November 2025 arXiv papers — page 91

Sriram Srinivasan, Srinivasan Aruchamy, Siva Ram Krisha Vadali

Seismic sensing has emerged as a promising solution for border surveillance and monitoring; the seismic sensors that are often buried underground are small and cannot be noticed easily, making them difficult for intruders to detect, avoid, or vandalize. This significantly enhances their effectiveness compared to highly visible cameras or fences. However, acc

Sean C. Lewis, Brooke Polak, Mordecai-Mark Mac Low, Stephen L. W. McMillan

Unstructured Voronoi mesh simulations offer many advantages for simulating self-gravitating gas dynamics on galactic scales. Adaptive mesh refinement (AMR) can be a powerful tool for simulating the details of star cluster formation and gas dispersal by stellar feedback. Zooming in from galactic to local scales using the star cluster formation simulation pack

Ali Salehi, Cassandra L. Jacobs

We investigate tokenization strategies for Kurdish word embeddings by comparing word-level, morpheme-based, and BPE approaches on morphological similarity preservation tasks. We develop a BiLSTM-CRF morphological segmenter using bootstrapped training from minimal manual annotation and evaluate Word2Vec embeddings across comprehensive metrics including simila

Minami Taniguchi

In [Z23], minimal tri-plane diagrams of surface-links listed in the Yoshikawa table are computed using ch-diagrams. In this paper, we obtain upper bounds for the L- and L*-invariants of several surface-links in the Yoshikawa table by using their tri-plane diagrams.

Rui Zhu, Xiaopu Zhou, Haixu Tang, Stephen W. Scherer

Trained on massive cross-species DNA corpora, DNA large language models (LLMs) learn the fundamental "grammar" and evolutionary patterns of genomic sequences. This makes them powerful priors for DNA sequence modeling, particularly over long ranges. However, two major constraints hinder their use in practice: the quadratic computational cost of self-attention

Rishu Kumar Singh, Navneet Shreya, Sarmistha Das, Apoorva Singh

Existing approaches to complaint analysis largely rely on unimodal, short-form content such as tweets or product reviews. This work advances the field by leveraging multimodal, multi-turn customer support dialogues, where users often share both textual complaints and visual evidence (e.g., screenshots, product photos) to enable fine-grained classification of

Jooho Kim, Yei Eun Shin

Two-phase sampling designs have been widely adopted in epidemiological studies to reduce costs when measuring certain biomarkers is prohibitively expensive. Under these designs, investigators commonly relate survival outcomes to risk factors using the Cox proportional hazards model. To fully utilize covariates collected in phase 1, multiple imputation (MI) m

Kallol Mondal, Ankush Kumar

Attention is the brain's ability to selectively focus on a few specific aspects while ignoring irrelevant ones. This biological principle inspired the attention mechanism in modern Transformers. Transformers now underpin large language models (LLMs) such as GPT, but at the cost of massive training and inference energy, leading to a large carbon footprint. Wh

P. O. Mchedlov-Petrosyan, L. N. Davydov

To describe the simultaneous order-disorder transformation and phase separation Eguchi, Oki and Matsumura [\doi{10.1557/proc-21-589}] introduced the system of two equations: one equation, governing the evolution of a conserved order parameter, and the second equation for the non-conserved order parameter. The key feature of their model is the free energy fun

Shiyar Jamo

Automatic age estimation is widely used for age verification, where input images often vary considerably in resolution. This study evaluates the effect of image resolution on age estimation accuracy using DeepFace and InsightFace. A total of 1000 images from the IMDB-Clean dataset were processed in seven resolutions, resulting in 7000 test samples. Performan

Clovis Gladstone, Zhao Fang, Spencer Dean Stewart

Historical and low-resource NLP remains challenging due to limited annotated data and domain mismatches with modern, web-sourced corpora. This paper outlines our work in using large language models (LLMs) to create ground-truth annotations for historical French (16th-20th centuries) and Chinese (1900-1950) texts. By leveraging LLM-generated ground truth on a

Huiyan Zou, Allison L. Lewis

Global sensitivity metrics are essential tools for assessing parameter importance in complex models, particularly when precise information about parameter values is unavailable. In many cases, such metrics are used to provide parameter rankings that allow for necessary dimension reduction in moderate-to-high dimensional systems. However, globally-derived sen

