December 2025 arXiv papers — page 118

Priyankush Ghosh, Namrata Rani, Jeehyun Yang, Karen Willacy

Sulfur chemistry plays a central role in controlling the atmospheric structure, cloud formation, and composition of Venus and Venus-like exoplanets. However, key reactions involving ground- and excited-state sulfur species remain poorly constrained, and existing photochemical models often rely on incomplete or uncertain kinetic data under high-temperature, C

Yuqing Lei, Yingjun Du, Yawen Huang, Xiantong Zhen

Vision-language models (VLMs) such as CLIP exhibit strong zero-shot generalization but remain sensitive to domain shifts at test time. Test-time prompt tuning (TPT) mitigates this issue by adapting prompts with fixed augmentations, which may falter in more challenging settings. In this work, we propose Meta Test-Time Prompt Tuning (MetaTPT), a meta-learning

Giovanni Saraceno, Anand N. Vidyashankar, Claudio Agostinelli

We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger distance. We define an adversarial objective based on this divergence and study its statistical properties within a general parametric framework. We establish the existence, uniquenes

Tomaž Košir, Petra Lazić, Matjaž Omladič

Almost seventy years old Marshall-Olkin copulas, then wider Marshall copulas, and finally even wider shock model (SM) copulas constitute a substantial part of nowadays copula theory due to numerous applications. Recently, Christian Genest with some coauthors introduced a new stochastic model for a special subclass of SM copulas which gives not only a new ang

Abhay Srivastava, Sam Jung, Spencer Mateega

We introduce MARKET-BENCH, a benchmark that evaluates large language models (LLMs) on introductory quantitative trading tasks by asking them to construct executable backtesters from natural language strategy descriptions and market assumptions. Each instance specifies one of three canonical strategies: scheduled trading on Microsoft (NASDAQ: MSFT), pairs tra

Isamu Iwanari

Koszul duality is a duality between algebras that provides deep connections between seemingly different algebraic objects and has important applications in various areas of mathematics. The classical form of Koszul duality concerns associative algebras. In this paper, we develop a categorified generalization of Koszul duality that treats duality phenomena am

Francesco Camellini, Wissam Ghantous, Andrea M. Lanocita, Layna E. Mangiapanello

Suppose we have $n$ dice, each with $s$ faces (assume $s\geq n$). On the first turn, roll all of them, and remove from play those that rolled an $n$. Roll all of the remaining dice. In general, if at a certain turn you are left with $k$ dice, roll all of them and remove from play those that rolled a $k$. The game ends when you are left with no dice to roll.

Filippo Riva

The approximation of brittle laws via steeper and steeper cohesive profiles is validated within the mechanical setting of debonding models, which describe the detachment process of a peeled elastic adhesive membrane. In a quasistatic framework, energetic solutions to a suitably rescaled cohesive debonding problem, formulated in terms of displacements, are pr

Ege Atacan Doğan, Peter F. Patel-Schneider

Traditional ontology design emphasizes disjoint and exhaustive top-level distinctions such as continuant vs. occurrent, abstract vs. concrete, or type vs. instance. These distinctions are used to structure unified hierarchies where every entity is classified under a single upper-level category. Wikidata, by contrast, does not enforce a singular foundational

Eshwar Srinivasan, Ramesh Hariharasubramanian

The class of word-representable graphs, introduced in connection with the study of the Perkins semigroup by Kitaev and Seif, has attracted significant attention in combinatorics and theoretical computer science due to its deep connections with graph orientations and combinatorics on words. A graph is word-representable if and only if it admits a semi-transit

Amelia Drew, Ivan Rybak

In the standard picture of cosmic strings, cusps are generic features of Nambu-Goto loops where the string momentarily reaches the speed of light. They have a characteristic sharp profile, following $y \sim x^{2/3}$ in the $(x,y)$ plane, and produce strong gravitational-wave (GW) bursts with frequency-domain strain $\mathop{\tilde{\!\kappa}}(\omega) \propto

Matjaž Omladič, Damjan Škulj

Bivariate copulas with prescribed diagonal section were first studied by Bertino. Their maximality was studied so far only from the point of view of upper bounds which brings quasi-copulas into the picture and limits the resulting set substantially. We propose to study maximality of these families in the order theoretic sense. A copula C with given diagonal

