April 2026 arXiv papers — page 164

Zhenshan Tan, Chenhan Lu, Yuxiang Huang, Ziwen He

Image Manipulation Localization (IML) aims to identify edited regions in an image. However, with the increasing use of modern image editing and generative models, many manipulations no longer exhibit obvious low-level artifacts. Instead, they often involve subtle but meaning-altering edits to an object's attributes, state, or relationships while remaining hi

Wang-Chen Xue, Wen-Jun Tan, Yu-Xiang Huang, Xiao-Bo Li

SGR J1935+2154 is the unique magnetar so far from which fast radio bursts have been detected. In October 2022, it resumed its burst activity, and we implemented a dedicated target-of-opportunity (ToO) observation on it from Oct. 13th to Nov. 1st, 2022 (about 940 ks in total) with \textit{Insight}-HXMT, while the KM40m radio telescope observed this source for

Sogand Beirami, Zahra Esmaeilzadeh, Ahmed Gomaa, Pluvio Stephan

Background: Manual delineation of target volumes in head and neck cancer (HNC) remains a significant bottleneck in radiotherapy planning, characterized by high inter-observer variability and time consumption. This study evaluates the integration of a Volume-Aware (VA) Dice loss function into a self-configuring deep learning framework to enhance the auto-segm

Henrik Johansson, Qianli Xing, Nathaniel Taylor, Xiongfei Wang

Grid-forming (GFM) inverters are expected in future inverter-dominated grids. In such grids, time-domain protection schemes, for example those based on instantaneous incremental quantities (IQs), are being advocated as potential solutions to the challenges faced by traditional phasor-based protection schemes, due to their ability to process nonlinear data. H

Wen-Tao Xu, Frank Pollmann, Michael Knap

The concept of gapped symmetry-protected topological (SPT) states has been generalized to gapless SPT (gSPT) states. Similar to gapped SPT states, gSPT states in one dimension exhibit universal degeneracies in their entanglement spectra. The entanglement spectra of gSPT states are further described by boundary conformal field theories, whose systematic predi

Longteng Jiang, DanDan Zheng, Qianqian Qiao, Heng Huang

The rapid advancement of AIGC-based video generation has underscored the critical need for comprehensive evaluation frameworks that go beyond traditional generation quality metrics to encompass aesthetic appeal. However, existing benchmarks remain largely focused on technical fidelity, leaving a significant gap in holistic assessment-particularly with respec

Congying Xu, Hengcheng Zhu, Songqiang Chen, Jiarong Wu

Metamorphic testing (MT) is a widely recognized technique for alleviating the oracle problem in software testing. However, its adoption is hindered by the difficulty of constructing effective metamorphic relations (MRs), which often require domain-specific or hard-to-obtain knowledge. In this work, we propose a novel approach that leverages the functional co

Dongli Wu, Jingyu Hu, Ka-Hei Hui, Xiaobao Wei

Existing single-image 3D indoor scene generators often produce results that look visually plausible but fail to obey real-world physics, limiting their reliability in robotics, embodied AI, and design. To examine this gap, we introduce a unified Physics Evaluator that measures four main aspects: geometric priors, contact, stability, and deployability, which

Zijin Zhou, Songan Zhang

Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Traffic Accident Detection (TAD) and Traffic Accident Understanding (TAU). However, existing studies mainly focus on describing and interpreting accident videos, leaving room for deeper causal reasoning and integration of legal knowledge. Traffic Accident Responsibility Allocation

Bernard Muller, Antonio Armando Ortiz Barrañón, LaVonne Roberts

Dysarthric speech severity assessment typically requires trained clinicians or supervised models built from labelled pathological speech, limiting scalability across languages and clinical settings. We present a training-free method that quantifies dysarthria severity by measuring degradation in phonological feature subspaces within frozen HuBERT representat

Ning Sun, Pengfei Zhang

The realization of unitary designs is of fundamental interest in quantum science and typically requires the ability to implement structured quantum circuits. Recent developments have explored the possibility of generating unitary designs using only a small number of quantum quenches, in which the evolution during each interval is governed by a static Hamilto

Salih Kibaroğlu

We explore black hole solutions in the context of Born-Infeld-f(R) gravity, a modified gravitational framework that extends both Born-Infeld and f(R) theories. By adopting a static, spherically symmetric spacetime ansatz, we derive an exact black hole solution and investigate its geometrical structure. We proceed to analyze the thermodynamic properties of th

