November 2025 arXiv papers — page 34

Yoonseul Choi, Jungsoon Choi

South Korea faces the dual challenge of managing growing distributed solar energy surpluses and the high energy demand of industries like Bitcoin mining. Leveraging mining operations as a flexible load to monetize this `net-metering surplus' presents a viable synergy, but requires a robust site selection methodology. Traditional GIS-based Multi-Criteria Deci

Ahmet Efe, Hüsrev Cılasun, Abhimanyu Kumar, Nafisa Sadaf Prova

Ising machines are emerging as a new technology for solving various classes of computationally hard problems of practical importance, yet their limits on structured SAT workloads, representative of numerous real-world applications, remain unexplored. We present the first systematic study of such problems, using semiprime factorization as a representative cas

Jionghao Han, Jiatong Shi, Zhuoyan Tao, Yuxun Tang

Singing voice synthesis (SVS) and singing voice conversion (SVC) have achieved remarkable progress in generating natural-sounding human singing. However, existing systems are restricted to human timbres and have limited ability to synthesize voices outside the human range, which are increasingly demanded in creative applications such as video games, movies,

Hanxi Pan, Wei Xu, Mowei Shen, Zaifeng Gao

As artificial intelligence systems become increasingly integrated into human social contexts, Artificial Social Intelligence (ASI) has emerged as a critical capability that enables AI to perceive, understand, and engage meaningfully in complex human social interactions. This chapter introduces a comprehensive framework for Human-Centered Artificial Social In

Qi He

The growth of large-scale AI systems is increasingly constrained by infrastructure limits: power availability, thermal and water constraints, interconnect scaling, memory pressure, data-pipeline throughput, and rapidly escalating lifecycle cost. Across hyperscale clusters, these constraints interact, yet the main metrics remain fragmented. Existing metrics,

Hakki Motorcu, Mujdat Cetin

Spatially varying image deblurring remains a fundamentally ill-posed problem, especially when degradations arise from complex mixtures of motion and other forms of blur under significant noise. State-of-the-art learning-based approaches generally fall into two paradigms: model-based deep unrolling methods that enforce physical constraints by modeling the deg

Cheng Yang, Hui Jin, Xinlei Yu, Zhipeng Wang

Diagnosing lung cancer typically involves physicians identifying lung nodules in Computed tomography (CT) scans and generating diagnostic reports based on their morphological features and medical expertise. Although advancements have been made in using multimodal large language models for analyzing lung CT scans, challenges remain in accurately describing no

Dinanath Padhya, Krishna Acharya, Bipul Kumar Dahal, Dinesh Baniya Kshatri

Automatic Modulation Classification (AMC) is a core technology for future wireless communication systems, enabling the identification of modulation schemes without prior knowledge. This capability is essential for applications in cognitive radio, spectrum monitoring, and intelligent communication networks. We propose an AMC system based on a hybrid Convoluti

Naoki Okamoto, Yohei Kono, Takahiro Onimaru, Keisuke T. Matsumoto

We performed field-angle-resolved magnetostriction and thermal-expansion measurements on PrIr$_2$Zn$_{20}$, a cubic non-Kramers compound exhibiting antiferroquadrupolar order below $T_{\rm Q}=0.125$ K. Thermal expansion exhibits two qualitatively different anomalies under magnetic fields applied along the $[001]$ direction, providing experimental support for

Anantha Padmanaban Krishna Kumar

Can in-context learning (ICL) override pre-trained label semantics, or does it merely refine an existing semantic backbone? We address this question by treating LLMs as prompt-induced classifiers and contrasting their behavior under \emph{natural} demonstrations (with correct labels) and \emph{inverted} demonstrations (systematically flipping label meanings)

Justin Cui, Kevin Pu, Tovi Grossman

Foundation models are increasingly used to personalize learning, yet many systems still assume fixed curricula or coarse progress signals, limiting alignment with learners' day-to-day needs. At the other extreme, lightweight incidental systems offer flexible, in-the-moment content but rarely guide learners toward mastery. Prior work privileges either continu

Mahdi Saki, Justin Lipman

Dairy farmers should decide to keep or cull a cow based on an objective assessment of her likely performance in the herd. For this purpose, farmers need to identify more resilient cows, which can cope better with farm conditions and complete more lactations. This decision-making process is inherently complex, with significant environmental and economic impli

