October 2025 arXiv papers — page 100

Marta Sewiło, Will R. M. Rocha, Martijn van Gelder, Maria Gabriela Navarro

We present the results of James Webb Space Telescope observations of the protostar ST6 in the Large Magellanic Cloud (LMC) with the Medium Resolution Spectrograph of the Mid-Infrared Instrument (4.9-27.9 $\mu$m). Characterized by one-third to half-solar metallicity and strong UV radiation fields, the environment of the LMC allows us to study the physics and

WenBo Xu, Liu Liu, Li Zhang, Ran Zhang

Articulated objects, such as laptops and drawers, exhibit significant challenges for 3D reconstruction and pose estimation due to their multi-part geometries and variable joint configurations, which introduce structural diversity across different states. To address these challenges, we propose KineDiff3D: Kinematic-Aware Diffusion for Category-Level Articula

Himanshu Gupta, Allen Herman, Alice Lacaze-Masmonteil, Roghayeh Maleki

The perfect matching association scheme is a set of relations on the perfect matchings of the complete graph on $2n$ vertices. The relations between perfect matchings are defined by the cycle structure of the union of any two perfect matchings, and each relation can be represented as a matrix. Each matrix is labeled by an integer partition whose parts corres

R. Bucik, S. T. Hart, M. A. Dayeh, M. I. Desai

Enhancements in 3He abundance, a characteristic feature of impulsive solar energetic particle (ISEP) events, are also frequently observed in gradual SEP (GSEP) events. Understanding the origin of this enrichment is crucial for identifying the mechanisms behind SEP generation. We investigate the origin of 3He enrichment in high-energy (25-50 MeV) solar proton

Shota Arai, Takashi Yoshidome

This work presents a novel algorithm for generating porous structures as an alternative to the PoreSpy program suite. Unlike PoreSpy, which often produces structures whose porosity deviates from the target value, our proposed algorithm generates structures whose porosity closely matches the specified input, within a defined error margin. Furthermore, paralle

Ioannis Tsaknakis, Bingqing Song, Shuyu Gan, Dongyeop Kang

Large Language Models (LLMs) excel at producing broadly relevant text, but this generality becomes a limitation when user-specific preferences are required, such as recommending restaurants or planning travel. In these scenarios, users rarely articulate every preference explicitly; instead, much of what they care about remains latent, waiting to be inferred.

Xin Gao, Jiyao Liu, Guanghao Li, Yueming Lyu

Recent advancements have explored text-to-image diffusion models for synthesizing out-of-distribution (OOD) samples, substantially enhancing the performance of OOD detection. However, existing approaches typically rely on perturbing text-conditioned embeddings, resulting in semantic instability and insufficient shift diversity, which limit generalization to

Usman Ali, Ali Zia, Abdul Rehman, Umer Ramzan

Industrial anomaly detection (IAD) increasingly benefits from integrating 2D and 3D data, but robust cross-modal fusion remains challenging. We propose a novel unsupervised framework, Multi-Modal Attention-Driven Fusion Restoration (MAFR), which synthesises a unified latent space from RGB images and point clouds using a shared fusion encoder, followed by att

Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Michael R. Lyu

Code generation, the task of creating executable programs from natural language requirements, has recently seen tremendous advances through Chain-of-Thought (CoT) reasoning, which enables Large Language Models (LLMs) to develop high-level reasoning plans before writing code. Recent research has proposed various methods to enhance models' CoT reasoning for co

Wenbing Tang, Meilin Zhu, Fenghua Wu, Yang Liu

Recent advancements in Large Language Models (LLMs) have greatly enhanced natural language understanding and content generation. However, these models primarily operate in disembodied digital environments and lack interaction with the physical world. To address this limitation, Embodied Artificial Intelligence (EAI) has emerged, focusing on agents that can p

Qisi Zhou, Qingqian Kang, Teng Zhao, Xin Su

Photon addition operations applied to squeezed states have been shown to significantly enhance phase sensitivity. In this study, we extend this approach by applying photon addition not only to coherent states but also within a Mach--Zehnder interferometer setup, using coherent and squeezed vacuum states as input. Both intensity-difference and homodyne detect

Rongzhong Xiao

Fix $c\in (1,23/22)$. Let $\alpha$ and $\beta$ be two distinct non-zero real numbers with $|\alpha|\neq |\beta|$. It is shown that for any measure preserving system $(X,\mathcal{X},\mu,T)$ and any $f,g\in L^{\infty}(\mu)$, the limit \begin{equation*} \lim_{N\to\infty}\frac{1}{N}\sum_{n=1}^{N}f(T^{\lfloor \alpha n^c \rfloor}x)g(T^{\lfloor \beta n^c \rfloor}x)

