October 2025 arXiv papers — page 62

Anh Nguyen, Viet Nguyen, Duc Vu, Trung Dao

Shortcut models represent a promising, non-adversarial paradigm for generative modeling, uniquely supporting one-step, few-step, and multi-step sampling from a single trained network. However, their widespread adoption has been stymied by critical performance bottlenecks. This paper tackles the five core issues that held shortcut models back: (1) the hidden

Daniele Girolimetto, Anastasios Panagiotelis, Tommaso Di Fonzo, Han Li

Methods for forecasting time series adhering to linear constraints have seen notable development in recent years, especially with the advent of forecast reconciliation. This paper extends forecast reconciliation to the open question of non-linearly constrained time series. Non-linear constraints can emerge with variables that are formed as ratios such as mor

Axel Maas, Simon Plätzer, Felix Pressler

It has been a long entertained idea that self-bound gravitons, so-called geons, could be a dark matter candidate or form (primordial) black holes. The development of viable candidates for quantum gravity allows now to investigate these ideas. Analytic methods show that the description of geons needs to be based on composite operators made out of the graviton

Andrea Gallese, Davide Lombardo

We describe an algorithm to compute the minimal field of definition of the Tate classes on powers of a Jacobian $J$ with potential complex multiplication. This field arises as a natural invariant of the Galois representations attached to $J$. We also give closed formulas expressing the periods of anti-holomorphic differential forms on $J$ in terms of the per

Michael Külper, Jan-Niclas Hilgert, Frank Breitinger, Martin Lambertz

Self-hosted cloud storage platforms like Nextcloud are gaining popularity among individuals and organizations seeking greater control over their data. However, this shift introduces new challenges for digital forensic investigations, particularly in systematically analyzing both client and server components. Despite Nextcloud's widespread use, it has receive

Rebekah Rousi, Toija Cinque, Katey O'Sullivan, Aska Mayer

The exhibition Butterfly: Glo-cal Effects of Data, Energy, and Industry is, at its core, a meditation on entanglement-between the global and the local, the ecological and the digital, the material and the virtual. It asks how we might reframe the infrastructures that shape our lives not only as technologies of efficiency or convenience, but as ecosystems the

Noah Oberweis, Semih Cayci

Continuous-time models provide important insights into the training dynamics of optimization algorithms in deep learning. In this work, we establish a non-asymptotic convergence analysis of stochastic gradient Langevin dynamics (SGLD), which is an It\^o stochastic differential equation (SDE) approximation of stochastic gradient descent in continuous time, in

Pengyu Xu, Shijia Li, Ao Sun, Feng Zhang

We propose OutboundEval, a comprehensive benchmark for evaluating large language models (LLMs) in expert-level intelligent outbound calling scenarios. Unlike existing methods that suffer from three key limitations - insufficient dataset diversity and category coverage, unrealistic user simulation, and inaccurate evaluation metrics - OutboundEval addresses th

Agnieszka Szysiak, Robert Tomala, Helena Węglarz, Juraj Kajan

Developing efficient Er3+,Yb3+:YAG eye-safe lasers is a priority of modern laser technology. This paper focuses on the influence of the concentration of Yb3+ ions on the spectroscopic properties of Er3+,Yb3+:YAG transparent ceramics. Four samples with different concentrations of Yb3+ ions were prepared by solid-state reaction sintering. The study revealed th

Yimeng Bai, Chang Liu, Yang Zhang, Dingxian Wang

Generative recommendation is emerging as a transformative paradigm by directly generating recommended items, rather than relying on matching. Building such a system typically involves two key components: (1) optimizing the tokenizer to derive suitable item identifiers, and (2) training the recommender based on those identifiers. Existing approaches often tre

Michal Outrata

This work deals with two groups of spectral analysis results for matrices arising in fully implicit Runge-Kutta methods used for linear time-dependent partial differential equations. These were applied for different formulations of the same problem and used different tools to arrive at results that do not immediately coincide. We show the equivalence of the

Joaquín Medina Dueñas, Santiago Giménez de Castro, Jose H. Garcia, Stephan Roche

The electrical generation of spin signals is of central interest for spintronics, where graphene stands as a relevant platform as its spin-orbit coupling (SOC) is tuned by proximity effects. Here, we propose an enhancement of spin-charge interconversion in graphene by controlling the intraparticle entanglement between the spin and pseudospin degrees of freed

