April 2026 arXiv papers — page 27

Yoichi Ishiwata, Ichidai Harada, Masaki Imamura, Kazutoshi Takahashi

We investigated the size dependence of the metal-insulator transition (MIT) in Cr-doped V2O3 nanocrystals by photoemission spectroscopy using complementary probing depths, together with magnetic susceptibility measurements. Photoemission spectra show that MIT signatures persist down to an average particle size of 5.6 nm, and magnetic susceptibility measureme

George Margaritis, Dionysios Kritsas, Dimitrios Katsaros, Yannis Manolopoulos

ACM and IEEE are the two premier associations on computing and electrical/electronics engineering which publish and organize the great majority of periodicals and conferences, respectively, serving these disciplines. Science is a constantly evolving process, and these publication fora are expected to follow the trends. In this article, we focus on the period

Yaofei Wang, Rui Wang, Weilong Pang, JiaLiang Han

Generative linguistic steganography (GLS) enables covert communication by embedding secret messages into the natural language generation process. In practical deployment, however, GLS is vulnerable to tokenization ambiguity: the same surface text may be re-tokenized into a different token sequence at the receiver, breaking the shared decoding state between t

Kotaro Imasaka, Dianhong Dong, Natsuki Kanda, Bing Xue

We demonstrate submicrometer focusing of isolated attosecond pulses (IAPs) in the extreme ultraviolet (XUV) region using a custom ellipsoidal mirror. The obtained focal spot sizes were verified using knife-edge measurements with a sharp silicon edge, confirming reproducible dimensions down to 0.46 $\mu$m $\times$ 0.36 $\mu$m (FWHM), approaching the diffracti

Akbota Senkebayeva

This paper presents two one-dimensional mathematical models describing automobile traffic flow on straight road segments at a signalized intersection. When the traffic light is permissive, the flow density and velocity are obtained by solving an initial-boundary value problem for a first-order hyperbolic system. When the signal is prohibitive, the same quant

Elias Greil, Alba de las Heras, Angel Rubio, Anna Galler

High-harmonic generation is a sensitive all-optical probe of symmetry and electron dynamics in solids. Here, we use first-principles time-dependent density functional theory (TDDFT) to study high-harmonic generation in T$_d$-WTe$_2$, a two-dimensional semimetal with switchable out-of-plane ferroelectric polarization driven by interlayer sliding. We show that

Dominik Borawski, Marta Szulc, Robert Chudy, Małgorzata Giedrowicz

Large Language Models (LLMs) have shown strong potential for narrative generation, but their use in complex, multi-layered role-playing game (RPG) worlds is still limited by issues of coherence, controllability, and structural consistency. This paper explores a dependency-aware, multi-stage prompt pipeline for procedural RPG content generation that models na

Arnau Vivet, Alex Arenas

We introduce a Hopfield-type associative memory in which effective connectivity is multiplicatively modulated by astrocytic gains evolving under an entropy-regularized replicator equation. The coupled neuron-astrocyte dynamics admit a Lyapunov function, ensuring global convergence. At fixed points, astrocytic gains implement a softmax-normalized allocation o

Yanshan Fan, Jianyong Hu, Shuxiao Wu, Zhixing Qiao

Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in photon limited scenarios. Here we report a photon level image classification method using quantum compressed sensing, which reformul

Ziyi Li, Fangming Zhao, Howard H. Yang

In this paper, we investigate the impact of channel probing and reservation on the Age of Information (AoI) in energy-harvesting (EH) random access networks, where each source relies solely on harvested energy for status updating. To mitigate collisions, each node may expend a small amount of energy to send a probing signal before transmission, and a success

Hanqing Yang, Qiang Zhou, Yongchao Du, Sashuai Zhou

Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance the reasoning-grounded planning for image editing, we propose DDA-Thinker, a Thinker-centric framework designed for the independent optimization of a planning module (Thinker) over a fixed generative model (Ed

Venkata Pushpak Teja Menta

Standard text-to-speech (TTS) evaluation measures intelligibility (WER, CER) and overall naturalness (MOS, UTMOS) but does not quantify accent. A synthesiser may score well on all four yet sound non-native on features that are phonemic in the target language. For Indic languages, these features include retroflex articulation, aspiration, vowel length, and th

Suman Karan, Aman Srivastava, Pratham Sachin Todkar, Anand K. Jha

A K-mirror rotates the wavefront of an incident optical field. However, the rotation always introduces polarization changes in the transmitted field. This is a serious concern for applications ranging from astronomical image derotation to orbital angular momentum spectrum characterization in photonic quantum technology. Recent efforts have shown that the pol

