October 2025 arXiv papers — page 126

Jihao Zhao, Zhiyuan Ji, Simin Niu, Hanyu Wang

The traditional RAG paradigm, which typically engages in the comprehension of relevant text chunks in response to received queries, inherently restricts both the depth of knowledge internalization and reasoning capabilities. To address this limitation, our research transforms the text processing in RAG from passive chunking to proactive understanding, defini

Mingkai Liu, Dikai Fan, Haohua Que, Haojia Gao

Efficient localization and high-quality rendering in large-scale scenes remain a significant challenge due to the computational cost involved. While Scene Coordinate Regression (SCR) methods perform well in small-scale localization, they are limited by the capacity of a single network when extended to large-scale scenes. To address these challenges, we propo

Lianzi Jiang, Jianxin Zhang, Xinyu Han, Huanhe Dong

Accurate motion response prediction for elastic Bragg breakwaters is critical for their structural safety and operational integrity in marine environments. However, conventional deep learning models often exhibit limited generalization capabilities when presented with unseen sea states. These deficiencies stem from the neglect of natural decay observed in ma

Qixin Deng, Bryan Pardo, Thrasyvoulos N Pappas

Understanding and modeling the relationship between language and sound are essential for applications such as music information retrieval, text-guided music generation, and audio captioning. Central to these tasks are joint language-audio embedding spaces, which map textual descriptions and auditory content into a shared representation. Although multimodal e

Zhaojun Chen

In this paper, we use the skein exact sequence and other techniques to compute the second-to-top term of HFK of closed 3-braids. We do it case-by-case according to Xu's classification. We also verify the rank inequality conjectured by Sivek.

Kentaro Takahira, Yuki Ueno

Effective real-time data presentation is essential in small-group interactive contexts, where discussions evolve dynamically and presenters must adapt visualizations to shifting audience interests. However, most existing interactive visualization systems rely on fixed mappings between user actions and visualization commands, limiting their ability to support

Jingwen Gu, Yiting He, Zhishuai Liu, Pan Xu

Decision-making under distribution shift is a central challenge in reinforcement learning (RL), where training and deployment environments differ. We study this problem through the lens of robust Markov decision processes (RMDPs), which optimize performance against adversarial transition dynamics. Our focus is the online setting, where the agent has only lim

Kaiyuan Zhao, Guangfen Yao, Xiaoyu Cheng, Luqiao Yin

Currently, the ASM-HEMT model, QPZD model and EPFL model are all based on the three-terminal potential as the core, and relate the electrical characteristics such as I-V and C-V to Vd, Vs and Vg, so as to accurately build the HEMT model with high accuracy and fast convergence. However, there has not yet been a model based on three-terminal potentials that ca

Miu Sumino, Mayu Ishii, Shun Kaizu, Daisuke Hisano

Optical camera communication (OCC) represents a promising visible light communication technology. Nonetheless, typical OCC systems utilizing frame-based cameras are encumbered by limitations, including low bit rate and high processing load. To address these issues, OCC system utilizing an event-based vision sensor (EVS) as receivers have been proposed. The E

Caolu Xu, Zhiyong Chen, Meixia Tao, Li Song

Immersive virtual reality (VR) applications impose stringent requirements on latency, energy efficiency, and computational resources, particularly in multi-user interactive scenarios. To address these challenges, we introduce the concept of spatial computing communications (SCC), a framework designed to meet the latency and energy demands of multi-user VR ov

Parsa Hejabi, Elnaz Rahmati, Alireza S. Ziabari, Morteza Dehghani

Large Language Models (LLMs) often produce inconsistent answers when faced with different phrasings of the same prompt. In this paper, we propose Flip-Flop Consistency ($F^2C$), an unsupervised training method that improves robustness to such perturbations. $F^2C$ is composed of two key components. The first, Consensus Cross-Entropy (CCE), uses a majority vo

Soumyya Kanti Datta, Tanvi Ranga, Chengzhe Sun, Siwei Lyu

The rise of manipulated media has made deepfakes a particularly insidious threat, involving various generative manipulations such as lip-sync modifications, face-swaps, and avatar-driven facial synthesis. Conventional detection methods, which predominantly depend on manually designed phoneme-viseme alignment thresholds, fundamental frame-level consistency ch

Jiayu Wang, Yifei Ming, Riya Dulepet, Qinglin Chen

Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for agentic systems. To rigorously evaluate this ability, four principles are essential: tasks should be (1) user-centric, reflecting realistic information needs, (2) dynamic, requiring