Su Gao, André Nies, Gianluca Paolini

We prove that topological isomorphism on procountable groups is not classifiable by countable structures, in the sense of descriptive set theory. In fact, the equivalence relation $\ell_\infty$ expressing that two sequences of reals have a bounded difference is Borel reducible to it. This marks substantial progress on an open problem of Kechris, Nies and Ten

Eric Vansteenberghe

This paper investigates how dispersion in banks' subjective inflation forecasts is a channel of the transmission of monetary policy to credit supply. We extend the Monti-Klein model of monopolistic banking by incorporating risk aversion, subjective beliefs, and ambiguity aversion. The model predicts that greater inflation uncertainty or asymmetry in beliefs

Silouanos Brazitikos, Christos Pandis

We prove Hunter's conjecture on complete homogeneous symmetric polynomials. For even $n$ and every integer $k\geq 1$, we show that under the constraint $\sum_{i=1}^n a_i^2=1$ the global minimum of the even-degree polynomial $h_{2k}(a_1,\dots,a_n)$ is attained precisely at the half-plus/half-minus vector and we compute the optimal value in closed form. The pr

Héctor Cancio, Pere Masjuan

We compute the Form Factors of both neutral and charged pion using a non-perturbative running of the strong coupling constant $\alpha_s$ obtained using a double-dilaton Holographic QCD model. These form factors remain poorly understood in the intermediate-energy region, which marks the transition between low- and high-energy physics. In particular, experimen

Kyosuke Nishishita, Atsuki Sato, Yusuke Matsui

Count-Min Sketch (CMS) is a memory-efficient data structure for estimating the frequency of elements in a multiset. Learned Count-Min Sketch (LCMS) enhances CMS with a machine learning model to reduce estimation error under the same memory usage, but suffers from slow construction due to empirical parameter tuning and lacks theoretical guarantees on intolera

Saneesh Babu, Gabriele Di Stefano, Aparna Lakshmanan S

The mutual-visibility chromatic number of a graph $G$ is the smallest number of colors needed to color the vertices of $G$ such that each color class is a mutual-visibility set. In this paper, we prove that determining the mutual-visibility chromatic number of a graph is NP-complete even when restricted to the class of graphs having diameter four and mutual-

Anna Perekhodko, Robert Ślepaczuk

Accurate volatility forecasting is essential in banking, investment, and risk management, because expectations about future market movements directly influence current decisions. This study proposes a hybrid modelling framework that integrates a Stochastic Volatility model with a Long Short Term Memory neural network. The SV model improves statistical precis

Partha Ghose

Quantum measurement is commonly posed as a dynamical tension between linear Schr\"odinger evolution and an ad hoc collapse rule. I argue that the deeper conflict is logical: quantum theory is inherently contextual, whereas the classical tradition presupposes a single global, Boolean valuation. Building on Bohr's complementarity, the Einstein--Podolsky--Rosen

Aahsan Iqbal, Sohail Khalid, Mujeeb ur Rehman

Hematology analyzers are essential diagnostic and monitoring tools for detecting blood diseases. Although contemporary analyzers produce only basic insights, they are often not as detailed as required under the personalized medicine paradigm. Next-Generation Hematology Analyzers (NGHAs) are revolutionary newcomers in the field, with significant advantages ov

Azzurra Ciliberti

We study skew-symmetrizable cluster algebras $\mathcal{A}$ associated with unpunctured surfaces $\tilde{\mathbf{S}}$ endowed with an orientation-preserving involution $\sigma$. We give a geometric realization of such cluster algebras by showing that cluster variables of $\mathcal{A}$ correspond to $\sigma$-orbits of arcs of $\tilde{\mathbf{S}}$, while cluste

I Putu Andika Bagas Jiwanta, Ayu Purwarianti

Detecting video moments and highlights from natural-language queries have been unified by transformer-based methods. Other works use generative Multimodal LLM (MLLM) to predict moments and/or highlights as text timestamps, utilizing its reasoning capability. While effective, text-based generation cannot provide direct gradients for frame-level predictions be

Anika Sharma, Tianyi Niu, Emma Wrenn, Shashank Srivastava

The phenomenon of sound symbolism, the non-arbitrary mapping between word sounds and meanings, has long been demonstrated through anecdotal experiments like Bouba Kiki, but rarely tested at scale. We present the first computational cross-linguistic analysis of sound symbolism in the semantic domain of size. We compile a typologically broad dataset of 810 adj

