November 2025 arXiv papers — page 6

Archisman Ghosh

The gamut of todays internet-connected embedded devices has led to increased concerns regarding the security and confidentiality of data. Most internet-connected embedded devices employ mathematically secure cryptographic algorithms to address security vulnerabilities. Despite such mathematical guarantees, as these algorithms are often implemented in silicon

Woncheol Lee, Mark E. Turiansky, Dominic Waldhör, Byounghak Lee

Bond breaking in the presence of highly energetic carriers is central to many important phenomena in physics and chemistry, including radiation damage, hot-carrier degradation, activation of dopant-hydrogen complexes in semiconductors, and photocatalysis. Describing these processes from first principles has remained an elusive goal. Here we introduce a compr

William Swartworth, David P. Woodruff, Samson Zhou

Perfect $L_p$ sampling in a stream was introduced by Jayaram and Woodruff (FOCS 2018) as a streaming primitive which, given turnstile updates to a vector $x \in \{-\text{poly}(n), \ldots, \text{poly}(n)\}^n$, outputs an index $i^* \in \{1, 2, \ldots, n\}$ such that the probability of returning index $i$ is exactly \[\Pr[i^* = i] = \frac{|x_i|^p}{\|x\|_p^p} \

Jonas Andersson, Dimitris Karlis

The literature on multivariate time series is, largely, limited to either models based on the multivariate Gaussian distribution or models specifically developed for a given application. In this paper we develop a general approach which is based on an underlying, unobserved, Gaussian Vector Autoregressive (VAR) model. Using a transformation, we can capture t

Zhiming Lian

Financial text classification has increasingly become an important aspect in quantitative trading systems and related tasks, such as financial sentiment analysis and the classification of financial news. In this paper, we assess the performance of the large language model Qwen3-8B on both tasks. Qwen3-8B is a state-of-the-art model that exhibits strong instr

Sahand Kiani, Constantino M. Lagoa

The synthesis of robust invariant sets for nonlinear systems has traditionally been hindered by the inherent non convexity and a strict reliance on exact analytical models. This paper presents a purely data-driven framework to compute robust polytopic contractive sets for unknown nonlinear systems operating under persistent bounded process noise and state-in

Chadi Bsila, Yiqi Tang, Kaiwen Wang

Sparse high-dimensional linear regression is a central problem in statistics, where the goal is often variable selection and/or coefficient estimation. We propose a mean-field variational Bayes approximation for sparse regression with spike-and-slab Laplace priors that replaces the standard Kullback-Leibler (KL) divergence objective with the Rényi's $α$

Kim Gerard A. Villanueva, Priyanka Kumar

Accurate and timely diagnosis of multi-class skin lesions is hampered by subjective methods, inherent data imbalance in datasets like HAM10000, and the "black box" nature of Deep Learning (DL) models. This study proposes a trustworthy and highly accurate Computer-Aided Diagnosis (CAD) system to overcome these limitations. The approach utilizes Deep Convoluti

Tzu-I Liao, Mahmoud Fakhry, Jibin Yesudas Varghese

Pith detection in tree cross-sections is essential for forestry and wood quality analysis but remains a manual, error-prone task. This study evaluates deep learning models -- YOLOv9, U-Net, Swin Transformer, DeepLabV3, and Mask R-CNN -- to automate the process efficiently. A dataset of 582 labeled images was dynamically augmented to improve generalization. S

Basilis Mamalis, Marios Perlitis

Flying Ad-hoc Networks (FANETs), formed by Unmanned Aerial Vehicles (UAVs), represent an emerging and promising communication paradigm. These networks face unique challenges due to UAVs high mobility, limited energy resources, and dynamic topology. In this work, we propose a novel multi-hop clustering algorithm aimed at creating stable, energy-efficient clus

Pablo Alvarez Romeo, Mehmet Ercan Altinsoy

Force feedback gloves in haptic applications remain constrained by limited adaptability, simplified feedback, and fixed architectures that limit force feedback versatility. To address these challenges, we present KinesCeTI, a modular force feedback exoskeleton for the index and thumb, designed as a multipurpose device adaptable to a wide range of hand sizes.

