November 2025 arXiv papers — page 15

Lior Tenenbaum

We discuss a method to estimate the measure of a compact set which is approximated using the Hausdorff distance by a sequence of compact sets. We do this by considering corresponding fattenings of the sequence of compact sets and showing their measures converge. We further review applications of this result to study the measure of a spectrum of an operator w

Divyanshi Tyagi, Saswata Bhattacharya

Hybrid organic--inorganic perovskites with broken inversion symmetry provide a fertile ground for uncovering coupled spin-orbit and ferroelectric phenomena. Here, we investigate the layered family (PA)$_2$CsY$_2$X$_7$ (Y = Pb, Sn; X = I, Br) using density functional theory, Berry-phase polarization analysis, and effective $\boldsymbol{k \cdot p}$ modeling. A

Yuanhong Chen, Federico Pichi, Zhen Gao, Gianluigi Rozza

Graph autoencoders have gained attention in nonlinear reduced-order modeling of parameterized partial differential equations defined on unstructured grids. Despite they provide a geometrically consistent way of treating complex domains, applying such architectures to parameterized dynamical systems for temporal prediction beyond the training data, i.e. the e

Changhun Kim, Yechan Mun, Hyeongwon Jang, Eunseo Lee

Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin critical decisions. While recent XAI methods have improved the explainability of time series models, they mostly analyze each time step independently, overlooking temporal dependencies.

Zuolei Li, Xingyu Gao, Xiaofan Wang, Jianlong Fu

Learning transferable latent actions from large-scale object manipulation videos can significantly enhance generalization in downstream robotics tasks, as such representations are agnostic to different robot embodiments. Existing approaches primarily rely on visual reconstruction objectives while neglecting physical priors, leading to sub-optimal performance

Yulai Huang

We investigate the right tail behavior of a certain class of GMC ratios, reminiscent of H\"older's inequality. We start with a heuristic argument to justify the optimal exponent in the tail estimate. Since Kahane's convexity inequality does not apply to GMC ratios, implementing the heuristic in the continuous setting is nontrivial from the viewpoint of GMC t

Shiwen Sun, Yi-Xin Wang, Xiao Liu, Yan Zhang

Subwavelength atomic arrays offer a powerful platform for engineering cooperative light-matter interactions and enabling quantum metasurfaces. We demonstrate that a two-dimensional array of three-level atoms operating under electromagnetically induced transparency can function as a tunable dual-band atomic mirror, where two independently controllable reflect

Changpeng Wang, Haozhe Wang, Xi Chen, Junhan Liu

Recent advances in vision-language reasoning underscore the importance of thinking with images, where models actively ground their reasoning in visual evidence. Yet, prevailing frameworks treat visual actions as optional tools, boosting metrics but leaving reasoning ungrounded and crops ineffective. This gap gives rise to the illusion of thinking with images

Tai Inui, Alexander Matsumura, Edgar Simo-Serra

Large-scale terrain generation remains a labor-intensive task in computer graphics. We introduce Geodiffussr, a flow-matching pipeline that synthesizes text-guided texture maps while strictly adhering to a supplied Digital Elevation Map (DEM). The core mechanism is multi-scale content aggregation (MCA): DEM features from a pretrained encoder are injected int

Wenshuo Wang, Fan Zhang

Zero-Shot Super-Resolution Spatiotemporal Forecasting requires a deep learning model to be trained on low-resolution data and deployed for inference on high-resolution. Existing studies consider maintaining similar error across different resolutions as indicative of successful multi-resolution generalization. However, deep learning models serving as alternat

Niko Lindvall, Mikko Heino, Robin Rajamäki, Mikko Valkama

This paper studies the effects of directional antenna element complex gain patterns and nonidealities in direction of arrival (DoA) estimation. We compare sparse arrays and classical uniform linear arrays, harnessing EM simulation tools to accurately model the electromagnetic behavior of both patch and Vivaldi antenna element including mutual coupling effect

Shoya Kasai, Shun Okumura, Yukitoshi Motome

Knots, characterized by topological invariants called the Hopf number $H$, arise from the intertwining of strings and exhibit diverse configurations. The knot structures have recently been observed in condensed matters, as examplified by a magnetic hopfion, sparking interest in controlling their topology. Here, we show that spin-orbit torque (SOT) enables dy

Kassem Kallas

Every day we share our personal information through digital systems which are constantly exposed to threats. For this reason, security-oriented disciplines of signal processing have received increasing attention in the last decades: multimedia forensics, digital watermarking, biometrics, network monitoring, steganography and steganalysis are just a few examp

