October 2023 arXiv papers — page 18

Xiong Xiong, Fan Yang, Li Su

Livestreaming commerce, a hybrid of e-commerce and self-media, has expanded the broad spectrum of traditional sales performance determinants. To investigate the factors that contribute to the success of livestreaming commerce, we construct a longitudinal firm-level database with 19,175 observations, covering an entire livestreaming subsector. By comparing th

Jiamin Lin, Balsam Alkouz, Athman Bouguettaya, Amani Abusafia

We propose a 3D simulator tailored for the Drone-as-a-Service framework. The simulator enables employing dynamic algorithms for addressing realistic delivery scenarios. We present the simulator's architectural design and its use of an energy consumption model for drone deliveries. We introduce two primary operational modes within the simulator: the edit mode

Xiong Xiong, Li Su, Jinguo Huang, Guixia Kang

Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space

David Chen, Helene C. W. Rytgaard, Edwin C. H. Fong, Jens M. Tarp

This article introduces the R package concrete, which implements a recently developed targeted maximum likelihood estimator (TMLE) for the cause-specific absolute risks of time-to-event outcomes measured in continuous time. Cross-validated Super Learner machine learning ensembles are used to estimate propensity scores and conditional cause-specific hazards,

Roman Ya. Kezerashvili, Shalva M. Tsiklauri, Andrew Dublin

We develop the theoretical formalism and study the formation of valley trions in transition metal dichalcogenide (TMDC) monolayers within the framework of a nonrelativistic potential model using the method of hyperspherical harmonics (HH) in four-dimensional space. We present the solution of the three-body Schr\"{o}dinger equation with the Rytova-Keldysh (RK

Ellie Zontou

The evolution of cellular networks has played a pivotal role in shaping the modern telecommunications landscape. This paper explores the journey of cellular network generations, beginning with the introduction of Japan's first commercial 1G network by Nippon Telegraph and Telephone (NTT) Corporation in 1979. This analog wireless network quickly expanded to b

J. P. Edelen, M. J. Henderson, J. Einstein-Curtis, C. C. Hall

Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual syste

Natalie Coleman, Xiangpeng Li, Tina Comes, Ali Mostafavi

After about a decade of research in this domain, what is missing is a systematic overview of the research agenda across different infrastructures and hazards. It is now imperative to evaluate the current progress and gaps. This paper presents a systematic review of equity literature on disrupted infrastructure during a natural hazard event. Following a syste

Ali Borji

Although extensive research has been carried out to evaluate the effectiveness of AI tools and models in detecting deep fakes, the question remains unanswered regarding whether these models can accurately identify genuine images that appear artificial. In this study, as an initial step towards addressing this issue, we have curated a dataset of 510 genuine i

Shuo Niu, Dilasha Shrestha, Abhisan Ghimire, Zhicong Lu

The openness and influence of video-sharing platforms (VSPs) such as YouTube and TikTok attracted creators to share videos on various social issues. Although social issue videos (SIVs) affect public opinions and breed misinformation, how VSP users obtain information and interact with SIVs is under-explored. This work surveyed 659 YouTube and 127 TikTok users

Dehong Xu, Ruiqi Gao, Wen-Hao Zhang, Xue-Xin Wei

Grid cells in the entorhinal cortex of mammalian brains exhibit striking hexagon grid firing patterns in their response maps as the animal (e.g., a rat) navigates in a 2D open environment. In this paper, we study the emergence of the hexagon grid patterns of grid cells based on a general recurrent neural network (RNN) model that captures the navigation proce

Gary Froyland, Stefano Galatolo

We consider the problem of optimal linear response for deterministic expanding maps of the circle. To each infinitesimal perturbation $\dot{T}$ of a circle map $T$ we consider (i) the response of the expectation of an observation function and (ii) the response of isolated spectral points of the transfer operator of $T$. In each case, under mild conditions on

Rodrigo Ribeiro

In this paper, we study a class of random walks that build their own tree. At each step, the walker attaches a random number of leaves to its current position. The model can be seen as a subclass of the Random Walk in Changing Environments (RWCE) introduced by G. Amir, I. Benjamini, O. Gurel-Gurevich and G. Kozma. We develop a renewal framework for the proce

