March 2024 arXiv papers — page 128

Hanzhi Yang, Nina Mahmoudian

Underwater gliders (UGs) have emerged as highly effective unmanned vehicles for ocean exploration. However, their operation in dynamic and complex underwater environments necessitates robust path-planning strategies. Previous studies have primarily focused on global energy or time-efficient path planning in explored environments, overlooking challenges posed

Lintao Zhang, Mengqi Wu, Lihong Wang, David C. Steffens

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correcti

Zhonglin Sun, Chen Feng, Ioannis Patras, Georgios Tzimiropoulos

In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through

Hong Hu, Yue M. Lu, Theodor Misiakiewicz

Recent advances in machine learning have been achieved by using overparametrized models trained until near interpolation of the training data. It was shown, e.g., through the double descent phenomenon, that the number of parameters is a poor proxy for the model complexity and generalization capabilities. This leaves open the question of understanding the imp

Alexander Davydov, Francesco Bullo

In this letter, we investigate sufficient conditions for the exponential stability of LTI systems driven by controllers derived from parametric optimization problems. Our primary focus is on parametric projection controllers, namely parametric programs whose objective function is the squared distance to a nominal controller. Leveraging the virtual system met

Jung Hoon Han

We analyze the recently proposed dipolar BF theory with couplings to charge and dipole currents. The quasiparticles of the theory are either charge-like or dipole-like, and the mutual braiding statistics between charge-like and dipole-like quasiparticles are dipolar, meaning that it depends on the position of the quasiparticle being encircled. The braiding s

Fuzhi Wu, Jiasong Wu, Youyong Kong, Chunfeng Yang

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerou

Ali Youssef, Francisco Vasconcelos

Feature point detection and description is the backbone for various computer vision applications, such as Structure-from-Motion, visual SLAM, and visual place recognition. While learning-based methods have surpassed traditional handcrafted techniques, their training often relies on simplistic homography-based simulations of multi-view perspectives, limiting

Douglas J. Buettner, Richard E. Griffiths, Nick Snell, John Stilley

Over the past several years, the misidentification of SpaceX Starlink satellites as Unidentified Aerial Phenomena (UAP) by pilots and laypersons has generated unnecessary aviation risk and confusion. The many deployment and orbital evolution strategies, coupled with changing sun specular reflection angles, contribute to this gap in space situational awarenes

Jianxin Xie, Bing Yao, Zheyu Jiang

Soil moisture is a key hydrological parameter that has significant importance to human society and the environment. Accurate modeling and monitoring of soil moisture in crop fields, especially in the root zone (top 100 cm of soil), is essential for improving agricultural production and crop yield with the help of precision irrigation and farming tools. Reali

Benjamin Doerr, Andrew James Kelley

In their recent work, C. Doerr and Krejca (Transactions on Evolutionary Computation, 2023) proved upper bounds on the expected runtime of the randomized local search heuristic on generalized Needle functions. Based on these upper bounds, they deduce in a not fully rigorous manner a drastic influence of the needle radius $k$ on the runtime. In this short arti

Gilhyun Ryou, Geoffrey Wang, Sertac Karaman

High-speed online trajectory planning for UAVs poses a significant challenge due to the need for precise modeling of complex dynamics while also being constrained by computational limitations. This paper presents a multi-fidelity reinforcement learning method (MFRL) that aims to effectively create a realistic dynamics model and simultaneously train a plannin

Józef Kuśba

To determine the effect of nonradiative excitation energy transfer on the fluorescence of a rigid multicomponent solution, a new analytical method was developed by treating this transfer as a time-resolved Markov chain (TRMC). In the TRMC approach, we assume that the Markov chain under consideration is governed by bivariate joint probability mass-density fun

Charles Edison Tripp, Jordan Perr-Sauer, Jamil Gafur, Amabarish Nag

Addressing the so-called ``Red-AI'' trend of rising energy consumption by large-scale neural networks, this study investigates the actual energy consumption, as measured by node-level watt-meters, of training various fully connected neural network architectures. We introduce the BUTTER-E dataset, an augmentation to the BUTTER Empirical Deep Learning dataset,