B. Dinçel, G. Paylı, S. K. Yerli, A. Ankay

The secondaries of the massive binary systems can be found as runaway stars after being ejected due to the supernova (SN) of the more massive component. We search for such stars inside the supernova remnants (SNRs), where a recent SN is guaranteed to have happened. In this paper, we present the massive runaway star HD~254577 as the pre-supernova binary compa

Kiera McCormick, Rafael Martínez-Galarza

Large Language Models have demonstrated the ability to generalize well at many levels across domains, modalities, and even shown in-context learning capabilities. This enables research questions regarding how they can be used to encode physical information that is usually only available from scientific measurements, and loosely encoded in textual description

Biaojie Zeng, Min Zhang, Juan Zhou, Fengrui Liu

Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these errors has become an important research direction. However, existing approaches \textit{mainly focus on self-correction within the model}, which falls short of the "teacher-style" correction required in educat

Vaskar Chakma, MD Jaheid Hasan Nerab, Abdur Rouf, Abu Sayed

Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a sys

Mrinal Dasgupta, Alexander Fraley, Pier Francesco Monni, Saad Nabeebaccus

We investigate the QCD transverse-energy ($E_T$) flow distribution within an azimuthal region of phase space, defined by an angular interval $\Delta \phi$ on the plane transverse to a chosen jet axis. Vetoes on the resulting $E_T$ are widely employed at the LHC to isolate missing transverse momentum in final states with invisible particles. We show that this

Shijun Liang, Ismail Alkhouri, Qing Qu, Rongrong Wang

Numerous diffusion model (DM)-based methods have been proposed for solving inverse imaging problems. Among these, a recent line of work has demonstrated strong performance by formulating sampling as an optimization procedure that enforces measurement consistency, forward diffusion consistency, and both step-wise and backward diffusion consistency. However, t

D. N. Purschke, D. Vick, A. Cárdenas, N. Haram

High-harmonic generation (HHG) is a strong-field phenomenon that is sensitive to the attosecond dynamics of tunnel ionization and coherent transport of electron-hole pairs in solids. While the foundations of solid HHG have been established, a deep understanding into the nature of decoherence on sub-cycle timescales remains elusive. Furthermore, there is a gr

Divij Sharma, James M. Sullivan, Kazuyuki Akitsu, Mikhail M. Ivanov

Primordial non-Gaussianity (PNG) is a common prediction of a wide class of inflationary models. Equilateral-type PNG, generically predicted by single-field inflationary models with higher-derivative interactions, imprints subtle but measurable signatures on the large-scale distribution of matter. An important parameter of these imprints is the PNG-induced bi

Haoran Li, Zhe Cheng, Muhao Guo, Yang Weng

Probabilistic load forecasting is widely studied and underpins power system planning, operation, and risk-aware decision making. Deep learning forecasters have shown strong ability to capture complex temporal and contextual patterns, achieving substantial accuracy gains. However, at the scale of thousands or even hundreds of thousands of loads in large distr

Yiyang Xu, Ziyou Shen, Yanqing Lv, Shutong Tan

AlphaFold 3 (AF3) is a powerful biomolecular structure-predicting tool based on the latest deep learning algorithms and revolutionized AI model architectures. A few of papers have already investigated its accuracy in predicting different biomolecular structures. However, the potential applications of AF3 beyond basic structure prediction have not been fully

R. J. Charity, J. Okołowicz, M. Płoszajczak, L. G. Sobotka

Recent studies have completed the A=16 isospin quintets for states with spin/parity J{\pi} =0+ and 2+. The dependence of their masses as a function of isospin projection shows evidence for deviations from quadratic behavior indicating isospin violation beyond the expectation from two- body forces. The deviation is most pronounced for the 2+ states. Predictio

Amanda Burcroff, Kyungyong Lee, Lang Mou, Gregg Musiker

We study rank-2 cluster scattering diagrams through moduli spaces of quiver representations and a recently developed combinatorial framework of tight gradings. Combining quiver-theoretic and combinatorial methods, we prove and extend a collection of conjectures posed by Elgin--Reading--Stella concerning the structural and enumerative properties of the wall-f

Ali Akbar Vali, Sadoon Azizi, Mohammad Shojafar, Rajkumar Buyya

The exponential growth of Internet of Things (IoT) devices has intensified the demand for efficient and responsive services. To address this demand, fog and edge computing have emerged as distributed paradigms that bring computational resources closer to end users, reducing latency, bandwidth limitations, and energy consumption. However, these paradigms pres