Shun-yi Yang, Guang-yue Hu, Chao Xiong, Tian-yi Li

Astrophysical systems exhibit a rich diversity of outflow morphologies, yet their mechanisms and existence conditions remain among the most persistent puzzles in the field. Here we present scaled laboratory experiments based on laser-driven plasma outflow into magnetized ambient gas, which mimic five basic astrophysical outflows regulated by interstellar med

Wangqian Chen, Junting Chen, Shuguang Cui

As communication networks evolve towards greater complexity (e.g., 6G and beyond), a deep understanding of the wireless environment becomes increasingly crucial. When explicit knowledge of the environment is unavailable, geometry-aware feature extraction from channel state information (CSI) emerges as a pivotal methodology to bridge physical-layer measuremen

Mykhailo Chaika

An important feature of Cr3+ is the ability to tune the absorption and emission spectra by changing the host. However, in some cases, different emission spectra can be detected in samples with similar Racah parameters. The present paper reports changes in the luminescence properties of Cr3+:YGG nanocrystals. Cr3+:YGG nanocrystals were synthesized by a modifi

Christoph Bühler, Matteo Biagiola, Luca Di Grazia, Guido Salvaneschi

Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model Context Protocol (MCP) has become the de facto standard for connecting agents with such resources, but security has lagged behind: thousands of MCP servers execute with unrestricted access to host systems, creating a

Carlo Rizza, Alessandra Contestabile, Maria Antonietta Vincenti, Giuseppe Castaldi

Electromagnetic temporal boundaries, emerging when the constitutive parameters of a medium undergo abrupt temporal variations, have garnered significant interest for their role in facilitating unconventional wave phenomena and enabling sophisticated field manipulations. A key manifestation is temporal reflection in an unbounded spatial domain, where a sudden

Allan John Gerrard, Kohei Motegi, Kazumitsu Sakai

We study a certain type of multiple commutation relations of the quantum affine algebra $U_q(\widehat{\mathfrak{gl}}_N)$. We show that all the coefficients in the multiple commutation relations between the $L$-operator elements are given in terms of the trigonometric weight functions for the vector representation, independent of the representation of the $L$

Waris Radji, Odalric-Ambrym Maillard

In reinforcement learning (RL) theory, the concept of most confusing instances is central to establishing regret lower bounds, that is, the minimal exploration needed to solve a problem. Given a reference model and its optimal policy, a most confusing instance is the statistically closest alternative model that makes a suboptimal policy optimal. While this c

Jason Hartline, Aleck Johnsen, Yingkai Li

We study auctions that are robust at any scale, i.e., they can be applied to sell both expensive and cheap items and achieve the best multiplicative approximation of the optimal revenue in the worst case. We first show that it is without loss of optimality to restrict attention to scale-invariant mechanisms whenever the family of possible distributions is cl

Jose Alfonso Pinzon Escobar, Markus Mühlhäußer, Hans-Joachim Bungartz, Philipp Neumann

In this work, algorithms for the parallel computation of three-body interactions in molecular dynamics are developed. While traversals for the computation of pair interactions are readily available in the literature, here, such traversals are extended to allow for the computation between molecules stored across three cells. A general framework for the comput

Chen-Yu Yang, Huan Ye, Xiao-Xiong Zeng

Using ray-tracing techniques, this paper investigates the optical and polarization images of rotating black holes in Kalb-Ramond (KR) gravity illuminated by thick accretion disks. We examine two accretion disk models: the phenomenological radiatively inefficient accretion flow (RIAF) model and the analytical ballistic approximation accretion flow (BAAF) mode

Xiang Li, Huizi Yu, Wenkong Wang, Yiran Wu

Objective: Emergency medical dispatch (EMD) is a high-stakes process challenged by caller distress, ambiguity, and cognitive load. Large Language Models (LLMs) and Multi-Agent Systems (MAS) offer opportunities to augment dispatchers. This study aimed to develop and evaluate a taxonomy-grounded, LLM-powered multi-agent system for simulating realistic EMD scen

Ke Sun, Jingyi Yan, Zhenglin Li, Shaorong Xie

The effectiveness of Data Injections Attacks (DIAs) critically depends on the completeness of the system information accessible to adversaries. This relationship positions information incompleteness enhancement as a vital defense strategy for degrading DIA performance. In this paper, we focus on the information-theoretic stealth attacks, where the attacker e

Hao-Xiang Jiang, Chao-Ran Cai, Ji-Qiang Zhang, Ming Tang

We propose a coupled dynamical model of resource allocation and epidemic spread, inspired by the hierarchical structure of real-world therapeutic resource allocation. In this framework, network nodes are assigned distinct roles as either resource allocators or resource recipients. As the average number of links per recipient from allocators increases, the pr

