October 2025 arXiv papers — page 38

Runyu Ma, Zenghui Fan, Hongxin Liu, Tianxing Ma

Competing orders represent a central challenge in understanding strongly correlated systems. In this work, we employ projector quantum Monte Carlo simulations to study a sign-problem-free bilayer extended Hubbard model. In this model, a charge stripe phase, characterized by a peak at momentum $k_x=2πδ$ is induced by highly anisotropic interlayer spin-exchang

Kosuke Nogaki, Youichi Yanase

Recent angle-resolved photoemission spectroscopy (ARPES) and density functional theory plus Hubbard $U$ (DFT+$U$) studies revealed that a heavy-fermion superconductor CeRh$_2$As$_2$ exhibits van Hove singularities and the Dirac point near the Fermi level $E_{\mathrm F}$, which are key signatures of strong-correlation effects and quantum geometry. We have con

Kazuki Ikeuchi, Yoshiki Matsuda, Shu Tanaka

Ising machines, including quantum annealing machines, are promising next-generation computers for combinatorial optimization problems. However, due to hardware limitations, most Ising-type hardware can only solve objective functions expressed in linear or quadratic terms of binary variables. Therefore, problems with higher-order terms require an order-reduct

SiYing Wang, ZhiXin Xia, Yue Yan, Xiang-Bin Wang

The surface code represents a promising candidate for fault-tolerant quantum computation due to its high error threshold and experimental accessibility with nearest-neighbor interactions. However, current exact surface code threshold analyses are based on the assumption of independent and identically distributed (i.i.d.) errors. Though there are numerical st

Nitin Shukla, Alessandro Romeo, Caterina Caravita, Michael Redenti

Developing and redesigning astrophysical, cosmological, and space plasma numerical codes for existing and next-generation accelerators is critical for enabling large-scale simulations. To address these challenges, the SPACE Center of Excellence (SPACE-CoE) fosters collaboration between scientists, code developers, and high-performance computing experts to op

F. Cavaliere, D. Ferraro, M. Carrega, G. Benenti

We demonstrate the possibility of a genuine quantum advantage in the efficiency of quantum batteries by analyzing a model that enables a consistent comparison between quantum and classical regimes. Our system consists of $N$ harmonic oscillator cells coupled to a common thermal reservoir, evolving through Gaussian states. We define the global efficiency as t

Ameir Shaa, Claude Guet, Xiasu Yang, Armand Albergel

Rapid and accurate urban wind field prediction is essential for modeling particle transport in emergency scenarios. Traditional Computational Fluid Dynamics (CFD) approaches are too slow for real-time applications, necessitating surrogate models. We develop a hybrid neural interpolation method for constructing surrogate models that can update urban wind maps

Hao Cao, Jian-Qiang Hu, Jiaqiao Hu

We present a kernel-based stochastic approximation (KBSA) framework for solving contextual stochastic optimization problems with differentiable objective functions. The framework only relies on system output estimates and can be applied to address a large class of contextual measures, including conditional expectations, conditional quantiles, CoVaR, and cond

Takuya Kawahara, Sean McGrath, Jessica G Young

In the presence of competing events, many investigators are interested in a direct treatment effect on the event of interest that does not capture treatment effects on competing events. Classical survival analysis methods that treat competing events like censoring events, at best, target a controlled direct effect: the effect of the treatment under a difficu

Geoffery Agorku, Sarah Hernandez, Hayley Hames, Cade Wagner

Accurate, real-time estimation of barge quantity on inland waterways remains a critical challenge due to the non-self-propelled nature of barges and the limitations of existing monitoring systems. This study introduces a novel method to use Automatic Identification System (AIS) vessel tracking data to predict the number of barges in tow using Machine Learnin

Guojing Cong, Tom Potok, Hamed Poursiami, Maryam Parsa

We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For prediction accuracy \hdgc outperforms major and popular graph neural network implementations as well as state-of-the-art hyperdimensional computing implementations for a collection of homo

Amit Kam, Kfir Sulimany, Shai Tsesses, Uzi Pereg

Encoding in a high-dimensional Hilbert space improves noise resilience in quantum information processing. This approach, however, may result in cross-mode coupling and detection complexities, thereby reducing quantum cryptography performance. This fundamental trade-off between correctness and secrecy motivates the search for quantum error-correction approach

Francesco Perciavalle, Nicola Lo Gullo, Francesco Plastina

We develop a framework based on the Kirkwood-Dirac quasiprobability distribution to quantify the contribution of coherence to work extraction during generic, cyclic quantum evolutions. In particular, we focus on ``anomalous processes'', counterintuitive scenarios in which, due to the negativity of the quasiprobability distribution, work can be extrac

