April 2026 arXiv papers — page 131

S. A. Klimin, N. D. Molchanova, N. N. Kuzmin, E. S. Sektarov

We have performed detailed temperature-dependent study of optical f-f transitions of the Yb3+ ions in h-YbMnO3 by means of Fourier-transform spectroscopy. The splitting of the ground Kramers doublet as a function of temperature, D0(T), for the Yb3+ ion at 4b site was determined. The D0(T) function follows the dynamics of the manganese magnetic moment below T

Yi Gui, Jiawan Zhang, Yina Wang, Tianran Ma

Front-end development constitutes a substantial portion of software engineering, yet converting design mockups into production-ready User Interface (UI) code remains tedious and costly. While recent work has explored automating this process with Multimodal Large Language Models (MLLMs), existing approaches typically rely solely on design images. As a result,

Dirk Romeis

To describe the interactions in magnetically soft particle systems either numerical full-field methods or dipole models are used. Whereas the former are computationally challenging, simple dipole interactions are largely underestimating the actual forces when particles get closer. Based on the full 2-body solution, an analytic approximation scheme for many-b

Nikita Misuna, Dmitry Ponomarev, Alexander Solomin

We show that the self-dual classical double copy can be straightforwardly extended to the higher-spin case when formulated in terms of light-cone gauge prepotentials. This allows us to construct a higher-spin extension for any self-dual spacetime that admits a Kerr-Schild form. We also discuss the counterpart of this procedure at the level of Weyl tensors. W

Suraj Prasad, Pinak Mahapatra

Automating the digitization of Piping and Instrumentation Diagrams (P&IDs) into structured process graphs would unlock significant value in plant operations, yet progress is bottlenecked by a fundamental data problem: engineering drawings are proprietary, and the entire community shares a single public benchmark of just 12 annotated images. Prior attempts at

Yu Lei, Minghuan Liu, Abhiram Maddukuri, Zhenyu Jiang

Co-training, which combines limited in-domain real-world data with abundant surrogate data such as simulation or cross-embodiment robot data, is widely used for training generative robot policies. Despite its empirical success, the mechanisms that determine when and why co-training is effective remain poorly understood. We investigate the mechanism of sim-an

Z. M. McIntyre, Abhikbrata Sarkar, Daniel Loss

Spin qubits have emerged as a leading platform for quantum information processing due to their long coherence times, small footprint, and compatibility with the existing semiconductor industry. We first provide an introduction to the different qubit implementations currently being investigated, including single electron-spin qubits, hole-spin qubits, donor q

Karl Bringmann, Danny Hermelin, Tomohiro Koana, Dvir Shabtay

The Lawler-Moore dynamic programming framework is a classical tool in scheduling on parallel machines. It applies when the objective is regular, i.e. monotone in job completion times, and each machine follows a fixed priority order such as Smith's Rule or Jackson's Rule. For the basic objectives $Pm||\sum w_jC_j$, $Pm||L_{\max}$, and $Pm||\sum w_jU_j

Yanchao Li, Mingying Zhong

In the present paper, we study the diffusion limit of the classical solution to the modified Vlasov-Poisson-Boltzmann (mVPB) System with initial data near a global Maxwellian. Based on the spectral analysis, weprove the convergence and establish the convergence rate of the global strong solution to the mVPB system towards the solution to an incompressible Na

Franz Herling, Mireia Torres-Sala, Dorye L. Esteras, Charlotte Evason

Van der Waals (vdW) heterostructures enable tailored electronic and magnetic phases by stacking atomically thin layers with pristine interfaces. Here, we investigate fully 2D Cr2Ge2Te6/WTe2 heterostructures and identify a strong enhancement of ferromagnetism in Cr2Ge2Te6 (CGT). Magnetotransport measurements across multiple devices with WTe2 thicknesses rangi

Pengfei Zhu

Heat transport exhibits distinct regimes ranging from ballistic propagation to diffusive relaxation, traditionally described by disparate theoretical frameworks. Here, we introduce a unified first-order operator formulation in which temperature and heat flux are treated as a coupled state vector, yielding a minimal dynamical closure of heat transport. The re

June Rousseau, Denis Carnier, Thomas Van Strydonck, Steven Keuchel

A key feature in trusted computing is attestation, which allows encapsulated components (enclaves) to prove their identity to (local or remote) distrusting components. Reasoning about software that uses the technique requires tracking how trust evolves after successful attestation. This process is security-critical and non-trivial, but no existing formal ver