Krishnendu Mandal, Shravan M. Hanasoge

Recently discovered inertial waves, observed on the solar surface, likely extend to the deeper layers of the Sun. Utilizing helioseismic techniques, we explore these motions, allowing us to discern inertial-mode eigenfunctions in both radial and latitudinal orientations. We analyze $8$ years of space-based observations ($2010 - 2017$) taken by the Helioseism

Ho Jin Choi, Satyajeet Das, Shaoting Peng, Ruzena Bajcsy

This paper explores the feasibility of employing EEG-based intention detection for real-time robot assistive control. We focus on predicting and distinguishing motor intentions of left/right arm movements by presenting: i) an offline data collection and training pipeline, used to train a classifier for left/right motion intention prediction, and ii) an onlin

Benjamin Bobin Ye, Shensheng Chen, Zhen-Gang Wang

In recent years, molecular dynamics (MD) simulations have emerged as a pivotal tool for understanding the structure, dynamics, and phase behavior in charged soft matter systems. To explore phenomena across greater length and time scales in MD simulations, molecules are often coarse-grained for better computational performance. However, commonly-used force fi

Michael Sun, Minghao Guo, Weize Yuan, Veronika Thost

Recent research in molecular discovery has primarily been devoted to small, drug-like molecules, leaving many similarly important applications in material design without adequate technology. These applications often rely on more complex molecular structures with fewer examples that are carefully designed using known substructures. We propose a data-efficient

Jurgen Julio-Batalla, Jimmy Petean

On a closed Riemannian manifold $(M^n ,g)$ with a proper isoparametric function $f$ we consider the equation $\Delta^2 u -\alpha \Delta u +\beta u = u^q$, where $\alpha$ and $\beta$ are positive constants satisfying that $\alpha^2 \geq 4 \beta$. We let ${\bf m}$ be the minimum of the dimensions of the focal varieties of $f$ and $q_f = \frac{n-{\bf m}+4}{n-{\

Yang Cai, Yingkai Li, Jinzhao Wu

This paper studies a joint design problem where a seller can design both the signal structures for the agents to learn their values, and the allocation and payment rules for selling the item. In his seminal work, Myerson (1981) shows how to design the optimal auction with exogenous signals. We show that the problem becomes NP-hard when the seller also has th

Elaheh Sanoubari, Atil Iscen, Leila Takayama, Stefano Saliceti

In this paper, we investigate the use of 'prosody' (the musical elements of speech) as a communicative signal for intuitive human-robot interaction interfaces. Our approach, rooted in Research through Design (RtD), examines the application of prosody in directing a quadruped robot navigation. We involved ten team members in an experiment to command a robot t

Artem O. Denisov, Veronika Reckova, Solenn Cances, Max J. Ruckriegel

The intrinsic valley degree of freedom makes bilayer graphene (BLG) a unique platform for semiconductor qubits. The single-carrier quantum dot (QD) ground state exhibits a two-fold degeneracy, where the two states that constitute a Kramers pair, have opposite spin and valley quantum numbers. Because of the valley-dependent Berry curvature, an out-of-plane ma

Alzayat Saleh, Alex Olsen, Jake Wood, Bronson Philippa

Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localisation. We present FieldNet, a novel deep learning framework for real-time shadow removal, optimised for resource-constrained hardware. FieldNet introduces a probabilistic enhancem

Xiaohang Chen, Joe Giacalone, Fan Guo, Kristopher G. Klein

Diffusion coefficients of energetic charged particles in turbulent magnetic fields are a fundamental aspect of diffusive transport theory but remain incompletely understood. In this work, we use quasi-linear theory to evaluate the spatial variation of the parallel diffusion coefficient $\kappa_\parallel$ from the measured magnetic turbulence power spectra in

Oliver M. Drozdowski, Ulrich S. Schwarz

Epithelial monolayers are a central building block of complex organisms. Topological defects have emerged as important elements for single cell behavior in flat epithelia. Here we theoretically study such defects in a three-dimensional vertex model for spherical epithelia like cysts or intestinal organoids. We find that they lead to the same generic morpholo

WenSheng Hong, Weishan Zhu, TianRui Wang, Xiaohu Yang

In the prevailing model of galaxy formation and evolution, the process of gas accretion onto central galaxies undergoes a transition from cold-dominated to hot-dominated modes. This shift occurs when the mass of the parent dark matter halos exceeds a critical threshold known as $M_{shock}$. Moreover, cold gas usually flows onto central galaxies through filam