Siqi Guan, Shangbin Yang, Xiao Guo

This study investigates the gravitational waves (GWs) generated by the emergence of magnetic flux tubes in the solar convection zone. We focus on the upward buoyancy of magnetic flux tubes, which leads to significant magnetic activity and the formation of active region sunspots. This study adopts parameters representative of a moderate-sized solar active reg

Zewen Li, Zitong Yu, Qilang Ye, Weicheng Xie

The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most traditional IAD methods lack the ability to provide multi-turn human-machine dialogues and detailed descriptions, such as the color of objects, the shape of an anomaly, or specific typ

Xiangjiang Bao, Lucas Kreiss, Clare B. Cook, Haoyu Gong

We present an epi-illumination multi-camera array microscope (epi-MCAM) designed for wide-field reflective imaging of non-transparent samples. The epi-MCAM contains 24 tightly packed and synchronized epi-illumination microscope units, arranged in a $4 \times 6$ planar array at 18 mm spacing. Each unit contains a unique CMOS image sensor (13 megapixels each),

Yingguang Yang, Xianghua Zeng, Qi Wu, Hao Peng

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness of these detection methods is still underexplored. Existing Gr

D. A. Garanin, E. M. Chudnovsky

Efficient numerical routines are developed for numerical studies of the dependence of the equilibrium magnetic states, excitations, and microwave power absorption on temperature and composition in transition-metal/rare-earth ferrites, including the reversal of the N\'eel vector occurring on both temperature and the concentration of the rare-earth atoms. It r

Daniel R. Venn, Steven J. Ruuth

We develop and test high-order methods for integration on surface point clouds. The task of integrating a function on a surface arises in a range of applications in engineering and the sciences, particularly those involving various integral methods for partial differential equations. Mesh-based methods require a curved mesh for high-order convergence, which

Zhiming Zhang, Qingfu Zhu, Xianzhen Luo, Yixuan Wang

Code translation aims to translate the code from its source language to the target language and is used in various software development scenarios. Recent developments in Large Language Models (LLMs) have showcased their capabilities in code translation, and parallel corpora play a crucial role in training models for code translation. Parallel corpora can be

Kama Svoboda, Tosiron Adegbija

This survey paper presents a comprehensive examination of Spiking Neural Network (SNN) architecture search (SNNaS) from a unique hardware/software co-design perspective. SNNs, inspired by biological neurons, have emerged as a promising approach to neuromorphic computing. They offer significant advantages in terms of power efficiency and real-time resource-co

Ning Han, Gu Gong, Bin Zhang, Yuexuan Xu

Manipulating three-dimensional (3D) deformable objects presents significant challenges for robotic systems due to their infinite-dimensional state space and complex deformable dynamics. This paper proposes a novel model-free approach for shape control with constraints imposed on key points. Unlike existing methods that rely on feature dimensionality reductio

Fanchao Meng, Jiaping Gui, Yunbo Li, Yue Wu

Modern Network Intrusion Detection Systems generate vast volumes of low-level alerts, yet these outputs remain semantically fragmented, requiring labor-intensive manual correlation with high-level adversarial behaviors. Existing solutions for automating this mapping-rule-based systems and machine learning classifiers-suffer from critical limitations: rule-ba

Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran, Siddhartha Jain

Competitive programming has become a rigorous benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). The International Olympiad in Informatics (IOI) stands out as one of the most prestigious annual competitions in competitive programming and has become a key benchmark for comparing human and AI-level programm

Nils Philipp Walter, Linara Adilova, Jilles Vreeken, Michael Kamp

Despite their empirical success, neural networks remain vulnerable to small, adversarial perturbations. A longstanding hypothesis suggests that flat minima, regions of low curvature in the loss landscape, offer increased robustness. While intuitive, this connection has remained largely informal and incomplete. By rigorously formalizing the relationship, we s

Hongsong Wang, Renxi Cheng, Yang Zhang, Chaolei Han

The rapid advancement of GAN and Diffusion models makes it more difficult to distinguish AI-generated images from real ones. Recent studies often use image-based reconstruction errors as an important feature for determining whether an image is AI-generated. However, these approaches typically incur high computational costs and also fail to capture intrinsic

Koki Yumoto, Toru Kouyama, Manabu Yamada, Yuya Mimasu

Observations of exoplanet transits by small satellites have gained increasing attention for reducing detection biases. However, no unambiguous detection of an exoplanet has yet been demonstrated using optics with apertures smaller than 60 mm. Here, we investigated the detectability of exoplanet transits using the telescopic Optical Navigation Camera (ONC-T)

