March 2026 arXiv papers — page 9

Niklas Risse, Marcel Böhme

\'Sliwerski, Zimmermann, and Zeller (SZZ) just won the 2026 ACM SIGSOFT Impact Award for asking: When do changes induce fixes? Their paper from 2005 served as the foundation for a wide array of approaches aimed at identifying bug-introducing changes (or commits) from fix commits in software repositories. But even after two decades of progress, the best-perfo

Ji Liu, Jianyu Li, Peng Zhang, Xiaosen Yang

Altermagnets - collinear, zero-net-moment magnets with momentum-odd spin splitting protected by crystalline symmetries - offer a tunable route to suppress long-range antiferromagnetism while preserving strong short-range spin fluctuations. We show that this environment robustly stabilizes unconventional superconductivity and naturally produces mixed-symmetry

Fabian Kabus, Julia Hindel, Jelena Bratulić, Meropi Karakioulaki

Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genetic skin disorder for which clinicians greatly benefit from finding similar cases using images and clinical text. However, off-the-shelf foundation models do not reliably capture clinically meaningful features for this heterogeneous, long-tail disease, and structured measurement of agreement wit

Christopher Goetze, Tim Schlippe, Daniel Lakey

Spacecraft anomaly detection is critical for mission safety, yet deploying sophisticated models on-board presents significant challenges due to hardware constraints. This paper investigates three approaches for spacecraft telemetry anomaly detection -- forecasting & threshold, direct classification, and image classification -- and optimizes them for edge dep

Simone Manti, Luca De Paolis, Leonardo Abbene, Francesco Artibani

Kaonic atoms provide a unique laboratory to investigate the interplay between atomic, nuclear, and strong-interaction physics. In heavy nuclei, atomic transitions can couple to low-lying collective nuclear excitations via the electric quadrupole interaction. When the energy difference between two kaonic atomic levels approaches that of a nuclear $2^+$ excita

Poulastya Kar, Bipin Singh Koranga

Sterile neutrinos arise naturally in extensions of the Standard Model and can affect cosmological evolution even with subdominant abundance. Their impact is often described by a constant shift in the effective number of relativistic species, Delta Neff, assuming a radiation-like equation of state. However, for finite mass sterile neutrinos with incomplete th

Yahan Li, Xinyi Jie, Wanjia Ruan, Xubei Zhang

Large language models (LLMs) are increasingly used for medical consultation and health information support. In this high-stakes setting, safety depends not only on medical knowledge, but also on how models respond when patient inputs are unclear, inconsistent, or misleading. However, most existing medical LLM evaluations assume idealized and well-posed patie

Lu Zhang, Jinchuan Zeng, Hui Zhang

Large-scale linear systems of the form $Ax=b$ are often doubly-noisy, in the sense that both its measurement matrix $A$ and measurement vector $b$ are noisy. In this paper, we extend the relaxed greedy randomized Kaczmarz (RGRK) method to the doubly-noisy systems to accelerate convergence. However, RGRK fails to converge to the least-squares solution for dou

Qing-Ming Cheng, Junqi Lai, Guoxin Wei

There is a well-known conjecture asserts that the round sphere should be the only compact embedded self-shrinker (i.e. $0$-hypersurface) which is diffeomorphic to a sphere. S. Brendle confirmed the conjecture for 2-dimensional $0$-hypersurfaces. For any dimensional $\lambda$-hypersurfaces, if $\lambda<0$, we constructed compact convex embedded $\lambda$-hype

Yinan Wang, Arpita Varadwaj, Teruyasu Mizoguchi, Masato Kotsugi

Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional theory (DFT) and machine learning (ML) to interpret how boron (B) and nitrogen (N) dopants influence the local electronic environments of graphene. A dataset of

Enlai Li, Zhe Lin, Sharad Sinha, Wei Zhang

Deep reinforcement learning has demonstrated remarkable success across various domains. However, the tight coupling between training and inference processes makes accelerating DRL training an essential challenge for DRL optimization. Two key issues hinder efficient DRL training: (1) the significant variation in computational intensity across different DRL al

Abrarul Karim, Joachim Falk, Jürgen Teich

The introduction of dynamic power management strategies such as clock gating and power gating in dataflow networks has been shown to provide significant energy savings when applied during idle times. However, these strategies can also degrade throughput due to shutdown and wake-up delays. Such throughput degradations might be particularly detrimental to sign

Moiz Sadiq Awan, Maryam Raza

Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars and thousands of physician hours each year. While large language models have shown promise across clinical text tasks, their ability to produce submission-ready prior authorization letters has received only limited attention, with

