December 2024 arXiv papers — page 173

Ramón J. Aliaga, Eva Pernecká, Richard J. Smith

We prove that every element of a Lipschitz-free space admits an expression as a convex series of elements with compact support. As a consequence, we conclude that all extreme points of the unit ball of Lipschitz-free spaces are elementary molecules, solving a long-standing problem. We also deduce that all elements of a Lipschitz-free space with the Radon-Nik

A. Bykov, E. Minguzzi, S. Suhr

We introduce a notion of Lorentzian metric space which drops the boundedness condition from our previous work and argue that the properties defining our spaces are minimal. In fact, they are defined by three conditions given by (a) the reverse triangle inequality for chronologically related events, (b) Lorentzian distance continuity and relative compactness

Andrea Pizzi

The eigenstates of a chaotic system can be enhanced along underlying unstable periodic orbits in so-called quantum scars, making it more likely for a particle launched along one such orbits to be found still there at long times. Unstable periodic orbits are, however, a negligible part of the phase space, and a question arises regarding the structure of the w

Sébastien Piérard, Anaïs Halin, Anthony Cioppa, Adrien Deliège

In the computer vision and machine learning communities, as well as in many other research domains, rigorous evaluation of any new method, including classifiers, is essential. One key component of the evaluation process is the ability to compare and rank methods. However, ranking classifiers and accurately comparing their performances, especially when taking

F. Yang, L. Q. Chen

Quantitative description of finite-temperature properties of displacive ferroelectrics, and in particular the critical behavior, is of fundamental importance to both theory and device design, going beyond the Landau-Ginzburg approach, which requires known knowledge of critical behaviors and temperature-dependent parameter fitting. Here within quantum statist

Changsheng Gao, Yifan Ma, Qiaoxi Chen, Yenan Xu

Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as

Chang Niu, Linjia Long, Yizhi Zhang, Zehao Lin

The ongoing demand for more energy-efficient, high-performance electronics is driving the exploration of innovative materials and device architectures, where interfaces play a crucial role due to the continuous downscaling of device dimensions. Tellurium (Te), in its two-dimensional (2D) form, offers significant potential due to its high carrier mobility and

Michihiro Yasunaga, Leonid Shamis, Chunting Zhou, Andrew Cohen

Recent approaches to large language model (LLM) alignment typically require millions of human annotations or rely on external aligned models for synthetic data generation. This paper introduces ALMA: Alignment with Minimal Annotation, demonstrating that effective alignment can be achieved using only 9,000 labeled examples -- less than 1% of conventional appr

Yizhou Wang, Kuan-Chuan Peng, Yun Fu

3D anomaly detection and localization is of great significance for industrial inspection. Prior 3D anomaly detection and localization methods focus on the setting that the testing data share the same category as the training data which is normal. However, in real-world applications, the normal training data for the target 3D objects can be unavailable due to

Roman Shendrik, Evgeny Radzhabov, Alexandra Myasnikova, Viktorija Pankratova

This study investigates the mechanisms underlying ultrafast cross-luminescence observed in BaF$_2$ crystals doped with LaF$_3$. We identified an ultrafast luminescent component with a decay time of approximately 150 ps, which emerges under excitation energies exceeding 24 eV as a novel radiative recombination process between electrons in the 5p core band of

Sen Yang, Yu-Peng Zhang, Tao Zhu, Li Zhao

In this paper, we focus on the gravitational waves emitted by a stellar-mass object in a quasi-circular inspiral orbit around a central supermassive polymerized black hole in loop quantum gravity. Treating the stellar-mass object as a massive test particle, we derive its equations of motion and the corresponding radial effective potential. We find that the p

Trong-Tung Nguyen, Quang Nguyen, Khoi Nguyen, Anh Tran

Recent advances in text-guided image editing enable users to perform image edits through simple text inputs, leveraging the extensive priors of multi-step diffusion-based text-to-image models. However, these methods often fall short of the speed demands required for real-world and on-device applications due to the costly multi-step inversion and sampling pro

Ziwei Huang, Wanggui He, Quanyu Long, Yandi Wang

Evaluating the quality of synthesized images remains a significant challenge in the development of text-to-image (T2I) generation. Most existing studies in this area primarily focus on evaluating text-image alignment, image quality, and object composition capabilities, with comparatively fewer studies addressing the evaluation of the factuality of T2I models