Stefan Floerchinger

Response theory describes the reaction of observales to perturbations in external fields. We review this formalism for quantum fiels in isolation that have unitary time evolution. An emphasis is put on consequences of causality and the resulting spectral representations for linear and nonlinear response functions, on functional techniques and generating func

Sicheng Wu, Minghui Liwang, Yangyang Gao, Deqing Wang

In air-ground integrated networks (AGINs), unmanned aerial vehicles (UAVs) provide on-demand edge services to ground vehicles. Realizing this vision requires carefully designed incentives to coordinate interactions among self-interested participants. This is exacerbated by the dynamic nature of AGINs, where spatio-temporal variations introduce significant un

Xinxin Fan, Wenxiong Chen, Quanliang Jing, Chi Lin

Graph adversarial attacks are usually produced from the two perspectives of topology/structure and node feature, both of them represent the paramount characteristics learned by today's deep learning models. Although some defense countermeasures are proposed at present, they fails to disclose the intrinsic reasons why these two aspects necessitate and how the

Xuwen Zhou, Fangxin Liu, Chao Wang, Xiao Zheng

Speculative decoding accelerates autoregressive generation by letting draft tokens bypass full verification, but conventional frameworks suffer from frequent false rejections, particularly when draft models produce semantically correct but lexically divergent outputs. In this paper, we present Calibrated Speculative Decoding (CSD), a training-free framework

Jingjing Qian, Zeyuan He, Chen Shi, Lei Xiao

Coordinating navigation and manipulation with robust performance is essential for embodied AI in complex indoor environments. However, as tasks extend over long horizons, existing methods often struggle due to catastrophic forgetting, spatial inconsistency, and rigid execution. To address these issues, we propose ESCAPE (Episodic Spatial Memory Coupled with

Yuga Hirai, Shota Ikari, Yosuke Ueno, Yasunari Suzuki

The logical S gate implemented via twist defect braiding in the surface code is one of the major sources of overhead in fault-tolerant quantum computing, since an S-gate correction is required in every logical T-gate teleportation. Existing logical S-gate implementations require spacetime volumes of \(2d \times 2d \times d\) or \(2d \times 1.5d \times d\), w

N. J. Adshead, P. F. L. Maxted, A. Hahlin

There are very few M-dwarfs with accurate independent measurements of their mass, radius and effective temperature (T$_{\rm eff}$) that can be used to test stellar models for these low-mass stars. We aim to use high-resolution, near-infrared spectroscopy to measure the mass of M-dwarfs in eclipsing binary systems with solar-type stars and to measure the flux

Xixun Lin, Yang Liu, Yancheng Chen, Yongxuan Wu

The performance of large language model (LLM) agents depends critically on the execution harness, the system layer that orchestrates tool use, context management, and state persistence. Yet this same architectural centrality makes the harness a high-value attack surface: a single compromise at the harness level can cascade through the entire execution pipeli

Oliver Goertsches, Grigory Solomadin

Given a GKM$_3$ action of a torus $K$ on a manifold $M$ with GKM graph $\Gamma$, we show that for any extension of $\Gamma$ to an abstract GKM graph the corresponding restriction map in equivariant graph cohomology is surjective. While the corresponding statement for extensions of actions is well-known, we observe that this graph-theoretical statement is fal

Nana Wang, Pelin Sekercioglu, Dimos V. Dimarogonas

We address the problem of interaction topology identification in open multi-agent systems (OMAS) with dynamic node sets and fast switching interactions. In such systems, new agents join and interactions change rapidly, resulting in intervals with short dwell time and rendering conventional segment-wise estimation and clustering methods unreliable. To overcom

H. S. Behmanush, M. Küçükaslan

In this paper, we introduce the notion of a $\gamma$-density point for Lebesgue-measurable subsets of $\mathbb{R}$, where $\gamma$ is a modulus function, and study its basic measure-theoretic properties. We show that every $\gamma$-density point is a Lebesgue density point, while under Condition~(A) the two notions coincide. Consequently, for such modulus fu

Xuewei Ju, Xiaoting Tong

In this paper, we consider the stochastic reaction-diffusion equation $\mathrm{d}u = (\mathcal{A} u + f(u))\mathrm{d}t + σ(u)\mathrm{d}W$ on a smooth bounded domain $\mathcal{O}$ with homogeneous Dirichlet boundary conditions. We investigate the long-time behavior of solutions with a strongly dissipative drift nonlinearity and superlinear multiplicative nois