Paolo Olivucci, David E. Rival, Richard Semaan

This work addresses the challenge of learning reduced-order models (ROMs) capable of generalizing to unobserved dynamical regimes across unseen control parameters. We introduce the Control-oriented Cluster-based Network Model (CNMc), a framework for synthesizing reduced-order dynamics at held-out operating conditions without requiring simulation data at thos

Juan Carlos Bravo-Rodríguez, Juan Carlos del-Pino-López, Francisco Casado-Machado

Unbalanced three-phase systems still lack a compact phasor-domain representation of power that makes phase asymmetry explicit while remaining consistent with established apparent-power definitions. This paper addresses that point through a complex-vector power formulation for sinusoidal steady-state operation. The proposed representation supplements the clas

Yi Geng, Pan Cao, Ting Zeng, Yongqian Deng

This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but can only opportunistically reuse irregular 5G/6G reference signals (RSs). We show that the velocity profile induced by such irregular time-domain patterns can be decomposed into a periodic-peak component and a

Zhaohui Li, Peng He, Zhiyuan Chen, Honglu Liu

The need to evaluate instructional materials for K-12 science education has become increasingly important, as more educators use generative AI to create instructional materials. However, the review of instructional materials is time-consuming, expertise-intensive, and difficult to scale, motivating interest in automated evaluation approaches. While large lan

Milena Skvortsova

We study the quasinormal spectrum of a massive test scalar field in the exact asymptotically flat regular black-hole geometry supported by a phantom Dirac--Born--Infeld scalar. Using high-order WKB approximation improved by Pad\'e resummation, together with characteristic time-domain integration and Prony extraction, we compute the fundamental mode and the f

Markus Heydenreich, Christian Hirsch, Matthias Löwe

The central question that we address is: How can structured information be stored in a hierarchical Hopfield model involving hidden layers? To this end, we develop a formalism of strokes and concepts that allows us to appropriately structure information: initial features are first classified into strokes, which in a second step are aggregated into concepts.

Qing Zhao, Di Xiao, Xue-Feng Wu

The rotation measure (RM) and dispersion measure (DM) of fast radio bursts (FRBs) serve as critical probes of the magneto-ionic environments along the line of sight. The significant temporal evolution of RM observed in some repeating FRBs is generally attributed to the local environment of the source, since the intergalactic medium is not expected to vary on

Die Gan, Siyu Xie, Zhixin Liu, Xuebo Zhang

This paper studies the distributed adaptiveestimation problems for stochastic large regression modelswith an infinite number of parameters. By constructing a re-cursive local cost function, we propose a novel distributedrecursive least squares algorithm to estimate the unknownsystem parameters, where the growth rate of regressors'dimension is characterized b

Shuchen Zhu, Zhengyang Huang, Yuqi Xu, Peijin Li

Federated learning increasingly operates in a large-model regime where communication, memory, and computation are all scarce. Typically, non-IID client data induce drift that degrades the stability and performance of local training. Existing remedies such as SCAFFOLD introduce heterogeneity-correction mechanisms to address this challenge, but they incur subs

Jingi Kim, Wonjun Kim

Recently, generalizable human Gaussian splatting from sparse-view inputs has been actively studied for the photorealistic human rendering. Most existing methods rely on explicit geometric constraints or predefined structural representations to accurately position 3D Gaussians. Although these approaches have shown the remarkable progress in this field, they s

Alessio Cipriani, Jon Woolf

We show that the faithful highest weight hearts in an algebraic triangulated category are the serially faithful glued hearts, equivalently the hearts containing a dual pair of full exceptional collections in the sense of Bodzenta--Bondal (arXiv:2601.22004). We then characterise faithful highest weight categories of perverse sheaves on topologically stratifie

Oliver Bause, Jörg Gamerdinger, Julia Werner, Oliver Bringmann

Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key challenge is their limited size, resulting in a short battery lifetime which conflicts with high energy consumption for image capturing and transmission to an on-body device. Thus, we propose an image compressi

Lotte Hollands, Subrabalan Murugesan

In this paper, we investigate the role of spectral networks in quantum Liouville theory, with particular emphasis on spectral networks of Fenchel-Nielsen-type. In the first part, we construct q-parallel transport for Fenchel-Nielsen networks through q-nonabelianisation, and compare with quantum parallel transport computed using the Moore-Seiberg formalism. T