Álvaro Castro-Castilla, Marcin Pawlowski, Hong-Sheng Zhou

We present Areon, a family of latency-friendly, stake-weighted, multi-proposer proof-of-stake consensus protocols. By allowing multiple proposers per slot and organizing blocks into a directed acyclic graph (DAG), Areon achieves robustness under partial synchrony. Blocks reference each other within a sliding window, forming maximal antichains that represent

Massimo Sorella, David Villringer

We construct a time-dependent, incompressible, and uniformly-in-time Lipschitz continuous velocity field on $\mathbb{T}^3$ that produces exponential growth of the magnetic energy along a subsequence of times, for every positive value of the magnetic diffusivity. Because this growth is not uniform in time but occurs only along a diverging sequence of times, w

Yushan Li, Jiabao He, Dimos V. Dimarogonas

Consensus networks are widely deployed in numerous civil and industrial applications. However, the process of reaching a common consensus among nodes can unintentionally reveal the network's topology to external observers by appropriate inference techniques. This paper investigates a feedback-based resistant inference design to prevent the topology from bein

Shubham Sawarkar, Pushpak Jagtap

We study the prescribed-time reach-avoid (PT-RA) control problem for nonlinear systems with unknown dynamics operating in environments with moving obstacles. Unlike robust or learning based Control Barrier Function (CBF) methods, the proposed framework requires neither online model learning nor uncertainty bound estimation. A CBF-based Quadratic Program (CBF

Kulin Shah, Bhuvesh Kumar, Neil Shah, Liam Collins

Generative recommendation (GR) with semantic IDs (SIDs) has emerged as a promising alternative to traditional recommendation approaches due to its performance gains, capitalization on semantic information provided through language model embeddings, and inference and storage efficiency. Existing GR with SIDs works frame the probability of a sequence of SIDs c

Diego Matessi, Arthur Renaudineau

Continuing the investigation of real Calabi-Yau hypersurfaces in toric varieties obtained by patchworking, we present a new theorem concerning the computation of their first Betti number using mirror symmetry. Although the proof of this result will appear elsewhere, we focus here on its consequences and applications to the topology of real Calabi-Yau hypersu

Markus Aspegren, Chris Mkolongo, Sebastian Lehmann, Kimberly Dick

We realize strongly confined quantum dots (QDs) in InAs nanowires (NWs) by combining epitaxial crystal-phase control with chemical wet etching. A strong axial confinement is first introduced by growing closely spaced wurtzite (WZ) tunnel barriers in NWs to enclose a zinc blende (ZB) QD. The NW cross-section is then reduced by isotropic etching to obtain very

Hibiki Yama, Kento Masuda, Yui Kawashima, Hajime Kawahara

We present atmospheric retrievals of the benchmark brown dwarf binary Luhman 16AB using high-resolution VLT/CRIRES spectra and the differentiable framework ExoJAX. We derive elemental abundances and temperature-pressure ($T$-$P$) profiles while explicitly testing the robustness of the results against major sources of systematic uncertainty. We first perform

Elham Ahmadi, Alireza Olama, Petri Välisuo, Heidi Kuusniemi

Reliable positioning in GNSS-challenged environments remains a critical challenge for navigation systems. Tightly coupled GNSS/IMU fusion improves robustness but remains vulnerable to non-Gaussian noise and outliers. We present a robust and adaptive factor graph-based fusion framework that directly integrates GNSS pseudorange measurements with IMU preintegra

Huijie Cheng, Binhan Sun, Aochen Zhang, Dirk Ponge

Infrastructure parts for a hydrogen (H) economy need alloys that are mechanically strong and at the same time resistant to the most dangerous and abrupt type of failure mode, namely, H embrittlement. These two properties are in fundamental conflict, as increasing strength typically amplifies susceptibility to H-related failure. Here, we introduce a new appro

Moritz Hütten

Understanding past and present maritime activity patterns is critical for navigation safety, environmental assessment, and commercial operations. An increasing number of services now openly provide positioning data from the Automatic Identification System (AIS) via ground-based receivers. We show that coastal vessel activity can be reconstructed from open ac

Michael Dumbser, Andrea Thomann, Maurizio Tavelli, Walter Boscheri

We introduce a novel structure-preserving vertex-staggered semi-implicit four-split discretization of a unified first order hyperbolic formulation of continuum mechanics that is able to describe at the same time fluid and solid materials within the same mathematical model. The governing PDE system goes back to pioneering work of Godunov, Romenski, Peshkov an

