November 2025 arXiv papers — page 138

Yiming Meng, Jun Liu

Verifying stability and safety guarantees for nonlinear systems has received considerable attention in recent years. This property serves as a fundamental building block for specifying more complex system behaviors and control objectives. However, estimating the domain of attraction under safety constraints and constructing a Lyapunov-barrier function remain

Daniel M. Jimenez-Gutierrez, Enrique Zuazua, Joaquin Del Rio, Oleksii Sliusarenko

Early and accurate pneumonia detection from chest X-rays (CXRs) is clinically critical to expedite treatment and isolation, reduce complications, and curb unnecessary antibiotic use. Although artificial intelligence (AI) substantially improves CXR-based detection, development is hindered by globally distributed data, high inter-hospital variability, and stri

Ruaridh Forbes, Paul Hockett, Rune Lausten

We report the generation of the fifth harmonic of Yb at 206 nm with pulse energies exceeding 16 $\mu$J and durations of approximately 100 fs at a repetition rate of 100 kHz. The deep ultraviolet pulses are produced using four-wave difference frequency mixing in a He-filled stretched hollow-core fiber, driven by a pump at 343 nm and seeded at 1030 nm. Guided

Alyssa VanZanten, Surbhi Punhani-Schillinger, M. Reed Blocksome, Aditya Ketkar

Polymer gels with photo-responsive cross-links enable tunable mechanics and surface morphologies, making them promising for adaptive materials. While prior work on coumarin cross-linked gels has focused on photo-mediated events in dilute solution, their network-level mechanical responses remain unclear. Here, we design PEG hydrogels with both permanent coval

Jaehyeok Choi, Eunwoo Lee

We investigate the \emph{quantum} cohomology of a supercharge $Q$ in $\mathcal{N}=4$ super Yang-Mills theory. Recent analyses have revealed a mismatch between the one-loop BPS spectra of the S-dual $SO(7)$ and $Sp(3)$ theories. The $SO(7)$ theory contains a pair of additional graviton (monotone) and non-graviton (fortuitous) cohomologies, whose net contribut

Yunkai Yu, Yingying Wang, Rong Zheng

The Internet of Things (IoT) sensors have been widely employed to capture human locomotions to enable applications such as activity recognition, human pose estimation, and fall detection. Motion capture (MoCap) systems are frequently used to generate ground truth annotations for human poses when training models with data from wearable or ambient sensors, and

Yiheng Wang, Han Qu, Jiafeng Lu, Huiyuan Wang

Warped structures are often observed in disk galaxies, yet their physical origin is still under investigation. We present a systematic study of warped edge-on disk galaxies based on imaging data from the DESI Legacy Imaging Surveys DR8, with the expectation that this large sample size, enabled by wide-area surveys, will offer new perspectives on the formatio

Xiaodan Li, Chengshi Wang, Yushu Zheng

We study the scaling limits of genealogical trees arising from Cannings models. Under suitable moment conditions, we show that the rescaled contour and height functions converge to a time change of Brownian motion conditioned on a given local time profile. This conditioned Brownian motion is a self-interacting diffusion constructed independently by Warren--Y

Runhao Li, Wenkai Guo, Zhenyu Wu, Changyuan Wang

Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation. However, these models struggle with long-horizon tasks due to their lack of memory and reliance solely on immediate sensory inputs. To address this limitation, we propose Memory-Augmented Prompting f

Fangqi Zhu, Zhengyang Yan, Zicong Hong, Quanxin Shou

Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sam

Rakesh Palariya, Sunil P. Singh

The mechanical response of intrinsically disordered proteins (IDPs) and polyampholyte (PA) chains is vital for understanding their biological functions and designing functional materials. We investigate the force-extension behavior of a PA chain with distinct charge sequences using molecular dynamics simulations and a theoretical approach based on the genera

Jingquan Yan, Yuwei Miao, Lei Yu, Yuzhi Guo

Exploring how genetic sequences shape phenotypes is a fundamental challenge in biology and a key step toward scalable, hypothesis-driven experimentation. The task is complicated by the large modality gap between sequences and phenotypes, as well as the pleiotropic nature of gene-phenotype relationships. Existing sequence-based efforts focus on the degree to

Diego Garcia Ovalle, Aurelien Manchon

We investigate spin- and orbital-to-charge conversion phenomena in nonmagnetic materials with broken inversion symmetry, treating the contributions from the Hall effect and the Rashba-Edelstein effect on an equal footing. We develop a general formalism for this interconversion based solely on macroscopic observables. The theory is validated through a case st

