March 2026 arXiv papers — page 121

Luis F. Abanto-Leon, Setareh Maghsudi

This work investigates the radio resource management (RRM) design for downlink integrated sensing and communications (ISAC) systems, jointly optimizing timeslot allocation, beam adaptation, functionality selection, and user-target pairing, with the goal of economizing resource consumption under imperfect information. Timeslot allocation assigns a number of d

Seungjun Cha, Chen Wang, Victor Fung, Guoxiang Hu

Halide perovskite nanocrystals are leading candidates for next-generation optoelectronics, yet the role of surface ligands in controlling their phonon dynamics remains poorly understood. These dynamics critically govern nonradiative relaxation, energy up-conversion, and phonon-assisted anti-Stokes emission. Conventional ab initio methods, while accurate, are

Vito Daniele Perfetta, Daniel Feliu-Talegon, Ebrahim Shahabi, Cosimo Della Santina

High-performance closed-loop control of truly soft continuum manipulators has remained elusive. Experimental demonstrations have largely relied on sufficiently stiff, piecewise architectures in which each actuated segment behaves as a distributed yet effectively rigid element, while deformation modes beyond simple bending are suppressed. This strategy simpli

Redwan Sony, Anil K Jain, Arun Ross

Multimodal Large Language Models (MLLMs) have recently been proposed as a means to generate natural-language explanations for face recognition decisions. While such explanations facilitate human interpretability, their reliability on unconstrained face images remains underexplored. In this work, we systematically analyze MLLM-generated explanations for the u

Sofia Arranz Regidor, Matthew Kozma, Stephen Hughes

We present a quantum dynamical study of pulsed few-photon scattering from a single artificial atom, consisting of a two-level system (TLS) or qubit, in a waveguide QED system, directly comparing and contrasting two different quantum theoretical simulation methods: (i) an input-output scattering approach that uses frequency-dependent scattering matrices, and

L. G. G. Olde Scholtenhuis, D. Perez Capelo, K. Karatsu, D. J. Thoen

Studying the polarization and spectral distortion of the Cosmic Microwave Background (CMB) in tandem with intensity fluctuations of the Cosmic Infrared Background (CIB) allows us to verify our assumptions on cosmic inflation and investigate the dynamics and evolution of galaxy clusters in the last 10 billion years. Because of its broadband emission and being

Snir Carmeli, Adir Morgan, Kiril Solovey

Marine oil spills damage ecosystems, contaminate coastlines, and disrupt food webs, while imposing substantial economic losses on fisheries and coastal communities. Prior work has demonstrated the feasibility of containing and cleaning individual spills using a duo of autonomous surface vehicles (ASVs) equipped with a towed boom and skimmers. However, existi

Zhen Zhao, Jianping Sun, Xin-Wei Yi, Ruwen Wang

Exploring and synthesizing materials with new crystal structures provides an important route to discovering exotic quantum phenomena. However, materials with unconventional lattice geometries remain largely unexplored. Here, we report the discovery of a new vanadium-based material, $\mathrm{Cs_3V_9Te_{13}}$, featuring a Reuleaux-triangle-like lattice. Electr

Elisabetta Boella, Nitin Shukla, Filippo Spiga, Mozhgan Kabiri Chimeh

The Particle-In-Cell (PIC) method is a computational technique widely used in plasma physics to model plasmas at the kinetic level. In this work, we present our effort to prepare the semi-implicit energy-conserving PIC code ECsim for exascale architectures. To achieve this, we adopted a pragma-based acceleration strategy using OpenACC, which enables high per

Ángel Ferran Pousa, Wilbert M. den Hertog, Severin Diederichs, Al berto Martinez de la Ossa

The accurate modeling of plasma-based accelerators relies on costly numerical simulations due to the complexity of laser-plasma and beam-plasma interactions. Several strategies can highly reduce the computational cost compared to 3D first-principles particle-in-cell simulations, such as exploiting the near axial symmetry and quasistatic nature of plasma wake

Ryo Kishino, Riku Shiomi, Hiroaki Yamagiwa, Momose Oyama

Instead of directly distilling a language model, this study addresses the problem of aligning a base model with a target model in distribution by designing the domain mixture of training data for pretraining or continued pretraining as a fixed training recipe. We propose a method for determining domain weights by viewing models as points in log-likelihood sp

Bernardo Williams, Harsha Vardhan Tetali, Arto Klami, Marcelo Hartmann

We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison geometry to map simplex-valued class probabilities to an unconstrained Euclidean representation, turning classification into a GP regression problem with fewer latent dimensions th

