November 2022 arXiv papers — page 75

Felipe A. Ramirez

We extend the Duffin--Schaeffer conjecture to the setting of systems of $m$ linear forms in $n$ variables. That is, we establish a criterion to determine whether, for a given rate of approximation, almost all or almost no $n$-by-$m$ systems of linear forms are approximable at that rate using integer vectors satisfying a natural coprimality condition. When $m

Sifan Yin, L Mahadevan

A combination of cellular contractility and active phase separation in cell-matrix composites is thought to be an enabler of spatiotemporal patterning in multicellular tissues across scales, from somitogenesis to cartilage condensation. To characterize these phenomena, we provide a general theory that incorporates active cellular contractility into the class

Mengjie Jin, Qingguo Li

In this paper, the concepts of $K$-subset systems and $k$-well-filtered spaces are introduced, which provide another uniform approach to $d$-spaces, $s$-well-filtered spaces (i.e., $\mathcal{U}_{S}$-admissibility) and well-filtered spaces. We prove that the $k$-well-filtered reflection of any $T_{0}$ space exists. Meanwhile, we propose the definition of $k$-

Yizheng Li, DIngguo Wang

In this paper, we construct a differential graded Lie algebra whose Maurer-Cartan elements are given by crossed homomorphisms on Leibniz algebras. This allows us to define cohomology for a crossed homomorphism. Finally, we study linear deformations, formal deformations and extendibility of finite order deformations of a crossed homomorphism in terms of the c

Yitian Zhang, Yue Bai, Huan Wang, Yi Xu

Existing action recognition methods typically sample a few frames to represent each video to avoid the enormous computation, which often limits the recognition performance. To tackle this problem, we propose Ample and Focal Network (AFNet), which is composed of two branches to utilize more frames but with less computation. Specifically, the Ample Branch take

Yizheng Li, Dingguo Wang

In this paper, we define the cohomology of a modified Rota-Baxter Leibniz algebra with coefficients in a suitable representation. As applications of our cohomology, we study formal one-parameter deformations and abelian extensions of modified Rota-Baxter Leibniz algebras.

Yang Liu, Brian T. Welsch, Gherardo Valori, Manolis K. Georgoulis

Magnetic free energy powers solar flares and coronal mass ejections (CMEs), and the buildup of magnetic helicity might play a role in the development of unstable structures that subsequently erupt. To better understand the roles of energy and helicity in large flares and eruptions, we have characterized the evolution of magnetic energy and helicity associate

Yin Li, Libin Lu, Chirag Modi, Drew Jamieson

The formation of the large-scale structure, the evolution and distribution of galaxies, quasars, and dark matter on cosmological scales, requires numerical simulations. Differentiable simulations provide gradients of the cosmological parameters, that can accelerate the extraction of physical information from statistical analyses of observational data. The de

Yin Li, Chirag Modi, Drew Jamieson, Yucheng Zhang

Rapid advances in deep learning have brought not only myriad powerful neural networks, but also breakthroughs that benefit established scientific research. In particular, automatic differentiation (AD) tools and computational accelerators like GPUs have facilitated forward modeling of the Universe with differentiable simulations. Based on analytic or automat

D. Osin

The goal of this note is to provide yet another proof of the following theorem of Golod: there exists an infinite finitely generated group $G$ such that every element of $G$ has finite order. Our proof is based on the Nielsen-Schreier index formula and is simple enough to be included in a standard group theory course.

