December 2025 arXiv papers — page 53

Xiyun Xu, Ming Xu

In this paper, we consider a left invariant complex Finsler metric $F$ on a complex Lie group. Using the technique of invariant frames, we prove the following properties for $(G,F)$. First, the metric $F$ must be a complex Berwald metric. Second, its complex spray $\chi=w^i\delta_{z^i}$ on $T^{1,0}G\backslash0$ can be extended to a holomorphic tangent field

The STAR Collaboration

We report high-statistics measurements of fifth- and sixth-order factorial cumulants and cumulant ratios of (net-)proton multiplicity distributions in Au+Au collisions at $\sqrt{s_{NN}} = 7.7$--27 GeV, using data from the STAR experiment collected during the Beam Energy Scan Phase~II at RHIC. Protons and antiprotons are identified at midrapidity ($|y| < 0.5$

Nawaf Bou-Rabee, Victor H. de la Peña

Consider a Markov chain $(X_i)_{i\ge0}$ with invariant measure $\mu$ that admits the representation $X_{i+1}=\Phi(X_i,U_i)$, where $(U_i)_{i\ge0}$ are i.i.d. random variables and $\Phi$ is a measurable map. We introduce a tangent-decoupled process $(\widetilde X_i)_{i\ge0}$ obtained by replacing $(U_i)$ with an independent copy. Conditional on the realized b

A. B. M. Ashikur Rahman, Saeed Anwar, Muhammad Usman, Irfan Ahmad

Sycophancy, an excessive tendency of AI models to agree with user input at the expense of factual accuracy or in contradiction of visual evidence, poses a critical and underexplored challenge for multimodal large language models (MLLMs). While prior studies have examined this behavior in text-only settings of large language models, existing research on visua

JiaWei Zhu, ZiHeng Liu

Hidden confounding remains a fundamental challenge in causal inference from observational data. Recent advances leverage Large Language Models (LLMs) to generate plausible hidden confounders based on domain knowledge, yet a critical gap exists: LLM-generated confounders often exhibit semantic plausibility without statistical utility. We propose VIGOR+ (Varia

Shubhankar Sahai

Let $k$ be a perfect ring of characteristic $p>0$, and let $R$ be an animated $k$-algebra. This note aims to show that the Nygaard filtered prismatization $R^{\mathrm{Nyg}}$ of $R$ is naturally isomorphic, as a stack over $k^{\mathrm{Nyg}}$, to the relative spectrum over $k^{\mathrm{Nyg}}$ of the Rees algebra of the Nygaard filtered prismatic cohomology of $

Pranav Vaidhyanathan, Gerard J. Milburn

Irreversibility implies a preferred flow of time, yet special relativity denies the existence of a preferred clock. This tension has long obstructed the formulation of a relativistic master equation: standard Markovian approximations either break Lorentz covariance, trigger catastrophic vacuum heating, or depend arbitrarily on the observer's foliation. In th

Nabajit Talukdar

For an irreducible character $\chi$ of a finite group $G$, its kernel is defined as $\text{ker }\chi=\{g\in G: \chi(g)=\chi(1)\}$. In this paper we characterize the finite groups of prime power order(for odd prime) in which kernels of all of the non-linear irreducible characters are of the same order.

Zhe Li, Shanwen Wang, Zhiqi Zhu

In this note, we make explicit the correspondence between Harish-Chandra parameters and Langlands-Vogan parameters for symplectic groups and orthogonal groups of equal rank over reals. As an application, we reformulate Moeglin's results and Paul's work on the Howe correspondence for symplectic-orthogonal dual pairs using Langlands-Vogan parameters.

Kiril Dichev, Filip Pawlowski, Albert-Jan Yzelman

Recommender systems are enablers of personalized content delivery, and therefore revenue, for many large companies. In the last decade, deep learning recommender models (DLRMs) are the de-facto standard in this field. The main bottleneck in DLRM inference is the lookup of sparse features across huge embedding tables, which are usually partitioned across the

Guanqi Qiu, Qianyi Ma, Deji Liu, Dongchi Cai

Optimizing laser and plasma parameters is crucial for enhancing accelerated proton energy in laser-driven proton acceleration with finite laser energy for applications such as cancer therapy. Tight focusing plays a significant role in improving laser-driven proton acceleration, which is generally believed as a result of the enhancement of laser intensity. Ho

Rashid Iqbal, Ahmed Zoha, Salama Ikki, Muhammad Ali Imran

Most intelligent reflecting surface (IRS)-aided indoor visible light communication (VLC) studies ignore the time delays introduced by reflected paths, even though these delays are inherent in practical wideband systems. In this work, we adopt a realistic assumption of IRS-induced time delay for physical layer security (PLS) enhancement. We consider an indoor