Olof Nebrin

In the standard model of cosmology ($\Lambda$CDM) the first stars, star clusters, and galaxies are expected to have formed in low-mass dark matter halos at high redshifts ($z \sim 6 - 30$). Attempts to predict the properties and abundances of these objects have mainly relied on numerically expensive cosmological simulations, which often lack the sub-parsec r

Dajun Liu, Jiaxuan Feng, Hanpeng Gao

Let $\Gamma = \Lambda[M]$ be the one-point extension of an algebra $\Lambda$ by a $\Lambda$-module $M$. We establish a method to lift projectively Wakamatsu tilting (PWT) modules from $\mathrm{mod}\,\Lambda$ to $\mathrm{mod}\,\Gamma$ by adding the new projective module, and prove that this lifting process perfectly preserves mutation relations under certain

Francesco Carlucci, Giovanni Pollo, Xiaying Wang, Massimo Poncino

Photoplethysmography (PPG)-based blood pressure (BP) estimation is a challenging task, particularly on resource-constrained wearable devices. However, fully on-board processing is desirable to ensure user data confidentiality. Recent deep neural networks (DNNs) have achieved high BP estimation accuracy by reconstructing BP waveforms or directly regressing BP

Nojod M. Alotaibi, Areej M. Alhothali

Major depressive disorder (MDD) is a prevalent mental disorder associated with complex neurobiological changes that cannot be fully captured using a single imaging modality. The use of multimodal magnetic resonance imaging (MRI) provides a more comprehensive understanding of brain changes by combining structural and functional data. Despite this, the effecti

Guglielmo Fucci, Mateusz Piorkowski, Jonathan Stanfill

We use the theory of entire functions of finite order to prove a universal spectral dependence of the blowup/decay rate of solutions of the Sturm-Liouville eigenvalue equation for problems with Schatten $p$-class resolvents. The general form of the asymptotics turns out to depend exclusively on the largest integer $\mathfrak{p}$ such that the underlying reso

Zehua Cheng, Wei Dai, Jiahao Sun, Thomas Lukasiewicz

The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from hallucinations, logical inconsistencies, and mode collapse when tasked with structured generation. Existing approaches, such as prompting or retrieval-augmented generation, lack the mechanisms to balance linguist

Bohan Li, Shengmin Li, Xinyu Shi, Enyi Yao

Graph Convolutional Networks (GCNs) are widely adopted for tasks involving relational or graph-structured data and can be formulated as two-stage sparse-dense matrix multiplication (SpMM) during inference. However, existing accelerators often struggle with the irregular workloads induced by power-law node degree distributions. In this work, we propose FlexVe

Xining Ge, Gengjia Chang, Weijun Yuan, Zhan Li

Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours, scene layout, and radiometric stability. Unlike visible-image super-resolution, thermal imagery is weakly textured and more sensitive to unstable local sharpening, which makes complementary local and global

Zahra Manzoor, Oded Schiller, Yonatan Plotnik, Mordechai Segev

Bound states in the continuum (BICs) are spatially localized eigenmodes that remain perfectly confined even though their energies reside within a continuum of radiating modes. BICs were predicted in 1929, but their experimental realization awaited more than 8 decades. Following their experimental observation, BICs were explored in a variety of wave systems,

Kai-Yuan Guo, Jiang Wang, Renjie Zhao, Tianyi Wang

Large Language Models (LLMs) have become a key foundation for enabling personalized smart home experiences. While existing studies have explored how smart home assistants understand user queries to control devices in real time, their ability to perform memory-driven device control remains challenging from both evaluation and methodological perspectives. In t

Ting-Chen Hsu, Wenran Chen, Jiangxu Lin, Fei Qin

This study examines how large language model-driven non-player characters (LLM-NPCs) affect players' cognitive load and gaming experience, with a particular focus on the underlying psychological mechanisms, differences across task scenarios, and the role of individual traits. Conducting a randomized between-subject experiment (N=130) in a self-developed

Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos, Kostas Daniilidis

Monocular head pose estimation is traditionally formulated as direct regression from a single image to an absolute pose. This paradigm forces the network to implicitly internalize a dataset-specific canonical reference frame. In this work, we argue that predicting the relative rigid transformation between two observed head configurations is a fundamentally e

