November 2025 arXiv papers — page 117

Liangshun Wu, Wen Chen, Qingqing Wu

In flexible-grid elastic optical networks (EONs), the ordering of frequency channels plays a crucial role in managing inter-channel interference and ensuring signal quality. We address the Channel Ordering Problem (COP) by reformulating it as a Bottleneck Traveling Salesman Problem (BTSP), where interference among channels is represented as edge weights in a

Pal Dave, Xiaohong Yuan, Madhuri Siddula, Kaushik Roy

Cyberbullying is a well-known social issue, and it is escalating day by day. Due to the vigorous development of the internet, social media provide many different ways for the user to express their opinions and exchange information. Cyberbullying occurs on social media using text messages, comments, sharing images and GIFs or stickers, and audio and video. Mu

Zhen Tao, Yuehang Cao, Yang Fang, Yunhui Liu

Dynamic recommendation, focusing on modeling user preference from historical interactions and providing recommendations on current time, plays a key role in many personalized services. Recent works show that pre-trained dynamic graph neural networks (GNNs) can achieve excellent performance. However, existing methods by fine-tuning node representations at lar

Christopher J. McDevitt, Jonathan S. Arnaud

An adjoint formulation of energetic particle confinement in axisymmetric tokamak geometry is derived and evaluated using a physics-informed neural network (PINN). The PINN estimates the mean escape time of energetic ions by solving an inhomogeneous adjoint of the drift kinetic equation with a Lorentz collision operator, yielding predictions of fast ion loss

Mani Amani, Behrad Beheshti, Reza Akhavian

Prompting robots with natural language (NL) has largely been studied as what task to execute (goal selection, skill sequencing) rather than how to execute that task safely and efficiently in semantically rich, human-centric spaces. We address this gap with a framework that turns a large language model (LLM) into a stochastic semantic sensor whose outputs mod

Steven Cao, Gregory Valiant, Percy Liang

We study the problem of entropy calibration, which asks whether a language model's entropy over generations matches its log loss on human text. Past work found that models are miscalibrated, with entropy per step increasing as generations grow longer, due to error accumulation. To calibrate the model and improve text quality, it has become standard practice

Howard Hao-Tse Huang, Wenbin Lu

Hills breakup of binary systems allows massive black holes (MBH) to produce hyper-velocity stars (HVSs) and tightly bound stars. The long timescale of orbital relaxation means that binaries must spend numerous orbits around the MBH before they are tidally broken apart. Repeated MBH tidal perturbations over multiple pericenter passages can perturb the binary

Nikola Janjušević, Amirhossein Khalilian-Gourtani, Yao Wang, Li Feng

Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability

Carlos Mosquera, Neven Elsayed, Ernst Kruijff, Joseph Newman

This paper presents a comprehensive study of virtual 3D object manipulation along 4DoF on real surfaces in mixed reality (MR), using hand-based and tangible interactions. A custom cylindrical tangible proxy leverages affordances of physical knobs and tabletop support for stable input. We evaluate both modalities across isolated tasks (2DoF translation, 1DoF

Rayan Aldajani

Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as low-cost alternatives using frontal body images and basic anthropometric data. The dataset consists of 535 sam

Shuning Zhang, Jiaqi Bai, Linzhi Wang, Shixuan Li

While communication strategies of Large Language Models (LLMs) are crucial for human-LLM interactions, they can also be weaponized to elicit private information, yet such stealthy attacks remain under-explored. This paper introduces the first adaptive attack framework for stealthy and targeted private information elicitation via communication strategies. Our

Surajit Das, Peu Majumder, Aleksei Eliseev

The rapid emergence of Generative AI, particularly ChatGPT, has sparked a global debate on the future of education, often characterized by alarmism and speculation. Moving beyond this, this study investigates the structured, grounded perspectives of a key stakeholder group: university educators. It proposes a novel theoretical model that conceptualizes the e

Leonardi Melo, Luís Gustavo, Dimmy Magalhães, Lucciani Vieira

This study presents a comparative analysis of three U-Net-based architectures for semantic segmentation of rock art petroglyphs from Brazilian archaeological sites. The investigated architectures were: (1) BEGL-UNet with Border-Enhanced Gaussian Loss function; (2) Attention-Residual BEGL-UNet, incorporating residual blocks and gated attention mechanisms; and

