April 2026 arXiv papers — page 159

Yubin Zhang, Qiwei Shi, Guilin Wu

This study investigates the boundary migration mechanisms near the sample surface of recrystallizing grains in high-purity Al subjected to cryogenic rolling. Local strain and stress tensors were characterized during \textit{in situ} annealing by combining high-resolution electron backscatter diffraction with microstructure-based digital image correlation str

Xiangchi Meng, Jianan Zhou, Jie Gao, Yifan Lu

Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has demonstrated superior efficiency over some traditional algorithms. While serving as a primary NCO approach for solving general VRPs, current cross-problem learning paradigms are still

Monika, Shishram Rebari

We present an analytical study of the relativistic quantum Otto cycle driven by a time-dependent harmonic oscillator. By imposing an asymmetry on the two adiabatic processes of this cycle, we obtain distinct scenarios of sudden compression and sudden expansion, and analyze how asymmetry affects the performance of the relativistic quantum Otto engine. By leve

Randy Martinez, Rong Tang, Lizhen Lin

While the manifold hypothesis is widely adopted in modern machine learning, complex data is often better modeled as stratified spaces -- unions of manifolds (strata) of varying dimensions. Stratified learning is challenging due to varying dimensionality, intersection singularities, and lack of efficient models in learning the underlying distributions. We pro

Rajveer Singh

We present a systematic empirical study of the spectral structure of LoRA weight updates. Through 2D Discrete Cosine Transform (DCT) analysis of trained adaptation matrices across BERT-base and RoBERTa-base on four GLUE benchmarks (SST-2, MNLI, CoLA, QQP), we establish that LoRA updates are universally dominated by low-frequency components: on average, just

Yohei Harata, Soramichi Akiyama

Downfall is a side-channel attack that leaks values in vector registers from a process to another on the same CPU core. This attack enables an attacker to achieve serious outcomes (e.g., stealing AES keys), and there is no fundamental countermeasure besides applying microcode-based hardware patches. Although the impact of this attack is discussed by the orig

Shaqi Luo, Yuanyuan Li, Youhao Hu, Chenhao Yu

UMI-style interfaces enable scalable robot learning, but existing systems remain largely visuomotor, relying primarily on RGB observations and trajectory while providing only limited access to physical interaction signals. This becomes a fundamental limitation in contact-rich manipulation, where success depends on contact dynamics such as tactile interaction

Dylan McDermott, Nobuko Yoshida

We provide the first denotational semantics for asynchronous multiparty session types with precise asynchronous subtyping. Our semantics enables us to reason about asynchronous message-passing, in which message-sending is non-blocking. It enables us to prove the correctness of communication optimisations, in particular, those involving reordering of messages

Neena Gupta, Sourav Sen

In this note we rectify the proof of Theorem 3.11 in [arXiv:2403.02876]. We also present a set of examples at the end discussing various cases.

Ido Beigelman, Moti Freiman

Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is correct using only signals from a single forward pass. Motivated by internal-signal hallucination detection in large language models, we investigate whether similar depth-wise signals exist in Vision Transfor

Ke Huang, Sankar Das Sarma, Xiao Li

We investigate the interplay of disorder and interaction in two-dimensional electron systems in a strong magnetic field, focusing on the transition between Wigner crystals and fractional quantum Hall liquids. We first study classical Wigner crystals with charged impurities, revealing an evolution from a coherent crystal to local crystalline domains with shor

Behrooz Razeghi

Synthetic face generators can produce many nominal identities, but nominal count does not determine how many are jointly distinguishable under a specified verification rule. We define finite-dimensional capacity as the supremum of codebook sizes over distinct latent identity codes whose induced identity-conditional embedding distributions satisfy per-identit

Alexander Schugardt

This paper presents a driving-cycle-aware shape and topology optimization workflow for interior permanent magnet synchronous machines used in traction drives. A k-means clustering approach reduces full driving cycles to representative operating points so that optimization remains computationally feasible while preserving realistic operating behavior. The wor

Jian Pang, Bingfeng Zhang, Jin Wang, Baodi Liu

Object detection in hazy environments is challenging because degraded objects are nearly invisible and their semantics are weakened by environmental noise, making it difficult for detectors to identify. Common approaches involve image enhancement to boost weakened semantics, but these methods are limited by the instability of enhanced modules. This paper pro

