December 2025 arXiv papers — page 63

Ilya Petrovanov, Anton Sergeev

Transport coding reduces message delay in packet-switched networks by introducing controlled redundancy at the transport layer: $k$ original packets are encoded into $n\ge k$ coded packets, and the message is reconstructed after the first $k$ successful deliveries, effectively shifting latency from the maximum packet delay to the $k$-th order statistic. We p

Qiong Lou, Han Yang, Fang Lu

Bone Age Assessment (BAA) is a widely used clinical technique that can accurately reflect an individual's growth and development level, as well as maturity. In recent years, although deep learning has advanced the field of bone age assessment, existing methods face challenges in efficiently balancing global features and local skeletal details. This study aim

Tatiana Tatarenko, Lucas Wey Hacker

We study generalized Nash equilibrium (GNE) problems in games with quadratic costs and individual linear equality constraints. Departing from approaches that require strong monotonicity and/or shared constraints, we reformulate the KKT conditions of the (generally non-monotone) games into a tractable convex program whose objective satisfies the Polyak-Lojasi

Guo Chen, Junjie Huang, Huaijin Xie, Fei Sun

Retrieval-Augmented Generation (RAG) effectively enhances Large Language Models (LLMs) by incorporating retrieved external knowledge into the generation process. Reasoning models improve LLM performance in multi-hop QA tasks, which require integrating and reasoning over multiple pieces of evidence across different documents to answer a complex question. Howe

Emmanuel Gottlob, David Gröters, Ulrich Schneider

Quasicrystals, structures that are ordered yet aperiodic, defy conventional band theory, confining most studies to finite-size real-space numerics. We overcome this limitation with a configuration-space framework that predicts and explains the positions and origins of energy gaps in quasicrystalline potentials. We find that a hierarchy of gaps stems from res

Meidong Xia, Min Fan, Wei Xu, Haiming Wang

Operating power amplifiers (PAs) at lower input back-off (IBO) levels is an effective way to improve PA efficiency, but often introduces severe nonlinear distortion that degrades transmission performance. Amplitude-phase-time block modulation (APTBM) has recently emerged as an effective solution to this problem. The intrinsic amplitude and phase constraints

Yi-Ran Xiao, Hua-Lei Yin, Wen-Ji Hua, Xiao-Yu Cao

Source-independent quantum secret sharing (SI QSS), while essential for secure multiuser cryptographic operations in quantum networks, faces significant implementation challenges stemming from the inherent complexity of generating and distributing multipartite entangled states. Recently, a resource-efficient SI QSS protocol utilizing entangled photon pairs c

Sergey Bobkov, Friedrich Götze

Energy bounds for Kantorovich transport distances are developed for convex cost functions. The main results extend estimates due to M. Ledoux for the Kantorovich distances $W_p$.

Tianlun Liu, Zhiliang Tian, Zhen Huang, Xingzhi Zhou

Text understanding often suffers from domain shifts. To handle testing domains, domain adaptation (DA) is trained to adapt to a fixed and observed testing domain; a more challenging paradigm, test-time adaptation (TTA), cannot access the testing domain during training and online adapts to the testing samples during testing, where the samples are from a fixed

Jiahao Lu

Regularization techniques such as L2 regularization (Weight Decay) and Dropout are fundamental to training deep neural networks, yet their underlying physical mechanisms regarding feature frequency selection remain poorly understood. In this work, we investigate the Spectral Bias of modern Convolutional Neural Networks (CNNs). We introduce a Visual Diagnosti

Sun-Hyun Youn

Most conventional studies on tennis serve biomechanics rely on phenomenological observations comparing professional and amateur players or, more recently, on AI-driven statistical analyses of motion data. While effective at describing \textit{what} elite players do, these approaches often fail to explain \textit{why} such motions are physically necessary fro

Paolo Bonicatto, Panu Lahti, Enrico Pasqualetto

We prove that a set of finite perimeter is indecomposable if and only if it is, up to a choice of suitable representative, connected in the 1-fine topology. This gives a topological characterization of indecomposability which is new even in Euclidean spaces. Our approach relies crucially on the metric space theory of functions of bounded variation, and we ar

Eren Caglar, Amirkia Rafiei Oskooei, Mehmet Kutanoglu, Mustafa Keles

This paper introduces a parallel and asynchronous Transformer framework designed for efficient and accurate multilingual lip synchronization in real-time video conferencing systems. The proposed architecture integrates translation, speech processing, and lip-synchronization modules within a pipeline-parallel design that enables concurrent module execution th