Derek Chen, Zoe Samuels, Lizzie Peiros, Sujaan Mukherjee

Screw-based propulsion systems offer promising capabilities for amphibious mobility, yet face significant challenges in optimizing locomotion across water, granular materials, and transitional environments. This study presents a systematic investigation into the locomotion performance of various screw configurations in media such as dry sand, wet sand, satur

Rory Barnes, Russell Deitrick, Jacob Haqq-Misra, Shintaro Kadoya

The Functionality of Ice Line Latitudinal EBM Tenacity (FILLET) project is a CUISINES exoplanet model intercomparison project (exo-MIP) that compares various energy balance models (EBMs) through a series of numerical experiments. The objective is to establish rigorous protocols that enable the identification of intrinsic differences among EBMs that could lea

Andreas Habring

Consider the Langevin diffusion process $\mathrm{d} X_t = \nabla \log p_t(X_t) + \sqrt{2}\mathrm{d} W_t$ guided by the time-dependent probability density $p_t(x)$. Let $q_t$ be the density of $X_t$. Recently, in order to analyze convergence in the Kullback-Leibler divergence, the time derivative of $t\mapsto \mathrm{KL}(q_t|p_t)$ has been used in several wor

Takayuki Suzuki, Anna B. Stephenson, Jinsik Yoon, Junghyun Bae

The growing use of triaxial particles in microfluidic, microrobotic, and biological systems makes it essential to understand how their rotational dynamics couples with lateral migration in microscale flows. Our experiments in inertial Poiseuille flow reveal that geometric asymmetry in triaxial, multifaceted disks governs their orientation, migration, and rot

Borchuluun Yadamsuren, Steven Keith Platt, Miguel Diaz

This study introduces a hybrid neuro-symbolic framework that achieves deterministic detection of statutory inconsistency in complex law. We use the U.S. Internal Revenue Code (IRC) as a case study because its complexity makes it a fertile domain for identifying conflicts. Our research offers a solution for detecting inconsistent provisions by combining Large

Yemko Pryor, Virum Ranka, Joao Pedro Donadio, Samantha C. Muller

Forensic DNA databases in the United States have expanded substantially over the past two decades. However, comprehensive, harmonized data describing database structure and composition remain limited. This dataset series documents forensic DNA infrastructure across national and state levels from 2001 to 2025. It includes a reconstructed time series of monthl

I. A. Sarmiento-Alvarado, Leonel Bixano, Tonatiuh Matos

In the present work, we develop and examine a series of exact solutions to Einstein's 5-dimensional field equations in the vacuum, which depend on two constant parameters, $p$ and $q$, which generalize the solutions of Lü and Mei [8] belonging to our class $p=2$. This category of solutions can be split into two sections: when $p$ is odd, it represents a

Mattheus Burkhard, Malte Kroj, Kévin Falque, Alberto Bramati

The Hawking effect amplifies fluctuations in the vicinity of horizons, both in black holes and in analogue platforms. Here, we consider a polariton simulator and numerically examine the \emph{stimulated} Hawking effect using a coherent probe incident on the horizon from the exterior. We implement an experimentally realistic effective spacetime that supports

Angqi Liu, Filippo Moro, Sebastian Billaudelle, Melika Payvand

Oscillatory dynamics have recently proven highly effective in machine learning (ML), particularly through State-Space-Models (SSM) that leverage structured linear recurrences for long-range temporal processing. Resonate-and-Fire neurons capture such oscillatory behavior in a spiking framework, offering strong expressivity with sparse event-based communicatio

Bingze Dai, Xi Zhang, Wei-Ning Lee

Volumetric ultrasound imaging faces a fundamental trade-off among image quality, frame rate, and hardware complexity. This study introduces three-dimensional Null Subtraction Imaging (3D NSI), a nonlinear beamforming framework that addresses this trade-off by combining computationally efficient null-subtraction process with multiplexing-aware sparse aperture

Musawenkosi K. Ncube, Pallab Barai, Selva Chandrasekaran Selvaraj, Larry A. Curtiss

Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermodynamically unstable against layered oxide cathodes. Here, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular d

A. Rothstein, K. Erickson, R. Conlin, A. Bortolon

A real-time safe multi-threading library was developed on the DIII-D plasma control system to optimize the realtime TORBEAM and real-time STRIDE physics codes. These physics codes are crucial for future fusion power plant operation as they provide information about electron cyclotron wave propagation and heating as well as inform about ideal plasma stability

