March 2026 arXiv papers — page 17

Minsen Yuan, Amanuel Adane, James Humann, Yue Yu

Energy-sharing UAV-UGV systems extend the endurance of Uncrewed Aerial Vehicles (UAVs) by leveraging Uncrewed Ground Vehicles (UGVs) as mobile charging stations, enabling persistent autonomy in infrastructure-sparse environments. Trajectory optimization for these systems is often challenging due to UGVs' terrain access constraints and the discrete nature of

Kang Lu, Weiqiang Wang, Alex Weekes

We formulate shifted affine iquantum groups of arbitrary quasi-split ADE types via Drinfeld presentations. We construct GKLO-type representations of shifted affine iquantum groups via algebras of difference operators, which allow us to construct truncated shifted affine iquantum groups. This provides a q-deformation of truncated shifted iYangians in our prio

Mohammed Barkatou, Mohamed El Morsalani

We investigate a geometric dynamical mechanism arising in the class $\mathcal{O}_C$ of domains containing a fixed convex set $C$ and satisfying two geometric normals properties introduced by Barkatou \cite{Barkatou2002}. The first property induces a radial structure linking the boundaries $\partial C$ and $\partial \Omega$ through a thickness function $d:\pa

Davide Di Gioia

Current Retrieval-Augmented Generation (RAG) systems predominantly rely on relevance-based dense retrieval, sequentially fetching documents to maximize semantic similarity with the query. However, in knowledge-intensive and real-world scenarios characterized by conflicting evidence or fundamental query ambiguity, relevance alone is insufficient for resolving

Yizhe Feng, Weiguo Gao, Jia Yin

We propose two novel data-driven dynamic mode decomposition (DMD)-type methods, the Crank--Nicolson DMD and the semi-implicit DMD, to predict the highly oscillatory dynamics of the semiclassical Schr\"odinger equations efficiently and accurately. Unlike many existing DMD-type methods which directly models the dynamics of the wave function, our approach is ba

Tobias Breiten, Shubhaditya Burela, Philipp Schulze

Solving optimal control problems for transport-dominated partial differential equations (PDEs) can become computationally expensive, especially when dealing with high-dimensional systems. To overcome this challenge, we focus on developing and deriving reduced-order models that can replace the full PDE system in solving the optimal control problem. Specifical

Sujan Mandal, Tamoghna Mukhopadhyay, Achintya Kumar Dutta

We present a computationally efficient relativistic formulation of the equation-of-motion coupled-cluster method for the double electron attachment problem. In this work, the exact two-component Hamiltonian within the atomic mean-field approximation is employed, yielding results that are in close agreement with the corresponding four-component calculations.

Yubo Zhang, Zhiguo Hao, Songhao Yang, Baohui Zhang

Additional active power control (AAPC) of wind turbines (WTs) is essential to improve the transient frequency stability of low-inertia power systems. Most of the existing research has focused on imitating the frequency response of the synchronous generator (SG), known as virtual inertia control (VIC), but are such control laws optimal for the power systems?

Ho Lee

We study the asymptotic behavior of small solutions to the Vlasov--Klein--Gordon system in high dimensions. The standard argument of Glassey and Strauss \cite{GS87} for studying small solutions to the Vlasov--Maxwell system does not apply to the Vlasov--Klein--Gordon system due to the massiveness of the Klein--Gordon field. In this paper we use the vector fi

Adrien Busnot Laurent, Hans Munthe-Kaas, Venkatesh G. S

Aromatic Butcher series were successfully introduced for the study and design of numerical integrators that preserve volume while solving differential equations in Euclidean spaces. They are naturally associated to pre-Lie-Rinehart algebras and pre-Hopf algebroids structures, and aromatic trees were shown to form the free tracial pre-Lie-Rinehart algebra. In

Marco Hidalgo-Araya, Raphaël Trésor, Bart Van Erp, Wouter W. L. Nuijten

Speech enhancement in hearing aids remains a difficult task in nonstationary acoustic environments, mainly because current signal processing algorithms rely on fixed, manually tuned parameters that cannot adapt in situ to different users or listening contexts. This paper introduces a unified modular framework that formulates signal processing, learning, and

Leon Witt, Kentaroh Toyoda, Wojciech Samek, Dan Li

The synergy between Federated Learning and blockchain has been considered promising; however, the computationally intensive nature of contribution measurement conflicts with the strict computation and storage limits of blockchain systems. We propose a novel concept to decentralize the AI training process using blockchain technology and Multi-task Peer Predic

