October 2025 arXiv papers — page 167

Tudor Manole, Daniel K. Mark, Wenjie Gong, Bingtian Ye

Benchmarking quantum devices is a foundational task for the sustained development of quantum technologies. However, accurate in situ characterization of large-scale quantum devices remains a formidable challenge: such systems experience many different sources of errors, and cannot be simulated on classical computers. Here, we introduce new benchmarking metho

Jin Ma, Weixuan Xia, Jianfeng Zhang

We present a unified approach for characterizing the boundary of a possibly nonconvex domain. Motivated by the well-known Pascoletti--Serafini method of scalarization, we recast the boundary characterization as a multi-criteria optimization problem with respect to a local partial order induced by a spherical cone with varying orient. Such an approach enables

Hernán de Alba, Cecilia Martínez-Reyes

It is known that for binary codes one can use Gr\"obner bases to obtain a subset of codewords of minimal support that can be used to determine the second generalized Hamming weight of the code. In this paper we establish conditions on a nonbinary code under which the same property holds. We also construct a family of codes over any nonbinary finite field whe

Manuel Segura, Pere Vergés, Richard Ky, Ramesh Arangott

Excess alcohol consumption leads to serious health risks and severe consequences for both individuals and their communities. To advocate for healthier drinking habits, we introduce a groundbreaking mobile smartwatch application approach to just-in-time interventions for intoxication warnings. In this work, we have created a dataset gathering TAC, acceleromet

Sicong Huang, Qianqi Yan, Shengze Wang, Ian Lane

Abstractive summarization using large language models (LLMs) has become an essential tool for condensing information. However, despite their ability to generate fluent summaries, these models sometimes produce unfaithful summaries, introducing hallucinations at the word, phrase, or concept level. Existing mitigation strategies, such as post-processing correc

Aditya Malusare, Vineet Punyamoorty, Vaneet Aggarwal

Recent breakthroughs in generative modeling have demonstrated remarkable capabilities in molecular generation, yet the integration of comprehensive biomedical knowledge into these models has remained an untapped frontier. In this study, we introduce K-DREAM (Knowledge-Driven Embedding-Augmented Model), a novel framework that leverages knowledge graphs to aug

Shangbin Feng, Wenhao Yu, Yike Wang, Hongming Zhang

Alignment training has tradeoffs: it helps language models (LMs) gain in reasoning and instruction following but might lose out on skills such as creativity and calibration, where unaligned base models are better at. We aim to make the best of both worlds through model collaboration, where different models in the training pipeline collaborate and complement

D. V. Brovko

The relevance of this research lies in the growing demand for unmanned aerial vehicles (UAVs) capable of operating reliably in complex environments where conventional navigation becomes unreliable due to interference, poor visibility, or camouflage. Hyperspectral imaging (HSI) provides unique opportunities for UAV-based computer vision by enabling fine-grain

Lingfa Meng, David Salvador Novo, Albert H. Werner, Samir Bhatt

We present a quantum algorithm in bioinformatics for solving the Binary Near-Perfect Phylogeny Problem (BNPP) with a complexity bound of $O(8.926^q + 8^q nm2)$, where n is the number of input taxa and m is the sequence length for each taxon with each character in the sequence being a binary bit using the QRAM model. We give another polynomial space exact alg

Mario A. Quiroz-Juarez, Marco A. Zurita, Horacio Olivares-Pilon, Adrian M. Escobar Ruiz

We analyze how quantum mechanics reinstates confinement in Hamiltonian systems that are classically unstable and exhibit chaotic dynamics. Specifically, we consider two paradigmatic models: the Contopoulos Hamiltonian, an isotropic oscillator perturbed by the quartic coupling $\alpha\, x^{2}y^{2}$, and the purely quartic Yang--Mills Hamiltonian $H=\tfrac{1}{

Damien Tageddine, Jean-Christophe Nave

We derive a numerical approximation of the Laplace-Beltrami operator on compact surfaces embedded in $\mathbb{R}^3$ with an axial symmetry. To do so we use a noncommutative Laplace operator defined on the space of finite dimensional hermitian matrices. This operator is derived from a foliation of the surface obtained under an $S^1$-action on the surface. We

Hao Yan, Heyan Zhang, Yongyi Guo

The rise of large-scale pretrained models has made it feasible to generate predictive or synthetic features at low cost, raising the question of how to incorporate such surrogate predictions into downstream decision-making. We study this problem in the setting of online linear contextual bandits, where contexts may be complex, nonstationary, and only partial