Vyacheslav Futorny, Zheng Li, Jian Zhang

The evaluation homomorphisms from the super Yangian $\Ymn$ to the universal enveloping algebra $\U(\gl_{m|n})$ allows one to regard the covariant tensor module of $\gl_{m|n}$ as $\Ymn$ modules. We study simple quotients of the submodules generated by a tensor product of highest weight vectors inside the tensor products of covariant evaluation modules. In the

Hayaki Kudo, Yuto Nogata

Let $q=p^n$, $r\in \mathbb{Z}_{\ge 2}$, $e=q-1$, and $k=\frac{q^r-1}{e}$. In this paper, we study the cyclotomic numbers $(a,b)_{q-1}$ over $\mathbb{F}_{q^r}$. We prove that $(a,b)_{q-1}\le \left\lceil \frac{k}{2}\right\rceil$ for all $0\le a,b\le q-2$ except when $q=2$ and $r\ge 3$. We also give sharper bounds for prime values of $r$, especially for $r=2$ a

Sayan Maity, Tristan A. Mauck, Ulrich Kleinekathöfer

In the theory of open quantum systems, spectral densities are key quantities for modeling the dynamics and spectroscopic properties of the system under investigation. In the case of light-harvesting complexes, they encode the frequency-dependent coupling of electronic excitations in pigment molecules to their environment, reflecting contributions from both i

Kenji Kitamura, Ryoji Tanabe

Stopping criteria automatically determine when to stop an evolutionary algorithm, so as not to waste function evaluations on a stagnant population. Although stopping criteria play an important role in real-world applications, they have attracted little attention in the evolutionary multi-objective optimization (EMO) community. In fact, new stopping criteria

Fabio D'Oronzio, Federico Putamorsi, Leonardo Zini, Marcella Cornia

Despite recent advances, single-image super-resolution (SR) remains challenging, especially in real-world scenarios with complex degradations. Diffusion-based SR methods, particularly those built on Stable Diffusion, leverage strong generative priors but commonly rely on text conditioning derived from semantic captioning. Such textual descriptions provide on

Nitin Venkateswaran, Jason Ang, Deep Adhikari, Tarun Krishna Dasari

We investigate linguistic biases in LLM-based restaurant and product recommendations given prompts varying across Southern American English (AE), Indian English (IE), and Code-Switched Hindi-English dialects, using the Yelp Open dataset (Yelp Inc., 2023) and Walmart product reviews dataset (PromptCloud,2020). We add lists of restaurant and product names bala

Bektur Ryskeldiev

Web accessibility rests on static standards and developer compliance. That model frays in platforms where content is user-generated: photos arrive blurry or off-frame, descriptions skip size and condition, and page structure shifts from listing to listing. Drawing on six studies conducted between 2022 and 2025 with blind, low-vision, and older adult users of

Taegyeong Kim, Youngtak Kim, Sathya Sheela Subramanian, Geun Ho Gu

High-entropy alloys are widely modeled as homogeneously mixed surfaces, yet the validity of this assumption for catalytic prediction remains unclear. Here, we reproduce high-throughput experimental measurements using thermodynamic simulations and show that surface ordering is essential for accurately capturing the compositional activity landscape. Homogeneou

Md. Abrar A Mushfik, Mohammad Ali Kaisar, Mohiminul Islam Bhuiyan Sahed, Idban Alamzadeh

Metasurfaces' ability to control electromagnetic wave propagation has led to a rapid paradigm shift in wireless operation. These metasurfaces are often called reconfigurable intelligent surfaces (RISs) due to active tuning elements distributed across the meta-atoms comprising the metasurface array. However, each of these dynamic meta-atoms requires additiona

Razin Hafid Hamdi, Ivana Margareth Hutabarat, Hanna Gresia Sinaga, Luluk Muthoharoh

Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper presents a comprehensive benchmarking study comparing a Machine Learning (ML) approach via the PyCaret AutoML framework against a

Bo-Hae Im, Hojin Kim, Tuan Ngo Dac

A fundamental conjecture formulated by Thakur in 2009, which has guided significant developments in function field arithmetic, asserts that multiple zeta values (MZV's) in positive characteristic of fixed weight are linearly independent over $\mathbb{F}_q$. In this paper we settle this conjecture by determining the precise threshold for this independence. We

M. Miyamoto, M. Horio, K. Moriya, A. Takahashi

Electron-doped cuprates require not only electron doping by chemical substitution but also post-growth reduction annealing for realizing superconductivity. However, electron concentration can also be varied by reduction annealing, making it challenging to disentangle the respective influences of electron concentration and oxygen non-stoichiometry. Here, by c