Aviraj Newatia, Michael Cooper, Viet Nguyen, Rahul G. Krishnan

Training machine learning models requires the storage of large datasets, which often contain sensitive or private data. Storing data is associated with a number of potential risks which increase over time, such as database breaches and malicious adversaries. Machine unlearning is the study of methods to efficiently remove the influence of training data subse

Jingliang Duan, Jie Li, Yinsong Ma, Liye Tang

Understanding the optimization landscape of linear quadratic regulation (LQR) problems is fundamental to the design of efficient reinforcement learning solutions. Recent work has made significant progress in characterizing the landscape of static output-feedback control and linear quadratic Gaussian (LQG) control. For LQG, much of the analysis leverages the

Wenda Liu, Zhigang Song, Shuai Nie, Guangyao Liu

LLM-based universal information extraction (UIE) methods often rely on additional information beyond the original training data, which increases training complexity yet often yields limited gains. To address this, we propose ProUIE, a Macro-to-Micro progressive learning approach that improves UIE without introducing any external information. ProUIE consists

Matteo Spanio, Valentina Frezzato, Antonio Rodà

Music and food traditions are both intangible cultural heritage, and the links between them, how a sound can make a taste seem sweeter or more bitter, are increasingly used in museum, exhibition and gastronomic-tourism settings. Modelling those links computationally runs into a data bottleneck familiar across cultural heritage computing: expert annotation is

João Paulo Biazotto, Daniel Feitosa, Paris Avgeriou, Elisa Yumi Nakagawa

Managing technical debt (TD) is critical to ensure the sustainability of long-term software projects. However, the time and cost involved in technical debt management (TDM) often discourage practitioners from performing this activity consistently. Continuous Integration and Continuous Delivery (CI/CD) pipelines offer an opportunity to support TDM by embeddin

Deobrat Singh, Li-Yun Tian, Moyses Araujo, Raquel Lizarraga

Solid-state lithium batteries have attracted considerable attention due to their potential to provide improved safety and higher energy density compared with conventional liquid electrolyte batteries. However, the stability of the interface between Li metal anodes and solid electrolytes remains a critical issue that strongly influences battery performance. I

Bronislav Sidik, Lior Rokach

Autonomous AI agents built on open-source runtimes such as OpenClaw expose every available tool to every session by default, regardless of the task. A summarization task receives the same shell execution, subagent spawning, and credential access capabilities as a code deployment task, a 15x overprovision ratio that we call the capability overprovisioning pro

Yang Cui, Jingyuan Sun, Yizheng Sun, Yifan Wang

How the brain supports language across different languages is a basic question in neuroscience and a useful test for multilingual artificial intelligence. Neuroimaging has identified language-responsive brain regions across languages, but it cannot by itself show whether the underlying processing is shared or language-specific. Here we use six multilingual l

S. S. Agaev, K. Azizi, H. Sundu

The resonance $X(6600)$ is explored as the all-charm tetraquark structure with spin-parities $J^{\mathrm{PC}}=2^{++}$. It is considered in the diquark-antidiquark picture and modeled as a tensor state $X$ composed of the axial-vector diquark $cCγ_μc$ and antidiquark $\overline{c}% γ_νC\overline{c}$ with $C$ being the charge conjugation matrix. The mass and d

Stephen Wiggins

Classical reaction dynamics suggests transport through an index-1 saddle is organized not just by flux, but by local symplectic width scales of bounded proxy neighborhoods near the bottleneck. We investigate if a related geometric effect appears in the quantum regime for highly squeezed Gaussian wavepackets. Building on de Gosson's symplectic approach, we an

Tomasz Kania, Mariusz Niwiński

We introduce a norm-controlled notion of semiprojectivity for Banach lattices, requiring liftability of contractive lattice homomorphisms through inductive limits of closed ideals with arbitrarily small loss of norm control. Our main result establishes that, for a compact metric space $X$, the Banach lattice $C(X)$ is semiprojective if and only if $X$ is an

Ye-Hang Qin, Ye Feng

In traditional phase-field modeling of multiphase materials, a significant challenge arises from the non-local nature of fracture energy regularization, where interfacial toughness is inherently coupled with the properties of the surrounding bulk phases. Achieving consistency with prescribed material properties typically necessitates complex corrections and

