March 2023 arXiv papers — page 122

Jyotish Robin, Elza Erkip

Massive Machine-Type Communications (mMTC) is a key service category in the current generation of wireless networks featuring an extremely high density of energy and resource-limited devices with sparse and sporadic activity patterns. In order to enable random access in such mMTC networks, base station needs to identify the active devices while operating wit

Daniel Azagra, Anthony Cappello, Piotr Hajłasz

We show a new, elementary and geometric proof of the classical Alexandrov theorem about the second order differentiability of convex functions. We also show new proofs of recent results about Lusin approximation of convex functions and convex bodies by $C^{1,1}$ convex functions and convex bodies.

Joyce Zhou, Elena Glassman, Daniel S. Weld

Scientists and science journalists, among others, often need to make sense of a large number of papers and how they compare with each other in scope, focus, findings, or any other important factors. However, with a large corpus of papers, it's cognitively demanding to pairwise compare and contrast them all with each other. Fully automating this review proces

Lucas Lamata

This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews specific high-impact topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their experimental reali

Jonathan Cervantes, Mike Krebs

In this paper, we take a modest first step towards a systematic study of chromatic numbers of Cayley graphs on abelian groups. We lose little when we consider these graphs only when they are connected and of finite degree. As in the work of Heuberger and others, in such cases the graph can be represented by an $m\times r$ integer matrix, where we call $m$ th

Yu Wang, Lei Cao, Yizhou Yan, Samuel Madden

Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evaluating outlier detection results and effectively turn the outliers into actionable insights, the users often expect a system to automatically produce interpretable summarizations of

Alberto Castillo Gómez, Christof Geiss

Inspired by the work of Geiss, Leclerc and Schr\"oer [Represent. Theory 20, (2016)] we realize the enveloping algebra of the positive part of an affine Kac-Moody Lie algebra of Dynkin type $\tilde{\mathsf{C}}_n$ as a generalized composition algebra of constructible functions on the varieties of locally free representations of the corresponding 1-Iwanaga-Gore

Youzhi Liang, Wen Liang, Jianguo Jia

Vibration signals have been increasingly utilized in various engineering fields for analysis and monitoring purposes, including structural health monitoring, fault diagnosis and damage detection, where vibration signals can provide valuable information about the condition and integrity of structures. In recent years, there has been a growing trend towards th

Paolo Lipparini

We study contact posets and show that every contact poset can be embedded into a Boolean poset with overlap contact relation. Contact posets and (nonadditive) contact semilattices have the superamalgamation property, Fra\"\i ss\'e limits and model completion. Some results apply to event structures with binary conflict, as introduced in computer science.

Prithvi Akella, Wyatt Ubellacker, Aaron D. Ames

Leveraging recent developments in black-box risk-aware verification, we provide three algorithms that generate probabilistic guarantees on (1) optimality of solutions, (2) recursive feasibility, and (3) maximum controller runtimes for general nonlinear safety-critical finite-time optimal controllers. These methods forego the usual (perhaps) restrictive assum

Michael Gleicher, Maria Riveiro, Tatiana von Landesberger, Oliver Deussen

Visualization researchers and visualization professionals seek appropriate abstractions of visualization requirements that permit considering visualization solutions independently from specific problems. Abstractions can help us design, analyze, organize, and evaluate the things we create. The literature has many task structures (taxonomies, typologies, etc.

Samuel Epstein

In recent results, it has been proven that all sampling methods produce outliers. In this paper, we extend these results to quantum information theory. Projectors of large rank must contain pure quantum states in their images that are outlying states. Otherwise, the projectors are exotic, in that they have high mutual information with the halting sequence. T

Tyler Parsotan, Sibasish Laha, David M. Palmer, Amy Lien

The Swift Burst Alert Telescope (BAT) is a coded aperture gamma-ray instrument with a large field of view that primarily operates in survey mode when it is not triggering on transient events. The survey data consists of eighty-channel detector plane histograms that accumulate photon counts over time periods of at least 5 minutes. These histograms are process

Xin Xiong, Eduardo Pavez, Antonio Ortega, Balu Adsumilli

User generated content (UGC) refers to videos that are uploaded by users and shared over the Internet. UGC may have low quality due to noise and previous compression. When re-encoding UGC for streaming or downloading, a traditional video coding pipeline will perform rate-distortion (RD) optimization to choose coding parameters. However, in the UGC video codi