Kristi Topollai, Tolga Dimlioglu, Anna Choromanska, Simon Odie

Contract management involves reviewing and negotiating provisions, individual clauses that define rights, obligations, and terms of agreement. During this process, revisions to provisions are proposed and iteratively refined, some of which may be problematic or unacceptable. Automating this workflow is challenging due to the scarcity of labeled data and the

Ruomeng Ding, Wei Cheng, Minglai Shao, Chen Zhao

Large language models (LLMs) are increasingly applied to sequential decision-making through in-context learning (ICL), yet their effectiveness is highly sensitive to prompt quality. Effective prompts should meet three principles: focus on decision-critical information, provide step-level granularity, and minimize reliance on expert annotations through label

Xiaoqiong Xia, Cesar de la Fuente-Nunez

Infections depend on interactions between pathogen and host proteins, but comprehensively mapping these interactions is challenging and labor intensive. Many biological networks have hierarchical, scale-free structure, so we developed a deep learning framework, ApexPPI, that represents protein networks in hyperbolic Riemannian space to capture these features

Neeti Gauniyal, Victor Turchin

We produce combinatorial formulas for invariants of smooth embeddings of $(2\ell-1)$-spheres into $\mathbb{R}^{3\ell}$ for $\ell\geq 2$. Furthermore, we obtain such a formula for the Haefliger invariant, which classifies smooth knots $S^{4k-1}\hookrightarrow \mathbb{R}^{6k}$ up to isotopy. Our approach is similar in spirit to the work of Goussarov, Polyak, a

Supranta S. Boruah, Michael Jacob, Bhuvnesh Jain, Riya Maiya

High-resolution mapping of cosmic mass distribution is essential for a variety of astrophysical applications including understanding cosmic structure formation, and galaxy formation and evolution. However dark matter is not directly observed and therefore we need advanced methods for solving inverse problems to reconstruct the underlying cosmic matter distri

Elkanah Nyabuto, Philipp Otto, Yarema Okhrin

We present an estimation procedure of spatial and temporal effects in spatiotemporal autoregressive panel data models using the Least Absolute Shrinkage and Selection Operator, LASSO (Tibshirani, 1996). We assume that the spatiotemporal panel is drawn from a univariate random process and that the data follows a spatiotemporal autoregressive process which inc

Milan Rosko

This paper investigates how global decision problems over arithmetically represented domains acquire reflective structure through class-quantification. Arithmetization forces diagonal fixed points whose verification requires reflection beyond finitary means, producing Feferman-style obstructions independent of computational technique. We use this mechanism t

W. Michael Brown, Anurag Ramesh, Thomas Lubinski, Thien Nguyen

As is intrinsic to the fundamental goal of quantum computing, classical simulation of quantum algorithms is notoriously demanding in resource requirements. Nonetheless, simulation is critical to the success of the field and a requirement for algorithm development and validation, as well as hardware design. GPU-acceleration has become standard practice for si

Xiaoqiong Xia, Cesar de la Fuente-Nunez

Peptide-based drugs can bind to protein interaction sites that small molecules often cannot, and are easier to produce than large protein drugs. However, designing effective peptide binders is difficult. A typical peptide has an enormous number of possible sequences, and only a few of these will fold into the right 3D shape to match a given protein target. E

Xia Cui, Ziyi Huang, Naeemeh Adel

Annotation bias in NLP datasets remains a major challenge for developing multilingual Large Language Models (LLMs), particularly in culturally diverse settings. Bias from task framing, annotator subjectivity, and cultural mismatches can distort model outputs and exacerbate social harms. We propose a comprehensive framework for understanding annotation bias,

Diana Romero, Xin Gao, Daniel Khalkhali, Salma Elmalaki

This paper explores how large language models can leverage multi-level contextual information to predict group coordination patterns in collaborative mixed reality environments. We demonstrate that encoding individual behavioral profiles, group structural properties, and temporal dynamics as natural language enables LLMs to break through the performance ceil

Alexandre-Xavier Labonté-Lamoureux, Simon Boyer

The goal of this paper is to explore the benefits of automatic pipeline provisioning and identify how it can be applied. Automatic pipeline provisioning can be defined as a process of quickly deploying a pipeline for a software engineering project. This research will focus on CI pipelines, although the outcomes of this approach on CD pipelines will likely be

