March 2024 arXiv papers — page 120

Zichao Zhang, Melda Yuksel, Halim Yanikomeroglu, Benjamin K. Ng

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output (MIMO) faster-th

Gabriel Gómez, Patrick Valageas

An exciting possibility to constrain dark matter (DM) scenarios is to search for their gravitational imprints on Black Hole (BH) observations. In this paper, we investigate the impact of self-interacting scalar field DM on the shadow radius of a Schwarzschild BH. We implement a self-consistent formulation, paying attention to the enhancement of the DM densit

Richard Tong, Haoyang Li, Joleen Liang, Qingsong Wen

The adoption of Artificial Intelligence in Education (AIED) holds the promise of revolutionizing educational practices by offering personalized learning experiences, automating administrative and pedagogical tasks, and reducing the cost of content creation. However, the lack of standardized practices in the development and deployment of AIED solutions has le

Geovana Franca dos Santos, Eugenio B. Castelan, Walter Lucia

This paper proposes an output feedback controller capable of ensuring steady-state offset-free tracking for ramp and sinusoidal reference signals while ensuring local stability and state and input constraints fulfillment. The proposed solution is derived by jointly exploiting the internal model principle, polyhedral robust positively invariant arguments, and

Masato Minamitsuji, Kei-ichi Maeda

We investigate thermodynamics of static and spherically symmetric black holes (BHs) in generalized Proca (GP) theories by applying the Iyer-Wald prescription. BH solutions in GP theories are divided into the two classes. The first class corresponds to the solutions obtained by the direct promotion of the BH solutions in shift-symmetric Horndeski theories, wh

Shijun Chu, Ashley P. Willis, Elena Marensi

It is well known that buoyancy suppresses, and can even laminarise turbulence in upward heated pipe flow. Heat transfer seriously deteriorates in this case. Through a new DNS model, we confirm that the deteriorated heat transfer within convective turbulence is related to a lack of near-wall rolls, which leads to a weak mixing between the flow near the wall a

Carlo Grigioni, Franca Corradini, Alessandro Antonucci, Jérôme Guzzi

Safe road-crossing by self-driving vehicles is a crucial problem to address in smart-cities. In this paper, we introduce a multi-sensor fusion approach to support road-crossing decisions in a system composed by an autonomous wheelchair and a flying drone featuring a robust sensory system made of diverse and redundant components. To that aim, we designed an a

Aditya Anand, Euiwoong Lee, Jason Li, Thatchaphol Saranurak

We study polynomial-time approximation algorithms for (edge/vertex) Sparsest Cut and Small Set Expansion in terms of $k$, the number of edges or vertices cut in the optimal solution. Our main results are $\mathcal{O}(\text{polylog}\, k)$-approximation algorithms for various versions in this setting. Our techniques involve an extension of the notion of sample

The H1 collaboration, V. Andreev, M. Arratia, A. Baghdasaryan

The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged boson defines the coordinate system such that the struck quark is back-scattered along the virtual photon momentum direction. In Quantum Chromodynamics (QCD), higher order perturbati

Ioannis V. Manousiouthakis, Vasilios I. Manousiouthakis

In this work, a theorem is first proved which presents a game theoretic formulation of a necessary and sufficient sustainizability over a set condition for a general system described by ordinary differential equations (ODEs). Then, two additional theorems are proved for the n-species Gause-Lotka-Volterra (GLV) population model, establishing necessary and suf

Olivia Weng, Alexander Redding, Nhan Tran, Javier Mauricio Duarte

With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time to go off-chip and retrieve weights. Even more so, off-chip memory such as DRAM does not have the bandwidth r

Qiming Cui, Duygu Tosun, Pratik Mukherjee, Reza Abbasi-Asl

Supervised deep learning techniques can be used to generate synthetic 7T MRIs from 3T MRI inputs. This image enhancement process leverages the advantages of ultra-high-field MRI to improve the signal-to-noise and contrast-to-noise ratios of 3T acquisitions. In this paper, we introduce multiple novel 7T synthesization algorithms based on custom-designed varia