A. R. Humphries, A. S. Eremin, Z. Wang

Delays are ubiquitous in applied problems, but often do not arise as the simple constant discrete delays that analysts and numerical analysts like to treat. In this chapter we show how state-dependent delays arise naturally when modeling and the consequences that follow. We treat discrete state-dependent delays, and delays implicitly defined by threshold con

Zhaoqi Su, Xikai Shan, Zhenwei Lyu, Junyao Zhang

Strongly lensed gravitational waves may pass through the stellar field of a lensing galaxy with additional modulations (on both phase and amplitude) due to gravitational microlensing effect of stars/remnants near the line of sight. These microlensed waveforms depend on the mass and location of thousands or more most relevant stars, so that their deterministi

Jeongwoo Lee, Dana Longcope, Junmu Youn, Navdeep K. Panesar

We present the first joint high-resolution observations of small-scale EUV jets using Solar Orbiter(SolO)'s Extreme Ultraviolet Imager and High Resolution Imager (HRI) and H$\alpha$ imaging from the Visible Imaging Spectrometer (VIS) installed on the 1.6~m Goode Solar Telescope (GST) at the Big Bear Solar Observatory (BBSO). These jets occurred on 2022-10-29

Chengxiu Hua, Jiawen Gu, Yushun Tang

Reinforcement learning (RL) has achieved significant success across a wide range of domains, however, most existing methods are formulated in discrete time. In this work, we introduce a novel RL method for continuous-time control, where stochastic differential equations govern state-action dynamics. Departing from traditional value function-based approaches,

Shifeng Xu, Yanzhu Liu, Adams Wai-Kin Kong

Diffusion models have become emerging generative models. Their sampling process involves multiple steps, and in each step the models predict the noise from a noisy sample. When the models make prediction, the output deviates from the ground truth, and we call such a deviation as \textit{prediction error}. The prediction error accumulates over the sampling pr

Tenghui Huang, Jinbo Wen, Jiawen Kang, Siyong Chen

Smart contracts play a significant role in automating blockchain services. Nevertheless, vulnerabilities in smart contracts pose serious threats to blockchain security. Currently, traditional detection methods primarily rely on static analysis and formal verification, which can result in high false-positive rates and poor scalability. Large Language Models (

Fenghua Wen, Xieyu Yin, Chufu Wen

We develop a theory of demand economics for an era of material abundance. The binding constraint on growth has shifted from insufficient aggregate demand to inadequate demand-tier upgrading. Our result is that, the new engine of growth lies in upgrading the demand hierarchy: higher-tier demands generate larger value-creation multipliers. The key mechanism is

Rishi Sonthalia, Raj Rao Nadakuditi

We introduce a novel regularization scheme for autoencoders based on matricial free energy. Our approach defines a differentiable loss function in terms of the singular values of the code matrix (code dimension x batch size). From the standpoint of free probability an d random matrix theory, this loss achieves its minimum when the singular value distribution

Soyoung Jung, Sung Park

The development of conversational agents (CAs) has shown strong potential in supporting mental health through dialogue. While many studies focus on CAs for individual psychological care, research on agents designed for couples facing relational or emotional challenges remains limited. This study aims to identify design considerations for CAs that address the

Caroline N. Cappetto, Penelope Messinger, Kaitlyn S. Yasumura, Miro Rothman

We present a minimal model for autonomous robotic swarms in one- and higher-dimensional spaces, where identical, field-driven agents interact pairwise to self-organize spacing and independently follow local gradients sensed through quantized digital sensors. We show that the collective response of a multi-agent train amplifies sensitivity to weak gradients b

Matthew Slattery-Holmes

In 2020, Hamaker, Pawlowski, and Sagan introduced the \emph{pattern quasisymmetric functions}, which are quasisymmetric functions associated with pattern-avoidance classes of permutations, and defined via expansions in fundamental quasisymmetric functions. They determined which subsets of the symmetric group $\mathfrak{S}_3$ index pattern quasisymmetric func

Wei Du, Nuowei Liu, Jie Wang, Jiahao Kuang

Language models trained with a fixed vocabulary struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations. Existing dynamic vocabulary approaches attempt to address this limitation but face challenges such as fragmented codebases, lack of support for modern LLMs, and limited inference scalab

Hodaka Kawachi, Tomoya Nakamura, Hiroaki Santo, SaiKiran Kumar Tedla

This paper introduces a method for using LED-based environmental lighting to produce visually imperceptible watermarks for consumer cameras. Our approach optimizes an LED light source's spectral profile to be minimally visible to the human eye while remaining highly detectable by typical consumer cameras. The method jointly considers the human visual system'

