March 2023 arXiv papers — page 55

Ruijie Hou, Xiuxian Li, Yang Shi

This paper investigates online composite optimization in dynamic environments, where each objective or loss function contains a time-varying nondifferentiable regularizer. To resolve it, an online proximal gradient algorithm is studied for two distinct scenarios, including convex and strongly convex objectives without the smooth condition. In both scenarios,

Daehyun Kim, Chihiro Morooka

We study the set of (stationary) feasible payoffs of overlapping generation repeated games that can be achieved by action sequences in which every generation of players plays the same sequence of action profiles. First, we completely characterize the set of feasible payoffs given any fixed discount factor of players and the length of interaction. This allows

Huixu Dong, Haotian Guo, Sihao Yang, Chen Qiu

Compliant grippers, owing to adaptivity and safety, have attracted considerable attention for unstructured grasping in real applications, such as industrial or logistic scenarios. However, accurately modeling the bidirectional relationship between shape deformation and contact force for such grippers, the Fin-Ray grippers as an example, remains stagnant to d

Yu Dong, Jie Liang, Longbing Cao, Daniel Catchpoole

Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncerta

Martí Pi, Francesco Ancilotto, Manuel Barranco, Samuel L. Butler

We have studied self-sustained, deformable, rotating liquid He cylinders of infinite length. In the normal fluid $^3$He case, we have employed a classical model where only surface tension and centrifugal forces are taken into account, as well as the Density Functional Theory (DFT) approach in conjunction with a semi-classical Thomas-Fermi approximation for t

Yihui Weng, Qin Li, Guozhuo Tan, Wei Su

Numerical simulation and analysis are carried out on interactions between a 2D/3D conical shock wave and an axisymmetric boundary layer with reference to the experiment by Kussoy et al., in which the shock was generated by a 15-deg half-angle cone in a tube at 15-deg angle of attack (AOA). Based on the RANS equations and Menter's SST turbulence model, the pr

Teerapat Jenrungrot, Michael Chinen, W. Bastiaan Kleijn, Jan Skoglund

We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec trains a Transformer language model to predict the fine tokens from the coarse ones in a g

George N. Wojcik, Lisa L. Everett, Shu Tian Eu, Ricardo Ximenes

The paradigm of portal matter represents a well-motivated extension to models with kinetic mixing/vector portal dark matter. In previous work, we constructed a simple leptonic portal matter model in which the portal matter fields could mediate a new physics correction to the anomalous magnetic moment of the muon consistent with the observed discrepancy betwe

Joseph Cohen, Xun Huan, Jun Ni

In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs. Used for the 2021 PHM Data Challenge, the new Commercial Modular Aero-Propulsion System Simulation dataset from NASA is an open-source benchmark containing simulated turbofan engin

Sihan Zeng, Thinh T. Doan, Justin Romberg

The aim of this paper is to improve the understanding of the optimization landscape for policy optimization problems in reinforcement learning. Specifically, we show that the superlevel set of the objective function with respect to the policy parameter is always a connected set both in the tabular setting and under policies represented by a class of neural n

Xiangyun Lei, Joseph Montoya

GMP-Featurizer is a lightweight, accurate, efficient, and scalable software package for calculating the Gaussian Multipole (GMP) features \cite{GMP} for a variety of atomic systems with elements across the periodic table. Starting from the GMP feature computation module from AmpTorch \cite{amptorch}, the capability of GMP-Featurizer has since been greatly im

Wei Zi, Qian Li, Xiaoming Sun

We propose a linear-size synthesis of the multi-controlled Toffoli gate on qudits with at most one borrowed ancilla. This one ancilla can even be saved when the qudit dimension is odd. Our synthesis leads to improvements in various quantum algorithms implemented on qudits. In particular, we obtain (i) a linear-size and one-clean-ancilla synthesis of multi-co