Claudia Neves, Chang Xu

When analysing extreme values, two alternative statistical approaches have historically been held in contention: the block maxima method (or annual maxima method, spurred by hydrological applications) and the peaks-over-threshold. Clamoured amongst statisticians as wasteful of potentially informative data, the block maxima method gradually fell into disfavou

Kenji Fukushima, Shuhei Minato

We present the derivation of an alternative representation of the real-time in-in formalism under a spatially homogeneous and time independent electric field. Because the system exhibits instability associated with pair production of particles and antiparticles, the perturbation theory should be reorganized depending on the choice of the reference vacuum. We

Siyuan Mei, Yan Xia, Fuxin Fan, Andreas Maier

The synthesis of computed tomography (CT) from magnetic resonance imaging (MRI) and cone-beam CT (CBCT) plays a critical role in clinical treatment planning by enabling accurate anatomical representation in adaptive radiotherapy. In this work, we propose GANeXt, a 3D patch-based, fully ConvNeXt-powered generative adversarial network for unified CT synthesis

Fei Han, Ruizhi Huang, Weiping Zhang

We prove that every almost flat spin^$c$ manifold bounds a compact orientable manifold, thereby settling, in the spin^$c$ case, a long-standing conjecture of Farrell--Zdravkovska and S. T. Yau.

Haohua Chen, Songbin Liu, Junjie Ma

We propose an orthogonal approximate message passing (OAMP) algorithm for signal estimation in the rectangular spiked matrix model with general rotationally invariant (RI) noise. We establish a rigorous state evolution that precisely characterizes the algorithm's high-dimensional dynamics and enables the construction of iteration-wise optimal denoisers. With

Clara Bertinelli Salucci, Hedvig Borgen Reiersrud, A. L. Read, Anders Kvellestad

Likelihood ratio tests are widely used in high-energy physics, where the test statistic is usually assumed to follow a chi-squared distribution with a number of degrees of freedom specified by Wilks' theorem. This assumption breaks down when parameters such as signal or coupling strengths are restricted to be non-negative and their values under the null hypo

Yueting Zhu, Yuehao Song, Shuai Zhang, Wenyu Liu

Whole Slide Images (WSIs) are typically analyzed using multiple instance learning (MIL) methods. However, the scale and heterogeneity of WSIs generate highly redundant and dispersed information, making it difficult to identify and integrate discriminative signals. Existing MIL methods either fail to discard uninformative cues effectively or have limited abil

Edwan Préau

The holographic product formula is used to determine the general form taken by holographic spectral functions in the near-extremal hydrodynamic regime, with energy $\omega$, momentum $k$ and temperature $T$ much smaller than a hard scale $\mu$. The resulting expressions simplify in the extremal limit $T \ll \omega,k\ll \mu$, for which the low-temperature gap

Guangyue Huang, Chunlei Luo, Hongru Song

In this paper, we study the overdetermined problem for the p-Laplacian equation on a compact Riemannian manifold with positive Ricci curvature. By introducing a new P-function which is related to the first nonzero eigenvalue for p-Laplacian, we obtain some integral identities. As their applications, the Heintze-Karcher type inequality and the Soap Bubble The

Bingqing Liu, David Watling, Joseph Y. J. Chow

We study the stochastic assignment game and extend it to model multimodal mobility markets with a regulator or a Mobility-as-a-Service (MaaS) platform. We start by presenting general forms of one-to-one and many-to-many stochastic assignment games. Optimality conditions are discussed. The core of stochastic assignment games is defined, with expected payoffs

Moamal Fadhil Abdul-Mahdi, Jonas Bruun Hubrechts, Thomas Martini Jørgensen, Emil Hovad

Automatically detecting and classifying strokes in table tennis video can streamline training workflows, enrich broadcast overlays, and enable fine-grained performance analytics. For this to be possible, annotated video data of table tennis is needed. We extend the public OpenTTGames dataset with highly detailed, frame-accurate shot type annotations (forehan

Philipp Schneider, Julian Werthmann

We study how the Massively Parallel Computation (MPC) model in the strongly sublinear regime relates to the classic, graph-centric distributed models, focusing on the Node-Capacitated Clique (NCC), a bandwidth-parametrized generalization of the Congested Clique. In MPC, $M$ machines with per-machine memory $S$ hold a partition of the input graph. In NCC, we