Qiang He, Wentian Qu, Jiajia Dai, Changsong Lei

Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangements and similarities in shape among adjacent teeth, existing methods struggle with accurate segmentation, because they often focus on local geometry while neglecting global context

Kevin T. Grosvenor, Mario Solís, Piotr Surówka

Plastic deformation is widely regarded as an intrinsically dissipative phenomenon and its theoretical description is largely phenomenological. We argue instead that plasticity possesses a non-dissipative, symmetry determined backbone: defect kinematics are fixed by symmetry prior to dissipation and separate from constitutive assumptions. Starting from the sp

Chunhai Jiao, Jin Guo, Haoyan Zhang, Jinqiao Duan

We study the problem of detecting the onset of path splitting in stochastic interpolation between probability distributions. This question is especially subtle when the target distribution is nonconvex or supported on disconnected components, where interpolating trajectories may separate into distinct branches. Motivated by the stochastic control and Schr\"o

Ruslan Zakirzyanov

Automated process control systems (APCS) are widely used in modern industrial enterprises. They address three key objectives: ensuring the required quality of manufactured products, ensuring process safety for people and the environment, and reducing capital and operating costs. At large industrial enterprises, APCSs are typically geographically distributed

Weiqin Jiao, Hao Cheng, George Vosselman, Claudio Persello

We tackle the problem of generating a complete vector map representation from aerial imagery in a single run: producing polygons for all land-cover classes with shared boundaries and without gaps or overlaps. Existing polygonization methods are typically class-specific; extending them to multiple classes via per-class runs commonly leads to topological incon

Quentin Claus, Gwenaël Joret, Clément Rambaud

We prove blow-up structure theorems for graphs excluding a tree or an apex-tree as a minor. First, we show that for every $t$-vertex tree $T$ with $t\geq 3$ and radius $h$, and every graph $G$ excluding $T$ as a minor, there exists a graph $H$ with pathwidth at most $2h-1$ such that $G$ is contained in $H\boxtimes K_{t-2}$ as a subgraph. This improves on a r

Dehui Kong, Martin Feick, Shi Liu, Alexander Maedche

Cognitive empathy, the ability to understand others' perspectives, is essential for effective communication, reducing biases, and constructive negotiation. However, this skill is declining in a performance-driven society, which prioritizes efficiency over perspective-taking. Here, the training of cognitive empathy is challenging because it is a subtle, hard-

Gergő Gyenizse, Miklós Maróti, László Zádori

A digraph $\mathbb G$ is called weakly connected, strongly connected, and extremely connected if any two vertices of $\mathbb G$ are connected respectively by an oriented, a directed, and a symmetric path in $\mathbb G$. We investigate the algebraic properties of digraphs that force some of these connectivity notions to coincide. We prove that for digraphs w

Zhengyang Wang, Nuttapong Rochanavibhata, Yuxiao Ren, Xusheng Du

In the early design stage of Japanese detached houses, the lack of a unified design representation among clients, sales representatives, and designers leads to design drift and inefficient feedback. Usually, sketches handed off by sales representatives may lose details for quick drawing, which reduces the fidelity of subsequent 3D generation using generative

Jean-Christophe Pain

We revisit Eisenstein's geometric proof of quadratic reciprocity and make explicit the involutive symmetry underlying Eisenstein's lattice-point argument. Building on Gauss's lemma, we interpret the Legendre symbols as counts of lattice points in a finite rectangle and construct a simple fixed-point-free involution corresponding to the central symmetry of th

Tangchao Zhan, Changfu Shi, Shuo Sun, Jianwei Mei

We study the sensitivity required for a future space-based detector to search for beyond general relativity effect in gravitational wave detection. To do this, we use the current design of TianQin, LISA, and $\mu$Ares as starting points, and study how their key noise parameters should be improved to adequately detect some target signals, for which we choose

Liqi Wu, Haoyu Jia, Kento Kawaharazuka, Hirokazu Ishida

Robotic grasping is a fundamental yet crucial component of robotic applications, as effective grasping often serves as the starting point for various tasks. With the rapid advancement of neural networks, data-driven approaches for robotic grasping have become mainstream. However, efficiently generating grasp datasets for training remains a bottleneck. This i

