December 2023 arXiv papers — page 90

Zhuofan Xia, Dongchen Han, Yizeng Han, Xuran Pan

Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the image. GRES poses challenges in modeling the complex spatial relationships of the instances in the image and identifying non-existing referents. Multimodal Large Language Models (MLLM

Dylan Langharst, Dongmeng Xi

In 1970, Schneider introduced the higher-order difference body and the associated Rogers-Shephard inequality. Recently, Haddad, Langharst, Putterman, Roysdon and Ye expanded the concept to a burgeoning higher-order Brunn-Minkowski theory. In 1991, Zhang introduced mean zonoids of a convex body, which was extended to the Firey-Brunn-Minkowski theory setting b

Gaoyu Li, Zhongquan Tan

This paper proves several weak limit theorems for the joint version of extreme order statistics and partial sums of independently and identically distributed random variables. The results are also extended to almost sure limit version.

Xiaolong Fan, Maoguo Gong, Yue Wu, Zedong Tang

Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an end-to-end graph structure learning model poses a challenge due to the impediment of gradient flow caused by the nearest neighbor sampling strategy. In this paper, we construct a d

Pekka Koskela, Zheng Zhu

For a continuous function $f:\mathbb{R}\to\mathbb{R}$, define the corresponding graph by setting \[\Gamma_f := {(x1, f(x1)) : x_1\in\mathbb{R}} .\] In this paper, we study the Sobolev extension property for the upper and lower domains over the graph $\Gamma_{\psi^\alpha_c}$ for $\psi^\alpha_c(x_1):=d(x_1, \mathcal C)^\alpha$, where $\mathcal C$ is the classi

Zhengdong Li

In recent years, deep generative models, such as Generative Adversarial Network (GAN), has grabbed significant attention in the field of computer vision. This project focuses on the application of GAN in image deblurring with the aim of generating clearer images from blurry inputs caused by factors such as motion blur. However, traditional image restoration

Anna C. Childs, Rebecca G. Martin, C. J. Nixon, Aaron M. Geller

We study the effects of general relativity (GR) on the evolution and alignment of circumbinary disks around binaries on all scales. We implement relativistic apsidal precession of the binary into the hydrodynamics code {\sc phantom}. We find that the effects of GR can suppress the stable polar alignment of a circumbinary disk, depending on how the relativist

Shengyao Zhang, Mi Zhang, Xudong Pan, Min Yang

To reduce the computation cost and the energy consumption in large language models (LLM), skimming-based acceleration dynamically drops unimportant tokens of the input sequence progressively along layers of the LLM while preserving the tokens of semantic importance. However, our work for the first time reveals the acceleration may be vulnerable to Denial-of-

Wenjie Qian, Weiwei Zhao, Tiezheng Qian, Qin Xu

Liquid droplets on soft solids, such as soft polymeric gels, can induce substantial surface deformations, leading to the formation of wetting ridges at contact points. While these contact ridges have been shown to govern the rich surface mechanics on compliant substrates, the inherently divergent characteristics of contact points and the multiphase nature of

Quang-Duy Tran, Bao Duong, Phuoc Nguyen, Thin Nguyen

Distinguishing the cause and effect from bivariate observational data is the foundational problem that finds applications in many scientific disciplines. One solution to this problem is assuming that cause and effect are generated from a structural causal model, enabling identification of the causal direction after estimating the model in each direction. The

Tiago Venzel Rosembach, Ana Luiza Novaes Dias, Ronald Dickman

We study a discrete-space model of active matter with excluded volume. Particles are restricted to the sites of a triangular lattice, and can assume one of three orientations. Varying the density and noise intensity, Monte Carlo simulations reveal a variety of spatial patterns. Ordered states occur in the form of condensed structures, which (away from the fu

Xiaofei Huang, Xiaolin Ma, Lei Cong, Wei Ji

Revealing the essence of dark matter (DM) and dark energy is essential for understanding our universe. Ultralight (rest energy $<$10 eV) bosonic particles, including pseudoscalar axions and axion-like particles (ALPs) have emerged among leading candidates to explain the composition of DM and searching for them has become an important part of precision-measur