Danijel Aleksić

In this paper, a novel test for testing whether data are Missing Completely at Random is proposed. Asymptotic properties of the test are derived utilizing the theory of non-degenerate U-statistics. It is shown that the novel test statistic coincides with the well-known Little's statistic in the case of a univariate nonresponse. Then, the extensive simulation

Ta-Ying Cheng, Matheus Gadelha, Soren Pirk, Thibault Groueix

We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets. Unlike other unsupervised 3D reconstruction methods, we assume that, within a large-enough dataset, there must exist images of objects with similar shapes but varying backgrounds, textures, and viewpoints. Our approach leverages the recent advances

Fayez H. Alruwaili, David W. Halim-Banoub, Jessica Rodgers, Adam Dalkilic

In this paper, we develop a virtual reality (VR) simulator for the Robossis robot-assisted femur fracture surgery. Due to the steep learning curve for such procedures, a VR simulator is essential for training surgeon(s) and staff. The Robossis Surgical Simulator (RSS) is designed to immerse user(s) in a realistic surgery setting using the Robossis system as

Nayra A. Álvarez Pari, V. K. Bharadwaj, R. Jaeschke-Ubiergo, A. Valadkhani

Topological semimetal antiferromagnets provide a rich source of exotic topological states which can be controlled by manipulating the orientation of the N\'eel vector, or by modulating the lattice parameters through strain. We investigate via ${ab\ initio}$ density functional theory calculations, the effects of shear strain on the bulk and surface states n t

H. J. Kim, H. Abdel-Raziq, X. Liu, A. Y. Siskovic

In this article, we present a mechanism and related path planning algorithm to construct light-duty barriers out of extruded, inflated tubes weaved around existing environmental features. Our extruded tubes are based on everted vine-robots and in this context, we present a new method to steer their growth. We characterize the mechanism in terms of accuracy r

Todd A. Oliynyk

On exponentially expanding Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) spacetimes, there is a distinguished family of spatially homogeneous and isotropic solutions to the relativistic Euler equations with a linear equation of state of the form $p=\sigma \rho$, where $\sigma \in [0,1]$ is the square of the sound speed. Restricting these solutions to a cons

Andrés F. Vargas, Andrew Melatos

Results are presented for a semi-coherent search for gravitational waves from the low-mass X-ray binary Scorpius X-1 in Observing Run 3 (O3) data from the Laser Interferometer Gravitational Wave Observatory, using an updated orbital parameter ephemeris and a hidden Markov model (HMM) to allow for spin wandering. The new orbital ephemeris corrects errors in p

Junjiao Tian, Yen-Cheng Liu, James Seale Smith, Zsolt Kira

Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring it to a downstream task. Recently, projected gradient descent has been successfully used in robust fine-tuning by constraining the deviation from the initialization of the fine-tun

Sayak Saha Roy, Poojitha Thota, Krishna Vamsi Naragam, Shirin Nilizadeh

The advanced capabilities of Large Language Models (LLMs) have made them invaluable across various applications, from conversational agents and content creation to data analysis, research, and innovation. However, their effectiveness and accessibility also render them susceptible to abuse for generating malicious content, including phishing attacks. This stu

Yao Yao, Peike Li, Boyu Chen, Alex Wang

With rapid advances in generative artificial intelligence, the text-to-music synthesis task has emerged as a promising direction for music generation. Nevertheless, achieving precise control over multi-track generation remains an open challenge. While existing models excel in directly generating multi-track mix, their limitations become evident when it comes

Joao Prazeres, Rafael Rodrigues, Manuela Pereira, Antonio M. G. Pinheiro

This paper reports on a subjective quality evaluation of static point clouds encoded with the MPEG codecs V-PCC and G-PCC, the deep learning-based codec RS-DLPCC, and the popular Draco codec. 18 subjects visualized 3D representations of distorted point clouds using a Head Mounted Display, which allowed for a direct comparison with their reference. The Mean O

Hung Pham

We introduce a new combinatorial condition that characterises the amenability for locally compact groups. Our condition is weaker than the well-known F{\o}lner's conditions, and so is potentially useful as a criteria to show the amenability of specific locally compact groups. Our proof requires us to give a quantitative characterisation of (relatively) weakl