Mengzhen Liu, Ming Li, Rang Liu, Qian Liu

Reconfigurable antennas (RAs), capable of dynamically adapting their radiation patterns, polarization states, and operating frequencies, have emerged as a promising technology to meet the stringent performance requirements of sixth-generation (6G) wireless networks. This article systematically introduces essential hardware implementations of RAs and investig

Haipeng Chen, Lai Jiang, Yufeng Wu

In this paper, we investigate the representations of rational numbers via continued fraction, Egyptian fraction, and Engel fraction expansions. Given $m \in \mathbb{N}$, denote by $C_m, E_m, E_m^*$ the sets of rational numbers whose continued fraction, Egyptian fraction, and Engel fraction expansions have length $m$, respectively. We first establish the Mink

Xiaoyu Guo, Shinobu Saito, Jianjun Zhao

With the growing interest in quantum computing, the emergence of quantum supremacy has marked a pivotal milestone in the field. As a result, numerous quantum programming languages (QPLs) have been introduced to support the development of quantum algorithms. However, the application of Model-Driven Development (MDD) in quantum system engineering remains large

Tianyang Xu, Dan Zhang, Kushan Mitra, Estevam Hruschka

Large language model (LLM) agents are increasingly deployed to tackle complex tasks, often necessitating collaboration among multiple specialized agents. However, multi-agent collaboration introduces new challenges in planning, coordination, and verification. Execution failures frequently arise not from flawed reasoning alone, but from subtle misalignments i

Yoonjin Lee, Munhee Kim, Hanbi Choi, Juhyeon Park

This study investigated LLM-based automation for analyzing non-financial data in corporate credit evaluation. Two systems were developed and compared: a Single-Agent System (SAS), in which one LLM agent infers favorable and adverse repayment signals, and a Popperian Multi-agent Debate System (PMADS), which structures the dual-perspective analysis as adversar

A. Balakrishna, I. Kukavica, W. S. Ożański

We consider any cover $\mathscr{C}$ of $\mathbb{R}^3$ by balls of radius bigger or equal $1$ satisfying two conditions: (i) any ball intersects at most $\sigma>0$ other balls, and (ii) intersecting balls have comparable sizes. We consider a natural Morrey-type space such that the $L^2_{\mathrm{uloc}}$ setting of Lemari\'e-Rieusset (Recent Developments in the

Chen Zhang, Weixin Bu, Wendong Xu, Runsheng Yu

Transformers have achieved remarkable success in time series modeling, yet their internal mechanisms remain opaque. This work demystifies the Transformer encoder by establishing its fundamental equivalence to a Graph Convolutional Network (GCN). We show that in the forward pass, the attention distribution matrix serves as a dynamic adjacency matrix, and its

Xiaogang Xu, Jian Wang, Yunfan Lu, Ruihang Chu

Diffusion-based methods, leveraging pre-trained large models like Stable Diffusion via ControlNet, have achieved remarkable performance in several low-level vision tasks. However, Pre-Trained Diffusion-Based (PTDB) methods often sacrifice content fidelity to attain higher perceptual realism. This issue is exacerbated in low-light scenarios, where severely de

Noah Franz, Bhagya Subrayan, Charles D. Kilpatrick, Griffin Hosseinzadeh

Kilonovae, the ultraviolet/optical/infrared counterparts to binary neutron star mergers, are an exceptionally rare class of transients. Optical follow-up campaigns are plagued by contaminating transients, which may mimic kilonovae, but do not receive sufficient observations to measure the full photometric evolution. In this work, we present an analysis of th

Shinji Ito, Kevin Jamieson, Haipeng Luo, Arnab Maiti

We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumulative loss incurred in each episode, rather than individual losses at each state-action pair. While prior work in this setting has focused exclusively on worst-case analysis, we i

Keigo Ushiyama, Hiroyuki Kajimoto

Kinesthetic illusions, which arise when muscle spindles are activated by vibration, provide a compact means of presenting kinesthetic sensations. Because muscle spindles contribute not only to sensing body movement but also to perceiving heaviness, vibration-induced illusions could potentially modulate weight perception. While prior studies have primarily fo

Lu Yin, Ziying Shi, Yinghao Wu, Xinyu Yi

Human motion capture with sparse inertial sensors has gained significant attention recently. However, existing methods almost exclusively rely on a template adult body shape to model the training data, which poses challenges when generalizing to individuals with largely different body shapes (such as a child). This is primarily due to the variation in IMU-me