Fan Peng, Renkuan Cao, Cui Nie, Tingyu Xu

We performed a series of molecular dynamics simulations on monodisperse polymer melts to investigate the formation of shear banding. Under high shear rates, shear banding occurs, which is accompanied with the entanglement heterogeneity intimately. Interestingly, the same linear relationship between the end-to-end distance $R_{ee}$ and entanglement density $Z

Chandan Setty, Matteo Baggioli, Alessio Zaccone

The role of anharmonicity on superconductivity has often been disregarded in the past. Recently, it has been recognized that anharmonic decoherence could play a fundamental role in determining the superconducting properties (electron-phonon coupling, critical temperature, etc) of a large class of materials, including systems close to structural soft-mode ins

Trung Pham, Mehran Maghoumi, Wanli Jiang, Bala Siva Sashank Jujjavarapu

Achieving robust and real-time 3D perception is fundamental for autonomous vehicles. While most existing 3D perception methods prioritize detection accuracy, they often overlook critical aspects such as computational efficiency, onboard chip deployment friendliness, resilience to sensor mounting deviations, and adaptability to various vehicle types. To addre

Wanqing Tian, Chang-Xing Ma

Confidence interval (CI) methods for stratified bilateral studies use intraclass correlation to avoid misleading results. In this article, we propose four CI methods (sample-size weighted global MLE-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, and complete MLE-based score CI) to investigate CIs of proportion ratios to clinical

Daniel Muñoz-Santiburcio, Jorge Kohanoff, Emilio Artacho

Traditionally, it has been assumed that the stopping of a swift ion travelling through matter can be understood in terms of two essentially independent components, i.e. electronic vs. nuclear. Performing extensive Ehrenfest MD simulations of the process of proton irradiation of water ice that accurately describe not only the non-adiabatic dynamics of the ele

Salik Ram Khanal, Prabin Sharma, Hugo Fernandes, João Barroso

Facial expression is a way of communication that can be used to interact with computers or other electronic devices and the recognition of emotion from faces is an emerging practice with application in many fields. There are many cloud-based vision application programming interfaces available that recognize emotion from facial images and video. In this artic

Wenbo Hu, Xin Sun, Qiang liu, Le Wu

Post-click conversion rate (CVR) is a reliable indicator of online customers' preferences, making it crucial for developing recommender systems. A major challenge in predicting CVR is severe selection bias, arising from users' inherent self-selection behavior and the system's item selection process. To mitigate this issue, the inverse propensity score (IPS)

David H. Sliski, Cullen H. Blake, Jason D. Eastman, Samuel Halverson

An optical fiber link to a telescope provides many advantages for spectrometers designed to detect and characterize extrasolar planets through precise radial velocity (PRV) measurements. In the seeing-limited regime, a multi-mode fiber is typically used so that a significant amount of starlight may be captured. In the near-diffraction-limited case, either wi

Xiantao Fan, Jian-Xun Wang

Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics based solvers are inadequate to handle the increasing demand for large-scale and long-period simulations. The ever-increasing avail

Janiya R. Peters, Orissa Rose

The global pandemic COVID-19 posed numerous challenges for U.S. restaurants and food services. Many businesses adopted contactless ordering and cashless payment policies to comply with emergency health mandates. Even with national and public health emergency mandates set to expire in May 2023, cashless payment services continue to thrive through online order

J. J. Toscano, O. Vázquez-Hernández

The renormalizable extension of a pure Yang-Mills theory with Lorentz violation is characterized by the CPT-Even $(k_F)_{\mu \nu \lambda \rho}$ and the CPT-Odd $(k_{AF})_\mu$ constant Lorentz coefficients. In this paper, the one-loop structure of the theory up to second order in these Lorentz violating coefficients is studied using the BFM-gauge. Results for

Tim Scargill, Sangjun Eom, Ying Chen, Maria Gorlatova

Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, enviro

Junjie Liu, Hanlin Nie

Dissipation has traditionally been considered a hindrance to quantum information processing, but recent studies have shown that it can be harnessed to generate desired quantum states. To be useful for practical applications, the ability to speed up the dissipative evolution is crucial. In this study, we focus on a Markovian dissipative state preparation sche