Sabyasachi Bandyopadhyay, Ahna Cecil, Jessica Sena, Andrea Davidson

Existing Intensive Care Unit (ICU) delirium prediction models do not consider environmental factors despite strong evidence of their influence on delirium. This study reports the first deep-learning based delirium prediction model for ICU patients using only ambient noise and light information. Ambient light and noise intensities were measured from ICU rooms

Subhash Nerella, Ziyuan Guan, Scott Siegel, Jiaqing Zhang

The intensive care unit (ICU) is a specialized hospital space where critically ill patients receive intensive care and monitoring. Comprehensive monitoring is imperative in assessing patients conditions, in particular acuity, and ultimately the quality of care. However, the extent of patient monitoring in the ICU is limited due to time constraints and the wo

Vishal Anand, Vivek Narsimhan

Particles in inertialess flows of shear thinning fluids are a model representation for several systems in biology, ecology, and micro-fluidics.In this paper, we analyze the motion of a spheroid in a pressure driven flow of a shear thinning fluid.The shear thinning rheology is characterized by the Carreau model.We use a combination of perturbative techniques

Wesley Chen, Andrew Edgley, Raunak Hota, Joshua Liu

In recent years, supervised learning has become the dominant paradigm for training deep-learning based methods for 3D object detection. Lately, the academic community has studied 3D object detection in the context of autonomous vehicles (AVs) using publicly available datasets such as nuScenes and Argoverse 2.0. However, these datasets may have incomplete ann

R. Nevin, L. Blecha, J. Comerford, J. Simon

It is difficult to accurately identify galaxy mergers and it is an even larger challenge to classify them by their mass ratio or merger stage. In previous work we used a suite of simulated mergers to create a classification technique that uses linear discriminant analysis (LDA) to identify major and minor mergers. Here, we apply this technique to 1.3 million

Shimon Edelman

What does it mean to know language? Since the Chomskian revolution, one popular answer to this question has been: to possess a generative grammar that exclusively licenses certain syntactic structures. Decades later, not even an approximation to such a grammar, for any language, has been formulated; the idea that grammar is universal and innately specified h

Anjali Pandey, Rajesh Kumar, Sudhir Kumar Srivastava

This paper considers the spherically symmetric gravitational collapse in the background of Chaplygin gas as dark energy component. The dark energy is assumed to be generalized and modified Chaplygin gas. The exterior of the star is considered to be Schwarzschild de-sitter/anti-de sitter metric and discusses the junction conditions. We have discussed the sing

Annu Jaiswal, Rajesh Kumar, Sudhir Kumar Srivastava, S. K. J. Pacif

A new class of self-gravitating collapsing star models with perfect fluid distributions is discussed in this work. The paper has a comprehensive analysis of a homogeneous gravitational collapsing system wherein using a parametrization scheme for the expansion-scalar $(Θ)$, the solutions of the Einstein Field Equations (EFEs) are determined independently. The

Tijana Levajkovic, Stevan Pilipovic, Dora Selesi, Milica Zigic

We study nonlinear stochastic partial differential equations with Wick-analytic type nonlinearities set in the framework of white noise analysis. These equations include the stochastic Fisher--KPP equations, stochastic Allen--Cahn, stochastic Newell--Whitehead--Segel, and stochastic Fujita--Gelfand equations. By implementing the theory of $C_0-$semigroups an

Steven D Miller

The 4/5 and 2/3 laws of turbulence can emerge from a theory of 'engineered' random vector fields $\mathcal{X}_{i}(x,t) =X_{i}(x,t)+\tfrac{\theta}{\sqrt{d(d+2)}} X_{i}(x,t)\psi(x)$ existing within $\mathbf{D}\subset\mathbf{R}^{d}$. Here, $X_{i}(x,t)$ is a smooth deterministic vector field obeying a nonlinear PDE for all $(x,t)\in\mathbf{D}\times\mathbf{R}^{+}

Yan Ding, Xiaohan Zhang, Chris Paxton, Shiqi Zhang

Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning is frequently needed in this process. However, achieving commonsense arrangements requires knowledge about objects, which is hard to transfer to robots. Large language models (LLMs) are one potential source of this knowledge, but they do not naively capture informatio