Zhenyu Huang, Shi Jin, Lei Li

The random batch method (RBM) proposed in [Jin et al., J. Comput. Phys., 400(2020), 108877] for large interacting particle systems is an efficient with linear complexity in particle numbers and highly scalable algorithm for $N$-particle interacting systems and their mean-field limits when $N$ is large. We consider in this work the quantitative error estimate

Shikhar Murty, Christopher Manning, Peter Shaw, Mandar Joshi

Following natural language instructions by executing actions in digital environments (e.g. web-browsers and REST APIs) is a challenging task for language model (LM) agents. Unfortunately, LM agents often fail to generalize to new environments without human demonstrations. This work presents BAGEL, a method for bootstrapping LM agents without human supervisio

Julia Mossbridge

Some crucial decisions in AI design tend to be overlooked or factor choices are assumed implicitly. The question often answered first is what the AI will do, not how it will interact with the rest of the world. This reduces our understanding of the possible types of AI that can be developed and their potential impacts on humanity. As an initial AI taxonomy,

Takumi Ogasawara, Ryo Ohashi, Kosuke Sakata, Shushi Harashita

In this paper we study genus-$4$ curves obtained as double covers of elliptic curves. Firstly we shall give explicit defining equations of such curves with explicit criterion for whether it is nonsingular, and show the irreducibility of the long polynomial determining whether the genus-4 curve is nonsingular or not, in any characteristic $\ne 2,3$. Secondly,

Armando Sánchez-Nungaray, José Rosales-Ortega, Carlos González-Flores

We consider symmetric separately radial (with corresponding group $S_n\rtimes \mathbb{T}^n$) and alternating separately radial (with corresponding group $A_n\rtimes \mathbb{T}^n$) symbols, as well as the associated Toeplitz operators on the weighted Bergman spaces on the unit ball on $\mathbb{C}^n$. Using a purely representation theoretic approach we obtain

Donghoon Shin, Lucy Lu Wang, Gary Hsieh

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However,

Kangfeng Ye, Jim Woodcock

RoboChart is a core notation in the RoboStar framework which brings modern modelling and formal verification technologies into software engineering for robotics. It is a timed and probabilistic domain-specific language for robotics and provides a UML-like architectural and state machine modelling. This work presents RoboCertProb for specifying quantitative p

Victoria G. Kurt, Astrid M. Veronig, Gregory D. Fleishman, Jürgen Hinterreiter

The 2003 October 28 (X17.2) eruptive flare was a unique event. The coronal electric field and the {\pi}-decay {\gamma}-ray emission flux had the highest values ever inferred in solar flares. This study reveals physical links between the magnetic reconnection process, the energy release, and the acceleration of electrons and ions to high energies in the chain

Andrej Dujella, Matija Kazalicki, Vinko Petričević

A set of $m$ distinct nonzero rationals $\{a_1, a_2,\ldots, a_m\}$ such that $a_i a_j+1$ is a perfect square for all $1\le i <j \le m$, is called a rational Diophantine $m$-tuple. If in addition, $a_i^2+1$ is a perfect square for $1\le i\le m$, then we say the $m$-tuple is strong. In this paper, we construct infinite families of rational Diophantine sextuple

P. V. G. M. Rathnayake, Stefano Bernardi, Asaph Widmer-Cooper

In this work, we compare existing non-polarizable force fields developed to study the solid or solution phases of hybrid organic-inorganic halide perovskites with the AMOEBA polarizable force field. The aim is to test whether more computationally expensive polarizable force fields like AMOEBA offer better transferability between solution and solid phases, wi

Yu-Chien Lin, Yan Xin, Ta-Sung Lee, Charlie

Acquiring downlink channel state information (CSI) at the base station is vital for optimizing performance in massive Multiple input multiple output (MIMO) Frequency-Division Duplexing (FDD) systems. While deep learning architectures have been successful in facilitating UE-side CSI feedback and gNB-side recovery, the undersampling issue prior to CSI feedback

Sahan Sanjaya, Aruna Jayasena, Prabhat Mishra

Side-channel attacks exploit variations in non-functional behaviors to expose sensitive information across security boundaries. Existing methods leverage side-channels based on power consumption, electromagnetic radiation, silicon substrate coupling, and channels created by malicious implants. Power-based side-channel attacks are widely known for extracting

Adrian Perez Dieguez, Min Choi, Mahmut Okyay, Mauro Del Ben

Tuning searches are pivotal in High-Performance Computing (HPC), addressing complex optimization challenges in computational applications. The complexity arises not only from finely tuning parameters within routines but also potential interdependencies among them, rendering traditional optimization methods inefficient. Instead of scrutinizing interdependenci

Silvia Goncalves, Serena Ng

A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size.