Mauro Di Nasso, Lorenzo Luperi Baglini

We present a proof of the sufficiency of Rado's condition for the partition regularity of linear Diophantine equations that avoids any use of van der Waerden's theorem. The proof is based on fundamental properties that are common knowledge in combinatorics of numbers and is entirely elementary, with the sole exception of a standard application of the

Chia-Yu Hung, Navonil Majumder, Haoyuan Deng, Liu Renhang

Vision--language--action (VLA) models have recently shown promising performance on a variety of embodied tasks, yet they still fall short in reliability and generalization, especially when deployed across different embodiments or real-world environments. In this work, we introduce NORA-1.5, a VLA model built from the pre-trained NORA backbone by adding to it

A. Crespo Gómez, Y. Tamura, L. Colina, J. Álvarez-Márquez

We present an analysis on the kinematics and physical properties of the ionized gas in the lensed galaxy RXCJ2248-ID at z=6.1 based on high-resolution JWST NIRSpec/IFU data in combination with ALMA observations. Our analysis reveals a high electron temperature ($T_e$$\sim$30000K) in the ionized gas, independent of the ionization level. We measure a wide rang

Ilario Bonacina, Maria Luisa Bonet, Sam Buss, Massimo Lauria

The concept of redundancy in SAT leads to more expressive and powerful proof search techniques, e.g., able to express various inprocessing techniques, and originates interesting hierarchies of proof systems [Heule et$.$al'20, Buss-Thapen'19]. Redundancy has also been integrated in MaxSAT [Ihalainen et$.$al'22, Berg et$.$al'23, Bonacina et$.$a

Ke Zhang, Haiyan Su

In this paper, we derive optimal L2- and H1-norm error estimates for a fully discrete convex-splitting decoupled finite element method (FEM) for the two-phase diffuse interface magnetohydrodynamics (MHD) system. We use the semi-implicit backward Euler scheme in time and employ the standard inf-sup stable Taylor--Hood or Mini elements to discretize the veloci

Weidao Chen, Yuxiao Yang, Yueming Wang

Major Depressive Disorder (MDD), affecting millions worldwide, exhibits complex pathophysiology manifested through disrupted brain network dynamics. Although graph neural networks that leverage neuroimaging data have shown promise in depression diagnosis, existing approaches are predominantly data-driven and operate largely as black-box models, lacking neuro

Ekaterina Spirande, Timofei Miryashkin, Andrei Kolmakov, Alexander Shapeev

Accurate free-energy calculations are essential for predicting thermodynamic properties and phase stability, but existing methods are limited: phonon-based approaches neglect anharmonicity and liquids, while molecular dynamics (MD) is computationally demanding, neglects low-temperature quantum effects, and often requires manual planning and post-processing o

Marius Dubosc, Yann Fischer, Zacharie Auray, Nicolas Boutry

Doppler holography is an emerging retinal imaging technique that captures the dynamic behavior of blood flow with high temporal resolution, enabling quantitative assessment of retinal hemodynamics. This requires accurate segmentation of retinal arteries and veins, but traditional segmentation methods focus solely on spatial information and overlook the tempo

Boris Alexeev, John Jasper, Dustin G. Mixon

An approximate Hadamard matrix is a well-conditioned square matrix with all entries in $\{\pm1\}$. We measure the quality of a matrix by its condition number, i.e., the ratio of its largest and smallest singular values. We prove that for any fixed positive $α<17/92$, every sufficiently large dimension admits an approximate Hadamard matrix with condition numb

Brendan Gho, Suman Muppavarapu, Afnan Shaik, Tyson Tsay

As foundation models are increasingly deployed as interacting agents in multi-agent systems, their collective behavior raises new challenges for trustworthiness, transparency, and accountability. Traditional coordination mechanisms, such as centralized oversight or adversarial adjudication, struggle to scale and often obscure how decisions emerge. We introdu

Mahmood Mazare, Hossein Ramezani

This paper proposes a novel Kernelized Data-Driven Predictive Control (KDPC) scheme for robust, offset-free tracking of nonlinear systems. Our computationally efficient hybrid approach separates the prediction: (1) kernel ridge regression learns the nonlinear map from past trajectories, and (2) analytical linearization of the kernel map approximates the effe

Jan Naumann

Recently developed applications in the field of machine learning and computational physics rely on automatic differentiation techniques, that require stable and efficient linear algebra gradient computations. This technical note provides a comprehensive and detailed discussion of the derivative of the truncated singular and eigenvalue decomposition. It summa