Muhammad Maaz, Liam DeVoe, Zac Hatfield-Dodds, Nicholas Carlini

Property-based testing (PBT) is a lightweight formal method, typically implemented as a randomized testing framework. Users specify the input domain for their test using combinators supplied by the PBT framework, and the expected properties or invariants as a unit-test function. The framework then searches for a counterexample, e.g. by generating inputs and

Jiaobiao Zhao, Kefeng Huang, Xiaoxiang Li, Mingxia Zhang

The terahertz (THz) band is a promising candidate for sixth-generation wireless networks, but its deploymen in outdoor environments is challenged by meteorological phenomena, particularly fog, which imposes variable and difficult-to-predict channel degradation. This article introduces dynamic channel model for the THz band explicitly driven by the time-evolv

Lenny Aharon, Keemin Lee, Karan Sikka, Selmaan Chettih

Multi-view pose estimation is essential for quantifying animal behavior in scientific research, yet current methods struggle to achieve accurate tracking with limited labeled data and suffer from poor uncertainty estimates. We address these challenges with a comprehensive framework combining novel training and post-processing techniques, and a model distilla

Ben Blum-Smith, Claudia Brugman, Thomas Conners, Soledad Villar

This essay develops a parallel between the Fundamental Theorem of Galois Theory and the Stone--Weierstrass theorem: both can be viewed as assertions that tie the distinguishing power of a class of objects to their expressive power. We provide an elementary theorem connecting the relevant notions of "distinguishing power". We also discuss machine lear

Lianhao Zhou, Hongyi Ling, Cong Fu, Yepeng Huang

Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to as agents, that accelerate discovery across varying levels of autonomy. These language agents provide a flexible and versatile framework that orchestrates interac

Yalda Ramezani, Sumit Sharma

Common force fields overestimate the hydration free energies of hydrophobic solutes, leading to an exaggerated hydrophobic effect. We compute the hydration free energies of linear alkanes from methane to eicosane (C${20}$H${42}$) using free energy perturbation with various three-site (SPC/E, OPC3) and four-site (TIP4P/2005, OPC) water models in combination w

Shunan Zheng, John Hasenbein

This paper studies information asymmetry in an unobservable single-server queueing system. While system managers have knowledge of the true arrival rate, customers may lack this information and instead form arbitrary beliefs. We propose a three-tier hierarchy of information asymmetry with increasing levels of information disclosure:customers keep private bel

Wonbin Kweon, Runchu Tian, SeongKu Kang, Pengcheng Jiang

Scientific document retrieval is a critical task for enabling knowledge discovery and supporting research across diverse domains. However, existing dense retrieval methods often struggle to capture fine-grained scientific concepts in texts due to their reliance on holistic embeddings and limited domain understanding. Recent approaches leverage large language

Dominik Stiller, Gregory J. Hakim

Earth's energy imbalance at the top of the atmosphere is a key climate system metric, but its natural variability is poorly constrained by the short observational record and large uncertainty in coupled climate models. While existing ocean heat content reconstructions offer a longer perspective, they cannot separate the contributions of shortwave and longwav

Yubo Li, Rema Padman

Modeling clinical time-series data is hampered by the challenge of capturing latent, time-varying dependencies among features. State-of-the-art approaches often rely on black-box mechanisms or simple aggregation, failing to explicitly model how the influence of one clinical variable propagates through others over time. We propose $\textbf{Chain-of-Influence

Junyuan Liu, Quan Qin, Guangsheng Dong, Xinglei Wang

Recent geospatial foundation models (GFMs) produce spatially extensive representations of the Earth's surface that capture rich physical and environmental patterns. Among them, the AlphaEarth Foundation (AE) represents a major step, generating 10 m embeddings from multi-source Earth Observation (EO) data that include diverse environmental and spectral charac

Guanming Chen, Lingzhi Shen, Xiaohao Cai, Imran Razzak

Personality detection from text aims to infer an individual's personality traits based on linguistic patterns. However, existing machine learning approaches often struggle to capture contextual information spanning multiple posts and tend to fall short in extracting representative and robust features in semantically sparse environments. This paper presents H

