March 2025 arXiv papers — page 158

C. S. Kannis, T. P. Rakitzis

We propose schemes to produce highly nuclear-spin polarized small molecules in an intense and cold molecular beam via microwave or infrared rotational excitation, followed by hyperfine-induced quantum beats. Repumping schemes can be used to achieve polarization above $90\%$ in cases where single-pumping schemes are insufficient. We discuss the possibility of

Shiyi Liu, Bei Liu, Igor P. Ivanov, Liangliang Ji

Vortex states of photons or electrons are a novel and promising experimental tool across atomic, nuclear, and particle physics. Various experimental schemes to generate high-energy vortex particles have been proposed. However, diagnosing the characteristics of vortex states at high energies remains a significant challenge, as traditional low-energy detection

Nam Van Tran

In this paper, we study inclusion problems where the involved operators may not be monotone in the classical sense. Specifically, we assume the operators to be generalized monotone, a weaker notion than classical monotonicity. This allows us to extend the applicability of our results to a broader class of operators. We apply the two-step inertial forward-ref

Lei Zhao, Ming Dong

In this paper, we study the receptivity of non-modal perturbations in hypersonic boundary layers over a blunt wedge subject to freestream vortical, entropy and acoustic perturbations. Due to the absence of the Mack-mode instability and the rather weak entropy-layer instability, the non-modal perturbation is considered as the dominant factor triggering transi

Paul Haubenwallner, Matthias Heller

We present a fully numerical framework for the optimization of molecule-specific quantum chemical basis functions within the quantics tensor train format using a finite-difference scheme. The optimization is driven by solving the Hartree-Fock equations (HF) with the density-matrix renormalization group (DMRG) algorithm on Cartesian grids that are iteratively

Chen Liao, Yan Shen, Dan Li, Zhongli Wang

Recently, Deep Unfolding Networks (DUNs) have achieved impressive reconstruction quality in the field of image Compressive Sensing (CS) by unfolding iterative optimization algorithms into neural networks. The reconstruction quality of DUNs depends on the learned prior knowledge, so introducing stronger prior knowledge can further improve reconstruction quali

Samapan Bhadury

With the help of a semi-classical kinetic theory, a new collision kernel is proposed, which simultaneously conserves the energy-momentum tensor and the spin tensor of a relativistic fluid of spin-1/2 particles irrespective of the frame and matching conditions, even when relaxation time is momentum dependent. The relativistic Boltzmann's equation is solved us

Yuan Gao, Anton Rodomanov, Jeremy Rack, Sebastian U. Stich

Modern machine learning tasks often involve massive datasets and models, necessitating distributed optimization algorithms with reduced communication overhead. Communication compression, where clients transmit compressed updates to a central server, has emerged as a key technique to mitigate communication bottlenecks. However, the theoretical understanding o

Lianting Wang, Marcelo Ponce

In this paper, we present an educational project aimed to introduce students to the technology behind Captive Portals infrastructures. For doing this, we developed a series of modules to emphasize each of the different aspects and features of this technology. The project is based on an open source implementation which is widely used in many computer network

Linqi Ge, Yinuo Zhao, Yubo Guo, Yuanyuan Liu

Background: Accurate myocardial T1 mapping at 5T remains a technical challenge due to field inhomogeneity and prolonged T1 values. The aim of this study is to develop an accurate and clinically applicable myocardial T1 mapping technique for 5T magnetic resonance imaging (MRI) systems and validate its performance in a multicenter study. Methods: The proposed

Ziqiang Cai, Zhenglong Ban, Lu Wang, Haiyuan Feng

The physical properties and optical appearance of a thin accretion disk surrounding a Schwarzschild-like black hole (BH) are investigated within the framework of bumblebee gravity. To understand how the Lorentz symmetry breaking (LSB) parameter $l$ affects the disk's behavior, we analyze main characteristics such as energy flux, temperature distribution, and

Wali Ullah Khan, Manzoor Ahmed, Chandan Kumar Sheemar, Marco Di Renzo

Beyond Diagonal Reconfigurable Intelligent Surfaces (BD-RIS) represent a groundbreaking innovation in sixth-generation (6G) wireless networks, enabling unprecedented control over wireless propagation environments compared to conventional diagonal RIS (D-RIS). This survey provides a comprehensive analysis of BD-RIS, detailing its architectures, operational pr