Liam Fallik, Sriram Balamurali, Alican Caglar, Rohith Acharya

Large-scale cryogenic quantum systems are constrained by an input-output bottleneck between room-temperature electronics and millikelvin stages, particularly in superconducting qubit platforms. This bottleneck is most acute for output lines, where bulky and expensive microwave components limit scalability. A promising approach for scalable characterization a

Zdeněk Nekula, Jakub Bělín, Andrea Konečná

Accurate wave-optical simulation in electron microscopy is severely constrained by the extreme sampling requirements imposed by short wavelengths and relatively large convergence angles. Conventional implementations of the angular spectrum method (ASM) rapidly become computationally intractable, often exceeding realistic memory and time limits. We present tw

Zeyang Ding, Xinglin Hu, Jicong Fan

Hallucinations in large language models (LLMs) remain a central obstacle to trustworthy deployment, motivating detectors that are accurate, lightweight, and broadly applicable. Since an LLM with a prompt defines a conditional distribution, we argue that the complexity of the distribution is an indicator of hallucination. However, the density of the distribut

Andi Liu, Guohui Hu

The precise encapsulation of deformable particles in multiphase flows involves complex transient Fluid-Structure Interactions (FSI) and topological interfacial changes. In the context of single-cell analysis, a numerical framework that couples the Cahn-Hilliard phase-field model with the Arbitrary Lagrangian-Eulerian (ALE) method is employed to investigate t

Aleksander Filip Żarnecki

Production of the leptophilic Z' boson at future colliders was proposed as one of the benchmark Beyond the Standard Model (BSM) scenarios for the Physics Briefing Book prepared for the 2026 update of the European Strategy for Particle Physics. Presented in this contributions are results on the sensitivity of the experiments at the International Linear Collid

Dillon G. Frost, Johann Bouchet, Mihai-Cosmin Marinica, Clovis Lapointe

Calculating diffusion rates of point defects in materials typically relies on the harmonic approximation to estimate migration free energies. However, anharmonic effects can have a large impact on diffusion properties, and explicitly accounting for them is usually computationally demanding and difficult to achieve in practice. In this work, we investigate th

Mo El-Haj

We introduce the Tarab Corpus, a large-scale cultural and linguistic resource that brings together Arabic song lyrics and poetry within a unified analytical framework. The corpus comprises 2.56 million verses and more than 13.5 million tokens, making it, to our knowledge, the largest open Arabic corpus of creative text spanning both classical and contemporar

Weijie Qiu, Dai Guan, Junxin Wang, Zhihang Li

Generative reward models (GRMs) for vision-language models (VLMs) often evaluate outputs via a three-stage pipeline: rubric generation, criterion-based scoring, and a final verdict. However, the intermediate rubric is rarely optimized directly. Prior work typically either treats rubrics as incidental or relies on expensive LLM-as-judge checks that provide no

Alessio Rimoldi, Carlo Cenedese, Alberto Padoan, Florian Dörfler

Urban traffic congestion is a key challenge for the development of modern cities, requiring advanced control techniques to optimize existing infrastructures usage. Despite the extensive availability of data, modeling such complex systems remains an expensive and time consuming step when designing model-based control approaches. On the other hand, machine lea

Stephan Pfannerer

Recently, Armon and Swanson introduced signed standard tableaux and a corresponding super major index that refines the classical major index. In this paper, we prove that signed standard tableaux of rectangular shape exhibit a cyclic sieving phenomenon (CSP) under the combined action of Sch\"utzenberger promotion and cyclic shift of the signs, with the sievi

S. Hekker, Y. Elsworth, S. Basu, F. Ahlborn

In the convective envelopes of relatively cool stars, oscillations are excited by turbulent convection. In these so-called solar-like oscillators, radial oscillation modes appear at nearly equally spaced frequencies. This spacing is referred to as the `large frequency separation'. Deviations from equally-spaced frequencies are a result of the internal struct

Fangjing Li, Zhihai Wang, Xinxin Ding, Haiyang Liu

Mounting posture is an important visual indicator of estrus in dairy cattle. However, achieving reliable mounting pose estimation in real-world environments remains challenging due to cluttered backgrounds and frequent inter-animal occlusion. We present FSMC-Pose, a top-down framework that integrates a lightweight frequency-spatial fusion backbone, CattleMou

Ardavan Rahimian

This paper develops a self-contained framework for studying a mobility-aware intelligent reflecting surface (IRS)-assisted multi-node uplink under simplified but explicit modeling assumptions. The considered system combines direct and IRS-assisted narrowband propagation, geometric IRS phase control with finite-bit phase quantization, adaptive IRS-user focusi

Athanasios Brofas, Fotios K. Diakonos

The search for the QCD critical end point (CEP) is a major objective of contemporary heavy-ion physics, motivating the study of fluctuation observables that are sensitive to critical dynamics. In particular, baryon-number fluctuations provide a natural probe because the net-baryon density can serve as an effective order parameter in the vicinity of the CEP.