Junbao Hu, Liyang Zhou, Jian Jiang

To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often h

Zi Huang, Akila Pemasiri, Simon Denman, Clinton Fookes

Radio signal recognition is a crucial function in electronic warfare. Precise identification and localisation of radar pulse activities are required by electronic warfare systems to produce effective countermeasures. Despite the importance of these tasks, deep learning-based radar pulse activity recognition methods have remained largely underexplored. While

Mengze Gao, Xiaozhi Cao, Daniel Abraham, Zihan Zhou

$B_1^+$ and $B_0$ field-inhomogeneities can significantly reduce accuracy and robustness of MRF's quantitative parameter estimates. Additional $B_1^+$ and $B_0$ calibration scans can mitigate this but add scan time and cannot be applied retrospectively to previously collected data. Here, we proposed a calibration-free sequence-adaptive deep-learning framewor

Md Shafayat Hossain, Qi Zhang, Zhiwei Wang, Nikhil Dhale

Electronic topological phases are renowned for their unique properties, where conducting surface states exist on the boundary of an insulating three-dimensional bulk. While the transport response of the surface states has been extensively studied, the response of the topological hinge modes remains elusive. Here, we investigate a layered topological insulato

Zixian Su, Jingwei Guo, Kai Yao, Xi Yang

While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to inaccurate target estimation. As previous attempts merely introduce source statistics to mitigate this issue, the fundamental problem of inaccurate target estimation still persist

Taekyun Kim, Dae San Kim

The aim of this paper is to study probabilistic versions of the degenerate Whitney numbers of the second kind and those of the degenerate Dowling polynomials, namely the probabilistic degenerate Whitney numbers of the second kind associated with Y and the probabilistic degenerate Dowling polynomials associated with Y. Here Y is a random variable whose moment

H. Ning, O. Mehio, X. Li, M. Buchhold

Ultrafast laser excitation provides a means to transiently realize long-range ordered electronic states of matter that are hidden in thermal equilibrium. Recently, this approach has unveiled a variety of thermally inaccessible ordered states in strongly correlated materials, including charge density wave, ferroelectric, magnetic, and intertwined charge-orbit

Toru Nishimura, Masakiyo Kitazawa, Teiji Kunihiro

We study how the dilepton production rates and electric conductivity are affected by the phase transition to color superconductivity and the QCD critical point. Effects of the soft modes associated with these phase transitions are incorporated through the photon self-energy called the Aslamazov-Larkin, Maki-Thompson, and density-of-states terms, which are re

Shitao Li, Minjia Shi, San Ling

The existence of $q$-ary linear complementary pairs (LCPs) of codes with $q> 2$ has been completely characterized so far. This paper gives a characterization for the existence of binary LCPs of codes. As a result, we solve an open problem proposed by Carlet $et~al.$ (IEEE Trans. Inf. Theory 65(3): 1694-1704, 2019) and a conjecture proposed by Choi $et~al.$ (

Qian Wang, Hefei Ling, Yingwei Li, Qihao Liu

In response to the rapidly evolving nature of adversarial attacks against visual classifiers, numerous defenses have been proposed to generalize against as many known attacks as possible. However, designing a defense method that generalizes to all types of attacks is unrealistic, as the environment in which the defense system operates is dynamic. Over time,

Ho-Weng Lee, Shang-Hong Lai

In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like ImageNet. In this work, we propose a novel f

Masaki Miyashita, Takashi Ui

This paper addresses information design in a workhorse model of network games, where agents have linear best responses, the information designer maximizes a quadratic objective, and the payoff-relevant state follows a multivariate Gaussian distribution. We formulate the problem as a semidefinite program and establish strong duality to characterize the optima

Falih Gozi Febrinanto, Kristen Moore, Chandra Thapa, Mujie Liu

Many multivariate time series anomaly detection frameworks have been proposed and widely applied. However, most of these frameworks do not consider intrinsic relationships between variables in multivariate time series data, thus ignoring the causal relationship among variables and degrading anomaly detection performance. This work proposes a novel framework

Nardo Giménez, Guillermo Matera, Mariana Pérez, Melina Privitelli

We study the set of common $\mathbb{F}_q$-rational solutions of "smooth" systems of multivariate symmetric polynomials with coefficients in a finite field $\mathbb{F}_q$. We show that, under certain conditions, the set of common solutions of such polynomial systems over the algebraic closure of $\mathbb{F}_q$ has a "good" geometric behavior. This allows us t