Runjian Chen, Wenqi Shao, Bo Zhang, Shaoshuai Shi

Deep-learning-based autonomous driving (AD) perception introduces a promising picture for safe and environment-friendly transportation. However, the over-reliance on real labeled data in LiDAR perception limits the scale of on-road attempts. 3D real world data is notoriously time-and-energy-consuming to annotate and lacks corner cases like rare traffic parti

Qiming Xia, Wenkai Lin, Haoen Xiang, Xun Huang

Unsupervised 3D object detection serves as an important solution for offline 3D object annotation. However, due to the data sparsity and limited views, the clustering-based label fitting in unsupervised object detection often generates low-quality pseudo-labels. Multi-agent collaborative dataset, which involves the sharing of complementary observations among

Ahmad Chaddad, Yan Hu, Yihang Wu, Binbin Wen

Objective. This paper presents an overview of generalizable and explainable artificial intelligence (XAI) in deep learning (DL) for medical imaging, aimed at addressing the urgent need for transparency and explainability in clinical applications. Methodology. We propose to use four CNNs in three medical datasets (brain tumor, skin cancer, and chest x-ray) fo

Luigi Lombardi

We show that any equivalence of bounded derived categories of coherent sheaves on a smooth projective complex variety supported in a closed algebraic subset preserves the dimension of the support in two cases: (i) the restriction of the (anti)canonical bundle to the support is ample; (ii) the supports are irreducible and the equivalence sends a skyscraper sh

Hao Wang

Human mobility, a pivotal aspect of urban dynamics, displays a profound and multifaceted relationship with urban sustainability. Despite considerable efforts analyzing mobility patterns over decades, the ranking dynamics of urban mobility has received limited attention. This study aims to contribute to the field by investigating changes in rank and size of h

Kento Fujita

Hirose, Murahara, and Saito proved that some $t$-adic symmetric multiple zeta values, for indices in which $1$ and $3$ appear alternately in succession, can be expressed as polynomials in Riemann zeta values, and conjectured similar formulas. In this paper, we prove the conjectured formula for indices that start with $1$ and end with $3$, showing that they a

Kwan Yun, Seokhyeon Hong, Chaelin Kim, Junyong Noh

Despite recent advancements in learning-based motion in-betweening, a key limitation has been overlooked: the requirement for character-specific datasets. In this work, we introduce AnyMoLe, a novel method that addresses this limitation by leveraging video diffusion models to generate motion in-between frames for arbitrary characters without external data. O

Weiyi Yang, Xiaolu Liu, Lei He, Yonghao Du

In the context of Vehicular ad-hoc networks (VANETs), the hierarchical management of intelligent vehicles, based on clustering methods, represents a well-established solution for effectively addressing scalability and reliability issues. The previous studies have primarily focused on centralized clustering problems with a single objective. However, this pape

Feiyang Wu, Zhuohang Bian, Guoyang Duan, Tianle Xu

The increasing demand for large language model (LLM) serving has necessitated significant advancements in the optimization and profiling of LLM inference systems. As these models become integral to a wide range of applications, the need for efficient and scalable serving solutions has grown exponentially. This work introduces TokenSim, a comprehensive hardwa

M. J. Bueno, G. Q. Garcia, A. M. de M. Carvalho, C. Furtado

This work examines the effect of disclinations on the scattering of quasipaticles in graphene with the presence of a topological defect. Using the tight-binding method, the electronic properties of graphene with disclination are described, where the topological defects are introduced in the lattice via geometric theory. The massless Dirac equation is modifie

Maciej Bazarnik, Anika Schlenhoff

Spin-resolved scanning tunneling microscopy and spectroscopy studies of image-potential states on Gr/Fe/Ir(111) show their sensitivity to the spatial variation of the Gr-Fe distance, and of the interfacial charge and spin transfer within the moir\'e unit cell. A stacking contrast between fcc and hcp sites, indistinguishable in the direct tunneling mode, is p

V. I. Gerasimenko, I. V. Gapyak

The article presents the concept of a cumulant representation for distribution functions describing the states of many-particle systems with topological nearest-neighbor interaction. A solution to the Cauchy problem for the hierarchy of nonlinear evolution equations for the cumulants of distribution functions of such systems is constructed. The connection be

Marcos Cirne, Hannah Menke, Alhasan Abdellatif, Julien Maes

Simulating reactive dissolution of solid minerals in porous media has many subsurface applications, including carbon capture and storage (CCS), geothermal systems and oil & gas recovery. As traditional direct numerical simulators are computationally expensive, it is of paramount importance to develop faster and more efficient alternatives. Deep-learning-base