Jianan Wang, Nailei Hei, Li He, Huanzhen Wang

Human animation aims to generate temporally coherent and visually consistent videos over long sequences, yet modeling long-range dependencies while preserving frame quality remains challenging. Inspired by the human ability to leverage past observations for interpreting ongoing actions, we propose FrameCache, a training-free, causality-consistent reference f

Zeyu Yao, Bowen Gang, Wenguang Sun

Dynamic decision-making in rapidly evolving research domains, including marketing, finance, and pharmaceutical development, presents a significant challenge. Researchers frequently confront the need for real-time action within a doubly sequential framework characterized by the continuous influx of high-volume data streams and the intermittent arrival of nove

HT To, S Nguyen, NH Pham

Multi-Agent Path Finding (MAPF) algorithms, including those for car-like robots and grid-based scenarios, face significant computational challenges due to expensive heuristic calculations. Traditional heuristic caching assumes that the heuristic function depends only on the state, which is incorrect in constraint-based search algorithms (e.g., CBS, MAPF-LNS,

Gabriele Bianchini, Orlando Luongo, Marco Muccino

The relativistic precession model (RPM) is widely-considered as a benchmark framework to interpret quasi-periodic oscillations (QPOs), albeit several observational inconsistencies suggest that the model remains incomplete. The RPM ensures \emph{structureless test particles} and attributes precession to geodesic motion alone. Here, we refine the RPM by incorp

Muhammad Junaid Khan, Rida Batool Sheraliyat

Cavity ring-down (CRD) spectroscopy represents a direct absorption technique of sample absorption measurement. Instead of measuring the amount of the absorbed light, this technique determines the rate at which light intensity decays inside an optical cavity. When using a pulsed or continuous-wave light source, CRD spectroscopy offers considerably higher sens

Maryam Mustafa, Umme Ammara, Amna Shahnawaz, Moaiz Abrar

We present the design, implementation, and in-situ deployment of a smartphone-based voice-enabled AI system for generating electronic medical records (EMRs) and clinical risk alerts in maternal healthcare settings. Targeted at low-resource environments such as Pakistan, the system integrates a fine-tuned, multilingual automatic speech recognition (ASR) model

Alessandro Ottazzi

We prove classical Taylor polynomial theorems for sub-Riemannian manifolds that are obtained as the submetric image of a Carnot group. For these theorems we also prove a sufficient condition for real analyticity and a result on L-harmonicity of Taylor polynomials.

Yinzhu Cheng, Haihua Xie, Yaqing Wang, Miao He

Semantic distance measurement is a fundamental problem in computational linguistics, providing a quantitative characterization of similarity or relatedness between text segments, and underpinning tasks such as text retrieval and text classification. From a mathematical perspective, a semantic distance can be viewed as a metric defined on a space of texts or

Jun-Muk Hwang, Qifeng Li

Let $G/H$ be a symmetric space of a complex linear algebraic group $G$ and let $X$ be a nonsingular equivariant compactification of $G/H$. We investigate the question: when are minimal rational curves on $X$ orbit-closures of 1-parameter subgroups of $G$? We show that this is the case if the variety of minimal rational tangents (VMRT) at a base point in $G/H

Sayantan Choudhury

This thesis investigates the design of algorithms for solving min-max optimization problems, which form the mathematical foundation of many modern applications in machine learning, game theory, and optimization. This work offers new theoretical insights and practical algorithms that address the limitations of existing methods in various problem settings.

Hironori Yamaguchi, Itsuki Shimamura, Akira Matsuo, Koichi Kindo

We report a spin-(1/2, 5/2) three-leg ladder realized in a radical-Mn polymer, exhibiting an antiferromagnetic transition and magnetization curves accurately described by classical mean-field theory. Although the underlying spin model intrinsically supports strong quantum fluctuations, as confirmed by quantum Monte Carlo simulations, the real system shows an

Murad Mehrab Abrar, Trevor W. Harrison

Accurate underwater localization remains a challenge for inexpensive autonomous platforms that require highfrequency position updates. In this paper, we present a robust, low-cost localization pipeline for buoyancy-driven microFloats operating in coastal waters. We build upon previous work by introducing a bidirectional acoustic Time-of-Flight (ToF) localiza

Anil Lal S, Mannu Yadav

This paper presents a unified and computationally efficient framework for predicting incompressible, irrotational (potential) flow around multiple immersed bodies in two-dimensional domains, with particular emphasis on quantifying irrotational interaction effects in multi-body configurations. The methodology integrates three components: a fast body-conformin