R. Bucik, G. M. Mason, S. M. Mulay, G. C. Ho

We examine 3He-rich solar energetic particles (SEPs) detected on 2023 October 24-25 by Solar Orbiter at 0.47 au. The measurements revealed that heavy-ion enhancements increase irregularly with mass, peaking at S. C, and especially N, Si, and S, stand out in the enhancement pattern with large abundances. Except for 3He, heavy ion spectra can only be measured

Junlan Feng, Fanyu Meng, Chong Long, Pengyu Cong

The hallucination and credibility concerns of large language models (LLMs) are global challenges that the industry is collectively addressing. Recently, a significant amount of advances have been made on post-training and inference techniques to mitigate these challenges. However, it is widely agreed that unsafe and hallucinations of LLMs intrinsically origi

Zhiyuan Fan, Arnab Maiti, Kevin Jamieson, Lillian J. Ratliff

In this paper, we study the classical Hedge algorithm in combinatorial settings. In each round, the learner selects a vector $\boldsymbol{x}_t$ from a set $X \subseteq \{0,1\}^d$, observes a full loss vector $\boldsymbol{y}_t \in \mathbb{R}^d$, and incurs a loss $\langle \boldsymbol{x}_t, \boldsymbol{y}_t \rangle \in [-1,1]$. This setting captures several im

Elias Hossain, Swayamjit Saha, Somshubhra Roy, Ravi Prasad

Even when prompts and parameters are secured, transformer language models remain vulnerable because their key-value (KV) cache during inference constitutes an overlooked attack surface. This paper introduces Malicious Token Injection (MTI), a modular framework that systematically perturbs cached key vectors at selected layers and timesteps through controlled

Hodek M. García, Marcelo Salgado

By implementing a full non-linear treatment of $f(R)$ gravity in static and spherically symmetric spacetimes, we analyze two scenarios. The first one within the context of the solar-system tests where we try to recover the chameleon effects without any approximations in the equations (e.g. linearization) from $f(R)$ models that are compatible with cosmology.

Suxuan Chen

Let $\psi:\mathbb{N}\rightarrow\mathbb{R}_+$ be a monotonically non-increasing function, and let $\psi_v:\mathbb{N}\rightarrow\mathbb{R}_+$ be defined by $\psi_v(q)=1/q^v$. In this article, we consider self-similar sets whose iterated function systems satisfy the open set condition. For functions $\psi$ that do not decrease too rapidly, we give a conjectural

Ruitong Gan, Junran Peng, Yang Liu, Chuanchen Luo

Planes are fundamental primitives of 3D sences, especially in man-made environments such as indoor spaces and urban streets. Representing these planes in a structured and parameterized format facilitates scene editing and physical simulations in downstream applications. Recently, Gaussian Splatting (GS) has demonstrated remarkable effectiveness in the Novel

Ryutaro Tomomatsu, Teruaki Suyama, Paolo Gondolo

When electromagnetic (or gravitational) waves propagate in the presence of a background magnetic field, a portion of the waves converts into gravitational (or electromagnetic) waves. This phenomenon, known as the (inverse) Gertsenshtein effect, is typically analyzed in Minkowski spacetime, neglecting the spacetime curvature induced by the magnetic field itse

Yunfeng Wen, Fang Yang, Jian Song, Zhu Han

Optical wireless integrated sensing and communication (OW-ISAC) is rapidly burgeoning as a complement and augmentation to its radio-frequency counterpart. In this paper, the channel capacity is analyzed to guide the design of a coherent OW-ISAC system based on frequency-modulated continuous wave (FMCW). Firstly, the system model of FMCW-based OW-ISAC is reca

Ryogo Niwa, Yoichi Ochiai, Tatsuki Fushimi

Accurate vibration measurement is vital for analyzing dynamic systems across science and engineering, yet noncontact methods often balance precision against practicality. Event cameras offer high-speed, low-light sensing, but existing approaches fail to recover vibration amplitude and frequency with sufficient accuracy. We present an event topology-based vis

Brian Chao, Laurent Saloff-Coste

Motivated by Euclidean boxes, we consider "thin" annular domains of the form $U=(a,b)\times U_0\subseteq \mathbb{R}^n$ in polar coordinates, where the spherical base $U_0\subseteq \mathbb{S}^{n-1}$ is an inner uniform domain. We show that, with respect to the measure $\varphi_U^2$ determined by the principal Dirichlet Laplacian eigenfunction $\varphi_U$, suc

Nathanael Ackerman, Cameron Freer, Kyle Gannon, James E. Hanson

We prove a model-theoretic representation theorem for the distribution of an ergodic exchangeable $k$-uniform hypergraph: every such measure arises as the pushforward of the countably-iterated Morley product of a global Borel-definable Keisler measure over the countable universal homogeneous $k$-uniform hypergraph. We show this by starting with a Borel $k$-h

Tung, Nguyen, Tuyen Nguyen

The growing demand for on-device large language model (LLM) inference is driving interest in deploying lightweight, cost-effective AI solutions on edge hardware. Single-board computers (SBCs) such as the Raspberry Pi and Orange Pi offer a promising platform for localized, privacy-preserving inference-but remain underexplored in the context of LLM workloads.