Suraj Kumar, Aditya Rallapalli, Nivriti Priyadarshini, Bharat Kumar GVP

Electric propulsion is used to maximize payload capacity in communication satellites. These orbit raising maneuvers span several months and hundreds of revolutions, making trajectory design a complex challenge. The literature typically addresses this problem using feedback laws, with Q-law being one of the most prominent approaches. However, Q-law suffers fr

Jun Li, Zongyu Lei

The traditional integer-pixel displacement search algorithm of digital image correlation method has low computational efficiency and has been gradually eliminated, and some intelligent optimization algorithms have their own strengths and weaknesses. The white shark optimizer has excellent global search capabilities. However, its calculation is cumbersome, pr

Kumarjit Pathak, Karthik K, Sachin Madan, Jitin Kapila

K-Nearest Neighbors (KNN) is one of the most used ML classifiers. However, if we observe closely, standard distance-weighted KNN and relative variants assume all 'k' neighbors are equally reliable. In heterogeneous feature space, this becomes a limitation that hinders reliability in predicting true levels of the observation. We propose DW-KNN (Double Weighte

Rin Saito, Anouk Sommer, Tatsuhiro Suga, Takahiro Suzuki

In the solution discovery problem for a search problem on graphs, we are given an initial placement of $k$ tokens on the vertices of a graph and asked whether this placement can be transformed into a feasible solution by applying a small number of modifications. In this paper, we study the computational complexity of solution discovery for several fundamenta

Yanjing Wang, Lizhou Wu, Sunfeng Gao, Yibo Tang

Conventional heterogeneous computing systems built on PCIe interconnects suffer from inefficient fine-grained host-device interactions and complex programming models. In recent years, many proprietary and open cache-coherent interconnect standards have emerged, among which compute express link (CXL) prevails in the open-standard domain after acquiring severa

Shoji Toyota, Yuto Miyatake

We address the problem of Bayesian inference for parameters in ordinary differential equation (ODE) models based on observational data. Conventional approaches in this setting typically rely on numerical solvers such as the Euler or Runge-Kutta methods. However, these methods generally do not account for the discretization error induced by discretizing the O

D. Sree Yashaswinee, Gargie Tambe, Y. Raghu Reddy, Karthik Vaidhyanathan

Digital twins (DT) have emerged as a transformative technology, enabling real-time monitoring, simulations, and predictive maintenance across various domains, though their Application in the networking domain remains underexplored. This paper focuses on issues such as increasing client density and traffic congestion by proposing a digital twin for computer n

Alessia Caponera, Vinicius Ferreira, Emilio Porcu

We develop a general framework for isotropic functional Gaussian fields on the $d$-dimensional sphere $\mathbb{S}^{d}$, where the field takes values in a separable Hilbert space $\mathcal{H}$. We establish an operator-valued extension of Schoenberg's theorem and show that the covariance structure of such fields admits a representation through a sequence of t

Yizheng Wang, Tao Jiang, Jinyan Bai, Zhengbin Zou

Software Requirement Document (RD) typically contain tens of thousands of individual requirements, and ensuring consistency among these requirements is critical for the success of software engineering projects. Automated detection methods can significantly enhance efficiency and reduce costs; however, existing approaches still face several challenges, includ

Alexander Ushakov, Yankun Wang

We prove that one variable equations in the lamplighter group $\MZ_2\wr \MZ$ are decidable and describe an algorithm for solving such equations. The algorithm has super-exponential time complexity in the worst case. We also show that, for most equations, decidability can be determined in nearly quadratic time; that is, the problem admits a nearly quadratic-t

Mehedi Hasan, Ishtiaq Murshed, Khayrul Islam, A. K. M. Masud

The development of next-generation electrochemical energy storage requires devices that combine the high energy density of batteries with the power capability and long cycle life of supercapacitors. However, the interfacial phenomena governing performance in these systems remain poorly unified. The solid-electrolyte interphase (SEI), a nanoscale film formed

Viktor Škorjanc, Stefanie Severin, Alexander Veber, Mauricio J. Prieto

Monolithic perovskite-silicon tandem solar cells experienced a significant increase in efficiency, making them viable for industrial applications. Among the various scalable and industry-compatible metal halide perovskite deposition techniques, co-evaporation stands out as particularly well-suited for perovskite-silicon tandem solar cells due to its ability