Hongyu Chen, Paul A. Ullrich, Julian Panetta

We introduce a fast, high-precision algorithm for calculating intersections between great circle arcs and lines of constant latitude on the unit sphere. We first propose a simplified intersection point formula with improved speed and numerical robustness over the ones traditionally implemented in geoscience software. We then show how algorithms based on the

Parsa Gooya, Reinel Sospedra-Alfonso

Seasonal forecast of Arctic sea ice concentration is key to mitigate the negative impact and assess potential opportunities posed by the rapid decline of sea ice coverage. Seasonal prediction systems based on climate models often show systematic biases and complex spatio-temporal errors that grow with the forecasts. Consequently, operational predictions are

Mauro L. Mugnai

Using a theoretical model we show that ideal ring polymers are stronger depletants than ideal linear polymers of equal radii of gyration, but not of equal hydrodynamic radii. The difference in the depletion-induced force profile is largely controlled by the thickness of the depletion layer. Theory suggests that this thickness is equal to the average extent o

Julius Huijts, Jom Luiten

The ultracold electron source is a unique approach to the generation of high-brightness electron beams. We give an overview of its development over the past 20 years, including the underlying physical principles, technical details and recent experiments, and give a flavor of the exciting prospects that the future may hold.

Yunlong Feng, Qiang Wu

We investigate robust nonparametric regression in the presence of heavy-tailed noise, where the hypothesis class may contain unbounded functions and robustness is ensured via a robust loss function $\ell_\sigma$. Using Huber regression as a close-up example within Tikhonov-regularized risk minimization in reproducing kernel Hilbert spaces (RKHS), we address

Yijin Ni, Simon Yu, Peng Qi

Vision and language models frequently ignore semantically critical input edits, defaulting to pretraining priors. For example, models will confidently assert a five-legged dog has four legs; consequently, on the VLMBias benchmark, GPT 5.2 and Claude Sonnet 4.6 achieve only $4.6\%$ and $0\%$ accuracy, respectively. Existing methods address this problem throug

Mohammad Tamim Kashifi

Over the past decade, there has been a growing trend toward integrating human factors (HF) into traffic flow models to better understand the complexities of human behavior and its impact on traffic dynamics. This research seeks to advance this trend by bridging the gap between traditional car-following models and the inherent variability of human driving beh

Waleed Razzaq, Yun-Bo Zhao

Prognostic Health Management (PHM) systems monitor and predict equipment health. A key task is Remaining Useful Life (RUL) estimation, which predicts how long a component, such as a rolling element bearing, will operate before failure. Many RUL methods exist but often lack generalizability and robustness under changing operating conditions. This paper introd

Soheila Farokhi, Xiaojun Qi, Hamid Karimi

Dynamic graph representation learning has become essential for analyzing evolving networks in domains such as social network analysis, recommendation systems, and traffic analysis. However, existing continuous-time methods face three key challenges: (1) some methods depend solely on node-specific memory without effectively incorporating information from neig

Hossein Entezari Zarch, Lei Gao, Chaoyi Jiang, Murali Annavaram

Large reasoning models (LRMs) achieve state-of-the-art performance on challenging benchmarks by generating long chains of intermediate steps, but their inference cost is dominated by decoding, where each new token must attend to the entire growing sequence. One approach to reduce this latency is to evict entries from the key-value (KV) cache, thereby reducin

Vishal Anand, Milad Alshomary, Kathleen McKeown

We present iBERT (interpretable-BERT), an encoder to produce inherently interpretable and controllable embeddings - designed to modularize and expose the discriminative cues present in language, such as semantic or stylistic structure. Each input token is represented as a sparse, non-negative mixture over k context-independent sense vectors, which can be poo

Minkwan Kim, Seungmin Lee, Junho Kim, Young Min Kim

Recent advances in novel-view synthesis can create the photo-realistic visualization of real-world environments from conventional camera captures. However, the everyday environment experiences frequent scene changes, which require dense observations, both spatially and temporally, that an ordinary setup cannot cover. We propose long-term Gaussian scene chron

Minkwan Kim, Changwoon Choi, Young Min Kim

We propose scene-adaptive strategies to efficiently allocate representation capacity for generating immersive experiences of indoor environments from incomplete observations. Indoor scenes with multiple rooms often exhibit irregular layouts with varying complexity, containing clutter, occlusion, and flat walls. We maximize the utilization of limited resource