Simone Ricci, Niccolò Biondi, Federico Pernici, Alberto Del Bimbo

Minimizing inconsistencies across successive versions of an AI system is as crucial as reducing the overall error. In image classification, such inconsistencies manifest as negative flips, where an updated model misclassifies test samples that were previously classified correctly. This issue becomes increasingly pronounced as the number of training classes g

Biyi Fang, Truong Vo, Jean Utke, Diego Klabjan

Convolutional Neural Networks (CNNs) have achieved remarkable success across a wide range of machine learning tasks by leveraging hierarchical feature learning through deep architectures. However, the large number of layers and millions of parameters often make CNNs computationally expensive to train, requiring extensive time and manual tuning to discover op

Yen-Wen Lu, Michael Mulligan

Strange metals are highly entangled gapless states of matter that exhibit anomalous transport, such as linear in temperature resistivity, over more than a decade of temperature. Why a single power law should be so robust is an open question. We propose a scenario in which interactions enhance the domain of certain scattering regimes, effectively suppressing

Barun Kumar Pal

We have revisited quasi-exponential model of inflation in the light of recent ACT-DR6 and Planck data along with latest constraint on the amplitude of primordial gravitational waves. For our analysis we have followed Mukhanov approach for inflationary equation-of-state employing Hamilton-Jacobi formulation. We find that the model is capable of mimicking late

F. Museur, J. Robert, F. Morineau, N. Bujault

The consecutive magnetic ordering of the Ho and Ru ions in the pyrochlore Ho2Ru2O7 and their interplay are investigated by neutron scattering, magnetic and specific heat measurements. The Ru moments order at 95 K into a $Γ_5$ easy-plane antiferromagnetic state. At 1.55 K the Ho moments order into an unusual $Γ_9$ ferromagnetic state with extensive ground sta

Nghia Thinh Nguyen, Tri Nhu Do

In this paper, we propose a new Temporal MDS-Vision Transformer (T-MDS-ViT) for multiclass target classification using millimeter-wave FMCW radar micro-Doppler spectrograms. Specifically, we design a transformer-based architecture that processes stacked range-velocity-angle (RVA) spatiotemporal tensors via patch embeddings and cross-axis attention mechanisms

I. Levin, S. L. Keller

Thin elastic sheets bend easily, leading to mechanical instabilities such as wrinkling. Here, we investigate wrinkles at edges of bi-strips, which consist of two thin sheets, one that swells and one that does not, joined side-by-side. It is well known that when bending rigidity is uniform across an isolated bi-strip, swelling results in axisymmetric shapes l

Zachary Couvillon, Anwesh Ray

We establish asymptotic lower bounds for the number of elliptic curves over $\mathbb{Q}$ with prescribed entanglement of division fields, ordered by naive height. Such elliptic curves are obtained as $1$-parameter families arising from certain genus $0$ modular curves. We apply techniques from the geometry of numbers and sieve methods to prove that the numbe

Tri Nhu Do

In this paper, we propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs), built on an open architecture that leverages open interfaces and artificial intelligence (AI)-based functionalities. This approach advances interoperability, flexibility, and intelligence in next-generation telecommunicat

Hadi Sheikhi, Chenyang Huang, Osmar R. Zaïane

Knowledge graph-based dialogue generation (KG-DG) is a challenging task requiring models to effectively incorporate external knowledge into conversational responses. While large language models (LLMs) have achieved impressive results across various NLP tasks, their ability to utilize external knowledge in KG-DG remains under-explored. We observe that LLMs of

Mohammed Temraz, Mark T Keane

In recent years, humanity has begun to experience the catastrophic effects of climate change as economic sectors (such as agriculture) struggle with unpredictable and extreme weather events. Artificial Intelligence (AI) should help us handle these climate challenges but its most promising solutions are not good at dealing with climate-disrupted data; specifi

Ling Wang, Yunfan Lu, Wenzong Ma, Huizai Yao

Clear imaging under hazy conditions is a critical task. Prior-based and neural methods have improved results. However, they operate on RGB frames, which suffer from limited dynamic range. Therefore, dehazing remains ill-posed and can erase structure and illumination details. To address this, we use event cameras for dehazing for the \textbf{first time}. Even