Linze Chen, Yufan Cai, Zhe Hou, Jin Song Dong

Legal decisions should be logical and based on statutory laws. While large language models(LLMs) are good at understanding legal text, they cannot provide verifiable justifications. We present L4L, a solver-centric framework that enforces formal alignment between LLM-based legal reasoning and statutory laws. The framework integrates role-differentiated LLM a

Yuxuan Zhu, Cong Fu, Yabo Ni, Anxiang Zeng

Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable tempora

Diego Vaca-Revelo, Benjamin Wilfong, Spencer H. Bryngelson, Aswin Gnanaskandan

We present a comprehensive validation, performance characterization, and scalability analysis of a hardware-accelerated phase-averaged multiscale solver designed to simulate acoustically driven dilute bubbly suspensions. The carrier fluid is modeled using the compressible Navier-Stokes equations. The dispersed phase is represented through two distinct subgri

Hanamantagouda P. Sankappanavar

This paper grew out of our investigation into a simple, but natural, question: Can 'F implies T' be distinct from F and T? To this end, we introduce five 'unorthodox' algebras that will play a major role, not only in providing a positive answer to the question, but also in their similarity to the 2-element Boolean algebra 2. Yet, they are remarkably dissimil

Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg

We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar input. Recent deep generative models, including diffusion models and flow matching, employ a single neural network to learn a time- or noise level-dependent vector field. Designing a

Yisheng Qiu, Tianwei Zhang, Tie Liu, Fengyao Zhu

Spectral lines from interstellar molecules provide crucial insights into the physical and chemical conditions of the interstellar medium. Traditional spectral line analysis relies heavily on manual intervention, which becomes impractical when handling the massive datasets produced by modern facilities like ALMA. To address this challenge, we introduce a nove

Marwa Ennaceur

We develop a systematic functional-analytic framework for Hom--Lie Banach algebras, introducing bounded $\alpha$-twisted derivations and almost periodic elements. Under natural continuity and compactness assumptions, we establish a complete Bohr--Fourier spectral decomposition of such derivations. We prove that the associated almost periodic and ergodic subs

Shijia Yang, Yunong Liu, Bohan Zhai, Ximeng Sun

Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current evaluation practices miss a fundamental question: Can captions stand-in for images in real downstream tasks? We propose a utility-based benchmark, CaptionQA, to evaluate model-generat

Qirui Yang, Yang Yang, Ying Zeng, Xiaobin Hu

Text-guided diffusion models have greatly advanced image editing and generation. However, achieving physically consistent image retouching with precise parameter control (e.g., exposure, white balance, zoom) remains challenging. Existing methods either rely solely on ambiguous and entangled text prompts, which hinders precise camera control, or train separat

Xiaoxu Xu, Guanghui Hu

This paper is concerned with an inverse boundary value problem for the Helmholtz equation over a bounded domain. The aim is to reconstruct two constant coefficients together with the location and shape of a Dirichlet polygonal obstacle from a single pair of Cauchy data. Uniqueness results are verified under some a priori assumptions and the one-wave factoriz

Haojian Huang, Jiahao Shi, Zhe Liu, Harold Haodong Chen

Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inconsistent, or even conflicting. Existing TMVC approaches predominantly rely on globally dense neighbor relationships to model intra-view dependencies, leading to high computational

Minghui Min, Jiahui Liu, Mingge Cao, Shiyin Li

Location-Based Services (LBSs) offer significant convenience to mobile users but pose significant privacy risks, as attackers can infer sensitive personal information through spatiotemporal correlations in user trajectories. Since users' sensitivity to location data varies based on factors such as stay duration, access frequency, and semantic sensitivity, im

Taehoon Kang, Taeyong Kim

Climate change has intensified the frequency and severity of wildfires, making rapid and accurate prediction of fire spread essential for effective mitigation and response. Physics-based simulators such as FARSITE offer high-fidelity predictions but are computationally intensive, limiting their applicability in real-time decision-making, while existing deep

Antonis Psistakis

Nowadays, avoiding system calls during cluster communication (e.g., in Data Centers and High Performance Computing) in modern high-speed interconnection networks has become a necessity, due to the high overhead of multiple data copies between kernel and user space. User-level zero-copy Remote Direct Memory Access (RDMA) technologies address this problem by i

Takamasa Kanai, Kengo Maeda, Daisuke Yoshida

We reformulate the construction of wormhole solutions as perturbations around near-horizon geometries of near-extremal Reissner-Nordstr\"{o}m black holes in four dimensions and equal-angular-momenta Myers-Perry black holes in five dimensions. When the negative Casimir energy is taken as the source, this framework reduces to the Maldacena-Milekhin-Popov const