Noah Thomas McDermott, Junfeng Yang, Chengzhi Mao

Large-scale language models achieved state-of-the-art performance over a number of language tasks. However, they fail on adversarial language examples, which are sentences optimized to fool the language models but with similar semantic meanings for humans. While prior work focuses on making the language model robust at training time, retraining for robustnes

Nikolai V. Ivanov

By a well known theorem of K.S. Brown an action of a discrete group on a simply-connected complex allows to construct a presentation of this group modulo the stabilizers of vertices. The main goal of the present paper is to provide a new proof of this theorem based on ideas of J.-L. Kozsul. In contrast with Brown proof, we start with a redundant but fairly c

Adam White, Margarita Saranti, Artur d'Avila Garcez, Thomas M. H. Hope

Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of the datasets available for learning, and how to effectively combine neuroimaging and tabular data (e.g. demog

Glenn Ledder

Analytical stability calculation is done to prove stability properties for systems with parameters that do not have explicit values. For systems with three components, the usual method of finding the characteristic polynomial as the determinant of J-lambda I and applying the Routh-Hurwitz conditions is reasonably efficient. For larger systems of four to six

Igal Sason

This paper delves into three research directions, leveraging the Lov\'{a}sz $\vartheta$-function of a graph. First, it focuses on the Shannon capacity of graphs, providing new results that determine the capacity for two infinite subclasses of strongly regular graphs, and extending prior results. The second part explores cospectral and nonisomorphic graphs, d

Srikumar Sastry, Subash Khanal, Aayush Dhakal, Di Huang

We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework unifies two SSL strategies: Contrastive Learning~(CL) and Masked Image Modeling~(MIM), while also enriching the embedding space with metadata available with ground-level imagery of

Zhenggqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning

A rare event is defined by a low probability of occurrence. Accurate estimation of such small probabilities is of utmost importance across diverse domains. Conventional Monte Carlo methods are inefficient, demanding an exorbitant number of samples to achieve reliable estimates. Inspired by the exact sampling capabilities of normalizing flows, we revisit this

Jimeng Shi, Vitalii Stebliankin, Giri Narasimhan

Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast

Himanshu Lohani

Narrow bandgap and its tuning are important aspects of materials for their technological applications. In this context group IV VI semiconductors are one of the interesting candidates. In this paper, we explore the possibility of bandgap tuning in one of the family member of this family GeSe by using isoelectronic Pb doping. Our study is first principles bas

Andrew Gould

I show that industrial-scale mass measurement of isolated black holes (BHs) can be achieved by combining a high-cadence, wide-field microlensing survey such as KMTNet, observations from a parallax satellite in solar orbit, and VLTI GRAVITY+ interferometry. I show that these can yield precision measurements of microlens parallaxes down to $\pi_{\rm E}\sim 0.0

Dzung Pham, Shreyas Kulkarni, Amir Houmansadr

Federated learning has emerged as a promising privacy-preserving solution for machine learning domains that rely on user interactions, particularly recommender systems and online learning to rank. While there has been substantial research on the privacy of traditional federated learning, little attention has been paid to the privacy properties of these inter

Timo Leppälä, Ahmed Gaber Abdelmagid, Hassan A. Qureshi, Konstantinos S. Daskalakis

Hybridisation of the cavity modes and the excitons to polariton states together with the coupling to the vibrational modes determine the linear optical properties of organic semiconductors in microcavities. In this article we compute the refractive index for such system using the Holstein-Tavis-Cummings model and determine then the linear optical properties

Chi Fai Chau

In this paper we study the Morse index for the $\overline{\partial}$-energy of a non-holomorphic disk in a strictly pseudoconvex domain in $\mathbb{C}^n$ or in a K\"ahler manifold with non-negative bisectional curvature. We give a proof that a $\overline{\partial}$-energy minimizing disk is holomorphic; in fact, more generally we show that a non-holomorphic

D. Winklehner, J. R. Alonso, J. M. Conrad

We are developing a high-current cyclotron as a driver for the IsoDAR neutrino experiment. It accelerates 5 mA H2+ to 60 MeV/amu, after which the electron is removed to produce a 10 mA, 60 MeV proton beam. The enabling innovations that offset space-charge effects occur at injection and in the first few turns, allowing one to construct cyclotrons with energie