A. Sultanov, E. Mutsenik, L. Kaczmarek, M. Schmelz

The emission of photon from an individual atom encodes the phase of its initialized quantum state. Using single-shot heterodyne detection, we measure the phase distribution of the emission from a superconducting transmon qubit in an open waveguide configuration and track its evolution over time. We demonstrate that the presence of a quantum superposition is

Luka Skolc, Sambuddha Chattopadhyay, Filip Marijanović, Qitong Li

Optical cavities enable strong, long-range, light-matter interactions that can drive collective ordering phenomena, such as superradiant self-organization in ultracold atomic gases. Extending these ideas to solid-state electron systems could enable continuous-wave optical control of electronic order, but is impeded by the mismatch between optical wavelengths

Xuan Deng, Xiandong Meng, Hengyu Man, Qiang Zhu

Although 3D Gaussian Splatting (3DGS) enables high-fidelity real-time rendering, its prohibitive storage overhead severely hinders practical deployment. Recent anchor-based 3DGS compression schemes reduce gaussian redundancy through some advanced context models. However, they overlook explicit geometric dependencies, leading to structural degradation and sub

Zhongping Ji

Orthogonal feature decorrelation is effective for low-bit online vector quantization, but dense random orthogonal transforms incur prohibitive $O(d^2)$ storage and compute. RotorQuant reduces this cost with blockwise $3$D Clifford rotors, yet the resulting $3$D partition is poorly aligned with modern hardware and offers limited local mixing. We propose \text

Daniel Gutierrez, Ruben Martinez, Leyre Arnedo, Antonio Cuesta

The demand for high-speed, low-latency, and energy-efficient object detection in autonomous systems -- such as advanced driver-assistance systems (ADAS), unmanned aerial vehicles (UAVs), and Industry 4.0 robotics -- has exposed the limitations of traditional Convolutional Neural Networks (CNNs). To address these challenges, we have developed AceleradorSNN, a

Xiaohang Nie, Zihan Guo, Kezhuo Yang, Zhichong Zheng

AI agents are rapidly expanding in both capability and population: they now write code, operate computers across platforms, manage cloud infrastructure, and make purchasing decisions, while open-source frameworks such as OpenClaw are putting personal agents in the hands of millions and embodied agents are spreading across smartphones, vehicles, and robots. A

Weiguang Zhao, Junting Dong, Rui Zhang, Kailin Li

Teleoperation is a key paradigm for transferring human dexterity to robots, yet most prior work targets objects that are initially static, such as grasping or manipulation. Dynamic object catch, where objects move before contact, remains underexplored. Pure teleoperation in this task often fails due to timing, pose, and force errors, highlighting the need fo

Kosei Fushimi, Kazunobu Serizawa, Junya Ikemoto, Kazumune Hashimoto

Signal Temporal Logic (STL) is widely used to specify timed and safety-critical tasks for cyber-physical systems, but writing STL formulas directly is difficult for non-expert users. Natural language (NL) provides a convenient interface, yet its inherent structural ambiguity makes one-to-one translation into STL unreliable. In this paper, we propose an \text

Victoria Leonenkova, Ekaterina Shumitskaya, Dmitriy Vatolin, Anastasia Antsiferova

This demonstration presents Digital-Physical Adversarial Attacks (DiPA), a new class of practical adversarial attacks against pervasive camera-based authentication systems, where an attacker displays an adversarial patch directly on a smartphone screen instead of relying on printed artifacts. This digital-only physical presentation enables rapid deployment,

Maik Gaerner, Judith Bünte, Finn Peters, Inga Ennen

The exchange bias (EB) in ferromagnetic/antiferromagnetic (FM/AFM) bilayer systems causes a shift of the magnetic hysteresis curve after field cooling through the N\'eel temperature of the AFM. In some cases, this shift is accompanied by an asymmetry between ascending and descending branches. In the past, this asymmetric magnetization reversal has been studi

Alejandra Avalos-Pacheco, Monia Lupparelli, Francesco C. Stingo

We introduce a novel class of graphical models, termed profile graphical models, that represent, within a single graph, how an external factor influences the dependence structure of a multivariate set of variables. This class is quite general and includes multiple graphs and chain graphs as special cases. Profile graphical models capture the conditional dist

Robin Kühn, Moritz Schappler, Thomas Seel, Dennis Bank

The complexity of teaching humanoid robots new tasks is one of the major reasons hindering their widespread adoption in the industry. While Imitation Learning (IL), particularly Action Chunking with Transformers (ACT), enables rapid task acquisition, there is no consensus yet on the optimal sensory hardware required for manipulation tasks. This paper benchma