Parikshit Gopalan, Raghu Meka, Prasad Raghavendra, Mihir Singhal

We study an extension of the standard two-party communication model in which Alice and Bob hold probability distributions $p$ and $q$ over domains $X$ and $Y$, respectively. Their goal is to estimate \[ \mathbb{E}_{x \sim p,\, y \sim q}[f(x, y)] \] to within additive error $\varepsilon$ for a bounded function $f$, known to both parties. We refer to this as t

Raymond Matson, Peter Samuelson

Skein algebras of surfaces quantize character varieties of topological surfaces, and in low genus, these quantizations are often related to algebras arising in representation theory. For example, Terwilliger defined a universal $SL_2$ spherical double affine Hecke algebra $A$; a combination of results in the literature shows $A$ is isomorphic to the $SL_2$ s

Ryosuke Kadono

Numerical simulations of muon spin relaxation ($\mu$SR) in ion diffusion were performed using the {\sl extended} Kubo-Toyabe (KT) relaxation function $G_z^{\rm EA}(t)$ that incorporates an Edwards-Anderson type autocorrelation function for the jump motion of ions. The analysis of the generated $\mu$SR spectra using the conventional KT function $G_z^{\rm KT}(

Hyunwook Jheonn, Jeong-Eun Lee, Jinho Lee, Seonjae Lee

We introduce a binary classification model, {\it the Double Filter Model}, utilizing various machine learning and deep learning methods to classify Young Stellar Objects (YSOs) and Asymptotic Giant Branch (AGB) stars. Since YSOs and AGB stars share similar infrared (IR) photometric characteristics due to comparable temperatures and the presence of circumstel

Sid Bharthulwar, Stone Tao, Hao Su

Massively parallel GPU simulation environments have accelerated reinforcement learning (RL) research by enabling fast data collection for on-policy RL algorithms like Proximal Policy Optimization (PPO). To maximize throughput, it is common to use short rollouts per policy update, increasing the update-to-data (UTD) ra- tio. However, we find that, in this set

Sanjaya Paudel, Cristiano G. Sabiu, Suk-Jin Yoon, Daya Nidhi Chhatkuli

Dwarf elliptical galaxies (dEs) are the dominant population in galaxy clusters and serve as ideal probes for studying the environmental impact on galactic evolution. A substantial fraction of dEs are known to harbor central nuclei, which are among the densest stellar systems in the Universe. The large-scale distribution and the underlying origin of nucleated

Md Tasnin Tanvir, Dr Santanu Kumar Dash, Ishan Shahnan, Nafis Fuad

The challenge of separating AI-generated text from human-authored content is becoming more urgent as generative AI technologies like ChatGPT become more widely available. In this work, we address this issue by looking at both the detection of content that has been entirely generated by AI and the identification of human text that has been reworded by AI. In

Constantinos Daskalakis, Vardis Kandiros, Rui Yao

We consider the problem of estimating Ising models over $n$ variables in Total Variation (TV) distance, given $l$ independent samples from the model. While the statistical complexity of the problem is well-understood [DMR20], identifying computationally and statistically efficient algorithms has been challenging. In particular, remarkable progress has occurr

Ioana Buhnila, Aman Sinha, Mathieu Constant

Large Language Models (LLMs) have proven efficient in giving definition-type answers to user input queries. While for humans giving various types of answers, such as examples and paraphrases, is an easy task, LLMs struggle to provide correct answers for other than definition-type queries. In this study, we evaluated this drop in performance using TrackList,

Zekai Zhang

This article aims to propose and elucidate the analytical concepts of "individual scientist" and "integrated scientist" to depict the fundamental transformation in the modes of scientific research actors throughout the history of science. The "individual scientist" represents an early modern scientific research panel characterized by independence, egalitaria

Jinpeng Wang, Chao Li, Ting Ye, Mengyuan Zhang

Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates significant potential in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing RLVR methods are often constrained by issues such as coarse-grained rewards, reward noise, and inefficient exploration, which lead to unstable training and entropy collapse. To add

Gonzalo De Polsi, Pawel Jakubczyk

We employ the derivative expansion of the nonperturbative renormalization group to address the phenomenon of anisotropic scale invariance and the associated functional fixed points, also known as Lifshitz points, in systems characterized by a scalar order parameter. We demonstrate the existence of the Lifshitz fixed point featuring a non-classical value of t