Seyed Muhammad Hossein Mousavi

In the domain of emotion recognition using body motion, the primary challenge lies in the scarcity of diverse and generalizable datasets. Automatic emotion recognition uses machine learning and artificial intelligence techniques to recognize a person's emotional state from various data types, such as text, images, sound, and body motion. Body motion poses un

Rongxiang Luo, Stefano Lepri

Simulation of transport properties of confined, low-dimensional fluids can be performed efficiently by means of Multi-Particle Collision (MPC) dynamics with suitable thermal-wall boundary conditions. We illustrate the effectiveness of the method by studying dimensionality effects and size-dependence of thermal conduction, properties of crucial importance for

Swapnil Kumar

The work focuses on gathering high-fidelity and low-fidelity numerical simulations data using Nektar++ (Solver based on Applied Mathematics) and XFOIL respectively. The utilization of the higher polynomial distribution in calculating the Coefficient of lift and drag has demonstrated superior accuracy and precision. Further, Co-kriging Data fusion and Adaptiv

Yansong Guo, Jie Hu, Yansong Qu, Liujuan Cao

Recent advances in interactive 3D segmentation from 2D images have demonstrated impressive performance. However, current models typically require extensive scene-specific training to accurately reconstruct and segment objects, which limits their applicability in real-time scenarios. In this paper, we introduce WildSeg3D, an efficient approach that enables th

Peter Frankl, Jian Wang

The matching number of a $k$-graph is the maximum number of pairwise disjoint edges in it. The $k$-graph is called $t$-resilient if omitting $t$ vertices never decreases its matching number. The complete $k$-graph on $sk+k-1$ vertices has matching number $s$ and it is easily seen to be $(k-1)$-resilient. We conjecture that this is maximal for $k=3$ and $s$ a

Yuhan Zhi, Xiaoyu Zhang, Longtian Wang, Shumin Jiang

Large language models (LLMs), as a new generation of recommendation engines, possess powerful summarization and data analysis capabilities, surpassing traditional recommendation systems in both scope and performance. One promising application is investment recommendation. In this paper, we reveal a novel product bias in LLM investment recommendation, where L

A. K. Stefanov, A. Suárez Mascareño, J. I. González Hernández, N. Nari

The low masses of M dwarfs create attractive opportunities for exoplanet radial-velocity (RV) detections. These stars, however, exhibit strong stellar activity that may attenuate or mimic planetary signals. We present a velocimetric analysis on one such M dwarf, GJ 3998 ($d=18.2\,\text{pc}$), with two published short-period super-Earths: GJ 3998 b and GJ 399

Elizaveta Kuznetsova, Ilaria Vitulano, Mykola Makhortykh, Martha Stolze

The purpose of this study is to assess how large language models (LLMs) can be used for fact-checking and contribute to the broader debate on the use of automated means for veracity identification. To achieve this purpose, we use AI auditing methodology that systematically evaluates performance of five LLMs (ChatGPT 4, Llama 3 (70B), Llama 3.1 (405B), Claude

Ben Priestley, Petros Wallden

The NP-hardness of the closest vector problem (CVP) is an important basis for quantum-secure cryptography, in much the same way that integer factorisation's conjectured hardness is at the foundation of cryptosystems like RSA. Recent work with heuristic quantum algorithms (arXiv:2212.12372) indicates the possibility to find close approximations to (constraine

Gayatri Mohan, Ronit Karmakar, Rupam Jyoti Borah, Umananda Dev Goswami

In this work, we analyze the strong lensing phenomenon and quasinormal modes (QNMs) in the case of black holes (BHs) surrounded by fluids within the framework of $f(R,T)$ gravity, adopting a minimally coupled model of the theory. Our analysis is conducted for three surrounding fields corresponding to three different values of the parameter $\omega$ of the eq

Liwei Zhang, Patrizia Mazzeo, Michele Nottoli, Edoardo Cignoni

The Kohn-Sham (KS) density matrix is one of the most essential properties in KS density functional theory (DFT), from which many other physical properties of interest can be derived. In this work, we present a parameterized representation for learning the mapping from a molecular configuration to its corresponding density matrix using the Atomic Cluster Expa

Maike Gremmel, Chandrashekhar Prakash Savant, Debaditya Bhattacharya, Georg Schönweger