Yasemin Büyükçolak

Vertex-edge domination is a natural variant of domination in which a vertex ve-dominates an edge whenever it is incident to the edge or adjacent to one of its endpoints. A set of vertices is a vedominating set if it ve-dominates every edge of the graph. In this paper, we introduce the notion of wellve- dominated graphs, namely graphs in which all minimal ve-

Jonathan Spraggett

Fall recovery is a critical skill for humanoid robots in dynamic environments such as RoboCup, where prolonged downtime often decides the match. Recent techniques using deep reinforcement learning (DRL) have produced robust get-up behaviors, yet existing methods require training of separate policies for each robot morphology. This paper presents a single DRL

Tianyu Zhang, Dong Liu, Chang Wen Chen

Ultra-low bitrate image compression (below 0.05 bits per pixel) is increasingly critical for bandwidth-constrained and computation-limited encoding scenarios such as edge devices. Existing frameworks typically rely on large pretrained encoders (e.g., VAEs or tokenizer-based models) and perform transform coding within their generative latent space. While thes

Huichang Yun, Seungho Yoo

Mobile robots in large-scale indoor environments, such as hospitals and logistics centers, require accurate 3D spatial representations. However, 3D maps consume substantial memory, making it difficult to maintain complete map data within limited computational resources. Existing SLAM frameworks typically rely on geometric distance or temporal metrics for mem

Ye Li, Jiahe Feng, Yuan Meng, Kangye Ji

Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. The temporal complexity of embodied tasks demands a dynamic and adaptable computation mode. Static and lossy acceleration methods, such as quantization, fail to handle such dynamic embodied tasks, while sp

Muhsin Aljaf, Ilias Cholis

The merger rate of primordial black hole (PBH) binaries can be used to understand the source population of the merging black hole binaries observable through gravitational-waves (GWs) and also to constrain the possible contribution of PBHs to dark matter. In the literature, the PBH merger rate is calculated analytically, assuming that PBH binaries stay in is

Chandrasekhar Gokavarapu

Classical spectral graph theory and graph signal processing rely on a symmetry principle: undirected graphs induce symmetric (self-adjoint) adjacency/Laplacian operators, yielding orthogonal eigenbases and energy-preserving Fourier expansions. Real-world networks are typically directed and hence asymmetric, producing non-self-adjoint and frequently non-norma

Karthik Prabhakar

Nystagmus patients with photosensitivity face significant daily challenges due to involuntary eye movements exacerbated by environmental brightness conditions. Current assistive solutions are limited to symptomatic treatments without predictive personalization. This paper proposes NystagmusNet, an AI-driven system that predicts high-risk visual environments

Maaz Khan, Gul Sher Khan, Ahsan Raza, Pir Sami Ullah

The increasing use of machine learning (ML) for Just-In-Time (JIT) defect prediction raises concerns about privacy leakage from software analytics data. Existing anonymization methods, such as tabular transformations and graph perturbations, often overlook contextual dependencies among software metrics, leading to suboptimal privacy-utility tradeoffs. Levera

Hongyi Luo, Wenyu Song, Daniel K. C. So, Zahra Mobini

With the explosive growth of data traffic and the ubiquitous connectivity of wireless devices, the energy demands of wireless networks have inevitably escalated. Reconfigurable intelligent surface (RIS) has emerged as a promising solution for 6G networks due to its energy efficiency (EE) and low cost, while cell-free massive multiple-input multiple-output (C

Xiang-Yuan Li, Xin-Yu Zhang, ChuanFeng Yue

In this work, the rate law for inhomogeneous concentration distributions has been formulated, by applying spatial integration over the products of species concentrations. Reaction rates for typical reactions have been investigated by assuming a linear concentration distribution in the grid. A few examples of one-dimensional concentration distributions, strai

Hamed Baghal Ghaffari, Ahmed Souabni

In this paper, we investigate the properties of Clifford prolate spheroidal wave functions (CPSWFs) through their associated eigenvalues. We prove that the expansion coefficients in CPSWFs series decay as both the order and the homogeneity degree increase. By establishing a precise connection between the radial CPSWFs and the eigenfunctions of the finite Han

Nathalie Alexander, Arnaud Gucciardi, Umberto Michelucci

Accurate segmentation of infant brain MRI is essential for quantifying developmental changes in structure and complexity. However, ongoing myelination and reduced tissue contrast make automated segmentation particularly challenging. This study systematically compared segmentation accuracy and its impact on volumetric and fractal dimension (FD) estimates in i