Wen-Tong Li, Yu-Biao Wu, Lin Zhuang, Jian-Tao Wang

Altermagnets host intrinsically chirality-splitting spin waves, which offer an ideal platform for chirality-based computing with low energy consumption and fast dynamics. However, achieving precise and efficient control over spin-wave chirality remains a challenge. Here, we propose a mechanism to switch the chirality of spin waves in altermagnets via electri

Min-Huan Chu, Manuel Colaço, Shohini Bhattacharya, Krzysztof Cichy

We extend the formalism of asymmetric frames of reference for generalized parton distributions (GPDs) to the case of nonzero skewness, i.e., including longitudinal momentum transfer. The framework, based on Lorentz-invariant amplitudes and previously developed and numerically implemented for unpolarized, helicity and transversity GPDs at zero skewness, gives

Xinyu Cao, Bimal Adhikari, Shangqing Zhao, Jingxian Wu

Radio frequency (RF) fingerprinting, which extracts unique hardware imperfections of radio devices, has emerged as a promising physical-layer device identification mechanism in zero trust architectures and beyond 5G networks. In particular, deep learning (DL) methods have demonstrated state-of-the-art performance in this domain. However, existing approaches

Bálint Gyevnár, Christopher G. Lucas, Stefano V. Albrecht, Shay B. Cohen

Autonomous multi-agent systems (MAS) are useful for automating complex tasks but raise trust concerns due to risks such as miscoordination or goal misalignment. Explainability is vital for users' trust calibration, but explainable MAS face challenges due to complex environments, the human factor, and non-standardised evaluation. Leveraging the counterfac

Jens Niklas Eberhardt, Arnaud Eteve

We prove a monoidal equivalence, called universal Koszul duality, between genuine equivariant K-motives on a Kac-Moody flag variety and constructible monodromic sheaves on its Langlands dual. The equivalence is obtained by a Soergel-theoretic description of both sides which extends results for finite-dimensional flag varieties by Taylor and the first author.

Tananun Songdechakraiwut

We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinica

Pramod Padmanabhan, Vivek Kumar Singh, Vladimir Korepin

The different forms of the tetrahedron equation appear when all possible ways to label the scattering process of infinitely long straight lines are considered in three dimensional spacetime. This is expected to lead to three dimensional integrability, analogous to the Yang-Baxter equation. Among the three possibilities, we consider two of them and their vari

Diana Aldana, João Paulo Lima, Daniel Csillag, Daniel Perazzo

Encoding input coordinates with sinusoidal functions into multilayer perceptrons (MLPs) has proven effective for implicit neural representations (INRs) of low-dimensional signals, enabling the modeling of high-frequency details. However, selecting appropriate input frequencies and architectures while managing parameter redundancy remains an open challenge, o

Soham Satyadharma, Fatemeh Sheikholeslami, Swati Kaul, Aziz Umit Batur

We introduce a novel, training free cascade for auto-prompting Large Language Models (LLMs) to assess product quality in e-commerce. Our system requires no training labels or model fine-tuning, instead automatically generating and refining prompts for evaluating attribute quality across tens of thousands of product category-attribute pairs. Starting from a s

Anastasia-Maria Leventi-Peetz, Jörg-Volker Peetz, Kai Weber, Nikolaos Zacharis

In this work, a three-dimensional multicomponent reaction-diffusion model has been developed, combining excitable-system dynamics with diffusion processes and sharing conceptual features with the FitzHugh-Nagumo model. Designed to capture the spatiotemporal behavior of biological systems, particularly electrophysiological processes, the model was solved nume

M. D. Amaral, J. G. Araújo

In this work, we investigate quantitative regularity estimates for degenerate parabolic partial differential equations, with a focus on Orlicz-type diffusive structures. Using a geometric tangential analysis tailored to these structures and a general notion of intrinsic scalings, we derive precise interior H\"older regularity estimates for bounded weak solut

Marcin Spoczynski, Marcela S. Melara

We present a security framework that strengthens distributed machine learning by standardizing integrity protections across CPU and GPU platforms and significantly reducing verification overheads. Our approach co-locates integrity verification directly with large ML model execution on GPU accelerators, resolving the fundamental mismatch between how large ML

Yuancheng Luo

Surround sound systems commonly distribute loudspeakers along standardized layouts for multichannel audio reproduction. However in less controlled environments, practical layouts vary in loudspeaker quantity, placement, and listening locations / areas. Deviations from standard layouts introduce sound-field errors that degrade acoustic timbre, imaging, and cl