Hyungchan Song, Sanyuan Chen, Zhuo Chen, Yu Wu

There is a surge in interest in self-supervised learning approaches for end-to-end speech encoding in recent years as they have achieved great success. Especially, WavLM showed state-of-the-art performance on various speech processing tasks. To better understand the efficacy of self-supervised learning models for speech enhancement, in this work, we design a

P. J. Wass, T. J. Sumner, H. M Araújo, D. Hollington

A model is presented that explains the charging rate of the LISA Pathfinder test masses by the interplanetary cosmic ray environment. The model incorporates particle-tracking from TeV to eV energies using a combination of GEANT4 and a custom low-energy particle generation and tracking code. The electrostatic environment of the test mass is simulated allowing

Xiaolin Sun, Jacob Masur, Ben Abramowitz, Nicholas Mattei

In representative democracies, the election of new representatives in regular election cycles is meant to prevent corruption and other misbehavior by elected officials and to keep them accountable in service of the ``will of the people." This democratic ideal can be undermined when candidates are dishonest when campaigning for election over these multiple cy

Giovanna Capraro, Maxim Lipkin, Michael Möller, Jens Bolten

Laser resonators have outputs with Gaussian spatial beam profiles. In laser interference lithography (LIL), using such Gaussian shaped beams leads to an inhomogeneous exposure of the substrate. As a result, dimensions of lithography defined features vary significantly across the substrate. In most LIL setups, pinholes are used as filters to remove optical no

Jiezhang Li, Junjun Li, Yue-Jiao Gong

Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated traffic subsystems, while the Traffic4cast 2022 competition is dedicated to exploring the traffic state dynamics of entire cities. Given one hour of sparse loop count data only, the

Geonho Hwang, Myungjoo Kang

The Convolutional Neural Network (CNN) is one of the most prominent neural network architectures in deep learning. Despite its widespread adoption, our understanding of its universal approximation properties has been limited due to its intricate nature. CNNs inherently function as tensor-to-tensor mappings, preserving the spatial structure of input data. How

Hiroyuki Hirashita

We propose an updated dust evolution model that focuses on the grain size distribution in a galaxy. We treat the galaxy as a one-zone object and include five main processes (stellar dust production, dust destruction in supernova shocks, grain growth by accretion and coagulation, and grain disruption by shattering). In this paper, we improve the predictions r

Yangjun Ruan, Saurabh Singh, Warren Morningstar, Alexander A. Alemi

Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning (SSL) enable leveraging large unlabeled corpora for state-of-the-art few-shot and supervised learning performance. In this paper, we explore how ensemble methods can improve recen

Jinxing Zhou, Dan Guo, Meng Wang

Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. It is pivotal to learn the discriminative features for each video segment. Unlike existing work focusing on audio-visual feature fusion, in this paper, we propose a n

Elham Ravanbakhsh, Mosab Rezaei, Ehsan Namjoo, Padideh Choobdar

This study aims to investigate implementing EM and FCM algorithms for skin color extraction. The capabilities of three well-known color spaces, namely, RGB, HSV, and YCbCr for skin-tone extraction are assessed by using statistical modeling of skin tones using EM and FCM algorithms. The results show that utilizing a Gaussian mixture model for parametric model

Aaron S. Chou, Marcelle Soares-Santos, Tim M. P. Tait, Rana X. Adhikari

This report summarizes the current status of Cosmic Frontier physics and the broad and exciting future prospects identified for the Cosmic Frontier as part of the 2021 Snowmass Process.

Abdullah-Al-Raihan Nayeem, Huikyo Lee, Dongyun Han, Mohammad Elshambakey

This paper introduces a novel visual analytics approach, DCPViz, to enable climate scientists to explore massive climate data interactively without requiring the upfront movement of massive data. Thus, climate scientists are afforded more effective approaches to support the identification of potential trends and patterns in climate projections and their subs

Evelyn Kent, Jason Gabriel, Anna Gunn-Golkin, Charles Langdon

The need for space test professionals is growing rapidly, due to establishment of the US Space Force and the extremely rapid growth of the commercial space industry. The future of space test looks bright and complex; a better training foundation is needed to impart test-mindedness to all space professionals to enable this future. Discussed from a military pe

Angel Kumchev, Wade McCormick, Nathan McNew, Ariana Park

We obtain explicit forms of the current best known asymptotic upper bounds for gaps between squarefree integers. In particular we show, for any $x \ge 2$, that every interval of the form $(x, x + 11x^{1/5}\log x]$ contains a squarefree integer. The constant 11 can be improved further, if $x$ is assumed to be larger than a (very) large constant.