Adir Morgan, Kiril Solovey, Oren Salzman

Inspection planning is concerned with computing the shortest robot path to inspect a given set of points of interest (POIs) using the robot's sensors. This problem arises in a wide range of applications from manufacturing to medical robotics. To alleviate the problem's complexity, recent methods rely on sampling-based methods to obtain a more manageable (dis

Frederik Beuth, Danny Kowerko

Collinearity is a visual perception phenomenon in the human brain that amplifies spatially aligned edges arranged along a straight line. However, it is vague for which purpose humans might have this principle in the real-world, and its utilization in computer vision and engineering applications even is a largely unexplored field. In this work, our goal is to

Ville Salo

If totally periodic points are dense in a subshift $X$, its automorphism group is residually finite. We show a weak converse: if periodic points are not dense in a subshift $X$, then the automorphism group of $X \times Y$ is not residually finite for full shifts $Y$ (and sufficiently full-shift-like subshifts). On the other hand, we show that the automorphis

Ji-Fu Li, Manyi Zhang, Xiaobo Xia, Han Bao

Microscaling floating-point (MXFP) formats have emerged as a promising standard for deploying Multi-modal Large Language Models (MLLMs) and Large Language Models (LLMs) on modern accelerator architectures. However, existing Post-Training Quantization (PTQ) methods, particularly rotation-based techniques designed for integer formats, suffer from severe perfor

Joonas Isometsä, Auguste Bieleviciute, Xiaolong Liu, Ville Vähänissi

Silicon carbide (SiC) is a highly promising material for the rapidly growing UV detection industry due to its visible-blindness, low dark current, and exceptional thermal and chemical stability. Despite these advantages, the performance of state-of-the-art SiC UV detectors remains limited due to high reflectance losses, even with the use of anti-reflection c

Pierre-Antoine Bannier

We present HistoAtlas, a pan-cancer computational atlas that extracts 38 interpretable histomic features from 6,745 diagnostic H&E slides across 21 TCGA cancer types and systematically links every feature to survival, gene expression, somatic mutations, and immune subtypes. All associations are covariate-adjusted, multiple-testing corrected, and classified i

Maurits Kaptein, Vassilis-Javed Khan, Andriy Podstavnychy

AI agents -- systems that plan, reason, and act using large language models -- produce non-deterministic, path-dependent behavior that cannot be fully governed at design time, where with governed we mean striking the right balance between as high as possible successful task completion rate and the legal, data-breach, reputational and other costs associated w

Francesco Bajardi

We study the Energy Conditions in modified $f(G)$ gravity, with $G$ being the topological Gauss-Bonnet term. Then we use the cosmographic parameters to constrain the functional form of the gravitational action and investigate the possibility to have standard inflation in the early time. Specifically, we select models containing symmetries within the modified

Eder M. Correa

We present a geometric mechanism for the emergence of spherical $3$-manifolds from the superspace of Riemannian metrics associated with flat ${\rm{SU}}(2)$-bundles over closed orientable hyperbolic surfaces. Our main result shows that any homogeneous spherical 3-manifold $(S,g_{S})$ can be realized as a boundary point in the Gromov-Hausdorff closure of a sup

Joe Standridge, Daniel Livescu, Paul Cizmas

Stiff dynamical systems present a challenge for machine-learning reduced-order models (ML-ROMs), as explicit time integration becomes unstable in stiff regimes while implicit integration within learning loops is computationally expensive and often degrades training efficiency. Time reparameterization (TR) offers an alternative by transforming the independent

Richard Aron, Verónca Dimant, Manuel Maestre

Recently, in arXiv:2304.07149, a bridge was made between the very active area of spaces of Lipschitz real functions on a metric space and holomorphic functions on an open subset of a Banach space. This was done by introducing and studying the space $\mathcal HL_0(B_X)$ of holomorphic Lipschitz functions defined on $B_X$, the open unit ball of the complex Ban