Jakob Vanhoefer, Antonia Körner, Domagoj Doresic, Jan Hasenauer

Model selection is a pivotal process in the quantitative sciences, where researchers must navigate between numerous candidate models of varying complexity. Traditional information criteria, such as the corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC), and Mallows's $\mathit{C_p}$, are valuable tools for identifying optimal

Minheng Ni, Zhengyuan Yang, Yaowen Zhang, Linjie Li

We study technical image generation, where a model must synthesize information-dense, scientifically precise illustrations from detailed descriptions rather than merely produce visually plausible pictures. To quantify the progress, we introduce TechImage-Bench, a rubric-based benchmark that targets biology schematics, engineering/patent drawings, and general

Zhi Chen, Jingcai Guo, Taotao Cai, Yuxiang Cai

Recognizing unseen fine-grained categories demands a model that can distinguish subtle visual differences. This is typically achieved by transferring visual-attribute relationships from seen classes to unseen classes. The core challenge is attribute entanglement, where conventional models collapse distinct attributes like color, shape, and texture into a sin

Rheeya Uppaal, Phu Mon Htut, Min Bai, Nikolaos Pappas

Reasoning-augmented vision language models (VLMs) generate explicit chains of thought that promise greater capability and transparency but also introduce new failure modes: models may reach correct answers via visually unfaithful intermediate steps, or reason faithfully yet fail on the final prediction. Standard evaluations that only measure final-answer acc

Hideaki Fujiwara

We present a comparative bibliometric analysis of eight astronomy journals over 1996--2024, including \textit{Publications of the Astronomical Society of Japan} (PASJ). Using data from Scopus and SciVal, we extract annual indicators of publication activity and scholarly impact, analyze time series, citation distributions, and citation age profiles, and bench

Yiqi Zhu, Apurva Gandhi, Graham Neubig

Prior works on training software engineering agents have explored utilizing existing resources such as issues on GitHub repositories to construct software engineering tasks and corresponding test suites. These approaches face two key limitations: (1) their reliance on pre-existing GitHub repositories offers limited flexibility, and (2) their primary focus on

Mihika Dusad

The admissibility problem in integral geometry asks for which collections of affine subspaces the Radon transform remains injective. In the discrete setting, this becomes a purely combinatorial question about recovering a function on a finite vector space from its sums over a prescribed family of affine subspaces. In this paper, we study the spatial X-ray tr

Sylvester Aboagye, Telex M. N. Ngatched

Visible light communication (VLC) is expected to be a key component of future wireless networks due to its abundant license-free spectrum, inherent high-level security, and the already deployed lighting infrastructure. VLC performance, however, depends on device orientation and the availability of an unobstructed line-of-sight (LoS) link, with transmitter se

Jun Li

Chapman-Enskog analysis of the lattice Boltzmann method (LBM) is adopted to recover the Navier-Stokes (N-S) equation for the magnetohydrodynamic (MHD) flows driven by external body forces other than the induced Lorentz force. Various numerical schemes are discussed for the implementation of external body forces, leading to different artefact terms. An order-

Ayush Noori, Joaquín Polonuer, Katharina Meyer, Bogdan Budnik

Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD)

Elizabeth Irenne Yuwono, Dian Tjondronegoro, Shawn Hunter, Amber Marshall

Financial exclusion remains a major barrier to digital public service delivery in resource-constrained and archipelagic nations. Traditional policy evaluations rely on retrospective data, limiting the ex-ante intelligence needed for agile resource allocation. This study introduces a predictive simulation framework to support anticipatory governance within go

Longchao Da, David Isele, Hua Wei, Manish Saroya

Being able to anticipate the motion of surrounding agents is essential for the safe operation of autonomous driving systems in dynamic situations. While various methods have been proposed for trajectory prediction, the current evaluation practices still rely on error-based metrics (e.g., ADE, FDE), which reveal the accuracy from a post-hoc view but ignore th

Yuting Tang, Weibang Jiang, Shanglin Li, Yong Li

Large-scale EEG foundation models have shown strong generalization across a range of downstream tasks, but their training remains resource-intensive due to the volume and variable quality of EEG data. In this work, we introduce EEG-DLite, a data distillation framework that enables more efficient pre-training by selectively removing noisy and redundant sample