Stephen W. Lovesey

Occhialini et al. (arXiv:2510.13767; DOI: 10.1103/yr5q-1v1s) add results to several recent experimental studies of bulk magnetism in the rutile compound RuO_2. It is of interest as a candidate altermagnet. The cited publication contains several serious errors. Notably, scattering amplitudes used to interpret measurements accomplished with resonant x-ray Brag

Mengting Hu, Jiyong Li, Bin Wang

This paper introduces a novel second-order splitting scheme for charged-particle dynamics in strong magnetic fields characterized by the maximal ordering. The proposed scheme is explicit and symmetric, which respectively ensure the efficiency of the algorithm and its long-term near-conservation of energy. We rigorously prove that the scheme achieves improved

Ruibin Li, Tao Yang, Fangzhou Ai, Tianhe Wu

Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window attention (SWA). However, SWA inevitably loses distant history during long video generation, and its computational overhead remains a critical challenge to real-time deployment. In this work, we propose Hybrid F

Jiang Li, Tian Lan, Shanshan Wang, Dongxing Zhang

The rapid development of large language models (LLMs) has extended text generation tasks into the literary domain. However, AI-generated literary creations has raised increasingly prominent issues of creative authenticity and ethics in literary world, making the detection of LLM-generated literary texts essential and urgent. While previous works have made si

Junying Guo, Yanjun Liu, Ziyi Wu, Di Xiao

By a result of Noritzsch, a finite solvable group whose non-linear character degrees have the same set of prime divisors is meta-abelian. In this note we investigate finite non-solvable groups whose non-linear character degrees have the same number of different prime divisors, and show that up to an abelian direct factor, such groups are exactly $L_2(4), L_2

Prasad Nimantha Madusanka Ukwatta Hewage, Midhun Chakkravarthy, Ruvan Kumara Abeysekara

Variational quantum circuits (VQCs) solving partial differential equations (PDEs) on near-term quantum hardware face a critical challenge: hardware noise degrades solution fidelity and disrupts convergence. We present a systematic study of three noise channels; depolarizing, amplitude damping, and bit-flip on VQCs constrained by PDE residual loss functions f

Ténéman Keita, Renaud Belmont, Thierry Stolarczyk

The Intergalactic Magnetic Field (IGMF), permeating cosmic voids, is thought to be a relic of primordial magnetic fields generated in the early Universe and that gave rise to all astrophysical magnetic fields. While it has escaped direct detection, lower limits on its intensity can be derived by characterising the time-delayed secondary emission initiated wh

Dongjie Huo, Haoyun Liu, Guoqing Liu, Dekang Qi

Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environments. Although Vision-Language-Action (VLA) models provide strong perception and intuitive responses, their open-loop nature limits long-horizon performance. Agents incorporating System 2 cognitive mechanisms i

Lorenzo Ruotolo, Giovanni Pollo, Mohamed Amine Hamdi, Matteo Risso

The growing complexity of cyber-physical systems (CPSs) calls for early prototyping tools that combine accuracy, speed, and usability. Virtual Platforms (VPs) provide fast functional simulation, but hybrid co-emulation solutions, in which key digital components are deployed on FPGA, become necessary when accurate timing modelling is required and RTL simulati

Yuri Cacchiò

We investigate the emergence of finite-amplitude non-zonal flows on the sphere $\mathbb{S}^2$ arising from stationary solutions to the 2D Euler equations. By restricting the Laplace-Beltrami eigenspace to the invariant subspace of the tetrahedral symmetry group $\mathbf{T}$, we bypass the $(2l+1)$-dimensional kernel degeneracy, obtaining a scalar Liapunov-Sc

Han Liu, Haotian Gao, Xiaotong Zhang, Changya Li

Large language models (LLMs) have shown remarkable performance in various domains, but they are constrained by massive computational and storage costs. Quantization, an effective technique for compressing models to fit resource-limited devices while preserving generative quality, encompasses two primary methods: quantization aware training (QAT) and post-tra

Nicholas Rummel, Tyler Jensen, Stephen Becker, Eduardo Corona

Direct numerical simulation of dense rigid body suspensions poses significant computational challenges. A popular approach to resolve collisions necessitates solving a linear complementary problem (LCP) per time step. Each matrix vector product (MVP) inside the LCP requires solving an expensive partial differential equation. In this work, we show the LCP can

Chen-Yen Lin, Susan Halabi, Taehwa Choi

Time-to-event endpoints are frequently used as outcomes in oncology and other disease areas where the outcome of interest may not be observed within a predetermined period. Although many analytical methods address the challenges of censoring in outcomes, limited research has focused on censored covariates. Conventional methods such as the complete case (CC)

Qihang Wu

We present EL-DRUIN, an ontological reasoning system for geopolitical intelligence analysis that combines formal ontology, finite semigroup algebra, and Lie algebra approximation to forecast long-run relationship trajectories. Current LLM-based political analysis systems operate as summarisation engines, producing outputs bounded by textual pattern matching.