Junho Choi, Teng-Yuan Chang, Namjung Kim, Youngjoon Hong

Ensemble simulations of high-dimensional flow models (e.g., Navier Stokes type PDEs) are computationally prohibitive for real time applications. Neural operators enable fast inference but are limited by costly data requirements and poor generalization to 3D flows. We present a data-free operator network for the Navier Stokes equations that eliminates the nee

Suman Raj, Radhika Mittal, Rajiv Mayani, Pawel Zuk

Drone fleets equipped with onboard cameras, computer vision, and Deep Neural Network (DNN) models present a powerful paradigm for real-time spatio-temporal decision-making. In wildfire response, such drones play a pivotal role in monitoring fire dynamics, supporting firefighter coordination, and facilitating safe evacuation. In this paper, we introduce AeroR

Enze Shi, Pankaj Bhagwat, Zhixian Yang, Linglong Kong

Machine learning models have achieved widespread success but often inherit and amplify historical biases, resulting in unfair outcomes. Traditional fairness methods typically impose constraints at the prediction level, without addressing underlying biases in data representations. In this work, we propose a principled framework that adjusts data representatio

Alec Sathiyamoorthy, Wenhao Zhou, Xiangmin Zhou, Xiaodong Li

The 2019-2020 Black Summer bushfires in Australia devastated 19 million hectares, destroyed 3,000 homes, and lasted seven months, demonstrating the escalating scale and urgency of wildfire threats requiring better forecasting for effective response. Traditional fire modeling relies on manual interpretation by Fire Behaviour Analysts (FBAns) and static enviro

Cheongho Han, Chung-Uk Lee, Ian A. Bond, Andrzej Udalski

In this study, we analyze microlensing events from the 2023 and 2024 observing seasons to identify cases likely caused by binary systems composed of BDs. By applying criteria that the binary-lens events exhibit well-resolved caustics, short time scales ($t_{\rm E} \lesssim 9$ days), and have small angular Einstein radii ($\theta_{\rm E} \lesssim 0.17$~mas),

Jayce R. Getz, Armando Gutiérrez Terradillos, Farid Hosseinijafari, Bryan Hu

Conjectures of Braverman and Kazhdan, Ng\^o and Sakellaridis have motivated the development of Schwartz spaces for certain spherical varieties. We prove that under suitable assumptions these Schwartz spaces are naturally a representation of a group that we christen the modulation group. This provides a broad generalization of the defining representation of t

Miguel Fernandez-de-Retana, Unai Zulaika, Rubén Sánchez-Corcuera, Aitor Almeida

Federated Learning (FL) allows for the training of Machine Learning models in a collaborative manner without the need to share sensitive data. However, it remains vulnerable to Gradient Leakage Attacks (GLAs), which can reveal private information from the shared model updates. In this work, we investigate the effectiveness of Differential Privacy (DP) mechan

Xirui Jin, Renbiao Jin, Boying Li, Danping Zou

Three-dimensional Gaussian Splatting (3DGS) has recently emerged as an efficient representation for novel-view synthesis, achieving impressive visual quality. However, in scenes dominated by large and low-texture regions, common in indoor environments, the photometric loss used to optimize 3DGS yields ambiguous geometry and fails to recover high-fidelity 3D

Emily Kim, Julieta Martinez, Timur Bagautdinov, Jessica Hodgins

We introduce TurboPortrait3D: a method for low-latency novel-view synthesis of human portraits. Our approach builds on the observation that existing image-to-3D models for portrait generation, while capable of producing renderable 3D representations, are prone to visual artifacts, often lack of detail, and tend to fail at fully preserving the identity of the

Raman Jha, Yang Zhou, Giuseppe Loianno

In this paper, we propose an adaptive keyframe selection method for improved 3D scene reconstruction in dynamic environments. The proposed method integrates two complementary modules: an error-based selection module utilizing photometric and structural similarity (SSIM) errors, and a momentum-based update module that dynamically adjusts keyframe selection th

Vladyslav Larin, Ihor Naumenko, Aleksei Ivashov, Ivan Nikitin

As centralized AI hits compute ceilings and diminishing returns from ever-larger training runs, meeting demand requires an inference layer that scales horizontally in both capacity and capability. We present Fortytwo, a novel protocol that leverages swarm intelligence principles and distributed pairwise ranking consensus to achieve superior performance in AI