YaLin Zhu, Jian-Chao He, Xi Chen

This study numerically investigates two-dimensional Rayleigh-Benard convection subjected to horizontal oscillation of the bottom plate, with Prandtl number Pr=4.3, Rayleigh numbers Ra ranging from 5e6 to 1e8, and oscillation frequencies f between 0.0001 and 0.5. The imposed oscillation breaks the up-down symmetry of the classical system, inducing a strong fr

Seiki Ubukata, Akira Notsu, Katsuhiro Honda

Self-Organizing Maps (SOM) are a classical method for unsupervised learning, vector quantization, and topographic mapping of high-dimensional data. However, existing SOM formulations often involve a trade-off between computational efficiency and a clearly defined optimization objective. Objective-based variants such as Soft Topographic Vector Quantization (S

Ian Drosos, Jo Vermeulen, George Fitzmaurice, Justin Matejka

People frequently use online forums to get help from experts to answer questions about feature-rich software. However, they may have to wait minutes, hours, or even days to receive advice. We investigate the potential to leverage experts to provide quicker help. We collected over 200 questions from online forums for two feature-rich software applications and

Jason Z Wang

We introduce MIRROR, a benchmark comprising eight experiments across four metacognitive levels that evaluates whether large language models can use self-knowledge to make better decisions. We evaluate 16 models from 8 labs across approximately 250,000 evaluation instances using five independent behavioral measurement channels. Core experiments are run across

Ahmet Tuğrul Bayrak, Mustafa Sertaç Türkel, Fatma Nur Korkmaz

Managing natural dialogue timing is a significant challenge for voice-based chatbots. Most current systems usually rely on simple silence detection, which often fails because human speech patterns involve irregular pauses. This causes bots to interrupt users, breaking the conversational flow. This problem is even more severe for languages like Turkish, which

Darij Grinberg

We study $n$-homomorphisms in the sense of Khudaverdian--Voronov, but generalized to maps from arbitrary rings to arbitrary commutative rings. We show that the sum of an $n$-homomorphism and an $m$-homomorphism is an $\left( n+m\right) $-homomorphism, and that the composition of an $n$-homomorphism and an $m$-homomorphism is an $nm$-homomorphism. The proofs

Akira Kawabata, Saku Sugawara

Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. However, most existing methods require costly rubric annotations, limiting scalability. Moreover, we find that rubric generation is vulnerable to a failure of cooperation; low-quality rubrics actively mislead

Masamichi Zaizen, Hiroki Nagakura

Fast neutrino-flavor conversion (FFC) can nontrivially alter neutrino radiation field in core-collapase supernovae (CCSN) and binary neutron-star merger (BNSM) remnants. However, its interplay with global geometry remains poorly understood because microscopic flavor conversion scales are much shorter than global transport scales. We perform global quantum ki

Lina Deschamps, Levin Maier, Tom Stalljohann

This paper develops new links between contact geometry, magnetic dynamics, and symmetry in exact magnetic systems. First, we establish an interpolation property for Killing magnetic systems on contact manifolds under an additional condition. Specifically, we show that the corresponding magnetic geodesic flow interpolates smoothly between the sub-Riemannian g

Jin'an Pang, Guangyin Jing, Xiaoqiang Feng, Kaige Wang

In nonlinear systems, small perturbations are conventionally attributed to negligible nonlinearity, justifying linear approximations. Here, we uncover a notable exception to this paradigm in an electrokinetic (EK) flow. Using a novel dual frequency excitation scheme with two high frequency AC electric fields ($> 10^{5}$ Hz), we efficiently excite flow pertur

Hao-Jie Lin, Tao Zhu, Jing-Fei Zhang, Xin Zhang

We study spontaneous scalarization of a scalar field in the magnetized Reissner--Nordstr\"om spacetime induced by parity-violating and parity-preserving interactions, represented by couplings to the electromagnetic Chern--Simons, gravitational Chern--Simons, and Gauss--Bonnet invariants, respectively. Working in the decoupling limit, we evolve scalar perturb

Alexey Dubinsky

We develop an analytic eikonal description of perturbations for four-dimensional regular black holes in quasi-topological gravity. Using first-order Schutz--Will WKB together with a small-coupling expansion and a large-$\ell$ expansion, we obtain closed quasinormal-mode formulas with explicit dependence on the black-hole parameters $(M,\mu,\nu,\alpha)$. We t

I. D. Kolesnikov, D. A. Maksimov, V. M. Moskvitin, N. Semenova

This study examines the impact of additive and multiplicative noise on both a single leaky integrate-and-fire (LIF) neuron and a trained spiking neural network (SNN). Noise was introduced at different stages of neural processing, including the input current, membrane potential, and output spike generation. The results show that multiplicative noise applied t