Yao Fu, Dong-Ki Kim, Jaekyeom Kim, Sungryull Sohn

Recent advances in large language models (LLMs) have empowered AI agents capable of performing various sequential decision-making tasks. However, effectively guiding LLMs to perform well in unfamiliar domains like web navigation, where they lack sufficient knowledge, has proven to be difficult with the demonstration-based in-context learning paradigm. In thi

Daisuke A. Takahashi

The basis of spinors in three-dimensional Euclidean space is expressed by differential forms. Its expression is found from the spectral decomposition of the modified Hamiltonian describing Weyl semimetals where the wavenumber parameters are replaced by the differential forms. The generalization of the definitions of differential forms including not only frac

Oscar Chacón-Rivera, Pablo Pérez-Lantero

Huemer et al. (Discrete Math, 2019) proved that for any two finite point sets $R$ and $B$ in the plane with $|R| = |B|$, the perfect matching that matches points of $R$ with points of $B$, and maximizes the total squared Euclidean distance of the matched pairs, has the property that all the disks induced by the matching have a nonempty common intersection. A

Chien-Yeah Seng

We introduce a useful framework for high-precision studies of the neutron beta decay by merging the current algebra description and the fixed-order effective field theory calculation of the electroweak radiative corrections to the neutron axial form factor. We discuss the advantages of this hybrid method and show that it only requires a minimal amount of lat

Jiuyi Zhu

We study the spectral inequalities of Schr\"odinger operator in the whole space for different potentials, which can be power growth or continuously vanishing at infinity. The spectral inequalities quantitatively depend on the density of the sensor sets with positive measure, growth rate of the potentials and spectrum (or eigenvalues). One important component

Ashish Sinha, Ghassan Hamarneh

Anatomical trees play a central role in clinical diagnosis and treatment planning. However, accurately representing anatomical trees is challenging due to their varying and complex topology and geometry. Traditional methods for representing tree structures, captured using medical imaging, while invaluable for visualizing vascular and bronchial networks, exhi

Christian A. Schiller

The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for Artificial Intelligence, introducing Large Language Models (LLMs) to the mainstream and setting new records in user adoption. LLMs, particularly ChatGPT, trained on extensive internet data, demonstrate remarkable conversational capabilities across various domains, suggesting a signi

Jorge-Humberto Urrea-Quintero, Michele Marino, Thomas Wick, Udo Nackenhorst

This work presents a comparative review and classification between some well-known thermodynamically consistent models of hydrogel behavior in a large deformation setting, specifically focusing on solvent absorption/desorption and its impact on mechanical deformation and network swelling. The proposed discussion addresses formulation aspects, general mathema

Corrine F Elliott, James PC Duncan, Tiffany M Tang, Merle Behr

Simulations play a crucial role in the modern scientific process. Yet despite (or due to) this ubiquity, the Data Science community shares neither a comprehensive definition for a "high-quality" study nor a consolidated guide to designing one. Inspired by the Predictability-Computability-Stability (PCS) framework for 'veridical' Data Science, we propose six

Minghan Li, Eric Gaussier

Recent studies have demonstrated that the ability of dense retrieval models to generalize to target domains with different distributions is limited, which contrasts with the results obtained with interaction-based models. Prior attempts to mitigate this challenge involved leveraging adversarial learning and query generation approaches, but both approaches ne

Alec G. Moore, Tiffany D. Do, Nayan N. Chawla, Antonia Jimenez Iriarte

In recent years, numerous researchers have begun investigating how virtual reality (VR) tracking and interaction data can be used for a variety of machine learning purposes, including user identification, predicting cybersickness, and estimating learning gains. One constraint for this research area is the dearth of open datasets. In this paper, we present a

Gopal Agarwal, Jorge-Humberto Urrea-Quintero, Henning Wessels, Thomas Wick

This study explores reduced-order modeling for analyzing the time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using

Qifeng Zhou, Wenliang Zhong, Yuzhi Guo, Michael Xiao

In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions. However, aligning WSIs with diagnostic captions presents a significant challenge. This difficulty arises from two main factors: 1) Gigapixel WSIs are unsuitable for direct input into deep learning mod