Xinyue Xu, Huifang Ma, Hongfei Wang, Long Feng

Elliptical factor models play a central role in modern high-dimensional data analysis, particularly due to their ability to capture heavy-tailed and heterogeneous dependence structures. Within this framework, Tyler's M-estimator (Tyler, 1987a) enjoys several optimality properties and robustness advantages. In this paper, we develop high-dimensional scatter m

Wei Tang, Yue Zhou

Let $\mathscr{S}_n(q)$ denote the set of symmetric bilinear forms over an $n$-dimensional $\mathbb{F}_q$-vector space. A subset $\mathcal{C}$ of $\mathscr{S}_n(q)$ is called a $d$-code if the rank of $A-B$ is larger than or equal to $d$ for any distinct $A$ and $B$ in $\mathcal{C}$. If $\mathcal{C}$ is further closed under matrix addition, then $|\mathcal{C}

Ezequiel Lopez-Rubio, Macoris Decena-Gimenez, Rafael Marcos Luque-Baena

A key module in neural transformer-based deep architectures is positional encoding. This module enables a suitable way to encode positional information as input for transformer neural layers. This success has been rooted in the use of sinusoidal functions of various frequencies, in order to capture recurrent patterns of differing typical periods. In this wor

Hassan Alhussein

Our constructions provide a systematic way to study cohomology tri-dendriform algebra via classical cohomology, simplifying computations and enabling the use of established techniques.

Yayuan Li, Jian Zhang, Jintao Guo, Zihan Cheng

The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised models into a unified model, requiring minimal or even no additional training. A core objective of model merging is to ensure the merged model retains the behavioural characterist

Anjali Varshney, Ritesh Dubey, Sushil Kumar, Tapas Goswami

The efficient and cost-effective conversion of nitro compounds to amines is crucial for industrial processes and environmental remediation, highlighting the growing demand for earth-abundant metal-based catalysts. In this study, magnetic Ni--NiO nanostructures and their composites with two-dimensional hexagonal boron nitride (\textit{h}-BN) were synthesized

Hassan Alhussein

We construct a cochain map embedding the cohomology complex of any dual Leibniz algebra $B$ into the Lie algebra cochain complex of $\mathfrak{g} \otimes B$, where $\mathfrak{g}$ is a Leibniz algebra. This reduces the study of dual Leibniz cohomology to classical Lie algebra cohomology, yielding computational simplifications and new structural insights.

Iván León, Riccardo Muolo, Hiroya Nakao, Keisuke Taga

The Vicsek model is the paradigmatic framework for collective motion in systems of self-propelled particles. In its continuous-time formulation and most of its extensions, alignment arises from pairwise interactions among neighboring particles. In this work, we consider a model in which each particle assigns weights to its neighbors according to their alignm

A. A. Gde Yogi Pramana, Jason Ray, Anthony Jaya, Michael Wijaya

Vision--Language Models (VLMs) show significant promise for Medical Visual Question Answering (VQA), yet their deployment in clinical settings is hindered by severe vulnerability to adversarial attacks. Standard adversarial training, while effective for simpler tasks, often degrades both generalization performance and the quality of generated clinical reason

Marica Muffoletto, Uxio Hermida, Charlène Mauger, Avan Suinesiaputra

Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a

Agustín Silva

We explore a new route toward a non-perturbative quantization of gravity based on a purely affine formulation, where the affine connection is the fundamental field and the metric, when it exists, emerges as a derived quantity. Starting from the Palatini formulation of General Relativity, we recall how an equivalent Eddington-type purely affine action arises

Pradyun Parayil, Amal Raj, Vivek Balachandran

Quantum circuit obfuscation is becoming increasingly important to prevent theft and reverse engineering of quantum algorithms. As quantum computing advances, the need to protect the intellectual property contained in quantum circuits continues to grow. Existing methods often provide limited defense against structural and statistical analysis or introduce con

Claude Carlet, and Alexander Kholosha

Much work has been devoted to bent functions in odd characteristic, but there still remains a gap between our knowledge of binary and nonbinary bent functions. In the first part of this paper, we attempt to partially bridge this gap by generalizing to any characteristic important properties known in characteristic two concerning the Walsh transform of deriva

Qilong Li, Yue Zhou

In this paper, we investigate the number of induced subgraphs and subdigraphs of Paley graphs and Paley tournaments where the (out-)degree of each vertex has the same parity. For Paley graphs, we establish a lower bound for the number of large even induced subgraphs, particularly those containing a constant proportion of vertices. We determine the number of

A. Sbaffoni, J. Liske, A. S. G. Robotham, L. J. M. Davies

Using the equatorial Galaxy and Mass Assembly (GAMA) dataset, we investigate how the low-redshift galaxy stellar mass function (GSMF) varies across different galaxy populations and as a function of halo mass. We find that: (i) The GSMF of passive and star-forming galaxies are well described by a double and a single Schechter function, respectively, although