Jingwen Zhang, Yuhong Nan, Kaiwen Ning, Mingxi Ye

Smart contracts are a critical component of blockchain systems. Due to the large amount of digital assets carried by smart contracts, their security is of critical importance. Although numerous tools have been developed for detecting smart contract vulnerability, their effectiveness remains limited, particularly due to the high false positives included in th

Amir Hossein Saleknia, Mohammad Sabokrou

In computer vision, a prevailing method for quantifying dataset bias is to train a model to distinguish between datasets. High classification accuracy is then interpreted as evidence of meaningful semantic differences. This approach assumes that standard image augmentations successfully suppress low-level, non-semantic cues, and that any remaining performanc

Aram Ebtekar, Michael K. Cohen

Reinforcement learners can attain high reward through novel unintended strategies. We study a Bayesian mitigation for general environments: we expand the agent's subjective reward range to include a large negative value $-L$, while the true environment's rewards lie in $[0,1]$. After observing consistently high rewards, the Bayesian policy becomes risk-avers

Muhammad Kashif, Hanzalah Mohamed Siraj, Nouhaila Innan, Alberto Marchisio

Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-quantum data encoding, quantum circuit architecture, measurement strategy and shots. In this paper, we present a comprehensive design space ex

Karsten Kruse, Christian Seifert

We study strongly continuous and locally equicontinuous families of operators on sequentially complete Hausdorff locally convex spaces. In case of Saks spaces, we relate the general notions to bi-continuity as well as equitightness. In this way, we recover and also generalise known results for special classes of operator families such as bi-continuous ($C$-)

Yuping Gao, Allan Lo, Songling Shan

Let $k \in \mathbb{N}$ and let $G$ be a simple graph with maximum degree $\Delta$. A $k$-colouring $\varphi$ of $G$ is an assignment of colours from $\{1,2,\ldots,k\}$ to the vertices of $G$. We call $\varphi$ proper if adjacent vertices receive distinct colours, and equitable if the sizes of any two colour classes differ by at most one. The celebrated Hajna

Zhiyong Chen, Shuhang Wu, Yingjie Duan, Xinkang Xu

This paper proposes an improved approach for open-set speaker identification based on pretrained speaker foundation models. Building upon the previous Speaker Reciprocal Points Learning framework (V1), we first introduce an enhanced open-set learning objective by integrating reciprocal points learning with logit normalization (LogitNorm) and incorporating ad

Ugur Demir, Philipp M. Srivastava, Aggelos Katsaggelos, Vicky Kalogera

Binary stellar evolution simulations are computationally expensive. Stellar population synthesis relies on these detailed evolution models at a fundamental level. Producing thousands of such models requires hundreds of CPU hours, but stellar track interpolation provides one approach to significantly reduce this computational cost. Although single-star track

Thomas Hübner

Electricity is typically traded in day-ahead auctions because many power system decisions, such as unit commitment, must be made in advance. However, when wind and solar generators sell power one day ahead, they face uncertainty about their actual production. In current day-ahead auctions, this uncertainty cannot be directly communicated, leading to ineffici

Xiaohua Wang, Muzhao Tian, Yuqi Zeng, Zisu Huang

Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multimodal large language models (MLLMs) toward human-preferred behaviors. However, these approaches introduce a systemic vulnerability: reward hacking, where models exploit imperfections in learned reward signals

Michael Leznik

We introduce Metric-Aware Principal Component Analysis (MAPCA), a unified framework for scale-invariant representation learning based on the generalised eigenproblem max Tr(W^T Sigma W) subject to W^T M W = I, where M is a symmetric positive definite metric matrix. The choice of M determines the representation geometry. The canonical beta-family M(beta) = Si

Nilo Mata-Cervera, Zhenyu Guo, Yijie Shen

Polarization of light can form skyrmionic textures, akin to nonlinear solitons in condensed matter, yet their disparate physical context has motivated extensive debate regarding their stability. Here we show that the topological charge of such structures (skyrmion number) changes when an arbitrarily small perturbation splits coalescent phase singularities. I

Fumikazu Konishi, Yuuki Tsubouchi, Hirofumi Tsuruta

SAKURAONE is a managed high performance computing (HPC) cluster developed and operated by the SAKURA Internet Research Center. It builds on the KOKARYOKU PHY bare metal GPU platform and is optimized for advanced workloads, including large language model (LLM) training. In ISC 2025 TOP500, SAKURAONE is ranked 49th by HPL and is the only top 100 system that us