M. W. Suffak, C. E. Jones, A. C. Carciofi

Recent simulations of Be stars in misaligned binary systems have revealed that misalignment between the disc and binary orbit can cause the disc to undergo Kozai-Lidov (KL) oscillations or disc-tearing. We build on our previous suite of three-dimensional smoothed particle hydrodynamics simulations of equal-mass systems by simulating eight new misaligned Be s

Zeyuan He, Kentaro Hayakawa, Makoto Ohsaki

A quad-mesh rigid origami is a continuously deformable panel-hinge structure where planar, rigid, zero-thickness quadrilateral panels are connected by rotational hinges in the combinatorics of a grid. This article provides a comprehensive exposition of two new families of infinitely refinable quad-mesh rigid origami, generated from linear and equimodular cou

Vladimir L. Kalashnikov, Alexander Rudenkov, Evgeni Sorokin, Irina T. Sorokina

In this study, we apply a thermodynamical approach to elucidate the primary constraints on the energy scaling of dissipative solitons (DS). We rely on the adiabatic theory of strongly chirped DS and define the DS energy scaling in terms of dissipative soliton resonance (DSR). Three main experimentally verifiable signatures identify a transition to DSR: i) gr

Jie Bao, Zhixin Zhou, Wen Jung Li, Rui Luo

Accurate medical image segmentation is essential for effective diagnosis and treatment planning but is often challenged by domain shifts caused by variations in imaging devices, acquisition conditions, and patient-specific attributes. Traditional domain generalization methods typically require inclusion of parts of the test domain within the training set, wh

Sandesh Rao Mattu, Imran Ali Khan, Venkatesh Khammammetti, Beyza Dabak

Much of the engineering behind current wireless systems has focused on designing an efficient and high-throughput downlink to support human-centric communication such as video streaming and internet browsing. This paper looks ahead to design of the uplink, anticipating the emergence of machine-type communication (MTC) and the confluence of sensing, communica

Sung Hoon Choi, Donggyu Kim

Several approaches for predicting large volatility matrices have been developed based on high-dimensional factor-based It\^o processes. These methods often impose restrictions to reduce the model complexity, such as constant eigenvectors or factor loadings over time. However, several studies indicate that eigenvector processes are also time-varying. To addre

Zhenglin Huang, Jinwei Hu, Xiangtai Li, Yiwei He

The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in digital content, resulting in severe repercussions. Despite some progress, academia has not yet created a larg

Mathurin Videau, Alessandro Leite, Marc Schoenauer, Olivier Teytaud

Recent advancements have highlighted that large language models (LLMs), when given a small set of task-specific examples, demonstrate remarkable proficiency, a capability that extends to complex reasoning tasks. In particular, the combination of few-shot learning with the chain-of-thought (CoT) approach has been pivotal in steering models towards more logica

Tong Zhao, Nikolce Murgovski, Baigen Cai, Wei ShangGuan

Coordinated control of connected and automated vehicles (CAVs) emerges as a promising technology to improve traffic safety, efficiency, and sustainability. Meanwhile, mixed traffic, where CAVs coexist with conventional human-driven vehicles (HDVs), represents an upcoming and necessary stage in the development of intelligent transportation systems. Considerin

Linfeng Jiang, Peidong Zhan, Ting Bai, Haoyong Yu

Traffic sign detection is crucial for improving road safety and advancing autonomous driving technologies. Due to the complexity of driving environments, traffic sign detection frequently encounters a range of challenges, including low resolution, limited feature information, and small object sizes. These challenges significantly hinder the effective extract

Shrawan Kumar

The main result of this note asserts that a strong form of the Matroid Minor Conjecture due to J. Draisma is not true, i.e., there exist properly ascending chains of $S_\infty$-stable ideals in the affine coordinate ring of the affine infinite Grassmannian, where $S_\infty$ is the infinite symmetric group. In fact, we explicitly construct such an ascending c

Shuhe Wang, Shengyu Zhang, Jie Zhang, Runyi Hu

Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in improving LLM capabilities, its implementation remains highly complex, requiring complex algorithms, reward modeling strategies, and optimization techniques. This complexity poses cha