Zahra Dehghanian, Morteza Abolghasemi, Hamid Beigy, Hamid R. Rabiee

Controllable video synthesis is a central challenge in computer vision, yet current models struggle with fine grained control beyond textual prompts, particularly for cinematic attributes like camera trajectory and genre. Existing datasets often suffer from severe data imbalance, noisy labels, or a significant simulation to real gap. To address this, we intr

Indranil Bhattacharjee, Vartika Narayani Srinet, Anirudha Bhattacharjee, Braj Bhushan

Understanding emotional responses in children with Autism Spectrum Disorder (ASD) during social interaction remains a critical challenge in both developmental psychology and human-robot interaction. This study presents a novel deep learning pipeline for emotion recognition in autistic children in response to a name-calling event by a humanoid robot (NAO), un

Jiangen He, Jiqun Liu

Conversational search systems increasingly provide source citations, yet how citation or source presentation formats influence user engagement remains unclear. We conducted a crowdsourcing user experiment with 394 participants comparing four source presentation designs that varied citation visibility and accessibility: collapsible lists, hover cards, footer

Jeongjun Park, Sunwook Hwang, Hyeonho Noh, Jin Mo Yang

Distracted driving contributes to fatal crashes worldwide. To address this, researchers are using driver activity recognition (DAR) with impulse radio ultra-wideband (IR-UWB) radar, which offers advantages such as interference resistance, low power consumption, and privacy preservation. However, two challenges limit its adoption: the lack of large-scale real

Xingyu Li, Qi Liu, Yufeng Li

Safety and security are critical to the reliable operation of connected and automated vehicles (CAVs). While existing research has identified correlations between the two domains, a theoretical framework to analyze their interaction mechanisms and guide co-design remains lacking. To address this gap, this paper proposes a copula-based joint safety-security a

Peizheng Li, Ioannis Mavromatis, Ajith Sahadevan, Tim Farnham

We present a large-scale, longitudinal visual dataset of urban streetlights captured by 22 fixed-angle cameras deployed across Bristol, U.K., from 2021 to 2025. The dataset contains over 526,000 images, collected hourly under diverse lighting, weather, and seasonal conditions. Each image is accompanied by rich metadata, including timestamps, GPS coordinates,

Yingqi Wen, Weidong Mei, Yike Xie, Beixiong Zheng

Conventional fixed-orientation antenna (FOA) arrays offer limited degrees of freedom (DoF) for flexible beamforming such as null steering. To address this limitation, we propose a new rotatable antenna array (RAA) architecture in this paper, which enables three-dimensional (3D) rotational control of an antenna array to provide enhanced spatial flexibility fo

Eric J. Elias, Michael Esswein, Jonathan P. How, David W. Miller

As the popularity of on-orbit operations grows, so does the need for precise navigation around unknown resident space objects (RSOs) such as other spacecraft, orbital debris, and asteroids. The use of Simultaneous Localization and Mapping (SLAM) algorithms is often studied as a method to map out the surface of an RSO and find the inspector's relative pose us

Yossi Azar, Niv Buchbinder, Tomer Epshtein

We study the classic fully dynamic load balancing problem on unrelated machines where jobs arrive and depart over time and the goal is minimizing the maximum load, or more generally the l_p-norm of the load vector. Previous work either studied the clairvoyant setting in which exact durations are known to the algorithm, or the unknown duration setting in whic

Seokcheon Lee

Cosmological constraints on a time-varying dark energy equation of state are fundamentally limited by the integral structure through which the equation of state enters cosmological observables. We rigorously derive the linear response kernel that maps perturbations in the equation of state \omega(z) to comoving distance fluctuations \delta D(z). By adopting

Predrag K. Nikolić, Robert Prentner

Large language models (LLMs) have often been characterized as "stochastic parrots" that merely reproduce fragments of their training data. This study challenges that assumption by demonstrating that, when placed in an appropriate dialogical context, LLMs can develop emergent conceptual structures and exhibit interaction-driven (re-)structuring of cognitive i

Rubing Han, Shuonan Wu, Hao Zhou

We develop and analyze a local discontinuous Galerkin (LDG) method for solving integral fractional Laplacian problems on bounded Lipschitz domains. The method is based on a three-field mixed formulation involving the primal variable, its gradient, and the corresponding Riesz potential, yielding a flux-based structure well suited for LDG discretizations while

Ercan Erkalkan, Vedat Topuz, Ayça Ak

This study introduces a lightweight perimeter tracking method designed for micro UAV teams operating over wildfire environments under limited bandwidth conditions. Thermal image frames generate coarse hot region masks through adaptive thresholding and morphological refinement, while RGB frames contribute edge cues and suppress texture related false detection