Christian Imenkamp, Andrea Maldonado, Hendrik Reiter, Martin Werner

Streaming process mining deals with the real-time analysis of streaming data. Event streams require algorithms capable of processing data incrementally. To systematically address the complexities of this domain, we propose AVOCADO, a standardized challenge framework that provides clear structural divisions: separating the concept and instantiation layers of

Zan Li, Rui Fan

Financial anomalies arise from heterogeneous mechanisms - price shocks, liquidity freezes, contagion cascades, and momentum reversals - yet existing detectors produce uniform anomaly scores without revealing which mechanism is failing or where risks concentrate. This hinders targeted responses: liquidity freezes call for market-making support, whereas price

Ziqing Zhu

Virtual power plants (VPPs) are becoming a cornerstone of future grids, aggregating distributed PV, wind, storage, and flexible loads for market participation and real-time balancing. As operations move to minute-- and second--level feedback, communication security shifts from a compliance item to an operational constraint: latency, reliability, and confiden

Xueqian Bai, Nicklas Hansen, Adabhav Singh, Michael T. Tolley

Soft robotic hands promise to provide compliant and safe interaction with objects and environments. However, designing soft hands to be both compliant and functional across diverse use cases remains challenging. Although co-design of hardware and control better couples morphology to behavior, the resulting search space is high-dimensional, and even simulatio

Fatemeh Mahmoudi, Chenxi Li, Kaida Cai, Xuewen Lu

Competing risks data refer to situations where the occurrence of one event pre- cludes the possibility of other events happening, resulting in multiple mutually exclusive events. This data type is commonly encountered in medical research and clinical trials, exploring the interplay between different events and informing decision-making in fields such as heal

Ivan C. H. Liu

This paper proposes a cognitive-Affective-Systemic (CAS) framework that integrates cognition, emotion, and systemic understanding to cultivate sustainability awareness through art. Drawing from eco-aesthetics, affect theory, complexity science, and posthuman ethics, the framework defines artistic practice as both epistemic and performative--a way of knowing

C. B. Z. Luo, Q. Tang, C. Guo, B. Wang

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purpose low background liquid scintillator detector, was proposed primarily to determine the neutrino mass ordering. To mitigate radioactivity from surrounding rock and enable cosmic muon tagging, its central detector is immersed in a Water Cherenkov Detector (WCD) containing 40~ktons of u

Ya-Dong Gu, Ji-Hai Yuan, Zhi-An Ren

A false zero resistance behavior was observed during our study on the search of superconductivity in Ge-doped GaNb4Se8. This zero resistance was proved to be caused by open-circuit in multi-phase samples comprised of metals and insulators by measuring with four-probe method. The evidence strongly suggests that the reported superconductivity in hydrides shoul

Zhiyi Zhang, Gang Cui, Kai Jiang, An-Chang Shi

The Landau-Brazovskii model provides a theoretical framework for describing various phases arising from competing short- and long-range interactions in many physical systems. In this work, we investigate phase transitions among various ordered phases within the three-dimensional Landau-Brazovskii model. We construct the phase diagram of this model, which enc

Andrew Eberhardt, Mateja Gosenca, Lam Hui

Due to wave interference, an ultralight light dark matter halo has stochastic, granular substructures which can scatter stars, leading to the heating of stellar distributions. Studies of this phenomenon have placed lower bounds on the ultralight dark matter mass. In this paper we investigate a number of relevant systematic effects, including: (1) the heating

Jad Berjawi, Yoann Dupas, Christophe C'erin

Multimodal object detection improves robustness in chal- lenging conditions by leveraging complementary cues from multiple sensor modalities. We introduce Filtered Multi- Modal Cross Attention Fusion (FMCAF), a preprocess- ing architecture designed to enhance the fusion of RGB and infrared (IR) inputs. FMCAF combines a frequency- domain filtering block (Freq

Dong Li, Jianping Li, Haisheng Ji

Decaying pulsations have been simultaneously detected in the low-energy X-rays of solar/stellar flares, which are supposed to be associated with standing slow magnetoacoustic or kink-mode waves. The physical mechanism behind rapidly decaying remains unknown. We present the detection of quasi-periodic pulsations (QPPs) with rapidly decaying in high-energy emi