Hardik Parwana, Dimitra Panagou

Control Barrier Functions offer safety certificates by dictating controllers that enforce safety constraints. However, their response depends on the classK function that is used to restrict the rate of change of the value of the barrier function along the system trajectories. This paper introduces the notion of a Rate-Tunable (RT) CBF, which allows for onlin

Xiaoke Huang, Yiji Cheng, Yansong Tang, Xiu Li

Efficiently digitizing high-fidelity animatable human avatars from videos is a challenging and active research topic. Recent volume rendering-based neural representations open a new way for human digitization with their friendly usability and photo-realistic reconstruction quality. However, they are inefficient for long optimization times and slow inference

Tao Yang

This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables. Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem can be solved. Our contributions are Fo

Sophia Hamer, Jennifer Sleeman, Ivanka Stajner

Poor air quality can have a significant impact on human health. The National Oceanic and Atmospheric Administration (NOAA) air quality forecasting guidance is challenged by the increasing presence of extreme air quality events due to extreme weather events such as wild fires and heatwaves. These extreme air quality events further affect human health. Traditi

Luca Baracco, Olga Bernardi, Christian Lange, Marco Mazzucchelli

We prove an analogue of the 4-dimensional local Viterbo conjecture for the higher Ekeland-Hofer capacities: on the space of 4-dimensional smooth star-shaped domains of unitary volume, endowed with the $C^3$ topology, the local maximizers of the $k$-th Ekeland-Hofer capacities are those domains symplectomorphic to suitable rational ellipsoids.

James Goff, Yu Zhang, Christian F. A. Negre, Andrew Rohskopf

A shadow molecular dynamics scheme for flexible charge models is presented, where the shadow Born-Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, a

Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. We review over 80 foundation models trained on

Islam Nassar, Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose ProtoCon, a novel SSL method aimed at the less-explored label-scarce SSL where such methods usually underperform. ProtoCon refines t

Piotr Hajłasz, Armin Schikorra

We develop a new method leading to an elementary proof of a generalization of Gromov's theorem about non existence of H\"older embeddings into the Heisenberg group.

Jiantao Wu, Shentong Mo, Xiang Yang, Muhammad Awais

One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity. Previous studies, which increase the information bottleneck during training, tend to lose the constraint of disentanglement, leading to the information diffusion problem. In this paper, we present a novel framewo

Florian Riedel

We introduce a notion of formally \'etale $\mathbb{E}_{\infty}$-coalgebras and show that they admit essentially unique, functorial lifts along square zero extensions of $\mathbb{E}_{\infty}$-rings. Using this, we show that for a perfect $\mathbb{F}_p$-algebra $k$, Weil restriction along the augmentation $\mathbb{W}(k)\to k$ induces a fully faithful functor f

Shicheng Wan, Hong Lin, Wensheng Gan, Jiahui Chen

Since the first appearance of the World Wide Web, people more rely on the Web for their cyber social activities. The second phase of World Wide Web, named Web 2.0, has been extensively attracting worldwide people that participate in building and enjoying the virtual world. Nowadays, the next internet revolution: Web3 is going to open new opportunities for tr

George Trimponias, Thomas G. Dietterich

Exogenous state variables and rewards can slow reinforcement learning by injecting uncontrolled variation into the reward signal. This paper formalizes exogenous state variables and rewards and shows that if the reward function decomposes additively into endogenous and exogenous components, the MDP can be decomposed into an exogenous Markov Reward Process (b

Neil Harrison, Mun K. Chan

A flattened electronic band is one of several possible routes for increasing the strength of the pairing interactions in a superconductor. With this in mind, we show here that thermodynamic measurements of the high-Tc cuprates reveal an appreciably stronger electron correlation-driven flattening of the quasiparticle bands than has previously been indicated.

