March 2024 arXiv papers — page 100

Yudong Luo, Yangchen Pan, Han Wang, Philip Torr

Reinforcement learning algorithms utilizing policy gradients (PG) to optimize Conditional Value at Risk (CVaR) face significant challenges with sample inefficiency, hindering their practical applications. This inefficiency stems from two main facts: a focus on tail-end performance that overlooks many sampled trajectories, and the potential of gradient vanish

Boshi Wang, Cunhua Pan, Hong Ren, Zhiyuan Yu

In this paper, we investigate a double-active-reconfigurable intelligent surface (RIS)-aided downlink wireless communication system, where a multi-antenna base station (BS) serves multiple single-antenna users with both double reflection and single reflection links. Due to the signal amplification capability of active RISs, they can effectively mitigate the

Al Amin, Deo Chimba, Kamrul Hasan, Emmanuel Samson

Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways and railroads intersect, pose significant safety concerns for the U.S. Federal Railroad Administration (FRA). Despite the critical importance of addressing accidents and traffic delays at highway-railroad intersections, there is a notable dearth of research on practical solutions f

Hadi Zayyani, Mehdi Korki

In this paper, an algorithm for estimation and compensation of second-order nonlinearity in wireless sensor setwork (WSN) in distributed estimation framework is proposed. First, the effect of second-order nonlinearity on the performance of Diffusion Least Mean Square (DLMS) algorithm is investigated and an upper bound for $l^2$-norm of the error due to nonli

Zhendong Fang

In this paper, we concentrate on the connection between Boltzmann equation and stationary equations. To our knowledge, the stationary Navier-Stokes-Fourier system, the stationary Euler equations and the stationary Stokes equations are formally derived by moment estimate in the first time and extend the results of Bardos, Golse, and Levermore in J. Statist. P

Xiaoji Zheng, Lixiu Wu, Zhijie Yan, Yuanrong Tang

Motion prediction is among the most fundamental tasks in autonomous driving. Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks. In this paper,

Zhihao Liang, Qi Zhang, Wenbo Hu, Ying Feng

The 3D Gaussian Splatting (3DGS) gained its popularity recently by combining the advantages of both primitive-based and volumetric 3D representations, resulting in improved quality and efficiency for 3D scene rendering. However, 3DGS is not alias-free, and its rendering at varying resolutions could produce severe blurring or jaggies. This is because 3DGS tre

Zhaobing Fan, Shaolong Han, Seok-Jin Kang, Young Rock Kim

Using new combinatorics of Young walls, we give a new construction of the arbitrary level highest weight crystal $B(\lambda)$ for the quantum affine algebras of types $A^{(2)}_{2n}$, $D^{(2)}_{n+1}$, $A^{(2)}_{2n-1}$, $D^{(1)}_n$, $B^{(1)}_n$ and $C^{(1)}_n$. We show that the crystal consisting of reduced Young walls is isomorphic to the crystal $B(\lambda)$

Pikkin Lau, Lingfeng Wang, Wei Wei, Zhaoxi Liu

In this paper, a novel cyber-insurance model design is proposed based on system risk evaluation with smart technology applications. The cyber insurance policy for power systems is tailored via cyber risk modeling, reliability impact analysis, and insurance premium calculation. A stochastic Epidemic Network Model is developed to evaluate the cyber risk by pro

Ye Wang, Zili Yi, Rui Ma

Personalized text-to-image (T2I) models not only produce lifelike and varied visuals but also allow users to tailor the images to fit their personal taste. These personalization techniques can grasp the essence of a concept through a collection of images, or adjust a pre-trained text-to-image model with a specific image input for subject-driven or attribute-

Jie Ren, Yaxin Li, Shenglai Zeng, Han Xu

Recent advancements in text-to-image diffusion models have demonstrated their remarkable capability to generate high-quality images from textual prompts. However, increasing research indicates that these models memorize and replicate images from their training data, raising tremendous concerns about potential copyright infringement and privacy risks. In our

Mariana Macedo, Akrati Saxena

Social media and digital platforms allow us to express our opinions freely and easily to a vast number of people. In this study, we examine whether there are gender-based differences in how communication happens via Twitter in regard to soccer. Soccer is one of the most popular sports, and therefore, on social media, it engages a diverse audience regardless