Vincent Bezold, Patrick Wagner, Jakob Hofmann, Marco Huber

This paper presents a trustworthy reinforcement learning approach for the control of industrial compressed air systems. We develop a framework that enables safe and energy-efficient operation under realistic boundary conditions and introduce a multi-level explainability pipeline combining input perturbation tests, gradient-based sensitivity analysis, and SHA

Aram Akram Mohammed

The genus of Rumex from the Polygonaceae family is widespread in the world, particularly in the northern hemisphere, and includes about 250 species. The species of this genus are used for medicinal purposes and their allelopathic impacts. Regarding allelopathy, many allelochemicals have been detected in different Rumex species. Therefore, plant extracts, lea

Baisali Ghadai, Kirti Kirti, Abinash Mishra, Sucheta Mondal

Thin amorphous-CoFeB (a-CFB) is deposited by rf-magnetron sputtering on a self-oxidized Si (100) substrate with different film thicknesses ranging from 0.7 nm to 20 nm. The 5-nm-thick a-CFB film is capped with a W layer for comparison. The surface morphology is investigated by using the atomic force microscopy technique. The low roughness of all the surface

Isabela Belciug, Simon Ferreira, Charles K. Assaad

Observational studies in fields such as epidemiology often rely on covariate adjustment to estimate causal effects. Classical graphical criteria, like the back-door criterion and the generalized adjustment criterion, are powerful tools for identifying valid adjustment sets in directed acyclic graphs (DAGs). However, these criteria are not directly applicable

Philipp Langsteiner, Jan-Niklas Dihlmann, Hendrik P. A. Lensch

Manual modeling of material parameters and 3D geometry is a time consuming yet essential task in the gaming and film industries. While recent advances in 3D reconstruction have enabled accurate approximations of scene geometry and appearance, these methods often fall short in relighting scenarios due to the lack of precise, spatially varying material paramet

Francesco Camilli, Pierluigi Contucci, Emanuele Mingione

We investigate multiscale Gibbs measures from a variational and probabilistic viewpoint, focusing on the structural asymmetry among conditional entropies that characterizes their construction. We show how this asymmetry emerges both from variational principles with entropic constraints and from stochastic processes with reinforcement. We thus introduce the r

Zeyu Zhang, Wei Zhai, Jian Yang, Yang Cao

The creation of high-fidelity, physically-based rendering (PBR) materials remains a bottleneck in many graphics pipelines, typically requiring specialized equipment and expert-driven post-processing. To democratize this process, we present MatE, a novel method for generating tileable PBR materials from a single image taken under unconstrained, real-world con

Melody Y. Guan, Miles Wang, Micah Carroll, Zehao Dou

Observability into the decision making of modern AI systems may be required to safely deploy increasingly capable agents. Monitoring the chain-of-thought (CoT) of today's reasoning models has proven effective for detecting misbehavior. However, this "monitorability" may be fragile under different training procedures, data sources, or even continued system sc

Ritesh Konka, Pranali Kurani

Instagram has become a key platform for global food brands to engage diverse audiences through visual storytelling. While previous research emphasizes content-based strategies, this study bridges the gap between content and context by examining how aesthetic elements -- such as dominant image colors and caption sentiment -- and structural factors like GDP, p

Harsh Rathva, Ojas Srivastava, Pruthwik Mishra

We introduce Embedded Safety-Aligned Intelligence (ESAI), a theoretical framework for multi-agent reinforcement learning that embeds alignment constraints directly into agents internal representations using differentiable internal alignment embeddings. Unlike external reward shaping or post-hoc safety constraints, internal alignment embeddings are learned la

Hao Chen, Shashank G. Markande, Matthias Saba, Gerd E. Schröder-Turk

This paper provides a construction and existence proof for a 1-parameter family of chiral unbalanced triply-periodic minimal surfaces of genus 4. We name these {\textit{gyrating H'-T} surfaces, because they are related to Schoen's H'-T surfaces in a similar way as the Gyroid is to the Primitive surface. Their chirality is manifest in a screw symmetry of orde