Shouvik Sadhukhan, C. S. Narayanamurthy

This study examines the influence of optical turbulence on field statistics using a nonlinear reconstruction and quantum phase-space formalism. Turbulence-distorted intensity sequences were processed through a nonlinear P3-type partial differential equation to retrieve the embedded phase, thereby reconstructing the complete complex optical field. The recover

Belle II Collaboration, M. Abumusabh, I. Adachi, L. Aggarwal

We measure the time- and phase-space-integrated $CP$ asymmetry $A_{CP}$ in $D^0\to\pi^+\pi^-\pi^0$ decays reconstructed in $e^+e^-\to c\bar c$ events collected by the Belle II experiment from 2019 to 2022. This sample corresponds to an integrated luminosity of 428 fb$^{-1}$. We require $D^0$ mesons to be produced in $D^{*+}\to D^0\pi^+$ decays to determine t

Kexuan Shi, Yandong Wen, Weiyang Liu

Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the parameter space, combining task vectors to mitigate conflicts, but remain constrained by parameter inconsistencies. We propose Functional Dual Anchors (FDAs), a framework that instead

Sukjoo Lee, Victor Przyjalkowski

In this article, we study how the rationality of a Fano threefold is reflected in its standard mirror Landau-Ginzburg model and its deformations. The main result is that a Fano threefold is rational if and only if the monodromy around every reducible fiber of its generic mirror Landau-Ginzburg model is unipotent.

Jiazheng Dou, Wen Zhao

Cosmic birefringence (CB) is a promising probe of parity-violating physics beyond the Standard Model, characterized by the rotation of the linear polarization plane of cosmic microwave background (CMB) photons. This effect, quantified by the birefringence angle $\beta$, generates non-zero $EB$ and $TB$ correlations that are otherwise absent in standard cosmo

Xun Su, Hiroyuki Kasai

Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation information into the generation process of the diffusion models. However, this presents an inherent dilemma: excessive integration can disrupt the generative process, while insufficient integration fails to emphasize the constra

Nishan Chatterjee, Veronika Bajt, Ana Zwitter Vitez, Senja Pollak

The rise of right-wing populism in Europe has brought to the forefront the significance of analysing social media discourse to understand the dissemination of extremist ideologies and their impact on political outcomes. Twitter, as a platform for interaction and mobilisation, provides a unique window into the everyday communication of far-right supporters. I

Xiaoyuan Zhang, Chengdong Ma, Yizhe Huang, Weidong Huang

World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure to model the crucial interplay between physical environments

Miroslav Bulíček, Petr Kaplický, Lucie Wintrová

We consider the flow of a generalized non-Newtonian incompressible heat-conducting fluid in a~bounded two-dimensional domain, subject to Dirichlet boundary conditions for velocity and temperature. The fluid obeys a power-law constitutive relation for the Cauchy stress with exponent~$p$. For $p\geq 2$ and finite-energy initial data, we establish the existence

Ali Mollabashi, Mohammad-Javad Vasli

We study the role of randomness in the scrambling of quantum information within integrable free-fermionic systems. Considering quadratic Hamiltonians with varying degrees of randomness, we analyze entanglement-based measures to characterize the scrambling structure. We show that the memory effect in the entanglement of disjoint subsystems of Gaussian states

Andreas Höring, Saverio Andrea Secci

We give a classification of smooth Fano fourfolds such that the base scheme of the anticanonical system is a smooth surface. As a consequence we show that there are exactly 22 deformation families of such manifolds and they are all obtained by the same geometric construction. These 22 families are closely related to the list of smooth Fano threefolds that ad

Shuoshuo Ding, Tiedong Zhang, Dapeng Jiang, Ming Lei

Visual degradation caused by limited visibility, insufficient lighting, and feature scarcity in underwater environments presents significant challenges to visual-inertial simultaneous localization and mapping (SLAM) systems. To address these challenges, this paper proposes a graph-based visual-inertial-acoustic-depth SLAM system that integrates a stereo came

Robert Gigiu

Artificial intelligence (AI) is increasingly embedded in NHS workflows, but its probabilistic and adaptive behaviour conflicts with the deterministic assumptions underpinning existing clinical-safety standards. DCB0129 and DCB0160 provide strong governance for conventional software yet do not define how AI-specific transparency, interpretability, or model dr