Uwe S. Pracht

This thesis presents and discusses optical low-temperature experiments on disordered NbN, granular Al thin-films, and the heavy-fermion compound CeCoIn5, offering a unified picture of quantum-critical superconductivity. It provides a concise introduction to the respective theoretical models employed to interpret the experimental results, and guides readers t

Qinghua Zhao, Xueling Gong, Xinyu Chen, Zhongfeng Kang

While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and optimization metrics across model scales (1B-32B). Our experiments

Chenxi Qiu, Tao Yue, Xuemei Hu

High throughput video acquisition is a challenging problem and has been drawing increasing attention. Existing high throughput imaging systems splice hundreds of sub-images/videos into high throughput videos, suffering from extremely high system complexity. Alternatively, with pixel sizes reducing to sub-micrometer levels, integrating ultra-high throughput o

Shi-Shun Chen, Shuai Gao, Xiao-Yang Li, Enrico Zio

Existing studies indicate that complex system degradation is characterized by degradation of multiple dependent parameters. Capturing the dependencies is crucial for accurate degradation modeling and effective degradation control. This work aims to uncover these dependencies through causal analysis, focusing on pairwise causal discovery. Firstly, considering

Mohammad Moradi, Morteza Moradi, Marco Grassia, Giuseppe Mangioni

Recent progress in brain-guided image generation has improved the quality of fMRI-based reconstructions; however, fundamental challenges remain in preserving object-level structure and semantic fidelity. Many existing approaches overlook the spatial arrangement of salient objects, leading to conceptually inconsistent outputs. We propose a saliency-driven dec

Fedor B. Lyudogovskiy

Let $G_n$ be the partition graph whose vertices are the partitions of $n$, with adjacency given by elementary transfers of one cell between parts, followed by reordering. We study the support of a partition -- the set of distinct part sizes -- as a global vertex invariant of $G_n$. We show that support size $r$ occurs in $G_n$ if and only if $T_r=r(r+1)/2\le

Woojeong Kim

Motivated by thrombus modeling, we study a modified Navier-Stokes-Cahn-Hilliard system and consider PINN-based numerical illustrations for the modified system. To enable the analysis, we introduce a diffusion-enhanced system for the deformation variable while preserving the associated dissipative energy structure. We prove local well-posedness for this new s

Koffi Titus Sergio Aglin, Anthony K. Muchiri, Celestin Nkundineza

Reliable image quality assessment is essential in applications where large volumes of images are acquired automatically and must be filtered before further analysis. In many practical scenarios, a pristine reference image is unavailable, making no reference image quality assessment (NR-IQA) particularly important. This paper introduces Multi-Metric Image Qua

Yang Yang, Zining Zhong, Jindong Li, Jiemin Wu

LLM-driven program evolution can discover high-quality programs, but its cost and run-to-run variance hinder reliable progress. We propose TurboEvolve, a multi-island evolutionary framework that improves sample efficiency and robustness under fixed evaluation budgets. Inspired by the multiple-offspring strategy in evolutionary algorithms, TurboEvolve introdu

Arushi Arushi, Naresh Kumar

Particle breakage due to collisional interactions plays a vital role in the development of several phenomena in science and engineering. The nonlinear collisional breakage equations (NCBEs) are a significant set of equations in this context. Solving the NCBE is computationally challenging due to its nonlinearity, high dimensionality, and complex kernel inter

Zesen Fu, Aolin Li, Wenzhe Zhou, Fangping Ouyang

The realization of multiferroic altermagnets featuring giant intrinsic spin splitting, hold great promise for next-generation spintronics. In this work, based on the recently proposed concept of spin-antiferroelectric (spin-AFE), we construct a class of two-dimensional (2D) multiferroic altermagnets, termed 2D spin-antiferroelectric altermagnets (2D spin-AFE

Yuchen Chen, Yuan Xiao, Chunrong Fang, Zhenyu Chen

The proliferation of large language models for code (CodeLMs) and open-source contributions has heightened concerns over unauthorized use of source code datasets. While watermarking provides a viable protection mechanism by embedding ownership signals, existing methods rely on detectable trigger-target patterns and are limited to source-code tasks, overlooki

Krishna Rajput, Vipul Gupta, Sudheesh K. Rajput, Yasuhiro Awatsuji

High-speed quantitative phase imaging enables non-intrusive visualization of transient compressible gas flows and energetic phenomena. However, phase maps reconstructed via the transport of intensity equation (TIE) suffer from spatially correlated low-frequency artifacts introduced by the inverse Laplacian solver, which obscure meaningful flow structures suc