Zhuoming Han, Tianmeng Zhang, Chao Liu, Chenxiaoji Ling

The distinction between stars and galaxies is a fundamental problem in the field of celestial classification. This issue has become challenging for these ongoing and upcoming digital surveys, which will produce terabytes and even petabytes of astronomical data. While deep learning offers a powerful solution for star-galaxy classification in large-scale datas

Kanggeon Lee, Soochahn Lee, Kyoung Mu Lee

We propose a robust alignment technique for Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which are challenging to align due to differences in scale, appearance, and the scarcity of distinctive features. Our method, termed Particle Diffusion Matching (PDM), performs alignment through an iterative Random Walk Correspondence Search (

Kavinda Athapaththu, Shiwei Chen, Yuan Fang, Sanchali Mitra

The past few years have witnessed vibrant efforts in discovering new two-dimensional (2D) semiconductor materials from both academia and the industry, due to their promising potential in resolving the severe performance deterioration of traditional semiconductors resulting from condensed silicon thickness. However, existing methods (e.g., Density Functional

Kanggeon Lee, Su Jeong Song, Soochahn Lee, Kyoung Mu Lee

Objective: The study aims to address the challenge of aligning Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which is difficult due to their substantial differences in viewing range and the amorphous appearance of the retina. Currently, no specialized method exists for this task, and existing image alignment techniques lack accurac

Xin An, Miranda Chang, Hao Cao, Vassilis Angelopoulos

Ion pickup at the outer planets' active moons is a fundamental plasma process in which newly ionized particles from moon exospheres interact with the ambient corotating plasma and are accelerated to match the background flow. Spacecraft observations have revealed intense electromagnetic wave activity commonly attributed to this pickup process. Here we invest

Franco Bagnoli, Luca Mencarelli

We investigate Boolean, totalistic cellular automata with a majority or frustrated majority vote rule, and an interaction range of variable span. These two models show a behavior which differs from the mean-field one. The majority vote model is characterized by the presence of absorbing states, and there is a related bifurcation according to the initial dens

Kanggeon Lee, Soochahn Lee, Kyoung Mu Lee

Real-world image pairs often exhibit both severe degradations and large viewpoint changes, making image restoration and geometric matching mutually interfering tasks when treated independently. In this work, we propose MatRes, a zero-shot test-time adaptation framework that jointly improves restoration quality and correspondence estimation using only a singl

Dawn Archey, Julian Buck, Javad Mohammadkarimi, N. Christopher Phillips

We establish comparison and divisibility properties for crossed product C*-algebras arising from automorphisms of algebras C (X, D) which lie over minimal homeomorphisms, from actions of compact groups which have finite Rokhlin dimension with commuting towers, and from actions of compact groups which have the restricted tracial Rokhlin property with comparis

Chao Xue, Yao Wang, Mengqiao Liu, Di Liang

Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent failure mode: even after convergence, models often fail to correctly reproduce a subset of their own supervised training data. We refer to this behavior as the Incomplete Learning Phenomenon(ILP). This paper pr

Saniah Kayenat Chowdhury, Muhammad E. H. Chowdhury

Student engagement is crucial for improving learning outcomes in group activities. Highly engaged students perform better both individually and contribute to overall group success. However, most existing automated engagement recognition methods are designed for online classrooms or estimate engagement at the individual level. Addressing this gap, we propose

Franco Bagnoli, Sara Dridi, Bassem Sellami, Amira Mouakher

In mathematics and engineering, control theory is concerned with the analysis of dynamical systems through the application of suitable control inputs. One of the prominent problems in control theory is controllability which concerns the ability to determine whether there exists a control input that can steer a dynamical system from an initial state to a desi

Shengjie Gong, Wenjie Peng, Hongyuan Chen, Gangyu Zhang

Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing methods typically decode text directly into executable code (e.g., bpy) without explicitly modeling assembly hierarchy or geometric constraints, which enlarges the search space, accumulates local errors, and often

Hongkang Li, Hancheng Min, Rene Vidal

Transformer-based diffusion models have demonstrated remarkable performance at generating high-quality samples. However, our theoretical understanding of the reasons for this success remains limited. For instance, existing models are typically trained by minimizing a denoising objective, which is equivalent to fitting the score function of the training data.