Christopher Alexander, Blake Temple, Zeke Vogler

We characterize the local instability of pressureless Friedmann spacetimes to radial perturbation at the Big Bang. The analysis is based on a formulation of the Einstein-Euler equations in self-similar variables $(t,\xi)$, with $\xi=r/t$, conceived to realize the critical ($k=0$) Friedmann spacetime as a stationary solution whose character as an unstable sad

Denizhan Pak

4E views of cognition seek to replace many of the long-held assumptions of tra- ditional cognitive science. One of the most radical shifts is the rejection of the sandwich model of cognition [8], which holds that mental processes are located be- tween action and perception. Subversion of such a long-held assumption requires an accessible theoretical alternat

Xiao Zheng, Wenchi Cheng, Jingqing Wang, Zhuohui Yao

Active reconfigurable intelligent surface (RIS) emerges as an effective technique to resist the double-fading attenuation of passive RIS. By embedding with power harvesting function, it further evolves to zero-power active RIS, which can effectively enhance the flexibility of RIS deployment without external power demand. Nevertheless, existing works neglecte

Motoya Takenaka, Shogo Yoshida, Yoshiki J. Sato, Ryuji Okazaki

We investigate the Sn-substitution effect on the thermoelectric transport properties of the layered cobaltite [Ca$_2$CoO$_3$]$_{0.62}$[CoO$_2$] single crystals, which exhibit a non-monotonic temperature variation of the electrical resistivity and the Seebeck coefficient owing to the complex electronic and magnetic states. We find that the onset temperature o

Fenglin Li

This paper investigates the homology groups of the clique complex associated with the zero-divisor graph of a finite commutative ring. Generalizing the construction introduced by F. R. DeMeyer and L. DeMeyer, we establish a Kunneth-type formula for the homology of such complexes and provide explicit computations for products of finite local rings. As a notab

Bo Li, Junwei Ma, Kai Yin, Yiming Xiao

The escalating frequency and severity of disasters routinely overwhelm traditional response capabilities, exposing critical vulnerability in disaster management. Current practices are hindered by fragmented data streams, siloed technologies, resource constraints, and the erosion of institutional memory, which collectively impede timely and effective decision

Sudarshan Srinivasa Ramanujam, Antonio Alonso, Saurabh Kataria, Siddharth Dangi

In large scale recommendation systems like the LinkedIn Feed, the retrieval stage is critical for narrowing hundreds of millions of potential candidates to a manageable subset for ranking. LinkedIn's Feed serves suggested content from outside of the member's network (based on the member's topical interests), where 2000 candidates are retrieved from a pool of

Benjamín Castro, Camilo Ramírez, Sebastián Espinosa, Jorge F. Silva

In Machine Learning (ML), a regression algorithm aims to minimize a loss function based on data. An assessment method in this context seeks to quantify the discrepancy between the optimal response for an input-output system and the estimate produced by a learned predictive model (the student). Evaluating the quality of a learned regressor remains challenging

Alida Vallejo-López, Cesar Noboa-Terán, Juana Kou-Guzmán, Josefina Ramírez-Amaya

Technology has become a global tool that allows us to obtain information and analyze data, streamlines communication, and allows us to share images, data, videos, texts, etc. Daily activities have gone from traditional to digital. Today, it is impossible to live without an electronic device. In this context, changes in people's health observed, with various

K. R. Dibert, M. Adamic, A. J. Anderson, P. S. Barry

We present the methodology and results of the on-sky responsivity calibration of the South Pole Telescope Shirokoff Line Intensity Mapper (SPT-SLIM). SPT-SLIM is a pathfinder line intensity mapping experiment utilizing the on-chip spectrometer technology, and was first deployed during the 2024-2025 Austral Summer season on the South Pole Telescope. During th

M. R. Young, M. Adamic, A. J. Anderson, P. S. Barry

The South Pole Telescope Shirokoff Line Intensity Mapper (SPT-SLIM) is a millimeter-wavelength line-intensity mapping experiment, which was deployed on the South Pole Telescope (SPT) during the 2024-2025 Austral summer season. This pathfinder experiment serves to demonstrate the on-sky operation of multi-pixel on-chip spectrometer technology. We report on th

Chaoyue Huang, Gejian Zhao, Hanzhou Wu, Zhihua Xia

As a valuable digital product, deep neural networks (DNNs) face increasingly severe threats to the intellectual property, making it necessary to develop effective technical measures to protect them. Trigger-based watermarking methods achieve copyright protection by embedding triggers into the host DNNs. However, the attacker may remove the watermark by pruni