Gargya Gokhale, Jonas Van Gompel, Bert Claessens, Chris Develder

Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery storage to minimize their energy bills and maximize renewable energy usage. This has spurred the development of advanced control algorithms that maximally achieve those goals. However, a common challenge faced while developing such controllers is the unavailability of accurate forecast

Tatiana Chakravorti, Chuhao Wu, Sai Koneru, Sarah Rajtmajer

Over the past decade, a crisis of confidence in scientific literature has gained attention, particularly in the West. In response, we have seen changes in policy and practice amongst individual researchers and institutions. Greater attention is given to the transparency of workflows and the appropriate use of statistical methods. Advances in scholarly big da

Hrutuj Raut

A total of 3 orifice plates was investigated, namely, 10, 9 and 5 holes. With each orifice, four pressure profiles were compared, 10, 7, 4 and 1 bar for each Pressure and Velocity Profiles were compared. Schnerr-Sauer Model was used in the ANSYS Fluent for CFD. An extensive comparison has been made between every model with respect to Pressure and Velocity Pr

Yaning Fan, Cheng Wang, Linfeng Jiang, Chao Sun

Understanding the dynamics of material objects advected by turbulent flows is a long standing question in fluid dynamics. In this perspective article we focus on the characterization of the statistical properties of non-interacting finite-sized massive spherical particles advected by a vigorous turbulent flow. We study the fluctuations and temporal correlati

Zexuan Zhong, Ziqing Huang, Alexander Wettig, Danqi Chen

Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications? In this work, we propose a novel attack for dense retrieval systems in which a malicious user generates a small number of adversarial passages by perturbing discrete tokens to maximize

Gargya Gokhale, Niels Tiben, Marie-Sophie Verwee, Manu Lahariya

Given its substantial contribution of 40\% to global power consumption, the built environment has received increasing attention to serve as a source of flexibility to assist the modern power grid. In that respect, previous research mainly focused on energy management of individual buildings. In contrast, in this paper, we focus on aggregated control of a set

Jishu Das, Neha Prabhu

For a prime ideal $\mathfrak{p}$ in a totally real number field $L$ with the adele ring $\mathbb{A}$, we study the distribution of angles $\theta_\pi(\mathfrak{p})$ coming from Satake parameters corresponding to unramified $\pi_\mathfrak{p}$ where $\pi_\mathfrak{p}$ comes from a global $\pi$ ranging over a certain finite set $\Pi_{\underline{k}}(\mathfrak{n}

Fayez H. Alruwaili, Michael P. Clancy, Marzieh S. Saeedi-Hosseiny, Jacob A. Logar

In the face of challenges encountered during femur fracture surgery, such as the high rates of malalignment and X-ray exposure to operating personnel, robot-assisted surgery has emerged as an alternative to conventional state-of-the-art surgical methods. This paper introduces the development of Robossis, a haptic system for robot-assisted femur fracture surg

Zachary Coalson, Gabriel Ritter, Rakesh Bobba, Sanghyun Hong

In this paper, we systematically evaluate the robustness of multi-exit language models against adversarial slowdown. To audit their robustness, we design a slowdown attack that generates natural adversarial text bypassing early-exit points. We use the resulting WAFFLE attack as a vehicle to conduct a comprehensive evaluation of three multi-exit mechanisms wi

S. Kuznetsova, S. Deleplanque, B. Dubus, M. Miniaci

The absorption of sound has great significance in many scientific and engineering applications, from room acoustics to noise mitigation. In this context, porous materials have emerged as a viable solution towards high absorption performance and lightweight designs. However, their performance is somehow limited in the low frequency regime. Inspired by the con

H. Lohani, K. Majhi, R. Ganesan, S. Gonzalez

Two quintuple layers of strong topological insulator Bi2Se3 are coupled by a Bi bilayer in BiSe crystal. We investigated its electronic structure using angle resolved photoelectron spectroscopy to study its topological nature. Dirac like linearly dispersive surface state bands are observed on the 001 surface of BiSe and Sb doped BiSe, similar to Bi2Se3. More