Manpreet Kaur, Arvind Kumar

We investigate the modification of the $J/\psi$ meson mass in asymmetric nuclear matter at zero and finite temperatures employing an effective Lagrangian approach that considers the contributions of $DD$ and $DD^*$ meson loops. The medium dependence of $D$ meson masses is determined using the hadronic chiral SU(3) model, where scalar condensates are calculat

Courtney Ford, Ojas Rane, Susan Leavy

Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we present a multi-jurisdictional Retrieval-Augmented Generation system for global AI regulation. Our corpus includes 242 documents across 68 jurisdictions, ranging from formal legislation like the EU AI Act to unst

Chiel C. van Heerwaarden, Menno A. Veerman, Imme Benedict, Lukas Brunner

Global storm-resolving models aspire to become digital twins of the Earth, delivering information at the local scale at which humans experience climate. We evaluated how well two such models, ICON and IFS-FESOM, reproduce the climate as classified by the K\"oppen-Geiger system, using 30-year (2020-2049) simulations from the nextGEMS project at 9~km global re

Liav Koram, Ohad Elishco

DNA-based storage has emerged as a promising alternative to traditional data storage methods, offering unmatched advantages in data density, longevity, and sustainability. Two main approaches have developed: in-vitro storage, where information is synthesized in controlled environments, and in-vivo storage, where data is embedded within an organism's DNA for

Yixiao Zhou, Dongzhou Cheng, zhiliang wu, Yi Yang

Large Language Models (LLMs) often fail to utilize their latent reasoning capabilities due to a distributional mismatch between ambiguous human inquiries and the structured logic required for machine activation. Existing alignment methods either incur prohibitive $O(N)$ costs by fine-tuning each model individually or rely on static prompts that fail to resol

Isidro Butaslac, Yota Nagaya, Almira Princess Redoble, Jordan Aiko Deja

In everyday life, physical effort is often minimized and convenience is prioritized, making it difficult for many people to sustain light exercise and stretching despite well-known long-term benefits. This challenge often arises not from objective movement limitations, but from whether an action feels doable in the moment and, therefore worth continuing. Thi

Grigori A. Karagulyan, Gor A. Melkumyan

We establish new quantitative estimates for general systems of functions with wavelet-type dyadic structure. These estimates are applied to obtain the optimal growth of various types of Weyl multipliers for certain wavelet-type systems. Some of our results are sufficiently general to allow the orthogonality assumption to be removed. In particular, as a conse

Venkata Pushpak Teja Menta

Commercial TTS systems produce near-native Indic audio, but the best open-source bases (Chatterbox, Indic Parler-TTS, IndicF5) trail them on measured phonological dimensions, and the most widely adopted multilingual base (Chatterbox, 23 languages) does not even tokenise Telugu or Tamil. We ask: what is the minimum intervention that brings such a non-Indic-na

Shabeeb Ameen, Tao Zhang, J. M. Schwarz

The task of learning patterns is typically associated with systems that update parameters on fixed architectures, such as neural networks, where learning proceeds through continuous optimization. Here, we demonstrate that pattern learning can also emerge in reconfigurable cellular tissue, where both mechanical parameters and network topology evolve. Using a

Evgeniya Usenko, Alexander Glamazda, Vladimir Valeev, Victor Karachevtsev

Interest in studying the interaction of small molecules with DNA is caused by the need to develop new, highly effective, and low-toxic drugs for cancer treatment. The strong and highly specific binding of thionine with DNA makes it a promising candidate for use in medicine and pharmacology. In this study, DNA-thionine complexes in aqueous solutions were inve

P. Žugec, N. Colonna, D. Rochman, M. Barbagallo

Energy-differential cross section of the $^{\mathrm{nat}}$C(n,p) and $^{\mathrm{nat}}$C(n,d) reactions was measured at the neutron time of flight facility n_TOF at CERN. The measurement was performed in the first experimental area (EAR1; flight path of 182.5 m). Two position-sensitive $\Delta E$-$E$ silicon telescopes were used. Two naturally occurring carbo

Changyu Li, Lu Wang, Ming Lei, Jiashen Liu

Source-free test-time adaptation (TTA) is appealing for mobile and wearable sensing because it enables on-device personalization from unlabeled test streams without centralizing private data. However, sensor-based human activity recognition (HAR) poses challenges that are less pronounced in standard vision benchmarks: behavioral inertial streams are temporal