Kaden Stillwagon, Alexandra Dunnum VandeLoo, Benjamin Magondu, Craig R. Forest

Instance segmentation enables the analysis of spatial and temporal properties of cells in microscopy images by identifying the pixels belonging to each cell. However, progress is constrained by the scarcity of high-quality labeled microscopy datasets. Many recent approaches address this challenge by initializing models with segmentation-pretrained weights fr

Muhammad Farhan, Henning Koehler

Modern route planners such as Google Maps and Apple Maps serve millions of users worldwide, optmizing routes in large-scale road networks where fast responses are required under diverse cost metrics including travel time, fuel consumption, and toll costs. Classical algorithms like Dijkstra or A$^*$ are too slow at this scale, and while index-based techniques

Eyad I. B. Hamid

H-EFT-VA established a physics-informed solution to the Barren Plateau (BP) problem via a hierarchical EFT UV-cutoff, guaranteeing gradient variance in Omega(1/poly(N)). However, localization restricts the ansatz to a polynomial subspace, creating a reference-state gap for states distant from |0>^N. We introduce Adaptive H-EFT-VA (A-H-EFT) to navigate the tr

Kazuyoshi Tsutsumi, Ernst Niebur

We propose a dynamical neural network model with a hierarchical and modular structure. The network architecture can be derived by minimizing an energy function that is originally designed based on two kinds of neurons with quite different time constants. It has multiple subspaces that are spanned by neural parameters employed in the energy function, and adja

Dmitriy Pochekutov

We study the complete diagonal of the Laurent series expansion of a rational function in $n$-complex variables. For a denominator that is nondegenerate for its Newton polyhedron, we prove that the complete diagonal, initially defined in a logarithmically convex domain, can be analytically continued along any path in the $r$-dimensional complex torus that avo

Vladi Vexler, Ofer Idan, Gil Lederman, Dima Sivov

Standard dense retrievers lack a native calculus for multi-atom logical constraints. We introduce Neuro-Symbolic Fuzzy Logic (NSFL), a framework that adapts formal t-norms and t-conorms to neural embedding spaces without requiring retraining. NSFL operates as a first-order hybrid calculus: it anchors logical operations on isolated zero-order similarity score

Luis Balderas, Miguel Lastra, José M. Benítez

Large language models are transforming all areas of academia and industry, attracting the attention of researchers, professionals, and the general public. In the trek for more powerful architectures, Mixture-of-Experts, inspired by ensemble models, have emerged as one of the most effective ways to follow. However, this implies a high computational burden for

Atef Lechiheb

We study time-fractional stochastic Navier-Stokes equations on a bounded domain of $\R^2$ (the restriction to dimension two is essential for the bilinear estimates via Sobolev embeddings) driven by a Hermite process $Z_H^k$ of order $k\ge1$ and Hurst parameter $H\in(1/2,1)$. This class of noises generalizes fractional Brownian motion ($k=1$) and the Rosenbla

Weitian Chen, Shixuan Sun, Cheng Chen, Yongmin Hu

Subgraph matching is a core operation in graph analytics, supporting a broad spectrum of applications from social network analysis to bioinformatics. Recent GPU-based approaches accelerate subgraph matching by leveraging parallelism but rely on a coarse-grained execution model that suffers from scalability and efficiency issues due to high memory overhead an

Alexey Chernov, Peter Hansbo, Erik Marc Schetzke

We construct and analyse a $hp$-FE/BE coupling on non-matching meshes, based on Nitsche's method. Both the mesh size and the polynomial degree are changed to improve accuracy. Nitsche's method leads to a positive definite formulation, thus, unlike the mortar method, it does not require the Babu\v{s}ka-Brezzi condition for stability. The method is stable prov

Mamdouh Alenezi

The rapid proliferation of large language models (LLMs) and agentic AI systems has created an unprecedented abundance of automatically generated code, challenging the traditional software engineering paradigm centered on manual authorship. This paper examines whether the discipline should be reoriented around orchestration, verification, and human-AI collabo

Yizhe Zhang, Jianping Li, Liangliang Yin, Zhen Dong

Human-in-the-loop (HITL) UAV operation is essential in complex and safety-critical aerial surveying environments, where human operators provide navigation intent while onboard autonomy must maintain accurate and robust state estimation. A key challenge in this setting is that resource-constrained UAV platforms are often limited to narrow-field-of-view LiDAR