Swastik Kopparty, Noga Ron-Zewi, Shubhangi Saraf

In this note, we give very simple constructions of unique neighbor expander graphs starting from spectral or combinatorial expander graphs of mild expansion. These constructions and their analysis are simple variants of the constructions of LDPC error-correcting codes from expanders, given by Sipser-Spielman [SS96] (and Tanner [Tan81]), and their analysis. W

Sabir Ramazanov

We discuss long-lasting gravitational wave sources arising and operating during radiation-dominated stage. Under a set of assumptions, we establish the correspondence between cosmological evolution of a source and the resulting gravitational wave spectrum. Namely, for the source energy density $\rho_s$ falling as a power law characterized by the exponent $\b

Junlong Chen, Yanbin Tang

In this paper we consider the homogenization problem of nonlinear evolution equations with space-time non-locality, the problems are given by Beltritti and Rossi [JMAA, 2017, 455: 1470-1504]. When the integral kernel $J(x,t;y,s)$ is re-scaled in a suitable way and the oscillation coefficient $\nu(x,t;y,s)$ possesses periodic and stationary structure, we show

Tuhin Chakrabarty, Kanishk Singh, Arkadiy Saakyan, Smaranda Muresan

Natural language instructions are a powerful interface for editing the outputs of text-to-image diffusion models. However, several challenges need to be addressed: 1) underspecification (the need to model the implicit meaning of instructions) 2) grounding (the need to localize where the edit has to be performed), 3) faithfulness (the need to preserve the ele

Andre Laestadius, Mihály A. Csirik, Markus Penz, Nicolas Tancogne-Dejean

The exchange-only virial relation due to Levy and Perdew is revisited. Invoking the adiabatic connection, we introduce the exchange energy in terms of the right-derivative of the universal density functional w.r.t. the coupling strength $\lambda$ at $\lambda=0$. This agrees with the Levy-Perdew definition of the exchange energy as a high-density limit of the

J. Keski-Rahkonen, X. -Y. Ouyang, S. Yuan, A. M. Graf

Quantum acoustics -- a recently developed framework parallel to quantum optics -- establishesa nonperturbative and coherent treatment of the electron-phonon interaction in real space. The quantum-acoustical representation reveals a displaced Drude peak hid ing in plain sight within the venerable Fr\"ohlich model: the optical conductivity exhibits a finite fr

Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the effici

Omar Maraqa, Sylvester Aboagye, Telex M. N. Ngatched

A critical concern within the realm of visible light communications (VLC) pertains to enhancing system data rate, particularly in scenarios where the direct line-of-sight (LoS) connection is obstructed by obstacles. The deployment of meta-surface-based simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) has emerged to comba

Michael Hitrik, Zhongkai Tao, Maciej Zworski

We establish exponential decay, as the angle of twisting goes to $ 0$, of eigenstates in a model of twisted bilayer graphene (TBG), near the hexagon connecting stacking points. That is done by adapting microlocal methods Kawai-Kashiwara and Sj\"ostrand used to establish analytic hypoellipticity by Tr\'epreau and Himonas. That replaces ellipticity, absent her

Md Nakhla Rafi, An Ran Chen, Tse-Hsun Chen, Shaohua Wang

For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. However, prior research indicates that Defects4J might include tests added post-bug report, embedding developer knowledge and affecting fault localization efficacy. In this paper, we ex

Nouman Khan, Kangle Mu, Mehrdad Moharrami, Vijay Subramanian

We study the problems of estimating the past and future evolutions of two diffusion processes that spread concurrently on a network. Specifically, given a known network $G=(V, \overrightarrow{E})$ and a (possibly noisy) snapshot $\mathcal{O}_n$ of its state taken at (a possibly unknown) time $W$, we wish to determine the posterior distributions of the initia

Suraj Singireddy, Precious Nwaorgu, Andre Beckus, Aden McKinney

Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes. However, deep RL methods have two weaknesses: collecting the amount of agent experience required for practical RL problems is prohibitively expensive, and the learned policies exhibit poor generalization on tasks outside the training data distributio