Zhibo Liu, Akira Watanabe

We compute the kaon gravitational form factor (GFF) using a bottom-up holographic QCD approach that incorporates SU(3) flavor symmetry breaking through the strange quark mass. We present the resulting Q^2 dependence of the kaon GFF and compare it with that of the pion. In the high-energy limit, the kaon GFF exhibits a 1/Q^2 falloff, in agreement with perturb

Yunlong Lin, Linqing Wang, Kunjie Lin, Zixu Lin

Agent-based editing models have substantially advanced interactive experiences, processing quality, and creative flexibility. However, two critical challenges persist: (1) instruction hallucination, text-only chain-of-thought (CoT) reasoning cannot fully prevent factual errors due to inherent information bottlenecks; (2) reward hacking, dynamic policy optimi

Marcel Theilen, Siegfried Kaidisch, Monja Stettner, Sarah Zajusch

Excitons, the correlated electron-hole pairs governing optical and transport properties in organic semiconductors, have long resisted direct experimental access to their full quantum-mechanical wave functions. Here, we use femtosecond time-resolved photoemission orbital tomography (trPOT), combining high-harmonic probe pulses with time- and momentum-resolved

Xiaoxuan Wang, Rolf Stadler

We study automated intrusion prediction in an IT system using statistical learning methods. The focus is on developing online attack predictors that detect attacks in real time and identify the current stage of the attack. While such predictors have been proposed in the recent literature, these works typically rely on constructing a monolithic predictor tail

Wancheng Liu

This paper presents a classification of the total spaces of $S^3$-bundles over $\mathbb{C}P^2$ up to orientation-preserving homotopy equivalence. Our approach proceeds in two steps: we first derive the PL-homeomorphism classification for these manifolds by computing their Kreck-Stolz invariants. Then, building upon this PL classification result and through a

Peng Kuang, Xiangxiang Wang, Wentao Liu, Jian Dong

Multimodal Large Language Models (MLLMs) have achieved impressive performances in mathematical reasoning, yet they remain vulnerable to visual hallucinations and logical inconsistencies that standard outcome-based supervision fails to mitigate. While Process Reward Models (PRMs) promise step-by-step verification, current approaches typically operate as scala

Minseong Kweon, Janghyun Kim, Ukcheol Shin, Jinsun Park

Recent advances in Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (3DGS) have achieved considerable performance in RGB scene reconstruction. However, multi-modal rendering that incorporates thermal infrared imagery remains largely underexplored. Existing approaches tend to neglect distinctive thermal characteristics, such as heat conduction and the

Ke Chen

This paper presents an analytical solution to the inverse kinematic problem(IKP) for the seven degree-of-freedom (7-DOF) Moz1 Robot Arm with offsets on wrist. We provide closed-form solutions with the novel arm angle . it allow fully self-motion and solve the problem of algorithmic singularities within the workspace. It also provides information on how the r

Yin Yang, Peihan Sun, Ye Shen, Zhijun Tu

Superconducting electrides have attracted growing attention for their potential to achieve high superconducting transition temperatures (TC) under pressure. However, many known electrides are chemically reactive and unstable, making high-quality single-crystal growth, characterization, and measurements difficult, and most do not exhibit superconductivity at

Fadi Dornaika, Danyang Sun

Cutmix-based data augmentation, which uses a cut-and-paste strategy, has shown remarkable generalization capabilities in deep learning. However, existing methods primarily consider global semantics with image-level constraints, which excessively reduces attention to the discriminative local context of the class and leads to a performance improvement bottlene

Moh Imam Faiz, Aviv Yuniar Rahman, Rangga Pahlevi Putra

The security of biometric authentication is increasingly critical as digital identity systems expand. Iris recognition offers high reliability due to its distinctive and stable texture patterns. Recent progress in deep learning, especially Vision Transformers ViT, has improved visual recognition performance. Yet, the effect of optimizer choice on ViT-based b

Cesare Donati, Fabrizio Dabbene, Constantino Lagoa, Carlo Novara

This paper addresses the problem of identifying contractive Lur'e-type systems. Specifically, it proposes an identification framework that integrates linear prior knowledge with a kernel representation of the nonlinear feedback while systematically enforcing contractivity via Lipschitz constant design. The resulting algorithms provide models that are accurat

Davide Donno, Donatello Elia, Gabriele Accarino, Marco De Carlo

Accurate tropical cyclones (TCs) tracking represents a critical challenge in the context of weather and climate science. Traditional tracking schemes mainly rely on subjective thresholds, which may introduce biases in their skills on the geographical region of application and are often computationally and data-intensive, due to the management of a large numb