George Nehma, Madhur Tiwari, Manasvi Lingam

The study of the Two-Body and Circular Restricted Three-Body Problems in the field of aerospace engineering and sciences is deeply important because they help describe the motion of both celestial and artificial satellites. With the growing demand for satellites and satellite formation flying, fast and efficient control of these systems is becoming ever more

Nicholas Filla, Beikang Gu, Jixin Hou, Kenan Song

The biomechanical properties of blood clots, which are dictated by their compositions and micro-structures, play a critical role in determining their fates, occlusion, persistency, or embolization in the human circulatory system. While numerous constitutive models have emerged to describe the biomechanics of blood clots, the majority of these models have pri

Misha Haywood, Sergey Khoperskov, Valeria Cerqui, Paola Di Matteo

We derive the metallicity profile of the Milky Way low-$\alpha$ disc population from 2 to 20 kpc from the Galactic centre in 1 Gyr age bins using the astroNN catalogue, and show that it is highly structured, with a plateau between 4 and 7 kpc and a break at 10-12 kpc. We argue that these features result from the two main bar resonances, the corotation and th

Eduardo Bedin, Junior Silva Souza, Gabriel Toshio Hirokawa Higa, Alexandre Pereira

The aim of this paper is to evaluate the use of D-CNN (Deep Convolutional Neural Networks) algorithms to classify pig body conditions in normal or not normal conditions, with a focus on characteristics that are observed in sanitary monitoring, and were used six different algorithms to do this task. The study focused on five pig characteristics, being these c

Paula Moraga Baez, Joel H. Kastner, Jesse Bublitz, Javier Alcolea

We present early results from our program of ALMA Band 6 (1.3mm) molecular line mapping of a sample of nearby, well-studied examples of high-excitation, bipolar/pinched-waist and molecule-rich planetary nebulae (Hubble 5 and NGC 2440, 2818, 2899, 6302, and 6445). We have mapped these planetary nebulae (PNe) in isotopologues of CO as well as various molecular

Leonardo F. Cavenaghi, Lino Grama

Since the advent of new pairwise non-diffeomorphic structures on smooth manifolds, it has been questioned whether two topologically identical manifolds could admit different geometries. Not surprisingly, physicists have wondered whether a smooth structure assumption different from some classical known models could produce different physical meanings. In this

Yoshitaka Inoue

Single-cell RNA sequencing (scRNA-seq) provides high-resolution measurements of cellular heterogeneity, but sparsity and technical zeros can obscure biological structure and complicate downstream analysis. We present scVGAE, a variational graph autoencoder for scRNA-seq imputation that integrates cell-cell graph propagation, a zero-inflated negative binomial

Roberto Guglielmi, Zhuqing Li

In this paper, we develop several necessary conditions of turnpike property for generalizaid linear-quadratic (LQ) optimal control problem in infinite dimensional setting. The term 'generalized' here means that both quadratic and linear terms are considered in the running cost. The turnpike property reflects the fact that over a sufficiently large time horiz

Mick Wright, Justin Janquart, Nathan K. Johnson-McDaniel

As the gravitational wave detector network is upgraded and the sensitivity of the detectors improves, novel scientific avenues open for exploration. For example, tests of general relativity will become more accurate as smaller deviations can be probed. Additionally, the detection of lensed gravitational waves becomes more likely. However, these new avenues c

Dhruv Toshniwal, Arpit Patil, Nancy Vachhani

In the competitive realm of sports, optimal performance necessitates rigorous management of nutrition and physical conditioning. Specifically, in badminton, the agility and precision required make it an ideal candidate for motion analysis through video analytics. This study leverages advanced neural network methodologies to dissect video footage of badminton

Rui Liu, Anish Gupta, Erfaun Noorani, Pratap Tokekar

Reinforcement Learning (RL) has shown exceptional performance across various applications, enabling autonomous agents to learn optimal policies through interaction with their environments. However, traditional RL frameworks often face challenges in terms of iteration efficiency and safety. Risk-sensitive policy gradient methods, which incorporate both expect