Arash Bahari Kordabad, Dean Brandner, Sebastien Gros, Sergio Lucia

In this paper, we propose a second-order deterministic actor-critic framework in reinforcement learning that extends the classical deterministic policy gradient method to exploit curvature information of the performance function. Building on the concept of compatible function approximation for the critic, we introduce a quadratic critic that simultaneously p

Raphael Morisco

This paper examines how the figure of the hacker is portrayed in German mainstream media and explores the impact of media framing on public discourse. Through a longitudinal content analysis of 301 articles from four of the most widely circulated German newspapers (Die Zeit, S\"uddeutsche Zeitung, Bild, and Der Spiegel), the study covers reporting between Ja

Enno Giese

Entanglement, a defining property of quantum mechanics in which two physical subsystems cannot be seen as independent entities, challenges our everyday experience and classical intuition. However, only such strong quantum correlations enable quantum technologies, including quantum computing or communication, while revealing the limits of our classical worldv

George-Rafael Domenikos, Victoria Leong

Complex systems produce high-dimensional signals that lack macroscopic variables analogous to entropy, temperature, or free energy. This work introduces a thermoinformational formulation that derives entropy, internal energy, temperature, and Helmholtz free energy directly from empirical microstate distributions of arbitrary datasets. The approach provides a

Dan Edidin, Ivan Gonzalez, Itzhak Tamo

Quantum state tomography seeks to reconstruct an unknown state from measurement statistics. A finite measurement (POVM) is \emph{pure-state informationally complete} (PSI-Complete) if the outcome probabilities determine any pure state up to a global phase. We study \emph{rank-one} POVMs that are minimally sufficient for this task. We call such a POVM \emph{v

Yilun Guan

Time-resolved observations of the Cosmic Microwave Background (CMB) offer a powerful probe of time-dependent cosmological signals, such as a stochastic gravitational wave background passing through Earth, which imprints a time-varying deflection on the CMB, and time-dependent cosmic birefringence, which induces an oscillating polarization rotation. However,

Abhijit Banerjee, Sujoy Majumder, Debabrata Pramanik, Nabadwip Sarkar

In this paper, we investigate meromorphic solutions in $\mathbb{C}^m$ of the nonlinear differential equation \[\displaystyle f^n\partial_u(f)g^n\partial_u(g)=1,\] where $\partial_u(f)=\sum_{j=1}^mu_j\partial_j(f)$ and $\sum_{j=1}^m u_j\neq 0$. Our results extend those of Yang and Hua [{\sc C. C. Yang} and {\sc X. H. Hua}, Uniqueness and value sharing of mero

Jerrin Bright, Yuhao Chen, John S. Zelek

Accurate 3D human pose estimation remains a critical yet unresolved challenge, requiring both temporal coherence across frames and fine-grained modeling of joint relationships. However, most existing methods rely solely on geometric cues and predict each 3D pose independently, which limits their ability to resolve ambiguous motions and generalize to real-wor

Guansu Wang, Peijie Sun

Recent advances in text-to-speech (TTS) have enabled models to clone arbitrary unseen speakers and synthesize high-quality, natural-sounding speech. However, evaluation methods lag behind: typical mean opinion score (MOS) estimators perform regression over entire utterances, while failures usually occur in a few problematic words. We observe that encoder-dec

Zixi Li

What is reasoning? This question has driven centuries of philosophical inquiry, from Aristotle's syllogisms to modern computational complexity theory. In the age of large language models achieving superhuman performance on benchmarks like GSM8K (95\% accuracy) and HumanEval (90\% pass@1), we must ask: have these systems learned to \emph{reason}, or have they

Fujiao Ji, Doowon Kim

Large language models (LLMs) have emerged as a promising phishing detection mechanism, addressing the limitations of traditional deep learning-based detectors, including poor generalization to previously unseen websites and a lack of interpretability. However, LLMs' effectiveness for phishing detection remains unexplored. This study investigates how to effec

Tengyuan Liang

We study the problem of denoising when only the noise level is known, not the noise distribution. Independent noise $Z$ corrupts a signal $X$, yielding the observation $Y = X + \sigma Z$ with known $\sigma \in (0,1)$. We propose \emph{universal} denoisers, agnostic to both signal and noise distributions, that recover the signal distribution $P_X$ from $P_Y$.