Shreshth Saini, Alan C. Bovik, Neil Birkbeck, Yilin Wang

High Dynamic Range (HDR) videos enhance visual experiences with superior brightness, contrast, and color depth. The surge of User-Generated Content (UGC) on platforms like YouTube and TikTok introduces unique challenges for HDR video quality assessment (VQA) due to diverse capture conditions, editing artifacts, and compression distortions. Existing HDR-VQA d

Milad Khanchi, Maria Amer, Charalambos Poullis

Multi-object tracking (MOT) methods often rely on Intersection-over-Union (IoU) for association. However, this becomes unreliable when objects are similar or occluded. Also, computing IoU for segmentation masks is computationally expensive. In this work, we use segmentation masks to capture object shapes, but we do not compute segmentation IoU. Instead, we f

Kangping Hu, Stephen Mussmann

Over the past couple of decades, many active learning acquisition functions have been proposed, leaving practitioners with an unclear choice of which to use. Bayesian-based active learning offers principled objectives with explainable intuition, including Expected Error Reduction (EER), Expected Predictive Information Gain (EPIG), and Bayesian Active Learnin

Luis Rojas, Sebastián Espinoza, Esteban González, Carlos Maldonado

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1)

Giorgio Tortone, Bozhidar Velichkov

In this survey we go through some of the recent results about the regularity of vectorial free boundary problems of Bernoulli type and free boundary systems. The aim is to illustrate the general methodologies as well as to outline a selection of notable open questions.

Margarete Jahrmann, Thomas Brandstetter, Stefan Glasauer

The paper presents the first results of an artistic research project investigating how Large Language Models (LLMs) curate and present collective memory. In a public installation exhibited during two months in Vienna in 2025, visitors could interact with five different LLMs (ChatGPT with GPT 4o and GPT 4o mini, Mistral Large, DeepSeek-Chat, and a locally run

Ada Chan, Venkata Raghu Tej Pantangi, Andriaherimanana Sarobidy Razafimahatratra, Peter Sin

We study perfect state transfer and multiple state transfer in oriented normal Cayley graphs. We construct examples in a variety of groups, ranging from abelian to nonsolvable, and establish some general restrictions and nonexistence results.

Nafiseh Nikeghbal, Amir Hossein Kargaran, Jana Diesner

Improvements in model construction, including fortified safety guardrails, allow Large language models (LLMs) to increasingly pass standard safety checks. However, LLMs sometimes slip into revealing harmful behavior, such as expressing racist viewpoints, during conversations. To analyze this systematically, we introduce CoBia, a suite of lightweight adversar

Andrey Vayner, Tanio Díaz-Santos, Carl D. Ferkinhoff, Peter R. M. Eisenhardt

Galaxy-scale outflows are of critical importance for galaxy formation and evolution. Dust grains are the main sites for the formation of molecules needed for star formation but are also important for the acceleration of outflows that can remove the gas reservoir critical for stellar mass growth. Using the MIRI medium-resolution integral field spectrograph ab

Sil Hamilton, Matthew Wilkens, Andrew Piper

We present NarraBench, a theory-informed taxonomy of narrative-understanding tasks, as well as an associated survey of 78 existing benchmarks in the area. We find significant need for new evaluations covering aspects of narrative understanding that are either overlooked in current work or are poorly aligned with existing metrics. Specifically, we estimate th

Christian Michael Ireland, Franziska Maria Maier, Einstein Dhayal, Erich Leistenschneider

Multi-reflection time-of-flight mass separators and spectrometers (MR-ToF MSs) are indispensable tools at radioactive ion beam (RIB) facilities. These electrostatic ion beam traps act as highly selective mass separators and high-precision mass spectrometers for rare and exotic nuclei. When well-tuned and designed to minimize higher-order flight-time aberrati

Philine van Vliet, Emilio Trevisani, Ingo Runkel, Bernardo Zan

Since the 1980s, many exact results have been discovered in $2d$ CFT, from critical exponents to correlation functions to complete solutions of certain models. In $d>2$, there is a wealth of numerical results as well as promising analytic approaches, but comparably fewer exact answers. The aim of this conference was to review the most promising analytic meth

Zhi Chen, Xin Yu, Xiaohui Tao, Yan Li

Vision-language models (VLMs) such as CLIP achieve zero-shot transfer across various tasks by pre-training on numerous image-text pairs. These models often benefit from using an ensemble of context prompts to represent a class. Despite being effective, conventional prompt ensembling that averages textual features of context prompts often yields suboptimal re