Arnesh Batra, Dev Sharma, Krish Thukral, Ruhani Bhatia

The rapid evolution of end-to-end AI music generation poses an escalating threat to artistic authenticity and copyright, demanding detection methods that can keep pace. While foundational, existing models like SpecTTTra falter when faced with the diverse and rapidly advancing ecosystem of new generators, exhibiting significant performance drops on out-of-dis

A. A. Vasil'eva

In the present paper we improve Besov's recent result about upper estimates for the entropy numbers of Sobolev classes on a H\"{o}lder domain (in the case when the definition of the Sobolev class involves all partial derivatives of order $r$). We also obtain upper estimates for the Kolmogorov, linear and the Gelfand widths.

Goutham Nalagatla, Shreyas Grandhe

Continual learning remains a fundamental challenge in artificial intelligence, with catastrophic forgetting posing a significant barrier to deploying neural networks in dynamic environments. Inspired by biological memory consolidation mechanisms, we propose a novel framework for generative replay that leverages predictive coding principles to mitigate forget

Kinkar Chandra Das, Akbar Ali

Let $G$ be a connected graph having more than two vertices and let $d_i$ denote the degree of vertex $v_i$ in $G$. Let $E(G)$ represent the edge set of $G$. Then, the augmented Sombor (ASO) index of $G$ is defined as $ASO(G) = \sum_{v_i v_j \in E(G)} \sqrt{(d_i + d_j - 2)^{-1}(d_i^2 + d_j^2)}.$ It is known that the cycle graph $C_n$ uniquely minimizes the AS

Omer Jauhar Khan

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, single-model responses often exhibit inconsistencies, hallucinations, and varying quality across different query domains. This paper presents ART (Adaptive Response Tuning), a novel framework that employs tournament-style ELO rank

Yongyu Wang

Graph Neural Networks (GNNs) have emerged as a dominant paradigm for learning on graph-structured data, thanks to their ability to jointly exploit node features and relational information encoded in the graph topology. This joint modeling, however, also introduces a critical weakness: perturbations or noise in either the structure or the features can be ampl

Wesley H. Holliday, Milan Mossé, Chase Norman, Eric Pacuit

Algorithms for resolving majority cycles in preference aggregation have been studied extensively in computational social choice. Several sophisticated cycle-resolving methods, including Tideman's Ranked Pairs, Schulze's Beat Path, and Heitzig's River, are refinements of the Split Cycle (SC) method that resolves majority cycles by discarding the weakest major

Martina Capasso, Liliana Lancellotti, Pavel Shumyatsky

Let $\gamma_k=[x_1,\dots,x_k]$ be the $k$-th lower central group-word. Given a group $G$, we write $X_k(G)$ for the set of $\gamma_k$-values and $\gamma_k(G)$ for the $k$-th term of the lower central of $G$. This paper deals with groups in which $\langle g^{X_k(G)} \rangle$ is a Chernikov group of size at most $(m,n)$ for all $g\in G$. The main result is tha

Goutham Nalagatla

Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topologies, several limitations remain: scalability challenges with large agent populations, communication overhead, lack of privacy guarantees, a

Md Fahad Equbal, M. A. H. Ahsan

We investigate the interaction-driven reorganization of spin and charge correlations in finite Hubbard clusters using exact diagonalization. Focusing on half-filled and lightly doped square lattices, we analyze spin-resolved charge-gaps, local observables, two-point correlation functions, entanglement measures, principal component analysis (PCA) of correlati

Siddhant Karki

Graph link prediction has long been a central problem in graph representation learning in both network analysis and generative modeling. Recent progress in deep learning has introduced increasingly sophisticated architectures for capturing relational dependencies within graph-structured data. In this work, we propose the Generalized Graph Transformer Variati

Franck Binard, Vanja Kljajevic

Prism is a small, compositional metalanguage for specifying the behaviour of tool-using software agents. Rather than introducing ad hoc control constructs, Prism is built around a fixed core context, Core1, which provides a minimal background grammar of categories numbers, strings, user prompts, tools together with abstract combinators for booleans, predicat

Bertrand Even, Luca Ganassali

We investigate the existence of a statistical-computational gap in multiple Gaussian graph alignment. We first generalize a previously established informational threshold from Vassaux and Massouli\'e (2025) to regimes where the number of observed graphs $p$ may also grow with the number of nodes $n$: when $p \leq O(n/\log(n))$, we recover the results from Va

Fernando D. Almeida García, Maria C. Luna Alvarado, Lenin P. Jiménez Jiménez, Gustavo Fraidenraich

This paper analyzes the outage performance of downlink NOMA systems with transmit antenna selection (TAS) and multi-antenna users over {\alpha}-{\mu} fading. Maximal-ratio combining (MRC) and equal-gain combining (EGC) are considered, with imperfect successive interference cancellation (ipSIC) explicitly modeled. Exact closed-form outage probability (OP) exp