Banafsheh Akbari, Tuval Foguel, Jack Schmidt

Consider a nonsolvable finite group G, where R(G) represents the solvable radical of G. For any element x in G, the solvabilizer of x in G, denoted by Sol_G(x), is defined as the set of all elements y in G such that the subgroup generated by x and y is solvable. Notably, the entirety of G can be expressed as the union over all x in G\R(G) of their respective

Nawaj KC

When $k$ is a field, the classical Jacobian criterion computes the singular locus of an equidimensional, finitely generated $k$-algebra as the closed subset of an ideal generated by appropriate minors of the so-called Jacobian matrix. Recently, Hochster-Jeffries and Saito have extended this result for algebras over any unramified discrete valuation ring of m

Harriette Phillips, Aiden Price, Owen Forbes, Claire Boulange

Globally, there is an increased need for guidelines to produce high-quality data outputs for analysis. No framework currently exists that provides guidelines for a comprehensive approach to producing analysis ready data (ARD). Through critically reviewing and summarising current literature, this paper proposes such guidelines for the creation of ARD. The gui

Stan Gudder

If $H_1$ and $H_2$ are finite-dimensional Hilbert spaces, a channel from $H_1$ to $H_2$ is a completely positive, linear map $\mathcal{I}$ that takes the set of states $\mathcal{S}(H_1)$ for $H_1$ to the set of states $\mathcal{S}(H_2)$ for $H_2$. Corresponding to $\mathcal{I}$ there is a unique dual map $\mathcal{I}^*$ from the set of effects $\mathcal{E}(H

Chensheng Peng, Chenfeng Xu, Yue Wang, Mingyu Ding

In this paper, we reimagine volumetric representations through the lens of quadrics. We posit that rigid scene components can be effectively decomposed into quadric surfaces. Leveraging this assumption, we reshape the volumetric representations with million of cubes by several quadric planes, which results in more accurate and efficient modeling of 3D scenes

Ling Han, Nanqing Luo, Hao Huang, Jing Chen

This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label removal. With the advent of regulations like the GDPR emphasizing data privacy and the right to be forgotten, machine learning models face the daunting task of unlearning sensitive i

Xiaodan Shao, Qijun Jiang, Rui Zhang

In this paper, we propose a new six-dimensional (6D) movable antenna (6DMA) system for future wireless networks to improve the communication performance. Unlike the traditional fixed-position antenna (FPA) and existing fluid antenna/two-dimensional (2D) movable antenna (FA/2DMA) systems that adjust the positions of antennas only, the proposed 6DMA system con

Robert J. Demyanovich

The micromixing time of impinging thin liquid sheets depends upon the energy dissipation rate. The kinetic energy released by the impingement has been previously studied and was found to be a function of the coefficient of restitution of the collision. In this work, the volume within which the released kinetic energy is dissipated was investigated. The volum

Akshay Kumar, Jarvis Haupt

This paper studies the gradient flow dynamics that arise when training deep homogeneous neural networks assumed to have locally Lipschitz gradients and an order of homogeneity strictly greater than two. It is shown here that for sufficiently small initializations, during the early stages of training, the weights of the neural network remain small in (Euclide

Shuang Guo, Guillermo Gallego

Event cameras are bio-inspired visual sensors that capture pixel-wise intensity changes and output asynchronous event streams. They show great potential over conventional cameras to handle challenging scenarios in robotics and computer vision, such as high-speed and high dynamic range. This paper considers the problem of rotational motion estimation using ev

Nicolau Andrés-Thió, Mario Andrés Muñoz, Kate Smith-Miles

Surrogate modelling techniques have seen growing attention in recent years when applied to both modelling and optimisation of industrial design problems. These techniques are highly relevant when assessing the performance of a particular design carries a high cost, as the overall cost can be mitigated via the construction of a model to be queried in lieu of