Bo Ma, Jinsong Wu, Weiqi Yan

Mamba selective state space models (SSMs) provide linear-time sequence modeling but remain sensitive to selective-scan chunk scheduling. We present COREY, a \emph{concept-and-feasibility} runtime scheduler that maps fixed-bin activation entropy to chunk size. We evaluate COREY in three tiers: a prototype cost model, real-checkpoint kernel timing, and routed

Yiyang Liu, Xianwei Li, Shaoyuan Li

This paper studies the problem of distributed state estimation of linear time-invariant (LTI) systems under event-triggered communication. For event-triggering mechanisms, the existence of positive minimum inter-event times (MIETs) is an essential property for ensuring practicality. It is widely recognized that dynamic event-triggering mechanisms can effecti

Ivan Rapoport, Ehud Behar, Vincent Desjacques

Low-ionization metal absorption lines provide a primary probe of cool gas in and around galaxies. We confront observations of metal-line absorption in quasar spectra with predictions from the IllustrisTNG cosmological simulation in order to benchmark how well current galaxy formation models reproduce the observed circumgalactic medium (CGM) and intergalactic

Evgenii Kruzhkov, Sven Behnke

Feed-forward geometric foundation models can infer dense point clouds and camera motion directly from RGB streams, providing priors for monocular SLAM. However, their predictions are often view-dependent and noisy: geometry can vary across viewpoints and under image transformations, and local metric properties may drift between frames. We present MonoEM-GS,

Maram Hasan, Md Aminur Hossain, Savitra Roy, Souparna Bhowmik

Effective foundation modeling in remote sensing requires spatially aligned heterogeneous modalities coupled with semantically grounded supervision, yet such resources remain limited at scale. We present GeoMeld, a large-scale multimodal dataset with approximately 2.5 million spatially aligned samples. The dataset spans diverse modalities and resolutions and

Weihua Zheng, Chang Liu, Zhengyuan Liu, Xin Huang

Multilingual Large Language Models (LLMs) struggle with cross-lingual tasks due to data imbalances between high-resource and low-resource languages, as well as monolingual bias in pre-training. Existing methods, such as bilingual fine-tuning and contrastive alignment, can improve cross-lingual performance, but they often require extensive parallel data or su

Pablo de los Riscos, Fernando J. Corbacho, Michael A. Arbib

We introduce a hierarchical categorical framework for Schema-Based Learning (SBL) structured across four interconnected levels. At the schema level, a free multicategory $Sch_{syn}$ encodes fundamental schemas and transformations. An implementation functor $\mathcal{I}$ maps syntactic schemas to representational languages, inducing via the Grothendieck const

Domagoj Herceg, Duarte Antunes

We present a distributionally robust PAC-Bayesian framework for certifying the performance of learning-based finite-horizon controllers. While existing PAC-Bayes control literature typically assumes bounded losses and matching training and deployment distributions, we explicitly address unbounded losses and environmental distribution shifts (the sim-to-real

Anqi Wang, Dongyijie Pan, Xin Tong, Pan Hui

Although Large Language Models (LLMs) demonstrate proficiency in knowledge-intensive tasks, current interfaces frequently precipitate cognitive misalignment by failing to externalize users' underlying reasoning structures. Existing tools typically represent intent as "flat lists," thereby disregarding the causal dependencies and revisable assumptions inheren

Giacomo Cignoni, Simone Magistri, Andrew D. Bagdanov, Antonio Carta

This paper explores Online Continual Self-Supervised Learning (OCSSL), a scenario in which models learn from continuous streams of unlabeled, non-stationary data, where methods typically employ replay and fast convergence is a central desideratum. We find that OCSSL requires particular attention to the stability-plasticity trade-off: stable methods (e.g. rep

Subramanyam Sahoo

Modern large language models (LLMs) are increasingly fine-tuned via reinforcement learning from human feedback (RLHF) or related reward optimisation schemes. While such procedures improve perceived helpfulness, we investigate whether sycophantic reward signals degrade calibration -- a property essential for reliable uncertainty quantification. We fine-tune Q

