March 2025 arXiv papers — page 76

Reza Mohammadpour, Paulo Varandas

We develop a higher-dimensional extension of multifractal analysis for typical fiber-bunched linear cocycles. Our main result is a relative variational principle, which shows that the topological entropy of Lyapunov exponent level sets can be approximated by the metric entropy of ergodic measures fully concentrated on those level sets, addressing a question

Chandan Yeshwanth, David Rozenberszki, Angela Dai

Generating text descriptions of objects in 3D indoor scenes is an important building block of embodied understanding. Existing methods do this by describing objects at a single level of detail, which often does not capture fine-grained details such as varying textures, materials, and shapes of the parts of objects. We propose the task of expressive 3D captio

Florian Honz, Bernhard Schrenk

We present a full-duplex 10Gb/s FSO bridge between two single-mode ports, utilizing centralized beamforming and simultaneous channel sounding. We further mitigate turbulence-induced fading through diversity reception enabled by wavelength-set coding.

Florian Honz, Winfried Boxleitner, Michael Hentschel, Philip Walther

We demonstrate focal plane array beamforming for semi-blind deployments of free-space optical QKD links. We accomplish a secure-key rate of 1.2 kb/s at a QBER of 9.1% over a 63-m out-door link during full sunshine.

A. Begué, M. W. Khaliq, N. Cotón, I. Lorenzo-Feijoo

In this work, we demonstrate the voltage control of magnetic anisotropy in a strain-mediated Ni90Fe10/BaTiO3(001) heterostructure. In the pristine state of the heterostructure, the Magneto-Optical Kerr Effect measurements show a transcritical hysteresis loop for the Ni90Fe10 film, indicating a weak perpendicular anisotropy. This was further confirmed by X-ra

Jeremy Barnes, Naiara Perez, Alba Bonet-Jover, Begoña Altuna

Automatic text summarization relies on automatic evaluation to quickly determine the quality of summarization models via automatic metrics and LLM-as-a-Judge models. However, these techniques require meta-evaluation to ensure that they capture human judgments correctly. In this paper, we explore this meta-evaluation beyond English by generating a new multili

Ashutosh Pradhan, Daniele Ottaviano, Yi Jiang, Haozheng Huang

The increasing complexity of embedded hardware platforms poses significant challenges for real-time workloads. Architectural features such as Intel RDT, Arm QoS, and Arm MPAM are either unavailable on commercial embedded platforms or designed primarily for server environments optimized for average-case performance and might fail to deliver the expected real-

Rebecca J. Herman, Jonas Wahl, Urmi Ninad, Jakob Runge

Causal discovery aims to extract qualitative causal knowledge in the form of causal graphs from data. Because causal ground truth is rarely known in the real world, simulated data plays a vital role in evaluating the performance of the various causal discovery algorithms proposed in the literature. But recent work highlighted certain artifacts of commonly us

Miguel Duarte, Justin C. Feng, Edgar Gasperín, David Hilditch

We employ an adapted version of H\"ormander's asymptotic systems method to show heuristically that the standard good-bad-ugly model admits formal polyhomogeneous asymptotic solutions near null infinity. In a related earlier approach, our heuristics were unable to capture potential leading order logarithmic terms appearing in the asymptotic solution of the go

Cyrus Malik, Josef Bajada, Joshua Ellul

The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates code features with transactional data to enhance reputabil

Enak Roubertie, Mathis Verdan, Andreas Kirchner, Jean-Yves Ollitrault

The cumulants of the distribution of anisotropic flow are measured accurately in Pb+Pb collisions at the LHC as a function of centrality classifiers (charged multiplicity and/or transverse energy). Using Bayesian inference, we reconstruct from these measurements the probability distribution of anisotropic flow in the ``theorists' frame'' where the impact par

Stephen Lloyd-Brown, Susan Francis, Caroline Hoad, Penny Gowland

An often overlooked problem in medical image segmentation research is the effective selection of training subsets to annotate from a complete set of unlabelled data. Many studies select their training sets at random, which may lead to suboptimal model performance, especially in the minimal supervision setting where each training image has a profound effect o

Mathieu Nicolas, Shuhui Yang, Christophe Méthivier, Souhir Boujday

Efficient peptide adsorption on metasurfaces is essential for advanced biosensing applications. In this study, we demonstrate how ellipsometric measurements coupled with numerical simulations allow for real-time tracking of temporin-SHa peptide adsorption on gold metasurfaces. By characterizing spectral shifts at 660 nm, 920 nm, and 1000 nm, we reveal a rapi