Xingwei Zhong, Kar Wai Fok, Vrizlynn L. L. Thing

Multimodal large language models (MLLMs) comprise of both visual and textual modalities to process vision language tasks. However, MLLMs are vulnerable to security-related issues, such as jailbreak attacks that alter the model's input to induce unauthorized or harmful responses. The incorporation of the additional visual modality introduces new dimensions to

Yulong Pan, Michael Lindsey

We develop a discontinuous Galerkin (DG) framework for automatically constructing adaptive basis sets for electronic structure calculations. By allowing basis functions to be discontinuous across element interfaces, our approach supports flexible combinations of atom-centered and polynomial basis sets, maintains favorable numerical conditioning, and induces

Nilanjana Dey Choudhury, P. Shalima, Keerthana U., J. Murthy

Far-Ultraviolet (FUV) halos have been detected around six bright stars by Murthy and Henry (2011) using GALEX observations. These halos are thought to be caused by forward scattering of the starlight by dust grains present in thin foreground clouds. The optical constants of grains producing such halos have been constrained earlier by using a single scatterin

Mithilesh K. Parit, Mingchen Huang, Ziting Chen, Yifei He

Light scattering plays an essential role in uncovering the properties of quantum states through light-matter interactions. Here, we explore the transition from Bose-Einstein condensate (BEC) to droplets in a dipolar $^{166}$Er gas by employing superradiant light scattering as both a probing and controlling tool. We observe that the efficiency of superradiant

Francesco Pivi, Simone Gazza, Davide Evangelista, Roberto Amadini

Generative models based on flow matching have demonstrated remarkable success in various domains, yet they suffer from a fundamental limitation: the lack of interpretability in their intermediate generation steps. In fact these models learn to transform noise into data through a series of vector field updates, however the meaning of each step remains opaque.

Zixiang Wan, Guochang Zhang, Yifeng He, Jianqiang Wei

Neural Audio Codecs (NACs) have gained growing attention in recent years as technologies for audio compression and audio representation in speech language models. While mainstream NACs typically require G-level computation and M-level parameters, the performance of lightweight and streaming NACs remains underexplored. This paper proposes SpecTokenizer, a lig

Yunlong Chu, Minglai Shao, Zengyi Wo, Bing Hao

Graph Neural Networks (GNNs) face a fundamental adaptability challenge: their fixed message-passing architectures struggle with the immense diversity of real-world graphs, where optimal computational strategies vary by local structure and task. While Mixture-of-Experts (MoE) offers a promising pathway to adaptability, existing graph MoE methods remain constr

Zipu Fan, Junchao Ma, Jinying Yang, Yan Sun

Precise probe and control of various quantum degrees of freedom in novel quantum matter are central to understanding fundamental quantum physics and hold promise for innovative routes to encode and process information. Chirality is one such degree of freedom that has recently attracted intense research interest, especially for Weyl fermions in topological We

Jingyu Wu, Zhihao Ouyang, Hubing Xiao, Elisa Prandini

In this work, we report, for the first time, a quasi-periodic oscillation (QPO) in the $\gamma$-ray band of 4FGL J0309.9-6058, also known as PKS 0308-611. We employed three analytical methods (the Lomb-Scargle periodogram, REDFIT, and the weighted wavelet Z-transform) to analyze the QPO signal using \textit{Fermi} $\gamma$-ray light curve data. The analysis

Xiyuan Zhang, Danielle C. Maddix, Junming Yin, Nick Erickson

Since the seminal work of TabPFN, research on tabular foundation models (TFMs) based on in-context learning (ICL) has challenged long-standing paradigms in machine learning. Without seeing any real-world data, models pretrained on purely synthetic datasets generalize remarkably well across diverse datasets, often using only a moderate number of in-context ex

Sophia Hatz

The analogy between Artificial Intelligence (AI) and nuclear weapons is prominent in academic and policy discourse on AI governance. This chapter reviews 43 scholarly works which explicitly draw on the nuclear domain to derive lessons for AI governance. We identify four problem areas where researchers apply nuclear precedents: (1) early development and gover

JunRu Luo, Difei Cheng, Bo Zhang

The Area Under the Curve (AUC) is an important performance metric for classification tasks, particularly in class-imbalanced scenarios. However, minimizing the AUC presents significant challenges due to the non-convex and discontinuous nature of pairwise 0/1 losses, which are difficult to optimize, as well as the substantial memory cost of instance-wise stor