Seyed Mahed Mousavi, Christian Moiola, Massimo Rizzoli, Simone Alghisi

Vision-Language Models (VLMs) are trained on data snapshots of documents, including images and texts. Their training data and evaluation benchmarks are typically static, implicitly treating factual knowledge as time-invariant. However, real-world facts are intrinsically time-sensitive and subject to erratic and periodic changes, causing model predictions to

D. T. Kattikat Melcom, I. Tsekhanovich, F. Guezet, A. Andreyev

Experimental setups commonly used to study fission properties of nuclei in the exotic neutron-deficient 180Hg region are based on the time-of-flight technique for the fission-product identification. The nuclei of interest are created via fusion reactions at excitation energies of several tens of MeV and identified with limited mass resolution. The deduced fi

Quanzhi Ye, Tony L. Farnham, Perry Cai, Lori Feaga

Short-period comet P/2010 H2 (Vales) underwent a significant outburst of $>7.5$~mag in 2010 and has not been detected since that apparition. Here we report our recovery attempt of P/Vales using the 4.3-m Lowell Discovery Telescope (LDT) during its 2015 and 2025 apparitions, as well as the data from the Transiting Exoplanet Survey Satellite (TESS) taken in 20

Zelin Zhang, Fei Cheng, Chenhui Chu

Although outcome-based reinforcement learning (RL) significantly advances the mathematical reasoning capabilities of Large Language Models (LLMs), its reliance on computationally expensive ground-truth annotations imposes a severe scalability bottleneck. Unsupervised RL guided by intrinsic rewards offers a scalable alternative, yet it suffers from opaque tra

Jose Manuel Sanchez, Miguel Angel Olivero, Ruben Heradio, Luis Cambelo

Feature models are widely used to capture the configuration space of software systems. Although automated reasoning has been studied for detecting problematic features and supporting configuration tasks, significantly less attention has been given to the systematic study of the structural properties of feature models at scale. The approach fills this gap by

Yong Zou, Haoran Li, Fanxiao Li, Shenyang Wei

Recent progress in image generation models (IGMs) enables high-fidelity content creation but also amplifies risks, including the reproduction of copyrighted content and the generation of offensive content. Image Generation Model Unlearning (IGMU) mitigates these risks by removing harmful concepts without full retraining. Despite growing attention, the robust

Titus von der Malsburg, Sebastian Padó

Transformers underlie almost all state-of-the-art language models in computational linguistics, yet their cognitive adequacy as models of human sentence processing remains disputed. In this work, we use a surprisal-based linking mechanism to systematically evaluate eleven autoregressive transformers of varying sizes and architectures on a more comprehensive

Daniel Witt, Thomas Bendall, Jemma Shipton

The accurate and efficient representation of atmospheric dynamics remains a central challenge in numerical weather prediction. A particular difficulty arises from the strong anisotropy of the atmosphere, in which horizontal and vertical motions occur on very different length scales, motivating numerical discretisations that can reflect this structure. In thi

Amirhossein Kazerouni, Maitreya Suin, Tristan Aumentado-Armstrong, Sina Honari

Recent advances in image restoration have enabled high-fidelity recovery of faces from degraded inputs using reference-based face restoration models (Ref-FR). However, such methods focus solely on facial regions, neglecting degradation across the full scene, including body and background, which limits practical usability. Meanwhile, full-scene restorers ofte

Hangting Ye, Peng Wang, Wei Fan, Xiaozhuang Song

Tabular data have been playing a mostly important role in diverse real-world fields, such as healthcare, engineering, finance, etc. The recent success of deep learning has fostered many deep networks (e.g., Transformer, ResNet) based tabular learning methods. Generally, existing deep tabular machine learning methods are along with the two paradigms, i.e., in

Laurent Cheret, Vincent Létourneau, Isar Nejadgholi, Chris Drummond

We study a simple unsupervised regularization scheme for autoencoders called Manifold-Matching (MMAE): we align the pairwise distances in the latent space to those of the input data space by minimizing mean squared error. Because alignment occurs on pairwise distances rather than coordinates, it can also be extended to a lower-dimensional representation of t

Jared Moore, Ashish Mehta, William Agnew, Jacy Reese Anthis

As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological effects, such as delusions, self-harm, and ``AI psychosis,'' have emerged in global media and legal discourse. However, it remains unclear how users and chatbots interact over the course of lengthy delusional ``spirals,'' limiting our ability to understa