Jiaojiao Feng, Ying-nan Mao, Kechen Wang

We evaluate discovery prospects for photophobic axion-like particles (ALPs) in the $WWjj$ final state at the High-Luminosity LHC (HL-LHC; $\sqrt{s}=14$ TeV, $L=3$ ab$^{-1}$). In the photophobic limit ($g_{a\gamma\gamma}=0$), ALPs couple to electroweak gauge bosons and are produced in association with two jets ($pp\to jj a$) via both $s$-channel electroweak e

Xiaoxing You, Qiang Huang, Lingyu Li, Chi Zhang

News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances, existing methods struggle with three key challenges: (1) incomplete information coverage, (2) weak cross-modal alignment, and (3) suboptimal visual-entity grounding. To address the

Y. Xu, T. Wu, A. Van Dyne, E. Lee

Background: True cognitive longitudinal decline can be obscured by repeated testing, which is called practice effects (PEs). We developed a modeling framework that aligns participants by baseline and estimates visit-specific PEs independently of age-related change. Method: Using real data ($N=175$), we estimated within-subject correlations via linear mixed-e

Seoyoung Choi, Rashmi Balegar Mohan, Heather Jin Hee Kim, Jisoo Ha

We present PileUp, a tufted pile e-textile sensing approach that offers unique affordances through the tactile expressiveness and richness of its continuous, threaded-volume construction. By integrating conductive yarns in looped or cut pile forms, PileUp transforms soft 3-dimensional textiles into multimodal sensors capable of detecting mechanical deformati

L. J. M. Davies, M. Bravo, R. H. W. Cook, A. Hashemizadeh

The Deep Extragalactic VIsible Legacy Survey (DEVILS) is a deep, high-completeness multi-wavelength survey based around spectroscopic observations using the Anglo-Australian Telescope's AAOmega spectrograph. The survey covers $\sim4.5$deg$^{2}$ over three extragalactic fields to Y$_{AB}<21.2$mag and probes sources at $0<z<1.2$, with a median redshift of $z=0

Shota Kanasugi, Yuya O. Nakagawa, Norifumi Matsumoto, Yuichiro Hidaka

Quantum Krylov algorithms have emerged as a promising approach for ground-state energy estimation in the near-term quantum computing era. A major challenge, however, lies in their inherently substantial sampling cost, primarily due to the individual measurement of each term in the Hamiltonian. While various techniques have been proposed to mitigate this issu

Jiaoyang Li, Jun Fang, Tianhao Gao, Xiaohui Zhang

Representation learning is fundamental to modern machine learning, powering applications such as text retrieval and multimodal understanding. However, learning robust and generalizable representations remains challenging. While prior work has demonstrated that active noise injection, a form of data augmentation, can enhance encoding performance, most existin

Jingxi Chen, Yixiao Zhang, Xiaoye Qian, Zongxia Li

Images can be viewed as layered compositions, foreground objects over background, with potential occlusions. This layered representation enables independent editing of elements, offering greater flexibility for content creation. Despite the progress in large generative models, decomposing a single image into layers remains challenging due to limited methods

Felix Biertümpfel, Bin Hu, Geir Dullerud, Peter Seiler

This paper provides the first finite-dimensional characterization for the complete set of full-block, circle criterion multipliers. We consider the interconnection of a discrete-time, linear time-invariant system in feedback with a non-repeated, sector-bounded nonlinearity. Sufficient conditions for stability and performance can be derived using: (i) dissipa

Yuxiao Xiang, Junchi Chen, Zhenchao Jin, Changtao Miao

Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain unsafe content even when the final answer is non-harmful, creating deployment risks. Existing multimodal safety guards primarily evaluate only the input question and the final answe

Shanwei Fan, Bin Zhang, Zhiwei Xu, Yingxuan Teng

Large language models (LLMs) offer strong high-level planning capabilities for reinforcement learning (RL) by decomposing tasks into subgoals. However, their practical utility is limited by poor planning-execution alignment, which reflects a critical gap between abstract plans and actionable, environment-compatible behaviors. This misalignment arises from tw

Zhiwen Zheng, Yiwei Ouyang, Zhao Huang, Tao Zhang

Accurately localizing and segmenting obscured objects from faint light patterns beyond the field of view is highly challenging due to multiple scattering and medium-induced perturbations. Most existing methods, based on real-valued modeling or local convolutional operations, are inadequate for capturing the underlying physics of coherent light propagation. M