Kathi Lakshmi Mani Thirdhana

Prime generation is a fundamental task in cryptography, number theory, and randomized algorithms. While the classical Sieve of Eratosthenes is simple and efficient in theory, its practical performance on modern central processing units is often limited by memory access inefficiencies. This paper introduces a cache-aware hybrid sieve that integrates segmentat

Jirui Jin, Cheng Zeng, Pawan Prakash, Ellad B. Tadmor

Key objectives in conditional molecular generation include ensuring chemical validity, aligning generated molecules with target properties, promoting structural diversity, and enabling efficient sampling for discovery. Recent advances in computer vision introduced a range of new guidance strategies for generative models, many of which can be adapted to suppo

Minghao Mou, Junjie Qin

Transportation electrification introduces strong coupling between the power and transportation systems. In this paper, we generalize the classical notion of Braess' paradox to coupled power and transportation systems, and examine how the cross-system coupling induces new types of Braess' paradoxes. To this end, we model the power and transportation networks

Xiaoxuan Tang, Xinping Lei, Chaoran Zhu, Shiyun Chen

Music-to-Video (M2V) generation for full-length songs faces significant challenges. Existing methods produce short, disjointed clips, failing to align visuals with musical structure, beats, or lyrics, and lack temporal consistency. We propose AutoMV, a multi-agent system that generates full music videos (MVs) directly from a song. AutoMV first applies music

Bin Xu, Ayan Banerjee, Sandeep K. S. Gupta

Digital twinning enables real-time simulation and predictive modeling by maintaining a continuously updated virtual representation of a physical system. In mission-critical applications, such as mid-air collision avoidance, these models must operate online with extremely low latency to ensure safety. However, executing complex Model Recovery (MR) pipelines o

Bin Xu, Ayan Banerjee, Midhat Urooj, Sandeep K. S. Gupta

Digital twins (DTs) can enable precision healthcare by continually learning a mathematical representation of patient-specific dynamics. However, mission critical healthcare applications require fast, resource-efficient DT learning, which is often infeasible with existing model recovery (MR) techniques due to their reliance on iterative solvers and high compu

Min-Won Seo, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia

Active robot exploration requires decision-making processes that integrate localization and mapping under tightly coupled uncertainty. However, managing these interdependent uncertainties over long-term operations in large-scale environments rapidly becomes computationally intractable. To address this challenge, we propose B-ActiveSEAL, a scalable informatio

Xuancheng Xu, Yaning Li, Sisi You, Bing-Kun Bao

Customized video generation aims to produce videos that faithfully preserve the subject's appearance from reference images while maintaining temporally consistent motion from reference videos. Existing methods struggle to ensure both subject appearance similarity and motion pattern consistency due to the lack of object-level guidance for subject and motion.

Ramon van den Akker, Bas J. M. Werker, Bo Zhou

Van den Akker, Werker, and Zhou (2025) showed that the limit experiment, in the sense of H\a'{a}jek-Le Cam, for (contextual) bandits whose arms' expected payoffs differ by $O(T^{-1/2})$, is Locally Asymptotically Quadratic (LAQ) but highly non-standard, being characterized by a system of coupled stochastic differential equations. The present paper considers

Zachary J. Wall, Justin D. Piel, Samuel R. Vizvary, Michael Bareian

We report a theoretical and experimental investigation of autoionizing resonances from the $5d6p\,{}^3\mathrm{D}_1^o$ manifold in neutral barium for efficient loading of ion traps. Our calculations predict large resonant cross sections for many narrow autoionizing resonances, but we find experimentally that for most of these, Doppler broadening during trap l

Xiang-dong Shi, Sheng-bang Qian, Li-ying Zhu, Lin-jia Li

We present a systematic investigation of $\beta$ Cephei (BCEP) stars by integrating photometric data from the Transiting Exoplanet Survey Satellite (TESS) with astrometric parameters from Gaia Data Release 3. Utilizing TESS's short-cadence (SC) and full-frame image (FFI) photometry, along with Gaia parallaxes and temperatures derived from the Extended Stella

Binze Tang, Chon-Hei Lo, Tiancheng Liang, Jiani Hong

Premelting plays a key role across physics, chemistry, materials and biology sciences but remains poorly understood at the atomic level due to surface characterization limitations. We report the discovery of a novel amorphous ice layer (AIL) preceding the quasi-liquid layer (QLL) during ice premelting, enabled by a machine learning framework integrating atom