Daniel Spokoyny, Nikolai Vogler, Xin Gao, Tianyi Zheng

Pig butchering, and similar interactive online scams, lower their victims' defenses by building trust over extended periods of conversation - sometimes weeks or months. They have become increasingly public losses (at least $75B by one recent study). However, because of their long-term conversational nature, they are extremely challenging to investigate at sc

Ningfeng Yang, Tor M. Aamodt

We study the problem of training neural networks with quantized parameters. Learning low-precision quantized parameters by enabling computation of gradients via the Straight-Through Estimator (STE) can be challenging. While the STE enables back-propagation, which is a first-order method, recent works have explored the use of zeroth-order (ZO) gradient descen

Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes

Accurate digital rock modeling of carbonate rocks is limited by the difficulty in acquiring morphological information on small-scale pore structures. Defined as microporosity phases in computed tomography (micro-CT) images, these small-scale pore structures may provide crucial connectivity between resolved pores (macroporosity). However, some carbonate rocks

Guohao Sun, Hang Hua, Jian Wang, Jiebo Luo

Chain-of-thought (CoT) reasoning is critical for improving the interpretability and reliability of Large Vision-Language Models (LVLMs). However, existing training algorithms such as SFT, PPO, and GRPO may not generalize well across unseen reasoning tasks and heavily rely on a biased reward model. To address this challenge, we reformulate reasoning in LVLMs

Dina Pisarevskaya, Arkaitz Zubiaga

We present a novel agent-based approach for the automated claim matching task with instruction-following LLMs. We propose a two-step pipeline that first generates prompts with LLMs, to then perform claim matching as a binary classification task with LLMs. We demonstrate that LLM-generated prompts can outperform SOTA with human-generated prompts, and that sma

Shashank Dhananjay Vyas, Satadru Dey

Connected and Autonomous Electrified Vehicles (CAEV) is the solution to the future smart mobility having benefits of efficient traffic flow and cleaner environmental impact. Although CAEV has advantages they are still susceptible to adversarial cyber attacks due to their autonomous electric operation and the involved connectivity. To alleviate this issue, we

Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni, Gene Louis Kim

Large Language Models have been shown to demonstrate stereotypical biases in their representations and behavior due to the discriminative nature of the data that they have been trained on. Despite significant progress in the development of methods and models that refrain from using stereotypical information in their decision-making, recent work has shown tha

Grant Hopkins, Sarah Teichman, Ellen Graham, Amy D Willis

Multi-category data arise in diverse fields including marketing, chemistry, public policy, genomics, political science, and ecology. We consider the problem of estimating ratios of category-specific means in a fully nonparametric setting, allowing for both observational units and categories to be preferentially sampled. We consider covariate-adjusted and una

Sebastian Grieninger, Dmitri E. Kharzeev, Eliana Marroquin

We investigate the quantum statistical properties of the confining string connecting a static fermion-antifermion pair in the massive Schwinger model. By analyzing the reduced density matrix of the subsystem located in between the fermion and antifermion, we demonstrate that as the interfermion separation approaches the string-breaking distance, the overlap

Emmanuel Sansusthy Tardio, Tian Chen, Theocharis Baxevanis

A macro-constitutive model for the deformation response of periodic rotating bistable auxetic surfaces is developed. Focus is placed on isotropic surfaces made of bistable hexagonal cells composed of six triangular units with two stable equilibrium states. Adopting a variational formulation, the effective stress-strain response is derived from a free energy

Christiam Figueroa

A translation surface in the Heisenberg group is constructed as the product of two planar curves. We classify a type of such surfaces with vanishing intrinsic curvature by analyzing the determinant of their Gauss map

Paul Ferrante, Connor Donovan, Chueng-Ryong Ji

We demonstrate the non-spreading behavior of Airy wave packets utilizing the Feynman path integral formulation of a linear potential, the Airy functions' zeros correspondence to heavy-meson mass spectroscopy, and their implications to the eigenstates of ultra-cold neutrons in Earth's gravitational field. We derive the linear kernel, and utilize the Feynman p

Arsenii Mustafin, Xinyi Sheng, Dominik Baumann

While Value Iteration (VI) is one of the most fundamental algorithms in Reinforcement Learning, its theoretical convergence guarantees still exhibit a persistent mismatch with empirical behavior. In the discounted-reward case, classical theory guarantees geometric convergence with rate $\gamma$, while in the average-reward case recent work suggests that only

Daniel Agassy, Dani Dorfman, Haim Kaplan

A $(\phi,\epsilon)$-expander-decomposition of a graph $G$ (with $n$ vertices and $m$ edges) is a partition of $V$ into clusters $V_1,\ldots,V_k$ with conductance $\Phi(G[V_i]) \ge \phi$, such that there are $O(\epsilon m)$ inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. [Agassy, Dorman, and Kaplan, ICALP 2023] (ADK) g