Jonas Mago, Joshua Brahinsky, Mark Miller, Charlotte Maschke

Criticality describes a regime between order and chaos that supports flexible yet stable information processing. Here we examine whether neural dynamics can be volitionally shifted toward criticality through the self-regulation of attention. We examined ten experienced practitioners of meditation during a 10-day retreat, comparing refined states of meditativ

Yu-Huan Wu, Zi-Xuan Zhu, Yan Wang, Liangli Zhen

Referring Camouflaged Object Detection (Ref-COD) segments specified camouflaged objects in a scene by leveraging a small set of referring images. Though effective, current systems adopt a dual-branch design that requires reference images at test time, which limits deployability and adds latency and data-collection burden. We introduce a Ref-COD framework tha

Guowei Dai, Yingxin Sun

The celebrated conjecture by Payne, P\'{o}lya and Weinberger (1956) states that for the fixed membrane problem, the ratio of the first two eigenvalues, $\lambda_2/\lambda_1$, is maximized by a disk. A more general dimensional version of this conjecture was later resolved by Ashbaugh and Benguria in the 1990s. For the Robin Laplacian, Payne and Schaefer (2001

Allison Li, Kristjan Greenewald, Thomas Parnell, Navid Azizan

Modern large language model (LLM) systems increasingly rely on multi-turn pipelines that are composed of multiple task-specific adapters, yet existing serving frameworks remain inefficient, incurring substantial recomputation overhead when switching between adapters. We present the first LLM serving engine that supports cross-model prefix cache reuse between

Davis Brown, Jesse He, Helen Jenne, Henry Kvinge

Evolutionary program synthesis systems such as AlphaEvolve, OpenEvolve, and ShinkaEvolve offer a new approach to AI-assisted mathematical discovery. These systems utilize teams of large language models (LLMs) to generate candidate solutions to a problem as human readable code. These candidate solutions are then 'evolved' with the goal of improving them beyon

Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong

Style transfer, a pivotal task in image processing, synthesizes visually compelling images by seamlessly blending realistic content with artistic styles, enabling applications in photo editing and creative design. While mainstream training-free diffusion-based methods have greatly advanced style transfer in recent years, their reliance on computationally inv

Xiaoya Wang, Richard J. Cook, Yeying Zhu, Tugba Akkaya-Hocagil

Methods for causal inference are well developed for binary and continuous exposures, but in many settings, the exposure has a substantial mass at zero-such exposures are called semi-continuous. We propose a general causal framework for such semi-continuous exposures, together with a novel two-stage estimation strategy. A two-part propensity structure is intr

Kengo Hashimoto

A combinatorial game is a two-player game without hidden information or chance elements. The main object of combinatorial game theory is to obtain the outcome, which player has a winning strategy, of a given combinatorial game. Positions of many well-known combinatorial games are naturally decomposed into a disjunctive sum of multiple components and can be a

Al Amin, Kamrul Hasan, Liang Hong, Sharif Ullah

Collaborative machine learning across healthcare institutions promises improved diagnostic accuracy by leveraging diverse datasets, yet privacy regulations such as HIPAA prohibit direct patient data sharing. While federated learning (FL) enables decentralized training without raw data exchange, recent studies show that model gradients in conventional FL rema

Junhan Liao, Minxian Xu, Wanyi Zheng, Yan Wang

To meet strict Service-Level Objectives (SLOs),contemporary Large Language Models (LLMs) decouple the prefill and decoding stages and place them on separate GPUs to mitigate the distinct bottlenecks inherent to each phase. However, the heterogeneity of LLM workloads causes producerconsumer imbalance between the two instance types in such disaggregated archit

Erhan Güler, Magdalena Toda

We consider a higher-order Henneberg-type minimal surfaces family using the generalized Weierstrass--Enneper representation in four-dimensional space $\mathbb{R}^4$. We derive explicit parametric equations for the surface and determine its differential geometric characteristics, including the normal vector fields $\mathbf{n}_1$ and $\mathbf{n}_2$, as well as

Rebecca Bryant, Jacob Grosek, Jay Gopalakrishnan

We apply a known theorem for simplifying dynamical systems with bounded error to a specific optical fiber waveguide problem, supplementing the physical intuition and heuristics used in the optics community with proper mathematical justification. Using techniques from averaging theory of dynamical systems, a reliable accelerated model based on the coupled mod