J. A. Osorio-Caballero, A. Rodríguez-González, Z. Meliani

Star formation bursts promote the ejection of material from the hosting galaxies due to the momentum and energy injected by winds from massive stars and supernova explosions. Numerical or analytical models generally consider that the mass, momentum, and energy injections result from bursts in a nuclear star formation region. However, star formation bursts ha

Martin Baránek, Dušan Lorenc, Tomáš Ščepka, Ján Šoltýs

We demonstrate a straightforward optoelectronic fiber alignment technique for superconducting nanowire single-photon detectors (SNSPDs) that exploits the temperature-dependent resistance of the nanowire under optical absorption. The target nanowire is illuminated via the fiber, and the local absorption of light heats the wire, causing a change in its resisti

Pablo Palacios Alonso, Raúl Pérez Peláez, Rafael Delgado-Buscalioni

We present a fast spectral solver for the linear response of viscoelastic structures under oscillatory flow either in free space or close to a flat moving wall. The scheme works in the frequency domain (using phasors) and couples the oscillatory Stokes equation with rigid or flexible structures, modeled by viscoelastic networks of immersed boundary kernels.

Vahid Salehi

This work will elaborate the fundamental principles of physical artificial intelligence (Physical AI) from a scientific and systemic perspective. The aim is to create a theoretical foundation that describes the physical embodiment, sensory perception, ability to act, learning processes, and context sensitivity of intelligent systems within a coherent framewo

A. C. Raga, Z. Meliani, A. Rodríguez-González, S. Cabrit

Stars predominantly form in compact, non-hierarchical clusters. The gas outflows ejected by protostars can intersect and interact with each other, resulting in complex interactions that affect the dynamics, morphology, and evolution of these outflows. Determining the probability of an encounter between them requires a Bayesian approach that considers the col

Tsogt-Ochir Enkhbayar

Although sparse autoencoders (SAEs) are crucial for identifying interpretable features in neural networks, it is still challenging to distinguish between real computational patterns and erroneous correlations. We introduce Model-X knockoffs to SAE feature selection, using knock-off+ to control the false discovery rate (FDR) with finite-sample guarantees unde

Tânia Paulista

Let $X$ be a finite set. Let $\mathcal{T}(X)$ be the transformation semigroup on $X$ and let $\mathcal{P}(X)$ be the partial transformation semigroup on $X$. This paper is a contribution to the problem of characterizing the largest commutative subsemigroups of $\mathcal{T}(X)$ (respectively, $\mathcal{P}(X)$). In the process of looking for these semigroups,

Octave Mestoudjian, Matt Wilson, Augustin Vanrietvelde, Pablo Arrighi

We extend the usual process-theoretic view on locality and causality in subsystems (based on the tensor product case) to general quantum systems (i.e.\ possibly non-factor, finite-dimensional von Neumann algebras). To do so, we introduce a primitive notion of splitting maps within dagger symmetric monoidal categories. Splitting maps give rise to subsystems t

Adam Tauman Kalai, Yael Tauman Kalai, Or Zamir

Motivated by undetectable risks in generative AI, we study a general robust aggregation problem: how to aggregate several probability distributions to boost safety. We present consensus sampling, a black-box algorithm that, given k distributions, has risk competitive with the average risk of the safest $s$ while abstaining when there is insufficient agreemen

Muhammed El Mustaqeem Mazelan, Noor Hazlina Abdul, Nouar AlDahoul

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns (e.g., 'P@ssw0rd1!'), giving users a false sense of security. To address this, we implement and evaluate a password strength sc

Devansh Bhardwaj, Evangelia Takou, Yingjia Lin, Kenneth R. Brown

Advancing quantum information processors and building fault-tolerant architectures rely on the ability to accurately characterize the noise sources and suppress their impact on quantum devices. In practice, noise often drifts over time, whereas conventional noise characterization and decoding methods typically assume stationarity or provide only a time-avera

Jiazheng Li, Yinsi Shou, Cheng Li, Xianzhe Jia

The icy surface of Europa is continuously bombarded by ions and electrons from Jupiter's magnetosphere. The bombardment of the particles dissociates water molecules on the surface of Europa and introduces impurities to the icy surface. Such processes lead to the generation of the nonwater species on the surface of Europa. These chemical species are closely r

Shuzhen Bi, Chang Song, Siyu Song, Jinze Lv

Supervised fine-tuning (SFT) of large language models (LLMs) for specialized tasks requires high-quality datasets, but manual curation is prohibitively expensive. Synthetic data generation offers scalability, but its effectiveness relies on complex, multi-stage workflows, integrating prompt engineering and model orchestration. Existing automated workflow met

Junqi Gao, Zhichang Guo, Dazhi Zhang, Yao Li

Rehearsal-based Continual Learning (CL) maintains a limited memory buffer to store replay samples for knowledge retention, making these approaches heavily reliant on the quality of the stored samples. Current Rehearsal-based CL methods typically construct the memory buffer by selecting a representative subset (referred to as coresets), aiming to approximate