Yujie Li, Jiuniu Wang, Mugen Peng, Guangzuo Li

Long-horizon Flexible Job-Shop Scheduling~(FJSP) presents a formidable combinatorial challenge due to complex, interdependent decisions spanning extended time horizons. While learning-based Rolling Horizon Optimization~(RHO) has emerged as a promising paradigm to accelerate solving by identifying and fixing invariant operations, its effectiveness is hindered

Chao Xue, Yao Wang, Mengqiao Liu, Di Liang

Recent advancements in the Generative Reward Model (GRM) have demonstrated its potential to enhance the reasoning abilities of LLMs through Chain-of-Thought (CoT) prompting. Despite these gains, existing implementations of GRM suffer from two critical limitations. First, CoT prompting is applied indiscriminately to all inputs regardless of their inherent com

Yiming Huang, Zhenbo Shi, Shuzheng Gao, Cuiyun Gao

Reinforcement Learning with Verifiable Rewards (RLVR) is an essential paradigm that enhances the reasoning capabilities of Large Language Models (LLMs). However, existing methods typically rely on static policy optimization schemes that misalign with the model's evolving reasoning capabilities. To address this issue, we propose Adaptive Power-Mean Policy Opt

Yiming Huang, Zhenbo Shi, Xin-Cheng Wen, Jichuan Zeng

Unsupervised reinforcement learning (RL) has emerged as a promising paradigm for enabling self-improvement in large language models (LLMs). However, existing unsupervised RL-based methods often lack the capacity to adapt to the model's evolving reasoning capabilities during training. Therefore, these methods can misdirect policy optimization in the absence o

Yebo Wu, Han Jin, Zhijiang Guo, Li Li

Multimodal Large Language Models (MLLMs) have demonstrated remarkable reasoning capabilities yet continue to suffer from hallucination, where generated text contradicts visual content. In this paper, we introduce Dual-Anchor Introspective Decoding (DaID), a novel contrastive decoding framework that dynamically calibrates each token generation by mining the m

L. Niedermeier, J. L. Krichmar

Microcontroller units (MCU), which have an order of magnitude lower Size, Weight and Power (SWaP) than standard computers, makes them suitable for applications at the edge. Neuromorphic computing, which can realize low SWaP, relies on Spiking Neural Networks (SNNs). Until now, software based simulations of SNNs required GPU-based workstations, application cl

Shuhei Yoshida, Atsushi Fukumoto, Manabu Yamamoto

We propose a novel holographic recording technique to improve the recording speed for holographic data storage (HDS). In this technique, holograms are recorded by scanning a digital micromirror device (DMD) that displays a data page with a focused, power density-increased line beam, while synchronously shifting the recording medium and a spherical reference

Zexi Fan, Bowen Li, Jianfeng Lu

In this note, we consider the underdamped Langevin dynamics with invariant measure $μ(\mathrm{d}x\,\mathrm{d}v) \propto e^{-U(x)-|v|^2/2}\,\mathrm{d}x\,\mathrm{d}v$. Assume that the position marginal $μ_x(\mathrm{d}x)\propto e^{-U(x)}\,\mathrm{d}x$ satisfies a Poincaré inequality with constant $m>0$, and that $\nabla^2 U\ge -K\,\mathrm{Id}$ for some $K\ge 0$

Huiwen Xu, Hanxiang Wu, Chang Li, Fei Pang

Two-dimensional (2D) SnSe is an emerging 2D material exhibiting intriguing properties such as ferroelectricity and nonlinear optical response. Here, high-quality single-crystalline SnSe nanosheets were synthesized via NaCl-assisted chemical vapor deposition (CVD) method. The addition of NaCl was found to significantly increase the surface coverage of the nan

Franco Bagnoli, Bassem Sellami, Amira Mouakher, Samira El Yacoubi

In this exploratory paper we introduce the problem of cognitive agents that learn how to modify their environment according to local sensing to reach a global goal. We concentrate on discrete dynamics (cellular automata) on a two-dimensional system. We show that agents may learn how to approximate their goal when the environment is passive, while this task b