Deepak Dhar, Tiago J. Oliveira, R. Rajesh, Jürgen F. Stilck

We consider the number of ways all the sites of a kagome lattice can be covered by non-overlapping linear rigid rods where each rod covers 3 sites. We establish a 2-to-1 correspondence between the configurations of trimers on the kagome lattice to the covering by dimers of a related hexagonal lattice to show that entropy of coverings per trimer $s_{\text{tri

Alvaro Becerra, Ruth Cobos

Providing timely and meaningful feedback remains a persistent challenge in higher education, especially in large courses where teachers must balance formative depth with scalability. Recent advances in Generative Artificial Intelligence (GenAI) offer new opportunities to support feedback processes while maintaining human oversight. This paper presents an stu

Remo Marini, Riccardo Arpe

Assessing cyber risk in complex IT infrastructures poses significant challenges due to the dynamic, interconnected nature of digital systems. Traditional methods often fall short, relying on static and largely qualitative models that do not scale with system complexity and fail to capture systemic interdependencies. In this work, we introduce a novel quantit

Xiaojie Chen, Bo Yang, Naoki Shinohara, Changjun Liu

In this article, we investigate the performance of an injection-locked 5.8-GHz continuous-wave magnetron with various load reflection levels. The load reflection is introduced to an equivalent magnetron model to theoretically evaluate the system performance. The effects of different load reflection levels on the magnetron's output are numerically analyzed. E

Michał Marcinkowski, Piotr Mizerka

Let $G$ be a finite group generated by $k$ elements. The well-known product replacement algorithm provides an effective method for sampling generating sets of $G$. We study a refinement of this algorithm that is designed to output individual elements of $G$. We show that after $O(k^2\log|G|)$ steps, the distribution of the output is close to uniform on $G$,

Yuanhao Xi, Xiaohuan Bing, Ramin Yahyapour

Vision Language Models (VLMs) have undergone significant advancements, particularly with the emergence of mobile-oriented VLMs, which offer a wide range of application scenarios. However, the substantial computational requirements for training these models present a significant obstacle to their practical application. To address this issue, Low-Rank Adaptati

Tanjim Taharat Aurpa, Md Shoaib Ahmed, Md Mahbubur Rahman, Md. Golam Moazzam

People use search engines for various topics and items, from daily essentials to more aspirational and specialized objects. Therefore, search engines have taken over as peoples preferred resource. The How To prefix has become familiar and widely used in various search styles to find solutions to particular problems. This search allows people to find sequenti

Suhas Vittal, Moinuddin Qureshi

This paper studies the impact of DRAM writes on DDR5-based system. To efficiently perform DRAM writes, modern systems buffer write requests and try to complete multiple write operations whenever the DRAM mode is switched from read to write. When the DRAM system is performing writes, it is not available to service read requests, thus increasing read latency a

A. Zaslavskiy, A. Skopenkov

This paper shares some experience in advanced mathematical education. We show how a high school student can be naturally and gradually introduced to basic steps of scientific research: developing intuition by finding and correcting mistakes through discussions and writing a paper, (transparent) anonymous peer review, recognition and award. We show that most

Jose I. Aizpurua

Power transformers are critical assets in power networks, whose reliability directly impacts grid resilience and stability. Traditional condition monitoring approaches, often rule-based or purely physics-based, struggle with uncertainty, limited data availability, and the complexity of modern operating conditions. Recent advances in machine learning (ML) pro

Amir Eskandari-asl, Adolfo Avella

We employ the Dynamical Projective Operatorial Approach (DPOA) to investigate the ultrafast optical excitations of germanium under intense, ultrashort pump pulses. The method has very low resource demand relative to many other available approaches and enables detailed calculation of the residual electron and hole populations induced by the pump pulse. It pro

Jose I. Aizpurua

The integration of physics-based knowledge with machine learning models is increasingly shaping the monitoring, diagnostics, and prognostics of electrical transformers. In this two-part series, the first paper introduced the foundations of Neural Networks (NNs) and their variants for health assessment tasks. This second paper focuses on integrating physics a

Sudip Chakrabarty, Pappu Bishwas, Rajdeep Chatterjee

Speech Emotion Recognition (SER) systems often degrade in performance when exposed to the unpredictable acoustic interference found in real-world environments. Additionally, the opacity of deep learning models hinders their adoption in trust-sensitive applications. To bridge this gap, we propose a Hybrid Transformer-CNN framework that unifies the contextual

José M. Zapata

We establish sharp large-deviation asymptotic estimates for the maximum order statistic of i.i.d.\ standard normal random variables on all Borel subsets of the positive real line. This result yields more accurate tail approximations than the classical Gumbel limit.