Jinpeng Xiao, Qianglin Hu, Zuodong Yu, Weipeng Chen

Higher-order topological superconductivity typically depends on spin-orbit interaction, and often necessitates well designed sample structures, nodal superconducting pairings or complex magnetic order. In this work, we propose a model that incorporates a Zeeman field, antiferromagnetic order, and $s$-wave superconducting pairing, all without the need for spi

Wanzhen Hui, Xue Li

Let $G$ be an additive finite abelian group, and let $\mathrm{disc}(G)$ denote the smallest positive integer $t$ with the property that every sequence $S$ over $G$ with length $|S|\geq t $ contains two nonempty zero-sum subsequences of distinct lengths. In recent years, Gao et al. established the exact value of $\mathrm{disc}(G)$ for all finite abelian group

Mingao Yuan

The global clustering coefficient is an effective measure for analyzing and comparing the structures of complex networks. The random annulus graph is a modified version of the well-known Erd\H{o}s-R\'{e}nyi random graph. It has been recently proposed in modeling network communities. This paper investigates the asymptotic distribution of the global clustering

Hurriyet Yuce-Cakir, Haoran Chen, Isaac Ogunniranye, Susanna M. Thon

Halide perovskites have emerged as promising candidates for high-performance solar cells. This study investigates the temperature-dependent optoelectronic properties of mixed-cation mixed-halide perovskite solar cells using electroluminescence (EL) and photoluminescence (PL) hyperspectral imaging, along with current-voltage analysis. Luminescence images, whi

K. J. McCollam, R. Reksoatmodjo, J. von der Linden, J. Sears

A novel matrix method of analyzing ion saturation current data from a general three-dimensional (3D) array of unmagnetized Mach probe tips is developed and used with data sets from two 3D Mach probes to make initial measurements of local plasma flow velocity in reversed-field pinch (RFP) experiments in the Madison Symmetric Torus (MST). The two 3D Mach probe

Beomseok Kang, Jiwon Song, Jae-Joon Kim

Multi-stage reasoning has emerged as an effective strategy for enhancing the reasoning capability of small language models by decomposing complex problems into sequential sub-stages. However, this comes at the cost of increased latency. We observe that existing adaptive acceleration techniques, such as layer skipping, struggle to balance efficiency and accur

Mingao Yuan

Random geometric graphs are widely used in modeling geometry and dependence structure in networks. In a random geometric graph, nodes are independently generated from some probability distribution $F$ over a metric space, and edges link nodes if their distance is less than some threshold. Most studies assume the distribution $F$ to be uniform. However, recen

Xiwang Cao, Keqin Feng, Hexiang Huang, Yulin Yang

As a fundamental metric for quantifying quantum advantage in non-local games, the quantum chromatic number reveals the power of entanglement in distributed tasks. In this paper, we investigate this parameter for $q$-ary Hamming graphs and a generalization of Hadamard graphs. Our main results establish an exponential separation between the quantum and classic

Chanuka A. S. Hewa Kaluannakkage, Rajkumar Buyya

Federated learning promises to revolutionize machine learning by enabling collaborative model training without compromising data privacy. However, practical adaptability can be limited by critical factors, such as the participation dilemma. Participating entities are often unwilling to contribute to a learning system unless they receive some benefits, or the

Gabriel Hernández-Morales, Brenda Cansino-Loeza, Arturo Jiménez-Gutiérrez, Victor M. Zavala

Simulation-based optimization of complex systems over discrete decision spaces is a challenging computational problem. Specifically, discrete decision spaces lead to a combinatorial explosion of possible alternatives, making it computationally prohibitive to perform simulations for all possible combinations. In this work, we present a new approach to handle

Bingsheng Yao, Bo Sun, Yuanzhe Dong, Yuxuan Lu

The emerging large language model role-playing agents (LLM RPAs) aim to simulate individual human behaviors, but the persona fidelity is often undermined by manually-created profiles (e.g., cherry-picked information and personality characteristics) without validating the alignment with the target individuals. To address this limitation, our work introduces t

Hurriyet Yuce-Cakir, John F. Roller, Haoran Chen, Tingting Zhu

All-perovskite tandem solar cells with narrow and wide bandgap perovskite absorbers are promising candidates for low-cost and high efficiency photovoltaic applications. However, the open circuit voltage of typical tandem structures is generally smaller than the sum of the individual voltages in the single-junction form; a quantity we call the voltage gap. Su