Yuma Kikuchi, Kiyoshi Katō, Takayuki Myo

Background: The complex scaling method (CSM) has been successfully used to describe many-body resonances as eigenvalues of the complex-scaled Hamiltonian in an appropriate $L^2$ basis representation. Its scope has subsequently been extended to many-body continuum states, strength functions, and scattering observables. However, a general framework that incorp

Jiacheng Wang, Weihong Qin, Jialing He, Changyuan Zhao

This survey examines intelligent forensics in next-generation mobile networks, arguing that future wireless security must move beyond real-time detection toward accountable post-incident reconstruction. Unlike traditional digital forensics, wireless investigations rely on short-lived, distributed, and heterogeneous evidence, including radio waveforms, channe

Tomoki Ishikura, Genichiro Matsuda, Takuya Kiyokawa, Kensuke Harada

As the amount of used home appliances is expected to increase despite the decreasing labor force in Japan, there is a need to automate disassembling processes at recycling plants. The automation of disassembling air conditioner outdoor units, however, remains a challenge due to unit size variations and exposure to dirt and rust. To address these challenges,

Jintao Sun, Hu Zhang, Gangyi Ding, Zhedong Zheng

Trajectory prediction seeks to forecast the future motion of dynamic entities, such as vehicles and pedestrians, given a temporal horizon of historical movement data and environmental context. A central challenge in this domain is the inherent uncertainty in real-time maps, arising from two primary sources: (1) positional inaccuracies due to sensor limitatio

Yacine Izza, Alexey Ignatiev, Xuanxiang Huang, Peter J. Stuckey

Tree ensembles (TEs) find a multitude of practical applications. They represent one of the most general and accurate classes of machine learning methods. While they are typically quite concise in representation, their operation remains inscrutable to human decision makers. One solution to build trust in the operation of TEs is to automatically identify expla

Zi-yi Zhou, Long Wang, Tereza Jerabkova, Zhenghao He

We present a suite of direct $N$-body simulations of the Hyades open cluster and its tidal stream in a Milky Way potential that includes a rotating bar and spiral arms. Using the high-resolution code PETAR and an AGAMA-based multi-component Galactic model, we vary the bar and spiral pattern speeds ($\Omega_b$, $\Omega_s$) on a discrete grid and quantify the

Derya Cögendez, Verena Zimmermann, Noé Zufferey

Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messaging). To assess to what extent an individual's personality can be inferred from user interactions with LLM-based conversational agents (CAs), we analyze and quantify related privacy risks of using CAs. We colle

Subhomoy Haldar, Diego Subero, Mukesh Kumar, Bayan Karimi

We investigate heat conduction and energy relaxation in an InAs semiconductor nanowire using a hybrid semiconductor-superconductor architecture. Local electronic temperatures are measured with an in-situ grown quantum dot thermometer, while controlled Joule heating is applied at different locations along the wire to probe temperature gradients at sub-kelvin

Tommy Sha, Stella Zhao

AI evaluation suites often report many scores without checking whether those scores carry independent information. We introduce Effective Dimensionality (ED), the participation ratio of a centered benchmark-score spectrum, as a fast, population-conditional upper-bound diagnostic of measurement breadth. Applied at per-instance granularity to 22 benchmarks acr

Kyeonghun Kim, Youngung Han, Seoyoung Ju, Yeonju Jean

The rapid progress of generative adversarial networks (GANs) and diffusion models has enabled the creation of synthetic faces that are increasingly difficult to distinguish from real images. This progress, however, has also amplified the risks of misinformation, fraud, and identity abuse, underscoring the urgent need for detectors that remain robust across d

Oliver Aleksander Larsen, Mahyar T. Moghaddam

Standing water in agricultural fields threatens vehicle mobility and crop health. This paper presents a deployed edge architecture for standing-water detection using Raspberry-Pi-class devices with optional Jetson acceleration. Camera input and environmental sensors (humidity, pressure, temperature) are combined in a finite-state machine (FSM) that acts as t

Alexander Mrokon, Sebastian Schöler, Leonard Vossgrag, Karsten Buse

A recirculating frequency-shifting loop (FSL) provides a highly flexible platform for generating coherent optically chirped light with tunable bandwidth, duration, chirp rate and repetition rate. The properties of the chirped light are controlled using low-frequency sinusoidal electronic drive signals, enabling deterministic waveform synthesis without comple