Ferhat Taş

We study a spinor-driven formulation of geometric evolution on closed $3$-manifolds, in which the spinor field is treated as the primary dynamical variable and the Riemannian metric is induced conformally by the spinor amplitude. We introduce a spinorial heat flow governed by the squared Dirac operator, \[ \partial_t \psi = - D_{g(\psi)}^{\,2} \psi , \] wher

JV Roig

Evaluating knowledge systems (LLMs, RAG, knowledge graphs, etc) faces fundamental challenges: static benchmarks are vulnerable to contamination, LLM-based judges exhibit systematic biases, and ground truth extraction requires expensive human annotation. We present RIKER (Retrieval Intelligence and Knowledge Extraction Rating), both a benchmark and a replicab

Alberto Cortijo

The possibility of a zero temperature, altermagnetic instability in anisotropic two dimensional electron systems in the diffusive regime is analyzed, in the presence and absence of spin-orbit coupling. Allowing for ferromagnetism, a phase diagram is built as a function of the parameter that controls anisotropy and the strength of the interactions. It is foun

Javier Vela-Tambo, Jorge Gracia, Fernando Dominguez-Castro

Understanding and monitoring the socio-economic impacts of climate hazards requires extracting structured information from heterogeneous news articles on a large scale. To that end, we have developed CienaLLM, a modular framework based on schema-guided Generative Information Extraction. CienaLLM uses open-weight Large Language Models for zero-shot informatio

Emir Devlet Ertörer, Cem Ünsalan

Binary neural networks provide a promising solution for low-power, high-speed inference by replacing expensive floating-point operations with bitwise logic. This makes them well-suited for deployment on resource-constrained platforms such as FPGAs. In this study, we present a fully custom BNN inference accelerator for handwritten digit recognition, implement

Abdelhanid Hassairi, Gérard Letac

The classification of natural exponential families started with the paper \cite {Morri} where Carl Morris unifies six very familiar families by the fact that their variance functions are polynomials of degree less or equal to two. Extension of this classification to $\R^n$ and to degree three is the subject of this paper. Keywords: Actions of the group $GL(n

Xu Zhang, Junyao Ge, Yang Zheng, Kaitai Guo

Large Vision--Language Models (LVLMs) hold great promise for advancing optical remote sensing (RS) analysis, yet existing reasoning segmentation frameworks couple linguistic reasoning and pixel prediction through end-to-end supervised fine-tuning, leading to weak geometric grounding and limited generalization across tasks. To address this, we developed Think

P. A. Duverne, W. K. Mouici, A. Coleiro, J. -G. Ducoin

Core-collapse supernovae (CCSNe) are expected to produce intense bursts of neutrinos preceding the emergence of their electromagnetic (EM) counterparts. The prompt detection of such neutrino signals offers a unique opportunity to trigger early follow-up observations in the EM domain. We aim to assess the feasibility and efficiency of an optical-NIR follow-up

Jun Li, Zikun Chen, Haibo Chen, Shuo Chen

Novel object synthesis by integrating distinct textual concepts from diverse categories remains a significant challenge in Text-to-Image (T2I) generation. Existing methods often suffer from insufficient concept mixing, lack of rigorous evaluation, and suboptimal outputs-manifesting as conceptual imbalance, superficial combinations, or mere juxtapositions. To

Haoyu Jiang, Fanjie Zeng, Boan Qu, Xiaojie Lin

In the global drive toward carbon neutrality, deeply coordinated smart energy systems underpin industrial transformation. However, the interdisciplinary, fragmented, and fast-evolving expertise in this domain prevents general-purpose LLMs, which lack domain knowledge and physical-constraint awareness, from delivering precise engineering-aligned inference and

Saurabh Mittal, Thomas Siegert, Francesca Calore, Pierluca Carenza

Axion-like particles (ALPs) are hypothetical pseudoscalar bosons that arise in many extensions of the Standard Model and are well-motivated dark matter candidates. Nearby massive stars in the late stages of stellar evolution provide a promising environment for enhanced ALP production due to their high core temperatures and densities. We search for a combined

Linzhi Chen, Yang Sun, Hongru Wei, Yuqi Chen

Low-Rank Adaptation (LoRA) has emerged as an efficient method for fine-tuning large language models (LLMs) and is widely adopted within the open-source community. However, the decentralized dissemination of LoRA adapters through platforms such as Hugging Face introduces novel security vulnerabilities: malicious adapters can be easily distributed and evade co