Fredy Maglorio Sobrado Suárez, Gilson Tumelero, Jackson Luchesi, Marieli Musial Tumelero

This manuscript studies a model of double-walled carbon nanotubes using two Timoshenko beams which are coupled by the Van der Walls force $(y-u)$. Kelvin-Voigt type dampings $(u_x-v)_{xt}$ and $(y_x-z)_{xt}$ and fractional dampings $(-\partial_{xx})^\alpha v_t$ and $(-\partial_{xx})^\beta z_t$ in both beams have been considered. We show that our proposed mod

CMS Collaboration

A hot and dense state of nuclear matter, known as the quark-gluon plasma, is created in collisions of ultrarelativistic heavy nuclei. Highly energetic quarks and gluons, collectively referred to as partons, lose energy as they travel through this matter, leading to suppressed production of particles with large transverse momenta ($p_\mathrm{T}$). Conversely,

Abuzer Gunduz

Let $R_1$ and $R_2$ be commutative rings with $1\neq 0,\;M$ and $N$ be unitary $R_1-$module and $R_2-$module, respectively. $f:R_1\rightarrow R_2$ be a ring homomorphism and $\varphi: M\rightarrow N$ be an $R-$module homomorphism. This article studied $2-$absorbing submodule that is a generalization of the concept of prime submodule. Firstly, some characteri

Carsten Hartmann, Edoardo De Din, Daniele Carta, Florian Middelkoop

Electric power systems are undergoing fundamental change. The shift to inverter-based generation challenges frequency stability, while growing digitalisation heightens vulnerability to errors and attacks. Here we identify an emerging risk at the intersection of cyber-physical coupling and control system design. We show that grid frequency time series worldwi

Jonas Böhm, Aurélie Champagne

Understanding ionic transport in halide solid electrolytes is essential for advancing next-generation solid-state batteries. This work demonstrates the effectiveness of fine-tuning the Crystal Hamiltonian Graph Network (CHGNet) universal machine learning interatomic potential to accurately predict total energies, relaxed geometries, and lithium-ion dynamics

Yu-Chi Kao, Anna C. Doner, Timo T. Pekkanen, Chuangchuang Cao

Ammonia is a promising zero-carbon fuel for industrial and transport applications, but its combustion is hindered by flame instabilities, incomplete oxidation, and the formation of nitrogen oxides. Accurate and detailed kinetic models are critical for designing optimal burners and engines. Despite numerous mechanisms published in recent years, large discrepa

Weijie Zhong

A provider sells a \emph{dynamic information service}---a real-time, capacity-constrained process that resolves a customer's uncertainty---to customers who differ privately in urgency. I characterize the revenue-optimal mechanism: deploy a \emph{single}, undistorted information process---the one a customer with unlimited access would most prefer---and sc

Eric Schwitzgebel

This is a skeptical overview of the literature on AI consciousness. We will soon create AI systems that are conscious according to some influential, mainstream theories of consciousness but are not conscious according to other influential, mainstream theories of consciousness. We will not be in a position to know which theories are correct and whether we are

Xiao Yang, Peifeng Yin, Abe Engle, Jinfeng Zhuang

The lightweight ad ranking layer, living after the retrieval stage and before the fine ranker, plays a critical role in the success of a cascaded ad recommendation system. Due to the fact that there are multiple optimization tasks depending on the ad domain, e.g., Click Through Rate (CTR) for click ads and Conversion Rate (CVR) for conversion ads, as well as

R. Casana, E. da Hora, F. C. Simas

We consider a $(1+1)$-dimensional theory with a single real scalar field $\phi$ whose kinematics is modified by a generalizing function $f(\phi)$. After briefly reviewing its Bogomol'nyi-Prasad-Sommerfield (BPS) structure, we focus on a particular $f(\phi)$ to obtain analytic BPS double-kink solutions in three different models governed by the $\phi^4$, $\phi

Juan Andrés Orozco Gutiérrez, Valente Santiago Vargas

Given a homological epimorphism $\pi:\mathcal{C}\longrightarrow \mathcal{C}/\mathcal{I}$ between $K$-categories, we show that if the ideal $\mathcal{I}$ satisfies certain conditions, then there exists an equivalence between the singularity categories $\mathbf{D}_{sg}(\mathrm{Mod}(\mathcal{C}))$ and $ \mathbf{D}_{sg}(\mathrm{Mod}(\mathcal{C}/\mathcal{I}))$. T