C. Z. Cao, J. Z. Han, M. Xiong, M. Deng

Generating and preserving metrologically useful quantum states is a central challenge in quantum-enhanced metrology. In low-field atomic magnetometry with multilevel atoms, the nonlinear Zeeman (NLZ) effect is both a resource and a limitation. It can generate internal spin squeezing within a single atomic qudit, but under fixed readout it also rotates and di

Gianluca Peri, Timoteo Carletti, Duccio Fanelli, Diego Febbe

Neural networks are fundamental tools of modern machine learning. The standard paradigm assumes binary interactions (across feedforward linear passes) between inter-tangled units, organized in sequential layers. Generalized architectures have been also designed that move beyond pairwise interactions, so as to account for higher-order couplings among computin

J. de la Nuez Gonzalez, Zaniar Ghadernezhad, Paolo Marimon, Michael Pinsker

To every $\omega$-categorical structure $M$ one can associate two spaces of symmetries which determine the structure up to first-order bi-interpretability: the topological group $\mathrm{Aut}(M)$ of its automorphisms and the topological monoid $\mathrm{EEmb}(M)$ of its elementary embeddings, both equipped with the topology of pointwise convergence $\tau_{\ma

Edoardo Signoroni, Pavel Rychlý

Lombard, an underresourced language variety spoken by approximately 3.8 million people in Northern Italy and Southern Switzerland, lacks a unified orthographic standard. Multiple orthographic systems exist, creating challenges for NLP resource development and model training. This paper presents the first study of automatic Lombard orthography classification

Malick Ebiele, Malika Bendechache, Rob Brennan

This paper proposes a new univariate filter feature selection (FFS) algorithm called KGroups. The majority of work in the literature focuses on investigating the relevance or redundancy estimations of feature selection (FS) methods. This has shown promising results and a real improvement of FFS methods' predictive performance. However, limited efforts have b

Alkis Sygkounas, Amy Loutfi, Andreas Persson

Reinforcement learning algorithms are defined by their learning update rules, which are typically hand-designed and fixed. We present an evolutionary framework for discovering reinforcement learning algorithms by searching directly over executable update rules that implement complete training procedures. The approach builds on REvolve, an evolutionary system

Weichao Cai, Weiliang Huang, Biao Xue, Chao Huang

Marine scene understanding and segmentation plays a vital role in maritime monitoring and navigation safety. However, prevalent factors like fog and strong reflections in maritime environments cause severe image degradation, significantly compromising the stability of semantic perception. Existing restoration and enhancement methods typically target specific

Siran Zhang, Shuming Cheng

The quantum approximate optimization algorithm (QAOA) is a leading variational approach to combinatorial optimization, but its practical performance depends strongly on objective design, parameter search, and shot allocation. We present a resource-efficient QAOA framework that uses the cut value of the most probable measured bitstring as the optimization obj

Wafa Labidi, Holger Boche, Christian Deppe, Marc Geitz

The coordination of large populations of highly constrained devices, such as micro- and nanoscale agents in biomedical applications, poses fundamental challenges to classical communication paradigms. In scenarios such as targeted drug delivery, devices operate under severe limitations in energy, size, and communication capabilities, while requiring precise a

M. Cocconcelli, A. Angotti, P. Florio, N. Pellizzi

Compact, low-power analog RF components are essential for next-generation microwave electronics and wireless systems. We demonstrate an all-electric integrated piezo-magnonic microelectromechanical system that enables efficient voltage control of GHz spin-wave signals via magnetoelastic coupling. Exploiting the large strain in a CoFeB magnonic waveguide inte

Manisha Dubey, Sebastiaan De Peuter, Wanrong Wang, Samuel Kaski

Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, despite real decision makers often exhibiting multiple latent preference archetypes across contexts. We propose an active preference learning framework for many-objective Bayesian optimization that infers latent p

Fangda Ye, Yuxin Hu, Pengxiang Zhu, Yibo Li

Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed rubrics, failing to evaluate the underlying research process. Most also offer limited multimodal coverage, rely on synthetic tasks that do not reflect real-world query complexity, a

Sven Krippendorf, Joseph Tooby-Smith

A recurring challenge in theoretical physics is to make reliable global statements about bounded but combinatorially large model spaces. Exhaustive scans quickly become opaque or impractical, while statistical exploration does not by itself provide theorem-backed guarantees. This motivates workflows in which the model-building problem itself is formalized in