J. Choi, J. Li, D. Van Tuan, H. Dery

When doped with a high density of mobile charge carriers, monolayer transition-metal dichalcogenide (TMD) semiconductors can host new types of composite many-particle exciton states that do not exist in conventional semiconductors. Such multi-particle bound states arise when a photoexcited electron-hole pair couples to not just a single Fermi sea that is qua

Igor G. Vladimirov

This paper is concerned with a class of multivariable stochastic Hamiltonian systems whose generalised position is related by an ordinary differential equation to the momentum governed by an Ito stochastic differential equation. The latter is driven by a standard Wiener process and involves both conservative and viscous damping forces. With the mass, diffusi

Daniel Mejia-Rodriguez

The $GW$ approximation has become an important tool for predicting charged excitations of isolated molecules and condensed systems. Its popularity can be attributed to many factors, including a favorable scaling and relatively good accuracy. In practical applications, the $GW$ is often performed as a one-shot perturbation known as $G_0W_0$. Unfortunately, $G

Chengchun Hao, Siqi Yang

We establish the a priori estimates and prove a blow-up criterion for the three-dimensional free boundary incompressible ideal magnetohydrodynamics equations. The fluid occupies a bounded region with a free boundary that is a closed surface, without assumptions of simple connectedness or periodicity of the region (thus, Fourier transforms cannot be applied),

Xinxiang Guo, Yifen Mu

This paper aims to solve the optimal strategy against a well-known adaptive algorithm, the Hedge algorithm, in a finitely repeated $2\times 2$ zero-sum game. In the literature, related theoretical results are very rare. To this end, we make the evolution analysis for the resulting dynamical game system and build the action recurrence relation based on the Be

Adel Ali, Alexey Belyanin

We propose a conceptual model of a toroidal flux qubit, which consists of a quantized toroidal magnetic flux coupled to a charged particle on a quantum ring through field-free interaction. Scaling the system to two or more flux qubits results in emergent field-free coupling between them. We show that the topological and nonlocal aspects of this system can ha

Juanzi Cai, Zhigang Wu, Mengqian Liu

In this paper, we mainly consider global well-posedness and long time behavior of compressible Navier-Stokes equations without heat conduction in $L^p$-framework. This is a generalization of Peng and Zhai \cite{peng}(SIMA, 55(2023), no.2, 1439-1463), where they obtained the corresponding result in $L^2$-framework. Based on the key observation that we can rel

Yuhan Jiang

The multispecies asymmetric simple exclusion process (mASEP) is a Markov chain in which particles of different species hop along a one-dimensional lattice. This paper studies the doubly asymmetric simple exclusion process $\mathrm{DASEP}(n,p,q)$ in which $q$ particles with species $1, \dots, p$ hop along a circular lattice with $n$ sites, but also the partic

Ugur Camci

We investigate the Noether symmetries of the minimal surface Lagrangian for four classes of metrics in G\"{o}del-type spacetimes. Then, calculating the Noether symmetries for all classes, namely, classes I, II, III and IV, we determine the conserved fields corresponding to each classes, allowing us to derive a comprehensive characterization of the minimal su

Douglas Schonholtz

As coding challenges become more complex, recent advancements in Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6\% solve rate on the HumanEval benchmark. Concurrently, there is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot and Tab Nine, aimed at enhancing developer

Govind Gandhi

Growing interest in modelling complex systems from brains to societies to cities using networks has led to increased efforts to describe generative processes that explain those networks. Recent successes in machine learning have prompted the usage of evolutionary computation, especially genetic programming to evolve computer programs that effectively forage

Faisal Alkaabneh

This work is motivated by solving a problem faced by big agriculture companies implementing precision agriculture operations for spraying practices using two types of operators, namely a tender tanker and a fleet of sprayers. We model this problem as a vehicle routing problem with multiple synchronization constraints and multiple trips with the objective of

Toshihito Osada

The electronic state of \alpha-type organic Dirac fermion systems such as \alpha-(ET)_2I_3 or \alpha-(BETS)_2I_3 has been studied under magnetic fields using the four-band tight-binding model with Peierls phase factors. The validity of the Dirac fermion picture in these materials was confirmed by the generated Hofstadter butterfly and its Chern numbers. The

Michaela Cully-Hugill, Nicol Leong

We provide explicit upper bounds of the order $\log t/\log\log t$ for $|\zeta'(s)/\zeta(s)|$ and $|1/\zeta(s)|$ when $\sigma$ is close to $1$. These improve existing bounds for $\zeta(s)$ on the $1$-line.