Michela Mancini, John A. Christian

Finding the intersection of two conics is a commonly occurring problem. For example, it occurs when identifying patterns of craters on the lunar surface, detecting the orientation of a face from a single image, or estimating the attitude of a camera from 2D-to-3D point correspondences. Regardless of the application, the study of this classical problem presen

Gianluca Aglieri Rinella, Giacomo Alocco, Matias Antonelli, Roberto Baccomi

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, p

Hung D. Nguyen

We consider the long time statistics of a one-dimensional stochastic Ginzburg-Landau equation with cubic nonlinearity while being subjected to random perturbations via an additive Gaussian noise. Under the assumption that sufficiently many directions of the phase space are stochastically forced, we find that the dynamics is attractive toward the unique invar

Ziyuan Lin, Deanna Needell

By removing irrelevant and redundant features, feature selection aims to find a good representation of the original features. With the prevalence of unlabeled data, unsupervised feature selection has been proven effective in alleviating the so-called curse of dimensionality. Most existing matrix factorization-based unsupervised feature selection methods are

Michael Desmond, Michelle Brachman

Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts to interact with and leverage LLMs. However, for complex tasks and tasks with specific requirements, prompt design

Nikolai Korchagin

Vortex states of particles - non-plane-wave solutions of the corresponding wave equation with a helicoidal wave front - open new opportunities for particle physics, unavailable in plane wave scattering. Here we demonstrate that $p\bar p$ annihilation with a vortex proton and antiproton provides access to the phase of proton electromagnetic form factors even

H. R. Tizhoosh

The paper reviews the state-of-the-art of foundation models, LLMs, generative AI, information retrieval and CBIR in digital pathology

Jiajun Shen, Fengjun Li, Morteza Hashemi, Huazhen Fang

In the swift evolution of Cyber-Physical Systems (CPSs) within intelligent environments, especially in the industrial domain shaped by Industry 4.0, the surge in development brings forth unprecedented security challenges. This paper explores the intricate security issues of Industrial CPSs (ICPSs), with a specific focus on the unique threats presented by int

Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren

In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data. In this work, we introduce the first lightweight detector designed to overcome these obstacles,

Xuansheng Wu, Haiyan Zhao, Yaochen Zhu, Yucheng Shi

Explainable AI (XAI) refers to techniques that provide human-understandable insights into the workings of AI models. Recently, the focus of XAI is being extended toward explaining Large Language Models (LLMs). This extension calls for a significant transformation in the XAI methodologies for two reasons. First, many existing XAI methods cannot be directly ap

Agustin Garcia Iglesias, Alfio Antonio Rodriguez

For each $\ell\geq 1$ and $\lambda,\mu\in\Bbbk$, we study the representations of a family of pointed Hopf algebras $\mathcal{A}_{\lambda,\mu}$. These arise as Hopf cocycle deformations of the graded algebra $\mathcal{FK}_3\#\Bbbk \mathbb{G}_{3,\ell}$, where $\mathcal{FK}_3$ is the Fomin-Kirillov algebra and $\mathbb{G}_{3,\ell}$ is a given non-abelian finite

Valerio Capraro, Roberto Di Paolo, Matjaz Perc, Veronica Pizziol

Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology, and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain

Jianlin Chen

Since the breakthrough of ChatGPT, large language models (LLMs) have garnered significant attention in the research community. With the development of LLMs, the question of text style transfer for conversational models has emerged as a natural extension, where chatbots may possess their own styles or even characters. However, standard evaluation metrics have

Suryaprakash Rajkumar, Cristian Tiriolo, Walter Lucia

This paper proposes a control solution to achieve collision-free platooning control of input-constrained mobile robots. The platooning policy is based on a leader-follower approach where the leader tracks a reference trajectory while followers track the leader's pose with an inter-agent delay. First, the leader and the follower kinematic models are feedback

Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez

Inference for Variational Autoencoders (VAEs) consists of learning two models: (1) a generative model, which transforms a simple distribution over a latent space into the distribution over observed data, and (2) an inference model, which approximates the posterior of the latent codes given data. The two components are learned jointly via a lower bound to the