Ashley S. Williams, Kien Nguyen-Cong, Jonathan T. Willman, Ivan I. Oleynik

Carbon at extreme conditions is the focus of intensive scientific inquiry due to its importance for applications in inertial confinement fusion experiments and for understanding the interior structure of carbon-rich exoplanets. The extreme metastability of diamond at very high pressures has been discovered in recent dynamic compression experiments. This work

Junfeng Wu, Yi Jiang, Qihao Liu, Xiang Bai

This technical report describes our 2nd-place solution for the ECCV 2022 YouTube-VIS Long Video Challenge. We adopt the previously proposed online video instance segmentation method IDOL for this challenge. In addition, we use pseudo labels to further help contrastive learning, so as to obtain more temporally consistent instance embedding to improve tracking

IceCube Collaboration, R. Abbasi, M. Ackermann, J. Adams

We report three searches for high energy neutrino emission from astrophysical objects using data recorded with IceCube between 2011 and 2020. Improvements over previous work include new neutrino reconstruction and data calibration methods. In one search, the positions of 110 a priori selected gamma-ray sources were analyzed individually for a possible surplu

Shao-Bo Chen, San-Dong Guo, Bing Lv, Mei Xu

In this paper, the electrical transport, thermal transport, and thermoelectric properties of three new Janus STe$_{2}$, SeTe$_{2}$, and Se$_{2}$Te monolayers are systematically studied by first-principles calculations, as well as the comparative with available literature's results using different methods. It is found that the Seebeck coefficient and conducti

Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read

Public conversations on Twitter comprise many pertinent topics including disasters, protests, politics, propaganda, sports, climate change, epidemics/pandemic outbreaks, etc., that can have both regional and global aspects. Spatial discourse analysis rely on geographical data. However, today less than 1% of tweets are geotagged; in both cases--point location

Michael Roberts, J. Ignacio Deza, Hisham Ihshaish, Yanhui Zhu

We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years, which was provided by a major academic publisher. We explore the subscription-type market within the context of customer defection and modelli

Wenlin Ma, Kexin Liu, Hong Guo, Weiguang Cui

We investigate the influence of active galactic nucleus (AGN) feedback on the galaxy cold gas content and its connection to galaxy quenching in three hydrodynamical simulations of Illustris, IllustrisTNG and SIMBA. By comparing to the observed atomic and molecular neutral hydrogen measurements for central galaxies, we find that Illustris over-predicts the co

Susan Athey, Emil Palikot

We evaluate two interventions facilitating technology-sector transitions for women in Poland: Mentoring, focused on expanding professional networks, and Challenges, focused on building credible skill signals. Randomizing oversubscribed admissions, we find both programs substantially increase technology employment at twelve months - by 15 percentage points fo

Abdullah-Al-Raihan Nayeem, Ignacio Segovia-Dominguez, Huikyo Lee, Dongyun Han

This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinate

Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid

In this report, we describe our submission to the Ego4D AudioVisual (AV) Speech Transcription Challenge 2022. Our pipeline is based on AVATAR, a state of the art encoder-decoder model for AV-ASR that performs early fusion of spectrograms and RGB images. We describe the datasets, experimental settings and ablations. Our final method achieves a WER of 68.40 on

Vinitha Balachandran, Dario Poletti

OTOC has been used to characterize the information scrambling in quantum systems. Recent studies showed that local conserved quantities play a crucial role in governing the relaxation dynamics of OTOC in non-integrable systems. In particular, slow scrambling of OTOC is seen for observables that has an overlap with local conserved quantities. However, an obse

Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Samson Zhou

We study fundamental problems in linear algebra, such as finding a maximal linearly independent subset of rows or columns (a basis), solving linear regression, or computing a subspace embedding. For these problems, we consider input matrices $\mathbf{A}\in\mathbb{R}^{n\times d}$ with $n > d$. The input can be read in $\text{nnz}(\mathbf{A})$ time, which deno

Rudy Rosas

We prove that a transversely product component of the singular set of a holomorphic foliation on $\mathbb P^n$ is necessarily a Kupka component.