Yufei Wei, Fuzhang Han, Yanmei Jiao, Zhuqing Zhang

Robot control loops require causal pose estimates that depend only on past and present measurements. At each timestep, controllers compute commands using the current pose without waiting for future refinements. While traditional visual SLAM systems achieve high accuracy through retrospective loop closures, these corrections arrive after control decisions wer

Erik Carlsson, Greg DePaul

In [3], the authors used the Legendre transform to give a tractable method for studying Topological Data Analysis (TDA) in terms of sums of Gaussian kernels. In this paper, we prove a variant for sums of cosine similarity-based kernel functions, which requires considering the more general "$c$-transform" from optimal transport theory [16]. We then apply thes

Ziyang Jiang, Zach Calhoun, Yiling Liu, Lei Duan

Causal reasoning is often challenging with spatial data, particularly when handling high-dimensional inputs. To address this, we propose a neural network (NN) based framework integrated with an approximate Gaussian process to manage spatial interference and unobserved confounding. Additionally, we adopt a generalized propensity-score-based approach to addres

Fuxiang Tao, Bahman Mirheidari, Madhurananda Pahar, Sophie Young

Dementia is associated with various cognitive impairments and typically manifests only after significant progression, making intervention at this stage often ineffective. To address this issue, the Prediction and Recognition of Cognitive Decline through Spontaneous Speech (PROCESS) Signal Processing Grand Challenge invites participants to focus on early-stag

Alex Albors, François Clément, Shosuke Kiami, Braeden Sodt

Given an initial point $x_0 \in \mathbb{R}^d$ and a sequence of vectors $v_1, v_2, \dots$ in $\mathbb{R}^d$, we define a greedy sequence by setting $x_{n} = x_{n-1} \pm v_n$ where the sign is chosen so as to minimize $\|x_n\|$. We prove that if the vectors $v_i$ are chosen uniformly at random from $\mathbb{S}^{d-1}$ then elements of the sequence are, on aver

Spiros Cotsakis

We review recent developments in structural stability as applied to key topics in general relativity. For a nonlinear dynamical system arising from the Einstein equations by a symmetry reduction, bifurcation theory fully characterizes the set of all stable perturbations of the system, known as the `versal unfolding'. This construction yields a comprehensive

Bingbing Hu, Yanyan Li, Rui Xie, Bo Xu

Capturing the temporal evolution of Gaussian properties such as position, rotation, and scale is a challenging task due to the vast number of time-varying parameters and the limited photometric data available, which generally results in convergence issues, making it difficult to find an optimal solution. While feeding all inputs into an end-to-end neural net

Daiheng Zhang, Yan Zeng, Xinyu Hong, Jinbo Xu

Accurately predicting protein melting temperature changes (Delta Tm) is fundamental for assessing protein stability and guiding protein engineering. Leveraging multi-modal protein representations has shown great promise in capturing the complex relationships among protein sequences, structures, and functions. In this study, we develop models based on powerfu

K. L. Kozlov, B. V. Sorin

The notion of a proper Ellis semigroup compactification is introduced. Ellis's functional approach shows how to obtain them from totally bounded equiuniformities on a phase space $X$ when the acting group $G$ is with the topology of pointwise convergence and the $G$-space $(G, X, \curvearrowright)$ is $G$-Tychonoff. The correspondence between proper Ellis se

Jinbin Bai, Wei Chow, Ling Yang, Xiangtai Li

We present HumanEdit, a high-quality, human-rewarded dataset specifically designed for instruction-guided image editing, enabling precise and diverse image manipulations through open-form language instructions. Previous large-scale editing datasets often incorporate minimal human feedback, leading to challenges in aligning datasets with human preferences. Hu

Alan Li, Angela P. Schoellig

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where objects may be textureless and in difficult poses, and occlusion between objects of the same type may cause confusion even in well-trained models. We propose a novel method of