Vuthea Chheang, Brian Thomas Weston, Robert William Cerda, Brian Au

Additive manufacturing (AM) techniques have been used to enhance the design and fabrication of complex components for various applications in the medical, aerospace, energy, and consumer products industries. A defining feature for many AM parts is the complex internal geometry enabled by the printing process. However, inspecting these internal structures req

Soheil Gharatappeh, Sepideh Neshatfar, Salimeh Yasaei Sekeh, Vikas Dhiman

In this paper, we present FogGuard, a novel fog-aware object detection network designed to address the challenges posed by foggy weather conditions. Autonomous driving systems heavily rely on accurate object detection algorithms, but adverse weather conditions can significantly impact the reliability of deep neural networks (DNNs). Existing approaches includ

Theodor Misiakiewicz, Basil Saeed

We consider learning an unknown target function $f_*$ using kernel ridge regression (KRR) given i.i.d. data $(u_i,y_i)$, $i\leq n$, where $u_i \in U$ is a covariate vector and $y_i = f_* (u_i) +\varepsilon_i \in \mathbb{R}$. A recent string of work has empirically shown that the test error of KRR can be well approximated by a closed-form estimate derived fro

Florian Tambon, Arghavan Moradi Dakhel, Amin Nikanjam, Foutse Khomh

Large Language Models (LLMs) for code have gained significant attention recently. They can generate code in different programming languages based on provided prompts, fulfilling a long-lasting dream in Software Engineering (SE), i.e., automatic code generation. Similar to human-written code, LLM-generated code is prone to bugs, and these bugs have not yet be

Peihong Yu, Manav Mishra, Alec Koppel, Carl Busart

Multi-Agent Reinforcement Learning (MARL) algorithms face the challenge of efficient exploration due to the exponential increase in the size of the joint state-action space. While demonstration-guided learning has proven beneficial in single-agent settings, its direct applicability to MARL is hindered by the practical difficulty of obtaining joint expert dem

Raphaël Monat, Aymeric Fromherz, Denis Merigoux

Legal expert systems routinely rely on date computations to determine the eligibility of a citizen to social benefits or whether an application has been filed on time. Unfortunately, date arithmetic exhibits many corner cases, which are handled differently from one library to the other, making faithfully transcribing the law into code error-prone, and possib

Javier Penuela, Cecile Ben, Stepan Boldyrev, Laurent Gentzbittel

Demand response (DR) programs currently cover about 2\% of the average annual global demand, which is far from contributing to the International Energy Agency's ``Net Zero by 2050'' roadmap's 20\% target. While aggregation of many small flexible loads such as individual households can help reaching this target, increasing the participation of industries that

Giuseppe Cartella, Vittorio Cuculo, Marcella Cornia, Rita Cucchiara

Creating high-quality and realistic images is now possible thanks to the impressive advancements in image generation. A description in natural language of your desired output is all you need to obtain breathtaking results. However, as the use of generative models grows, so do concerns about the propagation of malicious content and misinformation. Consequentl

S. Habib Mazharimousavi

In this research, we prove analytically that a generic spherically symmetric thin-shell wormhole (TSW) with its throat located at the innermost photonsphere of the bulk asymptotically flat black hole and supported by a generic surface barotropic perfect fluid is unstable against a radial linear perturbation. This is the generalization of the instability of t

Anik Mallik, Dawei Chen, Kyungtae Han, Jiang Xie

Connected and autonomous vehicles (CAVs) rely heavily upon time-sensitive information update services to ensure the safety of people and assets, and satisfactory entertainment applications. Therefore, the freshness of information is a crucial performance metric for CAV services. However, information from roadside sensors and nearby vehicles can get delayed i

Junse Lee, Francois Baccelli

This paper uses the theory of point processes and stochastic geometry to quantify the sky visibility experienced by users located in an urban environment. The general idea is to represent the buildings of this environment as a stationary marked point process, where the points represent the building locations and the marks their heights. The point process fra