Chenxin Li, Hengyu Liu, Yifan Liu, Brandon Y. Feng

Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for machine learning. Despite progress in generating 2D medical images, the complex domain of clinical video generation has largely remained untapped.This paper introduces \model, an innovative approach to generate medical videos that simulate

Zhen Zeng, William Watson, Nicole Cho, Saba Rahimi

The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requiring spontaneous or unpredictable tasks demanded by users. This paper introduces a novel approach, FlowMind, leveraging the capabilities of Large Language Models (LLMs) such as Gene

Mojtaba Rajabi, Taras Turiv, Bing-Xiang Li, Hend Baza

Electrophoresis is the motion of particles relative to a surrounding fluid driven by a uniform electric field. In conventional electrophoresis, the electrophoretic velocity grows linearly with the applied field. Nonlinear effects with a quadratic speed vs field dependence are gaining research interest since an alternating current field could drive them. Here

Ruhan Wang, Fahiz Baba-Yara, Fan Chen

Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate

Zhen Zeng, Rachneet Kaur, Suchetha Siddagangappa, Tucker Balch

Time series forecasting plays a crucial role in decision-making across various domains, but it presents significant challenges. Recent studies have explored image-driven approaches using computer vision models to address these challenges, often employing lineplots as the visual representation of time series data. In this paper, we propose a novel approach th

Jiawei Zhang

The advent of ChatGPT has sparked over a year of regulatory frenzy. However, few existing studies have rigorously questioned the assumption that, if left unregulated, AI chatbot's output would inflict tangible, severe real harm on human affairs. Most researchers have overlooked the critical possibility that the information market itself can effectively mitig

Victor F. dos Santos, Jonas Maziero

Recently, the entanglement dynamics of two harmonic oscillators initially prepared in a separable-coherent state was demonstrated to offer a pathway for prime number identification. This article presents a generalized approach and outlines a deterministic algorithm making possible the implementation of this theoretical concept on scalable fault-tolerant qubi

Ivan Romadanov, Yevgeny Raitses, Andrei Smolyakov

Laser-Induced Fluorescence (LIF) spectroscopy is an essential tool for probing ion and atom velocity distribution functions (VDFs) in complex plasmas. VDFs carry information about kinetic properties of the species critical for plasma characterization, yet their accurate interpretation is challenging due to multicomponent distributions, broadening effects, an

Franck Tonle, Henri Tonnang, Milliam Ndadji, Maurice Tchendji

Data mining, particularly the analysis of multivariate time series data, plays a crucial role in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal mis

Achraf Hsain, Hamza El Housni

This research explores the integration of chatbot technology powered by GPT-3.5 and GPT-4 Turbo into higher education to enhance internationalization and leverage digital transformation. It delves into the design, implementation, and application of Large Language Models (LLMs) for improving student engagement, information access, and support. Utilizing techn

Alejandro I. Maass, Francisco J. Vargas, Andres A. Peters, Juan I. Yuz

In this paper, we study (homogeneous) predecessor-following platoons in which the vehicle-to-vehicle (V2V) communications are affected by random packet losses. We model the overall platoon as a stochastic hybrid system and analyse its string stability via a small-gain approach. For nonlinear platoons, we illustrate how the different elements of the platoon h

Ji Li, Xiao Xiong, Fulin Yang

We provide full characterisation of the Schatten properties of $[M_b,T]$, the commutator of Calder\'{o}n--Zygmund singular integral $T$ with symbol $b$ $(M_bf(x):=b(x)f(x))$ on stratified Lie groups $\mathbb{G}$. We show that, when $p$ is larger than the homogeneous dimension $\mathbb{Q}$ of $\mathbb{G}$, the Schatten $\mathcal{L}_p$ norm of the commutator i

Adam K. Townsend, Eric E. Keaveny

Many biological fluids are composed of suspended polymers immersed in a viscous fluid. A prime example is mucus, where the polymers are also known to form a network. While the presence of this microstructure is linked with an overall non-Newtonian response of the fluid, swimming cells and microorganisms similar in size to the network pores and polymer filame