This study explores the influence of boron incorporation on the structural and electrical properties of ferroelectric Aluminum Scandium Nitride (Al_{1-x}Sc_xN ) thin films, focusing on leakage currents, wake-up effects, and imprint behavior. Al_{1-x}Sc_xN films were incorporated with varying boron concentrations and analyzed under different deposition condit

Jiawei Zhou, Lei Chen

In this paper, we analyze and empirically show that the learned relevance for conventional information retrieval (IR) scenarios may be inconsistent in retrieval-augmented generation (RAG) scenarios. To bridge this gap, we introduce OpenRAG, a RAG framework that is optimized end-to-end by tuning the retriever to capture in-context relevance, enabling adaptati

Yong He, Dong Liu, Yunjing Sun, Yalin Wang

Early work established convergence of the principal component estimators of the factors and loadings up to a rotation for large dimensional approximate factor models with weak factors in that the factor loading $\Lambda^{(0)}$ scales sublinearly in the number $N$ of cross-section units, i.e., $\Lambda^{(0)\top}\Lambda^{(0)}/N^{\alpha}$ is positive definite i

Wenyi Wu, Hao Zhang, Zhisen Wei, Xiao-Yuan Jing

Source-free domain adaptation (SFDA) has been exploited for cross-domain bearing fault diagnosis without access to source data. Current methods select partial target samples with reliable pseudo-labels for model adaptation, which is sub-optimal due to the ignored target samples. We argue that every target sample can contribute to model adaptation, and accord

Serena Fattori, Rino Persiani, Ugo Abundo

This study investigates the moderation of 14.1 MeV neutrons in a natural beryllium moderator arranged in a spherical geometry. The neutron interactions and moderation efficiency were analyzed using Monte Carlo simulations with the GEANT4 toolkit. Various sphere radii were tested to determine the optimal moderator thickness for neutron thermalization.

Viktor F. Majewski

We study Dirac operators on resolutions of Riemannian orbifolds by developing a uniform elliptic theory. The key idea is to view orbifolds as conically fibred singular (CFS) spaces and resolve them by gluing asymptotically conical fibrations (ACF) into the singular strata. This yields smooth Gromov--Hausdorff resolutions that preserve the large--scale struct

Joey De Pauw, Bart Goethals

Over recent years it has become well accepted that user interest is not static or immutable. There are a variety of contextual factors, such as time of day, the weather or the user's mood, that influence the current interests of the user. Modelling approaches need to take these factors into account if they want to succeed at finding the most relevant content

Zhanjie Zhang, Quanwei Zhang, Guangyuan Li, Junsheng Luan

Artistic style transfer aims to transfer the learned style onto an arbitrary content image. However, most existing style transfer methods can only render consistent artistic stylized images, making it difficult for users to get enough stylized images to enjoy. To solve this issue, we propose a novel artistic style transfer framework called DyArtbank, which c

P. R. Casale, J. E. Amaro, V. Belocchi, M. B Barbaro

In this work, we present a detailed analysis of the interference between meson exchange currents (MEC) and one-body currents in quasielastic electron scattering, with a focus on the sign of this interference in the transverse response for one-particle emission. We prove that the interference of both the Delta and pion-in-flight currents with the one-body cur

Lukas Krelle, Ryan Tan, Daria Markina, Priyanka Mondal

CrSBr is an air-stable magnetic van der Waals semiconductor with strong magnetic anisotropy, where the interaction of excitons with the magnetic order enables the optical identification of different magnetic phases. Here, we study the magnetic anisotropy of multi-layer CrSBr inside a three-axis vector magnet and correlate magnetic order and optical transitio

Lasai Barreñada, Bavo D. C. Campo, Laure Wynants, Ben Van Calster

Evaluation of clinical prediction models across multiple clusters, whether centers or datasets, is becoming increasingly common. A comprehensive evaluation includes an assessment of the agreement between the estimated risks and the observed outcomes, also known as calibration. Calibration is of utmost importance for clinical decision making with prediction m

Valentin Charraut, Waël Doulazmi, Thomas Tournaire, Thibault Buhet

Learning-based decision-making has the potential to enable generalizable Autonomous Driving (AD) policies, reducing the engineering overhead of rule-based approaches. Imitation Learning (IL) remains the dominant paradigm, benefiting from large-scale human demonstration datasets, but it suffers from inherent limitations such as distribution shift and imitatio