Koffi-Emmanuel Sadzi, Abdoulaye Ndao

We present a structurally simple yet functionally ver- satile reflective meta-array composed of phase-change Antimony trisulfide (Sb2S3) nanorods enabling broad- band and wide-angle beam deflection in near-infrared. The device achieves over 80% deflection efficiency over a 1000 nm wide passband, and covering from O-band (1260 nm-1360 nm) to U-band (1565 nm-1

Niraj Chaudhari, Manmeet Singh, Naveen Sudharsan, Amit Kumar Srivastava

Data fusion is an essential task in various domains, enabling the integration of multi-source information to enhance data quality and insights. One key application is in satellite remote sensing, where fusing multi-sensor observations can improve spatial and temporal resolution. In this study, we explore the design space of diffusion and flow models for data

Bailing Ma, Chueng-Ryong Ji

Performing the perturbative calculation of the vacuum polarization amplitude in QED1+1, one finds an anomalous axial vector Ward identity. We note that the photon self-energy function displays a singularity in the 1+1D case, in stark contrast to the 3+1D case. We discuss the nature of this singularity and its physical implications from the perspectives of ax

Liwei Wu, Yilin Zhang, Justin Leung, Jingyi Gao

The proliferation of virtual reality (VR) has led to its increasing adoption as an immersive medium for delivering presentations, distinct from other VR experiences like games and 360-degree videos by sharing information in richly interactive environments. However, creating engaging VR presentations remains a challenging and time-consuming task for users, hi

Kyum Kim, Yaqing Chen, Paromita Dubey

Regression with non-Euclidean responses -- e.g., probability distributions, networks, symmetric positive-definite matrices, and compositions -- has become increasingly important in modern applications. In this paper, we propose deep Fr\'echet neural networks (DFNNs), an end-to-end deep learning framework for predicting non-Euclidean responses -- which are co

Samuel Talkington, Daniel Turizo, Sergio A. Dorado-Rojas, Rahul K. Gupta

The power flow equations relate bus voltage phasors to power injections via the network admittance matrix. These equations are central to the key operational and protection functions of power systems (e.g., optimal power flow scheduling and control, state estimation, protection, and fault location, among others). As control, optimization, and estimation of n

Jean-Marie Dufour, Purevdorj Tuvaandorj

This paper introduces a likelihood ratio (LR)-type test that possesses the robustness properties of \(C(\alpha)\)-type procedures in an extremum estimation setting. The test statistic is constructed by applying separate adjustments to the restricted and unrestricted criterion functions, and is shown to be asymptotically pivotal under minimal conditions. It f

Zhe Luo, Wenjing Jia, Stuart Perry

Three-dimensional (3D) point clouds are becoming increasingly vital in applications such as autonomous driving, augmented reality, and immersive communication, demanding real-time processing and low latency. However, their large data volumes and bandwidth constraints hinder the deployment of high-quality services in resource-limited environments. Progres- si

Ioannis Anagnostides, Emanuel Tewolde, Brian Hu Zhang, Ioannis Panageas

Regret matching (RM) -- and its modern variants -- is a foundational online algorithm that has been at the heart of many AI breakthrough results in solving benchmark zero-sum games, such as poker. Yet, surprisingly little is known so far in theory about its convergence beyond two-player zero-sum games. For example, whether regret matching converges to Nash e

Zhichao Chen, Puneet Gupta

Three-dimensional (3D) integration continues to advance Moore's Law by facilitating dense interconnects and enabling multi-tier system architectures. Among the various integration approaches, Cu-Cu hybrid bonding has emerged as a leading solution for achieving high interconnect density in chiplet integration. In this work, we present YAP+, a yield modeling f

Cheng-Ting Wang

In this paper, we show some results about the gap between a prime number and its consecutive prime number for large enough prime numbers. We show that the gap between a prime number $p_n$ and its consecutive prime number is not larger than $2\log^2{p_n}$. We also show that the result implies the existence of a prime number in a certain type of interval for l

Shunan Sheng, Bohan Wu, Alberto González-Sanz

Mean-field variational inference (MFVI) is a widely used method for approximating high-dimensional probability distributions by product measures. It has been empirically observed that MFVI optimizers often suffer from mode collapse. Specifically, when the target measure $\pi$ is a mixture $\pi = w P_0 + (1 - w) P_1$, the MFVI optimizer tends to place most of