Zetao Lin, Shiping Liu

Using the Nakayama duality induced by a Nakayama functor, we provide a novel and concise account of the existence of Auslander-Reiten dualities and almost split sequences in abelian categories with enough projective objects or enough injective objects. As an example, we establish the existence of almost split sequences ending with finitely presented modules

Nikolay Nikolov, Julian Wykowski

We prove that the torsion-free lamplighter group $\Gamma = \mathbb{Z}^n \wr \mathbb{Z}$ of any rank $n \in \mathbb{N}$ is profinitely rigid in the absolute sense: the finite quotients of $\Gamma$ determine its isomorphism type uniquely among all finitely generated residually finite groups. The proof combines the theory of profinite rigidity for modules over

Alessandro Conigli, Julien Frison, Alejandro Sáez

We present lattice results for $f_K/f_{\pi}$ in the iso-symmetric limit of pure QCD (isoQCD) with $N_f=2+1$ flavours, along with a determination of $|V_{us}|/|V_{ud}|$ and a study on the unitarity of the first row of the Cabibbo-Kobayashi-Maskawa (CKM) matrix after introducing strong isospin-breaking and QED effects. The results obtained are based on a combi

Giulia Di Nunno, Barbara Martinucci, Serena Spina

We study a multi-type Ehrenfest process modeled as a finite quasi-birth-death (QBD) process. We assume that the transitions are allowed only to the two adjacent levels of the same phase and are characterized by linear rates. The crucial element lies in the phase switching mechanism at the origin, which is governed by an irreducible stochastic matrix. The pro

Ruijun Ma, Xin Chen, Xiaoqing Wen, Hui Xu

As the CMOS technology enters nanometer scales, integrated circuits (ICs) become increasingly sensitive to radiation-induced soft errors, which can corrupt the state of storage elements and cause severe reliability issues. Many hardened designs have been proposed to mitigate soft errors by using filtering elements. However, existing filtering elements only p

Raffaele Romagnoli, Soummya Kar

This paper investigates the stability properties of neural operators through the structured representation offered by the Hybrid B-spline Deep Neural Operator (HBDNO). While existing stability-aware architectures typically enforce restrictive constraints that limit universality, HBDNO preserves full expressive power by representing outputs via B-spline contr

K. Mistry, Y. Mei, D. R. Nygren

The next-generation of $0\nu\beta\beta$ searches are targeting half-life sensitivities towards 10$^{27}$--10$^{28}$ years. Gaseous xenon time projection chamber (GXeTPC) detectors may be able to meet this challenge due to their excellent energy resolution and background rejection power through event visualization. This paper explores how the design choices o

Longxiang Shao, Ulrich Dahmen, Juergen Rossmann

Rigid body dynamics simulators are important tools for the design, analysis and optimization of mechanical systems in a variety of technical and scientific applications. This study examines four different simulation environments (Adams, Simscape, OpenModelica, and VEROSIM), focusing in particular on the comparison of the modeling methods, the numerical solve

Davide Cugini, Francesco Ghisoni, Angela Rosy Morgillo, Francesco Scala

Estimating energy gaps, i.e. the energy difference between two different states, in quantum systems is crucial for understanding their properties. Conventionally, spectral gap estimation relies on independently computing the ground-state and first-excited-state energies and then taking their difference. This work introduces an alternative procedure for estim

Jiaao Wu, Xian Zhang, Fan Yang, Yinpeng Dong

We introduce Vibe Reasoning, a human-AI collaborative paradigm for solving complex mathematical problems. Our key insight is that frontier AI models already possess the knowledge required to solve challenging problems -- they simply do not know how, what, or when to apply it. Vibe Reasoning transforms AI's latent potential into manifested capability through

Sameera K. M., Serena Nicolazzo, Antonino Nocera, Vinod P.

Federated Learning (FL) has recently emerged as a revolutionary approach to collaborative training Machine Learning models. In particular, it enables decentralized model training while preserving data privacy, but its distributed nature makes it highly vulnerable to a severe attack known as Data Poisoning. In such scenarios, malicious clients inject manipula

Kuicheng Ma

In this paper, we study the behavior of some locally constrained inverse curvature flow in de Sitter space, with initial value any closed spacelike $k$-convex hypersurface satisfying some pinching condition. Assume further the Heintze-Karcher inequality for any closed spacelike mean convex hypersurface in de Sitter space, we derive a class of Alexandrov-Fenc

Abdelouahab Khelifati

We study deformation properties of balanced hyperbolicity, with a particular emphasis on degenerate balanced manifolds and their behavior under smooth modifications. From a different perspective, we introduce two new notions of hyperbolicity for compact complex non-K\"ahler manifolds $X$ of complex dimension $\dim_{\mathbb{C}}X=n$, in general degree $2p$ wit