Lijun Bo, Weiqiang Chang

Data markets are emerging as key mechanisms for trading personal and organizational data. Traditional data pricing studies -- such as query-based or arbitrage-free pricing models -- mainly emphasize price consistency and profit maximization but often neglect privacy constraints and strategic interactions. The widespread adoption of differential privacy (DP)

Gabrielle Laloy-Borgna, Maxime Fauconnier, Sibylle Grégoire, Stefan Catheline

The mechanical properties of cells play key roles in their physiology, function, physiological and pathological transformations. Micro-elastography has recently emerged as a promising tool to estimate cellular viscoelastic properties within a millisecond, without the need for mechanical modeling. Here, we report a fully contactless approach to single-cell mi

Matthew Deakin, Rahmat Heidari, Xu Deng

The mitigation of unbalanced grid voltages or currents by voltage source converters results in power ripple on the dc link, and is a key converter design parameter due to hardware or stability considerations. Despite the importance of this issue for system design and operation, the use of Optimal Power Flow (OPF)-based methods capturing the interaction betwe

Wenkai Li, Lynnette Hui Xian Ng, Andy Liu, Daniel Fried

The study of negotiation styles dates back to Aristotle's ethos-pathos-logos rhetoric. Prior efforts primarily studied the success of negotiation agents. Here, we shift the focus towards the styles of negotiation strategies. Our focus is the strategic dialogue board game Diplomacy, which affords rich natural language negotiation and measures of game success.

Zidong Gu, Shoufu Tian

Object detection in aerial imagery is a critical task in applications such as UAV reconnaissance. Although existing methods have extensively explored feature interaction between different modalities, they commonly rely on simple fusion strategies for feature aggregation. This introduces two critical flaws: it is prone to cross-modal noise and disrupts the hi

Ruiting Mao, Jeong Eun Lee, Matthew C. Edwards

The Laser Interferometer Space Antenna (LISA) data stream will inevitably contain gaps due to maintenance and environmental disturbances, introducing nonstationarities and spectral leakage that compromise standard frequency-domain likelihood evaluations. We present a scalable Simulation-Based Inference (SBI) framework capable of robust parameter estimation d

Jaume Alonso, Yuri B. Suris

Discrete Painlev\'e equations are integrable two-dimensional birational maps associated to a family of generalized Halphen surfaces. The latter can be seen either as $\mathbb P^2$ blown up at nine points or as $\mathbb P^1\times\mathbb P^1$ blown up at eight points. These maps become autonomous if the blow-up points are in a special position (support a penci

Shuai Wang, Yanmin Qian, Kai Yu

Developing a good speaker embedding has received tremendous interest in the speech community, with representations such as i-vector and d-vector demonstrating remarkable performance across various tasks. Despite their widespread adoption, a fundamental question remains largely unexplored: what properties are actually encoded in these embeddings? To address t

Mustafa Ghulam, Ramin Mehrabifard, Adriana Mišúthová, Zuzana Lukačová

This study explores plasma-activated water (PAW) effects on Common bean growth in laboratory and pot trials. Three treatments were assessed: PAW priming, spraying, and their combination. Laboratory trials showed no germination improvement. However, pot trials revealed notable increases in seedling length, biomass, and antioxidant enzyme activity. Enzymes SOD

David Mellado-Alcedo, Niurka R. Quintero

The instabilities observed in direct numerical simulations of the Gross-Neveu equation under linear and harmonic potentials are studied. The Lakoba algorithm, based on the method of characteristics, is performed to numerically obtain the two spinor components. We identify non-conservation of energy and charge in simulations with instabilities and we find tha

Xitong Ling, Minxi Ouyang, Xiaoxiao Li, Jiawen Li

Multiple Instance Learning (MIL) has enabled weakly supervised analysis of whole-slide images (WSIs) in computational pathology. However, traditional MIL approaches often lose crucial contextual information, while transformer-based variants, though more expressive, suffer from quadratic complexity and redundant computations. To address these limitations, we

Tianji Jiang, Wenqi Li, Jiqun Liu

In this study, we conducted semi-structured interviews with 21 IIR researchers to investigate their data reuse practices. This study aims to expand upon current findings by exploring IIR researchers' information-obtaining behaviors regarding data reuse. We identified the information about shared data characteristics that IIR researchers need when evaluating