Ryo Masumura, Shota Orihashi, Mana Ihori, Tomohiro Tanaka

This paper proposes a joint modeling method of the Big Five, which has long been studied, and HEXACO, which has recently attracted attention in psychology, for automatically recognizing apparent personality traits from multimodal human behavior. Most previous studies have used the Big Five for multimodal apparent personality-trait recognition. However, no st

Edgar P. Vidal, Alexander T. Gagliano, Carolina Cuesta-Lazaro

Time domain surveys such as the Vera C. Rubin Observatory are projected to annually discover millions of astronomical transients. This and complementary programs demand fast, automated methods to constrain the physical properties of the most interesting objects for spectroscopic follow up. Traditional approaches to likelihood-based inference are computationa

Joshua Tomlin

The Bauer-Furuta invariant of a family of smooth 4-manifolds is a stable cohomotopy refinement of the families Seiberg-Witten invariant and is constructed from a finite dimensional approximation of the Seiberg-Witten monopole map. We prove a general formula for the families Bauer-Furuta invariant of a fibrewise connected sum, extending Bauer's non-parameteri

Zhichao Wang, Andy Wong, Ruslan Belkin

After the pretraining stage of LLMs, techniques such as SFT, RLHF, RLVR, and RFT are applied to enhance instruction-following ability, mitigate undesired responses, improve reasoning capability and enable efficient domain adaptation with minimal data. SFT relies on the next-token prediction objective to strengthen instruction following in a base model using

Junyu Ren, Wensheng Gan, Guangyu Zhang, Wei Zhong

Existing transfer fault diagnosis methods typically assume either clean data or sufficient domain similarity, which limits their effectiveness in industrial environments where severe noise interference and domain shifts coexist. To address this challenge, we propose an information separation global-focal adversarial network (ISGFAN), a robust framework for c

Morium Akter Munny, Mahbub Alam, Sonjoy Kumar Paul, Daniel Timko

Toll scams involve criminals registering fake domains that pretend to be legitimate transportation agencies to trick users into making fraudulent payments. Although these scams are rapidly increasing and causing significant harm, they have not been extensively studied. We present the first large-scale analysis of toll scam domains, using a newly created data

German Villalobos, Johann Rudi, Andreas Mang

We investigate the use of neural networks (NNs) for the estimation of hidden model parameters and uncertainty quantification from noisy observational data for inverse parameter estimation problems. We formulate the parameter estimation as a Bayesian inverse problem. We consider a parametrized system of nonlinear ordinary differential equations (ODEs), which

Anne R. Kroo, Olav Solgaard

We introduce a modified corner cube reflector that encodes information from passive optical sensors in its retroreflected diffraction pattern, enabling remote sensor-state measurement over a single-ended optical link. The design interferes a reference path and a sensor-modulated path within the retroreflected beam to produce an interferometric signal suitabl

Göktuğ Bender, Samer Faraj, Anand Bhardwaj

Artificial intelligence (AI) has become increasingly central to precision medicine by enabling the integration and interpretation of multimodal data, yet implementation in clinical settings remains limited. This paper provides a scoping review of literature from 2019-2024 on the implementation of AI in precision medicine, identifying key barriers and enabler

Ning Ding, Haoshen Ye, Shan-Shan Wang, Shuai Dong

Altermagnets have garnered great interest due to their non-relativistic spin splitting and novel physical properties. However, the control of altermagnetic states remains underexplored. Here, we propose a unique multiferroic state, i.e. ferroelastic altermagnetic state, in which ferroelastic strain couples directly to the spin-splitting. Through symmetry ana

Long Chen, Xuehai Huang, Chao Zhang, Xinyue Zhao

This paper develops divergence-free mixed finite element methods for the Stokes equation. Using H(div)-conforming velocities and discontinuous pressures ensures the inf-sup condition for the velocity--pressure pair and yields pointwise divergence-free velocities. However, this choice makes the vector Laplacian difficult to discretize. Inspired by mass-conser

Beyza Zeynep Ucpinar, Sasan Razmkhah, Mustafa Altay Karamuftuoglu, Ali Bozbey

Superconducting quantum interference devices (SQUIDs) are among the most sensitive sensors, offering high precision through their well-defined flux-voltage characteristics. Building on this sensitivity, we designed, fabricated, and experimentally demonstrated a superconducting single flux quantum (SFQ)-based analog-to-digital converter (ADC) capable of detec