Songlin Cai, Xuan Liu, Xianwen Wang

Visa regimes constitute significant institutional barriers to the cross-border mobility of researchers. Utilizing China's phased implementation of a unilateral visa-free policy since 2023 as a quasi-natural experiment, this study employs a staggered difference-in-differences design to assess the policy's effect on international scientific collaboration. Resu

Mahmud Azam, Steven Rayan

In arXiv:2407.11958, a moduli stack parametrizing $I$--indexed diagrams of Higgs bundles over a base stack $X$ was constructed for any finite simplicial set $I$, inspiring speculations about extending the non-Abelian Hodge correspondence to these moduli stacks. In the present work, we formalize the de Rham side of this conjectural extension. We construct mod

David Gamba, Daniel M. Romero, Grant Schoenebeck

Rising animosity toward ideological opponents poses critical societal challenges. We introduce and test the Ideological Turing Test, a gamified framework requiring participants to adopt and defend opposing viewpoints, to reduce affective animosity and affective polarization. We conducted a mixed-design experiment ($N = 203$) with four conditions: modality (d

Mohammad Taghi Dabiri, Hossein Safi, Rula Ammuri, Mazen Hasna

High-speed vehicular environments require optical systems capable of joint sensing, positioning, and communication (JSPC) without mechanical tracking. Existing optical and integrated sensing-communication approaches often rely on point-source emitters or camera-based receivers, limiting spatial coverage and update rate under highway dynamics. This work intro

Jack Chen-An Chou, Tianyi Yu

Lam, Lee, and Shimozono introduced the double Stanley symmetric functions in their study of the equivariant geometry of the affine Grassmannian. They proved that the associated double Edelman--Greene coefficients, the double Schur expansion coefficients of these functions, are positive, a result later refined by Anderson. They further asked for a combinatori

Liangshou Zhang, Yanbin Liu, Hanchi Liu, Zheng Sun

Rehabilitation exoskeletons have shown promising results in promoting recovery for stroke patients. Accurately and timely identifying the motion intentions of patients is a critical challenge in enhancing active participation during lower limb exoskeleton-assisted rehabilitation training. This paper proposes a Dual-Channel Attentive Fusion Network (DCAF-Net)

Yihan Wang, Annan Yu, Lujun Zhang, Charuleka Varadharajan

Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on recurrent Long Short-Term Memory (LSTM) backbones and single-step training objectives, which limit their ability to capture long-range dependencies and produce coherent forecast tr

Xinyu Gao

Knowledge Graphs have become fundamental infrastructure for applications such as intelligent question answering and recommender systems due to their expressive representation. Nevertheless, real-world knowledge is heterogeneous, leading to a pronounced long-tailed distribution over relations. Previous studies mainly based on metric matching or meta learning.

Mohammad Taghi Dabiri, Rula Ammuri, Mazen Hasna, Khalid Qaraqe

Accurate and low-latency positioning is a key enabler for optical links with Low Earth Orbit (LEO) satellites, where millisecond-level beam alignment is required to maintain reliable high-data-rate communication. This paper presents a learning-driven dual-line laser scanning framework for fast and precise satellite positioning. Unlike conventional Gaussian-b

Jiawei Huang, Di Zhang, Yuanhao Cui, Xiaowen Cao

Wireless sensing has become a fundamental enabler for intelligent environments, supporting applications such as human detection, activity recognition, localization, and vital sign monitoring. Despite rapid advances, existing datasets and pipelines remain fragmented across sensing modalities, hindering fair comparison, transfer, and reproducibility. We propos

Siddhartha S. Nathan, Danilo Puggioni, Linding Yuan, James M. Rondinelli

Many studies of non-relativistic spin-splitting (NRSS), or altermagnetism, have focused on idealized, perfectly ordered crystals, relying on symmetry-based approaches to identify candidate materials. Here, we theoretically investigate how local short-range ordering (SRO) influences NRSS of energy bands in partially ordered collinear antiferromagnetic iron ox

Mohammad Taghi Dabiri, Meysam Ghanbari, Rula Ammuri, Mazen Hasna

Accurate characterization of free-space optical (FSO) channels requires joint estimation of transmitter pointing errors, receiver angle-of-arrival (AoA) fluctuations, and turbulence-induced fading. However, existing literature addresses these impairments in isolation, since their multiplicative coupling in the received signal severely limits conventional est