Kyungwon Cho, Hanbyul Joo

The proliferation of commercial egocentric devices offers a unique lens into human behavior, yet reconstructing full-body 3D motion remains difficult due to frequent self-occlusion and the 'out-of-sight' nature of the wearer's limbs. While head and hand trajectories provide sparse anchor points, current methods often overfit to specific hardware optics or re

Song Jiang, Quan Wang

This paper studies the two-dimensional inhomogeneous Navier--Stokes equations governing stratified flows in a bounded domain under a gravitational potential \(f\). Our main results are as follows. First, we provide a rigorous characterization of steady states, proving that under the Dirichlet condition \(\mathbf{u}|_{\partial \Omega} = \mathbf{0}\), all admi

Chang Dong, Jianfeng Tao, Chengliang Liu

Axial piston pumps are crucial components in fluid power systems, where reliable fault diagnosis is essential for ensuring operational safety and efficiency. Traditional data-driven methods require extensive labeled fault data, which is often impractical to obtain, while model-based approaches suffer from parameter uncertainties. This paper proposes a digita

Samuel Rosa, Radoslav Harman

Designing networks to optimize robustness and other performance metrics is a well-established problem with applications ranging from electrical engineering to communication networks. Many such performance measures rely on the Laplacian spectrum; notable examples include total effective resistance, the number of spanning trees, and algebraic connectivity. Thi

Sylwia Cichacz, Hubert Grochowski, Rita Zuazua

XOR-magic graph labelings form a special subclass of group distance magic labelings. A simple connected graph of order $2^n$ is called an open (respectively, closed) XOR-magic graph of power $n$ if its vertices can be labeled bijectively with vectors from $(\mathbb{Z}_2)^n$ such that the sum (over $(\mathbb{Z}_2)^n$) of labels in each open (respectively, clo

Ashley Z. Guo, Sam Wilken, Dov Levine, Paul M. Chaikin

We investigate a 2D dynamical absorbing state model of monodisperse disks, in which rich phase behavior arises from interactions consisting solely of repulsive displacements between overlapping particles. The phase diagram reveals several unconventional features, including a disordered and static absorbing configuration, where no particles overlap, separated

Ivan DeAndres-Tame, Chengwei Ye, Ruben Tolosana, Ruben Vera-Rodriguez

Generative AI (GenAI) models have revolutionized animation, enabling the synthesis of humans and motion patterns with remarkable visual fidelity. However, generating truly realistic human animation remains a formidable challenge, where even minor inconsistencies can make a subject appear unnatural. This limitation is particularly critical when AI-generated v

Abdellah Zakaria Sellam, Salah Eddine Bekhouche, Fadi Dornaika, Cosimo Distante

Pedestrian Attribute Recognition (PAR) involves predicting fine-grained attributes such as clothing color, gender, and accessories from pedestrian imagery, yet is hindered by severe class imbalance, intricate attribute co-dependencies, and domain shifts. We introduce VLM-PAR, a modular vision-language framework built on frozen SigLIP 2 multilingual encoders.

Mohammed Asheruddin N, Matheus Leal De Souza, Thomas Holland, Catherine Folkson

Degradation mode analysis (DMA) is widely used to decompose capacity fade into loss of lithium inventory (LLI) and loss of active material (LAM) from low-rate voltage-capacity data. Yet the measured trace is a pseudo-OCV (pOCV) that includes two non-degradation contributions: an SOC-dependent ohmic drop and intrinsic charge-discharge hysteresis, especially i

A. Cimarelli, N. Marras, B. Niceno, Y. Tessier Urrecha

We propose the use of an unifying paradigm for the assessment and development of closed forms of the coarse-grained Navier-Stokes equations in approaches ranging from the statistical to the scale-resolving ones. It consists in the exact formalism provided by the temporally filtered Navier-Stokes equations. The fundamental idea is that the smoothing action of

Xiaoyu Zhang, Zhiyun Fan, Wenyang Zhang, Di Wang

High-dimensional Kronecker-structured estimation faces a conflict between non-convex scaling ambiguities and statistical robustness. The arbitrary factor scaling distorts gradient magnitudes, rendering standard fixed-threshold robust methods ineffective. We resolve this via Scaled Robust Gradient Descent (SRGD), which stabilizes optimization by de-scaling gr