Marko Karbevski, Antonij Mijoski

We theoretically investigate whether the Query, Key, Value weight triplet can be reduced in encoder-only and decoder-only transformers. Under mild assumptions, we prove that one of the Query, Key or Value weights are redundant and can be replaced with the identity matrix, reducing attention parameters by 25\%. If applied to the Query or Key weights, this als

Arno Uhlig, Iris Braun, Matthias Wählisch

Allocating resources in a distributed environment is a fundamental challenge. In this paper, we analyze the scheduling and placement of virtual machines (VMs) in the cloud platform of SAP, the world's largest enterprise resource planning software vendor. Based on data from roughly 1,800 hypervisors and 48,000 VMs within a 30-day observation period, we highli

Khadija Omar Said, Yukta Pareek, Satadru Dey, Ashish Ranjan Kumar

Large-format lithium-ion batteries (LIBs) provide effective energy storage solutions for high-power equipment used in underground mining operations. They have high Columbic efficiency and minimal heat and emission footprints. However, improper use of LIBs, accidents, or other factors may increase the probability of thermal runaway (TR), a rapid combustion re

Vitaly Pronskikh

The introduction of the color quantum number is conventionally narrated as a linear progression from the quark-model statistics paradox to quantum chromodynamics (QCD). This paper challenges that teleology by arguing that "color" emerged as two conceptually distinct constructs during the Cold War. The first, originating with Han and Nambu and culminating in

M. T. Hassan, D. Zelenchuk, M. A. B. Abbasi

The sixth generation (6G) targets ultra reliable, low latency (URLLC) gigabit connectivity in mmWave bands, where directional channels require precise beam alignment. Reconfigurable intelligent surfaces (RIS) reshape wave propagation and extend coverage, but they enlarge the beam search space at the base station, making exhaustive sweeps inefficient due to c

Eddison Pham, Prisha Priyadarshini, Adrian Maliackel, Kanishk Bandi

Scene-level captioning in instructional videos can enhance learning by requiring an understanding of both visual cues and temporal structure. By aligning visual cues with textual guidance, this understanding supports procedural learning and multimodal reasoning, providing a richer context for skill acquisition. However, captions that fail to capture this str

Wasim Ahmad, Joachim Denzler, Maha Shadaydeh

Causal discovery uncovers complex relationships between variables, enhancing predictions, decision-making, and insights into real-world systems, especially in nonlinear multivariate time series. However, most existing methods primarily focus on pairwise cause-effect relationships, overlooking interactions among groups of variables, i.e., subsystems and their

Kexin Quan, Dina Albassam, Mengke Wu, Zijian Ding

Most AI systems today are designed to manage tasks and execute predefined steps. This makes them effective for process coordination but limited in their ability to engage in joint problem-solving with humans or contribute new ideas. We introduce MultiColleagues, a multi-agent conversational system that shows how AI agents can act as colleagues by conversing

Christos A. Athanasiadis

An $n$-dimensional lattice polytope ${\mathcal Q}_\sigma$ can be associated to any composition $\sigma$ of a positive integer $n$, as a special case of constructions due to Pitman--Stanley and Chapoton. The entries of the $h$-vector of $\sigma$, introduced by Chapoton, enumerate the lattice points in ${\mathcal Q}_\sigma$ by the number of their nonzero coord

Jans Solano, Diego Quiroz

Wheeled-legged robots combine the efficiency of wheels with the obstacle negotiation of legs, yet many state-of-the-art systems rely on costly actuators and sensors, and fall-recovery is seldom integrated, especially for wheeled-legged morphologies. This work presents a recovery-aware visual-inertial navigation system on a low-cost wheeled quadruped. The pro

Cristobal Heredia, Pedro Chumpitaz-Flores, Kaixun Hua

Mixed-integer programming (MIP) has emerged as a powerful framework for learning optimal decision trees. Yet, existing MIP approaches for regression tasks are either limited to purely binary features or become computationally intractable when continuous, large-scale data are involved. Naively binarizing continuous features sacrifices global optimality and of

Kuan-Po Chiu, Sumit Roy

This paper presents a new derivation of LEO-to-ground receiver channel model to address a clear gap in the prior art: the lack of an appropriate geometry aware characterization of non LOS (NLOS) link model represented by the power spectral density (PSD). Specifically, the main contribution is a coherent derivation of the PSD from 1st principles that is able

