December 2020 arXiv papers — page 67

Kanat Abdukhalikov, Duy Ho

We show that extended cyclic codes over $\mathbb{F}_q$ with parameters $[q+2,3,q]$, $q=2^m$, determine regular hyperovals. We also show that extended cyclic codes with parameters $[qt-q+t,3,qt-q]$, $1<t<q$, determine (cyclic) Denniston maximal arcs. Similarly, cyclic codes with parameters $[q^2+1,4,q^2-q]$ are equivalent to ovoid codes obtained from elliptic

Chenxin Xu, Siheng Chen, Maosen Li, Ya Zhang

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the tea

Sohei Ashida

The Hartree-Fock equation which is the Euler-Lagrange equation corresponding to the Hartree-Fock energy functional is used in many-electron problems. Since the Hartree-Fock equation is a system of nonlinear eigenvalue problems, the study of structures of sets of all solutions needs new methods different from that for the set of eigenfunctions of linear opera

Raheleh Sabbagh, Omid Zabeti

The main aim of the present note is to consider bounded orthomorphisms between locally solid vector lattices. We establish a version of the remarkable Zannen theorem regarding equivalence between orthomomorphisms and the underlying vector lattice to the case of all bounded orthomomorphisms. Furthermore, we investigate topological and ordered structures for t

Pan Shang, Lingchen Kong

l1-norm quantile regression is a common choice if there exists outlier or heavy-tailed error in high-dimensional data sets. However, it is computationally expensive to solve this problem when the feature size of data is ultra high. As far as we know, existing screening rules can not speed up the computation of the l1-norm quantile regression, which dues to t

Kevin Rao

Machine learning algorithms are designed to make accurate predictions of the future based on existing data, while online algorithms seek to bound some performance measure (typically the competitive ratio) without knowledge of the future. Lykouris and Vassilvitskii demonstrated that augmenting online algorithms with a machine learned predictor can provably de

Tianxiao Zhang, Xiaohan Zhang, Yiju Yang, Zongbo Wang

This paper focuses on the problem of online golf ball detection and tracking from image sequences. An efficient real-time approach is proposed by exploiting convolutional neural networks (CNN) based object detection and a Kalman filter based prediction. Five classical deep learning-based object detection networks are implemented and evaluated for ball detect

Seung Jun Moon, Sangwoo Mo, Kimin Lee, Jaeho Lee

Pre-trained language models have achieved state-of-the-art accuracies on various text classification tasks, e.g., sentiment analysis, natural language inference, and semantic textual similarity. However, the reliability of the fine-tuned text classifiers is an often underlooked performance criterion. For instance, one may desire a model that can detect out-o

Gary R. W. Greaves, Jack H. Koolen, Jongyook Park

In this paper, we study the order of a maximal clique in an amply regular graph with a fixed smallest eigenvalue by considering a vertex that is adjacent to some (but not all) vertices of the maximal clique. As a consequence, we show that if a strongly regular graph contains a Delsarte clique, then the parameter $\mu$ is either small or large. Furthermore, w

Edward Raff, William Fleshman, Richard Zak, Hyrum S. Anderson

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed $100$ MB, which corresponds to a time series with $T=100,000,000$ steps. To date, the closest approach to

Oliver H. E. Philcox

Conventional algorithms for galaxy power spectrum estimation measure the true spectrum convolved with a survey window function, which, for parameter inference, must be compared with a similarly convolved theory model. In this work, we directly estimate the unwindowed power spectrum multipoles using quadratic estimators akin to those introduced in the late 19

Eskil Forsell, Julie Beckley, Simon Ejdemyr, Veronica Hannan

We demonstrate the effectiveness of democratization and efficient computation as key concepts of our experimentation platform (XP) by presenting four new models supported by the platform: 1) Weighted least squares, 2) Quantile bootstrapping, 3) Bayesian shrinkage, and 4) Dynamic treatment effects. Each model is motivated by a specific business problem but is