Tianyi Chen, Tianyu Ding, Zhihui Zhu, Zeyu Chen

Compressing a predefined deep neural network (DNN) into a compact sub-network with competitive performance is crucial in the efficient machine learning realm. This topic spans various techniques, from structured pruning to neural architecture search, encompassing both pruning and erasing operators perspectives. Despite advancements, existing methods suffers

Hadia Mecheri, Islam Benamirouche, Feriel Fass, Djemel Ziou

In this study, we propose an approach for predicting rare events by exploiting time series in coevolution. Our approach involves a weighted autologistic regression model, where we leverage the temporal behavior of the data to enhance predictive capabilities. By addressing the issue of imbalanced datasets, we establish constraints leading to weight estimation

Gabriela Lichtenstein, Michael A. Schmidt, German Valencia, Raymond R. Volkas

We analyse the $Z$ and Higgs boson decays $Z\to \ell^+ \ell^- $ ($\ell = e, \mu,\tau$), $H\to \gamma\gamma$ and $H\to Z\gamma$ that are induced at one-loop level in models with a doubly-charged isosinglet scalar. After discussing current constraints, we derive the parameter space that will be probed by the HL-LHC and the possible future colliders the ILC, CE

Haim Grebnev

In this paper we formulate and prove a gauge equivalence for unitary connections and skew-Hermitian Higgs fields of suitable regularity that are mapped to the same function under the non-abelian X-ray transform on nontrapping asymptotically hyperbolic spaces with negative curvature and no nontrivial twisted conformal Killing tensor fields with certain regula

Kosuke Shigematsu

In this paper, we propose a method specifically aimed at improving small bird detection for the Small Object Detection Challenge for Spotting Birds 2023. Utilizing YOLOv7 model with test-time augmentation, our approach involves increasing the input resolution, incorporating multiscale inference, considering flipped images during the inference process, and em

Sanad Saha, Nischal Aryal, Leilani Battle, Arash Termehchy

Exploratory visual analysis (EVA) is an essential stage of the data science pipeline, where users often lack clear analysis goals at the start and iteratively refine them as they learn more about their data. Accurate models of users' exploration behavior are becoming increasingly vital to developing responsive and personalized tools for exploratory visual an

Pia M. Düring, Paul Rosenberger, Lutz Baumgarten, Fatima Alarab

Oxide electronics provide the key concepts and materials for enhancing silicon-based semiconductor technologies with novel functionalities. However, a basic but key property of semiconductor devices still needs to be unveiled in its oxidic counterparts: the ability to set or even switch between two types of carriers - either negatively (n) charged electrons

David Nesvorny, David Vokrouhlicky, Frank Shelly, Rogerio Deienno

Catalina Sky Survey (CSS) is a major survey of Near-Earth Objects (NEOs). In a recent work, we used CSS observations from 2005-2012 to develop a new population model of NEOs (NEOMOD). CSS's G96 telescope was upgraded in 2016 and detected over 10,000 unique NEOs since then. Here we characterize the NEO detection efficiency of G96 and use G96's NEO detections

Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega

In most work to date, graph signal sampling and reconstruction algorithms are intrinsically tied to graph properties, assuming bandlimitedness and optimal sampling set choices. However, practical scenarios often defy these assumptions, leading to suboptimal performance. In the context of sampling and reconstruction, graph irregularities lead to varying contr

Yikai Liu, Tushar K. Ghosh, Guang Lin, Ming Chen

Biased enhanced sampling methods utilizing collective variables (CVs) are powerful tools for sampling conformational ensembles. Due to high intrinsic dimensions, efficiently generating conformational ensembles for complex systems requires enhanced sampling on high-dimensional free energy surfaces. While methods like temperature-accelerated molecular dynamics

Cody Rucker, Brittany A. Erickson

Direct observations of earthquake nucleation and propagation are few and yet the next decade will likely see an unprecedented increase in indirect, surface observations that must be integrated into modeling efforts. Machine learning (ML) excels in the presence of large data and is an actively growing field in seismology. However, not all ML methods incorpora