N. T. Hunt-Smith, C. Cocuzza, W. Melnitchouk, N. Sato

Recently the possible existence of negative gluon helicity, $\Delta g$, has been observed to be compatible with existing empirical constraints, including from jet production in polarized proton-proton collisions at RHIC, and lattice QCD data on polarized gluon Ioffe time distributions. We perform a new global analysis of polarized parton distributions in the

Manuel Rivera, Daniel Tolosa

We prove that the cyclic chain complex of the categorical coalgebra of singular chains on an arbitrary topological space $X$ is naturally quasi-isomorphic to the $S^1$-equivariant chains of the free loop space of $X$. This statement does not require any hypotheses on $X$ or on the commutative ring of coefficients. Along the way, we introduce a family of poly

Vincent Freiberger, Erik Buchmann

Privacy policies are expected to inform data subjects about their data protection rights and should explain the data controller's data management practices. Privacy policies only fulfill their purpose, if they are correctly interpreted, understood, and trusted by the data subject. This implies that a privacy policy is written in a fair way, e.g., it does not

Arnab Dey, Vivek Khatana, Ankur Mani, Murti V. Salapaka

The integration of renewable energy sources (RES) into power distribution grids poses challenges to system reliability due to the inherent uncertainty in their power production. To address this issue, battery energy sources (BESs) are being increasingly used as a promising solution to counter the uncertainty associated with RES power production. During the o

Xiaozhou Ye, Kevin I-Kai Wang

In human activity recognition (HAR), the assumption that training and testing data are independent and identically distributed (i.i.d.) often fails, particularly in cross-user scenarios where data distributions vary significantly. This discrepancy highlights the limitations of conventional domain adaptation methods in HAR, which typically overlook the inhere

Sayma Sultana, Gias Uddin, Amiangshu Bosu

The presence of toxic and gender-identity derogatory language in open-source software (OSS) communities has recently become a focal point for researchers. Such comments not only lead to frustration and disengagement among developers but may also influence their leave from the OSS projects. Despite ample evidence suggesting that diverse teams enhance producti

Xiaozhou Ye, Kevin I-Kai Wang

In Human Activity Recognition (HAR), a predominant assumption is that the data utilized for training and evaluation purposes are drawn from the same distribution. It is also assumed that all data samples are independent and identically distributed ($\displaystyle i.i.d.$). Contrarily, practical implementations often challenge this notion, manifesting data di

Chirag Garg, Panagiotis Ch. Filippou, Ikhtiar, Yari Ferrante

Magnetic random access memory that uses magnetic tunnel junction memory cells is a high performance, non-volatile memory technology that goes beyond traditional charge-based memories. Today its speed is limited by the high magnetization of the memory storage layer. Here we show that fast and highly reliable switching is possible using a very low magnetizatio

Xiaozhou Ye, Waleed H. Abdulla, Nirmal Nair, Kevin I-Kai Wang

Human Activity Recognition (HAR) is a cornerstone of ubiquitous computing, with promising applications in diverse fields such as health monitoring and ambient assisted living. Despite significant advancements, sensor-based HAR methods often operate under the assumption that training and testing data have identical distributions. However, in many real-world s

Ruican Zhong, Donghoon Shin, Rosemary Meza, Predrag Klasnja

This paper explores the integration of causal pathway diagrams (CPD) into human-centered design (HCD), investigating how these diagrams can enhance the early stages of the design process. A dedicated CPD plugin for the online collaborative whiteboard platform Miro was developed to streamline diagram creation and offer real-time AI-driven guidance. Through a

Tamal K. Dey, Cheng Xin

For a $P$-indexed persistence module ${\sf M}$, the (generalized) rank of ${\sf M}$ is defined as the rank of the limit-to-colimit map for the diagram of vector spaces of ${\sf M}$ over the poset $P$. For $2$-parameter persistence modules, recently a zigzag persistence based algorithm has been proposed that takes advantage of the fact that generalized rank f

Xiaozhou Ye, Kevin I-Kai Wang

Current research on human activity recognition (HAR) mainly assumes that training and testing data are drawn from the same distribution to achieve a generalised model, which means all the data are considered to be independent and identically distributed $\displaystyle (i.i.d.) $. In many real-world applications, this assumption does not hold, and collected t