Xiaopeng Jiang, Thinh On, NhatHai Phan, Hessamaldin Mohammadi

Mobile apps, such as mHealth and wellness applications, can benefit from deep learning (DL) models trained with mobile sensing data collected by smart phones or wearable devices. However, currently there is no mobile sensing DL system that simultaneously achieves good model accuracy while adapting to user mobility behavior, scales well as the number of users

Prajjwal Bhargava, Pooyan Amini, Shahin Shayandeh, Chinnadhurai Sankar

As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists. In this work, we present AUTODIAL, a multi-task dialogue model that addresses the challenges of deploying dialogue model. AUTODIAL utilizes parallel decoders to perform tasks such as d

Stevan Pilipovic, Bojan Prangoski, Milica Zigic

We analyse various exponential off-diagonal decay rates of the elements of infinite matrices and their inverses. It is known that such decay of the elements of an infinite matrix does not imply inverse--closeness, i.e. the inverse, if exists, does not have the same order of decay. We discuss some consequences and extensions of this result.

Luca Franco, Paolo Mandica, Bharti Munjal, Fabio Galasso

Self-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the knowledge of the task matures during training. We prop

Vipul Gupta, Apurva Narayan

Deep Neural Networks (DNNs) are being used to solve a wide range of problems in many domains including safety-critical domains like self-driving cars and medical imagery. DNNs suffer from vulnerability against adversarial attacks. In the past few years, numerous approaches have been proposed to tackle this problem by training networks using adversarial train

Nikolay Konovalov

Let $\mathit{s}\mathcal{L}$ be the $\infty$-category of simplicial restricted Lie algebras over $\mathbf{F} = \overline{\mathbf{F}}_p$, the algebraic closure of a finite field $\mathbf{F}_p$. By the work of A. K. Bousfield et al. on the unstable Adams spectral sequence, the category $\mathit{s}\mathcal{L}$ can be viewed as an algebraic approximation of the $

Chengcheng Liu, Shahn Majid

Following steps analogous to classical Kaluza-Klein theory, we solve for the quantum Riemannian geometry on $C^\infty(M)\otimes M_2(\mathbb{C})$ in terms of classical Riemannian geometry on a smooth manifold $M$, a finite quantum geometry on the algebra $M_2(\mathbb{C})$ of $2\times 2$ matrices, and a quantum metric cross term. Fixing a standard form of quan

Pankaj Bhalla, Habib Rostami

In this theoretical investigation, we analyze light-induced nonlinear spin Hall currents in a gated single-layer 1T$'$-WTe$_2$, flowing transversely to the incident laser polarization direction. Our study encompasses the exploration of the second and third-order rectified spin Hall currents using an effective low-energy Hamiltonian and employing Kubo's forma

Xiaopeng Jiang, Cristian Borcea

Federated Learning (FL) is a privacy-preserving distributed deep learning paradigm that involves substantial communication and computation effort, which is a problem for resource-constrained mobile and IoT devices. Model pruning/sparsification develops sparse models that could solve this problem, but existing sparsification solutions cannot satisfy at the sa

A. S. Detinko, D. L. Flannery, A. Hulpke

We generalize our methodology for computing with Zariski dense subgroups of $\mathrm{SL}(n, \mathbb{Z})$ and $\mathrm{Sp}(n, \mathbb{Z})$, to accommodate input dense subgroups $H$ of $\mathrm{SL}(n, \mathbb{Q})$ and $\mathrm{Sp}(n, \mathbb{Q})$. A key task, backgrounded by the Strong Approximation theorem, is computing a minimal congruence overgroup of $H$.