Roberto Sanchez Peregrino

This paper builds on the research initiated by Boyadzhiev, but introduces generalized harmonic numbers, \[ H_n(\alpha)= \sum_{k=1}^n \frac{\alpha^{k}}{k}, \] which enable the derivation of new identities as well as the reformulation of existing ones. We also generalize Gould's identity, allowing classical harmonic numbers to be replaced by their generalized

Moritz Wolf, Sören Schweers, Philipp Maass

Particle currents flowing against an external driving are a fascinating phenomenon in both single-particle and interacting many-particle systems. Underlying physical mechanisms of such current reversals are not fully understood yet. Predicting their appearance is difficult, in particular for interaction-induced ones that emerge upon changes of the particle d

Zhangshuo Qi, Jingyi Xu, Luqi Cheng, Shichen Wen

Place recognition is a critical component of autonomous vehicles and robotics, enabling global localization in GPS-denied environments. Recent advances have spurred significant interest in multimodal place recognition (MPR), which leverages complementary strengths of multiple modalities. Despite its potential, most existing MPR methods still face three key c

Xianming Liu, Xu Sun

In this paper, we derive several criteria for (weak) synchronization by noise without the global swift transitivity property. Our sufficient conditions for (weak) synchronization are necessary and can be applied to scenarios involving degenerate or non-Gaussian noise. These results partially answer the open question posed by Flandoli et al. (Probab Theory Re

Ndagijimana Cyprien, Mehdi Sookhak, Hosein Zarini, Chandra N Sekharan

Joint deployment of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) has been shown to be an effective method to establish communications in areas affected by disasters. However, ensuring good Quality of Services (QoS) while using as few UAVs as possible also requires optimal positioning and trajectory planning for UAVs and UGVs. This pape

Hai-Cheng Feng, Sha-Sha Li, Mouyuan Sun, Ciro Pinto

Accretion onto supermassive black holes (SMBHs) powers active galactic nuclei (AGNs) and drives feedback that shapes galaxy evolution. Constraining AGN accretion disk structure is therefore essential for understanding black hole growth and feedback processes. However, direct constraints on disk size remain rare -- particularly from long-term, multi-season sp

Runze You, Shi Pu

Federated Learning (FL) methods are often designed for specific client participation patterns, limiting their applicability in practical deployments. We introduce the FedSUM family of algorithms, which supports arbitrary client participation without additional assumptions on data heterogeneity. Our framework models participation variability with two delay me

Adi Armoni, Dorin Weissman

Following Polchinski and Strassler [1] and our previous work [2], we study high-energy pion scattering in the holographic QCD hard-wall model. In particular, we focus on comparing our predictions for the angular dependence of $\pi^{+} \pi^{-} \to \pi^{+} \pi^{-}$ scattering with experimental data extracted from the process $\pi^{-} p \to \pi^{+} \pi^{-} n$.

Hee-Youl Kwak, Seong-Joon Park, Hyunwoo Jung, Jeongseok Ha

Quantum error correction (QEC) for fault-tolerant quantum computing requires a balanced decoding solution that offers high performance, low complexity, and low latency. However, the de facto standard, belief propagation (BP) combined with ordered statistics decoding (OSD), suffers from excessive iterations in the BP stage and high complexity in the OSD stage

Pulak Ranjan Giri

Quantum walk based hash functions have attracted a lot of attention in recent years because of its faster execution time and robust resistance against attacks compared to classical hash functions. It has been observed that the underlying graph and the way message controls the quantum walk iteration steps play a crucial role for the robustness of the hash fun

Se-Young Jang, Su-Yeon Yoon, Jae-Woong Jung, Dong-Hun Lee

With the accelerating pace of digital transformation and the widespread adoption of online platforms, both social and technical concerns regarding dark patterns-user interface designs that undermine users' ability to make informed and rational choices-have become increasingly prominent. As corporate online platforms grow more sophisticated in their design st

Eduard Feireisl, Maria Lukacova-Medvidova

We propose a new two-step selection criterion applicable to the dissipative measure--valued solutions of the Euler system of gas dynamics. The process consists of a successive maximisation of the entropy production rate and the total energy defect, i.e. maximisation of the turbulent energy. If the selected solution is a weak solution of the Euler system, the