Chi-Yuan Hsiao, Ke-Han Lu, Yu-Kuan Fu, Guan-Ting Lin

End-to-end full-duplex Speech Language Models (SLMs) require precise turn-taking for natural interaction. However, optimizing temporal dynamics via standard raw-token reinforcement learning (RL) degrades semantic quality, causing severe generative collapse and repetition. We propose ASPIRin, an interactivity-optimized RL framework that explicitly decouples w

Armin Gerami, Seyedehanita Madani, Ramani Duraiswami

Multimodal Transformers serve as the backbone for state-of-the-art vision-language models, yet their quadratic attention complexity remains a critical barrier to scalability. In this work, we investigate the viability of Linear Attention (LA) as a high-efficiency alternative within multimodal frameworks. By integrating LA, we reduce the computational overhea

Saad Mankarious, Nour Zeid, Iyad Ait Hou, Rebecca Hwa

Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift attention to \emph{intergroup} behavior, examining how two prominent neurodivergent communities, ADHD and autism, adjust their language when engaging with each other on Reddit. Grounded in Communication Accom

Suraj Kumar Behera, S. A. Kadam, Pratik P. Ray, B. Mishra

The $f(T)$ gravity is one of the extensions of teleparallel equivalent of general relativity, in which more general functions of the torsion scalar $T$ can be described. With the proposed functional form of $f(T) = \alpha T - \beta u^{-n} + \gamma u^m$, where $u = (-T/6)$, we have analyzed the cosmological parameters using dynamical system analysis and cosmo

Tuowei Wang, He Zhou, Chengru Song, Qiushi Li

Large vision-language models (VLMs) are enabling interactive video reasoning, giving rise to streaming long-video understanding. In this setting, frames arrive continuously, while the system preserves long-term context and generates responses under strict latency constraints. A central challenge is KVCache management: as video streams grow, KVCache expands r

Akshay Thirugnanam, Koushil Sreenath

In this paper, we discuss an efficient algorithm for computing the growth distance between two compact convex sets with representable support functions. The growth distance between two sets is the minimum scaling factor such that the sets intersect when scaled about some center points. Unlike the minimum distance between sets, the growth distance provides a

Tianyi Zhang, Wenhan Cao, Chang Liu, Yao Lyu

Accurate state estimation for robotic systems evolving on Lie group manifolds, such as legged robots, is a prerequisite for achieving agile control. However, this task is challenged by nonlinear observation models defined on curved manifolds, where existing filters rely on local linearization in the tangent space to handle such nonlinearity, leading to accum

Xunpei Sun, Wenwei Lin, Yi Chang, Gang Chen

Unsupervised optical flow methods typically lack reliable uncertainty estimation, limiting their robustness and interpretability. We propose U$^{2}$Flow, the first recurrent unsupervised framework that jointly estimates optical flow and per-pixel uncertainty. The core innovation is a decoupled learning strategy that derives uncertainty supervision from augme

Prakash Suman, Yanzhen Qu

This study addresses a key limitation in deep learning Automatic Modulation Classification (AMC) models, which perform well at high signal-to-noise ratios (SNRs) but degrade under noisy conditions due to conventional feature extraction suppressing both discriminative structure and interference. The goal was to develop a feature-preserving denoising method th

Tianyi Zhang, Wenhan Cao, Shengbo Eben Li

Popular Bayes filters often apply linearization techniques, such as Taylor expansion or stochastic linear regression, to enable the use of the Kalman filter structure, but this can lead to large errors in strongly nonlinear systems. The recently proposed NANO filter addresses this issue by interpreting the prediction and update steps of Bayesian filtering as

Awais Bilal, Kashif Sharif, Liehuang Zhu, Chang Xu

The rapid development and integration of intelligent technologies in the Internet of Vehicles (IoV) have revolutionized transportation systems by enhancing connectivity, automation, and safety. However, the complexity and connectivity of IoV networks also introduce security challenges, including data privacy concerns, cyber threats, and system vulnerabilitie

Jhon Astoquillca, Daniel Valesin

The voter model with anti-voter bonds is a variant of the classical voter model in which the edges of the underlying graph are assigned signs. At each update, a voter chooses a neighbour according to a transition kernel; interactions across a positive edge follow the usual voter dynamics, so that a site adopts the current opinion of its chosen neighbour, whe

Giulio Ciraolo, Pierpaolo Esposito, Xiaoliang Li

Given $N\geq 2$ and $\alpha>-1$, we consider the following weighted Liouville-type equation involving the $N$-Laplacian: \begin{equation*} \left\{ \begin{aligned} -& \Delta_N u = |x|^{N\alpha} e^u \quad \text{ in } \mathbb{R}^N && , \\ & \int_{\mathbb{R}^N} |x|^{N\alpha} e^u \, dx < + \infty\,. &&\end{aligned} \right. \end{equation*} Solutions have been comp