Jon Hasselgren, Zheng Zeng, Milos Hasan, Jacob Munkberg

We present a method for generating physically-based materials for 3D shapes based on a video diffusion transformer architecture. Our method is conditioned on input geometry and a text description, and jointly models multiple material properties (base color, roughness, metallicity, height map) to form physically plausible materials. We further introduce a cus

Dale T. Lowder, Gage Eichman, Yuxin Wan, Karthik Rao

Mesoscopic transport measurements are underexplored as probes of quasiparticles and their properties in correlated metals. The mixed valence compound YbAl$_3$ exhibits a single-ion Kondo temperature of 670 K, while thermodynamic and transport properties (probed with specific heat, magnetic susceptibility, Hall effect, and resistivity) imply the onset of cohe

Florian Bürger, Martim Dias Gomes, Adrián E. Granada, Noémie Moreau

Understanding non-genetic determinants of cell fate is critical for developing and improving cancer therapies, as genetically identical cells can exhibit divergent outcomes under the same treatment conditions. In this work, we present a deep learning approach for cell fate prediction from raw long-term live-cell recordings of cancer cell populations under ch

Manash Dey, Pralay Chakraborty, Dipshikha Das, Subhankar Roy

We plan to decipher the common origin of neutrino mass textures within a $\Delta(27)$ based framework, in association with Type-I+II seesaw mechanism. Motivated by the diversity of texture structures used in phenomenological analyses, we examine how a single framework can reproduce them without additional assumptions. We show that the interplay between vacuu

Tran Ngoc Thien, Vo Quoc Phong

The Hawking-Page phase transition represents a critical phenomenon in black hole thermodynamics, marking the point at which a thermal radiation state in anti-de Sitter (AdS) spacetime becomes unstable. In this work, we apply the Hamiltonian formalism to study the Hawking-Page phase transition of the Banados-Teitelboim-Zenelli (BTZ) black hole in on-shell and

Ana Vitória de Almeida Martinheira Braga, Murillo Gregorio Grefener da Silva, Aion Viana

The quest to identify the true nature of dark matter remains one of the most pressing challenges in modern physics. We present a novel approach to probe the Weakly Interacting Massive Particle (WIMP) paradigm by analyzing density enhancements, or ``mini-spikes,'' around stellar-mass black holes (sBHs) using 17 years of data from the \textit{Fermi} Large Area

Jiale Song, Jiaxin Luo, Xue-song Tang, Kuangrong Hao

Large Vision-Language Models (LVLMs) achieve strong performance on many multimodal tasks, but object hallucinations severely undermine their reliability. Most existing studies focus on the text modality, attributing hallucinations to overly strong language priors and insufficient visual grounding. In contrast, we observe that abnormal attention patterns with

Sangyeon Yoon, Sunkyoung Kim, Hyesoo Hong, Wonje Jeung

Large language models (LLMs) increasingly store user preferences in persistent memory to support personalization across interactions. However, in third-party communication settings governed by social and institutional norms, some user preferences may be inappropriate to apply. We introduce BenchPreS, which evaluates whether memory-based user preferences are

Farid Bikmukhametov, Ksenia Razrezova, Roman Smolnitsky, Yuri Shchelokov

Acoustic anechoic chambers allow free-field measurements required for the verification of effects under investigation and for the characterization of developing devices. However, the construction of an anechoic chamber is a labor-intensive process that requires a lot of resources, which is why these facilities are rather rare. An analogous statement can be m

Hojin Lee, Andreas Lintermann, Sangseung Lee, Mario Rüttgers

The analysis of flow around buildings has gained significant research interest across various domains, including pedestrian safety, pollutant dispersion, natural ventilation, and building energy efficiency. While these domains frequently include high-resolution computational fluid dynamics (CFD) data, predicting urban flow fields with machine learning (ML) m

Yifei Zhang, Mingyang Li, Henry Gao, Liang Zhao

Large language models (LLMs) demonstrate strong cognitive intelligence (IQ), yet many real-world interactions also require emotional intelligence (EQ) to produce responses that are both factually reliable and emotionally appropriate. In settings such as emotional support, technical assistance, and consultation, effective dialogue depends on how situations ar

Joel Bobadilla, Alberto Camjayi

Antiferromagnets are attractive for spintronics owing to their vanishing net magnetization and ultrafast spin dynamics, yet their spin-compensated electronic structure has long confined them to passive roles. Here we identify a symmetry selection rule that overcomes this limitation: in a collinear antiferromagnet, a spin-polarized dc charge current requires