Dong Zhao, Weiwei Fu, Ziqin Li, Jun He

Optical holography has undergone rapid development since its invention in 1948, but the accompanying speckles with randomly distributed intensity are still untamed now due to the fundamental difficulty of eliminating intrinsic fluctuations from irregular complex-field superposition. Despite spatial, temporal and spectral averages for speckle reduction, it is

Cem Anil, Ashwini Pokle, Kaiqu Liang, Johannes Treutlein

Designing networks capable of attaining better performance with an increased inference budget is important to facilitate generalization to harder problem instances. Recent efforts have shown promising results in this direction by making use of depth-wise recurrent networks. We show that a broad class of architectures named equilibrium models display strong u

Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi

Embodied AI agents continue to become more capable every year with the advent of new models, environments, and benchmarks, but are still far away from being performant and reliable enough to be deployed in real, user-facing, applications. In this paper, we ask: can we bridge this gap by enabling agents to ask for assistance from an expert such as a human bei

Jinchegn Hu, Jihao Li, Zhuoran Hou, Jingjing Jiang

The problem of robustness in adverse weather conditions is considered a significant challenge for computer vision algorithms in the applicants of autonomous driving. Image rain removal algorithms are a general solution to this problem. They find a deep connection between raindrops/rain-streaks and images by mining the hidden features and restoring informatio

Younghun Lee, Dan Goldwasser

Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved. This research proposes a neural model -- Subjective Ground Attention -- that learns subjective grounds of individuals and accounts for their jud

Keiji Sakakibara, Daniel M. Packwood

Advances in algorithms and hardware have enabled computers to design new materials atom-by-atom. However, in order for these computer-generated materials to truly address problems of societal importance, such as clean energy generation, it is not enough for them to have superior physical properties. It is also important for them to be adopted by as many user

Sergey A. Melikhov

Locally compact separable metrizable spaces are characterized among all metrizable spaces as those that admit a cofinal sequence $K_1\subset K_2\subset\cdots$ of compact subsets. Their \v{C}ech cohomology is well-understood due to Petkova's short exact sequence $0\to\lim^1 H^{n-1}(K_i)\to H^n(X)\to\lim H^n(K_i)\to 0$. We study a dual class of spaces. We call

Vassilios Vargiamidis, P. Vasilopoulos, Neophytos Neophytou

We investigate topological phases of monolayer jacutingaite (Pt$_2$HgSe$_3$) that arise when considering the competing effects of spin-orbit coupling (SOC), magnetic exchange interactions, and staggered sublattice potential V. The interplay between the staggered potential and exchange field offers the possibility of attaining different topological phases. By

Declan McIntosh, Tunai Porto Marques, Alexandra Branzan Albu, Rodney Rountree

Recent advancements in cabled ocean observatories have increased the quality and prevalence of underwater videos; this data enables the extraction of high-level biologically relevant information such as species' behaviours. Despite this increase in capability, most modern methods for the automatic interpretation of underwater videos focus only on the detecti

Yvette C. Perrott, Gopika SM, Alastair C. Edge, Keith J. B. Grainge

The cool-core galaxy cluster RXJ1720.1+2638 hosts extended radio emission near the cluster core, known as a minihalo. The origin of this emission is still debated and one piece of the puzzle has been the question of whether the supermassive black hole in the brightest central galaxy is actively powering jets. Here we present high-resolution e-MERLIN observat

Charles Audet, Pierre-Yves Bouchet, Loïc Bourdin

This note provides a counterexample to a theorem announced in the last part of the paper ''Analysis of direct searches for discontinuous functions'', Mathematical Programming Vol. 133, pp.~299--325, 2012. The counterexample involves an objective function $f: \mathbb{R} \to \mathbb{R}$ which satisfies all the assumptions required by the theorem but contradict