Miroslav Popovic, Marko Popovic, Pavle Vasiljevic, Miodrag Djukic

The Python Testbed for Federated Learning Algorithms is a simple FL framework targeting edge systems, which provides the three generic algorithms: the centralized federated learning, the decentralized federated learning, and the universal TDM communication in the current time slot. The first two were formally verified in a previous paper using the CSP proces

Chaoyi Pan, Changhao Wang, Haozhi Qi, Zixi Liu

Learning dexterous and agile policy for humanoid and dexterous hand control requires large-scale demonstrations, but collecting robot-specific data is prohibitively expensive. In contrast, abundant human motion data is readily available from motion capture, videos, and virtual reality, which could help address the data scarcity problem. However, due to the e

William Brach, Kristián Košťál, Lukas Galke Poech

Large Language Models (LLMs) are increasingly using external web content. However, much of this content is not easily digestible by LLMs due to LLM-unfriendly formats and limitations of context length. To address this issue, we propose a method for generating general-purpose, information-dense summaries that act as plain-text repositories of web content. Ins

Julio C. Bertua Marasca, Homer Dávila Gutiérrez, Víctor Huamán Ticona, Josué I. Mosquera Hadatty

We study five nearby galaxies (M100/NGC 4321, NGC 1300, M 74, M 60, and NGC 7331) by combining multiband imaging (optical, UV, NIR, and X-rays) with simple photometric measurements to show how each spectral window traces different physical components: UV/blue emphasizes recent star formation, NIR outlines the old stellar mass and internal structure, and X-ra

Lile Wang, Feng Long, Haifeng Yang, Ruobing Dong

The adsorption of volatile molecules onto dust grain surfaces fundamentally influences dust-related processes, including condensation of gas-phase molecules, dust coagulation, and planet formation in protoplanetary disks. Using advanced ab-initio density functional theory with r$^2$SCAN+rVV10 van der Waals functionals, we calculate adsorption energies of H$_

Niclas Boehmer, Lara Glessen, Jannik Peters

Despite extensive theoretical research on proportionality in approval-based multiwinner voting, its impact on which committees and candidates can be selected in practice remains poorly understood. We address this gap by (i) analyzing the computational complexity of several natural problems related to the behavior of proportionality axioms, and (ii) conductin

Renda Li, Hailang Huang, Fei Wei, Feng Xiong

Reinforcement learning (RL) has demonstrated considerable potential for enhancing reasoning in large language models (LLMs). However, existing methods suffer from Gradient Starvation and Policy Degradation when training directly on samples with mixed difficulty. To mitigate this, prior approaches leverage Chain-of-Thought (CoT) data, but the construction of

Andrew Hamara, Greg Hamerly, Pablo Rivas, Andrew C. Freeman

Planning in high-dimensional decision spaces is increasingly being studied through the lens of learned representations. Rather than training policies or value heads, we investigate whether planning can be carried out directly in an evaluation-aligned embedding space. We introduce SOLIS, which learns such a space using supervised contrastive learning. In this

Klaus Mattis, Swann Tubach

We show that every object of the stable \'etale motivic homotopy category over any scheme is $\eta$-complete. In some cases we show that in fact the fourth power of $\eta$ is null, whereas the third power of $\eta$ is always nonvanishing, similar to the situation in topology. Moreover, we prove an \'etale version of May's nilpotence conjecture, that states t

Anli Ji, Pranjal Patil, Chetraj Pandey, Manolis K. Georgoulis

Solar energetic particle (SEP) events, as one of the most prominent manifestations of solar activity, can generate severe hazardous radiation when accelerated by solar flares or shock waves formed aside from coronal mass ejections (CMEs). However, most existing data-driven methods used for SEP predictions are operated as black-box models, making it challengi

Selina F. Yang, Sophie M. McAtee, Benjamin J. Vaughan, Abigail T. Crites

We present the processing of an observation of Sagittarius A (Sgr A) with the Tomographic Ionized-carbon Mapping Experiment (TIME), part of the 2021-2022 commissioning run to verify TIME's hyperspectral imaging capabilities for future line-intensity mapping. Using an observation of Jupiter to calibrate detector gains and pointing offsets, we process the Sgr

Volker Betz, Tobias Schmidt, Mark Sellke

We study Brownian motion perturbed by a long range self-interaction. We provide variance bounds in terms of the spatial interaction strength and the order of time decay.