Divendar Murtadak, Yoon Kim, Trilokya Akula

This study investigates whether diagnostic prompting can improve Multimodal Large Language Model (MLLM) reliability for visual complexity assessment of Amazon Search Results Pages (SRP). We compare diagnostic prompting with standard gestalt principles-based prompting using 200 Amazon SRP pages and human expert annotations. Diagnostic prompting showed notable

Giorgos Bakoyannis, Aristofanis Rontogiannis, Ying Zhang, Wanzhu Tu

Analysis of competing risks data is often complicated by the incomplete or selectively missing information on the cause of failure. Standard approaches typically assume that the cause of failure is missing at random (MAR), an assumption that is generally untestable and frequently implausible in observational studies. We propose a novel sensitivity analysis f

Ivan Etoku Oiye, Ajay Sharma, Zinia Mohanta, Dinil Sasi Sankaralayam

Access to Magnetic Resonance Imaging system assembly knowledge can be expanded by leveraging open-source hardware and software, simplified installation requirements, and collaborative training initiatives. To this end, we conducted a three-day workshop to construct an operational 0.27T MRI scanner. The workshop hosted 16 participants, including faculty, post

Tao Zhang, Meixia Li, Fan Yang, Chunqin Zhou

In this paper, using anisotropic rearrangement techniques, we first establish the best constants for the singular anisotropic Adams' type inequality with exact growth in $\mathbb{R}^n$. Furthermore, by the same trick, we also prove the singular anisotropic Adams' type inequality on bounded domain $\Omega\subset \mathbb{R}^n$.

Junkai Hu, Li Xia

Efficiency and reliability are both crucial for energy management, especially in multi-microgrid systems (MMSs) integrating intermittent and distributed renewable energy sources. This study investigates an economic and reliable energy management problem in MMSs under a distributed scheme, where each microgrid independently updates its energy management polic

Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price, Victor Posligua

Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, sel

Yinwei Dai, Zhuofu Chen, Anand Iyer, Ravi Netravali

Agentic workflows have emerged as a powerful paradigm for solving complex, multi-stage tasks, but serving them at scale is computationally expensive given the many LLM inferences that each request must pass through. Configuration selection, or the cost-aware assignment of workflow agents to specific LLMs, can reduce these costs, but existing approaches bind

Zhisheng Zheng, Xiaohang Sun, Tuan Dinh, Abhishek Yanamandra

End-to-end speech-to-speech translation (S2ST) systems typically struggle with a critical data bottleneck: the scarcity of parallel speech-to-speech corpora. To overcome this, we introduce RosettaSpeech, a novel zero-shot framework trained exclusively on monolingual speech-text data augmented by machine translation supervision. Unlike prior works that rely o

Saurabhchand Bhati, Samuel Thomas, Hilde Kuehne, Rogerio Feris

Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate token sequences (e.g., 25 tokens/s), making attention computation costly and limiting scalability. In this paper, we explore techniques such as unsupervised segmentation, uniform average pooling, etc., to reduce the number

Jionghao Han, Jiatong Shi, Masao Someki, Yuxun Tang

With recent advances in automatic speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS) technologies, spoken dialogue systems (SDS) have become widely accessible. However, most existing SDS are limited to conventional spoken responses. We present SingingSDS, a cascaded SDS that responds through singing rather than speaking, fosteri

Marwa Ennaceur

We establish sharp stability results for of non--selfadjoint the ascent and descent spectra under strong resolvent convergence (SRS), a natural framework for finite element approximations of non-selfadjoint and singularly perturbed operators. The key quantitative hypothesis is the reduced minimum modulus $\gamma(T-\lambda)>0$, which guarantees closed range a

Sansrit Paudel

Real-world biosignal data is frequently corrupted by various types of noise, such as motion artifacts, and baseline wander. Although digital signal processing techniques exist to process such signals; however, heavily degraded signals cannot be recovered. In this study, we aim to classify two things: first, a binary classification of noisy and clean biosigna

Shenghua Yu, Youjun Lu, C. Simon Jeffery, Zhanwen Han

In sufficiently compact neutron star-white dwarf (NSWD) binary systems, orbital decay means the white dwarf eventually fills its shrinking Roche lobe, initiating a phase of mass transfer. The exchange of angular momentum-both internal and external-plays a critical role in determining the binary's evolutionary outcome. For neutron stars with relatively low ma