Rakib Hyder, M. Salman Asif

Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing entries. In this paper, our goal is to

Duc Nguyen, Anderson Y. Zhang

Item response theory (IRT) is the study of how people make probabilistic decisions, with diverse applications in education testing, recommendation systems, among others. The Rasch model of binary response data, one of the most fundamental models in IRT, remains an active area of research with important practical significance. Recently, Nguyen and Zhang (2022

Iman Saberi, Fatemeh H. Fard

Pre-trained Programming Language Models (PPLMs) achieved many recent states of the art results for many code-related software engineering tasks. Though some studies use data flow or propose tree-based models that utilize Abstract Syntax Tree (AST), most PPLMs do not fully utilize the rich syntactical information in source code. Still, the input is considered

Sabri Mustafa Kahya, Muhammet Sami Yavuz, Eckehard Steinbach

Out-of-distribution (OOD) detection has recently received special attention due to its critical role in safely deploying modern deep learning (DL) architectures. This work proposes a reconstruction-based multi-class OOD detector that operates on radar range doppler images (RDIs). The detector aims to classify any moving object other than a person sitting, st

Darpan Verma, Md Mohsinur Rahman Adnan, Sushovan Dhara, Chris Sturm

Polarization dependent photocurrent spectra are measured on a (001) $\beta$-Ga$_{2}$O$_{3}$ Schottky photodetector, where the linear polarization of light is rotated within the ab plane. Three spectral peaks at 4.92 eV, 5.15 eV, and 5.44 eV are observed that vary in intensity with the optical polarization direction. The peak transition energies are consisten

Deen Abdullah, Shamanth Nayak, Gandharv Suri, Yllias Chali

Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to re

Tijana Levajkovic, Stevan Pilipovic, Dora Selesi, Milica Zigic

We study nonlinear parabolic stochastic partial differential equations with Wick-power and Wick-polynomial type nonlinearities set in the framework of white noise analysis. These equations include the stochastic Fujita equation, the stochastic Fisher-KPP equation and the stochastic FitzHugh-Nagumo equation among many others. By implementing the theory of $C_

Hongyi Chen, Ruinian Xu, Shuo Cheng, Patricio A. Vela

Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal navigation requires the robot to search for a target object category in an unexplored environment, which may not be suitable for home-assistant robots that typically have some level

Youjin Lee, Gary Hettinger, Nandita Mitra

Policy interventions can spill over to units of a population that are not directly exposed to the policy but are geographically close to the units receiving the intervention. In recent work, investigations of spillover effects on neighboring regions have focused on estimating the average treatment effect of a particular policy in an observed setting. Our res

Wojciech Zając, Joanna Waczyńska, Piotr Borycki, Jacek Tabor

Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), which produces meshes of human faces that are fully controllable. Unfortunately, such models have problems with capturing geometric and appearance details. In contrast to mesh represen

Tijana Levajkovic, Stevan Pilipovic, Dora Selesi, Milica Zigic

We study parabolic stochastic partial differential equations (SPDEs), driven by two types of operators: one linear closed operator generating a $C_0-$semigroup and one linear bounded operator with Wick-type multiplication, all of them set in the infinite dimensional space framework of white noise analysis. We prove existence and uniqueness of solutions for t

Arthur Hernandez, Michael F. Staddon, Michael Moshe, M. Cristina Marchetti

Using a mean field approach and simulation, we study the non-linear mechanical response of the vertex model (VM) of biological tissue under compression and dilation. The VM is known to exhibit a transition between rigid and fluid-like, or floppy, states driven by geometric incompatibility. Target perimeter and area set a target shape which may not be geometr

Teresa Datta, John P. Dickerson

Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools. Human-centered evaluation of AI-based systems combines quantitative and qualitative analysis and human input. It has been explored to some depth in the explainable AI (XAI) and human-computer interaction (HCI) communities. Gaps remain

Kota Kondo, Reinaldo Figueroa, Juan Rached, Jesus Tordesillas

Communication delays can be catastrophic for multiagent systems. However, most existing state-of-the-art multiagent trajectory planners assume perfect communication and therefore lack a strategy to rectify this issue in real-world environments. To address this challenge, we propose Robust MADER (RMADER), a decentralized, asynchronous multiagent trajectory pl

Sunbochen Tang, Anuradha M. Annaswamy

In this paper, we propose a combined Magnitude Saturated Adaptive Control (MSAC)-Model Predictive Control (MPC) approach to linear quadratic tracking optimal control problems with parametric uncertainties and input saturation. The proposed MSAC-MPC approach first focuses on a stable solution and parameter estimation, and switches to MPC when parameter learni

Yann in 't Veld, Mikhail I. Katsnelson, Andrew J. Millis, Malte Rösner

Two-dimensional (2D) metals can host gapless plasmonic excitations, which strongly couple to electrons and thus may significantly affect superconductivity in layered materials. To investigate the dynamical interplay of the electron-electron and electron-phonon interactions in the theory of 2D superconductivity, we apply a full momentum- and frequency-depende