Zefeng Qian, Chongyang Zhang, Yifei Huang, Gang Wang

Few-shot Action Recognition (FSAR) constitutes a crucial challenge in computer vision, entailing the recognition of actions from a limited set of examples. Recent approaches mainly focus on employing image-level features to construct temporal dependencies and generate prototypes for each action category. However, a considerable number of these methods utiliz

Zhengyao Fang, Pengyuan Lyu, Jingjing Wu, Chengquan Zhang

Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and content from the source image and then fusing the style with the target content, while ensuring content consistency using a pre-trained recognition model. Despite notable progress, thes

P. U. Devanand, Alok C. Gupta, V. Jithesh, Paul J. Wiita

We present a comprehensive study of the X-ray spectral variability observed in 13 TeV photon emitting high energy peaked BL Lacs(HBLs). These data come from 54 XMM-Newton EPIC-PN pointed observations made during its operational period from June 2001 through July 2023. We performed spectral studies in the energy range 0.6-10 keV by fitting X-ray spectra of th

Weiyi Yang, Yingwu Chen, Xiaolu Liu, Jun Wen

The allocation of tasks to a large number of distributed satellites is a difficult problem owing to dynamic changes in massive tasks and the complex matching of tasks to satellites. To reduce the complexity of the problem, tasks that are geographically close can be divided into a predefined grid with a specific time window and processed together. The problem

Susu Sun, Dominique van Midden, Geert Litjens, Christian F. Baumgartner

Multiple Instance Learning (MIL) methods have succeeded remarkably in histopathology whole slide image (WSI) analysis. However, most MIL models only offer attention-based explanations that do not faithfully capture the model's decision mechanism and do not allow human-model interaction. To address these limitations, we introduce ProtoMIL, an inherently inter

Gerassimos Barbatis, Marianna Chatzakou, Achilles Tertikas

We prove $L^p$-Hardy inequalities with distance to the boundary for domains in the Heisenberg group ${\mathbb{H}}^n$, $n\geq 1$. Our results are based on a certain geometric condition. This is first implemented for the Euclidean distance in certain non-convex domains. It is then implemented for the distance defined by the gauge quasi-norm related to the fund

Jesus Zarzar, Tom Monnier, Roman Shapovalov, Andrea Vedaldi

We present the first large reconstruction model, Twinner, capable of recovering a scene's illumination as well as an object's geometry and material properties from only a few posed images. Twinner is based on the Large Reconstruction Model and innovates in three key ways: 1) We introduce a memory-efficient voxel-grid transformer whose memory scales only quad

Benjamin Kempinski, Tal Kachman

Power indices are essential in assessing the contribution and influence of individual agents in multi-agent systems, providing crucial insights into collaborative dynamics and decision-making processes. While invaluable, traditional computational methods for exact or estimated power indices values require significant time and computational constraints, espec

Georgina Cosma, Emeka Abakasanga, Rania Kousovista, Sharmin Shabnam

Objectives: To examine the distribution, temporal associations, and age/sex-specific patterns of multiple long-term conditions (MLTCs) in adults with intellectual disability (ID). Study Design: Observational study using longitudinal healthcare data. Methods: Analysis of 18144 adults with ID (10168 males and 7976 females) identified in the Clinical Practice R

Qingsong Xie, Zhao Zhang, Zhe Huang, Yanhao Zhang

Image tokenization has significantly advanced visual generation and multimodal modeling, particularly when paired with autoregressive models. However, current methods face challenges in balancing efficiency and fidelity: high-resolution image reconstruction either requires an excessive number of tokens or compromises critical details through token reduction.

Chunhe Li, Hongyi Bian, Zixiang Lin, Yi Man

The movement of microorganisms near solid-liquid interfaces is a topic of significant scientific interest due to its relevance in various natural and industrial contexts, such as biofilm formation and marine biofouling. In this study, we investigate the swimming behavior of C. reinhardtii near a sinusoidal periodic microstructure (SPM). Using fluorescence mi

Nico Bohlinger, Jonathan Kinzel, Daniel Palenicek, Lukasz Antczak

On-robot Reinforcement Learning is a promising approach to train embodiment-aware policies for legged robots. However, the computational constraints of real-time learning on robots pose a significant challenge. We present a framework for efficiently learning quadruped locomotion in just 8 minutes of raw real-time training utilizing the sample efficiency and

Dominique Bang, Alain Chenciner, Carles Simó

Examples are given of solutions of the planar N-body problem which remain the same for at least two systems of masses with the same sum and same center of mass. The least value of N achieved up to now with this property is 474, a number which had been announced in the first author's thesis.