Lizhi Ma, Yi-Xiang Hu, Yuke Wang, Yifang Zhao

With the rapid advancements in big data technologies, the Databricks platform has become a cornerstone for enterprises and research institutions, offering high computational efficiency and a robust ecosystem. However, managing the escalating operational costs associated with job execution remains a critical challenge. Existing solutions rely on static config

Himabindu Thogaru, Saisubramaniam Gopalakrishnan, Zishan Ahmad, Anirudh Deodhar

Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in modern production environments. Traditional approaches to analyzing simulation-based manufacturing data often create barriers between human decision-makers and critical operational in

Yucheng Fan, Jiawei Chen, Yu Tian, Zhaoxia Yin

As vision-language models (VLMs) become widely adopted, VLM-based attribute inference attacks have emerged as a serious privacy concern, enabling adversaries to infer private attributes from images shared on social media. This escalating threat calls for dedicated protection methods to safeguard user privacy. However, existing methods often degrade the visua

Haolong Zheng, Yekaterina Yegorova, Mark Hasegawa-Johnson

Children's speech recognition remains challenging due to substantial acoustic and linguistic variability, limited labeled data, and significant differences from adult speech. Speech foundation models can address these challenges through Speech In-Context Learning (SICL), allowing adaptation to new domains without fine-tuning. However, the effectiveness of SI

Philippe Manjakasoa Randriantsoa, Ravo Tokiniaina Ranaivoson, Raoelina Andriambololona, Roland Raboanary

Recent developments in the unification of quantum mechanics and relativity have emphasized the necessity of generalizing classical phase space into a relativistic quantum phase space which is a framework that inherently incorporates the uncertainty principle and relativistic covariance. In this context, the present work considers the derivation of linear and

M. Mehdi Kholoosi, Triet Huynh Minh Le, M. Ali Babar

Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management (SVM), such as vulnerability detection and repair, remains underexplored in industry settings. T

Crescenzo Tortora, Daniela Carollo, Leslie Hunt, Francine Marleau

Dwarf galaxies ($M_{\star} \lesssim 10^{9} M_{\odot}$) are the most numerous galaxies in the Universe and critical probes of dark matter, baryonic feedback, and galaxy formation. Despite significant progress from wide-field imaging surveys, the majority of dwarf candidates beyond the Local Group will lack spectroscopic follow-up, leaving fundamental question

Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua

We evaluate TCP BBRv3 on Wi-Fi 6 home networks under modern AQM schemes using a fully wireless testbed and a simple cross-layer model linking Wi-Fi scheduling, router queueing, and BBRv3's pacing dynamics. Comparing BBR Internet traffic with CUBIC across different AQMs (FIFO, FQ-CoDel, and CAKE) for uplink, downlink, and bidirectional traffic, we find that F

Sicong He, Brandon Schwendeman, George Tynan, Jaime Marian

The foundations of irradiation damage theory were laid in the 1950s and 60s within the framework of chemical reaction kinetics. While helpful to analyze qualitative aspects of irradiation damage, the theory contained gaps that delayed its implementation and applicability as a predictive tool. The advent of computer simulations with atomistic resolution in th

Youheng Zhang

Standard formulations of supervenience typically treat higher level properties as point valued facts strictly fixed by underlying base states. However, in many scientific domains, from statistical mechanics to machine learning, basal structures more naturally determine families of probability measures than single outcomes. This paper develops a general frame

Sirui Li, Wangyue Lu, Xiaorui Shi, Ke Weng

Automated Theorem Proving (ATP) represents a core research direction in artificial intelligence for achieving formal reasoning and verification, playing a significant role in advancing machine intelligence. However, current large language model (LLM)-based theorem provers suffer from limitations such as restricted domain coverage and weak generalization in m

Miha Brešar, Aleksandar Mijatović, Gareth Roberts

Establishing central limit theorems (CLTs) for ergodic averages of Markov chains is a fundamental problem in probability and its applications. Since the seminal work~\cite{MR834478}, a vast literature has emerged on the sufficient conditions for such CLTs. To counterbalance this, the present paper provides verifiable necessary conditions for CLTs of ergodic