Mrinmay Sen, A. K. Qin, Krishna Mohan C

In federated learning (FL), the significant communication overhead due to the slow convergence speed of training the global model poses a great challenge. Specifically, a large number of communication rounds are required to achieve the convergence in FL. One potential solution is to employ the Newton-based optimization method for training, known for its quad

Pablo G. Barrientos, Joel Angel Cisneros

We study the class of transitive skew-products associated with iterated function systems of circle diffeomorphisms. We can approximate any transitive skew-product by maps in this class that have a robustly zero Lyapunov exponent. In particular, we prove the existence of non-hyperbolic ergodic measures for an open and dense subset of transitive skew-products.

Felipe I. Flores

Let ${\sf G}$ be a locally compact group, $\mathscr C\overset{q}{\to}{\sf G}$ a Fell bundle and $\mathfrak B=L^1({\sf G}\,\vert\,\mathscr C)$ the algebra of integrable cross-sections associated to the bundle. We give conditions that guarantee the automatic continuity of an intertwining operator $\theta:\mathcal X_1\to\mathcal X_2$, where $\mathcal X_1$ is a

Rui Dong

Typological databases in linguistics are usually categorical-valued. As a result, it is difficult to have a clear visualization of the data. In this paper, we describe a workflow to analyze the topological shapes of South American languages by applying multiple correspondence analysis technique and topological data analysis methods.

Guangyu Cui, Sung Ha Kang

We propose Texture Edge detection using Patch consensus (TEP) which is a training-free method to detect the boundary of texture. We propose a new simple way to identify the texture edge location, using the consensus of segmented local patch information. While on the boundary, even using local patch information, the distinction between textures are typically

Dennis Fedorishin, Livio Forte, Philip Schneider, Srirangaraj Setlur

Sound event detection (SED) is an active area of audio research that aims to detect the temporal occurrence of sounds. In this paper, we apply SED to engine fault detection by introducing a multimodal SED framework that detects fine-grained engine faults of automobile engines using audio and accelerometer-recorded vibration. We first introduce the problem of

Peter Luvton, Alfredo Castillejos, Jim Zhao, Christina Chajo

Image denoising stands as a critical challenge in image processing and computer vision, aiming to restore the original image from noise-affected versions caused by various intrinsic and extrinsic factors. This process is essential for applications that rely on the high quality and clarity of visual information, such as image restoration, visual tracking, and

Che-Yung Shen, Jingxi Li, Tianyi Gan, Yuhang Li

Quantitative phase imaging (QPI) is a label-free technique that provides optical path length information for transparent specimens, finding utility in biology, materials science, and engineering. Here, we present quantitative phase imaging of a 3D stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. Utilizing multiple spa

Mithun Goutham, Stephanie Stockar

This paper presents a novel fleet management strategy for battery-powered robot fleets tasked with intra-factory logistics in an autonomous manufacturing facility. In this environment, repetitive material handling operations are subject to real-world uncertainties such as blocked passages, and equipment or robot malfunctions. In such cases, centralized appro

Rory Conlin, Patrick Kim, Daniel W. Dudt, Dario Panici

In this work we consider the problem of optimizing a stellarator subject to hard constraints on the design variables and physics properties of the equilibrium. We survey current numerical methods for handling these constraints, and summarize a number of methods from the wider optimization community that have not been used extensively for stellarator optimiza

Sadaf Khademi, Zohreh Hajiakhondi, Golnaz Vaseghi, Nizal Sarrafzadegan

Familial Hypercholesterolemia (FH) is a genetic disorder characterized by elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated genes. Early-stage and accurate categorization of FH is of significance allowing for timely interventions to mitigate the risk of life-threatening conditions. Conventional diagnosis approach, however, is com

Anand Chavan, Włodzimierz Zwonek

We show the (anti)holomorphicity of smooth Kobayashi isometries of the diamond, the domain defined as $\triangle:=\{z\in\mathbb C^2:|z_1|+|z_2|<1\}$. Additionally, we discuss the problem of uniqueness of real geodesics, left inverses and strict convexity of indicatrices.