Philip Thompson

We study least-squares trace regression when the parameter is the sum of a $r$-low-rank matrix and a $s$-sparse matrix and a fraction $\epsilon$ of the labels is corrupted. For subgaussian distributions and feature-dependent noise, we highlight three needed design properties, each one derived from a different process inequality: a "product process inequality

Hyerin Cho, Ben S. Prather, Ramesh Narayan, Priyamvada Natarajan

Fueling and feedback couple supermassive black holes (SMBHs) to their host galaxies across many orders of magnitude in spatial and temporal scales, making this problem notoriously challenging to simulate. We use a multi-zone computational method based on the general relativistic magneto-hydrodynamic (GRMHD) code KHARMA that allows us to span $7$ orders of ma

Liying Chen, Ashley P. Fidler, Alexander M. McKillop, Marissa L. Weichman

Molecular polaritons, hybrid light-matter states resulting from strong cavity coupling of optical transitions, may provide a new route to guide chemical reactions. However, demonstrations of cavity-modified reactivity in clean benchmark systems are still needed to clarify the mechanisms and scope of polariton chemistry. Here, we use transient absorption to o

Qi Guo, Yuanyuan Ke, Bernhard Ruf

In this study, we investigate the Spectrum Zero Problem of nonlinear Dirac equations with a focus on the behavior of zero at the boundaries of the spectral gap. We introduce a nonlinear particle-antiparticle interaction and demonstrate that the problem exhibits asymmetric behavior at the left and right boundaries of the spectrum. Specifically, when zero is a

Hyun Jung Kim, Matthew Julian, Calum Williams, David Bombara

Recent growth in space systems has seen increasing capabilities packed into smaller and lighter Earth observation and deep space mission spacecraft. Phase-change materials (PCMs) are nonvolatile, reconfigurable, fast-switching, and have recently shown a high degree of space radiation tolerance, thereby making them an attractive materials platform for spacebo

Ahmed Sabir, Lluís Padró

In this paper, we investigate the impact of objects on gender bias in image captioning systems. Our results show that only gender-specific objects have a strong gender bias (e.g., women-lipstick). In addition, we propose a visual semantic-based gender score that measures the degree of bias and can be used as a plug-in for any image captioning system. Our exp

Iztok Banic, Goran Erceg, Judy Kennedy, Van Nall

Let $(X,f)$ be a dynamical system. Using an equivalence relation $\sim$ on $X$, we introduce the quotient $(X/_{\sim},f^{\star})$ of the dynamical system $(X,f)$. In the first part of the paper, we give new results about sensitive dependence on initial conditions of $(X/_{\sim},f^{\star})$, transitivity of $(X/_{\sim},f^{\star})$, and periodic points in $(X/

Indrashish Saha, Ashwini Gupta, Lori Graham-Brady

Design and analysis of inelastic materials requires prediction of physical responses that evolve under loading. Numerical simulation of such behavior using finite element (FE) approaches can call for significant time and computational effort. To address this challenge, this paper demonstrates a deep learning (DL) framework that is capable of predicting micro

Ziheng Zeng, Suma Bhat

Accurate processing of non-compositional language relies on generating good representations for such expressions. In this work, we study the representation of language non-compositionality by proposing a language model, PIER, that builds on BART and can create semantically meaningful and contextually appropriate representations for English potentially idioma

Amirreza Zamani, Tobias J. Oechtering, Deniz Gündüz, Mikael Skoglund

A private compression design problem is studied, where an encoder observes useful data $Y$, wishes to compress it using variable length code and communicates it through an unsecured channel. Since $Y$ is correlated with private attribute $X$, the encoder uses a private compression mechanism to design encoded message $\cal C$ and sends it over the channel. An

Piotr Indyk, Haike Xu

Graph-based approaches to nearest neighbor search are popular and powerful tools for handling large datasets in practice, but they have limited theoretical guarantees. We study the worst-case performance of recent graph-based approximate nearest neighbor search algorithms, such as HNSW, NSG and DiskANN. For DiskANN, we show that its "slow preprocessing" vers

Andre Lustosa, Tim Menzies

A "partial ordering" is a way to heuristically order a set of examples (partial orderings are a set where, for certain pairs of elements, one precedes the other). While these orderings may only be approximate, they can be useful for guiding a search towards better regions of the data. To illustrate the value of that technique, this paper presents iSNEAK, an