Sravanth Kodavanti, Manjunath Arveti, Sowmya Vajrala, Srinivas Miriyala

Diffusion Transformers (DiT) have established a new state-of-the-art in high-fidelity image synthesis; however, their massive computational complexity and memory requirements hinder local deployment on resource-constrained edge devices. In this paper, we introduce EdgeDiT, a family of hardware-efficient generative transformers specifically engineered for mob

Pando Georgiev, Vasil Zhelinski, Boyan Zlatanov

We generalize an abstract variational principle in Banach spaces, introduced by Topalova \& Zlateva, by showing that the set $\mathbb{P}_0$ of perturbations for which a perturbed lower semi-continuous function $f$ is WPMC (Well Posed Modulus Compact) not only contains a dense $G_\delta$ subset, but is also a complement to a $\sigma$-porous subset in a specif

Jussi Behrndt, Friedrich M. Philipp, Carsten Trunk

One of the most important contributions of Heinz Langer in the area of operator theory in Krein spaces is the introduction of the notion of definitizable operators and the construction of the corresponding spectral function. In this note we obtain a new characterization for the subclass of non-negative operators in Krein spaces which is based on local sign t

Maria Carvalho, Fagner B. Rodrigues

We introduce the notion of dynamical metric order of a continuous map on a compact metric space, study its basic properties, and compute it for several classes of maps. This concept which is a counterpart of the metric mean dimension with the role of the box-counting dimension being played by the metric order. It is devised for maps acting on spaces with inf

Claudia Danti, Michiel Lambrechts, Hannah Diamond-Lowe

Microlensing detections are uniquely well-suited to probing the population of planets outside the water iceline, down to planetary masses comparable to the Earth. Here, we perform 1D pebble-accretion population synthesis simulations to explore a sample of iceline planets around stars with masses and metallicities similar to the target population of the Galac

Silvia Fasce, Diana Nelli, Luca Benzi, Georg Daniel Förster

Fivefold-twinned metal nanoparticles host a central wedge disclination that strongly influences their mechanical and catalytic properties. Yet the atomistic mechanisms governing the stability, migration, and annihilation of this topological defect remain incompletely understood. Here we present a systematic molecular dynamics study of gold Marks decahedra in

Rizoi Bakhromzod

Nine impact craters on Mercury bear the names of Persian-Tajik poets: Rudaki, Saadi, Nizami, Rumi, Navoi, Firdousi, Hafiz, Sanai, and Mahsati. We compile IAU-approved coordinates, diameters, quadrant designations, approval dates (1976--2025), and MESSENGER-derived geological characteristics for each crater, and place these data in the broader context of Sola

Camila Crispin, Lorenzo J. Díaz

We study partially hyperbolic homoclinic classes of $C^1$-generic diffeomorphisms with a one-dimensional central bundle, so that the central Lyapunov exponent $\chi^c(\mu)$ is well defined for any ergodic measure $\mu$ supported on the class. We focus on nonhyperbolic homoclinic classes supporting ergodic measures with positive, zero, and negative central ex

Luca Minnei, Cristian Manca, Giorgio Piras, Angelo Sotgiu

Machine Learning (ML)-based detectors are becoming essential to counter the proliferation of malware. However, common ML algorithms are not designed to cope with the dynamic nature of real-world settings, where both legitimate and malicious software evolve. This distribution drift causes models trained under static assumptions to degrade over time unless the

Jyoti Yadav, Mousumi Das, S Amrutha, Dimitra Rigopoulou

We present a far-ultraviolet (FUV) analysis of the star-forming complexes (SFCs) in the nearby spiral galaxy NGC\,2090, based on observations from the Ultraviolet Imaging Telescope (UVIT), and compare it with emission from the optical and infrared bands. NGC\,2090 exhibits prominent star formation in its extended outer disk, with FUV emission traced out to $

Carlos Güemes-Palau, Miquel Ferriol-Galmés, Jordi Paillisse-Vilanova, Pere Barlet-Ros

Network performance modeling is a field that predates early computer networks and the beginning of the Internet. It aims to predict the traffic performance of packet flows in a given network. Its applications range from network planning and troubleshooting to feeding information to network controllers for configuration optimization. Traditional network perfo

Peng Kuai, Yukun Yang, Shaolun Ruan, Junchi Xu

Rare disease diagnosis is inherently challenging due to heterogeneous symptoms, limited clinical familiarity, and fragmented evidence across specialties. Recent large language model (LLM)-based agentic systems have shown promise by simulating multidisciplinary team discussions to generate and evaluate diagnostic hypotheses. However, fully automated diagnosis