Ángel González-Prieto, Marina Logares, Javier Martínez, Vicente Muñoz

We describe the geometry of the character variety of representations of the fundamental group of the complement of a Hopf link with $n$ twists, namely $\Gamma_{n}=\langle x,y \,| \, [x^n,y]=1 \rangle$ into the group $\mathrm{SU}(r)$. For arbitrary rank, we provide geometric descriptions of the loci of irreducible and totally reducible representations. In the

Ronnie de Souza Santos, Gianisa Adisaputri, Paul Ralph

Background. The COVID-19 pandemic triggered a widespread transition to hybrid work models (combinations of co-located and remote work) as software professionals' demanded more flexibility and improved work-life balance. However, hybrid work models reduce the spontaneous, informal face-to-face interactions that promote group maturation, cohesion, and resilien

David G. Russell

The NASA Exoplanet Archive was searched for planets with an equilibrium temperature below 600 K, mass uncertainty less than 27 percent, and radius uncertainty less than 8 percent. This search produced 93 planets with mass from 0.3 to 1680 ME; and 101 planets if the Solar System planets are included. The characteristics of the sample in this catalog are: (1)

Yumeng Song, Yu Gu, Tianyi Li, Jianzhong Qi

Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data. To such learning, we propose a contrastive hypergraph ne

Vignesh Viswanathan, Yair Zick

We study the problem of allocating indivisible chores among agents with binary supermodular cost functions. In other words, each chore has a marginal cost of $0$ or $1$ and chores exhibit increasing marginal costs (or decreasing marginal utilities). In this note, we combine the techniques of Viswanathan and Zick (2022) and Barman et al. (2023) to present a g

Asaad Elkhidir, Donal O'Connell, Matteo Sergola, Ingrid A. Vazquez-Holm

We study classical radiation fields at next-to-leading order using the methods of scattering amplitudes. The fields of interest to us are sourced when two massive, point-like objects scatter inelastically, and can be computed from one-loop amplitudes. We show that the real and imaginary parts of the amplitudes both play important but physically distinct role

Anshumali Shrivastava, Zhao Song, Zhaozhuo Xu

Graph-based algorithms have demonstrated state-of-the-art performance in the nearest neighbor search (NN-Search) problem. These empirical successes urge the need for theoretical results that guarantee the search quality and efficiency of these algorithms. However, there exists a practice-to-theory gap in the graph-based NN-Search algorithms. Current theoreti

Shuai Yuan, Shuzhi Yu, Hannah Kim, Carlo Tomasi

Unsupervised optical flow estimation is especially hard near occlusions and motion boundaries and in low-texture regions. We show that additional information such as semantics and domain knowledge can help better constrain this problem. We introduce SemARFlow, an unsupervised optical flow network designed for autonomous driving data that takes estimated sema

Charles Godfrey, Michael G. Rawson, Davis Brown, Henry Kvinge

Linear neural network layers that are either equivariant or invariant to permutations of their inputs form core building blocks of modern deep learning architectures. Examples include the layers of DeepSets, as well as linear layers occurring in attention blocks of transformers and some graph neural networks. The space of permutation equivariant linear layer

Sheng Cheng

Super-resolution results are usually measured by full-reference image quality metrics or human rating scores. However, these evaluation methods are general image quality measurement, and do not account for the nature of the super-resolution problem. In this work, we analyze the evaluation problem based on the one-to-many mapping nature of super-resolution, a

Timothy Campion

We show that various cube categories (without diagonals, but with symmetries / connections / reversals) are Eilenberg-Zilber categories. This generalizes a result of Isaacson for one particular cubical site. Our method does not involve direct verification of any absolute pushout diagrams. While we are at it, we record some folklore descriptions of cube categ

Paolo Lipparini

We show that the theories of partially ordered sets, lattices, semilattices, Boolean algebras, Heyting algebras with a further coarser partial order, or a linearization, or an auxiliary relation have the strong amalgamation property, Fra\"\i ss\'e limits and, in many cases, an $ \omega$-categorical model completion with quantifier elimination. The same appli