Euclid Collaboration, R. Navarro-Carrera, K. I. Caputi, C. J. R. McPartland

Probing the presence and properties of massive galaxies at high redshift is one of the most critical tests for galaxy formation models. In this work, we search for galaxies with stellar masses M* > 10^10.25 Msun at z in [5,7], i.e., towards the end of the Epoch of Reionisation, over a total of ~23 deg^2 in two of the Euclid Quick Data Release (Q1) fields: th

Delmar G. A. Cabral, Brandon Allen, Fabijan Pavošević, Sharon Hammes-Schiffer

We introduce a multicomponent unitary coupled cluster framework for quantum simulations of molecular systems that incorporate both electronic and nuclear quantum effects beyond the Born-Oppenheimer approximation. Using the nuclear-electronic orbital formalism, we construct mcUCC ans\"atze for positronium hydride and molecular hydrogen with a quantum proton,

Christopher Sandino, Sayeri Lala, Geeling Chau, Melika Ayoughi

Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotations for clinical applications like sleep staging and seizure detection. While current EEG SSL methods predominantly use masked reconstruction strategies like masked autoencoders (MAE

Evan Redden

Recent work by Faizal et al. (2025) claims that G\"odelian undecidability of non-algorithmic truths in our universe imply the impossibility of a formal, algorithmic simulation of the universe. This paper clarifies the distinction between epistemic incompleteness: limits on what can be proven within a formal system, and ontological incompleteness: limits on w

Manya Bansal, Daniel Sainati, Joseph W. Cutler, Saman Amarasinghe

To achieve peak performance on modern GPUs, one must balance two frames of mind: issuing instructions to individual threads to control their behavior, while simultaneously tracking the convergence of many threads acting in concert to perform collective operations like Tensor Core instructions. The tension between these two mindsets makes modular programming

Sebastian A. R. Ellis, Daniel C. Hackett, Shirley Weishi Li, Pedro A. N. Machado

Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield diff

Omar Abdelrazik, Mohamed Elsayed, Noorul Wahab, Nasir Rajpoot

Ultrasound-based risk stratification of thyroid nodules is a critical clinical task, but it suffers from high inter-observer variability. While many deep learning (DL) models function as "black boxes," we propose a fully automated, two-stage framework for interpretable malignancy prediction. Our method achieves interpretability by forcing the model to focus

Stefano Profumo, Aria Koul, Anika Malladi, Ben Schmitt

We investigate how the large-scale heliosphere alters the arrival directions of high-energy cosmic-ray electrons and positrons and ask if and when this "heliospheric lens" can be ignored for anisotropy and source-association studies - an especially timely topic given, for instance, the persistent cosmic-ray positron fraction and its unknown origin. Using a m

Munib Mesinovic, Tingting Zhu

Deep-learning survival models for electronic health record (EHR) data are hard to compare across papers because the upstream preprocessing step, which includes cohort definition, time discretisation, missingness handling, and censoring rules, is typically undocumented and inconsistent. A reported difference in concordance between two mortality models can the

Karthikeyan K, Raghuveer Thirukovalluru, Bhuwan Dhingra, David Edwin Carlson

Large language model agents for data analysis typically generate and execute code directly on databases. However, when applied to sensitive data, this approach poses significant security risks. To address this issue, we propose a security-motivated alternative: restrict LLMs from direct code generation and data access, and require them to interact with data

J. C. de Albuquerque, J. Carvalho, Edcarlos D. Silva

We establish the existence and qualitative properties of nontrivial solutions for a class of Hartree-Fock type systems defined over the whole space $\mathbb{R}^2$. By introducing a suitable Nehari-Pohozaev manifold, we prove the existence, regularity and we describe the asymptotic behavior of solutions with respect to the interaction parameter $\beta > 0$. I

Saida Liu, Nikolay Atanasov, Shumon Koga

This paper proposes MATT-Diff: Multimodal Active Target Tracking by Diffusion Policy, a control policy for active multi-target tracking using a mobile agent. The policy enables multiple behavior modes for the agent, including exploration, tracking, and target reacquisition, without prior knowledge of the target numbers, states, or dynamics. Effective target