Soumendra Nath Ruz, Asim Ghosh

The analysis of income and wealth inequality is often constrained by the lack of reliable data. In this work, we introduce a proxy-based approach in which sports performance data are used to mimic economic distributions. In particular, the total runs scored by a batter in a single T20 season are treated as an analogue of annual income, while the cumulative r

Diptasikha Das, Kartick Malik

SnTe is a potential thermoelectric material in the mid temperature range. Detailed techniques to enhance the figure of merit by increasing the Power Factor, and reducing thermal conductivity, of SnTe-based TE materials are discussed. The key factors governing the figure of merit of a thermoelectric material are discussed to facilitate the optimization of the

Zhenduo Wang, Yasser Abduallah, Jason T. L. Wang, Haimin Wang

The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensitive and could improve the reaction of thermospheric density

Dongjie Xu, Hao Wu, Weijie Shi, Yue Cui

Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition loops. We find that this degeneration is driven by collapsed attention patterns, where a subset of heads locks onto a narrow suffix of the history, and is further stabilized by inference-time KV cache reuse.

Ken-Ichiro Imura, Kohei Kawabata

The non-Hermitian skin effect is nonreciprocity-induced localization phenomena in which a macroscopic number of eigenstates accumulate anomalously at the boundary, accompanied by the extreme sensitivity to boundary conditions. Here, we develop a geometric characterization of the non-Hermitian skin effect. We demonstrate that the localization length scale ass

Naoya Torii, Shigeru Ida, Ryuki Hyodo

Planetary rings are ubiquitous structure in our Solar System, but their formation mechanisms remain under debate. One of the proposed scenarios is the tidal disruption of a nearby passing body that enters within a planet's Roche limit, producing fragments that are gravitationally captured and finally form the rings. In this study, we investigate the detailed

Sunghun Park, Anton V. Parafilo, Leonid Y. Gorelik, Robert I. Shekhter

Self-sustained oscillators produce stable periodic motion robust to dissipation. Such motion is usually achieved by work fed back into the oscillator, but its performance is often limited by frequency-dependent operation. Here we propose a scheme for self-sustained vibrations without external feedback. We consider a movable Cooper-pair box attached to the fr

Noor Hussein, Anil K. Jain, Karthik Nandakumar

The primary goal of this work is to systematically evaluate the intra-finger variability of synthetic fingerprints (particularly latent prints) generated using a state-of-the-art diffusion model. Specifically, we focus on enhancing the latent style diversity of the generative model by constructing a comprehensive \textit{latent style bank} curated from seven

Duy Le Dinh Anh, Patrick Amadeus Irawan, Tuan Van Vo

Vision--language models (VLMs) have achieved impressive performance on complex multimodal reasoning tasks, yet they still fail on simple grounding skills such as object counting. Existing evaluations mostly assess only final outputs, offering limited insight into where these failures arise inside the model. In this work, we present an empirical study of VLM

Jackson A. Mickley, Waseem Kamleh, Derek B. Leinweber, Finn M. Stokes

Relativistic wavefunctions of nucleon excitations are scrutinised to understand their node structure and the underlying role of local interpolating fields in generating the nucleon spectrum. In addressing quark model perspectives, approximately 4000 propagators are employed on the heaviest PACS-CS ensemble at $m_π\simeq$ 702 MeV. We examine the ground and fo

Junjie Luo, Yuxuan Liu, Wei Ting Chen, Qing Wang

We present a metasurface imaging system capable of simultaneously capturing two images at close range (1-2~cm) and an additional image at long range (about 40~cm) on a shared photosensor. The close-range image pair focuses at 1.4~cm and 2.0~cm, respectively, which forms a focal stack, enabling passive ranging with an accuracy of $\pm$1~mm from 12~mm to 20~mm

Guoqing Cai, Kai Zeng, Shoulin Huang, Ting Ma

Deep Riemannian networks provide a powerful framework for Electroencephalography (EEG) decoding, but their practical applications are severely constrained. Accurately decoding EEG signals requires modeling complex temporal dynamics across multiple rhythms, which results in high-dimensional Riemannian inputs and significant computational costs. To address thi