Yulin An, Xueqi Zhao, Enrique del Castillo

We present a new method for the statistical process control of lattice structures using tools from Topological Data Analysis. Motivated by applications in additive manufacturing, such as aerospace components and biomedical implants, where hollow lattice geometries are critical, the proposed framework is based on monitoring the persistent homology properties

Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, Keerthiram Murugesan

Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chronic diseases. However, existing methods face two fundamental challenges: the limited reasoning capacity of single-agent systems and the complexity of designing effective multi-age

Bradley Saul

Beginning in the 1970s, statistician-cum-logician Per Martin-L\"of wrote a series of papers developing what became Martin-L\"of type theory, realizing a system where the distinction between mathematics and programming disappears. Inspired by this vision, this paper introduces dependent type theory (of which Martin-L\"of type theory is an example) to a statis

Shivam Patel, Neharika Jali, Ankur Mallick, Gauri Joshi

Large language model (LLM) query routers are critical to modern AI platforms as they seek to improve efficiency by assigning inference queries to accurate, yet low-cost models. Parametric routers typically use trained neural networks for LLM selection but suffer from retraining and maintenance overheads. Nonparametric routers are training-free, instead estim

Haci Ismail Aslan, Syed Muhammad Mahmudul Haque, Joel Witzke, Odej Kao

Modern applications increasingly span across cloud, fog, and edge environments, demanding orchestration systems that can adapt to diverse deployment contexts while meeting Quality-of-Service (QoS) requirements. Standard Kubernetes schedulers do not account for user-defined objectives such as energy efficiency, cost optimization, and global performance, often

Vasco Brattka, Emmanuel Rauzy

In computable analysis typically topological spaces with countable bases are considered. The Theorem of Kreitz-Weihrauch implies that the subbase representation of a second-countable $T_0$ space is admissible with respect to the topology that the subbase generates. We consider generalizations of this setting to bases that are representable, but not necessari

Ruizhe Zhu

The widespread application of large vision language models has significantly raised safety concerns. In this project, we investigate text prompt injection, a simple yet effective method to mislead these models. We developed an algorithm for this type of attack and demonstrated its effectiveness and efficiency through experiments. Compared to other attack met

Peixian Liang, Yifan Ding, Yizhe Zhang, Jianxu Chen

State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from background pixels. In order to identify cell instances from foreground pixels (e.g., pixel clustering), most methods decompose instance information into pixel-wise objectives, such

Said Muhammad, Lahlou Laaziz, Nadjia Kara, Phat Tan Nguyen

The dynamic adaptation of resource levels enables the system to enhance energy efficiency while maintaining the necessary computational resources, particularly in scenarios where workloads fluctuate significantly over time. The proposed approach can play a crucial role in heterogeneous systems where workload characteristics are not uniformly distributed, suc

Zhenjiang Fan, Zengyi Qin, Yuanning Zheng, Bo Xiong

Causal discovery from observational data is fundamental to scientific fields like biology, where controlled experiments are often impractical. However, existing methods, including constraint-based (e.g., PC, causalMGM) and score-based approaches (e.g., NOTEARS), face significant limitations. These include an inability to resolve causal direction, restriction

Digvijay Redekar, Ronald Askin, Feng Ju

Maritime transportation systems (MTS) play a crucial role in ensuring the uninterrupted supply of essential goods and services, impacting the economy, border security and general welfare. However, MTS operations face disruptions from natural disasters, man-made disturbances, or cascading combinations of these events. These threats can disrupt trade routes, p

Ana-Maria Boldeanu, Mircea Neagu

The aim of this paper is to develop, via the least squares variational method, the Lagrange-Hamilton geometry (in the sense of nonlinear connections, d-torsions and Lagrangian Yang-Mills electromagnetic-like energy) produced by a Lotka-Volterra dynamical system, a simple model of the population dynamics of species competing for some common resource. From a g

Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder

Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures

Samantha N. Hasler, Leonid Pogorelyuk, Riley Fitzgerald, Kerri Cahoy

Future missions, including the Habitable Worlds Observatory, will aim to image Earth-like exoplanets around Sun-like stars in reflected light. Determining whether an exoplanet is in the habitable zone of its star may be difficult in multi-planet systems when the observer does not know in advance which detection corresponds to which planet. This "confusion" p