Maria Mannone, Paulo Vitor Itaborai, Omar Costa Hamido, Miriam Goldack

We apply sonification strategies and quantum computing to the analysis of an episode of seizure. We first sonify the signal from a selection of channels (from real ECoG data), obtaining a polyphonic sequence. Then, we propose two quantum approaches to simulate a similar episode of seizure, and we sonify the results. The comparison of sonifications can give h

Xinyang Song, Libin Wang, Weining Wang, Zhiwei Li

Recent image generation approaches often address subject, style, and structure-driven conditioning in isolation, leading to feature entanglement and limited task transferability. In this paper, we introduce 3SGen, a task-aware unified framework that performs all three conditioning modes within a single model. 3SGen employs an MLLM equipped with learnable sem

Zhaoyang Liu, Weitao Zhou, Junze Wen, Cheng Jing

Collecting large-scale naturalistic driving data is essential for training robust autonomous driving planners. However, real-world datasets often contain a substantial amount of repetitive and low-value samples, which lead to excessive storage costs and bring limited benefits to policy learning. To address this issue, we propose an information-theoretic data

Yitian Zheng, Zhangchen Ye, Weijun Dong, Shengjie Wang

Recent advances in hierarchical robot systems leverage a high-level planner to propose task plans and a low-level policy to generate robot actions. This design allows training the planner on action-free or even non-robot data sources (e.g., videos), providing transferable high-level guidance. Nevertheless, grounding these high-level plans into executable act

Juliana Gerard, Morgan Macleod, Kelly Norwood, Aisling Reid

In this paper, we compare methodological approaches for comparing student and staff perceptions, and ask: how much do these measures vary across different approaches? We focus on the case of AI perceptions, which are generally assessed via a single quantitative or qualitative measure, or with a mixed methods approach that compares two distinct data sources -

Emmanouil Kaniolakis-Kaloudis, Nektarios Papadogiannis, Yannis Orphanos, Makis Bakarezos

This work presents a complete methodology for the precise characterization of the acoustic field inside crystal-based devices driven by high-frequency ultrasounds towards the generation of tunable narrowband and directional gamma radiation via undulation of ultra-relativistic charged particles. Such gamma-ray sources have long been anticipated by the scienti

Felix Lizalde-Arroyo, Frederik Van Loock, Victoria Bernardo, Miguel Angel Rodriguez-Perez

Nanocellular polyetherimide exhibits significant improvements in mechanical properties like toughness and impact resistance, commonly associated with the presence of nanoporosity. However, this work demonstrates these enhancements, often measured directly after processing, cannot be fully explained solely by the cellular structure but also originate from a m

Abhishek Kumar

The paper presents our work on cross-lingual ontology alignment system which uses embedding based cosine similarity matching. The ontology entities are made contextually richer by creating descriptions using novel techniques. We use a fine-tuned transformer based multilingual model for generating better embeddings. We use cosine similarity to find positive o

M. G. van der Schans, W. P. M. de Kleijne, M. A. Brozius, B. Koopmans

Skyrmions are highly stable chiral magnetic spin textures with non-trivial topology. They can act as quasi-particles that can be generated, manipulated and annihilated, and hold promise for future memory and logic devices. As of now, all-optical stochastic nucleation of skyrmion ensembles, mostly in small applied magnetic fields, has been shown. However, to

Zun-Xian Zhang, Mengchu Cai, Bolun Hu, Xiangyu Jiang

The leading-order low-energy constant $\Delta_{\rm mix}$ in mixed-action chiral perturbation theory is calculated using $2+1+1$-flavor gauge ensembles with HISQ fermions and a tadpole-improved Symanzik gauge action at four lattice spacings $a \in [0.048, 0.111]$ fm. By comparing our results to those from different actions and a $2+1$-flavor case, We find tha

Zonghan Wang, Zahra Mobini, Hien Quoc Ngo, Hyundong Shin

Integrated sensing and communication (ISAC) systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect legitimate targets from potential detection or exploitation by

Kaustav Bhattacharjee, Himanshu Grover, P. Arumugam

We present the design, implementation, and operation of a pyramidal horn radio telescope built for detecting the Galactic 21 cm neutral hydrogen line emission. The system employs an SDR-based pipeline to obtain drift-scan observations, which were calibrated and processed to generate HI sky maps, a Galactic rotation curve and spiral arm features. This demonst

Julia Osęka-Lenart, Marcin Płodzień, Maciej Lewenstein, Michał Eckstein

The Schr\"odinger-Newton equation aims at describing the dynamics of massive quantum systems subject to the gravitational self-interaction. As a deterministic nonlinear quantum wave equation, it is generally believed to conflict with the relativistic no-signalling principle. Here we challenge this viewpoint and show that it is of key importance to study the