Mohammad Nazeri, Junzhe Wang, Amirreza Payandeh, Xuesu Xiao

Humans excel at efficiently navigating through crowds without collision by focusing on specific visual regions relevant to navigation. However, most robotic visual navigation methods rely on deep learning models pre-trained on vision tasks, which prioritize salient objects -- not necessarily relevant to navigation and potentially misleading. Alternative appr

Hanning Chen, Wenjun Huang, Yang Ni, Sanggeon Yun

Task-oriented object detection aims to find objects suitable for accomplishing specific tasks. As a challenging task, it requires simultaneous visual data processing and reasoning under ambiguous semantics. Recent solutions are mainly all-in-one models. However, the object detection backbones are pre-trained without text supervision. Thus, to incorporate tas

Ieva Liepuoniute, Mario Motta, Thaddeus Pellegrini, Julia E. Rice

The simulation of chemical reactions is an anticipated application of quantum computers. Using a Diels-Alder reaction as a test case, in this study we explore the potential applications of quantum algorithms and hardware in investigating chemical reactions. Our specific goal is to calculate the activation barrier of a reaction between ethylene and cyclopenta

Herbert Wright, Weiming Zhi, Matthew Johnson-Roberson, Tucker Hermans

The ability to construct concise scene representations from sensor input is central to the field of robotics. This paper addresses the problem of robustly creating a 3D representation of a tabletop scene from a segmented RGB-D image. These representations are then critical for a range of downstream manipulation tasks. Many previous attempts to tackle this pr

Ofek Gila, Evrim Ozel, Michael T. Goodrich

In the 1960s, the world-renowned social psychologist Stanley Milgram conducted experiments that showed that not only do there exist ``short chains'' of acquaintances between any two arbitrary people, but that these arbitrary strangers are able to find these short chains. This phenomenon, known as the \emph{small-world phenomenon}, is explained in part by any

Diego Gamboa, Carlos Uzcategui-Aylwin

A coloring on a finite or countable set $X$ is a function $\varphi: [X]^{2} \to \{0,1\}$, where $[X]^{2}$ is the collection of unordered pairs of $X$. The collection of homogeneous sets for $\varphi$, denoted by $Hom(\varphi)$, consist of all $H \subseteq X$ such that $\varphi$ is constant on $[H]^2$; clearly, $Hom(\varphi) = Hom(1-\varphi)$. A coloring $\va

Ruslan Musaev

In the age of information abundance, the ability to provide users with contextually relevant and concise information is crucial. Keyword in Context (KIC) generation is a task that plays a vital role in and generation applications, such as search engines, personal assistants, and content summarization. In this paper, we present a novel approach to generating

Luca Anderlini, GaOn Kim

We examine ``tournament'' second-price auctions in which $N$ bidders compete for the right to participate in a second stage and contend against bidder $N+1$. When the first $N$ bidders are committed so that their bids cannot be changed in the second stage, the analysis yields some unexpected results. The first $N$ bidders consistently bid above their values

Xiaozhou Ye, Kouichi Sakurai, Nirmal Nair, Kevin I-Kai Wang

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies often assume uniform data distributions across datasets, contrasting with the varied nature of practical

P. R. V. Chintalapati, G. Gutierrez, M. H. L. S. Wang

This paper presents the results of a systematic study of projection biases in the Weak Lensing analysis of cosmic shear and the combination of galaxy clustering and galaxy-galaxy lensing using data collected during the first-year of running the Dark Energy Survey experiment. The study uses $\Lambda$CDM as the cosmological model and two-point correlation func

Jae Hun Ro, Srinadh Bhojanapalli, Zheng Xu, Yanxiang Zhang

Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices. SGD is the standard client optimizer for on device training in cross-device FL, favored for its memory and computational efficiency. However, in centralized training of neural language models,

Mohd. Tasleem Khan

The utilization of distributed arithmetic (DA) in AF algorithms has gained significant attention in recent years due to its potential to enhance computational efficiency and reduce resource requirements. This paper presents an exploration of the application of DA to adaptive filtering (AF) algorithms, analyzing trends, discussing challenges, and outlining fu