Quan Mu

Based on the convex hull construction algorithm, a new geometrical model of ice crystals is proposed to investigate the scattering properties of cirrus clouds particles. Light scattering matrices involving complete polarization information are calculated in geometric optics approximation for randomly oriented large crystals with random and given convex polyh

Yang Zhong, Yifan Yao, Tong Luo, Youcai Zhang

Food analysis is becoming a hot topic in health area, in which fine-grained food recognition task plays an important role. In this paper, we describe the details of our solution to the LargeFineFoodAI-ICCV Workshop-Recognition challenge held on Kaggle. We find a proper combination of Arcface loss[1] and Circle loss[9] can bring improvement to the performance

Yang Zhong, Zhiming Wang, Zhaoyang Li, Jinyu Ma

This paper introduces the 3rd place solution to the ICCV LargeFineFoodAI Retrieval Competition on Kaggle. Four basic models are independently trained with the weighted sum of ArcFace and Circle loss, then TTA and Ensemble are successively applied to improve feature representation ability. In addition, a new reranking method for retrieval is proposed based on

Prabir Rudra, Aritra Sanyal, Promila Biswas, Tuhina Ghorui

In this paper, we explore a new type of smooth and well-behaved polynomial redshift function that can avoid a future singularity. Using this function, we have proposed different redshift parametrizations of the dark energy equation of state, drawing motivation from different polynomial functions like conventional polynomial, Legendre polynomial, Laguerre pol

Zixiang Wan, Haoran Zhao, Guochang Zhang, Runqiang Han

This paper presents PhoenixCodec, a comprehensive neural speech coding and decoding framework designed for extremely low-resource conditions. The proposed system integrates an optimized asymmetric frequency-time architecture, a Cyclical Calibration and Refinement (CCR) training strategy, and a noise-invariant fine-tuning procedure. Under stringent constraint

Steffen Borgwardt, MacKenzie Carr, Ce Chen, Wayne Ge

Fomin, Kratochv\'il, Lokshtanov, Mancini, and Telle showed that every $C_{4}$-free graph is reconstructible from the \emph{multiset} of closed neighborhoods. We strengthen their result proving that every $C_{4}$-free graph is reconstructible from the \emph{set} of closed neighborhoods. This extends the work of Lafrance et al.\ by showing that all $C_{4}$-fre

Shaoheng Zhang

Let $(\mathbf{u},\mathbf{B})$ be an axisymmetric self-similar solution to the stationary MHD equations with magnetic diffusion, of the form $\mathbf{u}=u^r(r,z)\mathbf{e}_{r}+u^{\theta}(r,z)\mathbf{e}_{\theta}+u^z(r,z)\mathbf{e}_{z}$ and $\mathbf{B}=B^{\theta}(r,z)\mathbf{e}_{\theta}$ in cylindrical coordinates $(r,\theta,z)$, where $(\mathbf{e}_r,\mathbf{e}

Helena Grete Lillepalu, Tanel Alumäe

The availability of LLM benchmarks for the Estonian language is limited, and a comprehensive evaluation comparing the performance of different LLMs on Estonian tasks has yet to be conducted. We introduce a new benchmark for evaluating LLMs in Estonian, based on seven diverse datasets. These datasets assess general and domain-specific knowledge, understanding

Luca Demetrio, Giovanni Apruzzese, Kathrin Grosse, Pavel Laskov

How does the progressive embracement of Large Language Models (LLMs) affect scientific peer reviewing? This multifaceted question is fundamental to the effectiveness -- as well as to the integrity -- of the scientific process. Recent evidence suggests that LLMs may have already been tacitly used in peer reviewing, e.g., at the 2024 International Conference o

Adarsh Jain, Pawan Bharadwaj, Chandra Sekhar Seelamantula

Gravity data can be better interpreted after enhancing high-frequency information via downward continuation. Downward continuation is an ill-posed deconvolution problem. It has been tackled using regularization techniques, which are sensitive to the choice of regularization parameters. More recently, convolutional neural networks such as the U-Net have been

Mingrui Liu, Sixiao Zhang, Cheng Long, Kwok Yan Lam

As Large Language Models (LLMs) become integral to computing infrastructure, safety alignment serves as the primary security control preventing the generation of harmful payloads. However, this defense remains brittle. Existing jailbreak attacks typically bifurcate into white-box methods, which are inapplicable to commercial APIs due to lack of gradient acce

Yukun Jiang, Mingjie Li, Michael Backes, Yang Zhang

Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful content, a risk that has been further amplified by various jailbreak attacks. Existing jailbreak attacks mainly follow sequential logic, where LLMs understand and answer each given task one by one. However, concurrenc