Henry Adams, Julian Carvajal, Jake Rhodes, Niccolo Turillo

For $X$ a metric space and $r>0$, the Vietoris--Rips simplicial complex $\mathrm{VR}(X;r)$ has $X$ as its vertex set, and a finite subset $\sigma \subseteq X$ as a simplex whenever the diameter of $\sigma$ is less than $r$. In ``On Vietoris--Rips complexes of ellipses'', the authors studied the homotopy types of Vietoris--Rips complexes of ellipses $E_a=\{(x

Rohan Banerjee Ravindran, R. Rajesh

We analytically and numerically study the effect of finite spatial boundaries on the Takayasu model of diffusing and aggregating particles with steady monomer input in one dimension. Exact expressions are derived for the steady-state density profile, two-point correlation functions, and mean-squared density under both open and periodic boundary conditions. T

Ying Peng, Hongsen Ye, Changxin Huang, Xiping Hu

Vision Transformers (ViTs) have achieved strong performance in video action recognition, but their high computational cost limits their practicality. Lightweight CNNs are more efficient but suffer from accuracy gaps. Cross-Architecture Knowledge Distillation (CAKD) addresses this by transferring knowledge from ViTs to CNNs, yet existing methods often struggl

Izabela Skwira-Chalot, Przemysław Sekowski, Agata Taranienko, Adam Spyra

During proton therapy, the beam flux decreases due to inelastic interactions with nuclei. At the highest energies used in proton therapy around 25\% protons initiate nuclear reactions. This report presents the cross section measurements of proton-induced production of three $\beta^+$ emitters -- $^{11}$C, $^{13}$N, $^{15}$O -- with half-lives between 2 and 2

Jun-Xian Li, Shuang Wang

The Hubble tension problem is one of the most significant challenges in modern cosmology. In this paper, we study the Hubble tension problem in the framework of holographic dark energy (HDE). To perform a systematic and comprehensive analysis, we select six representative theoretical models from all four categories of HDE. For the observational data, we adop

Ali Rasteh, Amirreza Kiani, Marco Mezzavilla, Sundeep Rangan

Fully digital massive MIMO systems with large numbers (1000+) of antennas offer dramatically increased capacity gains from spatial multiplexing and beamforming. Designing digital receivers that can scale to these array dimensions presents significant challenges regarding both channel estimation overhead and digital computation. In the massive MIMO setting, l

Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang, Cosmin I. Bercea

The sharp rise in medical tomography examinations has created a demand for automated systems that can reliably extract informative features for downstream tasks such as tumor characterization. Although 3D volumes contain richer information than individual slices, effective 3D classification remains difficult: volumetric data encode complex spatial dependenci

Sofiia Lauten, Matthew Otten

Implementing quantum gates on quantum computers can require the application of carefully shaped pulses for high-fidelity operations. We explore the use of physics-informed neural networks (PINNs) for quantum optimal control to assess their usefulness in predicting such pulses. Our PINN is a feedforward neural network that utilizes an unsupervised learning ap

Kevin Keomanee-Dizon, Yaakov Clenman, Alejandra Duran, Sergey Ryabichko

High-numerical-aperture (NA) oblique plane microscopy enables noninvasive fluorescence imaging of subcellular dynamics without requiring radical sample modification. However, performance degrades at depth in multicellular specimens as scattering and refractive-index heterogeneity raise out-of-focus background. We report a two-photon oblique plane microscope

Michelle Wynne Sze, David Zsolt Manrique, David Muñoz Ramo, Nathan Fitzpatrick

As established in the seminal work by Berry et al.[1], expanding the time evolution operator using truncated Taylor series (up to some order $K$) makes a good candidate for simulating Hamiltonian dynamics. Here, we adapt the method but present an alternative quantum circuit that maintains an equivalent asymptotic elementary gate cost but has an exponentially

Étienne Fouvry, Emmanuel Kowalski, Philippe Michel, Will Sawin

We obtain non-trivial bounds for bilinear sums of trace functions below the P\'olya-Vinogradov range assuming only that the geometric monodromy group of the underlying ell-adic sheaf satisfies certain simple structural properties, in contrast to previous works which handled only special cases of Kloosterman and hypergeometric sheaves. Our approach builds on

Rahul R. Divekar, Sophia Guerra, Lisette Gonzalez, Natasha Boos

While Large Language Models (LLMs) have transformed the user interface for learning, moving from keyword search to natural language dialogue, their impact on educational outcomes remains unclear. We present a controlled study (N=20) that directly compares the learning interaction and outcomes between LLM and search-based interfaces. We found that although LL