Jianchuan Chen, Jingchuan Hu, Gaige Wang, Zhonghua Jiang

Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gaussian Splatting (3DGS) for lifelike avatar creation, existing methods struggle with fine-grained control of facial expressions and body movements in full-body talking tasks. Additi

Ioana Duminica, Calin Alexa, Ioan M. Dinu, Bogdan A. Dobrescu

We explore the discovery potential of an ultraheavy $(7-8.5$ TeV) diquark scalar produced in the collisions of two up quarks at the LHC. Assuming that the diquark scalar decays into two vectorlike quarks of mass around 2 TeV, each of them decaying into a $W^{+}$ boson and a $b$ quark, we focus on the fully-hadronic final state. We present a signal-from-backg

Snigdha Paul, Sambit Mallick, Anindya Sen

Computer vision has transformed medical diagnosis, treatment, and research through advanced image processing and machine learning techniques. Fracture classification, a critical area in healthcare, has greatly benefited from these advancements, yet accurate detection is challenged by complex patterns and image noise. Bit plane slicing enhances medical images

Junjie Hu, Shuyong Gao, Qianyu Guo, Yan Wang

Humans can intuitively decompose an image into a sequence of strokes to create a painting, yet existing methods for generating drawing processes are limited to specific data types and often rely on expensive human-annotated datasets. We propose a novel self-supervised framework for generating drawing processes from any type of image, treating the task as a v

M. Sparnaaij, D. C. Duives, S. P. Hoogendoorn

A pedestrian model's computation speed impacts the model applicability. However, little attention has been given to this model property in the field of pedestrian dynamics modelling. As such, no framework exists to guide the systematic analysis of a pedestrian models' computational speed. This contribution presents the APS framework (Assess Pedestrian model

Ziteng Cui, Jianfei Yang, Tatsuya Harada

In the computer vision community, the preference for pre-training visual models has largely shifted toward sRGB images due to their ease of acquisition and compact storage. However, camera RAW images preserve abundant physical details across diverse real-world scenarios. Despite this, most existing visual perception methods that utilize RAW data directly int

Irene Scalco, Giulia Colafrancesco, Matteo Cinelli

Climate change is one of the most critical challenges of the twenty-first century. Public understanding of climate issues and of the goals regarding the climate transition is essential to translate awareness into concrete actions. In this context, social media platforms play a crucial role in disseminating information about climate change and climate policy.

Joverlyn Gaudillo, Nicole Astrologo, Fabio Stella, Enzo Acerbi

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships. These tasks, ref

David Mildenberger, Paul Hager, Daniel Rueckert, Martin J Menten

Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it struggles to learn well-conditioned representations of datasets with long-tailed class distributions. This problem is potentially exacerbated for binary imbalanced distributions,

Jonas Conneryd, Susanna F. de Rezende, Jakob Nordström, Shuo Pang

We prove that polynomial calculus (and hence also Nullstellensatz) over any field requires linear degree to refute that sparse random regular graphs, as well as sparse Erd\H{o}s-R\'{e}nyi random graphs, are $3$-colourable. Using the known relation between size and degree for polynomial calculus proofs, this implies strongly exponential lower bounds on proof

Nicola Pellicciotta, Ojus Satish Bagal, Maria Cristina Cannarsa, Silvio Bianchi

The persistent dynamics of active particles makes them explore extended portions of an obstacle's boundary during collisions. From impact to escape, the net applied forces depend on the curvature of the wall and increase in the presence of concave features. Here we systematically investigate the forces exerted by swimming bacteria on microfabricated structur

Lukas Allwicher, Gino Isidori, Marko Pesut

We illustrate the potential of a future high-intensity $e^+ e^-$ collider running at the $Z$ pole in probing extensions of the Standard Model via precise measurements of flavor-changing processes. We illustrate this potential both within effective field theories and simplified models inspired by current $B$-physics data, focusing on selected flavor-physics m

Enrico Marzano, Giovanni Pagliarini, Riccardo Pasini, Guido Sciavicco

The range of potential applications of acoustic analysis is wide. Classification of sounds, in particular, is a typical machine learning task that received a lot of attention in recent years. The most common approaches to sound classification are sub-symbolic, typically based on neural networks, and result in black-box models with high performances but very