Weiheng Hua, Changyu Hao

Tacho-less rotational speed estimation is critical for vibration-based prognostics and health management (PHM) of rotating machinery, yet traditional methods--such as time-domain periodicity, cepstrum, and harmonic comb matching--struggle under noise, non-stationarity, and inharmonic interference. Probabilistic tracking offers a principled way to fuse multip

Bokang Jia, Samta Kamboj, Satheesh Katipomu, Seung Hun Han

We introduce Nomad, a system for autonomous data exploration and insight discovery. Given a corpus of documents, databases, or other data sources, users rarely know the full set of questions, hypotheses, or connections that could be explored. As a result, query-driven question answering and prompt-driven deep-research systems remain limited by human framing

Ana Kelly de Oliveira, Pedro A. S. Salomão

A contact form $\lambda$ on a closed contact three-manifold $(M,\xi)$ is called weakly convex if either it has no contractible Reeb orbit, or the first Chern class of $\xi$ vanishes on $\pi_2(M)$, and the index of every contractible Reeb orbit is at least $2$. We present conditions for a weakly convex contact form to admit a well-defined cylindrical contact

François-Grégoire Bierwart, Alexandre Mauroy

The Koopman operator provides an infinite-dimensional linear description of nonlinear dynamical systems that can be leveraged in the context of stability analysis. In particular, Lyapunov functions can be obtained in a systematic way via the eigenfunctions of the Koopman operator. However, these eigenfunctions are computed from finite-dimensional approximati

Marcos Masip

This work is devoted to the study of the relationships between graph theory and the qualitative analysis of ordinary differential equations, with a special focus on two-dimensional systems. In particular, we reinterpret classical results through the lens of boundary polynomials of graphs. The theory naturally leads to questions about limit cycles, which aris

Yi-Han Bai, Yue Wei, Chi Zhang, Weibin Li

We propose high-fidelity controlled-NOT (CNOT) gates in a hybrid system of polar molecules and Rydberg atoms based on the unconventional Rydberg pumping mechanism. By combining the rich internal structure of polar molecules with the strong dipole-dipole interactions of Rydberg atoms, we realize both two-to-one and one-to-two gate configurations. Numerical si

Lu Zhang, Hongzhen Chen, Hongxia Wang, Hui Zhang

Stochastic projection algorithms for solving convex feasibility problems (CFPs) have attracted considerable attention due to their broad applicability. In this paper, we propose a unified stochastic bilevel reformulation for possibly inconsistent CFPs that combines proximity function minimization and structural regularization, leading to a feasible bilevel m

Amane Watahiki, Tomoki Doi, Akari Kikuchi, Hiroshi Ohata

Narrative analysis is a cornerstone of qualitative research. One leading approach is the Labovian model, but its application is labor-intensive, requiring a holistic, recursive interpretive process that moves back and forth between individual parts of the transcript and the transcript as a whole. Existing Labovian datasets are available only in English, whic

Ona de Gibert, Lluís de Gibert

Esperanto is a widespread constructed language, known for its regular grammar and productive word formation. Besides having substantial resources available thanks to its online community, it remains relatively underexplored in the context of modern machine translation (MT) approaches. In this work, we present the first comprehensive evaluation of open-source

Javad Vahedi, Martin Garttner

We introduce the parafermionic truncated Wigner approximation ($p$TWA), a semiclassical phase-space framework for simulating the nonequilibrium dynamics of lattice systems with fractional exchange statistics. The method extends truncated Wigner approaches developed for bosonic and fermionic systems to $\mathbb{Z}_n$ Fock parafermions by expressing the Hamilt

Youngung Han, Kyeonghun Kim, Seoyoung Ju, Yeonju Jean

Medical image segmentation faces fundamental challenges including restricted access, costly annotation, and data shortage to clinical datasets through Picture Archiving and Communication Systems (PACS). These systemic barriers significantly impede the development of robust segmentation algorithms. To address these challenges, we propose FOSCU, which integrat

Bitna Kim, Seungyeon Lee, Yelan Lee, Juyeop Kim

Cell search engineers face significant challenge in reducing computation time to meet the requirements for fast initial access and radio link recovery. Since the majority of cell search time is consumed by Primary Synchronization Signal (PSS) detection, reducing the computational burden of this step is critical for shortening the overall procedure. This pape

Stephen M. Wilkins, Jack Turner, Connor Sant Fournier, Behnood Bandi

With traditional sources of funding for astronomical research under increasing pressure, it is timely to explore innovative alternative mechanisms. We therefore introduce GalaxyCoin, a novel cryptocurrency whose issuance, validation, and economic evolution are anchored to real astrophysical objects - galaxies. GalaxyCoin links digital scarcity to observation