Shiva Hanifi, Sasan Jafarnejad, Marc Köntges, Andrej Wentnagel

Reliable anomaly detection in photovoltaic (PV) modules is critical for maintaining solar energy efficiency. However, developing robust computer vision models for PV inspection is constrained by the scarcity of large-scale, diverse, and balanced datasets. This study introduces PV-DDPM, a spatially conditioned denoising diffusion probabilistic model that gene

Koen W. van Arem, Jakob Söhl, Mirjam Bruinsma, Geurt Jongbloed

With an average football (soccer) match recording over 3,000 on-ball events, effective use of this event data is essential for practitioners at football clubs to obtain meaningful insights. Models can extract more information from this data, and explainable methods can make them more accessible to practitioners. The Expected Threat model has been praised for

Mathias S. Fischer, David Grass, Martin C. Fischer

Mechanical translation of samples along several axes is often required in microscopy, and automated positioning requires motorizing the translation stages. Stepper motors are commonly employed but require specialized driver electronics for reliable operation. Here we describe a low-cost, open-source controller design that drives several stepper motors and im

Rintaro Otsubo, Kanta Sawafuji, Hideo Saito

Multi-Object Tracking (MOT) plays a critical role in analyzing player behavior from videos, enabling performance evaluation. Current MOT methods are often evaluated using publicly available datasets. However, most of these focus on everyday scenarios such as pedestrian tracking or are tailored to specific sports, including soccer and basketball. Despite the

Tobias R. Rebholz, Maxwell Uphoff, Christian H. R. Bernges, Florian Scholten

As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In our experiment, we examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytically traceable equilibrium. Participants (N = 129) played a C

Deqiao Gan, Xiaoxia Xu, Xiaohu Ge, Yuanwei Liu

To enable intelligent beam training, a large language model (LLM)-enabled beam training framework is proposed for the pinching antenna system (PASS) in downlink multi-user multiple-input multiple-output (MIMO) communications. A novel LLM-based beam training supervised learning mechanism is developed, allowing context-aware and environment-adaptive probing fo

Shane Chern, Yifeng Huang

We compute the Quot and finitized Coh zeta functions of the inert quadratic orders $\mathbb{F}_q[[T]]+T^{m}\mathbb{F}_{q^{2}}[[T]]$ for every $m\geq 1$ in terms of a $2m$-fold multisum, and then show this multisum equals an $m$-fold Bressoud sum. This proves a recent conjecture of the second author, rounding up the line of exploration in the series of work b

Amanta Sherfenaz, Nazmul Haque, Protiva Sadhukhan Prova, Md Asif Raihan

With rapid urbanization in recent decades, traffic congestion has intensified due to increased movement of people and goods. As planning shifts from demand-based to supply-oriented strategies, Intelligent Transportation Systems (ITS) have become essential for managing traffic within existing infrastructure. A core ITS function is traffic forecasting, enablin

Remi Luschei, Werner Brannath

We consider clinical trials with multiple, overlapping patient populations, that test multiple treatment policies specifically tailored to these populations. Such designs may lead to multiplicity issues, as false statements will affect several populations. For type I error control, often the family-wise error rate (FWER) is controlled, which is the probabili

Lipisha Chaudhary, Trisha Mittal, Subhadra Gopalakrishnan, Ifeoma Nwogu

Audio Descriptions (AD) are essential for making visual content accessible to individuals with visual impairments. Recent works have shown a promising step towards automating AD, but they have been limited to describing high-quality movie content using human-annotated ground truth AD in the process. In this work, we present an end-to-end pipeline, MCAD, that

Lukas Gianinazzi, Tal Ben-Nun, Torsten Hoefler

Spatial dataflow architectures like the Cerebras Wafer-Scale Engine deliver exceptional performance in AI and scientific computing by distributing scratchpad memory across hundreds of thousands of processing elements (PEs). Yet programming these architectures remains difficult: with no shared memory, data movement requires explicit configuration, and asynchr

Hongchao Shu, Lalithkumar Seenivasan, Mingxu Liu, Yunseo Hwang

Arthroscopic procedures can greatly benefit from navigation systems that enhance spatial awareness, depth perception, and field of view. However, existing optical tracking solutions impose strict workspace constraints and disrupt surgical workflow. Vision-based alternatives, though less invasive, often rely solely on the monocular arthroscope camera, making

Sumon Kanti Dey, Manvi S, Zeel Mehta, Meet Shah

Large Language Models (LLMs) have been positioned as having the potential to expand access to health information in the Global South, yet their evaluation remains heavily dependent on benchmarks designed around Western norms. We present insights from a preliminary benchmarking exercise with a chatbot for sexual and reproductive health (SRH) for an underserve