Evangelia Panourgia, Theodoros Plessas, Ilias Balampanis, Diomidis Spinellis

The rising popularity of deep learning (DL) methods and techniques has invigorated interest in the topic of SE4DL (Software Engineering for Deep Learning), the application of software engineering (SE) practices on deep learning software. Despite the novel engineering challenges brought on by the data-driven and non-deterministic paradigm of DL software, litt

Elnaserledinellah Mahmood Abdelwahab

Modern Logics, as formulated notably by Frege, Russell and Tarski involved basic assumptions about Natural Languages in general and Indo-European Languages in particular, which are contested by Linguists. Based upon those assumptions, formal Languages were designed to overcome what Logicians claimed to be 'defects' of Natural Language. In this paper we show

Amirreza Zamani, Tobias J. Oechtering, Mikael Skoglund

A private compression design problem is studied, where an encoder observes useful data $Y$, wishes to compress it using variable length code and communicates it through an unsecured channel. Since $Y$ is correlated with private data $X$, the encoder uses a private compression mechanism to design encoded message $\cal C$ and sends it over the channel. An adve

Augusto Martins, Achiles F. da Mota, Chris Stanford, Taylor Contreras

Metalenses are composed of nanostructures for focusing light and have been widely explored in many exciting applications. However, their expanding dimensions pose simulation challenges. We propose a method to simulate metalenses in a timely manner using vectorial wave and ray tracing models. We sample the metalens' radial phase gradient and locally approxima

Viatcheslav Kharlamov, Rareş Răsdeaconu

We give an expression for the Smith-Thom deficiency of the Hilbert square $X^{[2]}$ of a smooth real algebraic variety $X$ in terms of the rank of a suitable Mayer-Vietoris mapping in several situations. As a consequence, we establish a necessary and sufficient condition for the maximality of $X^{[2]}$ in the case of projective complete intersections, and sh

Tianhao Zhang, Shenglin Wang, Nidhal Bouaynaya, Radu Calinescu

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable pr

Rafayel Teymurazyan

In this note we give a glimpse of the fractional Laplacian. In particular, we bring several definitions of this non-local operator and series of proofs of its properties. It is structured in a way as to show that several of those properties are natural extensions of their local counterparts, with some key differences.

C. Strynar, R. M. Rajapakse

Some of the most impressive achievements of contemporary Machine Learning systems comes from the GAN (Generative Adversarial Network) structure. DALLE-2 and GPT- 3, two of the most impressive and recognizable feats of ML in recent years, were both trained using adversarial techniques. The world of Quantum Computing is already well aware of the value of such

V. Popkov, G. M. Schütz

Using mode coupling theory the conditions for all allowed dynamical universality classes for the conserved modes in one-dimensional driven systems are presented in closed form as a function of the stationary currents and their derivatives. With a view on the search for the golden ratio universality class the existence of some families of microscopic models i

Björn Schrinski, Johan A. Brimer, Anders S. Sørensen

Photon bound states have been identified as particular solutions to the scattering of two photons from a single emitter, but from these results the full nature of these states remains elusive. We study a novel, clear and unambiguous signature that these bound states are truly bound. To this end we consider a new configuration of close-by waveguides, each chi

Jiaying Weng, Kai Tan, Cheng Wang, Zhou Yu

Fr\'echet regression has received considerable attention to model metric-space valued responses that are complex and non-Euclidean data, such as probability distributions and vectors on the unit sphere. However, existing Fr\'echet regression literature focuses on the classical setting where the predictor dimension is fixed, and the sample size goes to infini

Jiayao Tan, Fan Lyu, Linyan Li, Fuyuan Hu

Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems. However, existing V2X perception methods focus on static scenes from mainly vehicle-based vision, which is constrained by sensor capabilities and communication loads. To adapt V2X

Mohammad Mehdi Rastikerdar, Jin Huang, Shiwei Fang, Hui Guan

While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance

Atila Poro, Ehsan Paki, Ailar Alizadehsabegh, Mehdi Khodadadilori

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary systems. In this investigation, two-dimensional (2D) relationships for P-M_V(system), P-L_1,2, M_1,2-L_1,2, and q-L_ratio were revisited. The sample used is related to 118 contact binary systems with an orbital peri