Jarrad Hope, Peter Ludlow

We argue that the principal application for blockchain technology will not be in the financial sector, but rather in maintaining decentralized human governance, from archives to transparent policies encoded in the blockchain in the form of smart contracts.. Such decentralized, blockchain-grounded governance comes not a moment too soon, as nation states are d

Johnathan Kowalski, Liangbo Liang

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It i

Hantian Zhang

The high-energy behaviour of scattering amplitudes involving massive particles has attracted interest in recent years. In these proceedings, we report on the analytic tool AsyInt for solving massive multi-loop Feynman integrals in the high-energy limit, which are fundamental building blocks for such amplitudes in the full Standard Model. We present recent an

Bing Hu, Jong-Hoon Park, Helen Chen, Young-Rae Cho

The role of Artificial Intelligence (AI) is growing in every stage of drug development. Nevertheless, a major challenge in drug discovery AI remains: Drug pharmacokinetic (PK) and Drug-Target Interaction (DTI) datasets collected in different studies often exhibit limited overlap, creating data overlap sparsity. Thus, data curation becomes difficult, negative

David Benavente-Rios, Juan Ruiz Rodriguez, Gustavo Gatica

This paper investigates the use of synthetic face data to enhance Single-Morphing Attack Detection (S-MAD), addressing the limitations of availability of large-scale datasets of bona fide images due to privacy concerns. Various morphing tools and cross-dataset evaluation schemes were utilized to conduct this study. An incremental testing protocol was impleme

Jyoti Yadav, Felix Janus, Tiago de Oliveira Schneider, Shalini Sharma

We study thin films of the high-entropy alloy system Al$_{x}$(CrMoW)$_{1-x}$, grown on Ta seed layers by magnetron co-sputtering. Between $x=0.2$ and $x=0.6$, a resistivity larger than 100$\mu\Omega$cm is achieved, with a peak of 180$\mu\Omega$cm at $x=0.5$. Around the stoichiometric composition AlCrMoW, the alloy forms a bcc solid solution. The harmonic Hal

Xin-Feng Zhang, Huan-Bo Luo, Josep Batle, Bin Liu

The present contribution explores phase transitions that occur in the ground state (GS) of spin-1 Bose-Einstein condensates (BECs) with spin-orbit coupling (SOC) under the action of gradient magnetic fields. By solving the corresponding linearized system in an exact fashion, we identify the conditions under which the GS phase transitions occur, thus transfor

Steph Campbell, David J. Rosario, Houda Haidar, Enrique López Rodríguez

Broadband mid-infrared (MIR) imaging with high spatial resolution is useful to study extended dust structures in the circumnuclear regions of nearby AGN. However, broadband imaging filters cannot distinguish dust continuum emission from emission lines, and so accounting for the emission line contamination becomes crucial in studying extended dust in these en

Kesler O'Connor, Julia M. Jess, Devlin Costello, Manuel E. Lladser

We address the problem of localizing the source of infection in an undirected, tree-structured network under a susceptible-infected outbreak model. The infection propagates with independent random time increments (i.e., edge-delays) between neighboring nodes, while only the infection times of a subset of nodes can be observed. We show that a reduced set of o

Michael Crawshaw, Chirag Modi, Mingrui Liu, Robert M. Gower

To define a steepest descent method over a neural network, we need to choose a norm for each layer, a way to aggregate these norms across layers, and whether to use normalization. We systematically explore different alternatives for aggregating norms across layers, both formalizing existing combinations of Adam and the recently proposed Muon as a type of non

Jialin Zheng, Zhong Liu, Xiaonan Lu

Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly

Mohsen Joneidi

We propose the Decomposer Networks (DecompNet), a semantic autoencoder that factorizes an input into multiple interpretable components. Unlike classical autoencoders that compress an input into a single latent representation, the Decomposer Network maintains N parallel branches, each assigned a residual input defined as the original signal minus the reconstr

Kamil Khadiev, Aliya Khadieva, Vadim Sagitov, Kamil Khasanov

In the paper, we consider quantum circuits for the Quantum Fourier Transform (QFT) algorithm. The QFT algorithm is a very popular technique used in many quantum algorithms. We present a generic method for constructing quantum circuits for this algorithm implementing on quantum devices with restrictions. Many quantum devices (for example, based on superconduc