Detai Xin, Shujie Hu, Chengzuo Yang, Chen Huang

We present LongCat-AudioDiT, a novel, non-autoregressive diffusion-based text-to-speech (TTS) model that achieves state-of-the-art (SOTA) performance. Unlike previous methods that rely on intermediate acoustic representations such as mel-spectrograms, the core innovation of LongCat-AudioDiT lies in operating directly within the waveform latent space. This ap

Bikram Adhikary, Md Abu Talhamainuddin Ansary

In this paper, a new one-parameter filled function approach is developed for nonlinear multi-objective optimization. Inspired by key filled function ideas from single-objective optimization, the proposed method is adapted to the multi-objective setting. It avoids scalarization weights and does not impose any prior preference ordering among objectives. A desc

Shohei Higashiyama, Masao Ideuchi, Masao Utiyama

Entity linking is the task of associating linguistic expressions with entries in a knowledge base that represent real-world entities and concepts. Language resources for this task have primarily been developed for English, and the resources available for evaluating Japanese systems remain limited. In this study, we develop a corpus design policy for the enti

Frederic V. Hessman, Christian Jooss

It is generally assumed that gravitational waves are extremely difficult to detect. However, we show that the call of the Mexican Burrowing Toad has an amazing resemblance to cosmic gravitational wave signals due to the merging of neutron stars and/or black holes. It is known that toads exhibit magnetoreception - the ability to detect magnetic fields - and t

Haoyue Song, Lianqiang Qu, Ting Yan, Yuguo Chen

Statistical inference in parametric models (e.g., the Bradley--Terry model and its variants) for paired-comparison data has been explored in the high-dimensional regime, in which the number of items involving in paired comparisons diverges. However, parametric models are highly susceptible to model misspecification. To relax the assumption of known distribut

Yunyue Wei, Chenhui Zuo, Shanning Zhuang, Haixin Gong

The embodied learning of human motor control requires whole-body neuro-actuated musculoskeletal dynamics, while the internal muscle-driven processes underlying movement remain inaccessible to direct measurement. Computational modeling offers an alternative, but inverse dynamics methods struggled to resolve redundant control from observed kinematics in the hi

Atsushi Tabei, Ken'ichiro Tanaka

We study the convergence analysis of continuous-time dynamical systems associated with optimization methods for strongly convex functions. Recent works have proposed systematic constructions of Lyapunov functions for such analysis, while also revealing limitations of the Lyapunov analysis. Aujol--Dossal--Rondepierre (2023) have proposed a technique to addres

Qing Guo, Angela Pistoia, Shixin Wen

We build blowing-up solutions to the critical elliptic system with Neumann boundary condition, \begin{equation*} \begin{cases} -\Delta u_1 + \lambda u_1 = u_1^{3} -\beta u_1u_2^2 & \text{in } \Omega, -\Delta u_2 + \lambda u_2 = u_2^{3} -\beta u_1^2u_2 & \text{in } \Omega, \frac{\partial u_1}{\partial\nu} = \frac{\partial u_2}{\partial\nu} = 0, & \text{on } \

Kavindu Herath, Joshua Zhao, Saurabh Bagchi

Backdoor attacks on federated learning (FL) are most often evaluated with synthetic corner patches or out-of-distribution (OOD) patterns that are unlikely to arise in practice. In this paper, we revisit the backdoor threat to standard FL (a single global model) under a more realistic setting where triggers must be semantically meaningful, in-distribution, an

Renato Falomo, Aldo Treves, Simona Paiano, Riccardo Scarpa

BL Lac objects (BLL) are defined by the presence of very weak (typically $<$ 5 {\AA}) or even absent spectral lines. This makes determining their distance particularly challenging, especially at high redshift, where the sources are fainter and the host galaxy contribution in the optical band becomes negligible. Yet measuring their distance is crucial for der

Daniel Williams

Real-time, deep learning-based vocal denoising has seen significant progress over the past few years, demonstrating the capability of artificial intelligence in preserving the naturalness of the voice while increasing the signal-to-noise ratio (SNR). However, many deep learning approaches have high amounts of latency and require long frames of context, makin

Samantha M. Lawler, Michele T. Bannister, Laura E. Revell

The commercial space industry is launching more satellites into Low Earth Orbit every year. Aotearoa New Zealand (NZ) has a thriving dairy and cattle industry. Unfortunately, these industries could come into (high speed) cow-llision, as the rapid launch rate and short operational lifetimes of satellites in megaconstellations like Starlink result in a high re