Muhammad Waseem, Muhammad Irfan, Shahid Qamar

Phenomena involving interactions among magnons, phonons, and photons in cavity magnomechanical systems have attracted considerable attention recently, owing to their potential applications in the microwave frequency range. One such important effect is the response of a probe field to such tripartite interaction between photon-magnon-phonon. In this paper, we

Richa Jayanti, Andrew Kim, Sean Pham, Athreya Raghavan

Plastid genomes (plastomes) of angiosperms are of great interest among biologists. High-throughput sequencing is making many such genomes accessible, increasing the need for tools to perform rapid comparative analysis. This exploratory analysis investigates whether the Pangenome Research Tool Kit (PGR-TK) is suitable for analyzing plastomes. After determinin

Huseyin Fuat Alsan, Taner Arsan

This paper explores post-disaster analytics using multimodal deep learning models trained with curriculum learning method. Studying post-disaster analytics is important as it plays a crucial role in mitigating the impact of disasters by providing timely and accurate insights into the extent of damage and the allocation of resources. We propose a curriculum l

B. L. Shanks, H. W. Sullivan, A. R. Shazed, M. P. Hoepfner

While Bayesian inference is the gold standard for uncertainty quantification and propagation, its use within physical chemistry encounters formidable computational barriers. These bottlenecks are magnified for modeling data with many independent variables, such as X-ray/neutron scattering patterns and electromagnetic spectra. To address this challenge, we ap

Shuang Zhou, Alfonso Aragón-Salamanca, Michael Merrifield, V. M. Sampaio

Even over relatively recent epochs, galaxies have evolved significantly in their location in the mass-metallicity plane, which must be telling us something about the latter stages of galaxy evolution. In this paper, we analyse data from the LEGA-C survey using semi-analytic spectral and photometric fitting to determine these galaxies' evolution up to their o

Antonin Sulc, Raimund Kammering, Annika Eichler, Tim Wilksen

Navigating the landscape of particle accelerators has become increasingly challenging with recent surges in contributions. These intricate devices challenge comprehension, even within individual facilities. To address this, we introduce PACuna, a fine-tuned language model refined through publicly available accelerator resources like conferences, pre-prints,

Rupok Chowdhury Protik

Satellites play a vital role in remote communication where traditional communication mediums struggle to provide benefits over associated costs and efficiency. In recent years, satellite communication has achieved utter interest in the industry due to the achievement of high data rates through the massive deployment of LEO satellites. Because of the complex

T. Powers, R. M. Rajapakse

Key properties of physical systems can be described by the eigenvalues of matrices that represent the system. Computational algorithms that determine the eigenvalues of these matrices exist, but they generally suffer from a loss of performance as the matrix grows in size. This process can be expanded to quantum computation to find the eigenvalues with better

Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut

The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures. Recent works have experimentally shown that two different solutions found after two runs of a stochastic training are often connected by very simple continuous paths (e.g., linear) modulo a permu

Yilong Zhao, Chien-Yu Lin, Kan Zhu, Zihao Ye

The growing demand for Large Language Models (LLMs) in applications such as content generation, intelligent chatbots, and sentiment analysis poses considerable challenges for LLM service providers. To efficiently use GPU resources and boost throughput, batching multiple requests has emerged as a popular paradigm; to further speed up batching, LLM quantizatio

Leonid Zelenko

For Schr\"odinger operator $H=-\Delta+ V({\mathbf x})\cdot$, acting in the space $L_2(\mathbb R^d)\,(d\ge 3)$, necessary and sufficient conditions for semi-boundedness and discreteness of its spectrum.are obtained without assumption that the potential $V({\mathbf x})$ is bounded below. By reduction of the problem to investigation of existence of regular solu

László Csató, László Marcell Kiss, Zsombor Szádoczki

Qualifications for several world championships in sports are organised such that distinct sets of teams play in their own tournament for a predetermined number of slots. Inspired by a recent work studying the problem with the tools from the literature on fair allocation, this paper provides an alternative approach based on historical matches between these se