Aoting Zhang, Dongbao Yang, Chang Liu, Xiaopeng Hong

Existing class incremental learning is mainly designed for single-label classification task, which is ill-equipped for multi-label scenarios due to the inherent contradiction of learning objectives for samples with incomplete labels. We argue that the main challenge to overcome this contradiction in multi-label class-incremental learning (MLCIL) lies in the

N Narinder, Elisabeth Fischer-Friedrich

Living cells exhibit non-equilibrium dynamics emergent from the intricate interplay between molecular motor activity and its viscoelastic cytoskeletal matrix. The deviation from thermal equilibrium can be quantified through frequency-dependent effective temperature or time-reversal symmetry breaking quantified e.g. through the Kullback-Leibler divergence. He

Haoyang Hong, Ioanna Papanikolaou, Sonali Parbhoo

Mitigating shortcuts, where models exploit spurious correlations in training data, remains a significant challenge for improving generalization. Regularization methods have been proposed to address this issue by enhancing model generalizability. However, we demonstrate that these methods can sometimes overregularize, inadvertently suppressing causal features

Shuang Wei, Muhua Zhang, Yun Gan, Deqing Huang

Nowadays, robots are increasingly operated in environments shared with humans, where conflicts between human and robot behaviors may compromise safety. This paper presents a proactive behavioral conflict avoidance framework based on the principle of adaptation to trends for quadruped robots that not only ensures the robot's safety but also minimizes interfer

Simon Suh, Jihyuk Bang, Ji Woo Han

This study employs the Paul-Elder Critical Thinking Model and Tan's argumentative writing framework to create a structured methodology. This methodology, ChatGPT Guideline for Critical Argumentative Writing (CGCAW) framework, integrates the models with ChatGPT's capabilities to guide L2 learners in utilizing ChatGPT to enhance their critical thinking skills.

Ziqi Ji, Gang Du, Penghao Duan

Vortex flows are ubiquitous in both natural processes and engineering applications, including phenomena such as typhoons, water currents, and aerospace fluid dynamics. The vortex particle method, a computational approach grounded in vortex dynamics, has been extensively applied in aerodynamics, oceanography, turbulence, and aeroacoustics. With the recent int

Aitor Brazaola-Vicario, Oscar Lage, Julen Bernabé-Rodríguez, Eduardo Jacob

Quantum key distribution (QKD) enables unconditionally secure symmetric key exchange between parties. However, terrestrial fibre-optic links face inherent distance constraints due to quantum signal degradation. Traditional solutions to overcome these limits rely on trusted relay nodes, which perform intermediate re-encryption of keys using one-time pad (OTP)

Veysel Kocaman, Yigit Gul, M. Aytug Kaya, Hasham Ul Haq

Assertion status detection is a critical yet often overlooked component of clinical NLP, essential for accurately attributing extracted medical facts. Past studies have narrowly focused on negation detection, leading to underperforming commercial solutions such as AWS Medical Comprehend, Azure AI Text Analytics, and GPT-4o due to their limited domain adaptat

I. Awada, M. Bornert, V. Langlois, J. Léopoldès

We experimentally study the heterogeneity of strain in a granular medium subjected to oscillatory shear in a rotating drum. Two complementary methods are used. The first method relies on optical imaging and grain tracking, allowing us to compute some components of the strain tensor and their variance. The second method, Diffusive Acoustic Wave Spectroscopy (

Michal Praszalowicz

The Chiral Quark--Soliton Model applied to baryons with one heavy quark predicts new exotic states belonging to three $\overline{\boldsymbol{15}}$ SU(3) multiplets. Here, we extend previous analysis of charm exotica to the case of beauty. All model parameters are fixed from the charm sector and from the nonexotic $b$--baryons. We present predictions for mass

Flavio Ronetti, Noé Demazure, Jérôme Rech, Thibaut Jonckheere

Anyons are quasiparticles with fractional statistics, bridging between fermions and bosons. We propose an experimental setup to measure the statistical angle of topological anyons emitted from a quantum point contact (QPC) source. The setup involves an droplet along a fractional quantum Hall liquid edge, formed by defining a droplet with two negatively biase

Runze Ma, Zhongyue Zhang, Zichen Wang, Chenqing Hua

Ribonucleic acid (RNA) binds to molecules to achieve specific biological functions. While generative models are advancing biomolecule design, existing methods for designing RNA that target specific ligands face limitations in capturing RNA's conformational flexibility, ensuring structural validity, and overcoming data scarcity. To address these challenges, w