Charles De Clercq, Nikita Karpenko, Anne Quéguiner-Mathieu

Given a prime number $p$, we perform the study of Chow motives and motivic decompositions, with coefficients in $\mathbb{Z}/p\mathbb{Z}$, of projective homogeneous varieties for $p'$-inner $p$-consistent reductive algebraic groups. Assorted with the known case of $p$-inner reductive groups, our results cover all absolutely simple groups of type not $^3\!D_4$

B. Dotson, D. Sanchez Valencia, C. Millwater, P. Easter

Shear strength and cohesion of granular materials are important geotechnical properties that play a crucial role in the stability and behavior of lunar and Martian regolith, as well as their terrestrial analog materials. To characterize and predict shear strength and cohesion for future space missions, it is also important to understand the effects of partic

David B. McMillon

Drawing on work spanning economics, public health, education, sociology, and law, I formalize theoretically what makes systemic discrimination "systemic." Injustices do not occur in isolation, but within a complex system of interdependent factors; and their effects may amplify as a consequence. I develop a taxonomy of these amplification mechanisms, connecti

Jiachen Li, Weixi Feng, Wenhu Chen, William Yang Wang

Latent Consistency Distillation (LCD) has emerged as a promising paradigm for efficient text-to-image synthesis. By distilling a latent consistency model (LCM) from a pre-trained teacher latent diffusion model (LDM), LCD facilitates the generation of high-fidelity images within merely 2 to 4 inference steps. However, the LCM's efficient inference is obtained

Abu Zahid Bin Aziz, Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Y. Elhabian

Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despite their success, transformer-based models require substantial resources for training, including data, memory,

Jonathan Dunn, Benjamin Adams, Harish Tayyar Madabushi

This paper measures the skew in how well two families of LLMs represent diverse geographic populations. A spatial probing task is used with geo-referenced corpora to measure the degree to which pre-trained language models from the OPT and BLOOM series represent diverse populations around the world. Results show that these models perform much better for some

Ziqi Lu, Jianbo Ye, Xiaohan Fei, Xiaolong Li

Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene. If objects are reconfigured, it is difficult to update the NeRF to reflect the new state of the scene without time-consuming data re-capturing and NeRF re-training. To address this limitation, we develop the fi

Victoria Desyatka, Evgeny Sevost'yanov

The manuscript is devoted to the boundary behavior of mappings with bounded and finite distortion, which has been actively studied recently. We consider mappings of domains of the Euclidean space that satisfy the inverse Poletsky inequality with an integrable majorant, are open, and discrete. Assume that the image of the boundary of the original domain is fi

Martino Banchio, Aranyak Mehta, Andres Perlroth

We study the optimal placement of advertisements for interactive platforms like conversational AI assistants. Importantly, conversations add a feature absent in canonical search markets -- time. The evolution of a conversation is informative about ad qualities, thus a platform could delay ad delivery to improve selection. However, delay endogenously shapes t

Minkyu Choi, Harsh Goel, Mohammad Omama, Yunhao Yang

The unprecedented surge in video data production in recent years necessitates efficient tools to extract meaningful frames from videos for downstream tasks. Long-term temporal reasoning is a key desideratum for frame retrieval systems. While state-of-the-art foundation models, like VideoLLaMA and ViCLIP, are proficient in short-term semantic understanding, t

Joel Luís Carbonera

The increasing digitalization in industry and society leads to a growing abundance of data available to be processed and exploited. However, the high volume of data requires considerable computational resources for applying machine learning approaches. Prototype selection techniques have been applied to reduce the requirements of computational resources that

Nick Pant, Kyle Bushick, Andrew McAllister, Woncheol Lee

The quantum efficiency of AlGaN ultraviolet light-emitting diodes (LEDs) declines (droops) at increasing operating powers due to Auger-Meitner recombination (AMR). Using first-principles density-functional theory, we show that indirect AMR mediated by electron-phonon coupling and alloy disorder can induce bulk $C$ coefficients as large as $\sim10^{-31}$ cm$^

Vincent A. Cicirello

The BTPE algorithm (Binomial, Triangle, Parallelogram, Exponential) of Kachitvichyanukul and Schmeiser is one of the faster and more widely utilized algorithms for generating binomial random variates. Cicirello's open source Java library, $\rho\mu$, includes an implementation of BTPE as well as a variety of other random number related utilities. In this repo