Tolga Kiel, Stephan Durr

We study the 3D Ising model in the infinite volume limit $N_{x,y,z}\to\infty$ by means of numerical simulations. We determine $T_c$ as well as the critical exponents $\beta,\gamma$ and $\nu$, based on finite-size scaling and histogram reweighting techniques. In addition, we study a ``dimensionally reduced'' scenario where $N_z$ is kept fixed (e.g. at 2, 4, 8

Zaid Alyafeai, Michael Pieler, Hannah Teufel, Jonathan Tow

Large Language Models (LLMs) have shown impressive results in multiple domains of natural language processing (NLP) but are mainly focused on the English language. Recently, more LLMs have incorporated a larger proportion of multilingual text to represent low-resource languages. In Arabic NLP, several Arabic-centric LLMs have shown remarkable results on mult

Yuwei Cao, Liangwei Yang, Zhiwei Liu, Yuqing Liu

Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate s

Kritchavat Ploddi, Matthew Sperrin, Glen P. Martin, Maurice M. O'Connell

Generalisability and transportability of clinical prediction models (CPMs) refer to their ability to maintain predictive performance when applied to new populations. While CPMs may show good generalisability or transportability to a specific new population, it is rare for a CPM to be developed using methods that prioritise good generalisability or transporta

Matan Schliserman, Tomer Koren

We study the problem of learning vector-valued linear predictors: these are prediction rules parameterized by a matrix that maps an $m$-dimensional feature vector to a $k$-dimensional target. We focus on the fundamental case with a convex and Lipschitz loss function, and show several new theoretical results that shed light on the complexity of this problem a

Haley Duba-Sullivan, Obaidullah Rahman, Singanallur Venkatakrishnan, Amirkoushyar Ziabari

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the re

Elliot Chane-Sane, Constant Roux, Olivier Stasse, Nicolas Mansard

We propose to learn legged robot locomotion skills by watching thousands of wild animal videos from the internet, such as those featured in nature documentaries. Indeed, such videos offer a rich and diverse collection of plausible motion examples, which could inform how robots should move. To achieve this, we introduce Reinforcement Learning from Wild Animal

Qingyang Mao, Qi Liu, Zhi Li, Mingyue Cheng

In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models (LLMs), which have revolutionized natural language applications. Existing LLM-based studies typically achieve step-by-step thinking for table reasoning guided by task semantics. While these approaches emphasize autono

Fabrizio Canfora, David Dudal, Thomas Oosthuyse, Luigi Rosa

Recently, dynamical edge modes (DEM) in Maxwell theory have been constructed using a specific local boundary condition on the horizon. We discuss how to enforce this boundary condition on an infinite parallel plate in the QED vacuum by introducing Lagrange multiplier fields into the action. We carefully introduce appropriate boundary ghosts to maintain BRST

H. N. Long, H. T. Hung, V. H. Binh, A. B. Arbuzov

The Peccei-Quinn ($PQ$) mechanism is applied to the $\mathrm{SU(3)_c \otimes SU(3)_L \otimes U(1)_X}$ model with $U(1)_{B-L}$ symmetry. The structures in the $PQ$ charges of all fermions and scalar fields in the model are investigated by applying the invariant condition under the symmetry group transformations on all Yukawa interaction terms. All defined $PQ

Wenyang Li, Hanxiao Wang, Jiongmin Yong

Motivated by the optimality system associated with controlled (forward) Volterra integral equations (FVIEs, for short), the well-posedness of coupled forward-backward Voterra integral equations (FBVIEs, for short) is studied. The main feature of FBVIEs is that the unknown $\{(X(t,s),Y(t,s))\}$ has two arguments. By taking $t$ as a parameter and $s$ as a (tim

Arnout Roebben, Toon van Waterschoot, Jan Wouters, Marc Moonen

In many speech recording applications, noise and acoustic echo corrupt the desired speech. Consequently, combined noise reduction (NR) and acoustic echo cancellation (AEC) is required. Generally, a cascade approach is followed, i.e., the AEC and NR are designed in isolation by selecting a separate signal model, separate cost function, and separate solution s

Chengwei Zhang, Yue Zhou, Rui Zhao, Yidong Chen

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the gap between speech and text modalities. Despite substantial p