William Graham, Minyoung Jeon, Scott Joseph Larson

Let $G = GL(n)$ and $K = GL(p) \times GL(q)$ with $p+q=n$, where the groups are taken over $\C$. In this paper we study a certain family of $K$-orbit closures on the flag variety $X$ of $G$. The geometry of these orbit closures plays a central role in the infinite-dimensional representation theory of the real Lie group $U(p,q)$, and has applications to degen

Upala Mukhopadhyay, Purba Mukherjee, Alexandre Tkatchenko

The $\Lambda$CDM model successfully explains a wide range of cosmological observations; however, persistent discrepancies most notably the $H_0$ tension between early and late time measurements challenge its completeness. No proposed extension has yet resolved this tension while retaining the overall success of $\Lambda$CDM. In this work, we investigate whet

Chedly Ben Azizi, Claire Guilloteau, Gilles Roussel, Matthieu Puigt

This dataset provides a large collection of 10,915 synthetic hyperspectral image cubes paired with pixel-level vegetation trait maps, designed to support research in radiative transfer emulation, vegetation trait retrieval, and uncertainty quantification. Each hyperspectral cube contains 211 bands spanning 400--2500 nm at 10 nm resolution and a fixed spatial

Francesco Giovanni Celiberto, Francesca Lonigro

We report on recent developments of the Heavy-Flavor Non-Relativistic evolution (HF-NRevo) scheme, a framework designed to describe heavy-hadron formation through leading-power fragmentation at moderate and large transverse momentum. The approach combines short-distance inputs obtained from next-to-leading-order NRQCD calculations with collinear scale evolut

Michele Campisi

The explicit expression of ergotropy (a.k.a. available energy) of a classical system is known for the case when the system phase space density is continuous and with no plateaus. Here we provide the general expression of ergotropy that applies without those limitations. It easily follows upon casting the ergotropy problem as a function rearrangement problem.

Alkis Sygkounas, Rishi Hazra, Andreas Persson, Pedro Zuidberg Dos Martires

A central challenge in building continually improving agents is that training environments are typically static or manually constructed. This restricts continual learning and generalization beyond the training distribution. We address this with COvolve, a co-evolutionary framework that leverages large language models (LLMs) to generate both environments and

Carlos S. Sepúlveda, Gonzalo A. Ruz

Maritime surveillance missions, such as search and rescue and environmental monitoring, rely on the efficient allocation of sensing assets over vast and geometrically complex areas. Traditional Coverage Path Planning (CPP) approaches depend on decomposition techniques that struggle with irregular coastlines, islands, and exclusion zones, or require computati

Junhui Cao, Alexey Kavokin

Artificial spin ice provides a versatile setting for emergent gauge fields and magnetic monopole excitations. Here we propose a driven-dissipative polariton realization of artificial spin ice, in which the circular polarization of each link mode plays the role of an Ising degree of freedom, while an auxiliary lossy vertex mode dynamically enforces a local ic

D. Rodkin, Yu. Tchuvilsky

A theoretical study of the 7Be(p,gamma)8B reaction in the astrophysical energy range with the use of ab initio methods is presented. The used approaches are No-Core Shell Model and Cluster Channels Orthogonal Functions Method. The scheme also contains elements of R-matrix theory and procedures for extrapolating various data obtained in ab initio computations

Mirai Ikebuchi

Inspired by Brown's collapsing method (or discrete Morse theory) to obtain a free resolution of $\bbZ$ over the monoid ring $\bbZ M$, we apply algebraic discrete Morse theory to compute the homology groups of Lawvere theories, which is defined as Tor of a certain module. We reinterpret known partial free resolutions arising from complete term rewriting syste

En-Ming Huang, Shih-Hao Hung

Static timing analysis (STA) is crucial for Electronic Design Automation (EDA) flows but remains a computational bottleneck. While existing GPU-based STA engines are faster than CPU, they suffer from inefficiencies, particularly intra-warp load imbalance caused by irregular circuit graphs. This paper introduces Warp-STAR, a novel GPU-accelerated STA engine t

Christos G. Tsagas

Cosmological peculiar velocities have traditionally been studied within the framework of Newtonian theory. Around the turn of the century, a few quasi-Newtonian analyses appeared in the literature, but led to equations and results identical to those of the purely Newtonian approach [1]. More recently, a series of studies introduced a relativistic treatment o