Jingyi Jia, Qinbin Li

Large Language Model (LLM) agents have emerged as powerful tools for automating complex tasks by leveraging the reasoning and decision-making abilities of LLMs. However, a major bottleneck in current agent frameworks lies in the high inference cost of tool selection, especially in approaches like ReAct that repeatedly invoke the LLM to determine which tool t

John M. Oyer, Ali Namvar, Benjamin A. Hoff, Wassim W. Labaki

Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an n

Lane Boswell, Ying Cao

Quantum information theory is a rapidly growing area of math and physics that combines two independent theories, quantum mechanics and information theory. Quantum entanglement is a concept that was first proposed in the EPR paradox. In quantum mechanics, particles can be in superposition, meaning they are in multiple different states at once. It is not until

Chenjing Bu, Young-Hoon Kiem

We introduce the notion of a generalized intersection pairing for an Artin stack with a proper good moduli space and nonempty stable part. For the moduli stack of semistable bundles over a smooth projective curve, there are four known constructions by partial desingularization, parabolic bundles, stable pairs and wall crossing. In this paper, we compare all

Derek Frydel

We study a class of stochastic resetting (SR) processes in which a diffusing particle alternates between free motion and confinement by an externally controlled potential. When the particle is recaptured, it undergoes a return trajectory that drives it toward a designated reset point. In standard SR, such returns are treated as instantaneous, but in realisti

Lucie Leboulleux, Niyati Desai, Daniel Echeverri, Evangelia Kleisioti

Recognizing and addressing under-representation, exclusion, and problematic behavior within astronomy and astrophysics is crucial. In 2019, a survey was conducted at the Spirit of Lyot conference to evaluate the socio-demographics and well-being of the exoplanet and disk imaging community. This paper presents the results of a second survey, conducted at the

Marco Baioletti, Fabrizio Fagiolo, Angelo Oddi, Riccardo Rasconi

This paper introduces the DIRSH algorithm for the Qubit Routing Problem (QRP), using a heuristic-guided randomized divide-and-conquer strategy. The method splits the circuit into chunks and optimizes each one with a stochastic selection of gates and swaps. It balances global search, via restarts and adaptive tuning of bandit parameters with depth-sensitive l

Alina Kononov, Minh Nguyen, Andrew D. Baczewski

Electronic response properties of high-energy density (HED) systems influence planetary structure, drive evolution of fusion targets, and underpin diagnostics in laboratory astrophysics. Real-time time-dependent density functional theory (TDDFT) offers a versatile modeling framework capable of accurately predicting the dynamic response of HED materials -- in

Hemant Kumar Sharma

Nonequilibrium charge transport through a trimer molecular transistor composed of three quantum dots arranged in a triangular geometry, which is placed on a substrate, has been studied in the presence of electron electron and electron phonon interactions. The entire system is described by an extended Anderson Holstein Caldeira Leggett Hamiltonian, in which t

Avi Bagchi, Dwight Hutchenson

Radio spectrum monitoring in contested environments motivates the need for reliable automatic signal classification technology. Prior work highlights deep learning as a promising approach, but existing models depend on brute-force Doppler augmentation to achieve real-world generalization, which undermines both training efficiency and interpretability. In thi

Tao Yang, Dandan Huang, Yunting Lin, Pengfei Wu

Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domain knowledge, and hallucinations. We assemble a large, domain

François Clément, Stefan Steinerberger

Consider an infinite sequence $(x_k)_{k=1}^{\infty}$ on the unit circle $\mathbb{S}^1$. We may interpret the first $n$ elements $(x_k)_{k=1}^{n}$ as places where the `circular stick' $\mathbb{S}^1$ is broken into a total of $n+1$ pieces. It is clear that they cannot all be the same length all the time. de Bruijn and Erd\H{o}s (1949) show that the ratio of th

Nyah Speicher, Prashant Chandrasekar

Evidence supports that reducing cognitive load (CL) improves task performance for people of all abilities. This effect is specifically important for blind-and-low-vision (BLV) individuals because they cannot rely on many common methods of managing CL, which are frequently vision-based techniques. Current accessible "solutions" for BLV developers only sporadi

A. W. Griffiths, J. Southworth, L. Alegre, F. Amadio

Hot Jupiters are Jupiter-mass planets with orbital periods of less than ten days. Their short orbital separations make tidal dissipation within the stellar host especially efficient, potentially leading to a measurable evolution of the orbit. One possible manifestation of this is orbital decay, which presents itself observationally through variations in the