Xiaoyu Fu, Gengsheng Wang, Huaiqiang Yu, Xiaomin Zhu

We establish an observability inequality from space-time measurable sets for a class of strongly coupled parabolic systems consisting of two equations, where the observation acts on a single-component. The model is motivated by parabolic equations with complex coefficients and serves as a prototypical example of strongly coupled systems. The main difficulty

Qin Zhou, Guoyan Liang, Qianyi Yang, Jingyuan Chen

Recent reinforcement learning (RL) approaches have advanced radiology report generation (RRG), yet two core limitations persist: (1) report-level rewards offer limited evidence-grounded guidance for clinical faithfulness; and (2) current methods lack an explicit self-improving mechanism to align with clinical preference. We introduce clinically aligned Evide

Kan Kuno, Daisuke Moriwaki, Yoshihiro Takenami

In centralized assignment problems, agents may have preferences over joint rather than individual assignments, such as couples in residency matching or siblings in school choice and daycare. Standard preference estimation methods typically ignore such complementarities. This paper develops an empirical framework that explicitly incorporates them. Using data

Luigi Forcella, Xiao Luo, Xiaolong Yang

We investigate standing waves for the energy critical Schr\"odinger system with three waves interaction arising as a model for the Raman amplification in a plasma. Several results are proved: simultaneous existence of stable and unstable standing waves, existence of global solutions, and absence of small data scattering. Our main results show some specific f

Weiming Xu, Tao Yang, Peng Zhang

Binary droplet collisions are ubiquitous in dense sprays. Traditional deterministic models cannot adequately represent transitional and stochastic behaviors of binary droplet collision. To bridge this gap, we developed a probabilistic model by using a machine learning approach, the Light Gradient-Boosting Machine (LightGBM). The model was trained on a compre

Zixuan Chen, Depeng Wang, Hao Lin, Li Luo

We present AVID, the first large-scale benchmark for audio-visual inconsistency understanding in videos. While omni-modal large language models excel at temporally aligned tasks such as captioning and question answering, they struggle to perceive cross-modal conflicts, a fundamental human capability that is critical for trustworthy AI. Existing benchmarks pr

Jiashuo Wang, Jiawen Duan, Jian Wang, Kaitao Song

Reasoning capabilities in large language models (LLMs) have generally advanced significantly. However, it is still challenging for existing reasoning-based LLMs to perform effective decision-making abilities in multi-agent environments, due to the absence of explicit foresight modeling. To this end, strategic reasoning, the most fundamental capability to ant

Song Tan, Alexander Warmuth, Frédéric Schuller, Yuandeng Shen

Solar jets are dynamic collimated plasma flows in the solar atmosphere that play crucial roles in coronal heating and solar wind acceleration. Their complex and diverse morphologies pose significant challenges for developing universal algorithms for automatic identification and extraction, particularly for on-disk jets affected by projection effects and back

Boss Chen, Hanqing Wang

We present Dehaze-then-Splat, a two-stage pipeline for multi-view smoke removal and novel view synthesis developed for Track~2 of the NTIRE 2026 3D Restoration and Reconstruction Challenge. In the first stage, we produce pseudo-clean training images via per-frame generative dehazing using Nano Banana Pro, followed by brightness normalization. In the second s

Xiaoxiang Yu, Rong-Xia Hao, Jianbing Liu, Zhiguo Li

Gross, Mansour, and Tucker [European J. Combin., 95 (2021): 103329] introduced the \emph{partial Petrial polynomial} of a ribbon graph $G$, denoted by $^{\partial}{\varepsilon^{\times}_{G}}(z)$. Beck and Mellor proved, in both orientable and non-orientable cases respectively, that the Euler genus of a bouquet equals the rank of a certain matrix over $\mathbb

Kai-Chun Chen, I-Hsiang Wang

Information velocity (IV) is a recently proposed notion to capture the speed of reliable information dissemination over a large-scale network. It is the speed at which reliable end-to-end communication over $k$ hops can be achieved within $t$ time instances, and is defined formally as the asymptotic ratio $k/t$ as $k$ tends to infinity subject to vanishing e

Ye Tian, Jiaji Ren, Tuo Wu, Wei Liu

High-precision direction-of-arrival (DOA) estimation, as a key sensing capability for 6G-enabled applications such as autonomous driving and extended reality, is increasingly dependent on the effective exploitation of spatial degrees of freedom (DOFs). This paper integrates two frontier DOFs-oriented paradigms and proposes a fluid antenna-enabled hybrid anal