Tatsuro Banno, Mizuki Takenawa, Leslie Wöhler, Satoshi Ikehata

We introduce a novel urban visualization system that integrates 3D urban model (CityGML) and 360{\deg} walkthrough videos. By aligning the videos with the model and dynamically projecting relevant video frames onto the geometries, our system creates photorealistic urban visualizations, allowing users to intuitively interpret geospatial data from a pedestrian

Eric Albers, Paul Marriott, Masami Tatsuno

In neuroscience, methods from information geometry (IG) have been successfully applied in the modelling of binary vectors from spike train data, using the orthogonal decomposition of the Kullback-Leibler divergence and mutual information to isolate different orders of interaction between neurons. While spike train data is well-approximated with a binary mode

Amir Karami

In response to the escalating threat of misinformation, social media platforms have introduced a wide range of interventions aimed at reducing the spread and influence of false information. However, there is a lack of a coherent macrolevel perspective that explains how these interventions operate independently and collectively. To address this gap, I offer a

Thai Thuan Quang

Let $\psi$ be a holomorphic function on the open unit ball $\BB \subset \C^N$, and let $\varphi$ be a holomorphic self-map of $\BB$, associated with normal weights $\nu$ and $\mu$. We consider the weighted composition operator $ W_{\psi,\varphi} : \mathcal H_\nu^{(n)} \to \mathcal H_\mu^{(m)}, \quad n,m \in \N,$ acting between weighted-type high-order growth

Pengkun Ren, Hai Dong, Nasrin Sohrabi, Zahir Tari

Byzantine Fault-Tolerant (BFT) consensus protocols ensure agreement on transaction ordering despite malicious actors, but unconstrained ordering power enables sophisticated value extraction attacks like front running and sandwich attacks - a critical threat to blockchain systems. Order-fair consensus curbs adversarial value extraction by constraining how lea

Jack Vanlyssel

Industrial Control Systems (ICS) underpin the United States' critical infrastructure, managing essential services such as power, water, and transportation that are vital to national security and public safety. However, increasing digital integration has exposed these systems to escalating cyber threats. Historical attacks like Stuxnet and the Ukraine power g

Mahmood Hegazy, Aaron Rodrigues, Azzam Naeem

We present MAFA (Multi-Agent Framework for Annotation), a production-deployed system that transforms enterprise-scale annotation workflows through configurable multi-agent collaboration. Addressing the critical challenge of annotation backlogs in financial services, where millions of customer utterances require accurate categorization, MAFA combines speciali

Zhiyuan Zheng, Yong Shi, Qiusheng Gu, Zhi-Yu Zhang

Star-forming activity in the host galaxies of high-redshift quasars is crucial to understanding the connection between supermassive black hole (SMBH) activity and galaxy evolution. While most existing studies are biased toward luminous quasars, we conduct carbon monoxide (CO) observations of 17 gravitationally lensed quasars that have four images using the I

Zhang XueGuang

Optical quasi-periodic oscillations (QPOs) with periodicity around hundreds to thousands of days have been accepted as an efficient indicator for sub-pc binary black hole systems (BBHs) in broad line active galactic nuclei (BLAGN). However, considering intrinsic variability (red noises) of BLAGN, it is still an open question on physical origin of detected op

Dhruv Sharma

We present a comprehensive computational framework for simulating nonadiabatic molecular dynamics with explicit inclusion of geometric phase (GP) effects. Our approach is based on a generalized two-level Hamiltonian model that can represent various electronic state crossings - conical intersections, avoided crossings, and elliptic intersections - through app

Jaehyeon Ryu, Andreas Seeger

Let $G$ be a two-step nilpotent Lie group, identified via the exponential map with the Lie-algebra $\mathfrak g=\mathfrak g_1\oplus\mathfrak g_2$, where $[\mathfrak g,\mathfrak g]\subset \mathfrak g_2$. We consider maximal functions associated to spheres in a $d$-dimensional linear subspace $H$, dilated by the automorphic dilations. $L^p$ boundedness results

Yuancheng Xu, Wenqi Xian, Li Ma, Julien Philip

We introduce a framework that enables both multi-view character consistency and 3D camera control in video diffusion models through a novel customization data pipeline. We train the character consistency component with recorded volumetric capture performances re-rendered with diverse camera trajectories via 4D Gaussian Splatting (4DGS), lighting variability

Nivedita Mahesh, Judd D Bowman, Bharat Gehlot, Danny Jacobs

Several radio telescopes have been planned or proposed to be deployed on the Lunar farside in the coming years. These will observe the unexplored ultra-long wavelengths of the electromagnetic spectrum from the lunar farside's unique radio-quiet and ionosphere-free environment. One such lunar radio array is the NASA-funded concept - the Farside Array for Radi