Aashi Shrinate, Tanmay Siddharth, Twinkle Tripathy

In this paper, we address the multiconsensus problem in networked systems, where agents are partitioned into disjoint subgroups and the states of agents within a subgroup are driven to consensus. Our objective is to present a distributed control law that leads to multiconsensus in signed digraphs. To this end, we examine the convergence of opinions under the

Boyu Jiang, Jiawei Shen, Kexue Li

This paper investigates the Cauchy problem for the nonlinear Schr\"odinger equation (NLS) in the mass-supercritical and energy-subcritical regime within three spatial dimensions. For initial data in the critical homogeneous Sobolev space $\dot{H}^{s_c}(\mathbb{R}^3)$ (where $s_c = \frac{5}{6}$), we get a uniform decay estimate for the long-time dynamics of s

Willem de Graaf, Alexander Elashvili, Mamuka Jibladze

We use the theory of theta-groups developed by Vinberg, along with computations in the computer algebra system GAP4, to classify the orbits of Spin(10,C)x SL(4,C) acting on the tensor product of the half spin module of Spin(10,C) and the natural module of SL(4,C).

Jing Yang, Lihua Feng, Rongrong Lu, Tingzeng Wu

A bi-Cayley graph over the cyclic group $(\mathbb{Z}_n, +)$ is called a bicirculant graph. Let $\Gamma=BC(\mathbb{Z}_n; R,T,S)$ be a bicirculant graph with $R=-R\subseteq \mathbb{Z}_n\setminus \{0\}$ and $T={-}T\subseteq \mathbb{Z}_n\setminus \{0\}$ and $S\subseteq \mathbb{Z}_n$. In this paper, using Chebyshev polynomials, we obtain a closed formula for the

Zetao Xie, Zehai Pang, Yi Yang

Optical parametric processes underpin quantum photonics, while free-electron--photon interactions offer agile pathways to generate nontrivial quantum photonic states. These threads have so far largely progressed independently, whereas placing free electrons in a driven nonlinear system can potentially activate coherent parametric interaction channels for joi

Zhen-Zhen Zhang, Hua-Lei Wang, Kui Xiao, Min-Liang Liu

Stimulated by the recent experimental discovery of the sub-$\mu$s fission nucleus $^{252}$Rf [Phys. Rev. Lett. 134 (2025) 022501], we perform an improved configuration-constrained potential-energy-surface calculation, revealing the mechanism of intricate balance for the enhanced stability due to the high-$K$ (e.g., $K^\pi = 6^+$) isomer, possibly building on

Carla Crivoi, Radu Tudor Ionescu

We present the first empirical study of machine unlearning (MU) in hybrid quantum-classical neural networks. While MU has been extensively explored in classical deep learning, its behavior within variational quantum circuits (VQCs) and quantum-augmented architectures remains largely unexplored. First, we adapt a broad suite of unlearning methods to quantum s

Simone Creo, Salvatore Fragapane

We study obstacle problems for the regional fractional $p$-Laplacian in a domain $\Omega\subset\mathbb{R}^2$ having as fractal boundary the Koch snowflake. We prove well-posedness results for the solution of the obstacle problem, as well as two equivalent formulations. Moreover, we study corresponding approximating obstacle problems in a sequence of domains

Ihlas Sovbetov

Decentralized finance (DeFi) lacks centralized oversight, often resulting in heightened volatility. In contrast, centralized finance (CeFi) offers a more stable environment with institutional safeguards. Institutional backing can play a stabilizing role in a hybrid structure (HyFi), enhancing transparency, governance, and market discipline. This study invest

Prathamesh Devadiga

Traditional auto-parallelizing compilers, reliant on rigid heuristics, struggle with the complexity of modern heterogeneous systems. This paper presents a comprehensive evaluation of small (approximately 1B parameter) language-model-driven compiler auto-parallelization. We evaluate three models: gemma3, llama3.2, and qwen2.5, using six reasoning strategies a

Yao Chen, Rui Luo, Chen Wang, Yong-Kun Zhang

Searching for fleeting radio transients like fast radio bursts (FRBs) with wide-field radio telescopes has become a common challenge in data-intensive science. Conventional algorithms normally cost enormous time to seek candidates by finding the correct dispersion measures, of which the process is so-called dedispersion. Here we present a novel scheme to ide

Shaunak Samant

Automated program repair using neural models has shown promising results on benchmark datasets, yet practical deployment remains limited. In this study, we examine whether a small transformer model can meaningfully repair real-world Java bugs and whether syntactic correctness is a reliable proxy for semantic correctness. We fine-tune CodeT5-small (60.5M para