Xiequn Wang, Zhan Zhuang, Yu Zhang

Continual learning (CL) requires models to continuously adapt to new tasks without forgetting past knowledge. In this work, we propose \underline{P}roactive \underline{L}ow-rank \underline{A}llocatio\underline{N} (PLAN), a framework that extends Low-Rank Adaptation (LoRA) to enable efficient and interference-aware fine-tuning of large pre-trained models in C

Tsun Hin Navin Tsung, Gregory R. Werner, Dmitri A. Uzdensky, Mitchell C. Begelman

We present two-dimensional (2D) particle-in-cell simulations of a magnetized, collisionless, relativistic pair plasma subjected to combined velocity and magnetic-field shear, a scenario typical at intermittent structures in plasma turbulence. We create conditions where only the Kelvin-Helmholtz (KH) and Drift-Kink (DK) instabilities can develop, while tearin

Benoît Collins, Sho Matsumoto

In this paper, we develop a novel approach to the Weingarten calculus by employing the notion of virtual isometries. Traditionally, Weingarten calculus provides explicit formulas for integrating polynomial functions over compact matrix groups with respect to the Haar measure, yet it faces limitations when evaluating high-degree integrals due to the non-inver

Sunghyun Kang, Stefano Scopel, Gaurav Tomar

We introduce WimPyC, a Python code for the calculation of the capture rate of Weakly Interacting Massive Particles (WIMPs) by celestial bodies through nuclear scattering in the optically thin regime. WimPyC is an extension of the WimPyDD code, that calculates WIMP-nucleus scattering signals in direct detection (DD) experiments, and allows to combine DD and c

Stephen Zhao, Aidan Li, Rob Brekelmans, Roger Grosse

Reinforcement learning (RL) has become a predominant technique to align language models (LMs) with human preferences or promote outputs which are deemed to be desirable by a given reward function. Standard RL approaches optimize average reward, while methods explicitly focused on reducing the probability of undesired outputs typically come at a cost to avera

Anwesha Mukherjee, Rajkumar Buyya

This paper proposes a generative adversarial network and federated learning-based model to address various challenges of the smart prediction and recommendation applications, such as high response time, compromised data privacy, and data scarcity. The integration of the generative adversarial network and federated learning is referred to as Generative Federa

Junzhe Zhang, Huixuan Zhang, Xiaojun Wan

The rapid progress of multimodal large language models (MLLMs) calls for more reliable evaluation protocols. Existing static benchmarks suffer from the potential risk of data contamination and saturation, leading to inflated or misleading performance evaluations. To address these issues, we first apply Graph formulation to represent a static or dynamic VQA s

Julio Jerison E. Macrohon, Gordon Hung

Coral reefs support numerous marine organisms and are an important source of coastal protection from storms and floods, representing a major part of marine ecosystems. However coral reefs face increasing threats from pollution, ocean acidification, and sea temperature anomalies, making efficient protection and monitoring heavily urgent. Therefore, this study

Shuo Li, Keqin Xu, Jie Liu, Dan Ye

Causal relationship discovery has been drawing increasing attention due to its prevalent application. Existing methods rely on human experience, statistical methods, or graphical criteria methods which are error-prone, stuck at the idealized assumption, and rely on a huge amount of data. And there is also a serious data gap in accessing Multivariate time ser

Ning Bian, Xianpei Han, Hongyu Lin, Baolei Wu

Reliable simulation of human behavior is essential for explaining, predicting, and intervening in our society. Recent advances in large language models (LLMs) have shown promise in emulating human behaviors, interactions, and decision-making, offering a powerful new lens for social science studies. However, the extent to which LLMs diverge from authentic hum

Frederik Wagner Madsen, Joy Dalmacio Billanes, Bo Nørregaard Jørgensen, Zheng Ma

The transition toward net-zero energy systems requires scalable and cost-effective deployment of Power-to-X technologies, particularly green hydrogen production. Despite increasing investments, a critical research gap remains in dynamically assessing how different operational strategies affect the feasibility of hydrogen production under real-world energy ma

Flora C. Shi, Martin J. Wainwright, Stephen Bates

We study hypothesis testing over a heterogeneous population of strategic agents with private information. Any single test applied uniformly across the population yields statistical error that is sub-optimal relative to the performance of an oracle given access to the private information. We show how it is possible to design menus of statistical contracts tha