Omar El Housni, Ulysse Hennebelle, Alfredo Torrico

To address efficiency and design challenges in choice-based matching platforms, we introduce a two-sided assortment optimization framework under general choice preferences. The goal in this problem is to maximize the expected number of matches by deciding which assortments are displayed to the agents and the order in which they are shown. In this context, we

Camilo Amaya, Evan Eames, Gintautas Palinauskas, Alexander Perzylo

As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements compared to conventional von Neumann comp

Nithin V. Sabu, Bige Deniz Unluturk

Electrochemical communication is a mechanism that enables intercellular interaction among bacteria within communities. Bacteria achieves synchronization and coordinates collective actions at the population level through the utilization of electrochemical signals. In this work, we investigate the response of bacterial biofilms to artificial potassium concentr

Alexandre Girouard, Panagiotis Polymerakis

In this note we establish an expression for the Steklov spectrum of warped products in terms of auxiliary Steklov problems for drift Laplacians with weight induced by the warping factor. As an application, we show that a compact manifold with connected boundary diffeomorphic to a product admits a family of Riemannian metrics which coincide on the boundary, h

Pietro d'Avenia, Jarosław Mederski

Our motivation is to consider an electromagnetic Lagrangian density $\mathcal{L}_q$, depending on a parameter such that, for $q=1$ it corresponds to the Born-Infeld Lagrangian density and for $q=2$ it restores the Maxwell one. The model in the presence of given charge and current densities is investigated. We solve the pure magnetostatic problem for $q\in(6/

Bertrand Meyer

Techniques to achieve various forms of test coverage, such as branch coverage, typically do not iterate loops; in other words, they treat a loop as a conditional, executed zero or one time. Existing work by the author and collaborators produces test suites guaranteeing full branch coverage. More recent work has shown that by "unrolling" loops the approach ca

Junaid Aftab, Dong An, Konstantina Trivisa

The well-conditioned multi-product formula (MPF), proposed by Low-Kliuchnikov-Wiebe (2019), is a high-order, time-independent Hamiltonian simulation algorithm that implements a linear combination of low-order product formulas. Prior work established its well-conditioned algorithmic construction and near-optimal time and precision dependence, but did not simu

Charilaos Efthymiou, Kostas Zampetakis

Spin-glasses are Gibbs distributions that have been studied in CS for many decades. Recently, they have gained renewed attention as they emerge naturally in learning, inference, optimisation etc. We consider the Edwards-Anderson (EA) spin-glass distribution at inverse temperature $\beta$ when the underlying graph is an instance of $G(n,d/n)$. This is the ran

Anthony Massidda

In this thesis, we study the properties of String theory amplitudes within the framework of Intersection Theory (IT) for twisted (co)homology, which, as recently proposed, offered a novel approach to analyze relations between scattering amplitudes, in string theory as well as in QFT. As only recently pointed out, thanks to IT, the analytic properties of scat

Carlos Olarte, Peter Csaba Ölveczky

In this paper we propose a language for conveniently defining a wide range of execution strategies for real-time rewrite theories, and provide Maude-strategy-implemented versions of most Real-Time Maude analysis methods, albeit with user-defined discrete and timed strategies. We also identify a new time sampling strategy that should provide both efficient an

Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar, Liu Ren

Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in interpreting the complex environment. However, a notable challenge has been the loss of clear supervision when it comes to Bird's Eye View elements. To address this limitation, we int

Gwyneth Moreland

Nef and effective cones of divisors have been the subject of much study. In contrast, their higher codimension analogues are much harder to compute and few examples exist in the literature. In this paper we compute the nef cones in codimensions 2 & 3 and the effective cones in dimensions 2 & 3 for the Hilbert scheme of three points in $\mathbb{P}^3$. Our com

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal

Many methods in differentially private model training rely on computing the similarity between a query point (such as public or synthetic data) and private data. We abstract out this common subroutine and study the following fundamental algorithmic problem: Given a similarity function $f$ and a large high-dimensional private dataset $X \subset \mathbb{R}^d$,

Ersin Das, Aaron D. Ames, Joel W. Burdick

This paper develops rollover prevention guarantees for mobile robots using control barrier function (CBF) theory, and demonstrates the method experimentally. We consider a safety measure based on a zero moment point condition through the lens of CBFs. However, these conditions depend on time-varying and noisy parameters. To address this issue, we present a d