Jacqueline Malayter, Yingyao Zhou, Natasha Devroye, Chih-Chun Wang

Recent advances in deep learning for wireless communications have renewed interest in channel output feedback codes. In the additive white Gaussian broadcast channel with feedback (AWGN-BC-F), feedback can expand the channel capacity region beyond that of the no-feedback case, but linear analytical codes perform poorly with even small amounts of feedback noi

Logan Nye

Standard simulations of Turing machines suggest a linear relationship between the temporal duration $t$ of a run and the amount of information that must be stored by known simulations to certify, verify, or regenerate the configuration at time $t$. For deterministic multitape Turing machines over a fixed finite alphabet, this apparent linear dependence is no

Benjamin Mayer, Felix M. Ehring, Clemens Strobl, Matthias Weiß

We study the impact of electrical contact barriers in hybrid WSe$_2$-LiNbO$_3$-based acoustoelectric and acousto-photoelectric devices using a combination of scanning photocurrent and acousto-electric current spectroscopy. Static scanning photocurrent measurements provide a qualitative measure of the band-bending and spatial distribution of the Schottky barr

Aurélien Delage, Romaric Gaudel

We study the combinatorial semi-bandit problem under matroid constraints. The regret achieved by recent approaches is optimal, in the sense that it matches the lower bound. Yet, time complexity remains an issue for large matroids or for matroids with costly membership oracles (e.g. online recommendation that ensures diversity). This paper sheds a new light o

Ivan Beldiev

We prove that the infinite-dimensional Lie algebra of polynomial vector fields on the affine space $\KK^n$ is generated by two explicitly given elements.

Dimitrios Amaxilatis, Themistoklis Sarantakos, Nikolaos Tsironis, Souvik Sengupta

Smart cities are increasingly adopting data-centric architectures to enhance the efficiency, sustainability, and resilience of urban services.

Ongeziwe Mpatani, Dominik Muth, Anton Krüger, Rajdeep Adhikari

Alloying and doping are crucial for enhancing the electronic and optical properties of semiconductors while simultaneously introducing disorder. This report explores the effects of alloying and Si (0.5 at.\%) doping on In$_{0.10}$Ga$_{0.90}$N thin films that were grown by metal-organic vapor phase epitaxy. Post-growth X-ray diffraction measurements indicate

Wanle Zhong, Keman Huang, Xiaoyong Du

The Open Digital Rights Language (ODRL) is a pivotal standard for automating data rights management. However, the inherent logical complexity of authorization policies, combined with the scarcity of high-quality "Natural Language-to-ODRL" training datasets, impedes the ability of current methods to efficiently and accurately translate complex rules from natu

Yining Yuan, Yifei Wang, Yichang Xu, Zachary Yahn

This paper presents LLMBugScanner, a large language model (LLM) based framework for smart contract vulnerability detection using fine-tuning and ensemble learning. Smart contract auditing presents several challenges for LLMs: different pretrained models exhibit varying reasoning abilities, and no single model performs consistently well across all vulnerabili

Boyang Gu, Hongjian Zhou, Bradley Max Segal, Jinge Wu

Recent advances in large language models (LLMs) have shown strong reasoning capabilities through large-scale pretraining and post-training reinforcement learning, demonstrated by DeepSeek-R1. However, current post-training methods, such as Grouped Relative Policy Optimization (GRPO), mainly reward correctness, which is not aligned with the multi-dimensional

Xinyuan Dou, Ming Jin, Guangbin Ren, Irene Sabadini

The theory of slice regular (also called hyperholomorphic) functions is a generalization of complex analysis originally given in the quaternionic framework, and then further extended to Clifford algebras, octonions, and to real alternative algebras. Recently, we have extended this theory to the case of (real) even-dimensional Euclidean space. We provided sev

C. F. Munafò, S. Bonfiglio, P. Rogolino

In this paper, we investigate a mathematical model describing the interactions between effector cells (E), cancer cells (T), and the IL-2 compound (IL). The model considered here is a generalization, taking into account some cross-diffusion effects, of a spatial cancer immunotherapy model proposed by S. Suddin et al in 2021. These modifications allow us to d

Yining Yuan, J. Ben Tamo, Wenqi Shi, Yishan Zhong

Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes exhibit substantial heterogeneity. Many existing fairness approaches rely on coarse demographic adjustments or post-hoc corrections, which fail to capture the latent structure of c