Yuzo Ishikawa, Nadia L. Zakamska, Yue Shen, Xin Liu

The search for dual supermassive black holes (SMBHs) is of immense interest in modern astrophysics. Galaxy mergers may fuel and produce SMBH pairs. Actively accreting SMBH pairs are observed as a dual quasar, which are vital probes of SMBH growth. Dual quasars at cosmic noon are not well characterized. Gaia observations have enabled a novel technique to iden

Marco Giulini, Raffaele Fiorentini, Luca Tubiana, Raffaello Potestio

Bottom-up coarse-grained (CG) models proved to be essential to complement and sometimes even replace all-atom representations of soft matter systems and biological macromolecules. The development of low-resolution models takes the moves from the reduction of the degrees of freedom employed, that is, the definition of a mapping between a system's high-resolut

Andrey R. Chekhlov, Peter V. Danchev, Patrick W. Keef

Trying to finalize in some way the present subject, this paper targets to generalize substantially the notions of Bassian and co-Bassian groups by introducing the so-called finitely (co-)Bassian groups, semi (co-)Bassian groups, fully generalized (co-)Bassian groups, absolutely generalized (co-)Bassian groups and establishing their crucial properties and cha

Alexei A. Deriglazov

Equations of motion of a charged symmetrical body in external constant and homogeneous electric and magnetic fields are deduced starting from the variational problem, where the body is considered as a system of charged point particles subject to holonomic constraints. The final equations are written for the center-of mass-coordinate, rotation matrix and angu

Imed Basdouri, Sami Benabdelhafidh, Mohamed Amin Sadraoui

In this paper, first, we introduce a notion of modified Rota-Baxter Lie algebras of weight $\mathrm{\lambda}$ with derivations (or simply modified Rota-Baxter LieDer pairs) and their representations. Moreover, we investigate cohomologies of a modified Rota-Baxter LieDer pairs with coefficients in a suitable representation. As applications, we study formal on

Aaron Ray, Christopher Bradley, Luca Carlone, Nicholas Roy

Recent work in the construction of 3D scene graphs has enabled mobile robots to build large-scale metric-semantic hierarchical representations of the world. These detailed models contain information that is useful for planning, however an open question is how to derive a planning domain from a 3D scene graph that enables efficient computation of executable p

José Murta, Vasco Amaral, Fernando Brito e Abreu

Classic automobiles are an important part of the automotive industry and represent the historical and technological achievements of certain eras. However, to be considered masterpieces, they must be maintained in pristine condition or restored according to strict guidelines applied by expert services. Therefore, all data about restoration processes and other

Sudipta Banerjee, Sai Pranaswi Mullangi, Shruti Wagle, Chinmay Hegde

Through a large-scale study over diverse face images, we show that facial attribute editing using modern generative AI models can severely degrade automated face recognition systems. This degradation persists even with identity-preserving generative models. To mitigate this issue, we propose two novel techniques for local and global attribute editing. We emp

A. Hacar, A. Socci, F. Bonanomi, D. Petry

(Abridged) Recent molecular surveys have revealed a rich gas organization of sonic-like fibers in all kind of environments prior to the formation of low- and high-mass stars. This paper introduces the EMERGE project aiming to investigate whether complex fiber arrangements could explain the origin of high-mass stars and clusters. We analyzed the EMERGE Early

Erlend Grong, Sylvie Vega-Molino

Landmark manifolds consist of a collection of distinct points, and dynamics on this manifold can be used to represent flows, such as solutions of ODEs and flows deforming a shape. We will consider landmark configurations in the Euclidean space and how such configuration can be connected through flows of vector field. For every dimension equal or larger than

Michael Rizvi, Maude Lizaire, Clara Lacroce, Guillaume Rabusseau

Transformers are ubiquitous models in the natural language processing (NLP) community and have shown impressive empirical successes in the past few years. However, little is understood about how they reason and the limits of their computational capabilities. These models do not process data sequentially, and yet outperform sequential neural models such as RN

Kento Katagiri, Bernard Kozioziemski, Eric Folsom, Sebastian Göde

Optimizing grain boundary characteristics in polycrystalline materials can improve their properties. Many processing methods have been developed for grain boundary manipulation, including the use of intense radiation in certain applications. In this work, we used X-ray free electron laser pulses to irradiate single-crystalline bismuth selenide (Bi2Se3) and o