Maria G. Mendoza, Addison Kalanther, Daniel Bostwick, Emma Stephan

Autonomous drone technology holds significant promise for enhancing search and rescue operations during evacuations by guiding humans toward safety and supporting broader emergency response efforts. However, their application in dynamic, real-time evacuation support remains limited. Existing models often overlook the psychological and emotional complexity of

Bin Han, Jiwoon Sim

The acoustic scattering problem is modeled by the exterior Helmholtz equation, which is challenging to solve due to both the unboundedness of the domain and the high dispersion error, known as the pollution effect. We develop high-order compact finite difference methods (FDMs) in polar coordinates to numerically solve the problem with multiple arbitrarily sh

Volodymyr Oleksiyuk

This note presents the multivariate Hermite criterion: a practical and powerful algorithm for determining the number of distinct real and complex roots of a zero-dimensional system of polynomials in any finite number of variables. The final section includes an implementation in Macaulay2, a free and open-source computer algebra system.

Kosei Uemura, Miaoran Zhang, David Ifeoluwa Adelani

Text embeddings are an essential building component of several NLP tasks such as retrieval-augmented generation which is crucial for preventing hallucinations in LLMs. Despite the recent release of massively multilingual MTEB (MMTEB), African languages remain underrepresented, with existing tasks often repurposed from translation benchmarks such as FLORES cl

Hoora Sobhani, Hyoseung Kim

In Autonomous Driving Systems (ADS), Directed Acyclic Graphs (DAGs) are widely used to model complex data dependencies and inter-task communication. However, existing DAG scheduling approaches oversimplify data fusion tasks by assuming fixed triggering mechanisms, failing to capture the diverse fusion patterns found in real-world ADS software stacks. In this

Jinxin Zhou, Jiachen Jiang, Zhihui Zhu

Extending CLIP models to semantic segmentation remains challenging due to the misalignment between their image-level pre-training objectives and the pixel-level visual understanding required for dense prediction. While prior efforts have achieved encouraging results by reorganizing the final layer and features, they often inherit the global alignment bias of

Rodrigo Falcão, Stefan Schweitzer, Julien Siebert, Emily Calvet

Background: Systems of systems are becoming increasingly dynamic and heterogeneous, and this adds pressure on the long-standing challenge of interoperability. Besides its technical aspect, interoperability has also an economic side, as development time efforts are required to build the interoperability artifacts. Objectives: With the recent advances in the f

Kebin Contreras, Emmanuel Martinez, Brayan Monroy, Sebastian Ardila

Cocoa bean quality assessment is essential for ensuring compliance with commercial standards, protecting consumer health, and increasing the market value of the cocoa product. The quality assessment estimates key physicochemical properties, such as fermentation level, moisture content, polyphenol concentration, and cadmium content, among others. This assessm

Jiaqi Xue, Yifei Zhao, Mansour Al Ghanim, Shangqian Gao

Text watermarking for large language models (LLMs) enables model owners to verify text origin and protect intellectual property. While watermarking methods for closed-source LLMs are relatively mature, extending them to open-source models remains challenging, as developers cannot control the decoding process. Consequently, owners of open-source LLMs lack pra

Richard Cao, Rishibh Prakash

For a fixed radius $r$ and a point $o$ in the curve complex of a surface, we define the sphere of radius $r$ to be the induced subgraph on the set of vertices of distance $r$ from $o$. We show that these spheres are almost simply connected for surfaces of high enough complexity, in the sense that loops in the sphere bound an embedded disk contained in a smal

Bruce Zimov

Robin's Inequality posits $G(n)<e^{\gamma}$ for $n>5040$. Robin also showed that if the Riemann Hypothesis (RH) is false, then $G(n)>e^{\gamma}\left(1+\displaystyle\frac{c}{(\log n)^{b}}\right)$ for infinitely many values of $n$. By analyzing the prime or semiprime quotient $\displaystyle\frac{n}{m}$ for consecutive Colossally Abundant (CA) numbers $m$ follo

Pritom Mozumdar, Michele Cappellari, Christopher D. Fassnacht, Tommaso Treu

Understanding how the internal kinematics of massive galaxies evolve is key to constraining the physical processes that drive their assembly. We investigate the evolution of rotational support in massive ($\log M_{\ast}/M_{\odot} \geq 10.6$) early-type galaxies (ETGs) over the past $\sim$7 Gyr. We use MUSE integral-field spectroscopic (IFS) data for 212 ETGs