Jiatu Li

As an experiment to the application of proof assistant for logic research, we formalize the model and proof system for multi-agent modal logic S5 with PAL-style dynamic modality in Lean theorem prover. We provide a formal proof for the reduction axiom of public announcement, and the soundness and completeness of modal logic S5, which can be typechecked with

Byoung S. Ham

Over the last several decades, entangled photon pairs generated from \c{hi}^((2)) nonlinear optical materials via spontaneous parametric down conversion processes have been intensively studied for various quantum correlations such as Bell inequality violation and anticorrelation. In a Mach-Zehnder interferometer, the photonic de Broglie wavelength has also b

Lavanya Umapathy, Mahesh Bharath Keerthivasan, Natalie M. Zahr, Ali Bilgin

Thalamic nuclei have been implicated in several neurological diseases. WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalam

Anna Little, Daniel McKenzie, James Murphy

New geometric and computational analyses of power-weighted shortest-path distances (PWSPDs) are presented. By illuminating the way these metrics balance density and geometry in the underlying data, we clarify their key parameters and discuss how they may be chosen in practice. Comparisons are made with related data-driven metrics, which illustrate the broade

Zhonghan Niu, Zhaoxi Chen, Linyi Li, Yubin Yang

As adversarial attacks against machine learning models have raised increasing concerns, many denoising-based defense approaches have been proposed. In this paper, we summarize and analyze the defense strategies in the form of symmetric transformation via data denoising and reconstruction (denoted as $F+$ inverse $F$, $F-IF$ Framework). In particular, we cate

Hongya Song, Yaoguang Ma, Yubing Han, Weidong Shen

Computational spectroscopic instruments with Broadband Encoding Stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters. However, conventional design manners of BEST filters are often heuristic and may fail to fully explore the encoding potential of BEST filters. The Parameter Constrained Spectral Encoder

Haoyue Bai, Rui Sun, Lanqing Hong, Fengwei Zhou

While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework to a wide range of applications is challenging, mainly

Liang Ma, Ting He, Ananthram Swami, Don Towsley

This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7.3 Performance, 2015.

Klavs Hansen, Piero Ferrari

While linear molecules in their vibrational ground state cannot carry angular momentum around their symmetry axis, the presence of vibrational excitations can induce deformations away from linearity and therefore also allow angular momentum along the molecular axis. In this work, a recurrence relation is established for the calculation of the vibrational lev

You-Cheng Chou, Leo Herr, Yuan-Pin Lee

We prove a formula expressing the $K$-theoretic log Gromov-Witten invariants of a product of log smooth varieties $V \times W$ in terms of the invariants of $V$ and $W$. The proof requires introducing log virtual fundamental classes in $K$-theory and verifying their various functorial properties. We introduce a log version of $K$-theory and prove the formula

Ziwei Wang, Yonhon Ng, Pieter van Goor, Robert Mahony

Event cameras output asynchronous events to represent intensity changes with a high temporal resolution, even under extreme lighting conditions. Currently, most of the existing works use a single contrast threshold to estimate the intensity change of all pixels. However, complex circuit bias and manufacturing imperfections cause biased pixels and mismatch co

P. Domenichini, F. N. Paris, M. G. Capeluto, M. Granada

The dynamics of micrometer-sized magnetic domains in ultra-thin ferromagnetic films is so dramatically slowed down by quenched disorder that the spontaneous elastic tension collapse becomes unobservable at ambient temperature. By magneto-optical imaging we show that a weak zero-bias AC magnetic field can assist such curvature-driven collapse, making the area

Qisheng Wang, Mingsheng Ying

Lexicographically minimal string rotation (LMSR) is a problem to find the minimal one among all rotations of a string in the lexicographical order, which is widely used in equality checking of graphs, polygons, automata and chemical structures. In this paper, we propose an $O(n^{3/4})$ quantum query algorithm for LMSR. In particular, the algorithm has averag

Xiang Cheng, Hanchao Yang, Archanaa S Krishnan, Patrick Schaumont

During a pandemic, contact tracing is an essential tool to drive down the infection rate within a population. To accelerate the laborious manual contact tracing process, digital contact tracing (DCT) tools can track contact events transparently and privately by using the sensing and signaling capabilities of the ubiquitous cell phone. However, an effective D

Keita Kunikawa, Yohei Sakurai

In this paper, on Riemannian manifolds with boundary, we establish a Yau type gradient estimate and Liouville theorem for harmonic functions under Dirichlet boundary condition. Under a similar setting, we also formulate a Souplet-Zhang type gradient estimate and Liouville theorem for ancient solutions to the heat equation.