Danish Nazir, Antoine Hanna-Asaad, Lucas Görnhardt, Jan Piewek

Existing multi-view three-dimensional (3D) object detection approaches widely adopt large-scale pre-trained vision transformer (ViT)-based foundation models as backbones, being computationally complex. To address this problem, current state-of-the-art (SOTA) \texttt{ToC3D} for efficient multi-view ViT-based 3D object detection employs ego-motion-based releva

Binghang Li, Jie Jiang, Yukun Luo

The large-scale magnetic field observed at the solar surface is produced by the interior dynamo process. Whether this surface field also provides the dominant seed for the subsequent dynamo cycle, however, remains controversial, with important consequences for the predictive skill of solar dynamo models.We investigate the physical conditions under which this

Jui-Te Huang, Tianshu Huang, Anthony Rowe, Michael Kaess

mmWave radars are robust to darkness and occlusions such as dust and smoke, and can directly constrain ego-velocity from a single frame via Doppler measurements, making them attractive sensors for odometry in visually denied conditions. However, almost all existing radar-inertial odometry systems rely on lossy radar point clouds that are highly sparse, gener

Qi Xia, Peishan Cong, Ziyi Wang, Yujing Sun

Accurately reconstructing human behavior in close-interaction scenarios is crucial for enabling realistic virtual interactions in augmented reality, precise motion analysis in sports, and natural collaborative behavior in human-robot tasks. Reliable reconstruction in these contexts significantly enhances the realism and effectiveness of AI-driven interactive

Alvin K. Y. Li, Peony K. K. Lai, Elwin K. Y. Li, Otto A. Hannuksela

The addition of KAGRA to the global gravitational-wave detector network introduces new baselines and complementary antenna response patterns that can enhance sky localization for compact binary coalescences. We investigate KAGRA's role in the LIGO-Virgo-KAGRA network using a systematic injection study of binary neutron star signals. Sky maps are constructed

Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou

Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass retrieval. We introduce MM-Doc-R1, a novel framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis. To in

Jiabao Gong, Qiang Tu

This article investigates the existence of closed, star-shaped hypersurfaces for a class of Hessian quotient type curvature equations, in which the operator $\frac{\sigma_k}{\sigma_l}(\Lambda)$ arising in these equations can be viewed as a generalization of the classical Hessian quotient operator. By combining a priori estimates with the continuity method, w

Yuichi Yoshida

We study property testing of directed acyclicity in the unidirectional bounded-degree oracle model, where a query to a vertex reveals its outgoing neighbors. We prove that there exist absolute constants $d_0\in\mathbb{N}$ and $\varepsilon>0$ such that for every constant $d\ge d_0$, any one-sided $\varepsilon$-tester for acyclicity on $n$-vertex digraphs of m

Rintaro Matsuki, Shoko Minami, Hisashi Kotegawa, Hisatomo Harima

We report on new results of 9Be nuclear magnetic resonance (NMR) measurements conducted on a single crystal of the heavy fermion superconductor UBe13. Our previous 2007 study [J. Phys. Soc. Jpn. 76 204705 (2007)] determined NMR and electric field gradient (EFG) parameters that successfully reproduced the NMR spectra at low magnetic fields. However, these par

Kazumasa A. Takeuchi, Daiki Nishiguchi

In this perspective article, we discuss bacterial populations as a model system of active matter. It allows for the exploration and characterization of various phases of active matter and brings rich implications for both physics and biology. Specifically, we focus on active gas, active liquid, active glass and active liquid crystal states observed in bacter

Alexandre Muzy

Brain digital twins aim to provide faithful, individualized computational representations of brains as dynamical systems, enabling mechanistic understanding and supporting prediction of clinical interventions. Yet current approaches remain fragmented across data pipelines, model classes, temporal scales, and computing platforms, which prevents the preservati

Jundong Chen, Honglei Zhang, Xiangmou Qu, Haoxuan Li

Cross-market recommendation (CMR) aims to enhance recommendation performance across multiple markets. Due to its inherent characteristics, i.e., data isolation, non-overlapping users, and market heterogeneity, CMR introduces unique challenges and fundamentally differs from cross-domain recommendation (CDR). Existing CMR approaches largely inherit CDR by adop

R Medina

In this paper, we study the Boltzmann equation in a close to the hydrodynamic limit regime, set in bounded spatial domains with non-isothermal Maxwell boundary conditions. We establish the existence, uniqueness, and asymptotic stability of a non-equilibrium steady state under suitable smallness assumption on the temperature fluctuations at the boundary.