Francesco Bajardi, Micol Benetti, Salvatore Capozziello, Abedennour Dib

Within the general framework of $f(R)$ gravity, we introduce a function of the electromagnetic curvature invariant $f(\mathbb{F})$ that couples minimally to gravitation to ensure a consistent treatment of curvature functions in these theories. We show that one of the solutions leads to field equations that are a generalization of the Klein-Gordon equation wh

Alexander Prutsch, David Schinagl, Horst Possegger

Accurate trajectory prediction can improve General Aviation safety in non-towered terminal airspace, where high traffic density increases accident risk. We present ASCENT, a lightweight transformer-based model for multi-modal 3D aircraft trajectory forecasting, which integrates domain-aware 3D coordinate normalization and parameterized predictions. ASCENT em

Jilles S. van Hulst, W. P. M. H. Heemels, Duarte J. Antunes

Electron microscopy has enabled many scientific breakthroughs across multiple fields. A key challenge is the tuning of microscope parameters based on images to overcome optical aberrations that deteriorate image quality. This calibration problem is challenging due to the high-dimensional and noisy nature of the diagnostic images, and the fact that optimal pa

Jean-Christophe Pain

We investigate inequalities for partial sums of complex numbers with bounded modulus and zero total sum, a topic referred to as "polygonal confinement". Starting from Steinitz's classical result, we provide detailed constructions yielding explicit bounds, including $\sqrt{5}$, $\sqrt{3}$, $2$, and $\sqrt{2}$, depending on geometric constraints or weighted se

Aditya Gupta

Adaptive physical and biological systems continually process fluctuating information from their environments. When the environment is nonstationary, inference itself becomes a nonequilibrium process with thermodynamic cost. We analyse a minimal stochastic model which is an overdamped particle in an adaptive double well potential whose control parameter track

Tom F. Sterkenburg

This chapter discusses the Solomonoff approach to universal prediction. The crucial ingredient in the approach is the notion of computability, and I present the main idea as an attempt to meet two plausible computability desiderata for a universal predictor. This attempt is unsuccessful, which is shown by a generalization of a diagonalization argument due to

Lei Wang, Min Huang, Eduard Dragut

Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--particularly in addressing complex tasks like extracting Aspect-Category-Opinion-Sentiment-Intensity (ACOSI) tuples--remains underexplored. In this work, we introd

Bhawna Piryani, Zehra Mert, Adam Jatowt

Large language models (LLMs) often rely on outdated knowledge when answering time-sensitive questions, leading to confident yet incorrect responses. Without explicit signals indicating whether up-to-date information is required, models struggle to decide when to retrieve external evidence, how to reason about stale facts, and how to rank answers by their val

Keita Nagaoka, Kentaro Uno, Kazuya Yoshida

Climbing robots face significant challenges when navigating unstructured environments, where reliable attachment to irregular surfaces is critical. We present a novel mobile climbing robot equipped with compliant pin-array structured grippers that passively conform to surface irregularities, ensuring stable ground gripping without the need for complicated se

Wanpeng Zhang, Hao Luo, Sipeng Zheng, Yicheng Feng

Offline post-training adapts a pretrained robot policy to a target dataset by supervised regression on recorded actions. In practice, robot datasets are heterogeneous: they mix embodiments, camera setups, and demonstrations of varying quality, so many trajectories reflect recovery behavior, inconsistent operator skill, or weakly informative supervision. Unif

Xiangzhi Cao

In this paper, we are devoted to obtain some results on p-biharmonic map from gradient Ricci soliton, especially on two dimensional cigar soliton.

Jonas Deré, Jeroen Gantois

The concept of a nice basis for a Lie algebra was introduced to study the Ricci curvature on nilpotent Lie groups equipped with a left-invariant metric. Despite the many applications in differential geometry, for example in the construction of Einstein manifolds, very little is known about the existence and number of nice bases on a given Lie algebra. This p

Yue Zhao, Daochang Zhang, Jingqian Li, Dijana Mosic

The motivation of this paper is to investigate the perturbation theory for the QT-Drazin inverse of quaternion tensors under the QT-product via the associated $z$-block circulant representation. A fundamental relationship between the QT-Drazin inverse of $\mathtt{bcirc}_z(\mathcal A)$ and the $z$-block circulant form of $\mathcal A^D$ is established. Moreove

Mangyu Kong, Jaewon Lee, Seongwon Lee, Euntai Kim

3D Gaussian Splatting (3DGS) has recently emerged as a powerful scene representation and is increasingly used for visual localization and pose refinement. However, despite its high-quality differentiable rendering, the robustness of 3DGS-based pose refinement remains highly sensitive to both the initial camera pose and the reconstructed geometry. In this wor

