G. Van de Walle, Lukas Razinkovas, Ronald Ulbricht

The charge state stability of nitrogen-vacancy (NV) centers critically affects their application as quantum sensors and qubits. Understanding charge state conversion and equilibration is critical not only for NV centers in diamond but also for defects and impurities in wide-bandgap materials in general. The mechanisms by which these centers change charge sta

Vicky P. Vital, Francis F. Balahadia, Maria Anna D. Cruz, Dolores D. Mallari

This study explores the perceptions of 213 Filipino teachers toward AI detection tools in academic settings. It focuses on the factors that influence teachers' trust, concerns, and decision-making regarding these tools. The research investigates how teachers' trust in AI detection tools affects their perceptions of fairness and decision-making in evaluating

Alessandro Goffi

We study interior $C^{2,\alpha}$ regularity estimates for solutions of fully nonlinear uniformly elliptic equations of the general form $F(D^2u)=0$ in two independent variables and without any geometric condition on $F$. By means of the theory of divergence form equations we prove that $C^2$ solutions of the previous equation are $C^{2,\bar\alpha(\lambda/\La

Arnaldo Spalvieri

The paper works out the canonical probability distribution of the occupancy numbers of a bosonic system and shows that canonical typicality applies to the canonical density operator of the occupancy numbers. The result is that, if, as it is today standard, the canonical system's mixed state is obtained by tracing out the environment from any typical pure sta

Yansong Liu, Ronnie Stafford, Pramit Khetrapal, Huriye Kocadag

For patients undergoing systemic cancer therapy, the time between clinic visits is full of uncertainties and risks of unmonitored side effects. To bridge this gap in care, we developed and prospectively trialed a multi-modal AI framework for remote patient monitoring (RPM). This system integrates multi-modal data from the HALO-X platform, such as demographic

Benedikt Kantz, Kevin Innerebner, Peter Waldert, Stefan Lengauer

Querying knowledge bases using ontologies is usually performed using dedicated query languages, question-answering systems, or visual query editors for Knowledge Graphs. We propose a novel approach that enables users to query the knowledge graph by specifying prototype graphs in natural language and visually editing them. This approach enables non-experts to

Liyao Li, Chao Ye, Wentao Ye, Yifei Sun

To migrate the remarkable successes of Large Language Models (LLMs), the community has made numerous efforts to generalize them to the table reasoning tasks for the widely deployed tabular data. Despite that, in this work, by showing a probing experiment on our proposed StructQA benchmark, we postulate that even the most advanced LLMs (such as GPTs) may stil

Alvaro Paredes Amorin, Andre Python, Christoph Weisser

Large language models (LLMs) play an increasingly important role in financial markets analysis by capturing signals from complex and heterogeneous textual data sources, such as tweets, news articles, reports, and microblogs. However, their performance is dependent on large computational resources and proprietary datasets, which are costly, restricted, and th

Hilene E. Hernandez, Ranie B. Canlas, Madilaine Claire B. Nacianceno, Jordan L. Salenga

While AI tools are transforming programming education, their adoption in underrepresented countries remains insufficiently studied. Understanding students' trust, perceived usefulness, and dependency on AI tools is essential to improving their integration into education. For these purposes, this study surveyed 508 first-year programming students in Pampanga,