Tomer Galanti, Aarya Bookseller, Korok Ray

We study a bilevel \emph{max-max} optimization framework for principal-agent contract design, in which a principal chooses incentives to maximize utility while anticipating the agent's best response. This problem, central to moral hazard and contract theory, underlies applications ranging from market design to delegated portfolio management, hedge fund fee s

Shadi Aljawarneh, Juan A. Lara, Muneer Bani Yassein

The Meteorology is a field where huge amounts of data are generated, mainly collected by sensors at weather stations, where different variables can be measured. Those data have some particularities such as high volume and dimensionality, the frequent existence of missing values in some stations, and the high correlation between collected variables. In this r

Tianyi Zhang, Mu Chen

Financial advisors and investors struggle with information overload from financial news, where irrelevant content and noise obscure key market signals and hinder timely investment decisions. To address this, we propose a novel Chain-of-Thought (CoT) summarization framework that condenses financial news into concise, event-driven summaries. The framework inte

Yujin Jo, Taesup Kim

Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated remarkable zero-shot generalization, enabling deployment in a wide range of real-world tasks without additional task-specific training. However, in real deployment scenarios with evolving environments or emerging classes, these models inevitably face distributional shifts and novel ta

Víctor Rampérez, Javier Soriano, David Lizcano, Shadi Aljawarneh

Cloud computing has been consolidated as a support for the vast majority of current and emerging technologies. However, there are some barriers that prevent the exploitation of the full potential of this technology. First, the major cloud providers currently put the onus of implementing the mechanisms that ensure compliance with the desired service levels on

Angshul Majumdar

This paper presents a unified matrix factorization framework for classical and robust clustering. We begin by revisiting the well-known equivalence between crisp k-means clustering and matrix factorization, following and rigorously rederiving an unpublished formulation by Bauckhage. Extending this framework, we derive an analogous matrix factorization interp

Qihang Zhou, Binbin Gao, Guansong Pang, Xin Wang

Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. However, existing methods typically rely on a single textual space to align with visual semantics across diverse objects and domains. The indiscriminate alignment hinders the model from accurately capturing varied anomaly semantics. We propose TokenCLIP, a

T. Long, M. J. Choi, P. H. Diamond

Inhomogeneous mixing and the consequent mesoscopic layered structure have been observed in many physical systems, including magnetically confined fusion plasmas. Especially, in plasmas, mixing can be enhanced through turbulence spreading by intermittent coherent structures (blobs/voids), or suppressed due to the formation of transport barriers (sheared zonal

Gaëtan Chenevier, Wee Teck Gan

We use the triality automorphism of simple algebraic groups of type $D_4$ to prove some new instances of global Langlands functorial lifting. In particular, we prove the (weak) spin lifting from ${\rm GSp}_6$ to ${\rm GL}_8$ and the tensor product lifting from ${\rm GL}_2 \times {\rm GSp}_4$ to ${\rm GL}_8$. As an arithmetic application, we establish the exp

Sandra Ranilla-Cortina, Diego A. Aranda, Jorge Ballesteros, Jesus Bonilla

In this paper, we address the challenge of multivariate time-series forecasting using quantum machine learning techniques. We introduce adaptation strategies that extend variational quantum circuit models, traditionally limited to univariate data, toward the multivariate setting, exploring both purely quantum and hybrid quantum-classical formulations. First,

Dogyun Park, Taehoon Lee, Minseok Joo, Hyunwoo J. Kim

Recently, Flow Matching models have pushed the boundaries of high-fidelity data generation across a wide range of domains. It typically employs a single large network to learn the entire generative trajectory from noise to data. Despite their effectiveness, this design struggles to capture distinct signal characteristics across timesteps simultaneously and i

Ryosuke Okumura, Naoto Nakatsuji, Takuto Kawakami, Mikito Koshino

We study the structural relaxation and electronic properties of a one-dimensional (1D) moir\'e system composed of a zigzag graphene nanoribbon (GNR) placed on a hexagonal boron nitride (hBN) substrate. Using an effective grid model derived from continuum elasticity theory, we calculate the relaxed atomic structure of the GNR/hBN system for various twist angl

Guozhong Li, Muhannad Alhumaidi, Spiros Skiadopoulos, Panos Kalnis

The rapid growth of high-resolution scientific simulations and observation systems is generating massive spatiotemporal datasets, making efficient, error-bounded compression increasingly important. Meanwhile, decoder-only large language models (LLMs) have demonstrated remarkable capabilities in modeling complex sequential data. In this paper, we propose LLMC