Manish Tamta, Nirmal Raj, Himanshu Verma

Gravitational microlensing constraints on non-standard compact objects are conventionally derived assuming lenses trace the dark matter halo with velocities following a Maxwell-Boltzmann distribution centered around $10^{-3}c$. However, a variety of theoretical scenarios predict populations of compact objects whose velocities deviate dramatically from those

Zachary Macaskill-Smith, Unmol Sharma, Melissa Warner, Kálmán Varga

Minor embedding is a required compilation step for quantum annealing, mapping logical problem graphs onto sparse hardware topologies. Despite its central role in determining solution quality, no standardized benchmark exists for comparing embedding algorithms: prior studies use incompatible graph libraries, inconsistent metrics, and non-reproducible experime

Zi-Yang Bo, Wei Lu, Hongruixuan Chen, Si-Bao Chen

Shadows are a prevalent problem in remote sensing imagery (RSI), degrading visual quality and severely limiting the performance of downstream tasks like object detection and semantic segmentation. Most prior works treat shadow detection and removal as separate, cascaded tasks, which can lead to cumbersome process and error accumulation. Furthermore, many dee

Quentin Remy, Stéphane Mangin

The discovery of femtosecond laser-induced ultrafast demagnetization in 1996 opened a new field, femtomagnetism, in which magnetic order can be quenched on timescales shorter than a picosecond. This seminal observation revealed that angular momentum can be transferred out of the spin system with unprecedented speed, launching intense efforts to disentangle t

Khagendra Joshi, Deepak Kumar Sahoo, Kamalesh Kumar K, Debidas Kundu

In this paper, a broadband 1-bit coding metasurface-based reconfigurable intelligent surface (RIS) is presented. The unit cell of the metasurface consists of a wide dipole modified with interdigital capacitors and loaded with an SMP 1340-040LF PIN diode. The proposed element offers cell miniaturization and a stable angular response. A phase difference of 180

Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao

Lattice Boltzmann (LB) on quantum devices must reconcile unitary gate evolution with the dissipative \emph{collision} step. In the multiple-relaxation-time (MRT) class, we work in the common setting of \emph{modewise diagonal} moment relaxation, $\delta m_r'=\lambda_r\,\delta m_r$ with $\lambda_r\in[-1,1]$ (overrelaxation if $\lambda_r<0$). Embedding that co

Zeyue Xue, Siming Fu, Jie Huang, Shuai Lu

While large-scale video diffusion models have demonstrated impressive capabilities in generating high-resolution and semantically rich content, a significant gap remains between their pretraining performance and real-world deployment requirements due to critical issues such as prompt sensitivity, temporal inconsistency, and prohibitive inference costs. To br

Akinobu Matsuyama

A cost-effective augmented reality (AR) system is presented for visualising three-dimensional magnetic field structures and charged-particle trajectories in magnetically confined fusion plasmas. The system presented in this study integrates an orbit-following simulation code with a marker-based AR framework using a web camera and the OpenCV library. By synch

Hiroki Kutsuma, Taro Yamashita

We clarified the physical mechanism of superconducting strip single photon detectors (SSPDs) with optical cavities by using transmission line and impedance models. By introducing the transmission line model, we derived the analytical formulae for the absorptance of SSPDs with optical cavities. We compared the absorptance obtained from the analytical formulae

Harikrishnan K J, Amit Kumar Pal

We formulate a bounded distance decoding strategy applicable to all stabilizer codes including both CSS and non-CSS code-families. The framework emerges out of the local Clifford equivalence between arbitrary stabilizer states and graph states. Using the graphical representation of the stabilizers and the syndromes, we constitute the bounded distance decodin

Yu Wang, Emmanuele Chersoni, Chu-Ren Huang

Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "zh\`e/n\`a" in Chinese, as a novel probe for grounded knowledge. Using 6,400 responses from 320 native speakers, we establish a human baseline: English speakers reli

Huriyeh Babak, Melanie Schaller

Efficient GPU execution of convolution operators is governed by memory-access efficiency, on-chip data reuse, and execution mapping rather than arithmetic throughput alone. This paper presents a controlled operator-level study of CUDA kernel optimization for the depthwise convolution used in Structured State Space Model Convolutional Diagonal (S4ConvD), toge

Xinjie Chen, Biao Fu, Jing Wu, Guoxin Chen

Reinforcement learning with verifiable rewards (RLVR) enhances the reasoning of large language models (LLMs), but standard RLVR often depends on human-annotated answers or carefully curated reward specifications. In machine-checkable domains, label-free alternatives such as majority voting or LLM-as-a-judge remove annotation cost but can introduce false posi