Sumin Hong, Xavier Briggs, Qingxiao Zheng, Yao Du

Speech sound disorder is among the most common communication challenges in preschool children. Home-based practice is essential for effective therapy and for acquiring generalization of target sounds, yet sustaining engaging and consistent practice remains difficult. Existing story-based activities, despite their potential for sound generalization and educat

James McCammon

Traditional fuzzy matching often fails when searching for quotes that are semantically identical but syntactically different across documents-a common issue when aligning official written records with speech-to-text transcripts. We introduce TimeStampEval, a benchmark for retrieving precise millisecond timestamps from long transcripts given non-verbatim quot

C. Cherubini, R. Moradi, J. A. Rueda, R. Ruffini

We present the invariant characterization of the region where vacuum breakdown into electron-positron ($e^+e^-$) pairs occurs due to an overcritical electric field, the dyadoregion, in the case of a Kerr black hole (BH) in the presence of an external, asymptotically uniform test magnetic field aligned with the BH rotation axis, using the Wald solution. We ca

Chandrasekhar Gokavarapu, Dr D Madhusudhana Rao

The theory of ternary $\Gamma$-semirings extends classical ring and semiring frameworks by introducing a ternary product controlled by a parameter set $\Gamma$. Building on the foundational axioms recently established by Rao, Rani, and Kiran (2025), this paper develops the first systematic ideal-theoretic study within this setting. We define and characterize

Tananun Songdechakraiwut, Michael Lutz

We explore a lightweight framework that adapts frozen large language models to analyze longitudinal clinical data. The approach integrates patient history and context within the language model space to generate accurate forecasts without model fine-tuning. Applied to neuropsychological assessments, it achieves accurate and reliable performance even with mini

Anshuman Chhabra, Shrestha Datta, Shahriar Kabir Nahin, Prasant Mohapatra

Agentic AI systems powered by large language models (LLMs) and endowed with planning, tool use, memory, and autonomy, are emerging as powerful, flexible platforms for automation. Their ability to autonomously execute tasks across web, software, and physical environments creates new and amplified security risks, distinct from both traditional AI safety and co

Adil Rasheed, Oscar Ravik, Omer San

This work investigates the use of digital twins for dynamical system modeling and control, integrating physics-based, data-driven, and hybrid approaches with both traditional and AI-driven controllers. Using a miniature greenhouse as a test platform, four predictive models Linear, Physics-Based Modeling (PBM), Long Short Term Memory (LSTM), and Hybrid Analys

Xidong Feng, Vivek Veeriah, Marcus Chiam, Michael Dennis

While Generative AI rapidly advances in various domains, generating truly creative, aesthetic, and counter-intuitive outputs remains a challenge. This paper presents an approach to tackle these difficulties in the domain of chess puzzles. We start by benchmarking Generative AI architectures, and then introduce an RL framework with novel rewards based on ches

Hanke Chen, Yuan Liu, Minchen Li

Text-driven 3D scene generation holds promise for a wide range of applications, from virtual prototyping to AR/VR and simulation. However, existing methods are often constrained to single-object generation, require domain-specific training, or lack support for full 360-degree viewability. In this work, we present a training-free approach to 3D scene synthesi

Ayse Irmak Ercevik, Ahmet Murat Ozbayoglu

Predictive maintenance (PdM) is crucial for optimizing efficiency and minimizing downtime of electric buses. While these vehicles provide environmental benefits, they pose challenges for PdM due to complex electric transmission and battery systems. Traditional maintenance, often based on scheduled inspections, struggles to capture anomalies in multi-dimensio

Dharma Teja Donepudi

Intra-sentence multilingual speech synthesis (code-switching TTS) remains a major challenge due to abrupt language shifts, varied scripts, and mismatched prosody between languages. Conventional TTS systems are typically monolingual and fail to produce natural, intelligible speech in mixed-language contexts. We introduce Script-First Multilingual Synthesis wi

Markus Ludwig, Furkan Ayhan, Thibault Voumard, Weichen Fan

Periodically poled thin film lithium niobate waveguides provide simultaneous access to efficient second and third order nonlinear processes, enabling broadband generation of coherent laser light. Here, we demonstrate the generation of a broadband mid-infrared continuum in a nanophotonic lithium niobate waveguide pumped by a telecom-wavelength femtosecond sou

Zhentong Shao, Nanpeng Yu

Quantifying locational carbon emissions in power grids is crucial for implementing effective carbon reduction strategies for customers relying on electricity. This paper presents a carbon-aware optimal power flow (OPF) framework that incorporates data-driven carbon tracing, enabling rapid estimation of nodal carbon emissions from electric loads. By developin