Alejandro Vigna-Gómez, Evgeni Grishin, Jakob Stegmann, Aleksandra Olejak

Short-period massive binary stars are predicted to undergo chemically homogeneous evolution (CHE), making them prime candidates for producing binary black holes (BBHs) that may merge within the age of the Universe. Most of these binaries have a tertiary companion, and here we explore how a nearby third body possibly influences this evolutionary channel. Our

Muhua Zhang, Lei Ma, Ying Wu, Kai Shen

This paper presents an autonomous exploration framework. It is designed for indoor ground mobile robots that utilize laser Simultaneous Localization and Mapping (SLAM), ensuring process completeness and precise mapping results. For frontier search, the local-global sampling architecture based on multiple Rapidly Exploring Random Trees (RRTs) is employed. Tra

Sophia Rupprecht, Yassine Hounat, Monisha Kumar, Giacomo Lastrucci

As large language models have shown remarkable capabilities in conversing via natural language, the question arises as to how LLMs could potentially assist chemical engineers in research and industry with domain-specific tasks. We generate dynamic chemical reactor models in Modelica code format from textual descriptions as user input. We fine-tune Llama 3.1

Jian Guan, Junfei Wu, Jia-Nan Li, Chuanqi Cheng

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users' divers

Weimin Wang, Yu Du, Ting Yang, Yu Liu

Owing to the capability for reliable and all-weather long-range sensing, the fusion of LiDAR and Radar has been widely applied to autonomous vehicles for robust perception. In practical operation, well manually calibrated extrinsic parameters, which are crucial for the fusion of multi-modal sensors, may drift due to the vibration. To address this issue, we p

Subhadeep Bandyopadhyay, Silvia Picozzi, Sayantika Bhowal

We investigate the role of atomic distortions in non-relativistic spin splitting in perovskite oxides with Pbnm symmetry. Using LaMnO3 as a representative material, we analyze its non-relativistic spin splitting through a combined phonon and multipolar analysis. Our study provides key insights into how structural distortions and magnetic ordering drive ferro

Maria Tubella Salinas, Alexandra González, Silverio Martínez-Fernández

Background: Despite its impact on innovation, gender diversity remains far from fully being achieved in open-source projects. Aims: We examine gender diversity in Hugging Face (HF) organizations, investigating its impact on innovation and team dynamics in open-source development projects. Method: We conducted a repository mining study, focusing on ML model d

Robin Delabays, Philippe Jacquod

We investigate species-rich mathematical models of ecosystems. While much of the existing literature focuses on the properties of equilibrium fixed points, persistent dynamics (e.g., limit cycles or chaos) have also been observed, both in natural or lab-controlled ecosystems and in mathematical models. Here we emphasize the emergence of limit cycles followin

Alessandro Maraio

Weak lensing galaxy surveys are currently undergoing a dramatic revolution as the dawn of the Stage-IV surveys are upon us. Hence, ensuring that our analysis methods are as accurate and precise as the raw data is of upmost importance. This motivated the development of a new implementation of the quadratic maximum likelihood power spectrum estimation techniqu

Veysel Kocaman, Muhammed Santas, Yigit Gul, Mehmet Butgul

We evaluate the performance of four leading solutions for de-identification of unstructured medical text - Azure Health Data Services, AWS Comprehend Medical, OpenAI GPT-4o, and John Snow Labs - on a ground truth dataset of 48 clinical documents annotated by medical experts. The analysis, conducted at both entity-level and token-level, suggests that John Sno

Qinghe Ma, Jian Zhang, Zekun Li, Lei Qi

Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged sword when adapting them to downstream tasks in specific domains. In the context of semi-supervised medical image segmentation with domain shift, foundation models like MedSAM tend

Zhongyue Zhang, Runze Ma, Yanjie Huang, Shuangjia Zheng

Structure-informed protein representation learning is essential for effective protein function annotation and \textit{de novo} design. However, the presence of inherent noise in both crystal and AlphaFold-predicted structures poses significant challenges for existing methods in learning robust protein representations. To address these issues, we propose a no