Mingcheng Ye, Jiaming Liu, Yiren Song

Interleaved text-image generation aims to jointly produce coherent visual frames and aligned textual descriptions within a single sequence, enabling tasks such as style transfer, compositional synthesis, and procedural tutorials. We present Loom, a unified diffusion-transformer framework for interleaved text-image generation. Loom extends the Bagel unified m

Jayendran S, Abhishek K G, Suresh R, Helmer Fjellvåg

Magnetic refrigeration presents an energy-efficient and environmentally benign alternative to traditional vapour-compression cooling technologies. It relies on the magnetocaloric effect, in which the temperature of a magnetic material changes in response to variations in an applied magnetic field. Optimal magnetocaloric materials are characterized by a signi

Kenichi Umeda, Noriyuki Kodera

Atomic force microscopy (AFM) enables nanoscale characterization and has been widely applied to a broad range of systems. Over the past two decades, advances in high-speed AFM have enabled not only the imaging of static structures but also the direct observation of nanoscale dynamics in real time. However, because the tip or sample is typically scanned using

Zhendong Cao, Shigang Ou, Lei Wang

Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structu

Anuradha Mahasinghe, Kaushika De Silva, Xavier Cadet, Peter Chin

Implementing polynomial functions of Hermitian matrices on quantum hardware is a foundational task in quantum computing, critical for accurate Hamiltonian simulation, quantum linear system solving, high-fidelity state preparation, machine learning kernels, and other advanced quantum algorithms. Existing state-of-the-art techniques, including Qubitization, Qu

Siqiao Mu, Diego Klabjan

The low-rank adaptation (LoRA) algorithm for fine-tuning large models has grown popular in recent years due to its remarkable performance and low computational requirements. LoRA trains two ``adapter" matrices that form a low-rank representation of the model parameters, thereby massively reducing the number of parameters that need to be updated at every step

Rui Xing, Runmin Cong, Yingying Wu, Can Wang

Understanding the dietary preferences of ancient societies and their evolution across periods and regions is crucial for revealing human-environment interactions. Seeds, as important archaeological artifacts, represent a fundamental subject of archaeobotanical research. However, traditional studies rely heavily on expert knowledge, which is often time-consum

Shiye Lei, Zhihao Cheng, Dacheng Tao

Behavioral cloning is a widely adopted approach for offline policy learning from expert demonstrations. However, the large scale of offline behavioral datasets often results in computationally intensive training when used in downstream tasks. In this paper, we uncover the striking data saturation in offline behavioral data: policy performance rapidly saturat

Wisnu Uriawan, Andika Nuralamsyah, Abidzar Giffari, Arief Rahman Mubarok

This research explores the implementation of a spherical trigonometry algorithm in the development of QiblatKita, a mobile-based Qibla direction application designed to address the need for accurate and accessible prayer direction tools for Muslims, especially in the context of global mobility and diverse geographical settings. Built using the Flutter framew

Xiao He, Chang Tang, Xinwang Liu, Wei Zhang

Hyperspectral images with high spectral resolution provide new insights into recognizing subtle differences in similar substances. However, object detection in hyperspectral images faces significant challenges in intra- and inter-class similarity due to the spatial differences in hyperspectral inter-bands and unavoidable interferences, e.g., sensor noises an

Zehao Liu, Xi Lin

Large Language Models (LLMs) have gained considerable popularity and protected by increasingly sophisticated safety mechanisms. However, jailbreak attacks continue to pose a critical security threat by inducing models to generate policy-violating behaviors. Current paradigms focus on input-level anomalies, overlooking that the model's internal psychometric s

Keng-Hung Steven Lin

We study Nash valuations on 3-fold terminal singularities, especially in type cAx/2. We find that, in type cAx/2, exceptional prime divisors computing the minimal discrepancy (which is 1/2 in this case) induce Nash valuations. We conjecture this in general for all 3-fold terminal singularities, and provide some evidence in the Gorenstein case.