Soumyakanti Bose, Yong-Siah Teo, Hyukjoon Kwon, Hyunseok Jeong

Despite several approaches proposed to operationally characterize quantum states of light-those that cannot be sampled with a positive distribution over classical states-most existing formulations suffer from limited practicality or rely on convex optimization procedures that are computationally demanding. In this work, we develop a general convex resource-t

Chaoyang Wang, Tianmeng Yang, Jingdong Wang, Yunhai Tong

Classifier-free guidance (CFG) has become a widely adopted and practical approach for enhancing generation quality and improving condition alignment. Recent studies have explored guidance mechanisms for unconditional generation, yet these approaches remain fundamentally tied to assumptions specific to diffusion models. In this work, we propose a spectrum-wea

Nan Zhuang, Wenshuo Wang, Lekai Qian, Yuxiao Wang

Recent studies have demonstrated that some Large Language Models exhibit choice-supportive bias (CSB) when performing evaluations, systematically favoring their chosen options and potentially compromising the objectivity of AI-assisted decision making. While existing debiasing approaches primarily target demographic and social biases, methods for addressing

Louisa Fay, Hajer Reguigui, Bin Yang, Sergios Gatidis

Deep learning models can excel on medical tasks, yet often experience spurious correlations, known as shortcut learning, leading to poor generalization in new environments. Particularly in medical imaging, where multiple spurious correlations can coexist, misclassifications can have severe consequences. We propose MIMM-X, a framework that disentangles causal

Yuta Oshima, Daiki Miyake, Kohsei Matsutani, Yusuke Iwasawa

Recent text-to-image generation models have acquired the ability of multi-reference generation and editing; that is, to inherit the appearance of subjects from multiple reference images and re-render them in new contexts. However, existing benchmark datasets often focus on generation using a single or a few reference images, which prevents us from measuring

J. Merc, J. Mikołajewska, C. Gałan, K. Iłkiewicz

We present a detailed analysis of Terz V 2513 (=2MASS J17334728-2719266), a poorly studied symbiotic star. Our motivation was a peculiar beating pattern in its light curves from all-sky surveys and our own observations. Using \textit{Gaia} DR3 and OGLE-IV photometry, we show that this variability arises from blending with a nearby, unrelated Mira variable (\

Renbin Li, Shuangshuang Li, Peihao Dong

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a key enabler for sixth-generation (6G) networks, offering massive spatial degrees of freedom. Despite these advantages, the coexistence of near-field and far-field effects in hybrid-field channels presents significant challenges for accurate estimation, where traditional methods often

Jayan Adhikari, Prativa Joshi, Sushish Baral

Deep learning models for breast cancer detection from mammographic images have significant reliability problems when presented with Out-of-Domain (OOD) inputs such as other imaging modalities (CT, MRI, X-ray) or equipment variations, leading to unreliable detection and misdiagnosis. The current research mitigates the fundamental OOD issue through a comprehen

Thibeau Wouters, Peter T. H. Pang, Tim Dietrich, Chris Van Den Broeck

Bayesian inference, widely used in gravitational-wave parameter estimation, depends on the choice of priors, i.e., on our previously existing knowledge. However, to investigate neutron star mergers, priors are often chosen in an agnostic way, leaving valuable information from nuclear physics and independent observations of neutron stars unused. In this work,

Dennis Zanutto, Christos Michalopoulos, Lydia Tsiami, André Artelt

The highly anticipated 'Battle of the Water Networks' is back with a new challenge for the water community. This competition will be hosted at the 4th International Joint Conference on Water Distribution Systems Analysis and Computing and Control in the Water Industry (WDSA/CCWI 2026), taking place in Paphos, Cyprus, from May 18-21, 2026. This competition em

J. W. Zhou, Amelie Saintonge, Sami Dib, Pavel Kroupa

We construct a model by integrating observational constraints from the Milky Way and nearby galaxies to predict cloud-scale star formation rates (SFRs). In the model, we first estimate the initial total mass of clumps in a cloud based on the cloud mass, and then generate the initial clump population of the cloud using the initial clump mass function. Next, w

Loris Di Cairano

Can a secret be hidden not in which quantum state is prepared, but in the way that state \emph{moves} through its space of possibilities? Motivated by this question, we propose an essential geometric perspective on quantum cryptography in which projective Hilbert space and its entanglement foliations play a central role. The basic ingredients are: (a) the Fu

Hong Zheng, Nan Mu, Han Su, Lin Feng

Noise reduction constitutes a crucial operation within Digital Signal Processing. Regrettably, it frequently remains neglected when dealing with the processing of convolutional features in segmentation networks. This oversight could trigger the butterfly effect, impairing the subsequent outcomes within the entire feature system. To complete this void, we con

Bertrand Eynard

We provide explicit expressions of ABCD tensors for the most classical classes of spectral curves. And we discuss algorithmic implementation of Topological Recursion.