Morten Nielsen

We construct smooth localised orthonormal bases compatible with anisotropic Triebel-Lizorkin and Besov type spaces on $\mathbb{R}^d$. The construction is based on tensor products of so-called univariate brushlet functions that are based on local trigonometric bases in the frequency domain, and the construction is painless in the sense that all parameters for

Guilherme Catumba, Fernando P. Panadero, Carlos Pena, Alberto Ramos

We present a new GPU-based open source package to perform Lattice simulations developed in Julia. The code currently supports generation of SU(2) and SU(3) (pure gauge) configurations with different actions and boundary conditions, and is able to perform measurements of flow observables (both gluonic and fermionic) as well as different fermionic two point fu

Tobias Brudermueller, Elgar Fleisch, Marina González Vayá, Thorsten Staake

Heat pumps are essential for decarbonizing residential heating but consume substantial electrical energy, impacting operational costs and grid demand. Many systems run inefficiently due to planning flaws, operational faults, or misconfigurations. While optimizing performance requires skilled professionals, labor shortages hinder large-scale interventions. Ho

Andrew C Dwyer, Lizzie Coles-Kemp, Clara Crivellaro, Claude P R Heath

In a 'digital by default' society, essential services must be accessed online. This opens users to digital deception not only from criminal fraudsters but from a range of actors in a marketised digital economy. Using grounded empirical research from northern England, we show how supposedly 'trusted' actors, such as governments,(re)produce the insecurities an

Yuze Li, Wei Zhu

We propose an efficient fine-tuning method for time series foundation models, termed TRACE: Time Series Parameter Efficient Fine-tuning. While pretrained time series foundation models are gaining popularity, they face the following challenges: (1) Unlike natural language tasks, time series data vary in frequency, channel numbers, historical/prediction length

Filip Jonsson Kling

Consider a standard graded artinian $k$-algebra $B$ and an extension of $B$ by a new variable, $A=B\otimes_k k[x]/(x^d)$ for some $d\geq 1$. We will show how maximal rank properties for powers of a general linear form on $A$ can be determined by maximal rank properties for different powers of general linear forms on $B$. This is then used to study Lefschetz

Tao Feng, Zhiyuan Zhao, Yifan Xie, Yuqi Ye

We present STFTCodec, a novel spectral-based neural audio codec that efficiently compresses audio using Short-Time Fourier Transform (STFT). Unlike waveform-based approaches that require large model capacity and substantial memory consumption, this method leverages STFT for compact spectral representation and introduces unwrapped phase derivatives as auxilia

Ying Ming, Shaoze Luo, Longfei Zhao, Ruijie Zhao

Accurate segmentation of pulmonary vessels plays a very critical role in diagnosing and assessing various lung diseases. Currently, many automated algorithms are primarily targeted at CTPA (Computed Tomography Pulmonary Angiography) types of data. However, the segmentation precision of these methods is insufficient, and support for NCCT (Non-Contrast Compute

Parteek Kumar, Arunava Mandal

Let $\mathbb F$ be a local field and $G$ be a linear algebraic group defined over $\mathbb F$. For $k\in\mathbb N$, let $g\to g^k$ be the $k$-th power map $P_k$ on $G(\mathbb F)$. The purpose of this article is two-fold. First, we study the power map on real algebraic group. We characterise the density of the images of the power map $P_k$ on $G(\mathbb R)$ i

Torsha Moitra, Lukas Konecny, Marius Kadek, Ofer Neufeld

Chiral systems exhibit unique properties traditionally linked to their asymmetric spatial arrangement. Recently, multiple laser pulses were shown to induce purely electronic chiral states without altering the nuclear configuration. Here, we propose and numerically demonstrate a simpler realization of light-induced electronic chirality that is long-lived and

Eduardo Abi Jaber, Elie Attal, Mathieu Rosenbaum

We investigate the weak limit of the hyper-rough square-root process as the Hurst index $H$ goes to $-1/2\,$. This limit corresponds to the fractional kernel $t^{H - 1 / 2}$ losing integrability. We establish the joint convergence of the couple $(X, M)\,$, where $X$ is the hyper-rough process and $M$ the associated martingale, to a fully correlated Inverse G

Aakash Singh, Anurag Kanaujia, Vivek Kumar Singh

Among the factors of production, human capital or skilled manpower is the one that keeps evolving and adapts to changing conditions and resources. This adaptability makes human capital the most crucial factor in ensuring a sustainable growth of industry/sector. As new technologies are developed and adopted, the new generations are required to acquire skills