Yuta Okamoto, Yuuki Ozaki

We investigate how to learn treatment effects away from the cutoff in multiple-cutoff regression discontinuity designs. Using a microeconomic model, we demonstrate that the parallel-trend type assumption proposed in the literature is justified when cutoff positions are assigned as if randomly and the running variable is non-manipulable (e.g., parental income

Pengfei Liang, Neill Lambert, Si Luo, Lingzhen Guo

A full understanding of open quantum systems requires the characterization of both system and environmental properties. However, the complexity of the environmental statistics in the presence of strong system-bath hybridization and long memory effects usually prevents effective non-perturbative methods from going beyond the analysis of the reduced system dyn

Graham L. Giller

A simple model-free and distribution-free statistic, the functional relationship between the number of "effective" degrees of freedom and portfolio size, or N*(N), is used to discriminate between two alternative models for the correlation of daily cryptocurrency returns within a retail universe of defined by the list of tradable assets available to account h

Ethan Bradley, Muhammad Roman, Karen Rafferty, Barry Devereux

Table extraction from document images is a challenging AI problem, and labelled data for many content domains is difficult to come by. Existing table extraction datasets often focus on scientific tables due to the vast amount of academic articles that are readily available, along with their source code. However, there are significant layout and typographical

John Dang, Shivalika Singh, Daniel D'souza, Arash Ahmadian

We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models. By leveraging several years of research at Cohere For AI and Cohere, including advancements

Ilán Carretero, Pablo Meseguer, Rocío del Amor, Valery Naranjo

Domain shift in the field of histopathological imaging is a common phenomenon due to the intra- and inter-hospital variability of staining and digitization protocols. The implementation of robust models, capable of creating generalized domains, represents a need to be solved. In this work, a new domain adaptation method to deal with the variability between h

Vaishali Vinay, Anjali Mangal

As command-line interfaces remain integral to high-performance computing environments, the risk of exploitation through stealthy and complex command-line abuse grows. Conventional security solutions struggle to detect these anomalies due to their context-specific nature, lack of labeled data, and the prevalence of sophisticated attacks like Living-off-the-La

Youssef Jeyar, Kevin Austry, Minggang Luo, Brahim Guizal

The design of metallic contact grids on the front side of thermophotovoltaic cells is critical since it can cause significant optical and electrical resistive losses, particularly in the near field. However, from the theoretical point of view, this effect has been either discarded or studied by means of extremely simplified models like the shadowing methods,

Hoang Ky Nguyen

We reveal a novel aspect of scale-invariant actions that allow matter to couple with a dilaton field: $\,$The dynamics of the dilaton can induce variations in the Planck constant $\hbar$ and speed of light $c$. $\,$Our mechanism for generating variable $\hbar$ and $c$ in $\textit{curved}$ spacetimes via the dilaton offers a viable alternative account for lat

Jonathan Morag, Noy Gabay, Daniel koyfman, Roni Stern

Multi-Agent Path Finding (MAPF) deals with finding conflict-free paths for a set of agents from an initial configuration to a given target configuration. The Lifelong MAPF (LMAPF) problem is a well-studied online version of MAPF in which an agent receives a new target when it reaches its current target. The common approach for solving LMAPF is to treat it as

Ali Pourghoraba, MohammadSadegh KhajueeZadeh, Ali Amini, Abolfazl Vahedi

Reliable mechanical fault detection with limited data is crucial for the effective operation of induction machines, particularly given the real-world challenges present in industrial datasets, such as significant imbalances between healthy and faulty samples and the scarcity of data representing faulty conditions. This research introduces an innovative meta-

Subash Neupane, Himanshu Tripathi, Shaswata Mitra, Sean Bozorgzad

This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a retrieval-based filtering module that extracts Subjective, Objective, Assessment, and Plan (SOAP) information from conversation transcripts, and an inference module powered by fine-

Somnath Jha, Debanjana Kundu, Dipramit Majumdar

We show that for $5/6$-th of all primes $p$, Hilbert's 10-th Problem is unsolvable for $\mathbb{Q}(\zeta_3, \sqrt[3]{p})$. We also show that there is an infinite set $S$ of square free integers such tha Hilbert's 10-th Problem is unsolvable over the number fields $\mathbb{Q}(\zeta_3, \sqrt{D}, \sqrt[3]{p})$ for every $D \in S$ and every prime $p \equiv 2,5 \