Tianyu Xu, Jihan Li, Penghe Zu, Pranav Sahay

In Extended Reality (XR), rendering sound that accurately simulates real-world acoustics is pivotal in creating lifelike and believable virtual experiences. However, existing XR spatial audio rendering methods often struggle with real-time adaptation to diverse physical scenes, causing a sensory mismatch between visual and auditory cues that disrupts user im

Ghani Imadouchene

We construct and analyze a symmetric bimetric cosmological model connecting Anti-de Sitter (AdS) and de Sitter (dS) regimes through a coupled scalar field. Starting from a Lagrangian with Einstein-Hilbert terms for two FLRW metrics and an inter-metric potential, we derive modified Friedmann and Klein-Gordon equations governing their evolution. In the symmetr

Ziyao Cui, Edric Tam

Graph neural networks (GNNs) are fundamental tools in graph machine learning. The performance of GNNs relies crucially on the availability of informative node features, which can be limited or absent in real-life datasets and applications. A natural remedy is to augment the node features with embeddings computed from eigenvectors of the graph Laplacian matri

Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo

Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations. It is known that applying PCA to a rank-one signal corrupted by a dense, homogeneous noise, in the large matrix size limit, the celebrated BBP transition occurs, where the emergence of an outlying eigenvalue and the alignment of the corresponding e

Mark L. Lewis, Ryan McCulloch

Let $G$ be a $p$-group. We begin to consider the relationship between the structure of the commuting graph and $|G:Z(G)|$. We also build a family of groups whose commuting graphs have more than one connected component whose diameter is at least $2$. For this, we introduce another graph related to the commuting graph that is associated with centralizers.

Yongkang Huo, Fulvio Forni, Rodolphe Sepulchre

This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winne

Karthikeyan K, Raghuveer Thirukovalluru, David Carlson

Large Language Models have advanced clinical text classification, but their opaque predictions remain a critical barrier to practical adoption in research and clinical settings where investigators and physicians need to understand which parts of a patient's record drive risk signals. To address this challenge, we introduce \textbf{CALM}, short for \textbf{Cl

Vladimir Berman

We study a deliberately simple, fully non-linguistic model of text: a sequence of independent draws from a finite alphabet of letters plus a single space symbol. A word is defined as a maximal block of non-space symbols. Within this symbol-level framework, which assumes no morphology, syntax, or semantics, we derive several structural results. First, word le

Liudong Xing, Janet, Lin

This chapter presents perspectives for challenges and future development in building reliable AI systems, particularly, agentic AI systems. Several open research problems related to mitigating the risks of cascading failures are discussed. The chapter also sheds lights on research challenges and opportunities in aspects including dynamic environments, incons

Eamon Earl, Chen Ding, Richard Valenzano, Drai Paulen-Patterson

In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs) have shown to be useful in this process, minimizing political disengagement and information overload by providing individuals with articles on topics that matter to them. Unfortu

Gabrielle B. Graham, Daniel A. Dale, Chase L. Smith, Elisabeth Brann

The joint capabilities of the Hubble Space Telescope (HST) and JWST allow for an unparalleled look at the early lives of star clusters at near- and mid-infrared wavelengths. We present here a multiband analysis of embedded young stellar clusters in 11 nearby, star-forming galaxies, using the PHANGS-JWST and PHANGS-HST datasets. We use the Zooniverse citizen

Petro Sarkanych

Two dimensional Potts model is a classical example where the symmetry of the order parameter controls the order of a phase transition: on a square lattice with nearest-neighbours interaction, when the number of states $q$ is less than or equal to 4, the second-order phase transition is observed, while for $q>4$ the first-order phase transition occurs. Recent

Michael J. Bommarito

Sequence models for binary analysis are bottlenecked by byte-level tokenization: raw bytes waste precious context window capacity for transformers and other neural network architectures, and many existing text-oriented tokenizers fail on arbitrary 0x00--0xFF sequences. To address this issue, we introduce the Binary BPE tokenizer family, a set of cross-platfo

Wieger Wesselink, Bram Grooten, Huub van de Wetering, Qiao Xiao

Multilayer perceptrons (MLPs) remain fundamental to modern deep learning, yet their algorithmic details are rarely presented in complete, explicit \emph{batch matrix-form}. Rather, most references express gradients per sample or rely on automatic differentiation. Although automatic differentiation can achieve equally high computational efficiency, the usage