Nitish Kumar, Sannu Kumar, S Akash, Manish Gupta

With the rapid proliferation of online sports journalism, extracting meaningful pre-game and post-game insights from articles is essential for enhancing user engagement and comprehension. In this paper, we address the task of automatically extracting such insights from articles published before and after matches. We curate a dataset of 7,900 news articles co

Bin Zhu, Qianghuai Jia, Tian Lan, Junyang Ren

Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems fro

Boumediene Hamzi, Marianne Clausel, Kamal Dingle, Marcus Hutter

Spurious correlations are common in time-series analysis because simple, low-complexity patterns can produce high Pearson correlations even between unrelated series. We argue that Kolmogorov complexity, interpreted as resistance to compression, provides a principled safeguard against such false positives. Using effective Hausdorff dimension, we show that the

Allison Chen, Isabella Pu

While artificial intelligence (AI) technology is becoming increasingly popular, its underlying mechanisms tend to remain opaque to most people. To address this gap, the field of AI literacy aims to develop various resources to teach people how AI systems function. Here we contribute to this line of work by proposing two games that demonstrate principles behi

Kagan Akcay, MohammadJavad Salehi, Giuseppe Caire

Coded caching (CC) exploits cumulative cache memory at user devices and coding to transform unicast traffic into multicast transmissions. While information theoretic results show significant gains over uncoded caching for various network topologies, its practical benefits remain unclear. In this work, we investigate CC for on-demand video streaming over larg

Soufiane Jhilal, Eleonora Pasqua, Caterina Marchesi, Riccardo Corradi

Neurodiverse learners often require reading supports, yet increasing scaffold richness can sometimes overload attention and working memory rather than improve comprehension. Grounded in the Construction-Integration model and a contingent scaffolding perspective, we examine how structural versus semantic scaffolds shape comprehension and reading experience in

Konstantinos Bountrogiannis, Anthony Ephremides, Panagiotis Tsakalides, George Tzagkarakis

This work introduces a framework for analyzing the Age of Incorrect Information (AoII) in a real-time monitoring system with a generic discrete-time Markov source. We study a noisy communication system employing a hybrid automatic repeat request (HARQ) protocol, subject to a transmission rate constraint. The optimization problem is formulated as a constraine

Muhammad S. Hasan, Karol Gietka

Simulating cavity quantum electrodynamics in synthetic platforms offers a promising route to exploring light-matter interactions without real photons, while enabling the transfer of cavity-based techniques to other systems. Among such platforms, Bose-Einstein condensates with synthetic spin-orbit coupling provide a controllable setting where internal and mot

Tao Xia, Jiawei Liu, Yukun Zhang, Ting Liu

Visual autoregressive (VAR) models have recently emerged as a promising family of generative models, enabling a wide range of downstream vision tasks such as text-guided image editing. By shifting the editing paradigm from noise manipulation in diffusion-based methods to token-level operations, VAR-based approaches achieve better background preservation and

Milton Zhou, Sizhong Qin, Yongzhi Li, Quan Chen

Short-form videos have become a primary medium for digital advertising, requiring scalable and efficient content creation. However, current workflows and AI tools remain disjoint and modality-specific, leading to high production costs and low overall efficiency. To address this issue, we propose AutoCut, an end-to-end advertisement video editing framework ba

Fernanda Miyuki Yamada, João Paulo Gois, Hiroki Takahashi

The rise of 3D anime-style avatars in gaming, virtual reality, and other digital media has driven significant interest in automated generation methods capable of capturing their distinctive visual characteristics. These include stylized proportions, expressive features, and non-photorealistic rendering. This paper reviews the advancements and challenges in u

P. I. Gerevenkov, Ia. A. Filatov, L. A. Shelukhin, P. A. Dvortsova

Laser-induced magnetization precession measurements in ferromagnets often reveal an anomalous decrease in the damping time near a field-induced second-order spin-orientation transition, a behavior that cannot be described by the linearized Landau-Lifshitz-Gilbert equation. Here we demonstrate that this anomaly is not a material property but results from inte

Jiho Park, Sieun Choi, Jaeyoon Seo, Minho Sohn

A sketch is a distilled form of visual abstraction that conveys core concepts through simplified yet purposeful strokes while omitting extraneous detail. Despite its expressive power, quantifying the efficiency of semantic abstraction in sketches remains challenging. Existing evaluation methods that rely on reference images, low-level visual features, or rec