Matthew Macaulay, Pierre Rognon

Dense granular flows are well described by several continuum models, however, their internal dynamics remain elusive. This study explores the contact force distributions in simulated steady and homogenous shear flows. Results demonstrate the existence of high magnitude contact forces in faster flows with stiffer grains. A proposed physical mechanism explains

Sawinder Kaur, Yi Xiao, Asif Salekin

AI's widespread integration has led to neural networks (NNs) deployment on edge and similar limited-resource platforms for safety-critical scenarios. Yet, NN's fragility raises concerns about reliable inference. Moreover, constrained platforms demand compact networks. This study introduces VeriCompress, a tool that automates the search and training of compre

Tzu-Quan Lin, Hung-yi Lee, Hao Tang

Self-supervised models have had great success in learning speech representations that can generalize to various downstream tasks. However, most self-supervised models require a large amount of compute and multiple GPUs to train, significantly hampering the development of self-supervised learning. In an attempt to reduce the computation of training, we revisi

Lue Wu, Weiqiang Xie, Chao Xiang, Lin Chang

Chip-integrated optical frequency combs are attractive optical sources in comb applications requiring high-repetition-rate, low power consumption, or compact size. Spontaneous soliton formation via Kerr parametric oscillation is a promising generation principle in these frequency combs, and has been demonstrated in several material platforms over the past de

Milinda Fernando, David Neilsen, Yosef Zlochower, Eric W. Hirschmann

We present results from the new Dendro-GR code. These include simulations of binary black hole mergers for mass ratios up to q=16. Dendro-GR uses Wavelet Adaptive Multi-Resolution (WAMR) to generate an unstructured grid adapted to the spacetime geometry together with an octree based data structure. We demonstrate good scaling, improved convergence properties

Jinrui Yang, Sheilla Njoto, Marc Cheong, Leah Ruppanner

Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a

Eduard Looijenga

We consider the moduli space of genus 4 curves endowed with a $g^1_3$ (which maps with degree 2 onto the moduli space of genus 4 curves). We prove that it defines a degree $\frac{1}{2}(3^{10}-1)$ cover of the 9-dimensional Deligne-Mostow ball quotient such that the natural divisors that live on that moduli space become totally geodesic (their normalizations

Hamed Jalali, Gjergji Kasneci

By distributing the training process, local approximation reduces the cost of the standard Gaussian Process. An ensemble technique combines local predictions from Gaussian experts trained on different partitions of the data. Ensemble methods aggregate models' predictions by assuming a perfect diversity of local predictors. Although it keeps the aggregation t

Nicholas Owen Ralph, Alexandre Marcireau, Saeed Afshar, Nicholas Tothill

As an emerging approach to space situational awareness and space imaging, the practical use of an event-based camera in space imaging for precise source analysis is still in its infancy. The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space image systems and advance the capabiliti

Shima Rafiei, Shahram Shirani

Computer-generated hologram (CGH) is promised to realize the next generation of 3D visual media with life-changing applications. However, one of the essential obstacles to this technology is the time-consuming hologram computation. Thus, facilitating the computation of the generated hologram is of significant importance in this area. We propose a progressive

Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz

Explaining the behavior of AI systems is an important problem that, in practice, is generally avoided. While the XAI community has been developing an abundance of techniques, most incur a set of costs that the wider deep learning community has been unwilling to pay in most situations. We take a pragmatic view of the issue, and define a set of desiderata that

Sanjiv K. Tiwari, Lucy A. Wilkerson, Navdeep K. Panesar, Ronald L. Moore

A challenge in characterizing active region (AR) coronal heating is in separating transient (bursty) loop heating from the diffuse background (steady) heating. We present a method of quantifying coronal heating's bursty and steady components in ARs, applying it to FeXVIII (hot94) emission of an AR observed by SDO/AIA. The maximum, minimum, and average bright

Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes

Large-scale transformer models have become the de-facto architectures for various machine learning applications, e.g., CV and NLP. However, those large models also introduce prohibitive training costs. To mitigate this issue, we propose a novel random and layerwise token dropping method (random-LTD), which skips the computation of a subset of the input token