Víctor Becerril

In this paper we characterize the relative Gorenstein weak global dimension of the generalized Gorenstein $\mathrm{FP}_n$-flat $R$-modules and Projective Coresolved $\mathrm{FP}_n$-flat $R$-modules recently studied by S. Estrada, A. Iacob, and M. A. P\'erez. As application we prove that the weak global dimension that comes from the Gorenstein $\mathrm{FP}_n$

Marcin Bownik

Akemann and Weaver showed Lyapunov-type theorem for rank one positive semidefinite matrices which is an extension of Weaver's KS$_2$ conjecture that was proven by Marcus, Spielman, and Srivastava in their breakthrough solution of the Kadison-Singer problem. They conjectured that a similar result holds for higher rank matrices. We prove the conjecture of Akem

Darshan Chalise, Carlos Juarez-Yescas, Beniamin Zahiri, Paul V. Braun

Inorganic solid-state battery electrolytes show high ionic conductivities and enable the fabrication of all solid-state batteries. In this work, we present the temperature dependence of spin-lattice relaxation time (T1), spin-spin relaxation time (T2), and resonance linewidth of the 7Li nuclear magnetic resonance (NMR) for four solid-state battery electrolyt

Shyam Sundar Saravanan, Tie Luo, Mao Van Ngo

Anomaly detection is widely used in network intrusion detection, autonomous driving, medical diagnosis, credit card frauds, etc. However, several key challenges remain open, such as lack of ground truth labels, presence of complex temporal patterns, and generalizing over different datasets. This paper proposes TSI-GAN, an unsupervised anomaly detection model

Hasan Salehi Najafabadi, Mark. A. Meier, Gary A. Hallock

An effect we have termed the acousto-thermoelectric effect is theorized for temperature gradients driven by acoustic modulation. The effect produces a dynamic and spatially varying voltage. Adiabatic acoustic fluctuations in a solid cause temperature variations and temperature gradients that generate quasi-static thermoelectric effects correlated with the ti

Yiqun Mei, He Zhang, Xuaner Zhang, Jianming Zhang

Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map. However, these methods are not intuitive for user interaction and lack precise lighting control. We introduce LightPainter, a scribble-based relighting system that allows users to interactively ma

Michael Hertneck, Frank Allgöwer

Most approaches for self-triggered control (STC) of nonlinear networked control systems (NCS) require measurements of the full system state to determine transmission times. However, for most control systems only a lower dimensional output is available. To bridge this gap, we present in this paper an output-feedback STC approach for nonlinear NCS. An asymptot

Vinicius V. Santana, Erbet Costa, Carine M. Rebello, Ana Mafalda Ribeiro

This study presents a systematic machine learning approach for creating efficient hybrid models and discovering sorption uptake models in non-linear advection-diffusion-sorption systems. It demonstrates an effective method to train these complex systems using gradient based optimizers, adjoint sensitivity analysis, and JIT-compiled vector Jacobian products,

Karin Verelst

Isaac Newton, in popular imagination the Ur-scientist, was an outstanding humanist scholar. His researches on, among others, ancient philosophy, are thorough and appear to be connected to and fit within his larger philosophical and theological agenda. It is therefore relevant to take a closer look at Newton's intellectual choices, at how and why precisely he

Wanqing Tian, Chang-Xing Ma

Intraclass correlation in bilateral data has been investigated in recent decades with various statistical methods. In practice, stratifying bilateral data by some control variables will provide more sophisticated statistical results to satisfy different research proposed in random clinical trials. In this article, we propose three test statistics (likelihood

Shrivats Agrawal

The potential of large language models (LLMs) to reason like humans has been a highly contested topic in Machine Learning communities. However, the reasoning abilities of humans are multifaceted and can be seen in various forms, including analogical, spatial and moral reasoning, among others. This fact raises the question whether LLMs can perform equally wel

Uwe Hernandez Acosta, Burkhard Kämpfer

The impact of a strong electromagnetic background field on otherwise perturbative QED processes is studied in the momentum-space formulation. The univariate background field is assumed to have finite support in time, thus being suitable to provide a model for a strong laser pulse in plane-wave approximation. The usually employed Furry picture in position spa