Ljubisa Bojic, Matteo Cinelli, Dubravko Culibrk, Boris Delibasic

This paper explores the potential of a multidisciplinary approach to testing and aligning artificial intelligence (AI), specifically focusing on large language models (LLMs). Due to the rapid development and wide application of LLMs, challenges such as ethical alignment, controllability, and predictability of these models emerged as global risks. This study

Mohanad Odema, Hyoukjun Kwon, Mohammad Abdullah Al Faruque

To address increasing compute demand from recent multi-model workloads with heavy models like large language models, we propose to deploy heterogeneous chiplet-based multi-chip module (MCM)-based accelerators. We develop an advanced scheduling framework for heterogeneous MCM accelerators that comprehensively consider complex heterogeneity and inter-chiplet p

Hamed Hatami, Kaave Hosseini, Shachar Lovett, Anthony Ostuni

The log-rank conjecture, a longstanding problem in communication complexity, has persistently eluded resolution for decades. Consequently, some recent efforts have focused on potential approaches for establishing the conjecture in the special case of XOR functions, where the communication matrix is lifted from a boolean function, and the rank of the matrix e

R. Tyler Sutherland, Stephen D. Erickson

We propose a method to dynamically decouple every magnetically sensitive hyperfine sublevel of a trapped ion from magnetic field noise, simultaneously, using integrated circuits to adiabatically rotate its local quantization field. These integrated circuits allow passive adjustment of the effective polarization of any external (control or noise) field. By ro

Krishna Mullia, Fujun Luan, Xin Sun, Miloš Hašan

High-fidelity 3D assets with materials composed of fibers (including hair), complex layered material shaders, or fine scattering geometry are ubiquitous in high-end realistic rendering applications. Rendering such models is computationally expensive due to heavy shaders and long scattering paths. Moreover, implementing the shading and scattering models is no

F. Thomas Bruss

{\bf Abstract.} The present article is an essay about mathematical intuition and Artificial intelligence (A.I.), followed by a guided excursion to a well-known open problem. It has two objectives. The first is to reconcile the way of thinking of a computer program as a sequence of mathematically defined instructions with what we face nowadays with newer deve

Can Cui, Zichong Yang, Yupeng Zhou, Yunsheng Ma

Integrating large language models (LLMs) in autonomous vehicles enables conversation with AI systems to drive the vehicle. However, it also emphasizes the requirement for such systems to comprehend commands accurately and achieve higher-level personalization to adapt to the preferences of drivers or passengers over a more extended period. In this paper, we i

Gerard t Hooft

All quantum field theories that describe interacting bosonic elementary particles, share the feature that the zeroth order perturbation expansion describes non-interacting harmonic oscillators. This is explained in the paper. We then indicate that introducing interactions still leads to classical theories that can be compared with the quantum theories, but o

Joseph S. Schafer, Kate Starbird

Traditional research ethics has mainly and rightly been focused on making sure that participants are treated safely, justly, and ethically, to avoid the violation of their rights or putting participants in harm's way. Information integrity research within CSCW has also correspondingly mainly focused on these issues, and the focus of internet research ethics

Dániel Horváth, Jesús Bujalance Martín, Ferenc Gábor Erdős, Zoltán Istenes

Even though reinforcement-learning-based algorithms achieved superhuman performance in many domains, the field of robotics poses significant challenges as the state and action spaces are continuous, and the reward function is predominantly sparse. Furthermore, on many occasions, the agent is devoid of access to any form of demonstration. Inspired by human le

Keke Long, Haotian Shi, Zhiwei Chen, Zhaohui Liang

The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its importance, the comprehensive acquisition of vehicle trajectory data for the calculation of these macroscopic measures presents a challenge. To bridge this gap, this study aims to cal

Matthew J. Patrick, James K. Eckstein, Javier R. Lopez, Silvia Toderas

Quantification of microstructures is crucial for understanding processing-structure and structure-property relationships in polycrystalline materials. Delineating grain boundaries in bright-field transmission electron micrographs, however, is challenging due to complex diffraction contrast in images. Conventional edge detection algorithms are inadequate; ins