K. Le Bourdonnec, L. Valeri, C. Proust-Lima

Mediation analysis aims to decipher the underlying causal mechanisms between an exposure, an outcome, and intermediate variables called mediators. Initially developed for fixed-time mediator and outcome, it has been extended to the framework of longitudinal data by discretizing the assessment times of mediator and outcome. Yet, processes in play in longitudi

Yunxiang Zhang, Nan Wu, Connor Z. Lin, Gordon Wetzstein

Diffusion models offer unprecedented image generation power given just a text prompt. While emerging approaches for controlling diffusion models have enabled users to specify the desired spatial layouts of the generated content, they cannot predict or control where viewers will pay more attention due to the complexity of human vision. Recognizing the signifi

Jeff Dominitz, Charles F. Manski

We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research on prediction. SDT provides a formal frequentist framework for performing comprehensive OOS evaluation across all possible (1) training samples,

Alireza Rowhanimanesh

In systems biology, attractor landscape analysis of gene regulatory networks is recognized as a powerful computational tool for studying various cellular states from proliferation and differentiation to senescence and apoptosis. Therefore, accurate identification of attractors plays a critical role in determination of the cell fates. On the other hand, in a

Onofrio Mazzarisi, Matteo Smerlak

Robert May famously used random matrix theory to predict that large, complex systems cannot admit stable fixed points. However, this general conclusion is not always supported by empirical observation: from cells to biomes, biological systems are large, complex, and often stable. In this paper, we revisit May's argument in light of recent developments in bot

Jiashun Jin, Zheng Tracy Ke, Gabriel Moryoussef, Jiajun Tang

Given a $K$-vertex simplex in a $d$-dimensional space, suppose we measure $n$ points on the simplex with noise (hence, some of the observed points fall outside the simplex). Vertex hunting is the problem of estimating the $K$ vertices of the simplex. A popular vertex hunting algorithm is successive projection algorithm (SPA). However, SPA is observed to perf

Elie Rouphael, Manas Mejari, Mihaly Petreczky, Lotfi Belkoura

In this paper, we study a class of stochastic Generalized Linear Switched System (GLSS), which includes subclasses of jump-Markov, piecewide-linear and Linear Parameter-Varying (LPV) systems. We prove that the output of such systems can be decomposed into deterministic and stochastic components. Using this decomposition, we show existence of state-space repr

David W. Hogg, Andrew R. Casey

When there are many observations of an astronomical source - many images with different dithers, or many spectra taken at different barycentric velocities - it is standard practice to shift and stack the data, to (for example) make a high signal-to-noise average image or mean spectrum. Bound-saturating measurements are made by manipulating a likelihood funct

Wolfgang Seiringer, Klaus Altendorfer, Thomas Felberbauer, Balwin Bokor

In many supply chains, the current efforts at digitalization have led to improved information exchanges between manufacturers and their customers. Specifically, demand forecasts are often provided by the customers and regularly updated as the related customer information improves. In this paper, we investigate the influence of forecast updates on the product

Fahim Faisal, Orevaoghene Ahia, Aarohi Srivastava, Kabir Ahuja

Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is language variation in the form of non-standard dialects or language varieties (hereafter, varieties). Most NLP benchmarks are limited to standard language varieties. To fill this gap, w

Janmesh Ukey, Tushar Kataria, Shireen Y. Elhabian

Statistical Shape Modeling (SSM) effectively analyzes anatomical variations within populations but is limited by the need for manual localization and segmentation, which relies on scarce medical expertise. Recent advances in deep learning have provided a promising approach that automatically generates statistical representations (as point distribution models

Robert Cass, Thibaud van den Hove, Jakob Scholbach

We construct a refinement of Gaitsgory's central functor for integral motivic sheaves, and show it preserves stratified Tate motives. Towards this end, we develop a reformulation of unipotent motivic nearby cycles, which also works over higher-dimensional bases. We moreover introduce Wakimoto motives and use them to show that our motivic central functor is t