Tadeu Freitas, Erick Silva, Rehana Yasmin, Ali Shoker

Vehicle cybersecurity has emerged as a critical concern, driven by the innovation in the automotive industry, e.g., automomous, electric, or connnected vehicles. Current efforts to address these challenges are constrained by the limited computational resources of vehicles and the reliance on connected infrastructures. This motivated the foundation of Vehicle

Xu Zhang, Hao Zhou, Haoming Qin, Xiaobin Lu

Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research. To address these limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework designed to enable fine-grained control over pre-traine

Mikoláš Janota, Bartosz Piotrowski, Karel Chvalovský

This short paper proposes to learn models of satisfiability modulo theories (SMT) formulas during solving. Specifically, we focus on infinite models for problems in the logic of linear arithmetic with uninterpreted functions (UFLIA). The constructed models are piecewise linear. Such models are useful for satisfiable problems but also provide an alternative d

Theresa Ullmann, Georg Heinze, Michal Abrahamowicz, Aris Perperoglou

In regression analysis, associations between continuous predictors and the outcome are often assumed to be linear. However, modeling the associations as non-linear can improve model fit. Many flexible modeling techniques, like (fractional) polynomials and spline-based approaches, are available. Such methods can be systematically compared in simulation studie

Haichao Zhang, Yun Fu

Token-based video representation has emerged as a promising approach for enabling large language models (LLMs) to interpret video content. However, existing token reduction techniques, such as pruning and merging, often disrupt essential positional embeddings and rely on continuous visual tokens sampled from nearby pixels with similar spatial-temporal locati

Jinbo Yan, Rui Peng, Zhiyan Wang, Luyang Tang

Building Free-Viewpoint Videos in a streaming manner offers the advantage of rapid responsiveness compared to offline training methods, greatly enhancing user experience. However, current streaming approaches face challenges of high per-frame reconstruction time (10s+) and error accumulation, limiting their broader application. In this paper, we propose Inst

Ruoqi Zhang, Ziwei Luo, Jens Sjölund, Per Mattsson

Diffusion models have shown impressive performance in capturing complex and multi-modal action distributions for game agents, but their slow inference speed prevents practical deployment in real-time game environments. While consistency models offer a promising approach for one-step generation, they often suffer from training instability and performance degr

Matteo Vandelli, Francesco Ferrari, Daniele Dragoni

Current algorithms for large-scale industrial optimization problems typically face a trade-off: they either require exponential time to reach optimal solutions, or employ problem-specific heuristics. To overcome these limitations, we introduce SPLIT, a general-purpose quantum-inspired framework for decomposing large-scale quadratic programs into smaller subp

Cyril Koenig, Enrico Zelioli, Frank K. Gürkaynak, Luca Benini

RISC-V allows for building general-purpose computing platforms with programmable accelerators around a single open-source ISA. However, leveraging heterogeneous SoCs within high-level applications is a tedious task. In this preliminary work, we modify the OpenBLAS library to offload selected linear kernels to a programmable manycore accelerator (PMCA) using

Weihao Yu, Xiaoqing Guo, Chenxin Li, Yifan Liu

Achieving meticulous segmentation of tooth point clouds from intra-oral scans stands as an indispensable prerequisite for various orthodontic applications. Given the labor-intensive nature of dental annotation, a significant amount of data remains unlabeled, driving increasing interest in semi-supervised approaches. One primary challenge of existing semi-sup

Xiaofeng Mao, Yuefeng Chen, Rong Zhang, Hui Xue

Deep neural networks (DNNs) has shown great promise in computer vision tasks. However, machine vision achieved by DNNs cannot be as robust as human perception. Adversarial attacks and data distribution shifts have been known as two major scenarios which degrade machine performance and obstacle the wide deployment of machines "in the wild". In order to break

Julian Junyan Wang, Victor Xiaoqi Wang

This study provides the first comprehensive assessment of consistency and reproducibility in Large Language Model (LLM) outputs in finance and accounting research. We evaluate how consistently LLMs produce outputs given identical inputs through extensive experimentation with 50 independent runs across five common tasks: classification, sentiment analysis, su

Wentao Jiang, Jingya Wang, Kaiyang Ji, Baoxiong Jia

Human action-reaction synthesis, a fundamental challenge in modeling causal human interactions, plays a critical role in applications ranging from virtual reality to social robotics. While diffusion-based models have demonstrated promising performance, they exhibit two key limitations for interaction synthesis: reliance on complex noise-to-reaction generator