Michael Herty, Yizhou Zhou

This work is concerned with the computation of the first-order variation for one-dimensional hyperbolic partial differential equations. In the case of shock waves the main challenge is addressed by developing a numerical method to compute the evolution of the generalized tangent vector introduced by Bressan and Marson (1995). Our basic strategy is to combine

Harry Iveson

We give a concise presentation for the group of pure symmetric outer automorphisms of a given splitting of a free product $G_{1}\ast\dots\ast G_{n}$. These are the (outer) automorphisms which preserve the conjugacy classes of the free factors $G_{i}$. This is achieved by considering the action of these automorphisms on a particular subcomplex of `Outer Space

Davood Momeni, Ratbay Myrzakulov

The quest to understand gravity's role in shaping the universe has led to the exploration of modified gravity theories. One such theory is Myrzakulov gravity, which incorporates both curvature and torsion. In this work, we investigate the effects of torsion within the framework of $f(R,T)$-gravity, a modification of General Relativity that includes both curv

Guillaume Lhost, Geoffrey Compère

Eccentric binary compact mergers are prime targets of current and future gravitational wave observatories. In the small mass ratio expansion, post-adiabatic inspirals have been modeled up to the separatrix, where first-principle modeling currently ends. In this paper, we derive the analytic late time solution to the adiabatic inspiral in terms of self-force

SC Weber, J Pallas, G Olson, D Love

STARR (STAnford Research Repository) is a clinical research support ecosystem that supports basic science research, population health research and translational research at Stanford University. STARR consists of raw and analysis ready multi-modal data, and tools for cohort analysis and self service data access. STARR data is accessible on secure shared compu

Marco Garosi, Riccardo Tedoldi, Davide Boscaini, Massimiliano Mancini

Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios. Recent works have explored vision-language models (VLMs) as a promising alternative, using multi-view rendering and textual prompting to identify object parts. However, naively applying VLMs in this conte

Rahnuma Rahman, Supriyo Bandyopadhyay

Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be imple

Bernd Prach, Christoph H. Lampert

Despite extensive research since the community learned about adversarial examples 10 years ago, we still do not know how to train high-accuracy classifiers that are guaranteed to be robust to small perturbations of their inputs. Previous works often argued that this might be because no classifier exists that is robust and accurate at the same time. However,

Rao Fu, Dingxi Zhang, Alex Jiang, Wanjia Fu

Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bimanual hand activities from 56 subjects

Yixin Zhang, Nicholas Konz, Kevin Kramer, Maciej A. Mazurowski

Image segmentation foundation models (SFMs) like Segment Anything Model (SAM) have achieved impressive zero-shot and interactive segmentation across diverse domains. However, they struggle to segment objects with certain structures, particularly those with dense, tree-like morphology and low textural contrast from their surroundings. These failure modes are

Xiang Chen

n this work, we propose a latent molecular diffusion model that can make the generated 3D molecules rich in diversity and maintain rich geometric features. The model captures the information of the forces and local constraints between atoms so that the generated molecules can maintain Euclidean transformation and high level of effectiveness and diversity. We

Stavros Garoufalidis, Peter Scholze, Campbell Wheeler, Don Zagier

We introduce the Habiro ring of a number field $\mathbb{K}$ and modules over it graded by $K_3(\mathbb{K})$. Elements of these modules are collections of power series at each complex root of unity that arithmetically glue with each other after applying a Frobenius endomorphism, and after dividing at each prime by a collection of series that depends solely on

Jakub Novotný, Pavel Stránský, Pavel Cejnar

We extend the standard semiclassical theory of Excited-State Quantum Phase Transitions (ESQPTs), based on a classification of stationary points in the classical Hamiltonian, to constrained systems. We adopt the method of Lagrange multipliers to find all stationary points and their properties directly from the Hamiltonian constrained by an arbitrary number of

Benet Eiximeno, Marcial Sanchís-Agudo, Arnau Miró, Ivette Rodríguez

A deep-learning-based closure model to address energy loss in low-dimensional surrogate models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a transformer-encoder block with easy-attention mechanism, the model predicts the spatial probability density function of fluctuations not captured by the truncated POD modes. The methodology