Riccardo Piccoli, Marco Bardellini, Stavroula Vovla, Linda Oberti

Structured light has gained significant attention in recent years, especially in the generation and application of vector beams. These beams, characterized by a spatially varying polarization state, are a powerful tool to enhance our capacity to control light-matter interactions. In this study, we demonstrate the synthesis of extreme-ultraviolet (EUV) vector

Antonia Freibert, David Mendive-Tapia, Oriol Vendrell, Nils Huse

Recent advancements in ultrashort and intense X-ray sources have enabled the utilisation of resonant inelastic X-ray scattering (RIXS) as a probing technique for monitoring photoinduced dynamics in molecular systems. To account for dynamic phenomena like non-adiabatic transitions across the entire electronic state manifold, a time-dependent framework is cruc

Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang

Graph neural networks (GNNs) have achieved remarkable success across a wide range of applications, such as recommendation, drug discovery, and question answering. Behind the success of GNNs lies the backpropagation (BP) algorithm, which is the de facto standard for training deep neural networks (NNs). However, despite its effectiveness, BP imposes several co

Juraj Bodik

The potential outcomes framework serves as a fundamental tool for quantifying causal effects. The average dose-response function (also called the effect curve), denoted as (\mu(t)), is typically of interest when dealing with a continuous treatment variable (exposure). The focus of this work is to determine the impact of an extreme level of treatment, potenti

Torben Sunkel, Lukas Hupe, Philip Bittihn

Growth drives cellular dynamics in dense aggregates including bacterial colonies, developing tissues, and tumors. We investigate the underlying physical principles emerging from the interplay of growth, steric repulsion, and motility in a minimal agent-based model of exponentially growing, three-dimensional spheroids. Our results reveal a motility-induced mi

Alexander H. Berger, Nico Stucki, Laurin Lux, Vincent Buergin

Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting. Recently, significant methodological advancements have brought well-founded concepts from algebraic topology to binary segmentation. However, these approaches have been underexpl

Ishaan Mahajan, Huzaifa Unjhawala, Harry Zhang, Zhenhao Zhou

Simulation can and should play a critical role in the development and testing of algorithms for autonomous agents. What might reduce its impact is the ``sim2real'' gap -- the algorithm response differs between operation in simulated versus real-world environments. This paper introduces an approach to evaluate this gap, focusing on the accuracy of sensor simu

Gabriele Lami

In this article we will explore the concept of speedrunning as a representation of a simplified version of quantum mechanics within a classical simulation. This analogy can be seen as a simplified approach to understanding the broader idea that quantum mechanics may emerge from classical mechanics simulations due to the limitations of the simulation. The con

Alina G. Goutor, Sergey V. Tikhonov

In this paper, we study properties of polynomials over division rings. Moreover, we present formulas for finding roots of some polynomials

Hamid Haghmoradi, Hauke Fischer, Alessandro Bertolini, Ivica Galić

During the past few decades, abundant evidence for physics beyond the two standard models of particle physics and cosmology was found. Yet, we are tapping into the dark regarding our understanding of the dark sector. For more than a century, open problems related to the nature of the vacuum remain unresolved. Besides the traditional high-energy frontier and

Yash Bhalgat, Iro Laina, João F. Henriques, Andrew Zisserman

Understanding complex scenes at multiple levels of abstraction remains a formidable challenge in computer vision. To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properti

Chinmay Vilas Samak, Tanmay Vilas Samak, Venkat Narayan Krovi

Multi-agent reinforcement learning (MARL) for cyber-physical vehicle systems usually requires a significantly long training time due to their inherent complexity. Furthermore, deploying the trained policies in the real world demands a feature-rich environment along with multiple physical embodied agents, which may not be feasible due to monetary, physical, e

Tingting Tang, Yue Niu, Salman Avestimehr, Murali Annavaram

Graph neural networks (GNNs) play a key role in learning representations from graph-structured data and are demonstrated to be useful in many applications. However, the GNN training pipeline has been shown to be vulnerable to node feature leakage and edge extraction attacks. This paper investigates a scenario where an attacker aims to recover private edge in

Fanjun Bu, Wendy Ju

This paper introduces our dataset featuring human-robot interactions (HRI) in urban public environments. This dataset is rich with social signals that we believe can be modeled to help understand naturalistic human-robot interaction. Our dataset currently comprises approximately 15 hours of video footage recorded from the robots' perspectives, within which w