Shreyas Sundara Raman, Vanya Cohen, Ifrah Idrees, Eric Rosen

Extracting commonsense knowledge from a large language model (LLM) offers a path to designing intelligent robots. Existing approaches that leverage LLMs for planning are unable to recover when an action fails and often resort to retrying failed actions, without resolving the error's underlying cause. We propose a novel approach (CAPE) that attempts to propos

Bolei Deng, Hang Shu, Jian Li, Chengyang Mo

In this letter we investigate the propagation of nonlinear pulses along the free surface of flexible metamaterials based on the rotating squares mechanism. While these metamaterials have previously been shown to support the propagation of elastic vector solitons through their bulk, here we demonstrate that they can also support the stable propagation of nonl

Leonardo Giusti, Lauren Bedal, Eiji Hayashi, Jin Yamanaka

In our everyday life, we intuitively use space to regulate our social interactions. When we want to talk to someone, we approach them; if someone joins the conversation, we adjust our bodies to make space for them. In contrast, devices are not as considerate: they interrupt us, require us to input commands, and compete for our attention. In this paper, we in

Hugh Lachlan Kennedy

Two ways of designing low-order discrete-time (i.e. digital) controls for low-order plant (i.e. process) models are considered in this tutorial. The first polynomial method finds the controller coefficients that place the poles of the closed-loop feedback system at specified positions for adroit controls, i.e. for a rapid and compressed transient response, w

Eduard Muslimov, Coralie Neiner

Arago is a concept of space mission submitted to the European Space Agency's M7 science program. It will target a number of science cases in stellar physics including the characterisation of star-planet interactions. The concept is based on a 1-m class Ritchey-Chretien F/13 telescope mounted on an Ariel-type platform. The scientific payload includes a common

Bishnu Paudel, Christopher Pinner

Let $\mathbb Z_n$ denote the cyclic group of order $n$. We show how the group determinant for $G= \mathbb Z_n \times H$ can be simply written in terms of the group determinant for $H$. We use this to get a complete description of the integer group determinants for $\mathbb Z_2 \times D_8$ where $D_8$ is the dihedral group of order 8, and $\mathbb Z_2 \times

Brody Kutt, Pralay Ramteke, Xavier Mignot, Pamela Toman

Producing labels for unlabeled data is error-prone, making semi-supervised learning (SSL) troublesome. Often, little is known about when and why an algorithm fails to outperform a supervised baseline. Using benchmark datasets, we craft five common real-world SSL data scenarios: few-label, open-set, noisy-label, and class distribution imbalance/misalignment i

Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis

We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs). The core element of the method is a SNN, trained to use spike-encoded information about the solution at previous timesteps to predict spike-encoded information at the next timestep. Af

Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas

Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides. Synthetic Aperture Radar (SAR) is an active remote sensing technique that is unaffected by weather conditions. Dee

Maxime M. C. Tortora, Daniel Jost

Despite their considerable practical and biological applications, the link between molecular properties, assembly conditions and self-organized structure in confined polymer solutions remains elusive. Here, we explore the lyotropic nematic ordering of semi-flexible chains in spherical confinement for multiple contour lengths across a wide regime of concentra

Yuntian He, Saket Gurukar, Srinivasan Parthasarathy

Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research indicates that these models can learn biased representations leading to discriminatory outcomes. A few works have been proposed to mitigate the bias in graph representations. However, most existing works require exceptional ti

Luis Rodrigues

This paper proposes a new Linear Matrix Inequality (LMI) for static output feedback control assuming that a Linear Quadratic Regulator (LQR) has been previously designed for the system. The main idea is to use a quadratic candidate Lyapunov function for the closed-loop system parameterized by the unique positive definite matrix that solves the Riccati equati

David S. Warren, Peter Van Roy, Yanhong A. Liu

This proceedings contains abstracts and position papers for the work presented at the second Logic and Practice of Programming (LPOP) Workshop. The workshop was held online, virtually in place of Chicago, USA, on November 15, 2010, in conjunction with the ACM SIGPLAN Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLA

Aldo Ejlli, Sander M. Vermeulen, Eyal Schwartz, Lorenzo Aiello

In this work, we propose polarimetry experiments to search for low-mass (sub-eV) bosonic field dark matter, including axions and axion-like particles. We show that a polarimetry configuration consisting of a thick birefringent solid inside a Fabry-P\'erot cavity is exceptionally sensitive to scalar field dark matter, which may cause oscillatory variations in

Rendani Mbuvha, Julien Yise Peniel Adounkpe, Wilson Tsakane Mongwe, Mandela Houngnibo

Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Afr

Lin-Ding Yuan, Xiuwen Zhang, Carlos Mera, Alex Zunger

Many textbook physical effects in crystals are enabled by some specific symmetries. In contrast to such "apparent effects", "hidden effect X" refers to the general condition where the nominal global system symmetry would disallow the effect X, whereas the symmetry of local sectors within the crystal would enable effect X. Known examples include the hidden Ra

Selim F. Yilmaz, Can Karamanli, Deniz Gunduz

We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation theorem holds when transmitting independent sources over a MAC in the asymptotic infinite block length regime. However, we are interested in the practical finite block length regime, in

Gregory Vaksman, Michael Elad

Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unkn

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

We present a minimal construction using leptonic portal matter that addresses the muon $g-2$ anomaly. While the chiral enhancement mechanism is reminiscent of that of fermiophobic $Z'$ gauge models, the parameter space motivated by the kinetic mixing/vector portal dark matter model paradigm is vastly different and can be readily explored in current and forth

Luis Rodrigues

This paper addresses the inverse optimal control problem of finding the state weighting function that leads to a quadratic value function when the cost on the input is fixed to be quadratic. The paper focuses on a class of infinite horizon discrete-time and continuous-time optimal control problems whose dynamics are control-affine and whose cost is quadratic

Rachel Luo, Rohan Sinha, Yixiao Sun, Ali Hindy

When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical. However, most existing methods for detecting distribution shift are not well-suited to robotics settings, where data often arrives in a streaming fashion and may be very high-dimensional. In this work, we present an online met

Lianlian Du, Rebecca Langhough Koscik, Tobey J Betthauser, Sterling C. Johnson

Preclinical Alzheimer's disease (AD), the earliest stage in the AD continuum, can last fifteen to twenty years, with cognitive decline trajectories nonlinear and heterogeneous between subjects. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease-modifying therapies may

J. D. Mireles James, Francis Motta, Vincent Naudot

This work concerns the dynamics of a certain class of delay differential equations (DDEs) which we refer to as state dependent delay maps. These maps are generated by delay differential equations where the derivative of the current state depends only on delayed variables, and not on the un-delayed state. However, we allow that the delay is itself a function

Zhenyu Wang, John H. L. Hansen

Adapting speaker recognition systems to new environments is a widely-used technique to improve a well-performing model learned from large-scale data towards a task-specific small-scale data scenarios. However, previous studies focus on single domain adaptation, which neglects a more practical scenario where training data are collected from multiple acoustic

Farouk Mokhtar, Raghav Kansal, Javier Duarte

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-makin

Christopher D. Woodgate, Julie B. Staunton

Using an all-electron, first principles, Landau-type theory, we study the nature of short-range order and compositional phase stability in equiatomic refractory high entropy alloys, NbMoTa, NbMoTaW, and VNbMoTaW. We also investigate selected binary subsystems to provide insight into the physical mechanisms driving order. Our approach examines the short-range

Md. Shafayat Hossain, M. K. Ma, Y. J. Chung, S. K. Singh

Electrons' multiple quantum degrees of freedom can lead to rich physics, including a competition between various exotic ground states, as well as novel applications such as spintronics and valleytronics. Here we report magneto-transport experiments demonstrating how the valley degree of freedom impacts the fractional quantum states (FQHSs), and the related m