Shivank S. Shukla, Christopher Kuenneth, Rampi Ramprasad

Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, pain

Thomas Manzini, Robin Murphy, David Merrick, Justin Adams

Data collected at Hurricane Ian (2022) quantifies the demands that small uncrewed aerial systems (UAS), or drones, place on the network communication infrastructure and identifies gaps in the field. Drones have been increasingly used since Hurricane Katrina (2005) for disaster response, however getting the data from the drone to the appropriate decision make

Berfu Buyukoz

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification and sentiment analysis of product reviews. A "cross-context" setting is enabled using test sets that are distinct from the training data. Specifically, in the news classificati

Davide Lazzati, Rosalba Perna, Benjamin Gompertz, Andrew Levan

Long and short gamma-ray bursts (GRBs), canonically separated at around 2 seconds duration, are associated with different progenitors: the collapse of a massive star and the merger of two compact objects, respectively. GRB 191019A was a long GRB ($T_{90}\sim64$ s). Despite the relatively small redshift z=0.248 and HST followup observations, an accompanying s

Yi Tian Xu, Jimmy Li, Di Wu, Michael Jenkin

With the continuous growth in communication network complexity and traffic volume, communication load balancing solutions are receiving increasing attention. Specifically, reinforcement learning (RL)-based methods have shown impressive performance compared with traditional rule-based methods. However, standard RL methods generally require an enormous amount

Abhisek Konar, Di Wu, Yi Tian Xu, Seowoo Jang

Communication load balancing aims to balance the load between different available resources, and thus improve the quality of service for network systems. After formulating the load balancing (LB) as a Markov decision process problem, reinforcement learning (RL) has recently proven effective in addressing the LB problem. To leverage the benefits of classical

Shanle Yao, Babak Rahimi Ardabili, Armin Danesh Pazho, Ghazal Alinezhad Noghre

Smart Video surveillance systems have become important recently for ensuring public safety and security, especially in smart cities. However, applying real-time artificial intelligence technologies combined with low-latency notification and alarming has made deploying these systems quite challenging. This paper presents a case study for designing and deployi

Mohammad Musavinezhad, Dominik Rattenbacher, Alexey Shkarin, Jan Renger

We measure the quantum efficiency (QE) of individual dibenzoterrylene (DBT) molecules embedded in para-dichlorobenzene at cryogenic temperatures. To achieve this, we apply two distinct methods based on the maximal photon emission and on the power required to saturate the zero-phonon line. We find that the outcome of the two approaches are in good agreement,

Yann Cado, Mariano Quirós

If the inflaton field is coupled to the hypercharge Chern-Simons density $F\tilde F$, an explosive production of helical gauge fields when inflation ends can trigger baryogenesis at the electroweak phase transition. Besides, Higgs inflation identifies the inflaton with the Higgs field $\mathcal H$, thus relating cosmological observables to properties of elec

Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L. Gao

Our goal is to develop a general strategy to decompose a random variable $X$ into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural exponential families, $X$ can be "thinned" into independent random variables $X^{(1)}, \ldots, X^{(K)}$, such that $X = \s

Tiantian Geng, Teng Wang, Jinming Duan, Runmin Cong

Existing audio-visual event localization (AVE) handles manually trimmed videos with only a single instance in each of them. However, this setting is unrealistic as natural videos often contain numerous audio-visual events with different categories. To better adapt to real-life applications, in this paper we focus on the task of dense-localizing audio-visual

Alexander Brown, P. Christian Schneider, Kevin France, Cynthia S. Froning

The high energy X-ray and ultraviolet (UV) radiation fields of exoplanet host stars play a crucial role in controlling the atmospheric conditions and the potential habitability of exoplanets. Major surveys of the X-ray/UV emissions from late-type (K and M spectral type) exoplanet hosts have been conducted by the MUSCLES and Mega-MUSCLES Hubble Space Telescop