Ryota Kobayashi, Shigeru Shinomoto

This article presents a mini-review about the progress in inferring monosynaptic connections from spike trains of multiple neurons over the past twenty years. First, we explain a variety of meanings of ``neuronal connectivity'' in different research areas of neuroscience, such as structural connectivity, monosynaptic connectivity, and functional connectivity

Konstantin Vorob'ev

We consider extended $1$-perfect codes in Hamming graphs $H(n,q)$. Such nontrivial codes are known only when $n=2^k$, $k\geq 1$, $q=2$, or $n=q+2$, $q=2^m$, $m\geq 1$. Recently, Bespalov proved nonexistence of extended $1$-perfect codes for $q=3$, $4$, $n>q+2$. In this work, we characterize all positive integers $n$, $r$ and prime $p$, for which there exist

Guangyao Shi, Shipeng Liu, Ellen Novoseller, Feifei Qian

We consider a new type of inverse combinatorial optimization, Inverse Submodular Maximization (ISM), for its application in human-in-the-loop multi-robot information gathering. Forward combinatorial optimization - solving a combinatorial problem given the reward (cost)-related parameters - is widely used in multi-robot coordination. In the standard pipeline,

Sachiraj Mishra, A Rajmohan Dora, Tusaradri Mohapatra, Colin Benjamin

Quantum noise is a fundamental probe of quantum transport phenomena, offering insights into current correlations and wave-particle duality. A particularly intriguing form of such noise, $\Delta_T$ noise, emerges under a finite temperature difference in the absence of charge current at zero voltage bias. In this work, we investigate $\Delta_T$ noise in mesosc

B. A. McCullian, V. Sharma, H. Y. Chen, J. C. Crossman

We use a bulk acoustic wave resonator to demonstrate coherent control of the excited orbital states in a diamond nitrogen-vacancy (NV) center at cryogenic temperature. Coherent quantum control is an essential tool for understanding and mitigating decoherence. Moreover, characterizing and controlling orbital states is a central challenge for quantum networkin

Bhimsen Shivamoggi, Daniel Reinhart

In this paper we give an exact invariant for a relativistic linear harmonic oscillator with time-dependent frequency. This is accomplished, following Eliezer and Gray \cite{EliezerGray}, for the non-relativistic case, by associating a relativistic plane isotropic harmonic oscillator with this problem. This exact invariant reflects the conservation of angular

Li-Yuan Tsao, Yi-Chen Lo, Chia-Che Chang, Hao-Wei Chen

Flow-based super-resolution (SR) models have demonstrated astonishing capabilities in generating high-quality images. However, these methods encounter several challenges during image generation, such as grid artifacts, exploding inverses, and suboptimal results due to a fixed sampling temperature. To overcome these issues, this work introduces a conditional

Cheng Peng, Anton Malandii, Stan Uryasev

The Fundamental Risk Quadrangle (FRQ) is a unified framework linking risk management, statistical estimation, and optimization. Distributionally robust optimization (DRO) based on $\varphi$-divergence minimizes the maximal expected loss, where the maximum is over a $\varphi$-divergence ambiguity set. This paper introduces the \emph{extended} $\varphi$-diverg

Jose Antonio Rebollo, Rafael Vazquez, Ignacio Alvarado, Daniel Limon

A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications. The target's three-dimensional non-periodic rotational dynamics as well as other state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eli

Alexander Meiburg

The zero-error capacity of a channel (or Shannon capacity of a graph) quantifies how much information can be transmitted with no risk of error. In contrast to the Shannon capacity of a channel, the zero-error capacity has not even been shown to be computable: we have no convergent upper bounds. In this work, we present a new quantity, the zero-error {\em uni

Fan Chen, Shahzeen Attari, Gayle Buck, Lei Jiang

To improve privacy and ensure quality-of-service (QoS), deep learning (DL) models are increasingly deployed on Internet of Things (IoT) devices for data processing, significantly increasing the carbon footprint associated with DL on IoT, covering both operational and embodied aspects. Existing operational energy predictors often overlook quantized DL models