Thorben Schey, Khaled Karoonlatifi, Michael Weyrich, Andrey Morozov

This paper builds on our Uncertainty-Guided Live Measurement Sequencing (UGLMS) method. UGLMS is a closed-loop test strategy that adaptively selects SAR ADC code edges based on model uncertainty and refines a behavioral mismatch model in real time via an Extended Kalman Filter (EKF), eliminating full-range sweeps and offline post-processing. We introduce an

Sinan Urgun, Seçkin Arı

This study aims to systematically evaluate the performance of large language models (LLMs) in abstract visual reasoning problems. We examined four LLM models (GPT-4.1-Mini, Claude-3.5-Haiku, Gemini-1.5-Flash, Llama-3.3-70b) utilizing four different reasoning architectures (single-shot, embedding-controlled repetition, self-reflection, and multi-agent) on the

Adam F. Kowalski

I report on the high resolution spectra of the remarkable X9 solar flare of 2024 Oct 03 (SOL2024-10-03T12:08) and evaluate the extent to which nonthermal electron beams that generate dense chromospheric condensations can power very bright kernels in solar flares. 1D Radiative-hydrodynamic models predict extreme H$\alpha$ near-wing broadening, bright continuu

Shizhou Xu, Yuan Ni, Stefan Broecker, Thomas Strohmer

As AI models are trained on ever-expanding datasets, the ability to remove the influence of specific data from trained models has become essential for privacy protection and regulatory compliance. Unlearning addresses this challenge by selectively removing parametric knowledge from the trained models without retraining from scratch, which is critical for res

Saeed Mohammadzadeh, Yao-Chang Tsan, Aaron Renberg, Hiba Kobeissi

Timelapse images of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) provide rich information on cell structure and contractile function. However, it is challenging to reproducibly generate tissue samples and conduct scalable experiments with these cells. The two-dimensional cardiac muscle bundle (2DMB) platform helps address these limi

Haoyan Xu, Ruizhi Qian, Jiate Li, Yushun Dong

Graph machine learning has advanced rapidly in tasks such as link prediction, anomaly detection, and node classification. As models scale up, pretrained graph models have become valuable intellectual assets because they encode extensive computation and domain expertise. Building on these advances, Graph Foundation Models (GFMs) mark a major step forward by j

R. Potapov

We study the large $N$ limit of the spectral duality between the classical $\mathfrak{gl}_M$ XXX spin chain and the $\mathfrak{gl}_N$ trigonometric Gaudin model in its rational reduced form. The infinite-dimensional limit of the Gaudin model is constructed within the framework of the noncommutative torus algebra, following the approach of Hoppe, Olshanetsky

Siyou Li, Huanan Wu, Juexi Shao, Yinghao Ma

Despite the recent advances in the video understanding ability of multimodal large language models (MLLMs), long video understanding remains a challenge. One of the main issues is that the number of vision tokens grows linearly with video length, which causes an explosion in attention cost, memory, and latency. To solve this challenge, we present Query-aware

Jiacheng Wu

A theoretical model of systemic-risk propagation of financial market is analyzed for stability. The state equation is an unsteady diffusion equation with a nonlinear logistic growth term, where the diffusion process captures the spread of default stress between interconnected financial entities and the reaction term captures the local procyclicality of finan

Afifa Khaled, Ebrahim Hamid Sumiea

Medical imaging and multi-modal clinical settings often face the challange of missing modality in their diagnostic pipelines. Existing imputation methods either lack representational capacity or are computationally expensive. We propose PI-NAIM, a novel dual-path architecture that dynamically routes samples to optimized imputation approaches based on missing

Huawei Zhang, Chunwei Xia, Zheng Wang

Language models (LMs) underpin emerging mobile and embedded AI applications like meeting and video summarization and document analysis, which often require processing multiple long-context inputs. Running an LM locally on-device improves privacy, enables offline use, and reduces cost, but long-context inference quickly hits a \emph{memory capacity wall} as t

Maksym Parymuda, Taras Krokhmalskii, Oleg Derzhko

We consider the $S=1/2$ Heisenberg antiferromagnet on the Tasaki square lattice (flat-band spin system) and study its low-temperature thermodynamics around the saturation magnetic field. To this end, we construct a mapping of the ground states in the subspaces with total $S^z=N/2,\ldots,N/3$ ($N$ is the number of lattice sites) on the hard squares on an auxi