Paula Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon

Hamilton-Jacobi partial differential equations (HJ PDEs) have deep connections with a wide range of fields, including optimal control, differential games, and imaging sciences. By considering the time variable to be a higher dimensional quantity, HJ PDEs can be extended to the multi-time case. In this paper, we establish a novel theoretical connection betwee

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $e^+e^-$ collision data corresponding to an integrated luminosity of 7.33~${\rm fb^{-1}}$ recorded by the BESIII detector at center-of-mass energies between 4.128 and 4.226~${\rm GeV}$, we present an analysis of the decay $D_{s}^{+} \to \pi^{+}\pi^{-} e^{+}\nu_{e}$, where the $D_s^+$ is produced via the process $e^+e^- \to D_{s}^{*\pm}D_{s}^{\mp}$. We

Giovanni Brigati, Jean Dolbeault, Nikita Simonov

This paper is devoted to stability results for the Gaussian logarithmic Sobolev inequality, with explicit stability constants.

Ryan J. MacDonald, Natasha E. Batalha

Exoplanet atmospheric retrieval is a computational technique widely used to infer properties of planetary atmospheres from remote spectroscopic observations. Retrieval codes typically employ Bayesian sampling algorithms or machine learning approaches to explore the range of atmospheric properties (e.g., chemical composition, temperature structure, aerosols)

Whitney Tso, Wenkun Wu, David N. Seidman, Olle G. Heinonen

To better understand the equilibrium $\gamma^\prime$(L1$_2$) precipitate morphology in Co-based superalloys, a phase field modeling sensitivity analysis is conducted to examine how four phase-field parameters [initial Co concentration ($c_0$), double-well barrier height ($\omega$), gradient energy density coefficient ($\kappa$), and lattice misfit strain ($\

Tomasz Downarowicz, Mateusz Więcek

We provide a definition of a $\prec$-asymptotic pair in a topological action of a countable group $G$, where $\prec$ is an order on $G$ of type $\mathbb Z$. We then prove that if $G$ is a countable amenable group and $(X,G)$ is a topological $G$-action of positive entropy, then for every multiorder $(\tilde{\mathcal O},\nu,G)$ and $\nu$-almost every order $\

Jacob R. Epifano, Ravi P. Ramachandran, Aaron J. Masino, Ghulam Rasool

In the last few years, many works have tried to explain the predictions of deep learning models. Few methods, however, have been proposed to verify the accuracy or faithfulness of these explanations. Recently, influence functions, which is a method that approximates the effect that leave-one-out training has on the loss function, has been shown to be fragile

Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo

The notion of replicable algorithms was introduced in Impagliazzo et al. [STOC '22] to describe randomized algorithms that are stable under the resampling of their inputs. More precisely, a replicable algorithm gives the same output with high probability when its randomness is fixed and it is run on a new i.i.d. sample drawn from the same distribution. Using

Trudi Di Qi, LouAnne Boyd, Scott Fitzpatrick, Meghna Raswan

Improving object manipulation skills through hand-object interaction exercises is crucial for rehabilitation. Despite limited healthcare resources, physical therapists propose remote exercise routines followed up by remote monitoring. However, remote motor skills assessment remains challenging due to the lack of effective motion visualizations. Therefore, ex

Philip Korman

This paper deals with various cases of resonance, which is a fundamental concept of science and engineering. Specifically, we study the connections between periodic and unbounded solutions for several classes of equations and systems. In particular, we extend the classical Massera's theorem, dealing with periodic systems of the type \[ x'=A(t)x+f(t) \,, \] a

Mitchell Bredice, Michael G. Rozman, Jonathan Smucker, Eric Farmer

In this study, molecular dynamics simulations were conducted to investigate the relaxation of the internal energy in nano-sized particles and its impact on the nucleation of atomic clusters. Quantum-mechanical potentials were utilized to analyze the growth and collision relaxation of the internal energy of Ar$_n$H$^+$ clusters in a metastable Ar gas. The res