Guo-Hua Wang, Liangfu Cao, Tianyu Cui, Minghao Fu

We introduce $\textbf{Ovis-Image}$, a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints. Built upon our previous Ovis-U1 framework, Ovis-Image integrates a diffusion-based visual decoder with the stronger Ovis 2.5 multimodal backbone, leveraging a text-cent

Yulu Ren, Yang Shen, Chengyang Xu, Wanfei Shan

ABCB-stacked tetralayer graphene, with intrinsic spontaneous polarization, offers a unique platform to explore electron correlation effects, whose interplay with spin-orbit coupling may engender topological phases. Here, employing a $\mathbf{k}\cdot\mathbf{p}$ model with self-consistent Hartree-Fock calculations, we investigate its electronic ground states.

Udi Boker, Thomas A. Henzinger, Jan Otop

The target discounted-sum problem is the following: Given a rational discount factor $0<\lambda<1$ and three rational values $a,b$, and $t$, does there exist a finite or an infinite sequence $w \in \{a,b\}^*$ or $w \in \{a,b\}^\omega$, such that $\sum_{i=0}^{|w|} w(i) \lambda^i$ equals $t$? The problem turns out to relate to many fields of mathematics and co

Huaixiao Tou, Ying Zeng, Yuemeng Li, Cong Ma

We present ShoppingComp, a challenging real-world benchmark for comprehensively evaluating LLM-powered shopping agents on three core capabilities: precise product retrieval, expert-level report generation, and safety critical decision making. Unlike prior e-commerce benchmarks, ShoppingComp introduces difficult product discovery queries with many constraints

Sumit Mamtani, Abhijeet Bhure

This paper investigates fake news detection as a downstream evaluation of Transformer representations, benchmarking encoder-only and decoder-only pre-trained models (BERT, GPT-2, Transformer-XL) as frozen embedders paired with lightweight classifiers. Through controlled preprocessing comparing pooling versus padding and neural versus linear heads, results de

Marcos Ruibal Ortigueira, Robert de Keijzer, Luke Visser, Oliver Tse

This work explores connections between the quantum relative entropy of two faithful states $\rho,\sigma$ (i.e. full-rank density matrices) and the Kullback-Leibler divergences of classical measures $\mu,\nu$. Here, $\mu$ and $\nu$ are measures on the space of pure states, realizing $\rho$ and $\sigma$ respectively. The motivation for this result is to establ

Jiachen Li, Shihao Li, Christopher Martin, Zijun Chen

Roll-to-roll manufacturing requires precise tension and velocity control to ensure product quality, yet controller commissioning and adaptation remain time-intensive processes dependent on expert knowledge. This paper presents an LLM-assisted multi-agent framework that automates control system design and adaptation for R2R systems while maintaining safety. T

Qiushi Yang, Yingjie Chen, Yuan Yao, Yifang Men

Text-to-video (T2V) generation has achieved remarkable progress in producing high-quality videos aligned with textual prompts. However, aligning synthesized videos with nuanced human preference remains challenging due to the subjective and multifaceted nature of human judgment. Existing video preference alignment methods rely on costly human annotations or u

Jinze Li, Yixing Xu, Guanchen Li, Shuo Yang

Large language models (LLMs) achieve strong performance across diverse tasks but suffer from high inference latency due to their autoregressive generation. Speculative Decoding (SPD) mitigates this issue by verifying candidate tokens in parallel from a smaller draft model, yet its strict exact-match verification discards many semantically valid continuations

Ali Tozar

We investigate the universality of Anderson localization transitions in one-dimensional non-Hermitian systems exhibiting the skin effect. By developing a numerically stable Log-Space Non-Hermitian Scaling (LNS) method, we overcome the severe floating-point overflow issues associated with the exponential growth of transmittance (T ~ exp(2 gamma L)), enabling

Francesco Immorlano, Elijah Tavares, Felix Draxler, Padhraic Smyth

Large ensembles of climate projections are essential for characterizing uncertainty in future climate and extreme weather events, yet computational constraints of numerical climate models limit ensemble sizes to a small number of realizations per model. We present a unified conditional diffusion model that dramatically reduces this computational barrier by l