Baisong Li

Multispectral and Hyperspectral Image Fusion (MHIF) aims to reconstruct high-resolution images by integrating low-resolution hyperspectral images (LRHSI) and high-resolution multispectral images (HRMSI). However, existing methods face limitations in modeling cross-scale interactions and spatial-spectral collaboration, making it difficult to achieve an optima

Norihiro Iizuka, Akihiro Miyata

We investigate how the junction law for multipartite entanglement is realized in confining holographic backgrounds, using genuine multi-entropy (GM) as our main diagnostic. We first study an AdS$_3$ hard-wall toy model as an analytic benchmark, where multi-way cuts and junction geometries can be analyzed explicitly. In this setup, we classify the relevant sa

Sebastian Jung, Artem Zholus, Martin Sundermeyer, Carl Doersch

Tracking-Any-Point (TAP) models aim to track any point through a video which is a crucial task in AR/XR and robotics applications. The recently introduced TAPNext approach proposes an end-to-end, recurrent transformer architecture to track points frame-by-frame in a purely online fashion -- demonstrating competitive performance at minimal latency. However, w

Deobrat Singh, Raquel Lizarraga

Understanding and engineering interfacial electrostatics in hybrid two-dimensional (2D) and biomolecular material systems is essential for advancing high-performance triboelectric nanogenerators (TENGs). In this work, we systematically investigate the strain-dependent electronic structure and triboelectric response of Janus MoSSe, silk fibroin, and their van

Arnon Turetzky, Avihu Dekel, Hagai Aronowitz, Ron Hoory

Spoken meaning often depends not only on what is said, but also on which word is emphasized. The same sentence can convey correction, contrast, or clarification depending on where emphasis falls. Although modern text-to-speech (TTS) systems generate expressive speech, it remains unclear whether they infer contextually appropriate stress from discourse alone.

Xuwei Ding, Skylar Zhai, Linxin Song, Jiate Li

Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit threats such as misuse and prompt injection, but overlook a subtle yet critical setting where user instructions are entire

P. A. Maksimov, L. V. Shvanskaya, O. S. Volkova, A. N. Vasiliev

We present a magnetic model for an antiferromagnetic compound $\gamma$-Mn$_3$(PO$_4$)$_2$, which was previously shown to exhibit a 1/3 magnetization plateau due to the trimer-based structure of the lattice of magnetic Mn$^{2+}$ ions with $S=5/2$. An exchange Hamiltonian that yields observed field transitions is obtained from fitting magnetization data. It is

Anqi Wang, Bingqian Wang, Huiyang Chen, Keqing Jiao

Large Language Models (LLMs) offer vast potential for creative ideation; however, their standard interaction paradigm often produces unstructured textual outputs that lead users to prematurely converge on sub-optimal ideas-a phenomenon known as fixation. While recent creativity tools have begun to structure these outputs, they remain compositionally opaque:

Hoigi Seo, Byung Hyun Lee, Jaehyun Cho, Sungjin Lim

Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable content, such as copyrighted ones. Concept erasure has emerged as a mitigation strategy, yet existing approaches struggle to balance scalability, precision, and robustness, which restricts their applicabilit

Bo Zhou, Qiuxia Lai, Zeren Sun, Xiangbo Shu

Robust 3D representation learning forms the perceptual foundation of spatial intelligence, enabling downstream tasks in scene understanding and embodied AI. However, learning such representations directly from unposed multi-view images remains challenging. Recent self-supervised methods attempt to unify geometry, appearance, and semantics in a feed-forward m

Sohan Kumar Jha

This study examines the combined effect of electric charge and Lorentz symmetry breaking (LSB) on the observables of strong gravitational lensing (SGL) and the dynamics of quasiperiodic oscillations (QPOs) around an electrically charged, Lorentz symmetry-violating (LV) black hole (QKR BH). We first explore the SGL, which unravels an interesting effect that t

Theodor Spiro

We test whether artificial intelligence architectural evolution obeys the same statistical laws as biological evolution. Compiling 935 ablation experiments from 161 publications, we show that the distribution of fitness effects (DFE) of architectural modifications follows a heavy-tailed Student's t-distribution with proportions (68% deleterious, 19% neutral,

Chao Li, Xing Su, Chao Fan, Yang Li

While climate-induced population migration has received rising attention, the role played by human climate endeavors remains underexplored. Here, we combine machine learning with attribution mapping to analyze the impacts of 4,713 heat-related policies (HPs) on 11,177 migration flows between U.S. counties. We find that heat adaptation policies (APs) and heat