Jianqiang Wang, Dandan Ding, Zhan Ma

This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud

Nicolas Boizard, Kevin El Haddad, Thierry Ravet, François Cresson

Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparis

Amine Yamlahi, Thuy Nuong Tran, Patrick Godau, Melanie Schellenberg

Surgical scene understanding is a key prerequisite for contextaware decision support in the operating room. While deep learning-based approaches have already reached or even surpassed human performance in various fields, the task of surgical action recognition remains a major challenge. With this contribution, we are the first to investigate the concept of s

Salma Afifi, Febin Sunny, Mahdi Nikdast, Sudeep Pasricha

Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision. However, the complex structure of these models creates challenges for accelerating their execution on conventional electronic platforms. We propose the first silicon photonic hardware neural network accelerator c

Ronnie de Souza Santos, Brody Stuart-Verner, Cleyton Magalhaes

Diversity is an essential aspect of software development because technology influences almost every aspect of modern society, and if the software industry lacks diversity, software products might unintentionally constrain groups of individuals instead of promoting an equalitarian experience to all. In this study, we investigate the perspectives of transgende

Andrew J. Levan, Daniele B. Malesani, Benjamin P. Gompertz, Anya E. Nugent

The majority of long duration ($>2$ s) gamma-ray bursts (GRBs) are believed to arise from the collapse of massive stars \cite{Hjorth+03}, with a small proportion created from the merger of compact objects. Most of these systems are likely formed via standard stellar evolution pathways. However, it has long been thought that a fraction of GRBs may instead be

Yuliya Mishura, Andrey Pilipenko, Anton Yurchenko-Tytarenko

The present paper investigates Cox-Ingersoll-Ross (CIR) processes of dimension less than 1, with a focus on obtaining an equation of a new type including local times for the square root of the CIR process. We utilize the fact that non-negative diffusion processes can be obtained by the transformation of time and scale of some reflected Brownian motion to der

Febin Sunny, Mahdi Nikdast, Sudeep Pasricha

Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference. Contemporary computing platforms such as CPUs, GPUs, and TPUs are struggling to keep up with the demands of these AI applications. Non-coherent optical computing represents a promising appr

Sudeep Pasricha

The next generation of computer engineers and scientists must be proficient in not just the technical knowledge required to analyze, optimize, and create emerging microelectronics systems, but also with the skills required to make ethical decisions during design. Teaching computer ethics in computing curricula is therefore becoming an important requirement w

Georgina Curto, Flavio Comim

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynam

Samik Sadhu, Hynek Hermansky

We show that training a multi-headed self-attention-based deep network to predict deleted, information-dense 2-8 Hz speech modulations over a 1.5-second section of a speech utterance is an effective way to make machines learn to extract speech modulations using time-domain contextual information. Our work exhibits that, once trained on large volumes of unlab

Theodore Broeren, Kristopher Klein

The upcoming NASA mission HelioSwarm will use nine spacecraft to make the first simultaneous multi-point measurements of space plasmas spanning multiple scales. Using the wave-telescope technique, HelioSwarm's measurements will allow for both the calculation of the power in wavevector-and-frequency space and the characterization of the associated dispersion

Jennifer Buoncore, Alexis Sobecki, Brian Lee Beatty

Atherosclerotic lesions within carotid and cerebral vessels are likely to influence hemodynamics and manifest into vascular pathologies, including Alzheimers Disease and ischemic stroke. Hemodynamics are influenced by changes in luminal diameter of vessels and wall shear stress derived from turbulence, which directly relates to the surface topography of the

Thomas Schraivogel, Evelin Martine Christlmaier, Pablo López Ríos, Ali Alavi

We demonstrate the accuracy of ground-state energies of the transcorrelated Hamiltonian, employing sophisticated Jastrow factors obtained from variational Monte Carlo, together with the coupled cluster and distinguishable cluster methods at the level of singles and doubles excitations. Our results show that already with the cc-pVTZ basis the transcorrelated