Thuraya Alzubaidi, Farhad R. Nezami, Muzammil Behzad

Foundation models trained via vision-language pretraining have demonstrated strong zero-shot capabilities across diverse image domains, yet their application to volumetric medical imaging remains limited. We introduce MedCT-VLM: Medical CT Vision-Language Model, a parameter-efficient vision-language framework designed to adapt large-scale CT foundation model

Jiahao Tian, Zhenkai Wang

Modern recommender systems struggle to effectively utilize the rich, yet high-dimensional and noisy, multi-modal features generated by Large Language Models (LLMs). Treating these features as static inputs decouples them from the core recommendation task. We address this limitation with a novel framework built on a key insight: deeply fusing multi-modal and

Bala Siva Sai Akhil Malepati

Modern AI inference faces an irreducible tension: no single computational resource simultaneously maximizes performance, preserves privacy, minimizes cost, and maintains trust. Existing orchestration frameworks optimize single dimensions (Kubernetes prioritizes latency, federated learning preserves privacy, edge computing reduces network distance), creating

Bipin Singh Koranga, Parnav Kumar, Baktiar Wasir Farooq

In this work, we investigate the influence of quantum gravity induced corrections on the entanglement entropy associated with two flavor neutrino oscillations in vacuum. Using the Von Neumann entropy as a measure of quantum correlations, we analyze how Planck scale suppressed modification implemented through quantum gravity motivated changes in the neutrino

Moritz Bechberger, David Breitbach, Abbas Koujok, Björn Heinz

Spin-torque and spin-Hall oscillators (SHOs) have emerged as promising candidates for building blocks in neuromorphic computing due to their ability to synchronize mutually, a process that can be mediated by propagating spin waves. We demonstrate a SHO that takes advantage of a low-damping magnetic garnet with dominant perpendicular magnetic anisotropy (PMA)

Mihir Chauhan, Damon Conover, Aniket Bera

Autonomous navigation in partially observable environments requires agents to reason beyond immediate sensor input, exploit occlusion, and ensure safety while progressing toward a goal. These challenges arise in many robotics domains, from urban driving and warehouse automation to defense and surveillance. Classical path planning approaches and memoryless re

Weihua Xiao, Zeng Wang, Minghao Shao, Raghu Vamshi Hemadri

Hardware Trojans (HT s) are a persistent threat to integrated circuits, especially when inserted at the register-transfer level (RTL). Existing methods typically first convert the design into a graph, such as a gate-level netlist or an RTL-derived dataflow graph (DFG), and then use a graph neural network (GNN ) to obtain an embedding of that graph, which (i)

Alla Chepurova, Aydar Bulatov, Mikhail Burtsev, Yuri Kuratov

Knowledge graphs (KGs) provide structured, verifiable grounding for large language models (LLMs), but current LLM-based systems commonly use KGs as auxiliary structures for text retrieval, leaving their intrinsic quality underexplored. In this work, we propose Wikontic, a multi-stage pipeline that constructs KGs from open-domain text by extracting candidate

Misaki Mizumoto, Hiromitsu Takahashi, Ehud Behar, Rozenn Boissay-Malaquin

We present the high-resolution X-ray spectrum of the Z-source Cygnus X-2, obtained with X-Ray Imaging and Spectroscopy Mission (XRISM). The observations have enabled a precise characterization of the Fe-K emission lines from the accretion disk corona (ADC) and a possible detection of an ultra-fast outflow (UFO). The ADC component has at least two distinct re

Armin Ghazi, Ahmad Moradpouri

In this paper, we study the $U(1)^2$ Kac--Moody algebra and generalize the standard Sugawara construction of the Virasoro algebra to an infinite family of new realizations. In this case, in addition to the standard invariant tensor $\delta^{ij}$, there exists another invariant tensor $\epsilon^{ij}$, which enables the construction of genuinely new realizatio

Antonio Siconolfi

We investigate time dependent, first order Mean Field Games on the torus comparing, in a broad and general framework, the classical differential formulation , given by a Hamilton Jacobi equation coupled with a continuity equation, with a variational approach based on fixed points of a multivalued map acting on probability measures over trajectories. We prove

Michael R. Doane

This work presents the development and evaluation of an NLP-enabled probabilistic classifier designed to estimate the probability of technical and regulatory success (pTRS) for clinical trials in the field of neuroscience. While pharmaceutical R&D is plagued by high attrition rates and enormous costs, particularly within neuroscience, where success rates are