Meiying Gu, Jiawei Zhang, Jiahe Li, Xiaohan Yu

Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, such optimization is prone to overfitting, leading to suboptimal reconstruction quality. Existing approaches address this challenge by employing flattened Gaussian primitives to bett

Yuchen Luo, Xinyu Li, Liuhua Peng, Mingming Gong

In multivariate time series forecasting (MTSF), accurately modeling the intricate dependencies among multiple variables remains a significant challenge due to the inherent limitations of traditional approaches. Most existing models adopt either \textbf{channel-independent} (CI) or \textbf{channel-dependent} (CD) strategies, each presenting distinct drawbacks

Kahaan Gandhi, Boris Bolliet, Inigo Zubeldia

We show that multi-agent systems guided by vision-language models (VLMs) improve end-to-end autonomous scientific discovery. By treating plots as verifiable checkpoints, a VLM-as-a-judge evaluates figures against dynamically generated domain-specific rubrics, enabling agents to correct their own errors and steer exploratory data analysis in real-time. Case s

Ivy Yuqian Yang, David Yu Zhang

Scientific idea generation and selection requires exploration following a target probability distribution. In contrast, current AI benchmarks have objectively correct answers, and training large language models (LLMs) via reinforcement learning against these benchmarks discourages probabilistic exploration. Here, we conducted systematic experiments requestin

Anadi Shakya, Primal Pappachan, David Maier, Roberto Yus

The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in database management systems (DBMSs). Existing approaches to enforcing FGAC policies do not scale to thousands of policies, leading to degraded query performance and reduced system e

Benjamin Asch

We give noise-robust, Probably Approximately Correct (PAC) guarantees of global $\varepsilon$-optimality for the Variational Quantum Eigensolver under explicit geometric conditions. For periodic ansatzes with bounded generators -- yielding a globally Lipschitz cost landscape on a toroidal parameter space -- we assume that the low-energy region containing the

Mingbo Li, Junhao Cai, Yawen Gao

Nanoprecipitation, the rapid solvent-displacement route to nanoscale phase separation, has matured from a simple batch operation into a versatile platform for nanomaterial synthesis. This review synthesizes recent progress in stimulus-assisted nanoprecipitation, wherein externally applied triggers (ultrasonic, electrical, supergravity, thermal, chemical, and

Shuyuan Fan, Guanru Pan, Herbert Werner

What limits how fast a Lyapunov function can decay under input bounds? We address this question by showing how the shape of Lyapunov comparison functions governs guaranteed decay for control affine systems. Using a windowed nominal exponential rate together with the endpoint cap induced by actuator limits, we establish a strict ordering: concave comparison f

Qingwei Ben, Botian Xu, Kailin Li, Feiyu Jia

Robust humanoid locomotion requires accurate and globally consistent perception of the surrounding 3D environment. However, existing perception modules, mainly based on depth images or elevation maps, offer only partial and locally flattened views of the environment, failing to capture the full 3D structure. This paper presents Gallant, a voxel-grid-based fr

Jing Li, Xindi Hu, Helin Gong, Wei Gong

We propose Alternating Phase-Field Fourier Neural Networks (APF-FNNs) as a unified and physics-based framework for topology optimization. The approach decouples the design problem by representing the state, adjoint, and topology fields with three separate Fourier neural networks, which are trained via a stable collaborative alternating scheme applicable to b

Michael Greenacre, Martin Graeve

In certain fields where compositional data are studied, the compositional components, called parts, can be combined into certain subsets, called amalgamations, that are based on domain knowledge. Furthermore, these subsets can form a natural hierarchy of amalgamations subdividing into sub-amalgamations. The authors, a statistician and a biochemist, demonstra

Xuanjing Chu, Jinho Park, Jesse Balgley, Sean Clemons

We develop an analytic framework to extract circuit parameters and loss tangent from superconducting transmission-line resonators terminated by reactive loads, extending analysis beyond the perturbative regime. The formulation yields closed-form relations between resonant frequency, participation ratio, and internal quality factor, removing the need for full

Md Fokhrul Islam, Sajeda Al-Hammouri, Christopher J. Arellano, Kavan Hazeli

Falls are a leading cause of injury and loss of independence among older adults. Vision-based fall prediction systems offer a non-invasive solution to anticipate falls seconds before impact, but their development is hindered by the scarcity of available fall data. Contributing to these efforts, this study proposes the Biomechanical Spatio-Temporal Graph Conv