David G. Costa, Hossein Tehrani

After a brief review of the definition of the Trudinger-Moser functions in dimension $N=2$ and some basic notions in the theory of ``Reproducing Kernel Hilbert Spaces (RKHS)'', we will show that there is a close connection between those two topics. More precisely, among other things, we start by considering a properly chosen multiple of the classical Truding

Joey Velez-Ginorio, Nada Amin, Konrad Kording, Steve Zdancewic

We don't program neural networks directly. Instead, we rely on an indirect style where learning algorithms, like gradient descent, determine a neural network's function by learning from data. This indirect style is often a virtue; it empowers us to solve problems that were previously impossible. But it lacks discrete structure. We can't compile most algorith

Kostiantyn Drach, Kateryna Tatarko

For a given $\lambda >0$, a convex body in $\mathbb R^n$ is $\lambda$-convex if it is the intersection of (finitely or infinitely many) balls of radius $1/\lambda$. In this note, we show that among all $\lambda$-convex bodies in $\mathbb R^n$, $n \geqslant 2$, with a given inradius, the $\lambda$-convex lens (i.e., the intersection of two balls of radius $1/

Kushlam Srivastava

Parabolic cut pairs in the boundaries of relatively hyperbolic group are a new and previously unexplored phenomenon. In this paper, we give a way to create examples of relatively hyperbolic groups with parabolic cut pairs on their boundary via a combination theorem, which states that a group $G$, splitting as a graph of relatively hyperbolic groups with cert

Xi Li, Nicholas Matsumoto, Ujjwal Pasupulety, Atharva Deo

Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remain a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling an

Arnav Singhvi, Vasiliki Bikia, Asad Aali, Akshay Chaudhari

Vision-language foundation models (VLMs) show promise for diverse imaging tasks but often underperform on medical benchmarks. Prior efforts to improve performance include model finetuning, which requires large domain-specific datasets and significant compute, or manual prompt engineering, which is hard to generalize and often inaccessible to medical institut

Shuo Liu, Wei Xiao, Calin A. Belta

In safety-critical control systems, ensuring both safety and feasibility under sampled-data implementations is crucial for practical deployment. Existing Control Barrier Function (CBF) frameworks, such as High-Order CBFs (HOCBFs), effectively guarantee safety in continuous time but may become unsafe when executed under zero-order-hold (ZOH) controllers due t

Thorben Schey, Khaled Karoonlatifi, Michael Weyrich, Andrey Morozov

This paper introduces a novel closed-loop testing methodology for efficient linearity testing of high-resolution Successive Approximation Register (SAR) Analog-to-Digital Converters (ADCs). Existing test strategies, including histogram-based approaches, sine wave testing, and model-driven reconstruction, often rely on dense data acquisition followed by offli

Lingxiao Li, Haobo Zhang, Bin Chen, Jiayu Zhou

Text-conditioned molecular generation aims to translate natural-language descriptions into chemical structures, enabling scientists to specify functional groups, scaffolds, and physicochemical constraints without handcrafted rules. Diffusion-based models, particularly latent diffusion models (LDMs), have recently shown promise by performing stochastic search

Yongqiang Liu, Laurentiu Maxim, Botong Wang

We revisit the equivariant spectral sequence considered by Papadima-Suciu, and show that all its differentials are computed by higher order Massey products. As a first application, we extend to arbitrary field coefficients results of Pajitnov relating the size of Jordan blocks for the eigenvalue 1 part of the Alexander modules to the length of nonvanishing M

Helmut Abels, Alice Marveggio, Andrea Poiatti

We propose a thermodynamically consistent phase-field model for the flow of a mixture of two different viscous incompressible fluids of equal density in a bounded domain. We prove the well-posedness of local-in-time strong solutions by means of maximal regularity and contraction mapping arguments. We introduce a suitable entropic weak formulation of the prob

Nawid Keshtmand, Roussel Desmond Nzoyem, Jeffrey Nicholas Clark

Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual explanations are widely used to provide actionable "what-if" recourse, but they typically remain instance-specific and do not quantify which features systematically drive outcome change

Camila Machado de Araujo, Egon P. B. S. Borges, Ricardo Marcelo Canteiro Grangeiro, Allan Pinto