Ivan Shestakov, Ualbai Umirbaev

We prove that every irreducible Poisson supermodule over the Grassmann Poisson superalgebra $G_n$ over a field of characteristic different from $2$ is isomorphic to the regular Poisson supermodule $\mathrm{Reg}\,G_n$ or to its opposite supermodule. Moreover, every unital Poisson supermodule over $G_n$ is completely reducible. If $P$ is a unital Poisson super

Hang Huang, Yevgeniya Tarasova, Matteo Varbaro, Emily Witt

We prove that smooth projective curves admitting a squarefree Groebner degeneration have genus 0.

Zoe Kristin Lange, Maryam Farhadizadeh, Holger Dette, Nadine Binder

Assessing whether two patient populations exhibit comparable event dynamics is essential for evaluating treatment equivalence, pooling data across cohorts, or comparing clinical pathways across hospitals or strategies. We introduce a statistical framework for formally testing the similarity of competing risks models based on transition probabilities, which r

Yue Jiang, Haiwei Xue, Minghao Han, Mingcheng Li

Satire, a form of artistic expression combining humor with implicit critique, holds significant social value by illuminating societal issues. Despite its cultural and societal significance, satire comprehension, particularly in purely visual forms, remains a challenging task for current vision-language models. This task requires not only detecting satire but

Helen Papadaki, Inam Mirza, Nadezhda M. Bulgakova, Evaggelos Kaselouris

Laser-induced periodic surface structures (LIPSS) on silicon, generated by ultrashort pulsed lasers, provide an efficient means to tailor surface functionality. This work presents a multiphysics finite element study on the thermomechanical dynamics of silicon wafers irradiated by picosecond laser pulses, focusing on the melting regime where thermomechanical

Giovanni Conforti, Alain Durmus, Le-Tuyet-Nhi Pham, Gael Raoul

Diffusion models for continuous state spaces based on Gaussian noising processes are now relatively well understood from both practical and theoretical perspectives. In contrast, results for diffusion models on discrete state spaces remain far less explored and pose significant challenges, particularly due to their combinatorial structure and their more rece

Mauro Carfora, Francesca Familiari

We present a geometrical approach that provides a non-perturbative technique, allowing the standard FLRW observer to evaluate a measurable, scale-dependent distance functional between her idealized FLRW past light cone and the actual physical past light cone. From the point of view of the FLRW observer, gathering data from sources at cosmological redshift $\

Miroslav Blšták

When solving tasks in the field of natural language processing, we sometimes need dictionary tools, such as lexicons, word form dictionaries or knowledge bases. However, the availability of dictionary data is insufficient in many languages, especially in the case of low resourced languages. In this article, we introduce a new conceptual dictionary for the Sl

Riccardo Ontani, Shubham Sinha, Weihong Xu

We study the virtual intersection theory of Hyperquot schemes parameterizing sequences of quotient sheaves of a vector bundle on a smooth projective curve. Our results generalize the Vafa--Intriligator formula for Quot schemes and provide a closed formula for virtual counts of maps from the curve to a partial flag variety.

Zain Ateeq, Muhammad Faryad

This work presents a differentiable geometric parameterization of quantum channels in Kraus representation, which can be efficiently probed to find an unknown quantum channel. We explore its feasibility in finding the quasi inverse channels, which can be a tedious analytically for complex noise processes and is often achievable only for a limited range of pa

Anuradha Gupta, Kajal Negi

In this paper, we establish the essential criteria for the hyponormality and quasinormality of the unbounded Toeplitz operator $T_{\varphi}$ with non-harmonic symbol, acting on the Fock-Sobolev space $F^{2, m}(\mathbb{C})$. The study shows that quasinormality does not inherently imply hyponormality of unbounded Toeplitz operator with non-harmonic symbols.