Fabian Isensee, Maximilian Rokuss, Lars Krämer, Stefan Dinkelacker

Accurate and efficient 3D segmentation is essential for both clinical and research applications. While foundation models like SAM have revolutionized interactive segmentation, their 2D design and domain shift limitations make them ill-suited for 3D medical images. Current adaptations address some of these challenges but remain limited, either lacking volumet

Haonan Chen, Junxiao Li, Ruihai Wu, Yiwei Liu

Garment folding is a common yet challenging task in robotic manipulation. The deformability of garments leads to a vast state space and complex dynamics, which complicates precise and fine-grained manipulation. Previous approaches often rely on predefined key points or demonstrations, limiting their generalization across diverse garment categories. This pape

Bariscan Bozkurt, Ben Deaner, Dimitri Meunier, Liyuan Xu

We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accomplish this task using two proxy variables related to the latent confounder: a treatment proxy (related to the treatment) and an outcome proxy (related to the outcome). Two approach

Xucheng Guo, Yiran Shen, Xiaofang Xiao, Yuanfeng Zhou

This paper presents Ev-Layout, a novel large-scale event-based multi-modal dataset designed for indoor layout estimation and tracking. Ev-Layout makes key contributions to the community by: Utilizing a hybrid data collection platform (with a head-mounted display and VR interface) that integrates both RGB and bio-inspired event cameras to capture indoor layou

Chris Sedgwick, Stephen Serjeant, Charles Weiner

We present predictions of the number and properties of strongly-lensed submillimetre galaxies, based on an adaption of the physically-motivated LensPop model covering galaxy-galaxy strong lensing by elliptical galaxies, which successfully predicted optical and near-infrared lenses. For submillimetre-luminous lensed galaxies, the most efficient observational

Chaoquan Jiang, Yunfan Yang, Rui Hu, Jitao Sang

Prompt tuning of Vision-Language Models (VLMs) such as CLIP, has demonstrated the ability to rapidly adapt to various downstream tasks. However, recent studies indicate that tuned VLMs may suffer from the problem of spurious correlations, where the model relies on spurious features (e.g. background and gender) in the data. This may lead to the model having w

Runling Long, Yunlong Wang, Jia Wan, Xiang Deng

Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets on which the methods are trained are based on passive cameras, restricting their ability to fully sense t

Sergey Stepanov, Irina Tsyganok

In the present paper, we study harmonic mappings of complete Riemannian manifolds, as well as minimal and stable minimal submanifolds of complete Riemannian manifolds. We examine classical theorems in the theory of these manifolds from the perspective of the generalized Bochner technique.

Miriam Goetze, Michael Hoffmann, Ignaz Rutter, Torsten Ueckerdt

We study 3-plane drawings, that is, drawings of graphs in which every edge has at most three crossings. We show how the recently developed Density Formula for topological drawings of graphs (KKKRSU GD 2024) can be used to count the crossings in terms of the number $n$ of vertices. As a main result, we show that every 3-plane drawing has at most $5.5(n-2)$ cr

Won-Ki Seo, Dakyung Seong

This paper proposes econometric methods for studying how economic variables respond to function-valued shocks. Our methods are developed based on linear projection estimation of predictive regression models with a function-valued predictor and other control variables. We show that the linear projection coefficient associated with the functional variable allo

Zhaiyu Chen, Yuqing Wang, Liangliang Nan, Xiao Xiang Zhu

Existing polygonal surface reconstruction methods heavily depend on input completeness and struggle with incomplete point clouds. We argue that while current point cloud completion techniques may recover missing points, they are not optimized for polygonal surface reconstruction, where the parametric representation of underlying surfaces remains overlooked.

Saisai He, Jize Zhao, Hong-Gang Luo, Shijie Hu

In this work, we revisit the phase diagram of the $t$-$t^\prime$-$\delta$ Fermi-Hubbard model on the square lattice to gain a more comprehensive understanding of this correlated model at half filling. This model has recently become a prominent topic of research because it hosts altermagnetic phases. Using mean-field analysis, we identify four metallic phases

Canxuan Gang, Yuhan Peng

This technical report analyzes non-contrast CT image segmentation in computer vision. It revisits a proposed method, examines the background of non-contrast CT imaging, and highlights the significance of segmentation. The study reviews representative methods, including convolutional-based and CNN-Transformer hybrid approaches, discussing their contributions,