Mamadou Eramane Bodian, Souhaibou Sambou, Sény Diatta, Salomon Sambou

In this paper, we consider the problem of solving the $\partial\overline{\partial}$ equation with discribed support for differential forms in a relatively compact domain $\Omega$ of a complex analytic manifold $X$. And as a consequence, we have the solution of the equation $\partial\overline{\partial}$ for extendable currents without the annulation assumptio

Shouhe Zhang, Dayong Ren, Sensen Song, Yurong Qian

Extreme exposure degrades both the 3D map reconstruction and semantic segmentation accuracy, which is particularly detrimental to tightly-coupled systems. To achieve illumination invariance, we propose a novel semantic SLAM framework with two designs. First, the Intrinsic Appearance Normalization (IAN) module proactively disentangles the scene's intrinsic pr

Adithya A. Vasista, Anushka Agrawal, Tanay Nag

We consider the three-spin Ising model to study the effect of three-spin interacting term on bi-partitie entanglement between adjacent spins. The three-dominated disordered region has tri-partite entanglement causing a vanishingly small concurrence, while it acquires maximum value around the critical points. Considering the above model as an environment, we

Niels Taubert

This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews stu

Eramane Bodian, Winnie Ossete Ingoba, Souhaibou Sambou, Papa Badiane

By H\"ormander's $L^2$-method, we study the operator $\alpha \partial^k \bar{\partial}^{k} + \beta \bar{\partial}^k +\gamma \partial^k + c$ for any order $k$ with $\alpha, \beta, \gamma \in \mathbb{R}$ such that $(\alpha, \beta, \gamma) \neq(0,0,0)$ in the weighted Hilbert space $L^2(\mathbb{C}, \mathrm{e}^{-|z|^2})$. We prove the existence of its right inve

Zhirui Liu, Kaiyang Ji, Ke Yang, Yahao Fan

Enabling humanoid robots to follow free-form natural language commands is a critical step toward seamless human-robot interaction and general-purpose embodied AI. However, existing methods remain limited, often constrained to simple instructions or forced to sacrifice motion diversity for physical plausibility. To address this gap, we present Humanoid-LLA, a

Andrew T. Pierce, Chirag Vaswani, Dimitri Pimenov, Sihong Xu

Two-dimensional transition metal dichalcogenide (TMD) semiconductors exhibit a wide range of novel phenomena at millielectronvolt (terahertz-frequency) energy scales, including superconducting and correlation-induced insulating gaps that are frequently accompanied by symmetry breaking. However, due to the subwavelength dimensions and the often low conductivi

Chen Li, Eric Peh, Basura Fernando

Recent advances in large vision-language models (VLMs) have shown significant promise for 3D scene understanding. Existing VLM-based approaches typically align 3D scene features with the VLM's embedding space. However, this implicit alignment often yields suboptimal performance due to the scarcity of 3D data and the inherent complexity of spatial relationshi

Eiichi Nakai, Menghao Tang, Dachun Yang, Wen Yuan

Let $Y(\mathcal{X})$ be a ball quasi-Banach function space on the space of homogeneous type $(\mathcal{X},\rho,\mu)$ satisfying some mild additional assumptions, $q\in(0,\infty)$, and $\dot{W}^{s,q}_Y(\mathcal{X})$ with $s\in(0,1)$ be the homogeneous fractional Sobolev space associated with $Y(\mathcal{X})$. In this article, we show that, for any $f\in Y(\ma

Minh Duc Vu, Mingshuo Liu, Doudou Zhou

Measuring how central or typical a data point is underpins robust estimation, ranking, and outlier detection, but classical depth notions become expensive and unstable in high dimensions and are hard to extend beyond Euclidean data. We introduce Fused Unified centrality Score Estimation (FUSE), a neural centrality framework that operates on top of arbitrary

Shiyu Shen, Zhe Gao, Taifeng Chai, Yang Huang

Deep learning has revolutionized solar image analysis, yet most approaches train task-specific encoders from scratch or rely on natural-image pretraining that ignores the unique characteristics of Solar Dynamics Observatory (SDO) data. We introduce SolarCHIP, a family of contrastively pretrained visual backbones tailored to multi-instrument SDO observations.