Simon Saunders

I define a notion of local causality LOC closely modelled on the Bell principle, construed as the condition that single case probabilities cannot be modified by actions at spacelike separation. The new principle, like that of Bell, forces Bell inequalities, but with two loopholes: one is retrocausation, known to Bell, but the other is nonuniqueness of remote

Yiqiao Chen, Zijian Huang, Juchi He, Fazheng Xu

Brain-Computer Interfaces (BCIs) based on Motor Execution (ME) and Motor Imagery (MI) electroencephalogram (EEG) signals offer a direct pathway for human-machine interaction. However, developing robust decoding models remains challenging due to the complex spatio-temporal dynamics of EEG, its low signal-to-noise ratio, and the limited generalizability of man

Evgeny Ivanov

Harmonic ${\cal N}=2$ superspace was discovered in 1984 as the powerful tool of the geometric superfield off-shell description of ${\cal N}=2, 4D$ supersymmetric field theories with the maximal spins 1, 2, and 1/2 (${\cal N}=2$ Yang-Mills theories, supergravity and matter hypermultiplets). My talk is a brief account of the basic achievements of the harmonic

Tarun Chintada, Uday Kiran Cheera

Analogy-Based Estimation (ABE) is a popular method for non-algorithmic estimation due to its simplicity and effectiveness. The Analogy-Based Estimation (ABE) model was proposed by researchers, however, no optimal approach for reliable estimation was developed. Achieving high accuracy in the ABE might be challenging for new software projects that differ from

Hao Shen, Scott Andrew Smith, Rongchan Zhu

We derive a form of master loop equations for the lattice Yang-Mills-Higgs theory with structure group $SO(N)$, $U(N)$ or $SU(N)$. Compared to the pure Yang-Mills setting, several new operations arise. In fact, to obtain a closed recursion we must broaden the class of observables to include open Wilson lines. Our approach is based on the conditional Langevin

Ece Yurtseven

Quantum machine learning has emerged as a promising approach to improve feature extraction and classification tasks in high-dimensional data domains such as medical imaging. In this work, we present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset, a standardized ben

Thomas Jaklitsch

In this paper we define a descending filtration on the Chow group of zero cycles for varieties of the form $A \times C_1 \times \cdots \times C_d$ where $A$ is an abelian variety and each $C_i$ is a smooth projective curve. We give explicit generators and relations for the successive quotients of this filtration by showing that they can be described by Somek

Alejandro De Las Penas Castano

For a product $E_1\times E_2$ of two elliptic curves over a $p$-adic field with good supersingular reduction, we produce infinitely many rational equivalences in the Chow group $\mathrm{CH}_0(X)$ of zero cycles via genus 2 covers of $E_1$ and $E_2$. We use this to obtain evidence for a conjecture of Colliot-Th\'el\`ene about the structure of the Albanese ker

Zdzisław Burda, Maciej Wołoszyn, Krzysztof Malarz, Krzysztof Kułakowski

We discuss the Heider model in the presence of an external social field. This field was introduced to break the symmetry between the probabilities of hostile and friendly relationships. We consider the system in the presence of fluctuations generated by thermal noise and present the results of a comparative study of two-dimensional triangular and square netw

Giuseppe Cavaliere, Sílvia Gonçalves, Morten Ørregaard Nielsen, Edoardo Zanelli

Nonparametric regression and regression-discontinuity designs suffer from smoothing bias that distorts conventional confidence intervals. Solutions based on robust bias correction (RBC) are now central to the economist's toolbox. In this paper, we establish a novel connection between RBC methods and bootstrap prepivoting. Revisiting RBC through the lens of b

Nicolas Gorlo, Lukas Schmid, Luca Carlone

Computer vision and robotics applications ranging from augmented reality to robot autonomy in large-scale environments require spatio-temporal memory frameworks that capture both geometric structure for accurate language-grounding as well as semantic detail. Existing methods face a tradeoff, where producing rich open-vocabulary descriptions comes at the expe

Naman Choudhary, Vedant Singh, Ameet Talwalkar, Nicholas Matthew Boffi

A key aspect of learned partial differential equation (PDE) solvers is that the main cost often comes from generating training data with classical solvers rather than learning the model itself. Another is that there are clear axes of difficulty--e.g., more complex geometries and higher Reynolds numbers--along which problems become (1) harder for classical so

S M Asiful Islam Saky, Md Rashidul Islam, Md Saiful Arefin, Shahaba Alam

Respiratory diseases remain major global health challenges, and traditional auscultation is often limited by subjectivity, environmental noise, and inter-clinician variability. This study presents an explainable multimodal deep learning framework for automatic lung-disease detection using respiratory audio signals. The proposed system integrates two compleme

U. van Kolck

Effective field theories have been successful in describing nuclei up to the alpha particle but face significant challenges for larger nuclei due to leading-order instabilities. These issues can be addressed with the introduction of a fake interaction range at leading order, whose effects are compensated for in perturbation theory at higher orders. The calcu