Hermann Kroll, Christin K. Kreutz, Bill Matthias Thang, Philipp Schaer

Digital libraries provide different access paths, allowing users to explore their collections. For instance, paper recommendation suggests literature similar to some selected paper. Their implementation is often cost-intensive, especially if neural methods are applied. Additionally, it is hard for users to understand or guess why a recommendation should be r

Masahiro Ikeda, César J. Niche, Gabriela Planas

We examine the energy-critical nonlinear heat equation in critical spaces for any dimension greater or equal than three. The aim of this paper is two-fold. First, we establish a necessary and sufficient condition on initial data at or below the ground state that dichotomizes the behavior of solutions. Specifically, this criterion determines whether the solut

Jiahao Zhang, Ryota Yoshihashi, Shunsuke Kitada, Atsuki Osanai

Large language models (LLMs) have proven effective for layout generation due to their ability to produce structure-description languages, such as HTML or JSON. In this paper, we argue that while LLMs can perform reasonably well in certain cases, their intrinsic limitation of not being able to perceive images restricts their effectiveness in tasks requiring v

Juan R. Loaiza, Miguel González-Duque

Data-driven approaches to philosophy have emerged as a valuable tool for studying the history of the discipline. However, most studies in this area have focused on a limited number of journals from specific regions and subfields. We expand the scope of this research by applying dynamic topic modelling techniques to explore the history of philosophy in Colomb

Aidin Rasti

One of the issues faced by the maintainers of popular open source software is the triage of newly reported issues. Many of the issues submitted to issue trackers are questions. Many people ask questions on issue trackers about their problem instead of using a proper QA website like StackOverflow. This may seem insignificant but for many of the big projects w

Maria Paola Priola

I combine detection and mitigation techniques to addresses hallucinations in Large Language Models (LLMs). Mitigation is achieved in a question-answering Retrieval-Augmented Generation (RAG) framework while detection is obtained by introducing the Negative Missing Information Scoring System (NMISS), which accounts for contextual relevance in responses. While

Beatriz Gato-Rivera

In this article we concisely explain: what antimatter is, its differentiation between primordial and secondary, how it is produced, where it can be found, the experiments carried out at CERN to create and analyze antiatoms, the problem of the matter-antimatter asymmetry, and the medical and technological applications of antimatter in our society.

Shihua Huang, Zhichao Lu, Xiaodong Cun, Yongjun Yu

We introduce DEIM, an innovative and efficient training framework designed to accelerate convergence in real-time object detection with Transformer-based architectures (DETR). To mitigate the sparse supervision inherent in one-to-one (O2O) matching in DETR models, DEIM employs a Dense O2O matching strategy. This approach increases the number of positive samp

Kale-ab Abebe Tessera, Arrasy Rahman, Amos Storkey, Stefano V. Albrecht

Adaptive cooperation in multi-agent reinforcement learning (MARL) requires policies to express homogeneous, specialised, or mixed behaviours, yet achieving this adaptivity remains a critical challenge. While parameter sharing (PS) is standard for efficient learning, it notoriously suppresses the behavioural diversity required for specialisation. This failure

Nanjangud C. Narendra, Ronak Kanthaliya, Venkatareddy Akumalla

The emergence and growth of 5G and beyond 5G (B5G) networks has brought about the rise of so-called ''programmable'' networks, i.e., networks whose operational requirements are so stringent that they can only be met in an automated manner, with minimal/no human involvement. Any requirements on such a network would need to be formally specified via intents, w

Binjie Li, Xiaoping Xie, Qin Zhou

This paper investigates the pathwise uniform convergence in probability of fully discrete finite-element approximations for the two-dimensional stochastic Navier-Stokes equations with multiplicative noise, subject to no-slip boundary conditions. We demonstrate that the full discretization achieves nearly $ 3/2$-order convergence in space and nearly half-orde

Vito Iacovino

We construct the higher genus Open-Closed Gromov-Witten potential as a solution of the quantum master equation defined up to quantum master isotopy.