Salim Meddahi

This study introduces a hybridizable discontinuous Galerkin (HDG) method for simulating low-frequency wave propagation in poroelastic media. We present a novel four-field variational formulation and establish its well-posedness and energy stability. Our \(hp\)-convergence analysis of the HDG method for spatial discretization is complemented by a Crank-Nicols

Stefano De Angelis, Ivan Visconti, Andrea Vitaletti, Marco Zecchini

Blockchains are among the most powerful technologies to realize decentralized information systems. In order to safely enjoy all guarantees provided by a blockchain, one should maintain a full node, therefore maintaining an updated local copy of the ledger. This allows one to locally verify transactions, states of smart contracts, and to compute any informati

Md Faizal Karim, Mohammed Saad Hashmi, Shreya Bollimuntha, Mahesh Reddy Tapeti

Dual-arm robotic grasping is crucial for handling large objects that require stable and coordinated manipulation. While single-arm grasping has been extensively studied, datasets tailored for dual-arm settings remain scarce. We introduce a large-scale dataset of 16 million dual-arm grasps, evaluated under improved force-closure constraints. Additionally, we

Niklas Knaepper, Gerald Enzner, Aleksej Chinaev

Wireless systems with inband full-duplex transceiver typically require multiple lines of defense against the effect of harsh self-interference, specifically, to avoid saturation of the analog-to-digital converter (ADC) in the receiver. We may unite the typical tandem operation of successive analog and digital self-interference cancellation (SIC) stages by me

Daphné Aurouet, Valentin Patilea

Motivated by the need to analyze continuously updated data sets in the context of time-to-event modeling, we propose a novel nonparametric approach to estimate the conditional hazard function given a set of continuous and discrete predictors. The method is based on a representation of the conditional hazard as a ratio between a joint density and a conditiona

Hugo Henneuse

This work investigates the nonparametric estimation of the vector field of a noisy Ordinary Differential Equation (ODE) in high-dimensional ambient spaces, under the assumption that the initial conditions are sampled from a lower-dimensional structure. Specifically, let \( f:\mathbb{R}^{D}\to\mathbb{R}^{D} \) denote the vector field of the autonomous ODE \(

Kai Qiu, Xiang Li, Jason Kuen, Hao Chen

Recent image generation schemes typically capture image distribution in a pre-constructed latent space relying on a frozen image tokenizer. Though the performance of tokenizer plays an essential role to the successful generation, its current evaluation metrics (e.g. rFID) fail to precisely assess the tokenizer and correlate its performance to the generation

Hamed Ahmadi, Mostafa Rahmani, Swarna Bindu Chetty, Eirini Eleni Tsiropoulou

The transition to 6G is expected to bring significant advancements, including much higher data rates, enhanced reliability and ultra-low latency compared to previous generations. Although 6G is anticipated to be 100 times more energy efficient, this increased efficiency does not necessarily mean reduced energy consumption or enhanced sustainability. Network

Ruiqi Zhang, Hao Zhu, Jingyi Zhao, Qi Zhang

3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud representations, there are ambiguities in distinguishing wire-like and flat surfaces, as well as transparent or reflective objects. To address these issues, we propose Gaussian Splatting

Bholanath Kumbhakar, Dwijendra Narain Pandey

In this paper, we discuss the existence of local strong solutions for the multivalued version of three-dimensional nonstationary Navier-Stokes equation in Banach spaces. Also, we considered a more general inclusion problem and studied the existence of solutions using the fixed point technique approach. We assume that the multivalued map possesses closed valu

T. V. Smirnova, M. O. Toropov, S. A. Tyul'bashev

The search for weak components outside the main pulse (MP) in the radiation of pulsars at a frequency of 110 MHz observed on the LPA LPI telescope in the Pushchino Multibeam Pulsar Search (PUMPS) has been carried out. The sample included 96 pulsars, for which the signal-to-noise ratio (S/N) in the MP of the average profile during accumulation over 10 years w

Qiang Zhang, Gang Han, Jingkai Sun, Wen Zhao

In recent years, research on humanoid robots has garnered significant attention, particularly in reinforcement learning based control algorithms, which have achieved major breakthroughs. Compared to traditional model-based control algorithms, reinforcement learning based algorithms demonstrate substantial advantages in handling complex tasks. Leveraging the

Philipp Bomatter, Henry Gouk

The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the generalisability and robustness of EEG-based ML models often hinge on the amount and diversity of training data. It is common practice to split EEG recordings into small segments, thereb