Andy Wingo

Can a memory manager be built with fast bump-pointer allocation, single-pass heap tracing, and a low upper bound on memory overhead? The Immix collector answered in the affirmative for the first two, but the granularity at which it reclaims memory means that in the worst case a tiny object can keep two 128-byte lines of memory from being re-used for allocati

Yingping Liang, Yutao Hu, Wenqi Shao, Ying Fu

Depth completion involves predicting dense depth maps from sparse LiDAR inputs. However, sparse depth annotations from sensors limit the availability of dense supervision, which is necessary for learning detailed geometric features. In this paper, we propose a two-stage knowledge distillation framework that leverages powerful monocular foundation models to p

Konstantin Grotov, Dmitry Botov

Computational notebooks, while essential for data science, are limited by their one-dimensional interface, which poorly aligns with non-linear developer workflows and complicates collaboration and human-AI interaction. In this work, we focus on features of Computational Canvas, a novel two-dimensional interface that evolves notebooks to enhance data analysis

Zhe Hu, Jing Li, Zhongzhu Pu, Hou Pong Chan

Vision Language Models exhibit impressive performance for various tasks, yet they often lack the sophisticated situational reasoning required for complex decision-making. This paper shows that VLMs can achieve surprisingly strong decision-making performance when visual scenes are replaced by textual descriptions, suggesting foundational reasoning can be effe

Jiadong Tang, Yu Gao, Dianyi Yang, Liqi Yan

Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rendering quality, providing a new avenue for 3D reconstruction from drone imagery. However, dynamic distractors in wild environments challenge the static scene assumption in radiance f

Wei Zhang, Mengting Ma, Yizhen Jiang, Rongrong Lian

Compared with natural images, remote sensing images (RSIs) have the unique characteristic. i.e., larger intraclass variance, which makes semantic segmentation for remote sensing images more challenging. Moreover, existing semantic segmentation models for remote sensing images usually employ a vanilla softmax classifier, which has three drawbacks: (1) non-dir

William F. Martin

Studies by microbiologists from the 1970s provided robust estimates for the energy supply and demand of a prokaryotic cell. The amount of ATP needed to support growth was calculated from the chemical composition of the cell and known enzymatic pathways that synthesize its constituents from known substrates in culture. Starting in 2015, geneticists and evolut

F. Valentini, T. M. O'Neil, D. H. Dubin

Electron acoustic waves (EAWs) are nonlinear plasma modes characterized by electron trapping, which suppresses the usual Landau damping. Despite being predicted in the 1990s, their excitation and decay mechanisms remain a subject of active research. This study investigates the nonlinear dynamics of EAWs, focusing on their excitation, decay instability, and t

Steve Benford, Eike Schneiders, Juan Pablo Martinez Avila, Praminda Caleb-Solly

As robots enter the messy human world so the vital matter of safety takes on a fresh complexion with physical contact becoming inevitable and even desirable. We report on an artistic-exploration of how dancers, working as part of a multidisciplinary team, engaged in contact improvisation exercises to explore the opportunities and challenges of dancing with c

Cosimo Agostinelli, Marco Mancastroppa, Alain Barrat

In recent years, networks with higher-order interactions have emerged as a powerful tool to model complex systems. Comparing these higher-order systems remains however a challenge. Traditional similarity measures designed for pairwise networks fail indeed to capture salient features of hypergraphs, hence potentially neglecting important information. To addre

Ki-Won Kim, Euijoon Kwon, Yongjoo Baek

Recent years have seen a growing interest in the thermodynamic cost of dissipative structures formed by active particles. Given the strong finite-size effects of such systems, it is essential to develop efficient numerical approaches that discretize both space and time while preserving the original dynamics and thermodynamics of active particles in the conti

Muhammad Risha, Mohamed Elsaadany, Paul Liu

Predicting microporosity and permeability in clastic reservoirs is a challenge in reservoir quality assessment, especially in formations where direct measurements are difficult or expensive. These reservoir properties are fundamental in determining a reservoir's capacity for fluid storage and transmission, yet conventional methods for evaluating them, such a

Ji-Hoon Kim, Jeongsoo Choi, Jaehun Kim, Chaeyoung Jung

The objective of this study is to generate high-quality speech from silent talking face videos, a task also known as video-to-speech synthesis. A significant challenge in video-to-speech synthesis lies in the substantial modality gap between silent video and multi-faceted speech. In this paper, we propose a novel video-to-speech system that effectively bridg