Hayato Goto

Many-hypercube codes, concatenated ${[[n,n-2,2]]}$ quantum error-detecting codes ($n$ is even), have recently been proposed as high-rate quantum codes suitable for fault-tolerant quantum computing. While the original many-hypercube codes with ${n=6}$ can achieve remarkably high encoding rates (about 30% and 20% at concatenation levels 3 and 4, respectively),

Boxuan Han, Zeyu Rao, Ming Gong

Brownian motion, as one of the most fundamental concepts in statistical physics, has everlasting interests in interdisciplinary fields in the past century. Although this motion with static potentials have been widely explored, its physics in time-dependent periodic potentials are far less well understood. Here we generalize this motion to the realm of time-d

Andronikos Paliathanasis, Tommaso Mengoni, Genly Leon, Orlando Luongo

The recent data release DR2 from the Dark Energy Spectroscopic Instrument (DESI) has reinforced compelling evidence supporting the dynamical nature of dark energy. In this respect, we here explore a two-scalar field cosmological model, dubbed Chiral-Quintom paradigm, that allows for a unified description of early- and late-time cosmic accelerations, namely i

Haomiao Chen, Keith W Jamison, Mert R. Sabuncu, Amy Kuceyeski

What visual information is encoded in individual brain regions, and how do distributed patterns combine to create their neural representations? Prior work has used generative models to replicate known category selectivity in isolated regions (e.g., faces in FFA), but these approaches offer limited insight into how regions interact during complex, naturalisti

Sina Salimian, Gias Uddin, Sumon Biswas, Henry Leung

The widespread deployment of Large Language Models (LLMs) has intensified concerns about subtle social biases embedded in their outputs. Existing guardrails often fail when faced with indirect or contextually complex bias-inducing prompts. To address these limitations, we propose a unified framework for both systematic bias evaluation and targeted mitigation

L. Izzo, C. Gall, N. Khetan, N. Earl

Type Ia supernovae (SNe Ia) are among the most precise cosmological distance indicators used to study the expansion history of the Universe. The vast increase of SN Ia data due to large-scale astrophysical surveys has led to the discovery of a wide variety of SN Ia sub-classes, such as transitional and fast-declining SNe Ia. However, their distinct photometr

Chang Hsin Chen, Arnab Moitro, Alexei Y. Poludnenko

While direct numerical simulations (DNS) are the most accurate method for studying turbulence, their large computational cost restricts their use to idealized configurations and to Reynolds numbers well below those found in practical systems. A recently proposed method, Large/Small Eddy Simulation (L/SES), aims to overcome this limitation while still providi

Bohan Zhang, Michael Chen, A. Pavan, N. V. Vinodchandran

Replicability is a fundamental challenge in reinforcement learning (RL), as RL algorithms are empirically observed to be unstable and sensitive to variations in training conditions. To formally address this issue, we study \emph{list replicability} in the Probably Approximately Correct (PAC) RL framework, where an algorithm must return a near-optimal policy

Subramanyam Sahoo, Vinija Jain, Saanidhya Vats, Siddharth Mohapatra

Current evaluation of mathematical reasoning in language models relies primarily on answer accuracy, potentially masking fundamental failures in logical computation. We introduce a diagnostic framework that distinguishes genuine mathematical reasoning from superficial pattern matching through four complementary axes: forward-backward consistency, transitivit

Yang Li, Chong Ma, Yuanzheng Li, Sen Li

Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer-a tailored quantum-enhanced Transformer architecture that embeds parameterized

Valery Asiryan

We study the Diophantine equation $a^k + b^k = c^k + d^k$ with integer variables and exponent $k>1$, under the linear constraint $(c+d) - (a+b) = h$. We analyze the geometry and arithmetic of these linear slices. On the central slice $h=0$, we prove strictly convex uniqueness: distinct unordered pairs with the same sum yield distinct power sums. For shifted

L. Anger, A. Schoenit, F. Wodrascka, C. Rossé

Cells within biological tissue are constantly subjected to dynamic mechanical forces. Measuring the internal stress of tissues has proven crucial for our understanding of the role of mechanical forces in fundamental biological processes like morphogenesis, collective migration, cell division or cell elimination and death. Previously, we have introduced Bayes

Naveen Gupta, Sivananthan Sampath

In this article, we study the convergence behavior of the regularization-based algorithm for solving the polynomial regression model when both input data and responses are from infinite-dimensional Hilbert spaces. We derive convergence rates for estimation and prediction error by employing general (spectral) regularization under a general smoothness conditio