Jiajie Li, Xiang Ji, Shenggao Zhou, Shengfeng Zhu

As widely used electrochemical storage devices, supercapacitors deliver higher power density than batteries, but suffer from significantly lower energy density. In this work, we propose a topology optimization model for electrode structure to maximize energy storage in supercapacitors. The existence of minimizers to the resulting optimal control problem, whi

Andrew T. Karl

SVEMnet is an R package for fitting Self-Validated Ensemble Models (SVEM) with elastic-net base learners and performing multi-response optimization in small-sample mixture-process design-of-experiments (DOE) studies with numeric, categorical, and mixture factors. SVEMnet wraps elastic-net and relaxed elastic-net models for Gaussian and binomial responses fro

Nicholas Van Nimwegen

In a previous work, B\'ona and Pantone studied permutations that avoided all but one pattern of length $k$ that began with a length $k-1$ increasing subsequence. We draw the connection between that idea and distant patterns, first discussed heavily in a work by Dimitrov, and study similar permutation classes, where the index not part of the increasing subseq

Takeshi Ikeda, Shinsuke Iwao, Mark Shimozono

We study the torus-equivariant homology $H_*^T(\mathrm{Gr}_G)$ of the affine Grassmannian $\mathrm{Gr}_G$, where $G=\mathrm{Sp}_{2n}(\mathbb{C})$ is the symplectic group. This homology admits a natural ring structure and a Schubert basis, giving rise to a well-defined Schubert calculus. We realize $H_*^T(\mathrm{Gr}_G)$ in terms of symmetric functions. Our f

Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar

Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities, traffic management, and pedestrian safety. However, efficiently managing and analyzing multi-camera feeds poses challenges due to the vast amount of data. Analyzing such huge video

Zhuohan Li, KyuJung Jun, Bowen Deng, Gerbrand Ceder

Solid-state batteries require lithium-ion conductors that combine high ionic conductivity with stability under harsh electrochemical and chemical conditions. Here, we investigate the chemical factors governing the stability of NASICON-type and garnet-type Li-ion conductors in highly alkaline environments. This is particularly relevant to solid-state Li-air c

Jerry Lin, Zeyuan Hu, Tom Beucler, Katherine Frields

Subgrid machine-learning (ML) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent on

Anna Merin Francis, Avirup De, Abhijit Biswas, Lily Mandal

We investigate the anomalous Nernst effect in epitaxial SrRuO$_3$ thin films grown on c-cut Al$_2$O$_3$ substrates, and in a polycrystalline SrRuO$_3$ slab. Through comprehensive measurements of the transverse thermoelectric response as a function of temperature and magnetic field, we observe a pronounced Nernst signal near $T_c$ in the (111) oriented SrRuO$

Kasidis Arunruangsirilert, Bo Wei, Hang Song, Jiro Katto

5G Standalone (SA) is the goal of the 5G evolution, which aims to provide higher throughput and lower latency than the existing LTE network. One of the main applications of 5G is the real-time distribution of Ultra High-Definition (UHD) content with a resolution of 4K or 8K. In Q2/2021, Advanced Info Service (AIS), the biggest operator in Thailand, launched

Soumojit Das, Nairanjana Dasgupta, Prashanta Dutta

Modern artificial intelligence systems make critical decisions yet often fail silently when uncertain -- even well-calibrated models provide no mechanism to identify \textit{which specific predictions} are unreliable. We develop a geometric framework addressing both calibration and instance-level uncertainty quantification for neural network probability outp

Pasapong Wongprasert, Kasidis Arunruangsirilert, Jiro Katto

All 3GPP-compliant commercial 5G New Radio (NR)-capable UEs on the market are equipped with 4x4 MIMO support for Mid-Band frequencies (>1.7 GHz) and above, enabling up to rank 4 MIMO transmission. This doubles the theoretical throughput compared to rank 2 MIMO and also improves reception performance. However, 4x4 MIMO support on low-band frequencies (<1 GHz)

Alejandro Cuadron, Pengfei Yu, Yang Liu, Arpit Gupta

Despite rapid progress in LLM agents, performance on long-horizon, tool-using tasks remains fragile. To better understand this fragility, we ask a simple question: \emph{do all actions contribute equally to failure?} Analyzing execution traces on $\tau$-Bench (Airline/Retail) and SWE-Bench Verified, we decompose trajectories into \emph{mutating} (environment