Phillip Baumann, Idriss Mazari-Fouquer, Kevin Sturm

In this paper we introduce the topological state derivative for general topological dilatations and explore its relation to standard optimal control theory. We show that for a class of partial differential equations, the shape dependent state variable can be differentiated with respect to the topology, thus leading to a linearised system resembling those occ

Marco Bornstein, Jin-Peng Liu, Jingling Li, Furong Huang

Large-scale non-convex optimization problems are expensive to solve due to computational and memory costs. To reduce the costs, first-order (computationally efficient) and asynchronous-parallel (memory efficient) algorithms are necessary to minimize non-convex functions in machine learning. However, asynchronous-first-order methods applied within non-convex

Seth Lovett, Paul M. Walker, Alexey Osipov, Alexey Yulin

We present and experimentally study the effects of the photonic spin-orbit coupling on real space propagetion of polariton wavepackets in planar semiconductor microcavities and polaritonic analogs of graphene. In particular, we demonstrate the appearance of an analog Zitterbewegung effect, a term which translates as 'trembling motion' in english, which was o

Ciaran Murphy-Royal, ShiNung Ching, Thomas Papouin

The participation of astrocytes in brain computation was formally hypothesized in 1992, coinciding with the discovery that these glial cells display a complex form of Ca2+ excitability. This fostered conceptual advances centered on the notion of reciprocal interactions between neurons and astrocytes, which permitted a critical leap forward in uncovering many

Craig D. Roberts

Atomic nuclei lie at the core of everything visible; and at the first level of approximation, their atomic weights are simply the sum of the masses of all the neutrons and protons (nucleons) they contain. Each nucleon has a mass $m_N \approx 1\,{\rm GeV}\approx 2000$-times the electron mass. The Higgs boson -- discovered at the large hadron collider in 2012,

José Luis Álvarez-Rebollar, Jorge Cravioto-Lagos, Nestaly Marín, Oriol Solé-Pi

Let $S$ be a set of $n$ points in the plane in general position. Two line segments connecting pairs of points of $S$ cross if they have an interior point in common. Two vertex disjoint geometric graphs with vertices in $S$ cross if there are two edges, one from each graph, which cross. A set of vertex disjoint geometric graphs with vertices in $S$ is called

Tirthak Patel, Daniel Silver, Devesh Tiwari

When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To mitigate the adverse effects of hardware noise, it is necessary to understand the effect of hardware noise on the program output and more fundamentally, understand the impact of har

John F. Donoghue

This is a review of some of the concepts and results of the effective field theory treatment of quantum general relativity. Included are lessons of low energy quantum gravity, and a discussion of the limits of effective field theory techniques.

Xiaochen Tang, Mario Renteria-Pinon, Wei Tang

This paper presents a dynamic predictive sampling (DPS) based analog-to-digital converter (ADC) that provides a non-uniform sampling of input analog continuous-time signals. The processing unit generates a dynamic prediction of the input signal using two prior-quantized samplings to compute digital values of an upper threshold and a lower threshold. The digi

Elia Portnoy

We prove a generalized isoperimetric inequality for a domain diffeomorphic to a sphere that replaces filling volume with $k$-dilation. Suppose $U$ is an open set in $\mathbb{R}^n$ diffeomorphic to a Euclidean $n$-ball. We show that in dimensions at least 4 there is a map from a standard Euclidean ball of radius about $vol(\partial U)^{1/(n-1)}$ to $U$, with

Drew Scott, Satyanarayana G. Manyam, David W. Casbeer, Manish Kumar

Mixed Integer Linear Programs (MILPs) are often used in the path planning of both ground and aerial vehicles. Such a formulation of the path planning problem requires a linear objective function and constraints, limiting the fidelity of the the tracking of vehicle states. One such parameter is the state of charge of the battery used to power the vehicle. Acc

Zhenyu Wang, John H. L. Hansen

Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks. This study introduces a simple attention module to infer 3-dim attention weights for the feature map in a convolutional layer, which then op