Kevin Flaherty, Peter Knowlton, Tasan Smith-Gandy, A. Meredith Hughes

Binary systems are a common site of planet formation, despite the destructive effects of the binary on the disk. While surveys of planet forming material have found diminished disk masses around medium separation ($\sim$10--100 au) binaries, less is known about tight ($<$10 au) binaries, where a significant circumbinary disk may escape the disruptive dynamic

Weiqing Luo, Zhen Tan, Yifan Li, Xinyu Zhao

Real-world vision-language applications demand varying levels of perceptual granularity. However, most existing visual large language models (VLLMs), such as LLaVA, pre-assume a fixed resolution for downstream tasks, which leads to subpar performance. To address this problem, we first conduct a comprehensive and pioneering investigation into the resolution p

Jiaxing Weng, Haijun Yang, Tongyu Wang

This paper introduces a tractable model to study incentive-compatible homophily under both external environments--such as exogenous shocks or policy constraints--and internal micromotives based on interactive attributes. We propose a set of invariants that capture main features of homophily and the well-defined partition dynamics leading to perfect global ho

Jusheng Zhang, Kaitong Cai, Yijia Fan, Ningyuan Liu

Multi-label image classification demands adaptive training strategies to navigate complex, evolving visual-semantic landscapes, yet conventional methods rely on static configurations that falter in dynamic settings. We propose MAT-Agent, a novel multi-agent framework that reimagines training as a collaborative, real-time optimization process. By deploying au

Gourab Kumar Sar, Kevin O'Keeffe, Joao U. F. Lizarraga, Marcus A. M. de Aguiar

Swarmalators, entities that combine the properties of swarming particles with synchronized oscillations, represent a novel and growing area of research in the study of collective behavior. This review provides a comprehensive overview of the current state of swarmalator research, focusing on the interplay between spatial organization and temporal synchroniza

Cheuk Yin Lee, Samy Tindel

In this note we prove that the Fourier dimension of the graph $G(B)$ of a fractional Brownian motion $B$ with Hurst parameter $H\in(0,1/2)$ is equal to 1. This finishes to solve a conjecture by Fraser and Sahlsten. It also yields an exact formula for the gap $\dim_{\rm H}(G(B)) - \dim_{\rm F}(G(B))$ between the Hausdorff dimension and the Fourier dimension o

Muhammad Hamza, Rizwan Jafar

Social media has become an essential part of the digital age, serving as a platform for communication, interaction, and information sharing. Celebrities are among the most active users and often reveal aspects of their personal and professional lives through online posts. Platforms such as Twitter provide an opportunity to analyze language and behavior for u

Samanta Rodriguez, Yiming Dou, Miquel Oller, Andrew Owens

Today's visuo-tactile sensors come in many shapes and sizes, making it challenging to develop general-purpose tactile representations. This is because most models are tied to a specific sensor design. To address this challenge, we propose two approaches to cross-sensor image generation. The first is an end-to-end method that leverages paired data (Touch2Touc

Bin Wang, W. Jeffrey Johnston, Stefano Fusi

Recent experiments in neuroscience reveal that task-relevant variables are often encoded in approximately orthogonal subspaces of neural population activity. These disentangled, or abstract, representations have been observed in multiple brain areas and across different species. These representations have been shown to support out of distribution generalizat

Yufei Wang, Adriana Kovashka, Loretta Fernández, Marc N. Coutanche

We investigate a new setting for foreign language learning, where learners infer the meaning of unfamiliar words in a multimodal context of a sentence describing a paired image. We conduct studies with human participants using different image-text pairs. We analyze the features of the data (i.e., images and texts) that make it easier for participants to infe

Emile Martinez, Felipe Garrido-Lucero, Umberto Grandi

The assignment game models a housing market where buyers and sellers are matched, and transaction prices are set so that the resulting allocation is stable. Shapley and Shubik showed that every stable allocation is necessarily built on a maximum social welfare matching. In practice, however, stable allocations are rarely attainable, as matchings are often su

Kemal Bidzhiev, Stefano Grava, Pablo le Henaff, Mauro Mendizabal

Simulating the dynamics of neutral atom arrays is a challenging problem. To address this, we introduce two emulators, emu-sv and emu-mps, as computational backends for Pasqal's pulser package. Emu-sv is designed for high-precision state-vector simulations, giving the possibility to emulate systems of up to $\thicksim 27$ qubits on an A100 40GB GPU, making it