Xi Chen, Chang Gao, Zuowen Wang, Longbiao Cheng

Recurrent Neural Networks (RNNs) are useful in temporal sequence tasks. However, training RNNs involves dense matrix multiplications which require hardware that can support a large number of arithmetic operations and memory accesses. Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware. Inspi

Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker

Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs. However, future superhuman models will behave in complex ways too difficult for humans to reliably evaluat

Grigori Jasnovidov

In this contribution we study the asymptotics of \begin{eqnarray*} P(\exists t\ge 0 : B_H(L(t))-cL(t)>u), \quad u \to \infty, \end{eqnarray*} where $B_H, H\in (0,1)$ is a fractional Brownian motion, $L(t)$ is a non-negative pure jumps L\'{e}vy process independent of $B_H$, $c>0$.

Jehn-Ruey Jiang, Qiao-Yi Lin

Grover's algorithm is a well-known unstructured quantum search algorithm run on quantum computers. It constructs an oracle and calls the oracle O($\sqrt N$) times to locate specific data out of N unsorted data. This represents a quadratic speedup compared to the classical unstructured data sequential search algorithm, which requires to call the oracle O(N) t

Christoforos Galazis, Samuel Shepperd, Emma Brouwer, Sandro Queirós

The functional analysis of the left atrium (LA) is important for evaluating cardiac health and understanding diseases like atrial fibrillation. Cine MRI is ideally placed for the detailed 3D characterization of LA motion and deformation but is lacking appropriate acquisition and analysis tools. Here, we propose tools for the Analysis for Left Atrial Displace

Sasan Razmkhah, Ali Bozbey

We present a 4-bit Arithmetic Logic Unit (ALU) utilizing superconductor technology. The ALU serves as the central processing unit of a processor, performing crucial arithmetic and logical operations. We have adopted a bit-parallel architecture to ensure an efficient and streamlined design with minimal fanin/fanout and optimal latency. In terms of fabrication

Robert Angarone

We prove that an infinite block-Toeplitz matrix with finite diagonal support is totally nonnegative if and only if it is the weight matrix of a cylindrical network. This generalizes a well-known theorem of Brenti concerning finite totally nonnegative matrices and planar networks; in particular, our work gives an alternative, self-contained proof of the non-s

Baike She, Lei Xin, Philip E. Paré, Matthew Hale

Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this work we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread through the difference on the logarithmic scale of the infected cases. We bound the variance of the predictions made by GPR, which quantifies

Fabio Pavanello, Cedric Marchand, Paul Jimenez, Xavier Letartre

In the contemporary security landscape, the incorporation of photonics has emerged as a transformative force, unlocking a spectrum of possibilities to enhance the resilience and effectiveness of security primitives. This integration represents more than a mere technological augmentation; it signifies a paradigm shift towards innovative approaches capable of

Manuel Rebol, Krzysztof Pietroszek, Claudia Ranniger, Colton Hood

We present the design of a mixed reality (MR) telehealth training system that aims to close the gap between in-person and distance training and re-training for medical procedures. Our system uses real-time volumetric capture as a means for communicating and relating spatial information between the non-colocated trainee and instructor. The system's design is

Shelley Hebert, Slawomir Klimek, Matt McBride, J. Wilson Peoples

We introduce and investigate some examples of C$^*$-algebras which are related to multiplication maps in the ring of $p$-adic integers. We find ideals within these algebras and use the corresponding short exact sequences to compute the $K$-Theory.

Bradley Eck, Duygu Kabakci-Zorlu, Amadou Ba

The classical two-sample test of Kolmogorov-Smirnov (KS) is widely used to test whether empirical samples come from the same distribution. Even though most statistical packages provide an implementation, carrying out the test in big data settings can be challenging because it requires a full sort of the data. The popular Apache Spark system for big data proc

Maxime Girard, Rémi Nahon, Enzo Tartaglione, Van-Tam Nguyen

In this paper, we explore prior research and introduce a new methodology for classifying mental state levels based on EEG signals utilizing machine learning (ML). Our method proposes an optimized training method by introducing a validation set and a refined standardization process to rectify data leakage shortcomings observed in preceding studies. Furthermor