Felix A. Palm, Nader Mostaan, Nathan Goldman, Fabian Grusdt

Coherent control and braiding of anyons remain central challenges in realizing topologically protected quantum operations. We propose a Ramsey interferometry protocol to directly access the geometric phases associated with anyons in fractional Chern insulators. Our approach employs impurities with individually addressable internal states that bind to the any

Leone V. Luzzatto, Mathias Casiulis, Stefano Martiniani, István A. Kovács

Critical phase transitions have proven to be a powerful concept to capture the phenomenology of many systems, including deeply non-equilibrium ones like living systems. The study of these phase transitions has overwhelmingly relied on two-point correlation functions. In this Letter, we show that cluster tomography -- the study of one-dimensional cross-sectio

Hongchao Shu, Roger D. Soberanis-Mukul, Jiru Xu, Hao Ding

Accurate intra-operative localization of the bronchoscope tip relative to patient anatomy remains challenging due to respiratory motion, anatomical variability, and CT-to-body divergence that cause deformation and misalignment between intra-operative views and pre-operative CT. Existing vision-based methods often fail to generalize across domains and patient

Till Dieminger, Saúl Alonso-Monsalve, Christoph Alt, Claudio Bruschini

Neutrino detectors, particle calorimeters and some dark matter detectors require dense and massive active materials. An extremely fine segmentation is desirable to achieve precise three-dimensional particle tracking. However, such systems introduce significant challenges in construction and demand a large number of readout electronics channels, leading to ex

Debanik Bhattacharjee, Guy Z. Ramon, Yaniv Edery

Capillarity-dominated two-phase displacement in porous media often continues beyond the initial invasion-percolation (IP) breakthrough, as surfactants alter interfacial properties and reopen pathways once sealed by capillary forces. This study examines such secondary invasion, where adsorption-driven reductions in interfacial tension and contact-angle shifts

Anirudh Pradhan, Takol Tangphati, Ayan Banerjee, Javlon Rayimbaev

We address the equilibrium configurations and stability properties of anisotropic compact stars whose interior is described by a modified Chaplygin gas (MCG) equation of state in the framework of the regularized four-dimensional Einstein-Gauss-Bonnet (4DEGB) theory. Applying a quasi-local prescription for the pressure anisotropy, we derive the modified Tolma

Sai Puppala, Ismail Hossain, Md Jahangir Alam, Tanzim Ahad

We propose a method that uses large language models to assist graph machine learning under personalization and privacy constraints. The approach combines data augmentation for sparse graphs, prompt and instruction tuning to adapt foundation models to graph tasks, and in-context learning to supply few-shot graph reasoning signals. These signals parameterize a

Akash Shukla, Jonathan Roeltgen, Michael Kotschenreuther, David R. Hatch

Low-recycling regimes are appealing because they entail a high edge temperature and low edge density which are good for core confinement. However, due to considerably enhanced heat flux, the exhaust problems become severe. In addition, in the low-recycling regime, the conventional fluid simulations may not capture the physics of the Scrape-Off Layer (SOL) pl

Zhou Xu, Qi Wang, Yuxiao Yang, Luyuan Zhang

Score Distillation Sampling (SDS) enables 3D asset generation by distilling priors from pretrained 2D text-to-image diffusion models, but vanilla SDS suffers from over-saturation and over-smoothing. To mitigate this issue, recent variants have incorporated negative prompts. However, these methods face a critical trade-off: limited texture optimization, or si

Noor Hazlina Abdul Mutalib, Aznul Qalid Md Sabri, Ainuddin Wahid Abdul Wahab, Erma Rahayu Mohd Faizal Abdullah

Intrusion Detection Systems (IDS) play a vital role in modern cybersecurity frameworks by providing a primary defense mechanism against sophisticated threat actors. In this paper, we propose an explainable intrusion detection framework that integrates Recursive Feature Elimination (RFE) with Random Forest (RF) to enhance detection of Advanced Persistent Thre

Rosario G. Viglione, Giovanni Castorina, Gaetano Calogero, Giuseppe Fisicaro

Neutral silicon-carbon divacancy (V$_{Si}$V$_{C}$) in cubic silicon carbide (3C-SiC) is a promising class of point defects for quantum technologies based on active crystalline centers. Within the theoretical framework of spin-polarized Density Functional Theory (DFT), this study examines the structural and electronic characteristics of V$_{Si}$V$_{C}$ center