Jeonghyeok Park, Hyukjoon Kwon, Hyunseok Jeong

We investigate the distribution of entanglement entropy in hybrid quantum circuits consisting of random unitary gates and local measurements applied at a finite rate. We demonstrate that higher moments of the entanglement entropy distribution, such as the ratio between the variance and the mean and the skewness, capture nontrivial features of the measurement

Tobias Stähle, Péter Ferenc Gyarmati, Thilo Spinner, Rita Sevastjanova

The rise of AI agents introduces a fundamental shift in Visual Analytics (VA), in which agents act as a new user group. Current agentic approaches - based on computer vision and raw DOM access - fail to perform VA tasks accurately and efficiently. This paper introduces the Visual Analytics Context Protocol (VACP), a framework designed to make VA applications

Nan Li, Shiyin Shen

The cosmological principle states that the universe is uniform and does not favor any specific position or direction. However, research conducted by \cite{Shen2025} has revealed that the universe demonstrates a notable inclination towards parity-odd states. Furthermore, it remains uncertain whether the universe also favors prime numbers. In this study, we ex

Katsunori Arai, Ryoya Kai

A quandle is an algebraic system whose axioms are motivated by Reidemeister moves in knot theory. A typical example is a conjugation quandle arising from a group. A quandle is said to be admissible if it is isomorphic to a conjugation quandle. Admissible quandles often yield knot invariants that coincide with those derived from the knot group, whereas nonadm

Ke Wang, Xiao-Xiong Zeng

In Horndeski gravity, we investigate how to extract energy from a rotating black hole immersed in a uniform magnetic field $B$ based on the Magnetic Penrose Process. We map the ergosphere and negative energy regions of this spacetime, and analyze the relationship between the energy extraction efficiency and the hair parameter through both theoretical analysi

Hongyi Nie, Xunyuan Liu, Yudong Bai, Yaqing Wang

Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use is highly personalized: users adopt diverse workflows and preferences, challenging agents to deliver customized assistance rather than generic solutions. Existing GUI agent benchm

Yingke Wang, Hao Li, Yifeng Zhu, Hong-Xing Yu

Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil painting images, can a robot infer and execute the stroke tra

Karel Dekimpe, Lore De Weerdt

We generalise Nielsen theory to coincidences of pairs $(f,g)$ where $f:X\multimap Y$ is $n$-valued multimap and $g:X\to Y$ is a single-valued map, for $X$ and $Y$ closed oriented triangulable manifolds of equal dimension. We prove a Wecken theorem in this setting, and formulas for the Nielsen, Lefschetz and Reidemeister numbers in terms of the analogous inva

Aryan Yazdan Parast, Khawar Islam, Soyoun Won, Basim Azam

Deep neural networks often rely on spurious features to make predictions, which makes them brittle under distribution shift and on samples where the spurious correlation does not hold (e.g., minority-group examples). Recent studies have shown that, even in such settings, the feature extractor of an Empirical Risk Minimization (ERM)-trained model can learn ri

Lei Ma

We present a concise dynamical picture of infant-driven household chaos. The framework has three postulations: recurrent daily chaos, overall entropy growth in household organization, and transient local ordering episodes with switching rules (a volatile Maxwell-demon effect). We illustrate entropy growth with a two-region toy model (organizer vs. play area)

Sota Hikasa, Wataru Namiki, Daiki Nishioka, Maki Nishimura

Recently, artificial-intelligence (AI) technologies have been increasingly utilized in a wide range of real-world applications. Speech recognition is one of these practical AI tasks and is regarded as a key application for edge AI systems. Consequently, speech recognition has been widely employed as a representative benchmark task for assessing the performan

Sh. Golghasemi Sorkhabi, R. Barille, M. Loumaigne, A. Korbut

A simple optical strategy to transform patchy PMMA azopolymer composite nanoparticles into complex, fully three-dimensional morphologies using controlled laser polarization is presented. The particles consist of a PMMA core decorated with nanoscale azopolymer patches that undergo localized photofluidization upon trans cis isomerization. Linear polarization d

Hyun Jong Yang, Howon Lee, Kyuhong Shim, Jeongho Kwak

Imagine advanced humanoid robots, powered by multimodal large language models (MLLMs), coordinating missions across industries like warehouse logistics, manufacturing, and safety rescue. While individual robots show local autonomy, realistic tasks demand coordination among multiple agents sharing vast streams of sensor data. Communication is indispensable, y

Youngju Cho, Yongwoong Lee, Kikyeong Kwon, Seunghwan Roh

We report spectroscopic results of the \(X^2\Sigma^+(v=2) \rightarrow A^2\Pi_{1/2}(v=1)\) transition in magnesium monofluoride (MgF). Using Doppler-free Laser-Induced Fluorescence (LIF) spectroscopy on the \(X^2\Sigma^+(v=2) \rightarrow A^2\Pi_{1/2}(v=1)\) transition, we resolved 47 hyperfine components distributed over 11 transition lines in X and A states.