Bingxue Xu, Emil Hedemalm, Ajinkya Khoche, Patric Jensfelt

The challenge of 3D multi-object tracking is achieving robustness in real-world applications, for example under adverse conditions and maintaining consistency as distance increases. To overcome these challenges, sensor fusion approaches that combine LiDAR, cameras, and radar have emerged. However, existing multimodal methods usually treat radar as another le

Fu Liu, Hongyu Li, Shanpu Shen

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs), originally in the passive form, have attracted attention due to their benefits in enhanced wave manipulating through flexible inter-element connections and element arrangements. To mitigate the severe multiplicative fading, the concept of active BD-RISs with signal amplification capability has re

Ruixuan Tian, Junjie Mao, Jiejia Liu, Hai Jin

DIffuse X-ray Explorer (DIXE) is a proposed high-resolution spectroscopic survey mission onboard the China Space Station. Equipped with microcalorimeters based on the Transition-edge sensor technology, it aims to survey the hot gas in the Milky Way. The performance of DIXE depends on the understanding of non X-ray background (NXB), which can strongly affect

Zhentao Yang, Yixiang Luomei, Zhuoyang Liu, Zhenyu Liu

Wideband spectrum sensing for low-altitude monitoring is critical yet challenging due to heterogeneous protocols,large bandwidths, and non-stationary SNR. Existing data-driven approaches treat spectrograms as natural images,suffering from domain mismatch: they neglect time-frequency resolution constraints and spectral leakage, leading topoor narrowband visib

Mahmoud Fakhry, Abeer FathAllah Brery

Heart sound signals, phonocardiography (PCG) signals, allow for the automatic diagnosis of potential cardiovascular pathology. Such classification task can be tackled using the bidirectional long short-term memory (biLSTM) network, trained on features extracted from labeled PCG signals. Regarding the non-stationarity of PCG signals, it is recommended to extr

Didier Henrion, Milan Korda, Martin Kružík, Karolına Sehnalová

In nonlinear elasticity, finding the deformation of a material which minimizes a given stored energy density is a challenging calculus of variations problem which may fail to have minimizers: the energy optimal material forms infinitely fine microstructures (wrinkles) rather than deforming smoothly. In the case where the energy function is non-convex but fra

Yunkai Dang, Minxin Dai, Yuekun Yang, Zhangnan Li

Ultra-high-resolution (UHR) remote sensing imagery couples kilometer-scale context with query-critical evidence that may occupy only a few pixels. Such vast spatial scale leads to a quadratic explosion of visual tokens and hinders the extraction of information from small objects. Previous works utilize direct downsampling, dense tiling, or global top-k pruni

Chao-Hui Wang, Shao-Wen Wei, Tao Zhu, Yu-Xiao Liu

Multiple potential wells for massive test particles, allowing distinct families of bound orbits to coexist, are a characteristic feature of certain exotic compact objects beyond general relativity. Taking the dyonic black hole as a representative example, we demonstrate that such multi-well geometries generically support multiple coexisting branches of bound

Nadège Polette, Olivier Le Maître, Pierre Sochala, Alexandrine Gesret

This paper addresses the challenge of dimension reduction (DR) in Bayesian inference of high-resolution two-or three-dimensional fields, where a priori parametrizations require a large number of terms. The underlying idea is common to state-of-the-art methods in which the parameter space is decomposed into two subspaces, one informed by the likelihood and on

Alireza Azadi, Paul Rigge, Ethan Mahintorabi, Kenneth B. Kent

High-level synthesis (HLS) tools offer limited support for Engineering Change Orders (ECOs), making late-stage design modifications challenging and costly. This paper introduces a graph-based ECO methodology tailored for Google XLS. A Graph Edit Distance (GED) algorithm is used to detect structural differences between original and revised intermediate repres

A. Senol, B. S. Ozaltay, M. Tekin, H. Denizli

We investigate flavor-changing neutral current (FCNC) interactions of the top quark at a future muon collider operating at a center-of-mass energy of $\sqrt{s}=10~\mathrm{TeV}$. The process $μ^{+}μ^{-}\rightarrowν_μ\,μ^{+}\,b\,j$ together with its charge-conjugate channel is considered as a probe of anomalous $tqZ$ and $tqγ$ interactions within a model-indep

Shivika, Kartik Bose, Pankaj Gupta

Vision-language models trained with contrastive learning on paired medical images and reports show strong zero-shot diagnostic capabilities, yet the effect of training batch composition on learned representations remains unexplored for 3D medical imaging. We reproduce Merlin, a dual-encoder model that aligns 3D abdominal CT volumes with radiology reports usi