Bingyi Zhang, Viktor Prasanna

Graph Neural Network (GNN) inference is used in many real-world applications. Data sparsity in GNN inference, including sparsity in the input graph and the GNN model, offer opportunities to further speed up inference. Also, many pruning techniques have been proposed for model compression that increase the data sparsity of GNNs. We propose Dynasparse, a compr

N. J. Abdulameer, U. Acharya, C. Aidala, Y. Akiba

PHENIX presents a simultaneous measurement of the production of direct $\gamma$ and $\pi^0$ in $d$$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV over a $p_T$ range of 7.5 to 18 GeV/$c$ for different event samples selected by event activity, i.e. charged-particle multiplicity detected at forward rapidity. Direct-photon yields are used to empirically estimate

Richard Tarbell, Kim-Kwang Raymond Choo, Glenn Dietrich, Anthony Rios

Electronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct access to EMR data without needing a da

Abram C. Ellison, Keith Julien

Gyroscopic alignment of a fluid occurs when flow structures align with the rotation axis. This often gives rise to highly spatially anisotropic columnar structures that in combination with complex domain boundaries pose challenges for efficient numerical discretizations and computations. We define gyroscopic polynomials to be three-dimensional polynomials ex

Lingwei Cheng, Alexandra Chouldechova

Algorithm aversion occurs when humans are reluctant to use algorithms despite their superior performance. Studies show that giving users outcome control by providing agency over how models' predictions are incorporated into decision-making mitigates algorithm aversion. We study whether algorithm aversion is mitigated by process control, wherein users can dec

Basem Abdellatif, Mostafa M. Shibl, Tamer Khattab, John Tadrous

The deployment of low earth orbit (LEO) satellites with terrestrial networks can potentially increase the efficiency and reduce the cost of relaying content from a data center to a set of edge caches hosted by 6G and beyond enabled macro base stations. In this work, the characteristics of the communication system and the mobility of LEO satellites are thorou

Emmanouil Grigoriou, Carlos Navarrete-Benlloch

Equilibrium phase transitions usually emerge from the microscopic behavior of many-body systems and are associated to interesting phenomena such as the generation of long-range order and spontaneous symmetry breaking. They can be defined through the non-analytic behavior of thermodynamic potentials in the thermodynamic limit. This limit is obtained when the

Ashutosh Singh, Maria Sebastian, Yuanping Chen, Po-Yao Chang

In mirror-symmetric systems, there is a possibility of the realization of extended gapless electronic states characterized as nodal lines or rings. Strain induced modifications to these states lead to emergence of different classes of nodal rings with qualitatively different physical properties. Here we study optical response and the electromagnetic wave pro

Thanh-Dung Le, Philippe Jouvet, Rita Noumeir

Transformer-based models have shown outstanding results in natural language processing but face challenges in applications like classifying small-scale clinical texts, especially with constrained computational resources. This study presents a customized Mixture of Expert (MoE) Transformer models for classifying small-scale French clinical texts at CHU Sainte

Adam M. Lehavi, Seongtae Kim

In the realm of cybersecurity, intrusion detection systems (IDS) detect and prevent attacks based on collected computer and network data. In recent research, IDS models have been constructed using machine learning (ML) and deep learning (DL) methods such as Random Forest (RF) and deep neural networks (DNN). Feature selection (FS) can be used to construct fas

Aicha Baya Goumeidane, Djemel Ziou, Nafaa Nacereddine

Inertia Axes are involved in many techniques for image content measurement when involving information obtained from lines, angles, centroids... etc. We investigate, here, the estimation of the main axis of inertia of an object in the image. We identify the coincidence conditions of the Scale Space Radon Transform (SSRT) maximum and the inertia main axis. We

Ou Zheng, Mohamed Abdel-Aty, Zijin Wang, Shengxuan Ding

Crash data of autonomous vehicles (AV) or vehicles equipped with advanced driver assistance systems (ADAS) are the key information to understand the crash nature and to enhance the automation systems. However, most of the existing crash data sources are either limited by the sample size or suffer from missing or unverified data. To contribute to the AV safet