Francesco Morri, Hélène Le Cadre, David Salas, Didier Aussel

In dynamic noncooperative games, each player makes conjectures about other players' reactions before choosing a strategy. However, resulting equilibria may be multiple and do not always lead to desirable outcomes. These issues are typically addressed separately, for example, through opponent modelling and incentive design. Drawing inspiration from conjectura

Federico Capannoli, Emilio Cruciani, Hlafo Alfie Mimun, Matteo Quattropani

We study a nonlinear dynamics of binary opinions in a population of agents connected by a directed network, influenced by two competing forces. On the one hand, agents are stubborn, i.e., have a tendency for one of the two opinions; on the other hand, there is a disruptive bias, $p\in[0,1]$, that drives the agents toward the other opinion. The disruptive bia

Sizhe Wang, Yifan Yang, Yongkang Luo, Daheng Li

Dexterous functional tool-use grasping is essential for effective robotic manipulation of tools. However, existing approaches face significant challenges in efficiently constructing large-scale datasets and ensuring generalizability to everyday object scales. These issues primarily arise from size mismatches between robotic and human hands, and the diversity

Brian Rogers, Micah Bowles, Chris J. Lintott, Steve Croft

Accessing information in learned representations is critical for annotation, discovery, and data filtering in disciplines where high-dimensional datasets are common. We introduce What We Don't C, a novel approach based on latent flow matching that disentangles latent subspaces by explicitly removing information included in conditional guidance, resulting in

Nicolas Escobar-Velasquez, Jaroslaw Harezlak

Statistical analysis on non-Euclidean spaces typically relies on distances as the primary tool for constructing likelihoods. However, manifold-valued data admits richer structures in addition to Riemannian distances. We demonstrate that simple, tractable models that do not rely exclusively on distances can be constructed on the manifold of symmetric positive

Hamna Aslam, Frédéric Holweck

Entanglement classification of pure multipartite quantum states is a challenging problem in quantum information theory that can be mathematically stated as orbit classification for some given group action on the ambient Hilbert space. The group action depends on the grained classification one expects, the finer-grained one being the classification up to loca

Abdelmalek Boumali, Nosratollah Jafari, Bekdaulet Shukirgaliyev, Fadila Serdouk

We examine the thermal and statistical properties of the one dimensional Klein-Gordon oscillator within two prominent Doubly Special Relativity (DSR) frameworks: Amelino-Camelia and Magueijo-Smolin. Using the modified dispersion relations specific to each formulation, we derive the positive energy spectra, construct the partition function via the Euler-Macla

Pouya Shiri, Amirali Baniasadi

Capsule Network (CapsNet) classifier has several advantages over CNNs, including better detection of images containing overlapping categories and higher accuracy on transformed images. Despite the advantages, CapsNet is slow due to its different structure. In addition, CapsNet is resource-hungry, includes many parameters and lags in accuracy compared to CNNs

Johannes Hulsman, Elisa Alessi, Leonardo Andreasi, Philipp Azzarello

LunPAN (Lunar Particle Analyzer Network) is a three-year mission proposal designed to comprehensively map the particle spectra in the lunar radiation field. It aims to provide precise measurements of Galactic Cosmic Rays (GCR), Solar Energetic Particles (SEP), and albedo particles, including charged particles, neutrons, and gamma-rays, originating from the M

Hossein Mohanna, Ali Ait-Bachir

Text classification with hierarchical taxonomies is a fundamental requirement in IT Service Management (ITSM) systems, where support tickets must be categorized into tree-structured taxonomies. We present a dual-embedding centroid-based classification framework that maintains separate semantic and lexical centroid representations per category, combining them

Mohamed El Gorrim

Continual learning remains challenging due to catastrophic forgetting, where neural networks lose previously acquired knowledge when learning new tasks. Inspired by memory consolidation in neuroscience, we propose FSC-Net (Fast-Slow Consolidation Networks), a dual-network architecture that separates rapid task learning from gradual knowledge consolidation. O

Francesco Scala, Giacomo Guarnieri, Aurelien Lucchi

Quantum noise is known to strongly affect quantum computation, thus potentially limiting the performance of currently available quantum processing units. Even learning models based on variational quantum algorithms, which were designed to cope with the limitations of state-of-the art noisy hardware capabilities, are affected by noise-induced barren plateaus,

Georgios Pantazis, Nicola Mignoni, Raffaele Carli, Mariagrazia Dotoli

We study virtual energy storage services based on the aggregation of EV batteries in parking lots under time-varying, uncertain EV departures and state-of-charge limits. We propose a convex data-driven scheduling framework in which a parking lot manager provides storage services to a prosumer community while interacting with a retailer. The framework yields