Ruobing Mei, Yi-Fan Zhao, Chong Wang, Yafei Ren

In this work, we propose an intrinsic mechanism to understand the even-odd effect, namely the opposite signs of the anomalous Hall resistance and the different shapes of hysteresis loops for even and odd septuple layers (SLs), of MBE-grown MnBi2Te4 thin films with electron doping. In particular, we show that the non-zero hysteresis loops in the anomalous Hal

Ilia Itenberg, Eugenii Shustin

We introduce new invariants of the projective plane (and, more generally, of certain toric surfaces) that arise from the appropriate enumeration of real elliptic curves. These invariants admit a refinement (according to the quantum index) similar to the one introduced by Grigory Mikhalkin in the rational case. We also construct tropical counterparts of the r

Vinit Katariya, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi

Vehicle Trajectory datasets that provide multiple point-of-views (POVs) can be valuable for various traffic safety and management applications. Despite the abundance of trajectory datasets, few offer a comprehensive and diverse range of driving scenes, capturing multiple viewpoints of various highway layouts, merging lanes, and configurations. This limits th

Alexander Shmakov, Alejandro Yankelevich, Jianming Bian, Pierre Baldi

The complex events observed at the NOvA long-baseline neutrino oscillation experiment contain vital information for understanding the most elusive particles in the standard model. The NOvA detectors observe interactions of neutrinos from the NuMI beam at Fermilab. Associating the particles produced in these interaction events to their source particles, a pro

Mohammad S. Ramadan, Ahmad Al-Tawaha, Mohamed Shouman, Ahmed Atallah

Dynamic Programming (DP) suffers from the well-known ``curse of dimensionality'', further exacerbated by the need to compute expectations over process noise in stochastic models. This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density

Binghui Wang, Meng Pang, Yun Dong

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph learning tasks. However, recent studies show that GNNs are vulnerable to both test-time evasion and training-time poisoning attacks that perturb the graph structure. While existing attack methods have shown promising attack performance, we would like to design an attack fra

Yuchen Zhou, Yuxin Chen

This paper is concerned with estimating the column subspace of a low-rank matrix $\boldsymbol{X}^\star \in \mathbb{R}^{n_1\times n_2}$ from contaminated data. How to obtain optimal statistical accuracy while accommodating the widest range of signal-to-noise ratios (SNRs) becomes particularly challenging in the presence of heteroskedastic noise and unbalanced

Zach Yarbrough, E. Baron, James M. DerKacy, I. Washington

Type Ia supernovae (SNe Ia) are important cosmological probes and contributors to galactic nucleosynthesis, particularly of the iron group elements. To improve both their reliability as cosmological probes and to understand galactic chemical evolution, it is vital to understand the binary progenitor system and explosion mechanism. The classification of SNe I

Sounak Sinha Biswas, Ghanasyam Remesh, Venu Gopal Achanta, Ayan Banerjee

The interaction of light beams with resonant structures has led to the development of various optical platforms for sensing, particle manipulation, and strong light-matter interaction. In the current study, we investigate the manifestations of the bound states in continuum (BIC) on the in plane and out of plane shifts (referred to as Goos-Hanchen (GH) and Im

William F. Godoy, Pedro Valero-Lara, T. Elise Dettling, Christian Trefftz

We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Epyc CPUs and MI250X graphical processing units (GPUs) on Frontier's test bed Crusher system and Ampere's Arm-based CPUs and NVIDIA's A100 GPUs on the Wombat system at the Oak Ridge

Qutaiba Shuaib Al-Nema, Ghazwan Qasim Hasan, Omar Abdulazeez Alhamd

Protoplasts represent a unique experimental system for the circulation and formation of genetically modified plants. Here, protoplasts were isolated from genetically modified hairy root tissues of Brassica oleracea L. induced by the Agrobacterium rhizogenes strain (ATCC13332). The concentration of enzyme solutions utilized for protoplast isolation was 1.5 %

Fangda Li, Zhiqiang Hu, Wen Chen, Avinash Kak

Immunohistochemical (IHC) staining highlights the molecular information critical to diagnostics in tissue samples. However, compared to H&E staining, IHC staining can be much more expensive in terms of both labor and the laboratory equipment required. This motivates recent research that demonstrates that the correlations between the morphological information