Zhao-Qian Yao, Daniele Binosi, Zhu-Fang Cu, Craig D. Roberts

A symmetry-preserving truncation of the quantum field equations describing hadron properties is used to deliver parameter-free predictions for all nucleon elastic electromagnetic form factors and their flavour separation to large values of momentum transfer, $Q^2$. The proton electric form factor, $G_E^p$, possesses a zero, whereas that of the neutron, $G_E^

Di Kevin Gao, Andrew Haverly, Sudip Mittal, Jiming Wu

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbu

Cameron Michie, Ivan Tomasic

We develop a theory of internal Hochschild cohomology in a ringed topos. We construct it via the internal Hochschild cochain complex, as well as through derived functor/topos cohomology theory, and discuss its relationship to the absolute Hochschild cohomology. By specialising to the topos of difference sets, we obtain a theory of internal difference Hochsch

Daniel C. Stumpp, Himanshu Akolkar, Alan D. George, Ryad Benosman

As the use of neuromorphic, event-based vision sensors expands, the need for compression of their output streams has increased. While their operational principle ensures event streams are spatially sparse, the high temporal resolution of the sensors can result in high data rates from the sensor depending on scene dynamics. For systems operating in communicat

Anthony I., Wasserman

This paper describes the historical background that led to the development of the innovative Software through Pictures multi-user development environment, and the principles for its integration with other software products to create a software engineering environment covering multiple tasks in the software development lifecycle.

Xiaoxia Liu, Robert R Butler, Prashnna K Gyawali, Frank M Longo

Alzheimer's disease (AD) is a pervasive neurodegenerative disorder that leads to memory and behavior impairment severe enough to interfere with daily life activities. Understanding this disease pathogenesis can drive the development of new targets and strategies to prevent and treat AD. Recent advances in high-throughput single-cell RNA sequencing technology

Robert C. Kirby, Scott P. MacLachlan

Irksome is a library based on the Unified Form Language (UFL) that enables automated generation of Runge--Kutta methods for time-stepping finite element spatial discretizations of partial differential equations (PDE). Allowing users to express semidiscrete forms of PDE, it generates UFL representations for the stage-coupled variational problems to be solved

Shreevathsa Chalathadka Subrahmanya, Christian Darsow-Fromm, Oliver Gerberding

Precise measurements of the frequency and phase of an electrical or optical signal play a key role in various branches of science and engineering. Tracking changing laser frequencies is especially demanding when the lasers themselves are noisy or if the frequencies rapidly change because they encode highly dynamic signals in, e.g., Doppler-ranging or dynamic

Zongxian Wang, Jie Song

As the most critical production factor in the era of the digital economy, data will have a significant impact on social production and development. Energy enterprises possess data that is interconnected with multiple industries, characterized by diverse needs, sensitivity, and long-term nature. The path to monetizing energy enterprises' data is challenging y

Yingcong Li, Yixiao Huang, M. Emrullah Ildiz, Ankit Singh Rawat

Transformer-based language models are trained on large datasets to predict the next token given an input sequence. Despite this simple training objective, they have led to revolutionary advances in natural language processing. Underlying this success is the self-attention mechanism. In this work, we ask: $\textit{What}$ $\textit{does}$ $\textit{a}$ $\textit{

Leandro Farias Maia, David Huckleberry Gutman

This work provides the first convergence analysis for the Randomized Block Coordinate Descent method for minimizing a function that is both H\"older smooth and block H\"older smooth. Our analysis applies to objective functions that are non-convex, convex, and strongly convex. For non-convex functions, we show that the expected gradient norm reduces at an $O\

Yi Ji, Simon Mak, Ryan Lekivetz, Joseph Morgan

Software testing is essential for the reliable development of complex software systems. A key step in software testing is fault localization, which uses test data to pinpoint failure-inducing combinations for further diagnosis. Existing fault localization methods have two key limitations: they (i) largely do not incorporate domain and/or structural knowledge

Giovanni Bordiga, Eder Medina, Sina Jafarzadeh, Cyrill Boesch

Harnessing the rich nonlinear dynamics of highly-deformable materials has the potential to unlock the next generation of functional smart materials and devices. However, unlocking such potential requires effective strategies to spatially design optimal material architectures for desired nonlinear dynamic responses such as guiding of nonlinear elastic waves,