Zhe Kong, Yong Zhang, Tianyu Yang, Tao Wang

Personalization is an important topic in text-to-image generation, especially the challenging multi-concept personalization. Current multi-concept methods are struggling with identity preservation, occlusion, and the harmony between foreground and background. In this work, we propose OMG, an occlusion-friendly personalized generation framework designed to se

Panteleimon Kruglov, Charles Shaw

This study explores the relationship between R&D intensity, as a measure of innovation, and financial performance among S&P 500 companies over 100 quarters from 1998 to 2023, including multiple crisis periods. It challenges the conventional wisdom that larger companies are more prone to innovate, using a comprehensive dataset across various industries. The a

Vanessa Wirth, Johanna Bräunig, Danti Khouri, Florian Gutsche

Despite an emerging interest in MIMO radar, the utilization of its complementary strengths in combination with optical depth sensors has so far been limited to far-field applications, due to the challenges that arise from mutual sensor calibration in the near field. In fact, most related approaches in the autonomous industry propose target-based calibration

Zhongqi Yang, Yuning Wang, Ken S. Yamashita, Maryam Sabah

Emotional states, as indicators of affect, are pivotal to overall health, making their accurate prediction before onset crucial. Current studies are primarily centered on immediate short-term affect detection using data from wearable and mobile devices. These studies typically focus on objective sensory measures, often neglecting other forms of self-reported

Guanpu Chen, Gehui Xu, Fengxiang He, Dacheng Tao

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in

Shuaishuai Guo, Jianheng Guo, Jie Su, Dongdong Yan

Hot Jupiters, orbiting their host stars at extremely close distances, undergo tidal evolution, with some being engulfed by their stars due to angular momentum exchanges induced by tidal forces. However, achieving double synchronization can prolong their survival. Using the MESA stellar evolution code, combined with the magnetic braking model of Matt et al. (

Hang Yin, Liyao Xiang, Dong Ding, Yuheng He

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), and this type of entity remains unlabeled. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and then entity alignment. Lambda features a GNN-based encod

Georgios Koukis, Sotiris Skaperas, Ioanna Angeliki Kapetanidou, Vassilis Tsaoussidis

The CODECO Experimentation Framework is an open-source solution designed for the rapid experimentation of Kubernetes-based edge cloud deployments. It adopts a microservice-based architecture and introduces innovative abstractions for (i) the holistic deployment of Kubernetes clusters and associated applications, starting from the VM allocation level; (ii) de

Amritha Premkumar, Prajit T Rajendran, Vignesh V Menon, Adam Wieckowski

Traditional per-title encoding schemes aim to optimize encoding resolutions to deliver the highest perceptual quality for each representation. XPSNR is observed to correlate better with the subjective quality of VVC-coded bitstreams. Towards this realization, we predict the average XPSNR of VVC-coded bitstreams using spatiotemporal complexity features of the

Olivia Mallick, Muktish Acharyya

Three dimensional anisotropic XY ferromagnet driven by elliptically polarized propagating magnetic field wave has been extensively investigated by Monte Carlo simulation with Metropolis single spin flip algorithm. Both the effects of the bilinear exchange type and the single site anisotropies are thoroughly investigated. The time average magnetisation (over

Pablo Echenique-Robba

In the framework of classical statistical mechanics and assuming the electronic ground-state Born-Oppenheimer approximation, we show in this work that a dependence of the equilibrium bond-length on the available volume is to be expected for a dilute gas of diatomic molecules. In a nutshell, this dependence is controlled by the relation between the potential

Chao Li, Yong-Chul Chae

We developed a 6D multi-particle tracking program CETASim in C++ programming language to simulate intensity-dependent effects in electron storage rings. The program can simulate the beam collective effects due to short-range/long-range wakefields for single/coupled-bunch instability studies. It also features to simulate interactions among charged ions and th

Oleg D. Lavrentovich

Elastic constants of splay K_11, twist K_22, and bend K_33 of nematic liquid crystals are often assumed to be equal to each other in order to simplify the theoretical description of complex director fields. Here we present examples of how the disparity of K_11 and K_33 produces effects that cannot be described in a one-constant approximation. In a lyotropic