Peng Liu

Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages incl

Shamistan Karimov, Elian Neppel, Shreya Santra, Kentaro Uno

Modular robots offer reconfigurability and fault tolerance essential for lunar missions, but require controllers that adapt safely to real-world disturbances. We build on our previous hardware-agnostic actuator synchronization in Motion Stack to develop a new controller enforcing adaptive velocity bounds via a dynamic hypersphere clamp. Using only real-time

Wei-Jie Sheng, Xin-Tian Zhang

This paper is concerned with curved fronts of combustion reaction-diffusion equations in spatially periodic media in $\mathbb{R}^N$ $(N\geq2)$. Under the assumption that there are moving pulsating fronts for any given propagation direction $e \in \mathbb{S}^{N-1}$, and by constructing suitable super- and sub-solutions, we prove the existence of a curved fron

Varshika Srinivasavaradhan, Morgan Vigil-Hayes, Ellen Zegura, Elizabeth Belding

Characterizing cellular network performance is complex. Current representations of cellular coverage, such as service provider and FCC coverage maps, focus only on the minimal level of available bandwidth (e.g., 35/3Mbps download/upload speed for 5G) and omit critical dimensions of quality: network usability and stability over space and time. Because cellula

Minghao Sun, Hanqun Cao, Zhou Zhang, Chen Wei

Designing RNA sequences that reliably adopt specified three-dimensional structures while maintaining thermodynamic stability remains challenging for synthetic biology and therapeutics. Current inverse folding approaches optimize for sequence recovery or single structural metrics, failing to simultaneously ensure global geometry, local accuracy, and ensemble

Guanlin Wu, Boyan Su, Yang Zhao, Pu Wang

How to integrate and verify spatial intelligence in foundation models remains an open challenge. Current practice often proxies Visual-Spatial Intelligence (VSI) with purely textual prompts and VQA-style scoring, which obscures geometry, invites linguistic shortcuts, and weakens attribution to genuinely spatial skills. We introduce Spatial Intelligence Grid

Roson Nongthombam, Aman Verma, Amarendra K. Sarma

Post-selecting against quantum jumps into the ground state confines the evolution of the three-level system to the excited states manifold, effectively realizing a PT-symmetric non-Hermitian qubit. In this work, by introducing post-selection efficiencies for both decay channels, the second-excited to first-excited and the first-excited to ground-state transi

Takafumi Kitazawa, Yasuyuki Shimura, Takahiro Onimaru, Shun Tsuchida

We investigated electron-nuclear spin entanglement in the paramagnetic ground state of the Ho-based cubic compound HoCo2Zn20. From analyses of magnetization and specific heat data, we determined the cubic crystalline electric field (CEF) parameters, the magnetic exchange constant, and the hyperfine coupling constant between the 4f magnetic moment and the 165

Frank Garvan, Avi Mukhopadhyay

Ramanujan introduced mock theta functions in his last letter to G.H.Hardy. He provided examples and various relations between them. G.N.Watson found transformations for the third order mock theta functions $f(q)$ and $\omega$(q). Zwegers in 2000 built on Watson's techniques to complete these mock theta functions and connected them to real analytic modular fo

Dong Yan, Ke Zhou, Zirun Wang, Xin-Jiang He

In this paper, we investigate a portfolio selection problem with transaction costs under a two-factor stochastic volatility structure, where volatility follows a mean-reverting process with a stochastic mean-reversion level. The model incorporates both proportional exogenous transaction costs and endogenous costs modeled by a stochastic liquidity risk proces

Dandan Liang, Jianing Zhang, Evan Chen, Zhe Li

Split Federated Learning (SFL) enables scalable training on edge devices by combining the parallelism of Federated Learning (FL) with the computational offloading of Split Learning (SL). Despite its great success, SFL suffers significantly from the well-known straggler issue in distributed learning systems. This problem is exacerbated by the dependency betwe

Lianghong Chen, Dongkyu Eugene Kim, Mike Domaratzki, Pingzhao Hu

Designing de novo 3D molecules with desirable properties remains a fundamental challenge in drug discovery and molecular engineering. While diffusion models have demonstrated remarkable capabilities in generating high-quality 3D molecular structures, they often struggle to effectively control complex multi-objective constraints critical for real-world applic

Yuxin Ye, Jingtao Shi

This paper is concerned with a linear-quadratic non-zero sum differential game with asymmetric delayed information. To be specific, two players exist time delays simultaneously which are different, leading the dynamical system being an asymmetric information structure. By virtue of stochastic maximum principle, the stochastic Hamiltonian system is given whic