Chiara Pozzi, Mauro Pontani, Alessandro Beolchi, Elena Fantino

Gateway will represent a primary logistic infrastructure in cislunar space. The identification of efficient orbit transfers capable of connecting Earth, Moon, and Gateway paves the way for enabling refurbishment, servicing, and utilization of this orbiting platform. This study is devoted to determining two-way minimum-time low-thrust orbit transfers that con

David Ribar, Clifford E. Woodward, Jan Forsman

Experiments using the Surface Force Apparatus (SFA) have found anomalously long-ranged interactions between charged surfaces in concentrated salt solutions. Ion clustering have been suggested as a possible origin of this behaviour. In this work, we demonstrate that if such stable clusters indeed form, they are able to induce remarkably strong free energy bar

Sébastien Piérard, Anaïs Halin, Anthony Cioppa, Adrien Deliège

Ranking entities such as algorithms, devices, methods, or models based on their performances, while accounting for application-specific preferences, is a challenge. To address this challenge, we establish the foundations of a universal theory for performance-based ranking. First, we introduce a rigorous framework built on top of both the probability and orde

Nicolas Bongiorno

We study the multi-height distribution of rational points of smooth, projective and split toric varieties over $\mathbf{Q}$ using the lift of the number of points to universal torsors.

Keita Kume, Isao Yamada

In this paper, we address a manifold constrained nonsmooth optimization problem involving the composition of a weakly convex function and a smooth mapping under the availability of a parametrization of the manifold. To find a stationary point of the target problem, we propose a variable smoothing-type algorithm by combining the ideas of (i) translating the c

G. Mazzolari, R. Gilli, R. Maiolino, I. Prandoni

We explore the radio emission of JWST-selected Broad Line AGN (BLAGN, or type 1) in the GOODS-N field. We use deep radio data at different frequencies (144\,MHz, 1.5\,GHz, 3\,GHz, 5.5\,GHz, 10\,GHz), and we find that none of the {37} sources investigated is detected at any of the aforementioned frequencies. Similarly, the radio stacking analysis does not rev

I. R. Khairulin, A. A. Romanov, A. A. Silaev, M. Yu. Ryabikin

A method is proposed for the formation of femto- and subfemtosecond pulses of the deep ultraviolet and vacuum ultraviolet radiation via generating the third harmonic of femtosecond laser pulses during their resonant interaction with alkali atoms or alkaline earth ions. The pulse formation occurs due to two-photon Rabi oscillations between quasi-equidistant e

Anichur Rahman, MD. Zunead Abedin Eidmum, Dipanjali Kundu, Mahir Hossain

In the developing topic of smart cities, Vehicular Ad-Hoc Networks (VANETs) are crucial for providing successful interaction between vehicles and infrastructure. This research proposes a distributed Blockchain-based Vehicular Ad-hoc Network (DistB-VNET) architecture that includes binary malicious traffic classification, Software Defined Networking (SDN), and

Serge Bouc

Let $k$ be an algebraically closed field of positive characteristic $p$. We describe the full lattice of subfunctors of the diagonal $p$-permutation functor $kR_k$ obtained by $k$-linear extension from the functor $R_k$ of linear representations over $k$. This leads to the description of the ``composition factors'' $S_P$ of $kR_k$, which are parametrized by

Chenyang Zhu, Bin Xiao, Lin Shi, Shoukun Xu

The recent Segment Anything Model (SAM) represents a significant breakthrough in scaling segmentation models, delivering strong performance across various downstream applications in the RGB modality. However, directly applying SAM to emerging visual modalities, such as depth and event data results in suboptimal performance in multi-modal segmentation tasks.

Noé Clément, Yuki Nakamura, Michel Blanc, Yuxian Wang

The dynamics of giant planet magnetospheres is controlled by a complex interplay between their fast rotation, their interaction with the solar wind, and their diverse internal plasma and momentum sources. In the ionosphere, the Hall and Pedersen conductances are two key parameters that regulate the intensity of currents coupling the magnetosphere and the ion

Yongtao Jia, Ligong Bian

Axions have long been considered plausible candidates for dark matter. The axion dark matter emitted from cosmic strings after the Peccei-Quinn (PQ) symmetry breaking in the early Universe was extensively simulated. In this work, we study dark matter and gravitational waves through the lattice simulation of the Axion-Higgs string. We gave the dark matter ove