Guoqing Luo, Iffat Maab, Lili Mou, Junichi Yamagishi

While reasoning-based large language models excel at complex tasks through an internal, structured thinking process, a concerning phenomenon has emerged that such a thinking process can aggregate social stereotypes, leading to biased outcomes. However, the underlying behaviours of these language models in social bias scenarios remain underexplored. In this w

J. Guilhot, E. Little, J. Parkinson

We introduce the notion of a bounded weight function on a language, and show that the set of bounded weight functions on a regular language is a rational polyhedral cone. We study the cell recognised by a bounded weight function (that is, the set of elements of the language where the bound is attained), and show that if the language is regular then this cell

Yiqi Huang, Wenshuai Jiang

We show that the intrinsic diameter of mean curvature flow in $\mathbb{R}^3$ is uniformly bounded as one approaches the first singular time $T$. This confirms the bounded diameter conjecture of Haslhofer. In addition, we establish several sharp quantitative estimates: the second fundamental form $A$ has uniformly bounded $L^1$-norm on each time slice, $A$ be

Hassan Hamad, Yuou Qiu, Peter A. Beerel, Keith M. Chugg

While advancements in quantization have significantly reduced the computational costs of inference in deep learning, training still predominantly relies on complex floating-point arithmetic. Low-precision fixed-point training presents a compelling alternative. This work introduces a novel enhancement in low-precision logarithmic fixed-point training, geared

Nikolaus Howe, Micah Carroll

Chain-of-Thought (CoT) monitoring has emerged as a compelling method for detecting harmful behaviors such as reward hacking for reasoning models, under the assumption that models' reasoning processes are informative of such behaviors. In practice, LLM training often produces unintended behaviors due to imperfect reward signals, leading models to develop misa

Michael James McCulloch

The Free Energy Principle (FEP) states that self-organizing systems must minimize variational free energy to persist, but the path from principle to implementable algorithm has remained unclear. We present a constructive proof that the FEP can be realized through exact local credit assignment. The system decomposes gradient computation hierarchically: spatia

Gavin Wilks, Xu Sun, Zhenyu Ye

The STAR Collaboration has reported a significant $ϕ$-meson global spin alignment ($ρ_{00}$) signal in Au+Au collisions at $\sqrt{s_{NN}}\leq62$ GeV by measuring the polar angle distribution of $ϕ$-meson daughters with respect to the orbital angular momentum (OAM) direction of the collision system. In this paper, a new method is explored for studying vector-

Sofian Tur-Dorvault

We give a general construction of the motivic fundamental groupoid at tangential basepoints, extending previous works of P. Deligne, A. B. Goncharov, and M. Levine, which were limited to ordinary basepoints or to specific varieties. Given a smooth variety over a field endowed with a simple normal crossings divisor, we encode its tangential basepoints using t

Fabrizio Corelli, Paolo Pani, Andrea P. Sanna

General Relativity is expected to break down in the high-curvature regime. Beyond an effective field theory treatment with higher-order operators, it is important to identify consistent theories with higher-curvature terms at the nonperturbative level. Two well-studied examples are $f(\mathcal{R})$ gravity and Einstein-dilaton-Gauss-Bonnet (EdGB) gravity. Th

Aneesh Baburaj, Quinn M. Konopacky, Christopher A. Theissen, Roman Gerasimov

The era of JWST has enabled measurements of abundances of elements such as C, O, and even Na, S, K, and Fe in planetary atmospheres to very high precisions ($\sim$0.1 dex). Accurate inference of planet formation using these elemental abundances require the corresponding abundance measurements for the host star. We present the second set of results from our h

Hao Wang, Jiayu Yuan, Hongkai Shi, Haojie Li

Detection of the Higgs mode in superconductors using nonlinear terahertz spectroscopy is a key area of interest in condensed matter physics. We investigate the influence of disorder on the nonlinear terahertz response and the Higgs mode in NbN thin films with varying Ioffe-Regel parameters ($k_Fl$). In strongly disordered films near the superconductor-insula

Siva Sai, Abhishek Sawaika, Prabhjot Singh, Rajkumar Buyya

Federated learning (FL) focuses on collaborative model training without the need to move the private data silos to a central server. Despite its several benefits, the classical FL is plagued with several limitations, such as high computational power required for model training(which is critical for low-resource clients), privacy risks, large update traffic,

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

The angular distribution of gravitational-wave power from persistent sources may exhibit anisotropies arising from the large-scale structure of the Universe. This motivates directional searches for astrophysical and cosmological gravitational-wave backgrounds, as well as continuous-wave emitters. We present results of such a search using data from the first