Yuya Kano, Yohei Kawakami, Shumpei Masuda, Tomohiro Yamaji

We experimentally investigate how interactions between Kerr parametric oscillators (KPOs) degrade their bit-flip times, where a bit flip is defined as a transition between the two degenerate ground states of a KPO. Interactions between KPOs cause quantum states of KPOs to leak outside the computational subspace, leading to bit flips. Bit flips degrade fideli

Lucy M. Thomas, Katerina Chatziioannou, Sam Johar, Taylor Knapp

Accurate inclusion of both spin precession and orbital eccentricity effects in gravitational waveform models represents a key hurdle in our ability to fully characterize the properties of compact binaries. Virtually all efforts to model precession rely on a coprecessing frame transformation, a time-dependent spatial rotation that tracks the dominant emission

Tomohiro Arai, Kurando Baba

We study anti-self-dual contact instantons on 5-dimensional Sasakian manifolds with transverse Calabi-Yau structures. In this case, the leaf space is a Calabi-Yau orbifold, and the moduli space of irreducible anti-self-dual contact instantons is a hyperkahler manifold. Using the singularity data of the leaf spaces, we prove that the transverse Levi-Civita co

Qi Zheng, Yansong Ma, Xueguang Zhang, Qirong Yuan

The spectra of type 1 active galactic nuclei (AGNs) often exhibit broad component in [O III]$\lambda$5007, which are typically blue-shifted and associated with strong outflows. We systematically analyze the [O III] emission-line properties of type 1 AGNs with broad components to investigate how these kinematic features relate to the physical properties of th

Qinye Zhu, Yu Xiang, Jun Zhang, Wenyong Wang

The precise fusion of computational fluid dynamic (CFD) data, wind tunnel tests data, and flight tests data in aerodynamic area is essential for obtaining comprehensive knowledge of both localized flow structures and global aerodynamic trends across the entire flight envelope. However, existing methodologies often struggle to balance high-resolution local fi

Chuanxin Xu, Tao Xu

In this paper, the theory of inverse scattering transform (IST) is developed for the discrete PT-symmetric nonlocal nonlinear Schr\"{o}inger equation under large nonzero boundary conditions (NZBCs). By considering that the data at infinity have constant amplitudes, two cases are studied where the previous IST theory fails for large NZBCs. Based on a suitable

Jiaju Ma, R. Kenny Jones, Jiajun Wu, Maneesh Agrawala

Self-consistency has proven to be an effective technique for improving LLM performance on natural language reasoning tasks in a lightweight, unsupervised manner. In this work, we study how to adapt self-consistency to visual domains. Specifically, we consider the generation and verification of LLM-produced motion graphics trajectories. Given a prompt (e.g.,

William Luke Matthewson

We describe here, for the first time, a lower bound on the total number of fundamental constants required for a mathematical description of our physical universe to be complete. The answer is shown to be one. The formal arithmetized meta-mathematical proof of this is left to the reader.

Ryan McCulloch

In this note we provide a negative answer to the question: ``Is it true that for every positive rational number $r$ there exists a finite abelian group $G$ such that $|\mathrm{Aut}(G)|/|G| = r$?". We show that if $r = a/b$ is a rational number (with $a$ and $b$ coprime integers) so that $r = |\mathrm{Aut}(G)|/|G|$ for a finite abelian group $G$, then $b$ is

Soumyadip Hazra, Sraboni Dey, Arijit Kayal, Narendra Shah

Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correlation between such spatially non-uniform strain and modifications to the local electronic structure remains challenging. Here, density functional theory is com

Haoran Zhou, Gim Hee Lee

Realistic reconstruction of dynamic 4D scenes from monocular videos is essential for understanding the physical world. Despite recent progress in neural rendering, existing methods often struggle to recover accurate 3D geometry and temporally consistent motion in complex environments. To address these challenges, we propose MotionScale, a 4D Gaussian Splatti

Yaning Zhang, Linlin Shen, Zitong Yu, Chunjie Ma

Current deepfake attribution or deepfake detection works tend to exhibit poor generalization to novel generative methods due to the limited exploration in visual modalities alone. They tend to assess the attribution or detection performance of models on unseen advanced generators, coarsely, and fail to consider the synergy of the two tasks. To this end, we p