Alexandru Chirvasitu, Andre Kornell

We obtain two related characterizations of discrete quantum groups and discrete quantum groups of Kac type as allegorical group objects in the symmetric monoidal dagger category of quantum sets and relations, of interest to quantum predicate logic and quantum information theory. Specifically, we characterize discrete quantum groups by the existence of an inv

Haoming Lu, David Kocharian, Humphrey Shi

As a widely used operation in image editing workflows, image composition has traditionally been studied with a focus on achieving visual realism and semantic plausibility. However, in practical editing scenarios of the modern content creation landscape, many compositions are not intended to preserve realism. Instead, users of online platforms motivated by ga

Sanchit Kaul, Kevin Nhu, Jason Eissayou, Ivan Eser

This empirical investigation elucidates the limitations of deterministic, unidimensional productivity heuristics by operationalizing the SPACE framework through extensive repository mining. Utilizing a dataset derived from open-source repositories, the study employs rigorous statistical methodologies including Generalized Linear Mixed Models (GLMM) and RoBER

Tong Xia, Jiankun Zhang, Ruiwen You, Ao Xu

Urban research aims to understand how cities operate and evolve as complex adaptive systems. With the rapid growth of urban data and analytical methodologies, the central challenge of the field has shifted from data availability to the integration of heterogeneous data into coherent, verifiable urban knowledge through multidisciplinary approaches. Recent adv

Elias Gabriel Minian

We introduce a subsampling method for topological data analysis based on strong collapses of simplicial complexes. Given a point cloud and a scale parameter $\delta$, we construct a subsampling that preserves both global and local topological features while significantly reducing computational complexity of persistent homology calculations. We illustrate the

Tianyue Liu, Shuang Ming, Xin Sun, Baojun Wu

In this paper, we study the asymptotics of the $6j$-symbols for the principal series of the modular double of $\mathrm U_q\mathfrak{sl}(2;\mathbb R)$, and of their analytic extension -- what we call the $b$-$6j$ symbols, relating them in various cases to the volume of truncated hyperideal tetrahedra in the hyperbolic and the anti-de Sitter geometry. To the b

J. F. Parisi, A. Rutkowski

Producing valuable isotopes with high-flux high-energy neutrons generated by muon-catalyzed fusion ($\mu$CF) reactions could substantially improve the economic prospects for muon-catalyzed fusion. Because no external heating is required for $\mu$CF, heat flux constraints are significantly relaxed compared with fusion systems requiring external heating. This

Yaoyue Wang, Arian Ashourvan, Guilherme Ramos, Paul Bogdan

Medically uncontrolled epileptic seizures affect nearly 15 million people worldwide, resulting in enormous economic and psychological burdens. Treatment of medically refractory epilepsy is essential for patients to achieve remission, improve psychological functioning, and enhance social and vocational outcomes. Here, we show a state-of-the-art method that st

Richard Canary, Tengren Zhang, Andrew Zimmer

Farre, Pozzetti and Viaggi proved that any (d-k)-hyperconvex subgroup of PSL(d,C) is virtually isomorphic to a convex cocompact Kleinian group and that its k-th simple root critical exponent is at most 2. We show that a (d-k)-hyperconvex subgroup is isomorphic to a uniform lattice in PSL(2,C) if and only if its k-th simple root critical exponent is exactly 2

Yoshiki Hatta, Takashi Sekii

Theoretical study of high-order gravity-mode period spacing ($\Delta P_g$) pattern is relevant for the better understanding of internal properties of intermediate-mass ($1.5 \, M_\odot < M < 8 \, M_{\odot}$) main-sequence g-mode pulsators. In this paper, we carry out the first-order perturbative analysis to evaluate effects of a sharp, though not discontinuo

Tianyi Hao, Joseph Sullivan, Sivaprasad Omanakuttan, Michael A. Perlin

Recent experimental progress in realizing surface code on hardware, including demonstrations of break-even logical memory on devices with up to hundreds of physical qubits, has materially advanced the prospects for fault-tolerant quantum computation. This progress creates urgency for the development of compilation workflows that directly target the forthcomi

Xiao-Xi Yao, Bo Zhang Yusuf Turek

The heralded optical parametric amplifier (OPA) has emerged as a promising tool for quantum state engineering. However, its potential has been limited to coherent state inputs. Here, we introduce a generalized heralded OPA protocol that unlocks a vastly expanded class of quantum phenomena by accepting arbitrary non-classical inputs. With a squeezed vacuum in