Taras Holovatch, Yuri Kozitsky, Krzysztof Pilorz, Yurij Holovatch

The following model is studied analytically and numerically: point particles with masses $m,μ,m, \dots$ ($m\geqμ$) are distributed over the positive half-axis. Their dynamics is initiated by giving a positive velocity to the particle located at the origin; in its course the particles undergo elastic collisions. We show that, for certain values of $m/μ$, star

Julien Zouein, Vibhoothi Vibhoothi, Anil Kokaram

This paper presents a comprehensive analysis of motion vectors extracted from AV1-encoded video streams and their application in accelerating optical flow estimation. We demonstrate that motion vectors from AV1 video codec can serve as a high-quality and computationally efficient substitute for traditional optical flow, a critical but often resource-intensiv

Keivan Faghih Niresi, Zepeng Zhang, Olga Fink

Time series data are often affected by various forms of corruption, such as missing values, noise, and outliers, which pose significant challenges for tasks such as forecasting and anomaly detection. To address these issues, inverse problems focus on reconstructing the original signal from corrupted data by leveraging prior knowledge about its underlying str

U. V. B. L Udugama, George Vosselman, Francesco Nex

Deploying real-time spatial perception on edge devices requires efficient multi-task models that leverage complementary task information while minimizing computational overhead. This paper introduces Multi-Mono-Hydra (M2H), a novel multi-task learning framework designed for semantic segmentation and depth, edge, and surface normal estimation from a single mo

Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba

We report an interpretation method for deep learning models that allows us to handle high-dimensional spectral data in materials science. The proposed method uses feature extraction and clustering analysis to categorize materials into classes based on similarities in both spectral data and chemical characteristics such as elemental composition and atomic arr

Yu Tokizane, Hiroki Kishikawa, Takumi Kikuhara, Miezel Talara

Sixth-generation (6G) back-haul links will require terahertz (THz) carriers above 350 GHz to escape the congested 300 GHz band and support >100 Gbps data rates. Photonic THz transmitters have so far remained below 350 GHz because high-frequency photomixing suffers from phase noise and power limits. Here we demonstrate single-channel wireless transmission at

Muhammed Furkan Dasdelen, Ivan Kukuljan, Peter Lienemann, Fatih Ozlugedik

Peripheral blood smears remain a cornerstone in the diagnosis of hematological neoplasms, offering rapid and valuable insights that inform subsequent diagnostic steps. However, since neoplastic transformations typically arise in the bone marrow, they may not manifest as detectable aberrations in peripheral blood, presenting a diagnostic challenge. In this pa

Simone Ricci, Niccolò Biondi, Federico Pernici, Ioannis Patras

Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learned representations, there is significant interest in facilitating communication between representations and ensuring compatibility across independently trained neural networks. In the literature, two primary ap

Liying Chen, Alexander M. McKillop, Ashley P. Fidler, Marissa L. Weichman

Polaritons - hybrid light-matter states formed from the strong coupling of a bright molecular transition with a confined photonic mode - may offer new opportunities for optical control of molecular behavior. Vibrational strong coupling (VSC) has been reported to impact ground-state chemical reactivity, but its influence on electronic excited-state dynamics r

Karina Marin, Mauricio Poletti, Filiphe Veiga

We establish a relation between the continuity of the fiber entropy and the continuity of the fiber Lyapunov exponents for skew products with 2-dimensional fibers. This result extends the theorem for surfaces proved by Buzzi-Crovisier-Sarig. As a consequence, we are able to obtain classes of skew products that satisfies the strong positive recurrence (SPR) p

Alina Shalukhina

We prove that the Hardy--Littlewood maximal operator $M$ is bounded on the variable Lebesgue space $L^{p(\cdot)}(X,d,μ)$, with $1<p_-\le p_+<\infty$, over an unbounded space of homogeneous type $(X,d,μ)$ with a Borel-semiregular measure $μ$, if and only if the averaging operators $T_\mathcal{Q}$ are bounded on $L^{p(\cdot)}(X,d,μ)$ uniformly over all familie

Ritik Shah, Marco F. Duarte

High-spatial-resolution hyperspectral images (HSI) are essential for applications such as remote sensing and medical imaging, yet HSI sensors inherently trade spatial detail for spectral richness. Fusing high-spatial-resolution multispectral images (HR-MSI) with low-spatial-resolution hyperspectral images (LR-HSI) is a promising route to recover fine spatial

Erika Ortiz, Ciro S. Campolina, Alexei A. Mailybaev

The predictability of turbulent flows remains a challenging problem for mathematicians, physicists, and meteorologists. In this context, we consider the 3D incompressible Navier-Stokes equations with small-scale random forcing on logarithmic lattices in Fourier space. Our goal is to probe the phenomenon of spontaneous stochasticity in this system, which mean