Hevish Cowlessur, Chandra Thapa, Tansu Alpcan, Seyit Camtepe

Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely used hard-parameter-sharing setting, a shared backbone is combined with task-specific prediction heads. However, task-specific parameters can grow rapidly with the number of tasks. Therefore, designing multi-ta

Zhenyu Wan, Gong Chen, Qing Huang, Xiaoyuan Xie

Scenario testing is an important technique for detecting errors in web systems. Testers draft test scenarios and convert them into test scripts for execution. Early methods relied on testers to convert test scenarios into test scripts. Recent LLM-based scenario testing methods can generate test scripts from natural language descriptions of test scenarios. Ho

Peiwen Jiang, Yushuo Feng, Jiajia Guo, Chao-Kai Wen

The increasing deployment of agentic artificial intelligence (AI) systems has intensified the demand for efficient agent to agent communication, particularly over bandwidth limited wireless links. In embodied AI applications, agents must exchange task related information under strict latency and reliability constraints. Existing agent communication methods p

Ryan Brunet, Andrew Melatos, Pedro Rossetto

Hydromagnetic instabilities modify the structure of a magnetically confined mountain on an accreting neutron star, once the accreted mass exceeds a critical value. Ideal magnetohydrodynamics and flux freezing break down, and mass diffuses across magnetic field lines locally, wherever instabilities are excited. Here a self-consistent, iterative, numerical sch

You Wu, Ziheng Chen, Yizhen Zhang, Haoyi Wu

Cross-layer key-value (KV) compression has been found to be effective in efficient inference of large language models (LLMs). Although they reduce the memory consumption of the KV cache, such methods usually introduce non-negligible performance degradation. In this work, we aim to enhance the performance of YOCO, a cross-layer KV compression method that shar

K. Acharya, S. Ale, R. Kadel

Phishing websites now rely heavily on visual imitation-copied logos, similar layouts, and matching colours-to avoid detection by text- and URL-based systems. This paper presents a deep learning approach that uses webpage screenshots for image-based phishing detection. Two vision models, ConvNeXt-Tiny and Vision Transformer (ViT-Base), were tested to see how

Muhammad Adeel Ijaz

We present SQL Query Engine, an open-source, self-hosted service that translates natural language questions into validated PostgreSQL queries through a two-stage LLM pipeline. The first stage performs automatic schema introspection and SQL generation; a multi-strategy response parser extracts SQL from any LLM output format (JSON, code blocks, or raw text) wi

Ji Ho Bae

We determine the quantum query complexity of oracle identification on the hyperoctahedral group $B_N = \{\pm 1\}^N \rtimes S_N$ with respect to the natural representation: $Q_{LV}(B_N) = 2(N-1)$ for all $N \ge 2$. This is twice the symmetric-group value $Q_{LV}(S_N) = N-1$; the doubling arises from an $\varepsilon$-parity obstruction that restricts the bottl

Zikai Zhou, Wenyan Wang, Deng Hu, Zheyu Wang

As a unique kagome superconductor displaying clear signatures of strong electronic correlations, CsCr$_3$Sb$_5$ has drawn much attention. Its rich temperature-pressure phase diagram features intertwined orders including pressure-induced superconductivity and two density-wave-like phases, making it an outstanding platform to explore the complex coexistence an

Kaiwen Zheng, Kai Zhou, Jinwu Hu, Te Gu

Large language models (LLMs) demonstrate strong reasoning capabilities, but their performance often degrades under distribution shift. Existing test-time adaptation (TTA) methods rely on gradient-based updates that require white-box access and need substantial overhead, while training-free alternatives are either static or depend on external guidance. In thi

Cunda Wang, Ziying Ma, Po Hu, Weihua Wang

Entity alignment (EA) aims to identify entities referring to the same real-world object across different knowledge graphs (KGs). Recent approaches based on large language models (LLMs) typically obtain entity embeddings through knowledge representation learning and use embedding similarity to identify an alignment-uncertain entity set. For each uncertain ent

Sunchao Huang, Xihang Shi, Xiaoqiuyan Zhang, Suguo Chen

Smith Purcell radiation has emerged as a crucial platform for investigating light-matter interactions and developing compact, tunable light sources that span from microwaves to X-rays. In classical theory, it is believed that Cherenkov radiation exhibits an energy threshold for electrons, while Smith Purcell radiation is considered free of such a threshold.

Elton Cao, Hod Lipson

Reconstructing 3D geometry from 2D engineering line drawings is an inherently ambiguous problem: while visible strokes determine the object's projected structure, they do not specify the depth of each stroke. Rather than treating this problem as sketch-based asset generation, where models often infer unobserved structure, we study projection-faithful wir