Carmen Ng

LLM-enabled robots prioritizing scarce assistance in social settings face pluralistic values and LLM behavioral variability: reasonable people can disagree about who is helped first, while LLM-mediated interaction policies vary across prompts, contexts, and groups in ways that are difficult to anticipate or verify at contact point. Yet user-facing guardrails

Harry Sticker

Any representational enterprise must omit variation in order to function. NASA still uses Newtonian mechanics, though Einstein superseded Newton, and the standard picture of scientific progress cannot explain how. A description that omitted nothing would be identical to its subject and would explain nothing. This paper argues that omission is not a defect bu

Vassilios Tsounis, Guirec Maloisel, Christian Schumacher, Ruben Grandia

We present Kamino, a GPU-based physics solver for massively parallel simulations of heterogeneous highly-coupled mechanical systems. Implemented in Python using NVIDIA Warp and integrated into the Newton framework, it enables the application of data-driven methods, such as large-scale reinforcement learning, to complex robotic systems that exhibit strongly c

Viktor Stein, Wuchen Li, Gabriele Steidl

Transformers owe much of their empirical success in natural language processing to the self-attention blocks. Recent perspectives interpret attention blocks as interacting particle systems, whose mean-field limits correspond to gradient flows of interaction energy functionals on probability density spaces equipped with Wasserstein-$2$-type metrics. We extend

Agustín Muñoz González

This work extends the theory presented in Mean Field Games with a Dominating Player by Bensoussan, Chau and Yam on mean field games with a dominating player, to the case in which the utility and cost functions depend not only on the law of the states, but on the joint state--control law. We incorporate the conditional distribution of the state--control pair

Shambel Sahlu, Andronikos Paliathanasis, Genly Leon, Amare Abebe

We investigate the viability of a cosmological scenario with interacting dark sector, which can describe the coexistence between dark energy and dark matter. The model possesses an analytical solution for the Hubble function and we constrain the free parameters by applying the newly released cosmic chronometers data (31 old data and 3 new data from DESI), th

Sanjib Dey, Mir Faizal

We show that quartic modifications of relativistic dispersion relations arise generically from deformation-quantized phase spaces under minimal kinematical assumptions relevant to quantum gravity. When the kinematics admits an integral symplectic structure, a compatible almost-complex structure, and a gauge-invariant two-form sector, the leading Planck-scale

Kentaro Uno, Masazumi Imai, Kazuki Takada, Teruhiro Kataonami

In lunar and planetary exploration, legged robots have attracted significant attention as an alternative to conventional wheeled robots, which struggle to traverse rough and uneven terrain. To enable locomotion over highly irregular and steeply inclined surfaces, limbed climbing robots equipped with grippers on their feet have emerged as a promising solution

Fan Zhang, Zhiming Li

To analyze the uncertain data frequently encountered in practice, this paper proposes novel fixed-effects models that incorporate an uncertain measure to investigate variables of interest and nuisance variables in factor designs. First, an uncertain fixed-effects (UFE) model of a single-factor design is established, and uncertain estimation and hypothesis te

Agustín Muñoz González

This paper extends the theoretical framework introduced in Liquidity Pools as Mean Field Games: A New Framework, where the interactions among traders in a constant product market-making protocol were modeled using mean field games (MFG). In this extension, transaction costs are incorporated into the traders' inventory dynamics, modeling the impact of pool fe

Qianru Wei, Jihaoyu Yang, Cheng Zhang, Jinming Yang

With the development of information technology, the application of artificial intelligence and machine learning in the field of education shows great potential. This study aims to explore how to utilize K-means clustering algorithm to provide accurate career guidance for college students. Existing methods mostly focus on the prediction of career paths, but t

Aya Alnajjarine, Jacques Laskar, Federico Mogavero

We explore a hybrid expansion of the disturbing function in planetary dynamics that combines elements of the classical Laplace and Legendre developments. This formulation retains the structure of the Laplace expansion, but expresses the inverse of the mutual distance as a series whose terms keep an exact dependence on both the eccentricity and the semi-major

Baptiste Claudon, Alexis Lucas, Jean-Philip Piquemal, César Feniou

Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This work introduces a method for preparing quantum states whose amplitudes are given by a degree$-d$ polynomial, using circuits with logarithmic depth in the number $n