Cankut Tasci, Mohammad Hassan, Leon Orlov-Sullivan, Leonardo A. Cunha

Strong light-matter coupling enables hybrid states in which photonic and electronic degrees of freedom become correlated even in the ground state. While many-body effects in long-range dispersion interactions are known to reshape electronic properties under such conditions, their impact on quantum-optical observables remains largely unexplored. Here, we addr

Pan Ben Wong, Chengli Wu, Hanyue Lu

Real-time Video Frame Interpolation (VFI) has long been dominated by flow-based methods like RIFE, which offer high throughput but often fail in complicated scenarios involving large motion and occlusion. Conversely, recent diffusion-based approaches (e.g., Consec. BB) achieve state-of-the-art perceptual quality but suffer from prohibitive latency, rendering

Yonghwa Cho

We study the cohomology of divisors on a Burniat surface $X$ with $K_X^2=6$. We provide an algorithm for computing the cohomology groups of arbitrary divisors on $X$. As an application, we prove that there are no Ulrich line bundles\,(with respect to an arbitrary polarization), and that there exists an Ulrich vector bundle of rank 2 with respect to $3K_X$. T

Ding Jia, Jingyu Zhu, Yu Sun, Aoqian Zhang

Multivariate time series alignment is critical for ensuring coherent analysis across variables, but missing values and timestamp inconsistencies make this task highly challenging. Existing approaches often rely on prior imputation, which can introduce errors and lead to suboptimal alignments. To address these limitations, we propose a constraint-based alignm

Shahram Najam Syed, Yitian Hu, Yuchao Yao

Photorealistic 3-D reconstruction from monocular video collapses in large-scale scenes when depth, pose, and radiance are solved in isolation: scale-ambiguous depth yields ghost geometry, long-horizon pose drift corrupts alignment, and a single global NeRF cannot model hundreds of metres of content. We introduce a joint learning framework that couples all th

Lixiang Yan, Xibin Han, Yu Zhang, Samuel Greiff

As generative AI systems become increasingly embedded in collaborative work, they are evolving from visible tools into human-like communicative actors that participate socially rather than merely providing information. Yet little is known about how such agents shape team dynamics when their artificial nature is not recognised, a growing concern as human-like

Anup B. Dixit, Nikhil S Kumar

Let $ \lfloor {x} \rfloor $ denote the greatest integer less than or equal to a real number $x$. Given real numbers $0<\alpha_1 < \alpha_2 < \cdots< \alpha_k < 1$ satisfying a certain condition, we show that there are infinitely many positive integers $n$ for which all of $ \lfloor{n^{\alpha_1}}\rfloor, \lfloor{n^{\alpha_2}}\rfloor,\ldots, \lfloor{n^{\alpha_

Stephen Ni-Hahn, Rico Zhu, Jerry Yin, Yue Jiang

Hierarchical representations provide powerful and principled approaches for analyzing many musical genres. Such representations have been broadly studied in music theory, for instance via Schenkerian analysis (SchA). Hierarchical music analyses, however, are highly cost-intensive; the analysis of a single piece of music requires a great deal of time and effo

Aryan Chaudhary, Sanchit Goyal, Pratik Narang, Dhruv Kumar

Vision-Language Models have demonstrated remarkable capabilities in understanding visual content, yet systematic biases in their spatial processing remain largely unexplored. This work identifies and characterizes a systematic spatial attention bias where VLMs consistently prioritize describing left-positioned content before right-positioned content in horiz

Hyeon Jeon

Visual analytics now plays a central role in decision-making across diverse disciplines, but it can be unreliable: the knowledge or insights derived from the analysis may not accurately reflect the underlying data. In this dissertation, we improve the reliability of visual analytics with a focus on dimensionality reduction (DR). DR techniques enable visual a

Sylvain L. Walsh, Yalu Zhu, Feng Liu, William A. Sirignano

This work examines the physical consistency of the conventional Flamelet Progress Variable (FPV) model for diffusion flame simulations and and introduces a new compressible flamelet formulation that employs the turbulent kinetic energy dissipation rate, $\epsilon$, as the tracking variable. Two-dimensional Reynolds-averaged Navier-Stokes (RANS) simulations a

Lichen Yang, Qiang Wang, Zhonghao Yang, Daojing He

Graph Neural Networks (GNNs) have demonstrated remarkable efficacy in handling graph-structured data; however, they exhibit failures after deployment, which can cause severe consequences. Hence, conducting thorough testing before deployment becomes imperative to ensure the reliability of GNNs. However, thorough testing requires numerous manually annotated te

Takuya OKi, Yuan Liu

Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we exa

Deepit Sapru

This paper presents an applied AI pipeline for realtime geolocation from noisy microblog streams, unifying statistical hashtag segmentation, part-of-speech-driven proper-noun detection, dependency parsing around disaster lexicons, lightweight named-entity recognition, and gazetteer-grounded disambiguation to infer locations directly from text rather than spa