Marco Locatelli, Arjen Hommersom, Roberto Clemens Cerioli, Daniela Besozzi

Learning the parameters of Partially Observable Markov Decision Processes (POMDPs) from limited data is a significant challenge. We introduce the Fuzzy MAP EM algorithm, a novel approach that incorporates expert knowledge into the parameter estimation process by enriching the Expectation Maximization (EM) framework with fuzzy pseudo-counts derived from an ex

Severin Kohler, Jordi Piera Jiménez, Michael Anywar, Lars Fuhrmann

Healthcare interoperability between openEHR and HL7 FHIR remains challenging due to fundamental differences in their data modeling approaches and the absence of standardized transformation mechanisms. This paper presents FHIRconnect, a novel domain-specific language and open-source transformation engine that enables standardized, bidirectional data exchange

Ruoyu Qin, Weiran He, Weixiao Huang, Yangkun Zhang

Reinforcement Learning (RL) has emerged as a critical technique for advancing modern Large Language Models (LLMs), yet existing synchronous RL systems face severe performance bottlenecks. The rollout phase, which dominates end-to-end iteration time, suffers from substantial long-tail latency and poor resource utilization due to inherent workload imbalance. W

Lei Chen, Weiyan Chen

Justin Lanier and the authors recently determined the group normally generated by a single bounding pair map of genus $n$. We related this subgroup with the Chillingworth subgroup and the Casson--Morita's $d$ map. In this paper, we extend the results to the case when $n=0$. Let $\mathcal{M}_g^1$ be the mapping class group, $\text{Ch}_g^1$ be the Chillingwort

Nam-Gyu Kim

Recent advances in expressive text-to-speech (TTS) have introduced diverse methods based on style embedding extracted from reference speech. However, synthesizing high-quality expressive speech remains challenging. We propose SpotlightTTS, which exclusively emphasizes style via voiced-aware style extraction and style direction adjustment. Voiced-aware style

Yunfeng Zhang

Let $M$ be a product of rank-one symmetric spaces of compact type, each of dimension at least $3$. We establish sharp $L^p$ bounds for the restriction of Laplace--Beltrami eigenfunctions on $M$ to arbitrary submanifolds contained in a maximal flat, for all $p \ge 2$. The proof combines precise asymptotics of Jacobi polynomials and positivity of Fourier coeff

Mohammad Vali Sanian, Arshia Hemmat, Amirhossein Vahidi, Jonas Maaskola

A scalable and robust 3D tissue transcriptomics profile can enable a holistic understanding of tissue organization and provide deeper insights into human biology and disease. Most predictive algorithms that infer ST directly from histology treat each section independently and ignore 3D structure, while existing 3D-aware approaches are not generative and do n

Richard M. Höfer, A. Mecherbet, R. Schubert

We consider a microscopic model of spherical particles with inertia in a Stokes flow. As the particle number grows to infinity and their size goes to zero we derive the monokinetic Vlasov-Stokes equations as mean-field limit. We do this under the assumption that the particles have initial velocities given by a Lipschitz velocity profile and prove the mean-fi

Charles Cheng Ji, Brandon Kong

Mobile Web3 faces catastrophic retention (< 5%) yielding effective acquisition costs of \$500 - \$1,000 per retained user. Existing solutions force an impossible tradeoff: embedded wallets achieve moderate usability but suffer inherent click-jacking vulnerabilities; app wallets maintain security at the cost of 2 - 3% retention due to download friction and co

Vladimir Saleev, Kirill Shilyaev

In the article, the study of unpolarized $J/\psi$ production in proton-proton collisions is presented. The Soft Gluon Resummation approach as a TMD framework was used for description of small-$p_T^{}$ production cross section. The Improved Color Evaporation model was considered as an approach to describe hadronization of produced quarks into charmonium state

Niklas Knobel

We study the three-dimensional incompressible magnetohydrodynamic (MHD) equations near Couette flow with a constant magnetic field perpendicular to the shear plane. Couette flow induces mixing and generates magnetic induction, while the constant magnetic field stabilizes $z$-dependent modes. In contrast, the $z$-averaged magnetic field exhibits algebraic gro

Dave Dice, Alex Kogan

We present Hapax Locks, a novel locking algorithm that is simple, enjoys constant-time arrival and unlock paths, provides FIFO admission order, and which is also space efficient and generates relatively little coherence traffic under contention in the common case. Hapax Locks offer performance (both latency and scalability) that is comparable with the best s