Oleksandr Bobrovnikov, Madison Jones, Shriya Prasanna, Josiah Smith

We discuss the following inverse problem: given the run-up data of a tsunami wave, can we recover its initial shape? We study this problem within the framework of the non-linear shallow water equations, a model widely used to study tsunami propagation and inundation. Previously, it has been demonstrated that in the case of infinite sloping bathymetry, it is

Changdao He

We study the unweighted throughput scheduling problem on a single machine in the preemption-revoke model, where a running job may be aborted at any time, but all progress is permanently lost and the job cannot be restarted. Each job $J_i=(r_i,p_i,s_i)$ is defined by a release time $r_i$, a processing time $p_i$, and a slack $s_i$, and must start no later tha

Roger Creus Castanyer, Faisal Mohamed, Pablo Samuel Castro, Cyrus Neary

Reinforcement learning (RL) algorithms are highly sensitive to reward function specification, which remains a central challenge limiting their broad applicability. We present ARM-FM: Automated Reward Machines via Foundation Models, a framework for automated, compositional reward design in RL that leverages the high-level reasoning capabilities of foundation

Yueyun Chen, Xin Yi Ling, Jared Lodico, Tristan P. O`Neill

Electronic devices are engineered at increasingly smaller length scales; new metrologies to understand nanoscale thermodynamics are needed. Temperature and pressure are fundamental thermodynamic quantities whose nanoscale measurement is challenging as physical contact inevitably perturbs the system. Here we demonstrate Kikuchi diffraction thermometry (KDTh),

Xinyu Yang, Shan-Shan Wang, Shuai Dong

The development of altermagnets is fundamentally important for advancing spintronic device technology, but remains unpractical for the weak spin splitting in most cases, especially in two-dimensional materials. Based on spin group symmetry analysis and first-principles calculations, a novel hydroxyl rotation strategy in collinear antiferromagnets has been pr

Zhuo Chen

The acceleration and unbinding of the common envelope during the plunge-in phase are governed by complex physical processes that often manifest observationally as luminous red novae. We investigate the dynamics of this phase using one-dimensional radiation hydrodynamic simulations evolved with the code {\tt Guangqi}. We perform a parameter survey to quantify

Yuchao Su, Srikar Chundury, Jiajia Li, Frank Mueller

Hamiltonian simulation is a key workload in quantum computing, enabling the study of complex quantum systems and serving as a critical tool for classical verification of quantum devices. However, it is computationally challenging because the Hilbert space dimension grows exponentially with the number of qubits. The growing dimensions make matrix exponentiati

Jack Vanlyssel

The U.S. power grid underpins national security, public safety, and economic stability, but faces growing cyber risks from vulnerabilities in industrial control systems, remote access, and poor cyber hygiene. Despite its critical importance, current policy remains fragmented and reactive. This paper proposes a dual policy approach to strengthen grid cybersec

Marco Bochicchio, Giacomo Santoni

It has been known for many years that, in Yang-Mills theories with $\mathcal{N}=4,2,2^*$ supersymmetry, certain nontrivial supersymmetric Wilson loops exist with v.e.v. either trivial or computable by localization that arises from a cohomological field theory, which also computes the nonperturbative prepotential in $\mathcal{N}=2,2^*$ theories. Moreover, som

Praphul Singh, Corey Barrett, Sumana Srivasta, Amitabh Saikia

Clinical conversations mix explicit directives (order a chest X-ray) with implicit reasoning (the cough worsened overnight, we should check for pneumonia). Many systems rely on LLM rewriting, adding latency, instability, and opacity that hinder real-time ordering. We present JEDA (Joint Embedding for Direct and Ambient clinical orders), a domain-initialized

Barbara Lopez-Doriga, Anya R. M. Jones, Kunihiko Taira

Historically, investigations on gust encounters have been limited to thin airfoils. In this work, we examine vortex-gust encounters by a family of airfoils at a chord-based Reynolds number Re_c=100, which includes variations in the gust ratio, initial gust position, gust radius, angle of attack, airfoil thickness, and airfoil camber. We examine differences i

Xiangyu Luo, Ludovica Zullo, Sahaj Patel, Dongjin Oh

Flat electronic bands, which amplify electron correlations by quenching kinetic energy, provide an ideal foundation for exotic quantum phases. However, prevailing strategies -- including geometrically frustrated lattices, moire superlattices and heavy-fermion physics -- suffer from inherent trade-offs among robustness, tunability and orbital selectivity, lim