Ferran Bohigas-Daranas, Oriol Gomis-Bellmunt, Eduardo Prieto-Araujo

This paper presents a critical and practical approach to the evolution of distribution network reconfiguration algorithms, tracing their development from foundational heuristic methods introduced in 1975 to contemporary state-of-the-art techniques. The article systematically reviews seven different methodologies, including classical heuristic algorithms (Mer

Atsushi Kitazawa, Chihaya Ito, Yuta Yoshida, Takamitsu Shioi

A long line of concurrency-control (CC) protocols argues correctness via a single serialization point (begin or commit), an assumption that is incompatible with snapshot isolation (SI), where read-write anti-dependencies arise. Serial Safety Net (SSN) offers a lightweight commit-time test but is conservative and effectively anchored on commit time as the sol

Shrihari Sridharan, Sourjya Roy, Anand Raghunathan, Kaushik Roy

Large Language Models (LLMs) have achieved state-of-the-art accuracies in a variety of natural language processing (NLP) tasks. However, this success comes at the cost of increased model sizes which leads to additional computational burden. Mixture of Experts (MoEs) overcome this bottleneck by decoupling model capacity from computation by only activating a s

Jiachen Li, Shihao Li, Christopher Martin, Wei Li

Roll-to-roll (R2R) manufacturing requires precise tension and velocity control under operational constraints. Model predictive control demands gradient computation, while sampling-based methods like MPPI struggle with hard constraint satisfaction. This paper presents an adaptive trajectory bundle method that achieves rigorous constraint handling through deri

Yuji Muta, Naoki Terai

In this paper, we study rooted products of graphs from the perspective of combinatorial commutative algebra. For edge ideals, we introduce the 2-Cohen-Macaulayness with respect to a vertex and use it to investigate when edge ideals of rooted products of graphs are Cohen-Macaulay. Moreover, we completely determine when attaching a graph on at most six vertice

Jiachen Li, Shihao Li, Jian Chu, Dongmei Chen

Data Enabled Predictive Control (DeePC) is an established model free approach to predictive control, but it faces two open challenges: computational complexity that scales cubically with dataset size and performance degradation when data are corrupted. This paper introduces Robust Data Selection DeePC (RDS DeePC), a framework that addresses both obstacles th

I. S. Galtsov, R. V. Muratov, G. V. Vyskvarko, S. A. Murzov

Molecular dynamics is widely used to study various phenomena, such as diffusion, shock wave propagation, and plasma dynamics. A wide range of software packages supports the expanding scope of molecular dynamics applications. However, the quality of simulations depends on force field approximations, ranging from simple models to direct quantum solutions. Rece

Haiyang Mei, Qiming Huang, Hai Ci, Mike Zheng Shou

Accurate robot segmentation is a fundamental capability for robotic perception. It enables precise visual servoing for VLA systems, scalable robot-centric data augmentation, accurate real-to-sim transfer, and reliable safety monitoring in dynamic human-robot environments. Despite the strong capabilities of modern segmentation models, surprisingly it remains

Zizhuo Zhao, Yuefeng Di, Ligong Bian, Jing Shu

Stable domain wall (DW) must decay to avoid overclose the Universe. A commonly used solution is to slightly break the PQ symmetry by introducing a bias term in the potential. In this work, we propose an alternative, symmetry-preserving mechanism: coupling the axion field to a helical primordial magnetic field (PMF) via the Chern-Simons term. Using three-dime

Taeyeong Kim, SeungJoon Lee, Jung Uk Kim, MyeongAh Cho

Domain generalization in semantic segmentation faces challenges from domain shifts, particularly under adverse conditions. While diffusion-based data generation methods show promise, they introduce inherent misalignment between generated images and semantic masks. This paper presents FLEX-Seg (FLexible Edge eXploitation for Segmentation), a framework that tr

Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, Shivdeep Singh

Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, and not verifiable accounts of actual program behavior. Models trained on such data learn logically fla

E. Ballico, S. Canino

In this paper we address the postulation problem of zero-dimensional schemes on a surface of length at most 4. We prove some general results and then we focus on the case of P2, P1xP1 and Hirzebruch surfarces. In particular, we prove that except for few well-known exceptions, a general union of schemes of length at most 4 has always good postulation in P2 an

Daniel Max Hoffmann, Tomasz Rzepecki

We continue the study of the semigroup of global invariant types introduced by Gannon, Hoffmann, and Krupiński and the associated convolution semigroup of invariant Keisler measures. The first part of the paper concerns the Idempotent Measure Conjecture, studied in [CGK24] and [GHK25], which predicts that idempotent fim Keisler measures should be precisely t

Jin Han, Tianfan Fu, Wu-Jun Li

Protein inverse folding, the design of an amino acid sequence based on a target protein structure, is a fundamental problem of computational protein engineering. Existing methods either generate sequences without leveraging external knowledge or relying on protein language models~(PLMs). The former omits the knowledge stored in natural protein data, while th