Faizan Shafi, Rahul Jashvantbhai Pandya, Christo Kurisummoottil Thomas, Sridhar Iyer

In this work, a self-attention based conditional generative adversarial network (SA-cGAN) framework for the sixth generation (6G) semantic communication system is proposed, explicitly designed to balance the trade-off between distortion criticality and information representability under varying channel conditions. The proposed SA-cGAN model continuously lear

Huan Zhang, Wei Cheng, Wei Hu

Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly external resources such as powerful teacher models or reliable test units. However, in real-world scenarios, it is much harder to obtain reference solutions and test oracles than proble

Guozhi Qiu, Zhiwei Chen, Zixu Li, Qinlei Huang

Composed Image Retrieval (CIR) uses a reference image and a modification text as a query to retrieve a target image satisfying the requirement of ``modifying the reference image according to the text instructions''. However, existing CIR methods face two limitations: (1) frequency bias leading to ``Rare Sample Neglect'', and (2) susceptibility of similarity

Fang-Yu Hong

A fundamental obstacle to scalable solid-state quantum networks is the lack of a universal interface providing strong light-matter coupling, deterministic nonreciprocal photon routing, and efficient extraction. Here we propose a plasmonic platform overcoming these challenges using a Tomonaga-Luttinger liquid (TLL) in a single-walled carbon nanotube (SWCNT) m

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian, Mohsen Amini Salehi

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the en

Atharva Naik, Shounok Kar, Varnika Sharma, Ashwin Rajadesingan

Social and personal decisions in relational domains such as matchmaking are deeply entwined with cultural norms and historical hierarchies, and can potentially be shaped by algorithmic and AI-mediated assessments of compatibility, acceptance, and stability. In South Asian contexts, caste remains a central aspect of marital decision-making, yet little is know

Sebastian Grieninger, Dmitri E. Kharzeev, Eliana Marroquin

We compute the entanglement entropy and the entanglement spectrum of the vacuum state in the massive Schwinger model at a finite $\theta$ angle. The $\theta$ term is implemented through a chirally rotated lattice Hamiltonian that preserves the periodicity in $\theta$ already at the operator level and maintains the correct massless limit without $\theta$-depe

Atif Ghafoor, Santeri Neuvonen, Thinh Tran, Oscar Moreno Segura

Deterministic control of excitonic properties is key to advancing nanoscale optoelectronic and quantum technologies and to understanding diverse physical, optical, chemical, and biological phenomena. At the molecular scale, these properties can be tuned through chemical modification, local-environment influence or charge-state manipulation. Yet, direct contr

Jianjun Xiao, Cixiao Wang

Connectivist MOOCs (cMOOCs) rely on learner-driven interaction, yet their intentionally light facilitation makes it difficult to design generative AI participation that is both scalable and educationally productive. This design-based research study examined how human-AI interaction can be designed for discussion facilitation through a collaborative AI-in-the

Shiva Shakeri, Péter Baranyi, Mehran Mesbahi

We propose the Polytopic Receding-Horizon Policy Gradient (P-RHPG) algorithm for synthesizing Parallel Distributed Compensation (PDC) controllers via Tensor Product (TP) model transformation. Standard LMI-based PDC synthesis grows increasingly conservative as model fidelity improves; P-RHPG instead solves a finite-horizon integrated cost via backward-stage d

Xiaolei Chu, Guanren Zhou, Marco Broccardo, Didier Sornette

Large-scale hazards affect societies not only through direct physical impacts but also through emotions that spread across populations. Fueled by social amplification and networked communication, collective emotions often diverge markedly from underlying physical threats, pressuring policymakers toward suboptimal decisions that erode long-term societal resil

Amirreza Rouhi, Parikshit Sakurikar, Satya Sai Reddy, Narsimha Menga

A critical gap exists between the general-purpose visual understanding of state-of-the-art physical AI models and the specialized perceptual demands of structured real-world deployment environments. We present PRISM, a 270K-sample multi-view video supervised fine-tuning (SFT) corpus for embodied vision-language-models (VLMs) in real-world retail environments