Jiaxin Guo, Tongfan Guan, Wenzhen Dong, Wenzhao Zheng

Recent advances in 3D Gaussian Splatting (3DGS) have enabled generalizable, on-the-fly reconstruction of sequential input views. However, existing methods often predict per-pixel Gaussians and combine Gaussians from all views as the scene representation, leading to substantial redundancies and geometric inconsistencies in long-duration video sequences. To ad

Arnob Mukherjee, Biplab Sanyal, Annica M. Black-Schaffer, Ankita Bhattacharya

Altermagnets are a recently discovered class of compensated magnets with momentum-dependent spin splittings and unusual transport properties, even without a net magnetization. In the presence of combined four-fold rotation and time-reversal ($C_4\mathcal{T}$) symmetry, linear and also second-order, driven by a Berry curvature dipole, anomalous Hall responses

Zhunyun Tang, Xiaoxia Wang, Jin Li, Chaoyu He

The pursuit of materials combining light constituent elements with ultralow lattice thermal conductivity ($κ_{\mathrm{L}}$) is crucial to advancing technologies like thermoelectrics and thermal barrier coatings, yet it remains a formidable challenge to date. Herein, we achieve ultralow $κ_{\mathrm{L}}$ in lightweight cyanide-bridged framework materials (CFMs

G. R. Boroun, Phuoc Ha, A. V. Kotikov, A. V. Lipatov

We investigate the gluon distribution in a proton at very low $x$, both integrated and transverse momentum dependent, using the Laplace transform technique. By accounting for leading and main next-to-leading contributions, we derive compact analytical expressions for the gluon densities valid in the asymptotic limit $x \to 0$. Our results closely match those

Ming Zhong, Weifang Weng, Zhenya Yan

We investigate the asymmetric integrable turbulence and rogue waves (RWs) emerging from the modulation instability (MI) of plane waves for the DNLS equation. The \(n\)-th moments and ensemble-averaged kinetic and potential energy exhibit oscillatory convergence towards their steady-state values. Specifically, the amplitudes of oscillations for these indexes

Sebastian Baader, Jasmin Jörg, Danica Kosanović

A system of simple closed curves on a surface of genus $g$ is said to be sparse if their average pairwise intersection number does not exceed one. We show that the maximal size of a sparse curve systems grows roughly like a function of type $c^{\sqrt{g}}$, with $c$ between $2$ and $81938$.

Dominik Krengel, Jian Chen, Shun Nomura, Shunsuke Ota

Intruder mechanics in a granular aggregate is a common subject in engineering and geotechnical applications. However, most studies are limited to spherical intruders or small displacement regimes up to the point of failure. In this work we investigate the vertical pullout of a plate-like intruder buried within a granular aggregate well past the point of fail

Nicolas Steinacker-Olsztyn, Devashish Gosain, Ha Dao

Large Language Models (LLMs) are increasingly relying on web crawling to stay up to date and accurately answer user queries. These crawlers are expected to honor robots.txt files, which govern automated access. In this study, for the first time, we investigate whether reputable news websites and misinformation sites differ in how they configure these files,

Sebastian Stengele, Ángela Capel, Li Gao, Angelo Lucia

We consider the class of Davies quantum semigroups modelling thermalization for translation-invariant Calderbank-Shor-Steane (CSS) codes in D dimensions. We prove that conditions of Dobrushin-Shlosman-type on the quantum Gibbs state imply a modified logarithmic Sobolev inequality with a constant that is uniform in the system's size. This is accomplished

Zoha Laraib, Sherwood Richers

The neutrino fast flavor instability dominates the evolution of neutrino flavor within the engines of core-collapse supernovae and neutron star mergers. However, theoretical models of neutrino flavor change that include many-body quantum correlations can differ starkly from similar mean-field calculations. We demonstrate for the first time that the inhomogen

Reja H. Wilke, Henning Schlömer, Simon M. Linsel, Annabelle Bohrdt

Since the discovery of phase transitions driven by topological defects, the classification of phases of matter has been significantly extended beyond Ginzburg and Landau's paradigm of spontaneous symmetry breaking (SSB). In particular, intrinsic and symmetry-protected topological (SPT) orders have been discovered in (mostly gapped) quantum many-body grou

Yang Liu, Guochun Wu, Xin Zhong

We investigate the global stability of large solutions to the compressible isentropic Navier-Stokes equations in a three-dimensional (3D) bounded domain with Navier-slip boundary conditions. It is shown that the strong solutions converge to an equilibrium state exponentially in the $L^2$-norm provided the density is essentially uniform-in-time bounded from a