Mohammad Hosseini, Sadia Afrin, Anthony Yosick, Emma Schenker

Osteoporosis and osteopenia remain vastly underdiagnosed. Current clinical screening relies almost exclusively on dual-energy X-ray absorptiometry (DXA), which measures bone mineral density (BMD) but fails to capture the compositional changes that lead to BMD loss. We investigated whether Spatially Offset Raman Spectroscopy (SORS) applied to excised finger b

Eswari Jayakumar, Niladri Sekhar Dash, Debasmita Mukherjee

While Large Language Model (LLM)-based agents can be used to create highly engaging interactive applications through prompting personality traits and contextual data, effectively assessing their personalities has proven challenging. This novel interdisciplinary approach addresses this gap by combining agent development and linguistic analysis to assess the p

Domenec Espriu

The chart of nuclei could be enlarged with a branch describing neutron stars that are huge nuclei of a few solar masses held together by gravity force and sustained by the pressure due to the degenerate Fermi sea. We contend in this manuscript that yet another branch could be added: objects with a large weak charge, with masses around $10^{-3}$ solar masses

Yichi Zhang, Ignacio Martinez

Large Language Models (LLMs) are increasingly used to automate classification tasks in business, such as analyzing customer satisfaction from text. However, the inherent stochasticity of LLMs can create measurement error when the outcome is considered deterministic. This problem is often neglected with the empirical practice of a single round of output, or a

Zhentong Shao, Jingtao Qin, Xianbang Chen, Nanpeng Yu

The integration of distributed energy resources (DERs) into wholesale electricity markets, as mandated by FERC Order 2222, imposes new challenges on system operations. To remain consistent with existing market structures, regional transmission organizations (RTOs) have advanced the aggregation of transmission-node-level DERs (T-DERs), where a nodal virtual p

Andrey Gogolev, Martin Leguil, Federico Rodriguez Hertz

We consider two transitive $3$-dimensional Anosov flows which do not preserve volume and which are continuously conjugate to each other. Then, disregarding certain exceptional cases, such as flows with $C^1$ regular stable or unstable distributions, we prove that either the conjugacy is smooth or it sends the positive SRB measure of the first flow to the neg

Tânia Paulista

The aim of this paper is to study commuting graphs of completely $0$-simple semigroups, using the characterization of these semigroups as $0$-Rees matrix semigroups over a groups. We establish a method to decide whether the commuting graph of this semigroup construction is connected or not. If it is not connected, we also supply a way to identify the connect

Marianne Menglin Liu, Sai Ashish Somayajula, Syed Fahad Allam Shah, Sujith Ravi

We present OraPlan-SQL, our system for the Archer NL2SQL Evaluation Challenge 2025, a bilingual benchmark requiring complex reasoning such as arithmetic, commonsense, and hypothetical inference. OraPlan-SQL ranked first, exceeding the second-best system by more than 6% in execution accuracy (EX), with 55.0% in English and 56.7% in Chinese, while maintaining

Zhentong Shao, Jingtao Qin, Nanpeng Yu

The increasing integration of intermittent distributed energy resources (DERs) has introduced significant variability in distribution networks, posing challenges to voltage regulation and reactive power management. This paper presents a novel neural two-stage stochastic Volt-VAR optimization (2S-VVO) method for three-phase unbalanced distribution systems con

Paul Rosu, Muchang Bahng, Erick Jiang, Rico Zhu

This work presents a physics-conditioned latent diffusion model tailored for dynamical downscaling of atmospheric data, with a focus on reconstructing high-resolution 2-m temperature fields. Building upon a pre-existing diffusion architecture and employing a residual formulation against a reference UNet, we integrate a partial differential equation (PDE) los

Chloe Marple, Helen Wong

We calculate the Roger-Yang skein algebra of the annulus with two interior punctures, $ \mathcal S^{RY}(\Sigma_{0, 2, 2})$, and show there is a surjective homomorphism from this algebra to the Kauffman bracket skein algebra of the closed torus. Using this homomorphism, we characterize the irreducible, finite-dimensional representations of $ \mathcal S^{RY}(\

Pritom Mozumdar, Michele Cappellari, Christopher D. Fassnacht, Tommaso Treu

We present a sample of 212 early-type galaxies (ETGs) at redshifts $0.25 < z < 0.75$. We combine deep integral-field spectroscopy from the MUSE-DEEP survey with high-resolution HST imaging to study the structure, kinematics, and stellar populations of these galaxies. We measure spatially resolved stellar kinematics and use the specific angular momentum proxy