Chang Liu, Bo He

In this paper, we consider the exponential Diophantine equation \( (2^k-1)(b^k-1)=y^q \) with $k\ge 2$, odd integer $b$ and an odd prime exponent $q$ and obtain effective upper bounds for $q$ in terms of $b$. In particular, we show that $q\le \log_2(b+1)$ holds apart from a finite, explicitly determined set of exceptional pairs $(b,q)$ when $3\le b<10^6$. As

Sandika Biswas, Qianyi Wu, Biplab Banerjee, Hamid Rezatofighi

Real-world human-built environments are highly dynamic, involving multiple humans and their complex interactions with surrounding objects. While 3D geometry modeling of such scenes is crucial for applications like AR/VR, gaming, and embodied AI, it remains underexplored due to challenges like diverse motion patterns and frequent occlusions. Beyond novel view

Joud El-Shawa, Elham Bagheri, Sedef Akinli Kocak, Yalda Mohsenzadeh

Heatwaves are intensifying worldwide and are among the deadliest weather disasters. The burden falls disproportionately on marginalized populations and the Global South, where under-resourced health systems, exposure to urban heat islands, and the lack of adaptive infrastructure amplify risks. Yet current numerical weather prediction models often fail to cap

Akrati Saxena, Harshith Kumar Yadav, Bart Rutten, Shashi Shekhar Jha

The Influence Maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM a

Aliaksei Kachanovich

The rare decay $H \to Z\gamma$ has been investigated by both the ATLAS and CMS Collaborations, with each reporting an excess in Run~2 in 2023 characterized by $\mu = 2.2 \pm 0.7$. This anomaly was initially attributed to possible modifications of the $HZ\gamma$ vertex. However, because the $H \to Z\gamma$ signal is reconstructed via the $H \to \ell\ell\gamma

Tatsuki Sonoyama, Tomoki Sano, Takumi Suzuki, Kazuma Takahashi

We propose a pulse and continuous wave (CW) hybrid architecture of continuous-variable measurement-based optical quantum computation utilizing the strengths of both pulsed and CW light. In this architecture, input and ancillary non-Gaussian quantum states necessary for fault-tolerance and universality are generated with pulsed light, whereas quantum processo

Luchang Jin, Joshua Swaim

We present a new method for calculating quantum tunneling rates using lattice Monte Carlo simulations in imaginary time. This method is designed with the goal of studying false vacuum decay non-perturbatively on the lattice. We derive a new formula, which is similar in form to Fermi's Golden Rule, which gives the decay rate in terms of an implicit decay ampl

Sayeedul I. Sheikh, V. Subhasree Navya, Riya Sharma, Sudip Roy

Surfactants play an important role in determining the cleaning performance and stability of detergents. However, the design of new surfactants using traditional methods is often time-consuming, complex, and largely based on trial and error. Recent studies have incorporated data-driven and computational approaches to generate new surfactants and predict prope

Soham Ghosh, Arpit Bohra, Karthik Saikumar

The North American commercial electric vehicle (EV) sector is undergoing rapid expansion, with unit sales rising from 21,120 in 2022 to 36,491 in 2023 - a 73% increase, according to the International Energy Agency. However, this accelerating adoption brings emerging technical challenges. One critical concern is the impact of low to extreme winter temperature

Xiang Ma, Franz Pedit, Peng Wang

This paper resolves a long-standing open problem by providing a classification of Willmore $2$-spheres in $S^n$. We show that any such $2$-sphere is either totally isotropic--originating from the projection of a special twistor curve in the twistor bundle over an even-dimensional sphere--or strictly $k$-isotropic, obtained via $(m-k)$ steps of adjoint transf

Yongkang Hu, Yu Cheng, Yushuo Zhang, Yuan Xie

The widespread misuse of image generation technologies has raised security concerns, driving the development of AI-generated image detection methods. However, generalization has become a key challenge and open problem: existing approaches struggle to adapt to emerging generative methods and content types in real-world scenarios. To address this issue, we pro

Robert Baraldi, Michael Hintermüller, Qi Wang

Many large-scale optimization problems arising in science and engineering are naturally defined at multiple levels of discretization or model fidelity. Multilevel methods exploit this hierarchy to accelerate convergence by combining coarse- and fine-level information, a strategy that has proven highly effective in the numerical solution of partial differenti