Shao-Wen Wei, Yu-Xiao Liu

Recent explorations of topological aspects in black hole thermodynamics have achieved unprecedented progress. By utilizing topological numbers, different black hole systems can be categorized into distinct universality classes. This universal classification is particularly evident in thermodynamic limits, offering valuable insights for developing a comprehen

Adrián Sánchez-Fernández, Wouter Ryssens, Stéphane Goriely

Modeling fission properties, such as barriers and rates, is highly challenging. The most microscopic methods available are based on energy density functionals (EDFs) and rely on a limited set of collective coordinates to describe the evolution of a fissioning nucleus from its ground state to scission. Leveraging the efficiency of the MOCCa nuclear structure

Veith Weilnhammer, Lennart Luettgau, Christopher Summerfield, Viknesh Sounderajah

AI chatbots are increasingly used for health advice, but their performance in psychiatric triage remains undercharacterized. Psychiatric triage is particularly challenging because urgency must often be inferred from thoughts, behavior, and context rather than from objective findings. We evaluated the performance of 15 frontier AI chatbots on psychiatric tria

Pierre-Yves Bienvenu, Arne Winterhof

We compare several complexity measures for self-mappings of finite fields. In particular, we show that Carlitz rank and additive index cannot be small simultaneously up to trivial exceptions. That is, these two measures detect cryptographic weaknesses of different classes of functions. We also study the relationship between additive index and degree or weigh

Sylvain Ravets

Synthetic materials are obtained by assembling atoms or artificial atoms into regular arrays, thereby forming artificial crystals that offer powerful platforms to emulate and explore condensed-matter phenomena in highly controlled settings. They enable probing outstanding questions in many-body physics and designing new phases of matter with no direct analog

S. A. Narawade, S. A. Kadam

In this study, we explore the cosmological evolution of the Universe in the framework of covariant $f(Q)$ gravity, with a coupling function that evolves dynamically in proportion to the Hubble parameter. Two specific forms of the function are examined: a power-law model and a logarithmic model. By rewriting the cosmological field equations as an autonomous d

Eskil Hansen, Tony Stillfjord, Teodor Åberg

In recent previous work [E. Hansen, T. Stillfjord and T. \r{A}berg, SIAM J. Numer. Anal., to appear], we analyzed the convergence of operator splitting methods applied to operator-valued differential Riccati equations (DRE). In this paper, we extend these results by analyzing the convergence of a full discretization based on finite elements in space and Lie

Beatrice Franzolini, Francesco Pozza

Posterior inference for Dirichlet process mixture models is analytically intractable and typically relies on Markov chain Monte Carlo methods, which can become computationally prohibitive at moderate to large sample sizes. In this work, we investigate the performance of Laplace and skew-Laplace posterior approximations for density estimation in this setting.

Penghao Kuang, Haoyi Wu, Kewei Tu

Probabilistic Transformer (PT), a white-box probabilistic model for contextual word representation, has demonstrated substantial similarity to standard Transformers in both computational structure and downstream task performance on small models and small to medium sized datasets. However, PT is less robust to hyperparameter choices than standard Transformers

Runjie Wang, Weiling Chen, Tiesong Zhao, Chang Wen Chen

Low-level image processing has long been evaluated mainly from the perspective of visual fidelity. However, with the rise of deep learning and generative models, processed images may preserve perceptual quality while altering semantic content, making conventional Image Quality Assessment (IQA) insufficient for semantic-level assessment. In this paper, we for

Diego A. Baron Moreno, Christoph Englert, Yvonne Peters

Destructive interference between signal and background processes poses a fundamental challenge in searches for top-philic scalar resonances, significantly reducing experimental sensitivity to well-motivated extensions of the Higgs sector. Traditional bump-hunting strategies fail in this instance because interference effects invalidate the narrow-width approx

Ying-Hui Shao, Yan-Hong Yang, Yun Zhang

Connectedness measures quantify aggregate risk spillovers but obscure the local interaction patterns that generate systemic risk. We develop a motif-based framework that first extracts multiscale backbones from quantile connectedness networks and then identifies directed triadic motifs whose frequencies exceed randomization baselines. To distinguish how asse

Markus Käppeler, Özgün Çiçek, Yakov Miron, Abhinav Valada

Camera-based 3D object detection and tracking are central to autonomous driving, yet precise 3D object localization remains fundamentally constrained by depth ambiguity when no expensive, depth-rich online LiDAR is available at inference. In many deployments, however, vehicles repeatedly traverse the same environments, making static point cloud maps from pri