Abhishek Arora, Amit Acharya

Important physical observations in rupture dynamics such as static fault friction, short-slip, self-healing, and supershear phenomenon in cracks are studied. A continuum model of rupture dynamics is developed using the field dislocation mechanics (FDM) theory. The energy density function in our model encodes accepted and simple physical facts related to rock

Ana Fernández Vilas, Rebeca Díaz Redondo, Antón Lorenzo García

There is a consensus about the good sensing characteristics of Twitter to mine and uncover knowledge in financial markets, being considered a relevant feeder for taking decisions about buying or holding stock shares and even for detecting stock manipulation. Although Twitter hashtags allow to aggregate topic-related content, a specific mechanism for financia

Robin Henry, Rahul Gupta

The increasing adoption of measurement units in electrical power distribution grids has enabled the deployment of data-driven and measurement-based control schemes. Such schemes rely on measurement-based estimated models, where the models are first estimated using raw measurements and then used in the control problem. This work focuses on measurement-based e

Neil Lindquist, Piotr Luszczek, Jack Dongarra

Parker and L\^e introduced random butterfly transforms (RBTs) as a preprocessing technique to replace pivoting in dense LU factorization. Unfortunately, their FFT-like recursive structure restricts the dimensions of the matrix. Furthermore, on multi-node systems, efficient management of the communication overheads restricts the matrix's distribution even mor

Gabriel Kuntz, Junxiang Huang, Mitchell Rask, Alex Lindgren-Ruby

Living objects are able to consume chemical energy and process information independently from others. However, living objects can coordinate to form ordered groups such as schools of fish. This work considers these complex groups as living materials and presents imaging-based experiments of laboratory schools of fish to understand how this non-equilibrium ac

Mahabba El Sahili, Faisal N. Abu-Khzam

Given a graph $G$, an integer $k\geq 0$, and a non-negative integral function $f:V(G) \rightarrow \mathcal{N}$, the Vector Domination problem asks whether a set $S$ of vertices, of cardinality $k$ or less, exists in $G$ so that every vertex $v \in V(G)\setminus S$ has at least $f(v)$ neighbors in $S$. The problem generalizes several domination problems and i

Arvin Rasoulzadeh, Martin Kilian, Georg Nawratil

A smooth T-surface can be thought of as a generalization of a surface of revolution in such a way that the axis of rotation is not fixed at one point but rather traces a smooth path on the base plane. Furthermore, the action, by which the aforementioned surface is obtained does not need to be merely rotation but any ``suitable" planar equiform transformation

Yuexun Huang, Francisco Salces--Carcoba, Rana X Adhikari, Amir H. Safavi-Naeini

The vacuum beam guide (VBG) presents a completely different solution for quantum channels to overcome the limitations of existing fiber and satellite technologies for long-distance quantum communication. With an array of aligned lenses spaced kilometers apart, the VBG offers ultra-high transparency over a wide range of optical wavelengths. With realistic par

Jehyun Lee, Melissa Keranen

We consider the existence problem of uniformly resolvable decompositions of $K_v$ into subgraphs such that each resolution class contains only blocks isomorphic to the same graph. We give a complete solution for the case in which one resolution class is $K_2$ and the rest are $K_{1,5}$.

Mahmoud Jahanshahi, Audris Mockus

In Open Source Software, the source code and any other resources available in a project can be viewed or reused by anyone subject to often permissive licensing restrictions. In contrast to some studies of dependency-based reuse supported via package managers, no studies of OSS-wide copy-based reuse exist. This dataset seeks to encourage the studies of OSS-wi

Avner May, Dmitriy Serdyuk, Ankit Parag Shah, Otavio Braga

Audio-visual automatic speech recognition (AV-ASR) models are very effective at reducing word error rates on noisy speech, but require large amounts of transcribed AV training data. Recently, audio-visual self-supervised learning (SSL) approaches have been developed to reduce this dependence on transcribed AV data, but these methods are quite complex and com

Andrei Manolache

Deep anomaly detection methods have become increasingly popular in recent years, with methods like Stacked Autoencoders, Variational Autoencoders, and Generative Adversarial Networks greatly improving the state-of-the-art. Other methods rely on augmenting classical models (such as the One-Class Support Vector Machine), by learning an appropriate kernel funct