Yansong Li, Shuo Han

Stackelberg games have been widely used to model interactive decision-making problems in a variety of domains such as energy systems, transportation, cybersecurity, and human-robot interaction. However, existing algorithms for solving Stackelberg games often require knowledge of the follower's cost function or learning dynamics and may also require the follo

A. Ali, J. M. Khalil, A. Mindil

Context: this paper describes the detection of wide binary and multiple central stars (CSs) of Galactic planetary nebulae (PNe) using the most up-to-date data available from the Gaia Data Release 3 (Gaia DR3). Aims: the objective of this study is to benefit from the Gaia DR3's reliable measurements of parallax and proper motion to reveal the binary, ternary,

O. S. Stashko, O. V. Savchuk, L. M. Satarov, I. N. Mishustin

We study self-gravitating multi-pion systems (pion stars) in a state of the Bose condensate. To ensure stability of such stars, it is assumed that they are immersed in the lepton background. Two different phenomenological equations of state (EoS) for the pion matter are used, some of them having the first order phase transition. The model parameters are chos

Ram M Kripa, Andy Zou, Ryan Jia, Kenny Huang

Federated Learning systems use a centralized server to aggregate model updates. This is a bandwidth and resource-heavy constraint and exposes the system to privacy concerns. We instead implement a peer to peer learning system in which nodes train on their own data and periodically perform a weighted average of their parameters with that of their peers accord

C. J. Sayers, G. Cerullo, Y. Zhang, C. E. Sanders

1$T$-TaSe$_2$ is widely believed to host a Mott metal-insulator transition in the charge density wave (CDW) phase according to the spectroscopic observation of a band gap that extends across all momentum space. Previous investigations inferred that the occurrence of the Mott phase is limited to the surface only of bulk specimens, but recent analysis on thin

Ghazwan Qasim Hasan, Shimal Younis Abdulhadi

Pleurotus ostreatus is a common cultivated edible mushroom worldwide. The fruiting bodies of P. ostreatus is a rich source of a $\beta$-glucans polysaccharide. The current study aimed to investigate the effectiveness of $\beta$-glucans as a natural polysaccharide produced by P. ostreatus as an antioxidant, antimicrobial, and anticancer. The molecular identif

Nicholas S. Caros, Xiaotong Guo, Yunhan Zheng, Jinhua Zhao

Remote work has expanded dramatically since 2020, upending longstanding travel patterns and behavior. More fundamentally, the flexibility for remote workers to choose when and where to work has created much stronger connections between travel behavior and organizational behavior. This paper uses a large and comprehensive monthly longitudinal survey over near

Tatyana Barron, Alexander Kazachek

Let $H_k$, $k\in {\mathbb{N}}$, be the Hilbert spaces of geometric quantization on a K\"ahler manifold $M$. With two points in $M$ we associate a Bell-type state $b_k \in H_k\otimes H_k$. When $M$ is compact or when $M$ is ${\mathbb{C}}^n$, we provide positive lower bounds for the entanglement entropy of $b_k$ (asymptotic in $k$, as $k\to\infty$).

Gabriele Barbieri, Mauro Spera

In this note we prove that the symplectic Frobenius Reciprocity established in the paper "Symplectic Induction, Prequantum Induction and Prequantum Multiplicities" as a set bijection is indeed a diffeological diffeomorphism, as conjectured by its authors Ratiu and Ziegler. The same holds in the prequantum space context.

Daphné Giorgi, Sarah Kaakai, Vincent Lemaire

The R Package IBMPopSim aims to simulate the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs). The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arri

Haiyang Huang, Newsha Ardalani, Anna Sun, Liu Ke

Mixture-of-Experts (MoE) models have gained popularity in achieving state-of-the-art performance in a wide range of tasks in computer vision and natural language processing. They effectively expand the model capacity while incurring a minimal increase in computation cost during training. However, deploying such models for inference is difficult due to their

Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh

Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning. However, these datasets focus on detecting a subset of disease labels that could be present, thus making them distributed and non-iid with partial labels. Recent literature has indicated the impact of batch normalization layers on the conve

Junyu Chen, Yihao Liu, Yufan He, Yong Du

Transformers have recently shown promise for medical image applications, leading to an increasing interest in developing such models for medical image registration. Recent advancements in designing registration Transformers have focused on using cross-attention (CA) to enable a more precise understanding of spatial correspondences between moving and fixed im