Ron Wettenstein, Alexander Nadel, Udi Boker

Decision-tree ensembles are a cornerstone of predictive modeling, and SHAP is a standard framework for interpreting their predictions. Among its variants, Background SHAP offers high accuracy by modeling missing features using a background dataset. Historically, this approach did not scale well, as the time complexity for explaining n instances using m backg

Kewei Zhu, Cameron Wilson, Bartosz Mazur, Yi Li

Systematic chemical names, such as IUPAC-style nomenclature for metal-organic frameworks (MOFs), contain rich structural and compositional information in a standardized textual format. Here we introduce ReadMOF, which is, to our knowledge, the first nomenclature-free machine learning framework that leverages these names to model structure-property relationsh

Tuğrulcan Elmas, Filipi Nascimento Silva, Manita Pote, Priyanka Dey

Coordinated campaigns on social media play a critical role in shaping crisis information environments, particularly during the onset of conflicts when uncertainty is high and verified information is scarce. We study the interplay between coordinated campaigns and information integrity through a case study of the 2023 Israel-Hamas War on Twitter (X). We analy

Chang Su, Ming Li, Zhanglin Shangguan, Zhaojian Wang

Coordinated operation of alkaline water electrolysis (AWE) systems with multiple electrolyzers under fluctuating renewable power input is challenging due to varying power availability and dynamic safety constraints. Moreover, the conventional separation between optimization and control may result in inconsistent decisions across timescales. To address these

Masaya Iwashima, Ryo Onishi

We developed a new explicit cloud microphysical model, based on direct numerical simulation (DNS) with Lagrangian particle tracking. The model employs a vertically-elongated quasi-1D computational domain extending from the ground to the cloud top to explicitly capture the vertical structure of clouds. This allows us to simulate the all warm-cloud microphysic

Taihei Oki, Ryosuke Sato

We develop a unified ascending-auction framework for computing Walrasian equilibria in combinatorial markets with strong substitutes valuations and piecewise-linear payment functions. Our auction extends the celebrated ascending auctions of Gul and Stacchetti (2000) and Ausubel (2006) to accommodate payment frictions (e.g., transaction taxes or commission fe

Yida Lin, Bing Xue, Mengjie Zhang, Sam Schofield

This paper presents a stereo-vision-based system mounted on a drone for detecting and localising radiata pine branches to support autonomous pruning. The proposed pipeline comprises two stages: branch segmentation and depth estimation. For segmentation, YOLOv8, YOLOv9, and Mask R-CNN variants are compared on a custom dataset of 71 stereo image pairs captured

Eyal Buks

The hypothesis that disentanglement spontaneously occurs in quantum systems is motivated by some outstanding issues in the foundations of quantum mechanics. However, for some cases, spontaneous disentanglement enables the violation of the causality principle. To mitigate the conflict with causality, a formulation for the hypothesis, which is based on the max

Nikodem Tomczak

Profiled Sparse Networks (PSN) replace uniform connectivity with deterministic, heterogeneous fan-in profiles defined by continuous, nonlinear functions, creating neurons with both dense and sparse receptive fields. We benchmark PSN across four classification datasets spanning vision and tabular domains, input dimensions from 54 to 784, and network depths of

Minghui Min, Peng Zhang, Jiayang Xiao, Ruixin Yang

With the rapid growth of Multi-access Edge Computing (MEC), secure and efficient computation offloading from user equipment (UEs) to edge access points (APs) is critical. However, DISCO intelligent reflective surface-based fully-passive jammers (DIRS-based FPJs) use random time-varying phase shifts to launch DISCO jamming attacks, disrupting offloading perfo

Parviz Sahandi, Nematollah Shirmohammadi

Let $\Gamma$ be a cancellation monoid and $R=\bigoplus_{\alpha \in \Gamma}R_{\alpha}$ be a $\Gamma$-graded ring. It is shown that $R$ is graded left semihereditary if and only if $R$ is graded left coherent and every graded submodule of a flat left $R$-module is flat. Hence it gives a new characterization of graded-Pr\"{u}fer domains.