Leo Basov, Georgii Oblapenko, Martin Grabe

We propose a novel approach for modeling chemical reactions within the particle-based Fokker-Planck framework for gas flow simulations which conserves mass, momentum, and energy while retaining the performance advantages of the Fokker-Planck approach over the Direct Simulation Monte Carlo (DSMC) method in areas of high density. We show an application of the

Thomas Gawne, Sebastian Schwalbe, Thomas Chuna, Uwe Hernandez Acosta

We introduce a new open-source Python x-ray tracing code for modelling Bragg diffracting mosaic crystal spectrometers: High Energy Applications Ray Tracer (HEART). HEART's high modularity enables customizable workflows as well as efficient development of novel features. Utilizing Numba's just-in-time (JIT) compiler and the message-passing interface (MPI) all

Javier J. Poveda Rodrigo, Mohamed Amine Ahmdi, Alessio Burrello, Daniele Jahier Pagliari

The recent exponential growth of Large Language Models (LLMs) has relied on GPU-based systems. However, CPUs are emerging as a flexible and lower-cost alternative, especially when targeting inference and reasoning workloads. RISC-V is rapidly gaining traction in this area, given its open and vendor-neutral ISA. However, the RISC-V hardware for LLM workloads

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The Standard Model of particle physics-the theory of particles and interactions at the smallest scale-predicts that matter and antimatter interact differently due to violation of the combined symmetry of charge conjugation ($C$) and parity ($P$). Charge conjugation transforms particles into their antimatter particles, whereas the parity transformation invert

Jannis Brugger, Mattia Cerrato, David Richter, Cedric Derstroff

Deep learning approaches are becoming increasingly attractive for equation discovery. We show the advantages and disadvantages of using neural-guided equation discovery by giving an overview of recent papers and the results of experiments using our modular equation discovery system MGMT ($\textbf{M}$ulti-Task $\textbf{G}$rammar-Guided $\textbf{M}$onte-Carlo

H. M. Broadley, S. J. Lind, J. R. C. King

Here we perform the first analysis of high-fidelity simulations of the propagation of lean hydrogen flames through porous media, taking cylindrical arrays a representative example geometry. In this fundamental study we discuss the impact of confinement on both thermodiffusive and thermoacoustic instabilities. Flame propagation in these complex geometries is

Lei Chong

Stack-based memory corruption vulnerabilities have long been exploited by attackers to execute arbitrary code or perform unauthorized memory operations. Various defense mechanisms have been introduced to mitigate stack memory errors, but they typically focus on specific attack types, incur substantial performance overhead, or suffer from compatibility limita

Keisuke Hasegawa

In this study, a method for readily and inexpensively generating real-time reconfigurable intense midair ultrasound field is proposed. Recent investigations and applications of midair convergent high-power ultrasound have been increasingly growing. For generating such ultrasound fields, specifically designed ultrasound sources or phased arrays of ultrasound

Yinhan Zhang, Yue Ma, Bingyuan Wang, Qifeng Chen

We present Follow-Your-Color, a diffusion-based framework for multi-instance sketch colorization. The production of multi-instance 2D line art colorization adheres to an industry-standard workflow, which consists of three crucial stages: the design of line art characters, the coloring of individual objects, and the refinement process. The artists are require

Henning Samtleben

We review exceptional field theories as the duality-covariant reformulation of maximal supergravity theories in ten and eleven dimensions, that make the underlying exceptional symmetries explicit. Beyond their structural role in unifying the various maximal supergravities, we illustrate how they also provide access to very efficient techniques for tackling c

Nicholas Storm, Maria Bergemann, Philipp Eitner, Richard Hoppe

Historically, various methods have been employed to understand the origin of the elements, including observations of elemental abundances which have been compared to Galactic Chemical Evolution (GCE) models. It is also well known that 1D Local Thermodynamic Equilibrium (LTE) measurements fail to accurately capture elemental abundances. Non-LTE (NLTE) effects

Ibtissam Saadi, Abdenour Hadid, Douglas W. Cunningham, Abdelmalik Taleb-Ahmed

Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high complexity, and poor alignment between textual and visual representations. Additionally, existing methods struggle with ineffective temporal modeling. To address these issues, we pro