Noah Palmer, Heather L. Cihak, Daniele Avitabile, Zachary P. Kilpatrick

Persistent neural activity underlying working memory requires sustained synaptic transmission, yet the metabolic and neurotransmitter support provided by astrocyte networks is largely absent from spatially extended neural circuit models. We introduce a coupled astrocyte-neural field model in which synaptic efficacy is regulated by depletion and recovery of a

Fumitaka Iwaki, Miho Fuyama, Hayato Saigo, Tatsuji Takahashi

In this study, we developed a computational implementation for a model of metaphor comprehension based on the theory of indeterminate natural transformation (TINT) proposed by Fuyama et al. We simplified the algorithms implementing the model to be closer to the original theory and verified it through data fitting and simulations. The outputs of the algorithm

Bonmu Ku

This paper reports the first documented instance of a language model achieving a perfect score on an officially disclosed Law School Admission Test (LSAT). Controlled experiments on eight reasoning models show that varying the prompt, shuffling answer choices, and sampling multiple responses have no meaningful effect as drivers of performance. Ablating the t

Raja Solanki

Understanding the late-time cosmic phenomenon of the universe commonly referred to as the dark energy problem, which is one of the prominent tension in the field of theoretical as well as observational cosmology. In this work, we attempt to analyze the nature of the missing fluid of the universe. In order to do so, we employ a poly tropic equation of state c

Chi Zhang, Jingpu Cheng, Zhixian Wang, Ping Liu

While modern generative models such as diffusion-based architectures have enabled impressive creative capabilities, they also raise important safety and ethical risks. These concerns have led to growing interest in concept erasure, the process of removing unwanted concepts from model representations. Existing approaches often achieve strong erasure performan

Mengfan Li, Xuanhua Shi, Yang Deng

Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demonstrate promising performance on standard ToM benchmarks, we observe that they often fail to generalize to complex task-specific scenarios, relying heavily on prompt scaffolding to mimic reasoning. The critical

Gordon Chen, Ziqi Huang, Ziwei Liu

Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events in real-world videos and lack explicit mechanisms to control when semantic concepts appear, how long they persist, and the order in which multiple events occur. Such control is espe

Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Lorenzo Maggi

Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper, we explore the ability of agentic AI to autonomously design wireless communication algorithms. To that end, we implement a dedicated framework that leverages large language models (LLMs) to iteratively genera

Zongwei Wang, Min Gao, Hongzhi Yin, Junliang Yu

Large language model-empowered agentic recommender systems (ARS) reformulate recommendation as a multi-turn interaction between a recommender agent and a user agent, enabling iterative preference elicitation and refinement beyond conventional one-shot prediction. However, existing ARS are mainly optimized in a Reflexion-style paradigm, where past interaction

Hiroshi Iritani

We discuss arithmetic and Hodge-theoretic properties of the isomorphisms appearing in the decomposition theorem for quantum cohomology of blowups. These properties underpin the application to the rationality questions by Katzarkov-Kontsevich-Pantev-Yu.

Jakkapat Seeyangnok, Udomsilp Pinsook

We investigate the structural, electronic, and superconducting properties of a hexagonal BP3 monolayer using first-principles calculations combined with anisotropic Migdal-Eliashberg theory. The optimized structure exhibits a stable, slightly buckled configuration, as confirmed by phonon dispersion analysis and ab initio molecular dynamics simulations. The p

I. P. Fernando, D. Keller

We recast the case for quantum advantage in hadronic physics as an observable-by-observable question rather than a blanket claim about Quantum Chromo-Dynamics (QCD). Focusing on hadronic tomography, we analyze why Compton form factors (CFF), generalized parton distributions (GPDs), Transverse Momentum-dependent Distributions (TMDs), and Generalized Transvers

Shenghe Zheng, Minyu Zhang, Tianhao Liu, Hongzhi Wang

With the growing availability of open-sourced adapters trained on the same diffusion backbone for diverse scenes and objects, combining these pretrained weights enables low-cost customized generation. However, most existing model merging methods are designed for classification or text generation, and when applied to image generation, they suffer from content

Miriam Wanner, Hannah Collison, William Jurayj, Benjamin Van Durme

Weird generalization is a phenomenon in which models fine-tuned on data from a narrow domain (e.g. insecure code) develop surprising traits that manifest even outside that domain (e.g. broad misalignment)-a phenomenon that prior work has highlighted as a critical safety concern. Here, we present an extended replication study of key weird generalization resul