Peter E. Francis, Evita Nestoridi, Peter Winkler

Xavier and Yushi run a ``random race'' as follows. A continuous probability distribution $\mu$ on the real line is chosen. The runners begin at zero. At time $i$ Xavier draws $\mathbf{X}_i$ from $\mu$ and advances that distance, while Yushi advances by an independent drawing $\mathbf{Y}_i$. After $n$ such moves, Xavier wins a valuable prize provided he not o

Md Mahedi Hasan, Shoaib Meraj Sami, Nasser Nasrabadi

State-of-the-art face recognition (FR) models often experience a significant performance drop when dealing with facial images in surveillance scenarios where images are in low quality and often corrupted with noise. Leveraging facial characteristics, such as freckles, scars, gender, and ethnicity, becomes highly beneficial in improving FR performance in such

Sahal Shaji Mullappilly, Abdelrahman Shaker, Omkar Thawakar, Hisham Cholakkal

Climate change is one of the most significant challenges we face together as a society. Creating awareness and educating policy makers the wide-ranging impact of climate change is an essential step towards a sustainable future. Recently, Large Language Models (LLMs) like ChatGPT and Bard have shown impressive conversational abilities and excel in a wide vari

Yumu Yang, Mauricio Hippert, Enrico Speranza, Jorge Noronha

We compute first and second-order bulk-viscous transport properties due to weak-interaction processes in $npe$ matter in the neutrino transparent regime. The transport coefficients characterize the out-of-beta-equilibrium pressure corrections, which depend on the weak-interaction rates and the equation of state. We calculate these coefficients for realistic

Ehsan Shahoseini, Abbas Maarefparvar

Let $K/F$ be a finite extension of number fields and $S$ be a finite set of primes of $F$, including all the archimedean ones. In this paper, using some results of Gonz\'alez-Avil\'es \cite{Aviles}, we generalize the notions of the relative P\'olya group $\Po(K/F)$ \cite{ChabertI,MR2} and the Ostrowski quotient $\Ost(K/F)$ \cite{SRM} to their $S$-versions. U

Sahil Nokhwal, Nirman Kumar

Catastrophic forgetting(CF) is a significant challenge in continual learning (CL). In regularization-based approaches to mitigate CF, modifications to important training parameters are penalized in subsequent tasks using an appropriate loss function. We propose the RTRA, a modification to the widely used Elastic Weight Consolidation (EWC) regularization sche

Payal Kamboj, Ayan Banerjee, Varina L. Boerwinkle, Sandeep K. S. Gupta

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI were collected from 52 children with RE who had subs

Filipo Sharevski, Jennifer Vander Loop

Children encounter misinformation on social media in a similar capacity as their parents. Unlike their parents, children are an exceptionally vulnerable population because their cognitive abilities and emotional regulation are still maturing, rendering them more susceptible to misinformation and falsehoods online. Yet, little is known about children's experi

Henrique F. de Arruda, Kleber A. Oliveira, Yamir Moreno

Priority users (e.g., verified profiles on Twitter) are social media users whose content is promoted by recommendation algorithms. However, the impact of this heterogeneous user influence on opinion dynamics, such as polarization phenomena, is unknown. We conduct a computational mechanistic investigation of such consequences in a stylized setting. First, we

G. Alexi Rodriguez-Arelis, William J. Welch

Dimensional analysis (DA) pays attention to fundamental physical dimensions such as length and mass when modelling scientific and engineering systems. It goes back at least a century to Buckingham's Pi theorem, which characterizes a scientifically meaningful model in terms of a limited number of dimensionless variables. The methodology has only been exploite

Sahil Nokhwal, Nirman Kumar

Rehearsal-based techniques are commonly used to mitigate catastrophic forgetting (CF) in Incremental learning (IL). The quality of the exemplars selected is important for this purpose and most methods do not ensure the appropriate diversity of the selected exemplars. We propose a new technique "DSS" -- Diverse Selection of Samples from the input data stream

Subhodh Kotekal, Chao Gao

We study estimation of an $s$-sparse signal in the $p$-dimensional Gaussian sequence model with equicorrelated observations and derive the minimax rate. A new phenomenon emerges from correlation, namely the rate scales with respect to $p-2s$ and exhibits a phase transition at $p-2s \asymp \sqrt{p}$. Correlation is shown to be a blessing provided it is suffic