Xiaoxing You, Qiang Huang, Jun Yu

This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in high-stakes human-centered settings, their success depends not only on correctness or fluency but on faithful preservation of human perspectives. Yet, current LLMs systematically fail at this requirement: even

Zhengnan Guo, Fei Tan

While Diffusion Large Language Models (dLLMs) have emerged as a promising non-autoregressive paradigm comparable to autoregressive (AR) models, their faithfulness, specifically regarding hallucination, remains largely underexplored. To bridge this gap, we present the first controlled comparative study to evaluate hallucination patterns in dLLMs. Our results

Shifna P R, S. M. Sunoj

Measures of inequality are often limited in their ability to capture multidimensional aspects that arise from the joint distribution of multiple socio-economic variables. In this paper, we develop bivariate extensions of the Zenga inequality measure using bivariate quantile functions. We propose new bivariate Zenga surfaces and study their theoretical proper

Yapeng Meng, Lin Yang, Yuguo Chen, Xiangru Chen

Motion blur arises when rapid scene changes occur during the exposure period, collapsing rich intra-exposure motion into a single RGB frame. Without explicit structural or temporal cues, RGB-only deblurring is highly ill-posed and often fails under extreme motion. Inspired by the human visual system, brain-inspired vision sensors introduce temporally dense i

Xinping Yi

Graph neural networks have demonstrated excellent applicability to a wide range of domains, including social networks, biological systems, recommendation systems, and wireless communications. Yet a principled theoretical understanding of their generalization behavior remains limited, particularly for graph classification tasks where complex interactions betw

D. S. Katkov, M. V. Parfenov, I. S. Burmistrov

The spin quantum Hall effect is a relative of the integer quantum Hall effect, characterized by integer quantized spin Hall conductance. In this work, we formulate and investigate a quantum network model consisting of $\textsf{N}$ channels per chiral link, preserving the fundamental symmetries of the spin quantum Hall effect. We demonstrate that, in the gene

Hengxiao Cheng, Xudong Zhu, Zijia Su, Zhongbin Dai

The rapid development of artificial intelligence (AI), Internet of Things (IoT), and edge computing applications has posed severe challenges to conventional memory technologies in terms of density, speed, and energy consumption. Herein, a single-ion transport mechanism is proposed to achieve picosecond (ps) switching capability. For monolayer hexagonal boron

Xin Li, Jiachao Gong, Xijun Wang, Shiyao Xiong

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-UGC) video restoration benchmark, termed KwaiVIR, which is contributed by USTC and Kuaishou Technology. It contains both synthetically distorted videos and real-world short-form UG

Natsuki Tsutsumi, Kengo Nakai, Yoshitaka Saiki

The Gaussian radial function-based Regression (RfR) method is a data-driven modeling approach that utilizes physically understandable variables from scalar time series, constructed using delay coordinates and Gaussian radial basis functions. Even when a model successfully describes an approximate trajectory of the original system, data-driven models rarely r

Yuan Sun, Hong Yi, Jinyuan Liu

Personalized learning systems are almost universally designed around a single objective: help people acquire knowledge and skills more efficiently. We argue this framing misses the more consequential problem. The most damaging failures in human life-financial ruin, health collapse, professional obsolescence-are rarely caused by insufficient knowledge acquisi

Fanxing Li, Shengyang Wang, Yuxiang Huang, Fangyu Sun

Obstacle avoidance is a fundamental vision-based task essential for enabling quadrotors to perform advanced applications. When planning the trajectory, existing approaches both on optimization and learning typically regard quadrotor as a point-mass model, giving path or velocity commands then tracking the commands by outer-loop controller. However, at high s

Shiyin Jiang, Wei Long, Minghao Han, Zhenghao Chen

The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (VQ) offers strong structural fidelity, existing methods lack a principled mechanism for joint rate-distortion (RD) optimization due to the disconnect between representation learning

Yi-Fan Cao, Kento Shigyo, Yitong Gu, Xiyuan Wang

Large Language Models (LLMs) have advanced self-learning tools, enabling more personalized interactions. However, learners struggle to engage in meaningful dialogue and process complex information. To alleviate this, we incorporate epistemological frameworks within an LLM-based approach to self-learning, reducing the cognitive load on learners and fostering

Shunyu Wu, Jiawei Huang, Weibin Feng, Boxin Li

Time series foundation models (TSFMs) have recently achieved remarkable success in universal forecasting by leveraging large-scale pretraining on diverse time series data. Complementing this progress, incorporating frequency-domain information yields promising performance in enhancing the modeling of complex temporal patterns, such as periodicity and localiz