High-resolution volumetric imaging techniques, such as X-ray tomography and advanced microscopy, generate increasingly large datasets that challenge existing tools for efficient processing, segmentation, and interactive exploration. This work introduces new capabilities to Annotat3D through Harpia, a new CUDA-based processing library designed to support scal

Chiyoung Kim, Ryan Jacobs, Jack H. Duffy, Kyle S. Brinkman

Proton ceramic fuel cells (PCFCs) achieve high efficiency at reduced operating temperatures, but their performance is often limited by slow oxygen reduction reaction (ORR) kinetics at the cathode. The BaCoFeZrY (BCFZY) perovskite family is a promising triple-conducting air-electrode material, yet the role of Y dopants in governing oxygen transport remains un

CMS Collaboration

A search for signatures of a dark analog to quantum chromodynamics is performed. The analysis targets long-lived dark mesons that decay into standard-model particles, with a high branching fraction of the dark mesons decaying into muons. The dark mesons are formed by the hadronisation of dark partons, which are produced by a decay of the Higgs boson. The sea

Francois Foucart

Modeling disk formation and mass ejection in binary neutron star systems is an important component in the construction of models for the electromagnetic signals powered by these events. Most models rely on analytical formulae for the disk mass and dynamical ejecta that are fitted to the results of numerical simulations, yet these fits have large uncertaintie

Kausar Patherya, Ashutosh Dhekne, Francisco Romero

Smart cities and pervasive IoT deployments have generated interest in IoT data analysis across transportation and urban planning. At the same time, Large Language Models offer a new interface for exploring IoT data - particularly through natural language. Users today face two key challenges when working with IoT data using LLMs: (1) data collection infrastru

Eric Hua Qing Zhang, Julia Ive

Mental health disorders impose a substantial global socioeconomic burden. While large language models (LLMs) offer 24/7, non-judgmental interactions to address this gap, pretrained models lack contextual coherence and emotional alignment for appropriate therapeutic dialogue. Existing methods suffer from three critical methodological gaps: 1) Supervised Fine-

Karthikeyan K, Raghuveer Thirukovalluru, David Carlson

Clinical notes contain valuable, context-rich information, but their unstructured format introduces several challenges, including unintended biases (e.g., gender or racial bias), and poor generalization across clinical settings (e.g., models trained on one EHR system may perform poorly on another due to format differences) and poor interpretability. To addre

Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon

Accurate population estimates are essential for wildlife management, providing critical insights into species abundance and distribution. Traditional survey methods, including visual aerial counts and GNSS telemetry tracking, are widely used to monitor muskox populations in Arctic regions. These approaches are resource intensive and constrained by logistical

Zhengxin Zhang, Chengyu Huang, Aochong Oliver Li, Claire Cardie

Large Language Models (LLMs) have achieved remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR), yet still rely heavily on external supervision (e.g., curated labels). Adversarial learning, particularly through self-play, offers a promising alternative that enables models to iteratively learn from themselves - thus reducing relia

David Montero, Miguel D. Mahecha, Francesco Martinuzzi, César Aybar

Monitoring the spatiotemporal dynamics of forest CO$_2$ uptake (Gross Primary Production, GPP), remains a central challenge in terrestrial ecosystem research. While Eddy Covariance (EC) towers provide high-frequency estimates, their limited spatial coverage constrains large-scale assessments. Remote sensing offers a scalable alternative, yet most approaches

Yaghmorassene Hebib

Table-top proposals to test gravity-mediated entanglement aim to distinguish coherent gravitational interactions from classical dephasing processes that generate identical phases on both interferometers. A particularly important contribution is platform-invariant timing noise, which can be accessed through optical-clock cross-spectra and frequency-transfer l

Fernanda Bufon Färber, Iago Alves Brito, Julia Soares Dollis, Pedro Schindler Freire Brasil Ribeiro

While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages. This creates a critical barrier for other languages, as simple translation fails to capture unique clinical and cultural nuances, such as endemic diseases. To address this, we introduce MedPT, the first large-scale, re

Ploy Thajchayapong, Suzanne Carbonaro, Tim Couper, Blaine Helmick

As artificial intelligence (AI) becomes more deeply integrated into educational ecosystems, the demand for scalable solutions that enable personalized learning continues to grow. These architectures must support continuous data flows that power personalized learning and access to meaningful insights to advance learner success at scale. At the National AI Ins