Zhihao Lv, Xiaoyong Zhang, Mengfan Zhang, Xiaoyu Song

Multimodal locomotion is crucial for an animal's adaptability in unstructured wild environments. Similarly, in the human gastrointestinal tract, characterized by viscoelastic mucus, complex rugae, and narrow sphincters like the cardia, multimodal locomotion is also essential for a small-scale soft robot to conduct tasks. Here, we introduce a small-scale comp

Yipeng Yu

With the advancement of large language models (LLMs) in their knowledge base and reasoning capabilities, their interactive modalities have evolved from pure text to multimodality and further to agentic tool use. Consequently, their applications have broadened from question answering to AI assistants and now to general-purpose agents. Deep research (DR) repre

Kangkang Sun, Jun Wu, Jianhua Li, Minyi Guo

Uncertainty estimation in multi-LLM systems remains largely single-model-centric: existing methods quantify uncertainty within each model but do not adequately capture semantic disagreement across models. To address this gap, we propose Collaborative Entropy (CoE), a unified information-theoretic metric for semantic uncertainty in multi-LLM collaboration. Co

Charalampos Agiropoulos

This paper studies high-dimensional M-estimation in the proportional asymptotic regime (p/n -> gamma > 0) when the noise distribution has infinite variance. For noise with regularly-varying tails of index alpha in (1,2), we establish that the asymptotic behavior of a regularized M-estimator is governed by a single geometric property of the loss function: the

Lu Hao

We study boundary value problems at infinity for the graph $p$-Laplacian on infinite, connected, locally finite weighted graphs. Our main result is a Wiener criterion for $p$-massiveness. Assuming volume doubling and a weak $(1,p)$-Poincar\'e inequality, we show that every infinite connected $p$-massive set satisfies a dyadic capacitary condition expressed t

Ha Anh Vu

The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning models to improve classification performance. The proposed system integrates multiple classifiers, including ResNet101, D

V. I. Tselyaev

In the paper, one of the physical consequences of the recently developed theory of dual relativity (TDR) is considered. The general framework of TDR is described and some results previously obtained within this theory are summarized. The total action functional of TDR includes the action functionals of matter fields of two kinds: ordinary and dual. Based on

Diana Csontosová, Kyo-Hoon Ahn, Jan Kuneš

We study the instability in RuO$_2$ using the Hartree-Fock approximation followed by the random phase approximation. We employ a three-orbital Hubbard model without spin-orbit coupling. An analysis of the eigenvalues and eigenvectors of the static susceptibility in the non-magnetic phase for various local interaction parameters $U$, $J_H$, and hole doping $n

Karel Vařeka, Michal Potoček, Adam Očkovič, Tomáš Šikola

Chemical waves of CO oxidation on platinum surfaces exhibit complex spatio-temporal self-oscillations, yet the local electronic mechanisms driving their propagation remain poorly understood under operando conditions. In this work, we combine operando scanning electron microscopy with frequency-modulated Kelvin probe force microscopy (FM-KPFM) to simultaneous

Li-Heng Chen, Ke Cheng, Yahui Liu, Lei Shi

Driving video generation has achieved much progress in controllability, video resolution, and length, but fails to support fine-grained object-level controllability for diverse driving videos, while preserving the spatiotemporal consistency, especially in long video generation. In this paper, we present a new driving video generation technique, called VistaG

Sawon Pratiher

This article provides a simple trigonometric method for determining how many roots of a quintic equation are real and how many are complex, without solving the equation. The approach transforms a depressed quintic $t^5 + mt^3 + nt^2 + pt + q = 0$ with $m < 0$ into the trigonometric equation $f(\theta) = \alpha\cos^2\!\theta + \beta\cos\theta + \cos 5\theta +

Urja Khurana, Michiel van der Meer, Enrico Liscio, Antske Fokkens

Subjective judgments are part of several NLP datasets and recent work is increasingly prioritizing models whose outputs reflect this diversity of perspectives. Such responses allow us to shed light on minority voices, which are frequently marginalized or obscured by dominant perspectives. It remains a question whether our evaluation practices align with thes

Xinyue Luo, Masahiro Yamamoto, Jin Cheng

We consider a two-component semilinear reaction-diffusion system in a bounded spatial domain $\Omega$ over a time interval $(0,T)$, which governs the water density $u(x,t)$ and the vegetation biomass density $v(x,t)$ for $x\in\Omega$ and $0<t<T$. In this system, called the Klausmeier-Gray-Scott model, we assume that an unknown source depends only on the spat