Thomas C. Nicholas, Adam E. Stones, Adam Patel, F. Marc Michel

Amorphous calcium carbonate (ACC) is an important precursor for biomineralisation in marine organisms. Among the key outstanding problems regarding ACC are how best to understand its structure and how to rationalise its metastability as an amorphous phase. Here, we report high-quality atomistic models of ACC generated by using state-of-the-art interatomic po

Khadija Hanifi, Ramin F Fouladi, Basak Gencer Unsalver, Goksu Karadag

Developing automated and smart software vulnerability detection models has been receiving great attention from both research and development communities. One of the biggest challenges in this area is the lack of code samples for all different programming languages. In this study, we address this issue by proposing a transfer learning technique to leverage av

Dmitry Makarov, Sergey Khoperskov, Danila Makarov, Lidia Makarova

Dwarf galaxies provide a unique opportunity for studying the evolution of the Milky Way (MW) and the Local Group as a whole. Analysing the running solar apex based on the kinematics of the MW satellites, we discovered an unexpected behaviour of the dipole term of the radial velocity distribution as a function of the galactocentric distance. The nearby satell

Petros Papadopoulos, Farnaz Fallahi, Murat Yildirim, Ahmed Aziz Ezzat

The rapid increase in scale and sophistication of offshore wind (OSW) farms poses a critical challenge related to the cost-effective operation and management of wind energy assets. A defining characteristic of this challenge is the economic trade-off between two concomitant processes: power production (the primary driver of short-term revenues), and asset de

Xander Davies, Lauro Langosco, David Krueger

A principled understanding of generalization in deep learning may require unifying disparate observations under a single conceptual framework. Previous work has studied \emph{grokking}, a training dynamic in which a sustained period of near-perfect training performance and near-chance test performance is eventually followed by generalization, as well as the

Nazish Tahir, Ramviyas Parasuraman

When a mobile robot lacks high onboard computing or networking capabilities, it can rely on remote computing architecture for its control and autonomy. This paper introduces a novel collaborative Simulation Twin (ST) strategy for control and autonomy on resource-constrained robots. The practical implementation of such a strategy entails a mobile robot system

Wanrong Zhang, Ruqi Zhang

Bayesian inference provides a principled framework for learning from complex data and reasoning under uncertainty. It has been widely applied in machine learning tasks such as medical diagnosis, drug design, and policymaking. In these common applications, data can be highly sensitive. Differential privacy (DP) offers data analysis tools with powerful worst-c

Dimitrios Dimou, Jose Santos-Victor, Plinio Moreno

We present a force feedback controller for a dexterous robotic hand equipped with force sensors on its fingertips. Our controller uses the conditional postural synergies framework to generate the grasp postures, i.e. the finger configuration of the robot, at each time step based on forces measured on the robot's fingertips. Using this framework we are able t

Sirui Qi, Yingheng Li, Sudeep Pasricha, Ryan Gary Kim

To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed. However, designing such complex systems with multiple objectives can be challenging due to the huge associated design space and long evaluation time

Junyu Chen, Yihao Liu, Yufan He, Yong Du

In the past, optimization-based registration models have used spatially-varying regularization to account for deformation variations in different image regions. However, deep learning-based registration models have mostly relied on spatially-invariant regularization. Here, we introduce an end-to-end framework that uses neural networks to learn a spatially-va

Angelina Wang, Olga Russakovsky

Transfer learning is beneficial by allowing the expressive features of models pretrained on large-scale datasets to be finetuned for the target task of smaller, more domain-specific datasets. However, there is a concern that these pretrained models may come with their own biases which would propagate into the finetuned model. In this work, we investigate bia

Stéphane Bijakowski, Andrea Marrama

Let $p$ be a prime number, let $\mathcal{O}_F$ be the ring of integers of a finite field extension $F$ of $\mathbb{Q}_p$ and let $\mathcal{O}_K$ be a complete valuation ring of rank $1$ and mixed characteristic $(0,p)$. We introduce and study the "integral Hodge polygon", a new invariant of $p$-divisible groups $H$ over $\mathcal{O}_K$ endowed with an action