H. P. Khandagale, Sangram Patil, V. S. Gavali, S. V. Chavan

Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual

David Jordan, Inseok Song

Several planet formation models have been proposed to explain the gap in the population of planets between $1.8$ $R_\oplus$ to $2.0$ $R_\oplus$ known as the Radius Valley. To apply these models to confirmed exoplanets, accurate and precise host star and planet parameters are required to ensure the observed measurements correctly match model predictions. Prev

Chanyoung Kim, Dayun Ju, Jinyeong Kim, Woojung Han

As recent text-conditioned diffusion models have enabled the generation of high-quality images, concerns over their potential misuse have also grown. This issue is critical in the medical domain, where text-conditioned generated medical images could enable insurance fraud or falsified records, highlighting the urgent need for reliable safeguards against unet

Judicaël Mohet, Alexandre Mauroy, Joseph J. Winkin

The Koopman operator approach to the state estimation problem for nonlinear systems is a promising research area. The main goal of this paper is an attempt to provide a rigorous theoretical framework for this approach. In particular, the (linear) dual Koopman system is introduced and studied in an infinite dimensional context. Moreover, new concepts of obser

Lorenzo Mur-Labadia, Josechu Guerrero, Ruben Martinez-Cantin

Environment understanding in egocentric videos is an important step for applications like robotics, augmented reality and assistive technologies. These videos are characterized by dynamic interactions and a strong dependence on the wearer engagement with the environment. Traditional approaches often focus on isolated clips or fail to integrate rich semantic

Tim Weiland, Marvin Pförtner, Philipp Hennig

Mechanistic knowledge about the physical world is virtually always expressed via partial differential equations (PDEs). Recently, there has been a surge of interest in probabilistic PDE solvers -- Bayesian statistical models mostly based on Gaussian process (GP) priors which seamlessly combine empirical measurements and mechanistic knowledge. As such, they q

Chongjun Tu, Peng Ye, Dongzhan Zhou, Lei Bai

Multi-Modal Large Language Models (MLLMs) stand out in various tasks but still struggle with hallucinations. While recent training-free mitigation methods mostly introduce additional inference overhead via retrospection strategy and contrastive decoding, we propose attention reallocation (AttnReal) to mitigate hallucinations with nearly zero extra cost. Our

Yuan Tian

In this paper we study the oriented swap process on the positive integers and its asymptotic properties. Our results extend a theorem by Angel, Holroyd, and Romik regarding the trajectories of particles in the finite oriented swap process. Furthermore, we study the evolution of the type of a particle at the leftmost position over time. Our approach relies on

Unnikrishnan Kunnath Ganesan, Giuseppe Durisi, Matteo Zecchin, Petar Popovski

We investigate a lossy source compression problem in which both the encoder and decoder are equipped with a pre-trained sequence predictor. We propose an online lossy compression scheme that, under a 0-1 loss distortion function, ensures a deterministic, per-sequence upper bound on the distortion (outage) level for any time instant. The outage guarantees app

Bin Huang, Binzhong He, Yanhan Chen, Zhili Liu

Deep learning has significantly advanced PET image re-construction, achieving remarkable improvements in image quality through direct training on sinogram or image data. Traditional methods often utilize masks for inpainting tasks, but their incorporation into PET reconstruction frameworks introduces transformative potential. In this study, we pro-pose an ad

Jingkai Sun, Qiang Zhang, Gang Han, Wen Zhao

In recent years, research on humanoid robots has garnered increasing attention. With breakthroughs in various types of artificial intelligence algorithms, embodied intelligence, exemplified by humanoid robots, has been highly anticipated. The advancements in reinforcement learning (RL) algorithms have significantly improved the motion control and generalizat

Ratnangshu Das, Aiman Aatif Bayezeed, Pushpak Jagtap

This paper provides a discretization-free solution to the synthesis of approx-imation-free closed-form controllers for unknown nonlinear systems to enforce complex properties expressed by $\omega$-regular languages, as recognized by Non-deterministic B\"uchi Automata (NBA). In order to solve this problem, we first decompose NBA into a sequence of reach-avoid

Runwei Guan, Jianan Liu, Ningwei Ouyang, Shaofeng Liang

Embodied outdoor scene understanding forms the foundation for autonomous agents to perceive, analyze, and react to dynamic driving environments. However, existing 3D understanding is predominantly based on 2D Vision-Language Models (VLMs), which collect and process limited scene-aware contexts. In contrast, compared to the 2D planar visual information, point