José Garre Rubio, András Molnár, Norbert Schuch, Frank Verstraete

We study the conditions under which Matrix Product States (MPS) or Matrix Product Operators are exact eigenvectors of an extensive local operator, such as a Hamiltonian. By suitably choosing the local operator, this covers a wide range of settings: Exact eigenstates of Hamiltonians, including scar states, exact MPS trajectories for driven quantum systems, st

Giulia Pusceddu

Integrating social robots in our group-based society, beyond the technical challenges, requires considering the social group dynamics. Following the results from preliminary exploratory studies on the influence of social robots on group decisions, the proposed research investigates whether social robots can foster cooperation among group members. To achieve

Andrey E. Mironov, Siyao Yin

In this paper, we continue to study billiards inside cones $K\subset \mathbb{R}^n$ over strictly convex closed $C^3$ manifolds with non-degenerate second fundamental form. Recently we proved that the billiard is superintegrable, i.e., the billiard admits first integrals whose values uniquely determine all billiard trajectories. In this paper we prove that th

Wan Tian, Hui Yang, Zhouhui Lian, Lingyue Zhang

Efficient estimation of high-dimensional matrices-including covariance and precision matrices-is a cornerstone of modern multivariate statistics. Most existing studies have focused primarily on the theoretical properties of the estimators (e.g., consistency and sparsity), while largely overlooking the computational challenges inherent in high-dimensional set

Omer Gurevich, Tal Mor, Ido Ram

Catalytic carbon fixation to formic acid is important for studying the reduction of carbon footprint and the emergence of life. Can discrete quantum exhaustive search merged with other methods help reduce the carbon footprint? We suggest merging quantum, quantum inspired, and classical tools for a better simulation of various relevant processes. Quantum tool

He Du, Qiming Ge, Jiakai Hu, Aijun Yang

We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented post-training recipe. On the agent side, Kernel-Smith maintains a population of executable candidates and iteratively improves them using an archive of top-performing and diverse p

William Layton, Nanda Nechingal Raghunathan

Classical eddy viscosity models of turbulence add an eddy viscosity term based on the Kolmogorov-Prandtl parameterization by a turbulent length scale $l$ and a turbulent kinetic energy $k^{\prime }$. Approximations of the unknowns $l,k^{\prime }$ are typically constructed by solving multi-parameter systems of nonlinear convection-diffusion-reaction equations

Xinyan Yu, Marius Hoggenmueller, Xin Lu, Ozan Balci

Urban HCI investigates how digital technologies shape human behaviour within the social, spatial, temporal dynamics of public space. Meanwhile, robotic furniture research demonstrates how the purposeful animation of mundane utilitarian elements can influence human behaviour in everyday contexts. Taken together, these strands highlight an untapped opportunity

Ekaterina Torubarova, Jura Miniota, Andre Pereira

In this paper, we investigated how the choice of a Wizard-of-Oz (WoZ) interface affects communication with a robot from both the user's and the wizard's perspective. In a conversational setting, we used three WoZ interfaces with varying levels of dialogue input and output restrictions: a) a restricted perception GUI that showed fixed-view video and ASR trans

Julio C. Serrano, Joonas Kevari, Rumy Narayan

Systematic literature reviews in the social sciences overwhelmingly follow arborescent logics -- hierarchical keyword filtering, linear screening, and taxonomic classification -- that suppress the lateral connections, ruptures, and emergent patterns characteristic of complex research landscapes. This research note presents the Rhizomatic Research Agent (V3),

Rongyu Zhang, Hongyu Dong, Gaole Dai, Ziqi Qiao

The rapid adoption of data-driven methods in biomedicine has intensified concerns over privacy, governance, and regulation, limiting raw data sharing and hindering the assembly of representative cohorts for clinically relevant AI. This landscape necessitates practical, efficient privacy solutions, as cryptographic defenses often impose heavy overhead and dif

Heecheol Yun, Eunho Yang

With the widespread adoption of autonomous vehicles and robotics, amodal completion, which reconstructs the occluded parts of people and objects in an image, has become increasingly crucial. Just as humans infer hidden regions based on prior experience and common sense, this task inherently requires physical knowledge about real-world entities. However, exis

Yue Wang, Guangyi He, Liepeng Zhang, Lukas Gonon

Adversarial training is a standard defense against malicious input perturbations in security-critical machine-learning systems. Its main burden is structural: before every parameter update, the current model must first be attacked to find a new adversarial perturbation, making training increasingly expensive and hard to sustain at large-model scale. Here we