David Perez-Saura, Jose Andres Millan-Romera, Miguel Fernandez-Cortizas, Holger Voos

Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed from sensor data, to a prior map. This process is particularly challenging in environments with repetitive or symmetric layouts, where structural cues alone are often insufficient to resolve ambiguities. We

Maochun Xu, Yunqi Liang, Yi Hong

Persistent shifts in term-structure dynamics undermine the stability of single-regime models in long samples. We develop an arbitrage-free regime-switching generalized CIR (RS-GCIR) model that jointly prices the Chinese government bond (CGB) curve and corporate bond curves. To capture the systematic transmission from interest-rate conditions to credit spread

Nils Lid Hjort

The sudoku puzzles have a long history, with variations going back more than a hundred years, but its current and perhaps surprising world-wide prominence goes back to certain initiatives and then puzzle-generating computer programmes from just after 2000. To solve a sudoko puzzle, a statistician can put up a probabilitymodel on the enormous space of $9\time

Marco Bressan, T-H. Hubert Chan, Qipeng Kuang, Mauro Sozio

In recent years, the problem of computing the frequencies of the induced $k$-vertex subgraphs of a graph, or \emph{$k$-graphlets}, has become central. One approach for this problem is to sample $k$-graphlets randomly. Classic algorithms for $k$-graphlet sampling require loading the entire graph into main memory, making them impractical for massive graphs. To

Jun Gao, Yun Peng, Qian Qiao, Changhai Zhou

Despite strong performance on code generation tasks, it remains unclear whether large language models (LLMs) genuinely reason about code execution. Existing code reasoning benchmarks primarily evaluate final output correctness under a single canonical implementation, leaving two critical aspects underexplored: (1) whether LLMs can maintain consistency to fun

Julien Dechanxhe, Spencer W. Jolly, Pascal Kockaert

In this work, we show that injecting a single space-time-coupled light pulse-beam into a multimode graded-index fiber generates a train of multimode solitons. Space-time couplings excite the spatial modes with distinct temporal profiles. Due to nonlinear interactions, with a properly chosen input power these profiles split into several unique multimode solit

Alana Cavalcante, Maurício Corrêa, Fernando Lourenço, Elaheh Shahsavaripour

We introduce excess logarithmic residues for one-dimensional holomorphic foliations tangent to a divisor. They arise from the comparison between the logarithmic normal sheaf and the ordinary normal sheaf of the foliation, and measure the local variation between the logarithmic and classical Baum--Bott contributions. We prove a global residue formula expressi

Sittinon Jirattikansakul, Teeradej Kittipassorn, Kraiwich Kongsiri, Nitipon Moonwichit

We prove the congruence $\sum_{1 \leq k < \sqrt{N}} \sigma_0 (N - k^2) \equiv 0 \pmod 4$, where $\sigma_0(m)$ denotes the number of positive divisors of $m$, for $N = An + B$ with $(A,B) \in \{ (16,14),$ $(36,30),$ $(72,42),$ $(196,70),$ $(252,114) \}$. Our proof relies on a result of Keith which states that $\nu_2 (N) \equiv 0 \pmod 4$, where $\nu_2(N)$ is

Tatsuya Tanaka, Huimin Li, Shota Yamanaka, Ellen H. Fukuda

Many practical optimization problems involve uncertain parameters that are strictly positive. However, the most common uncertainty sets used in robust optimization are the box and the ellipsoidal sets, which may include non-positive values when the level of uncertainty is large. This can lead to overly conservative solutions or make the corresponding robust

Mutia Alfi Mayzaroh, Dwi Fitria Ningsih, Nindi Destriani, Martin C. T. Manullang

This paper benchmarks a classical machine learning approach based on PyCaret AutoML against a deep learning approach based on IndoBERT fine-tuning for binary sentiment analysis of Indonesian-language Twitter comments related to Ibu Kota Nusantara (IKN). The dataset contains 1,472 manually labeled samples, consisting of 780 negative and 692 positive comments.