Tanay Wakhare

Let $\lambda_1(G)\ge \lambda_2(G)\ge \cdots \ge \lambda_n(G)$ denote the adjacency eigenvalues of a graph $G$ of order $n$. We prove that for every $k\geq 2$ and every graph $G$ on $n\geq k$ vertices, $$ \lambda_k(G)\le \frac{\lambda_{\mathbb{R}}(k-1)}{2(k-1)}\,n-1, $$ where $$ \lambda_{\mathbb{R}}(r)=\sup_{N\ge r}\frac1N \max_{Q\in \mathcal P_r(N)}\sum_{i,j

Roy A. Lacey

A recent Letter~\cite{Borsanyi:2025dyp} employs lattice QCD calculations of the equation of state, combined with entropy-density contour analysis, to place a lower bound of $\mu_B \gtrsim 450$~MeV on the location of the QCD critical endpoint (CEP). While the underlying lattice calculations represent an important advance in precision and systematic control, t

Henry Chan, Daniel Cheng, Lior Gishboliner, Xiangyu Li

We study the minimum degree threshold $\delta_{r,q}$ guaranteeing the existence of $K_r$-tilings of high discrepancy in any $q$-edge-coloring. Balogh, Csaba, Pluh\'ar and Treglown handled the 2-color case, proving that $\delta_{r,2} = \frac{r}{r+1}$ for all $r \geq 3$. Here we determine $\delta_{r,q}$ for all $q$ large enough, namely $q \geq \binom{r}{2}$. F

Lu Zhang, Jinchuan Zeng, Hongxia Wang, Hui Zhang

Recently, the randomized sparse Kaczmarz method has been accelerated by designing heavy ball momentum adaptively via a minimal-error principle. In this paper, we develop a new adaptive momentum method based on the minimal dual function principle to go beyond the exact measurement restriction of the minimal-error principle. Moreover, by integrating the new ad

Huipeng Gu, Mingchao Cai, Jingzhi Li, Yu Jiang

This paper introduces a unified model for thermo-poroelasticity and multiple-network poroelasticity, reformulated into a total-pressure-based system. We first establish the well-posedness of the problem via a Galerkin-based argument and subsequently introduce a robust space-time finite element approximation. To efficiently solve the fully coupled system, we

Zhenxuan Li, Meng Huang

The low-rank matrix recovery problem seeks to reconstruct an unknown $n_1 \times n_2$ rank-$r$ matrix from $m$ linear measurements, where $m\ll n_1n_2$. This problem has been extensively studied over the past few decades, leading to a variety of algorithms with solid theoretical guarantees. Among these, gradient descent based non-convex methods have become p

Mikhail Kovalev, Alexey Kniazev, Oleg Malkov

We confirmed four spectroscopic binary candidates using new observations obtained with SALT. Three SB2 systems (HD 20784, HD 43519A, HD 62153A) exhibit circular orbits with periods shorter than 10 days, whereas one hierarchical triple system (HD 56024) contains a close binary with an inner eccentric orbit with a period of approximately 14 days, composed of n

Shiori Nakamura, Masato Kikuchi, Tadachika Ozono

Point of purchase (POP) materials can be created to assist non-experts by combining large language models (LLMs) with human insight. Persuasive POP texts require both customer understanding and expressive writing skills. However, LLM-generated texts often lack creative diversity, while human users may have limited experience in marketing and content creation

Soomin Park, Eunseong Lee, Kwang Bin Lee, Sung-Hee Lee

We present MaskAdapt, a framework for flexible motion adaptation in physics-based humanoid control. The framework follows a two-stage residual learning paradigm. In the first stage, we train a mask-invariant base policy using stochastic body-part masking and a regularization term that enforces consistent action distributions across masking conditions. This y

Wenyang Chen, Zhanxuan Hu, Yaping Zhang, Hailong Ning

Training-free open-vocabulary remote sensing segmentation (OVRSS), empowered by vision-language models, has emerged as a promising paradigm for achieving category-agnostic semantic understanding in remote sensing imagery. Existing approaches mainly focus on enhancing feature representations or mitigating modality discrepancies to improve patch-level predicti

Puspita Majumdar, Surbhi Mittal, Saheb Chhabra, Mayank Vatsa

The problem of bias persists in the deep learning community as models continue to provide disparate performance across different demographic subgroups. Therefore, several algorithms have been proposed to improve the fairness of deep models. However, a majority of these algorithms utilize the protected attribute information for bias mitigation, which severely

Zigeng Ding, Fan Lin, Xinyang Wang

In this study, we investigate the phase structure of magnetized QCD matter by determining the field-dependent parameters of the Nambu-Jona-Lasinio (NJL) model through a physics-informed machine learning framework. Specifically, we focus on extracting the optimal functional forms for the running coupling constant $G(eB)$ and the quark anomalous magnetic momen