Agustin Olivares, Paul Leger, Rodrigo Poblete

In the Chilean local alcohol industry (pisco indus- try), for one liter of alcohol produced 10-15 liters of vinasse as the main wastewater of the process. To comply with industrial waste regulations, vinasse must be stored, which enables evaporation, leaving behind a residual sludge. However, treating vinasse remains an environmental and industrial challenge

Shreya Adrita Banik, Niaz Nafi Rahman, Tahsina Moiukh, Farig Sadeque

Detecting political bias in news media is a complex task that requires interpreting subtle linguistic and contextual cues. Although recent advances in Natural Language Processing (NLP) have enabled automatic bias classification, the extent to which large language models (LLMs) align with human judgment still remains relatively underexplored and not yet well

M. Mirzakhani, R. Mahapatra, M. Platt

We report on the scintillation characterization of a 1 cm^3 CsI(Tl) crystal coupled to a 6 x 6 mm^2 SiPM read out with a custom transimpedance amplifier at cryogenic temperatures. The crystal was prepared with optical-grade surfaces and enclosed in a multi layer shielding system to suppress ambient light and background radiation. The detector response was st

Yilin Zhang, Leo D. Westbury, Elaine M. Dennison, Nicholas C. Harvey

Poor bone health is a significant public health concern, and low bone mineral density (BMD) leads to an increased fracture risk, a key feature of osteoporosis. We present XAttn-BMD (Cross-Attention BMD), a multimodal deep learning framework that predicts femoral neck BMD from hip X-ray images and structured clinical metadata. It utilizes a novel bidirectiona

Yilu Fang, Jordan G. Nestor, Casey N. Ta, Jerard Z. Kneifati-Hayek

Patients with acute kidney injury (AKI) are at high risk of developing chronic kidney disease (CKD), but identifying those at greatest risk remains challenging. We used electronic health record (EHR) data to dynamically track AKI patients' clinical evolution and characterize AKI-to-CKD progression. Post-AKI clinical states were identified by clustering patie

E. J. Ferrer, J. M. Perez-Fernandez

In this paper, we will demonstrate that a dense quark-matter system in the dual chiral density wave (DCDW) phase behaves as a ferromagnet in the sense that its magnetic-field dependent magnetization remains different from zero even at $B\rightarrow 0$. The corresponding permanent magnetization is a function of the baryonic chemical potential $\mu$, decreasin

Ludovic Petitdemange, Salomé Nashed

Blind and Visually Impaired (BVI) Individuals face significant challenges in science due to the discipline's reliance on visual elements such as graphs, diagrams, and laboratory work. Traditional learning materials, such as Braille and large-print textbooks, are often scarce or delayed, while practical experiments are rarely adapted for accessibility. Additi

Nathaniel Putera, Daniel Vilet Rodríguez, Noah Videcrantz, Julia Machnio

Accurate modeling of cognitive decline in Alzheimer's disease is essential for early stratification and personalized management. While tabular predictors provide robust markers of global risk, their ability to capture subtle brain changes remains limited. In this study, we evaluate the predictive contributions of tabular and imaging-based representations, wi

Dengyun Huang, Yonghua Zhu

While Large Language Models (LLMs) make symbolic music generation increasingly accessible, producing music with distinctive composition and rich expressiveness remains a significant challenge. Many studies have introduced emotion models to guide the generative process. However, these approaches still fall short of delivering novelty and creativity. In the fi

Chih-Chun Wang

Density functional theory (DFT) is an indispensable ab initio method in both quantum chemistry and condensed matter physics. Based on recent advancements in reduced density matrix functional theory (RDMFT), a variant of DFT that is believed to be better suited for strongly correlated systems, we construct a mathematical framework generalizing all ground stat

Dongqing Xie, Yonghuang Wu, Zisheng Ai, Jun Min

The accurate segmentation of brain tumors from multi-modal MRI is critical for clinical diagnosis and treatment planning. While integrating complementary information from various MRI sequences is a common practice, the frequent absence of one or more modalities in real-world clinical settings poses a significant challenge, severely compromising the performan

Noam Dahan, Omer Kidron, Gabriel Stanovsky

High quality summarization data remains scarce in under-represented languages. However, historical newspapers, made available through recent digitization efforts, offer an abundant source of untapped, naturally annotated data. In this work, we present a novel method for collecting naturally occurring summaries via Front-Page Teasers, where editors summarize