Alex Levering, Diego Marcos, Devis Tuia

In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However

Heejung Roh, Dong-Ha Kim, Yeongsu Cho, Young-Moo Jo

Metal-organic frameworks (MOFs) are promising materials for gas sensing but are often limited to single-use detection. We demonstrate a hybridization strategy synergistically deploying conductive MOFs (cMOFs) and conductive polymers (cPs) as two complementary mixed ionic-electronic conductors in high-performing stand-alone chemiresistors. Our work presents s

Philip Chrostoski, Scott Bisson, David Farley, Frank Narducci

Despite the fact that atom interferometry has been a successful application of quantum sensing, a major topic of interest is the further improvement of the sensitivity of these devices. In particular, the area enclosed by the interferometer (which controls the sensitivity) can be increased by providing a larger momentum kick to the atom cloud, increasing the

Martijn Janse, Dennis G. Uitenbroek, Loek van Everdingen, Jaimy Plugge

A consistent theory describing the dynamics of quantum systems interacting on a classical space-time was recently put forward by Oppenheim et al..[1, 2]. Quantum states may retain their coherence, at the cost of some amount of stochasticity of the spacetime metric, characterized by a spacetime diffusion parameter. Here, we report existing experimental upper

Stan Barmentloo, Anders Jerkstrand, Koichi Iwamoto, Izumi Hachisu

Nitrogen is produced by CNO-cycling in massive stars, and can be ejected in significant amounts in supernova explosions. While in H-rich SNe, its [\ion{N}{II}] 6548, 6583 emission becomes obscured by strong H$\alpha$, in explosions of He stars, this nitrogen emission becomes more visible. We here explore the formation of this line, using the \texttt{SUMO} co

Ángel Aso-Mollar, Eva Onaindia

There is a growing interest in the application of Reinforcement Learning (RL) techniques to AI planning with the aim to come up with general policies. Typically, the mapping of the transition model of AI planning to the state transition system of a Markov Decision Process is established by assuming a one-to-one correspondence of the respective action spaces.

Ana Bokulić, Tajron Jurić, Ivica Smolić

In classical Maxwell's electromagnetism, monopole term of the electric field is proportional to $r^{-2}$, while higher order multipole terms, sourced by anisotropic sources, fall-off faster. However, in nonlinear electromagnetism even a spherically symmetric field has multipole-like contributions. We prove that the leading subdominant term of the electric fi

Kang Gu, Md Rafi Ur Rashid, Najrin Sultana, Shagufta Mehnaz

With the rapid development of Large Language Models (LLMs), we have witnessed intense competition among the major LLM products like ChatGPT, LLaMa, and Gemini. However, various issues (e.g. privacy leakage and copyright violation) of the training corpus still remain underexplored. For example, the Times sued OpenAI and Microsoft for infringing on its copyrig

Dongwon Shin, Hyeonbeom Kim, Sung Ju Hong, Sehwan Song

Graphene, with spin and valley degrees of freedom, fosters unexpected physical and chemical properties for the realization of next-generation quantum devices. However, the spin symmetry of graphene is rather robustly protected, hampering manipulation of the spin degrees of freedom for the application of spintronic devices such as electric gate tunable spin f

Konstantinos Theofilatos

This note summarizes the lectures given in the tutorial session of the Introduction to the Terascale school at DESY on March 2023. The target audience are advanced bachelor and master physics students. The tutorial aims to best prepare the students for starting an LHC experimental physics thesis. The cross section of the top quark pair production is detailed

Rabimba Karanjai, Lei Xu, Weidong Shi

The advent of large language models (LLMs) has marked a significant milestone in the realm of artificial intelligence, with their capabilities often matching or surpassing human expertise in various domains. Among these achievements, their adeptness in translation tasks stands out, closely mimicking the intricate and preliminary processes undertaken by human