Tung-Duong Le-Duc, Hoang-Quoc Nguyen-Son, Minh-Son Dao

Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity. Recent generative approaches based on Retrieval-Augmented Generation (RAG) and Large Multimodal Models (LMMs) leverage candidates retrieved from fixed databases for reasoning, but often struggle with scenes tha

Silvia Bodei, Duncan P. Brumby, Katie Fisher, Jon Mella

Despite AI tools becoming increasingly embedded in academic practice, little is known about how university students integrate them into their writing processes. We examine how students engage with AI across different writing tasks, and how this engagement is shaped by individual factors including AI literacy, writing confidence, trust, authorship concerns, a

Muhammad Shaheer, Miguel Fernandez-Cortizas, Asier Bikandi-Noya, Holger Voos

Architectural floor plans are widely available priors which contain not only geometry but also the semantic information of the environment, yet existing localization methods largely ignore this semantic information. To address this, we present COMPASS, an algorithm that exploits both geometric and semantic priors from floor plans to estimate the pose of a ro

Ming Huang, Shuting Xu, Leying Yang, Huanzhang Hu

Direction-of-arrival (DOA) estimation is an important task in microphone array processing and many downstream applications. The steered response power with phase transform (SRP-PHAT) method has been widely adopted for DOA estimation in recent years. However, accurate SRP-PHAT estimation in 3D scenarios requires evaluating steering responses over thousands of

Suchismita Banerjee, Koyena Ghosh, Urna Basu, Banasri Basu

We study statistical properties of atmospheric particulate matter fluctuations using six years of daily PM2.5 concentration data from fifty-four Indian cities. Despite diverse urban settings and heterogeneous climatic conditions, we find that the fluctuations show strikingly universal behaviour in both the distributional properties and temporal dynamics. Aft

Massimo Pietroni, Fabian Schmidt

We develop a field-level posterior for cosmological data by marginalizing over initial conditions and noise in a general forward model. While our focus is on large-scale structure data, the results generalize to any weakly non-Gaussian observable. Moreover, the construction is non-perturbative with respect to the forward model and applies equally well to per

Kexue Wang, Yinfeng Yu, Liejun Wang

To establish empathy with machines, it is essential to fully understand human emotional changes. However, research in multimodal emotion recognition often overlooks one problem: individual expressive traits vary significantly, which means that different people may express emotions differently. In our daily lives, we can see this. When communicating with diff

Siu-Ming Tam

Tam [2026] shows that combining Bethel multivariate allocation with Hierarchical Bayes (HB) small area models can substantially reduce survey sample sizes while maintaining domain-level precision and near-nominal coverage of posterior credible intervals (CrIs). This paper extends that framework to cross-classified statistics derived from HBcalibrated unit re

Enqi Liu, Liyuan Pan, Zhi Gao, Yan Yang

Recent advancements in Graphical User Interface (GUI) agents have predominantly focused on training paradigms like supervised fine-tuning (SFT) and reinforcement learning (RL). However, the challenge of high-dynamic GUI environments remains largely underexplored. Existing agents typically rely on a single screenshot after each action for decision-making, lea

Yeeun Lim, Narim Jeong, Donghwan Lee

Ensuring safety during reinforcement learning (RL) training is critical in real-world applications where unsafe exploration can lead to devastating outcomes. While most safe RL methods mitigate risk through constraints or penalization, they still allow exploration of unsafe states during training. In this work, we adopt a stricter safety requirement that eli

Ya-Juan Wang, Yi-Shuai Niu, Artan Sheshmani, Shing-Tung Yau

Unrestricted mean-variance-skewness-kurtosis portfolio optimization can capture asymmetry and tail risk, but sample-moment formulations become computationally impractical when the asset universe is large: they produce dense nonconvex quartic objectives with prohibitive coskewness and cokurtosis tensors and anisotropic, ill-conditioned level sets. We develop

Ke Dong, Kejie Huang, Tao Luo, Bo Wang

Shifted-and-Duplicated-Kernel (SDK) mapping has emerged as an effective strategy to accelerate convolutional layers on compute-in-memory (CIM) hardware. However, existing SDK variants (e.g., VWC-SDK) merely optimize mapping for a single CIM macro, leaving inter-macro parallelism unexplored. Moreover, their mapping methodologies are still suboptimal. To addre

Qianqian Chen, Anglin Liu, Jingyang Zhang, Yudong Zhang

Accurate brain lesion segmentation in MRI is vital for effective clinical diagnosis and treatment planning. Due to high annotation costs and strict data privacy regulations, universal models require employing Continual Learning (CL) to adapt to evolving clinical tasks without losing previously acquired knowledge. However, existing CL paradigms often suffer f

Messali Rouaida, Mattia Coccolo, Miguel A. F. Sanjuán

This paper investigates resonance transmission in two unidirectionally coupled Duffing oscillators with fractional damping, where the driver is harmonically forced and the receiver is connected through a linear coupling spring. Particular attention is paid to how fractional damping in the receiver modifies amplitude amplification, energy redistribution, and