Yuksel Arslantas, Ege Yuceel, Muhammed O. Sayin

In this paper, we explore the susceptibility of the independent Q-learning algorithms (a classical and widely used multi-agent reinforcement learning method) to strategic manipulation of sophisticated opponents in normal-form games played repeatedly. We quantify how much strategically sophisticated agents can exploit naive Q-learners if they know the opponen

V. B. Eltsov, J. J. Hosio, M. Krusius

In rotating 3He superfluids the Kelvin-Helmholtz (KH) instability of the AB interface has been found to follow the theoretical model above 0.4 Tc. A deviation from this dependence has been assumed possible at the lowest temperatures. Our NMR and thermal bolometer measurements down to 0.2 Tc show that the critical KH rotation velocity follows the extrapolatio

Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain

We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives. We frame this as retrieval augmented generation, where perspectives are retrieved from a knowledge base and the LLM is tasked with generating

Zhongsheng Li, Wuji Li, Yudong He

Virtual reality games always provide the player with the most verisimilitude experience. With the advancement of VR hardware, it may become mainstream how people feel and attach to a virtual world. The paper discusses a possible solution to finding a better balance between the two classical genres of VR games, sensory stimulation and storytelling. To this en

Yatian Pang, Tanghui Jia, Yujun Shi, Zhenyu Tang

We present Envision3D, a novel method for efficiently generating high-quality 3D content from a single image. Recent methods that extract 3D content from multi-view images generated by diffusion models show great potential. However, it is still challenging for diffusion models to generate dense multi-view consistent images, which is crucial for the quality o

Pratyush Kumar Singh, Kathryn A. Farrell-Maupin, Danial Faghihi

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-maki

Hussein A. Ammar, Raviraj Adve, Shahram Shahbazpanahi, Gary Boudreau

We study the problem of managing handoffs (HOs) in user-centric cell-free massive MIMO (UC-mMIMO) networks. Motivated by the importance of controlling the number of HOs and by the correlation between efficient HO decisions and the temporal evolution of the channel conditions, we formulate a partially observable Markov decision process (POMDP) with the state

Thales Azevedo, Daniel E. A. Matamoros, Gabriel Menezes

We propose a candidate Compton amplitude which is valid for any (integer) quantum spin and free from any spurious poles. We consider the cases of electromagnetism and gravity. We obtain such amplitudes by calculating the corresponding ones from superstring theory involving states on the leading Regge trajectory. To extract the associated field-theory amplitu

Aaron Brunk, Jan Giesselmann, Maria Lukacova-Medvidova

In this work, we derive a $\gamma$-robust a posteriori error estimator for finite element approximations of the Allen-Cahn equation with variable non-degenerate mobility. The estimator utilizes spectral estimates for the linearized steady part of the differential operator as well as a conditional stability estimate based on a weighted sum of Bregman distance

Heidi V. Kastenholz, Michael I. Topper, Warren S. Warren, Martin C. Fischer

Carbon-based black pigments, a widely used class of pigments, are difficult to differentiate with the noninvasive techniques currently used in cultural heritage science. We utilize pump-probe microscopy to distinguish four common carbon-based black pigments as pure pigments, as two-component black pigment mixtures, and as a mixture of a black and a colorful

Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky

We consider a distributed setup for reinforcement learning, where each agent has a copy of the same Markov Decision Process but transitions are sampled from the corresponding Markov chain independently by each agent. We show that in this setting, we can achieve a linear speedup for TD($\lambda$), a family of popular methods for policy evaluation, in the sens

Jacob S. Cohen, Christopher D. Fassnacht, Conor M. O'Riordan, Simona Vegetti

The flux ratios of strongly lensed quasars have previously been used to infer the properties of dark matter. In these analyses it is crucial to separate the effect of the main lensing galaxy and the low-mass dark matter halo population. In this work, we investigate flux-ratio perturbations resulting from general third- and fourth-order multipole perturbation

Sean Reiter, Steffen W. R. Werner

The root mean squared error is an important measure used in a variety of applications such as structural dynamics and acoustics to model averaged deviations from standard behavior. For large-scale systems, simulations of this quantity quickly become computationally prohibitive. Classical model order reduction techniques attempt to resolve this issue via the