Hongxiao Wang, Hao Zheng, Jianxu Chen, Lin Yang

Automatic histopathology image segmentation is crucial to disease analysis. Limited available labeled data hinders the generalizability of trained models under the fully supervised setting. Semi-supervised learning (SSL) based on generative methods has been proven to be effective in utilizing diverse image characteristics. However, it has not been well explo

Pengju Zhang, Yihong Wu, Jiagang Zhu

Non-local operations are usually used to capture long-range dependencies via aggregating global context to each position recently. However, most of the methods cannot preserve object shapes since they only focus on feature similarity but ignore proximity between central and other positions for capturing long-range dependencies, while shape-awareness is benef

Gary Ruben, Isaac Pinar, Jeremy M. C. Brown, Florian Schaff

In X-ray imaging, photons are transmitted through and absorbed by the subject, but are also scattered in significant quantities. Previous attempts to use scattered photons for biological imaging used pencil or fan beam illumination. Here we present 3D X-ray Scatter Tomography using full-field illumination. Synchrotron imaging experiments were performed of a

Zhendong Chu, Haiyun Jiang, Yanghua Xiao, Wei Wang

Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issues. Additional information sources are urgently needed to supplement the training data and overcome these issues. In this paper, we introduce two widely-existing sources in knowled

Ravi Sharma, Sri Divya Pagadala, Pratool Bharti, Sriram Chellappan

In this paper, we report experimental results on assessing the impact of COVID-19 on college students by processing free-form texts generated by them. By free-form texts, we mean textual entries posted by college students (enrolled in a four year US college) via an app specifically designed to assess and improve their mental health. Using a dataset comprisin

Kendrick M. Shepherd, René R. Hiemstra, Thomas J. R. Hughes

Quadrilateral layouts on surfaces are valuable in texture mapping, and essential in generation of quadrilateral meshes and in fitting splines. Previous work has characterized such layouts as a special metric on a surface or as a meromorphic quartic differential with finite trajectories. In this work, a surface quadrilateral layout is alternatively characteri

Tsz-Chiu Au

Drones have a fairly short range due to their limited battery life. We propose an adaptive exploration techniques to extend the range of drones by taking advantage of physical structures such as tall buildings and trees in urban environments. Our goal is to extend the coverage of a drone delivery service by generating paths for a drone to reach its destinati

Angel Garcia-Chung, James B. Mertens, Saeed Rastgoo, Yaser Tavakoli

The linearized Einstein field equations provide a low-energy wave equation for the propagation of gravitational fields which may originate from a high energy source. Motivated by loop quantum gravity, we propose the polymer quantization scheme to derive the effective propagation of such waves on a classical Friedmann-Lemaitre-Robertson-Walker (FLRW) spacetim

Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus

Despite significant progress in monocular depth estimation in the wild, recent state-of-the-art methods cannot be used to recover accurate 3D scene shape due to an unknown depth shift induced by shift-invariant reconstruction losses used in mixed-data depth prediction training, and possible unknown camera focal length. We investigate this problem in detail,

Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang

Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy preserving DNN models from either algorithmic perspective or cryptographic perspective. The former mainly splits the

J. Kang, P. A. R. Ade, Z. Ahmed, M. Amiri

BICEP3 is a 520 mm aperture on-axis refracting telescope at the South Pole, which observes the polarization of the cosmic microwave background (CMB) at 95 GHz to search for the B-mode signal from inflationary gravitational waves. In addition to this main target, we have developed a low-elevation observation strategy to extend coverage of the Southern sky at

Malgorzata Peszynska, Ralph E. Showalter

We develop a practical discrete model of hysteresis based on nonlinear play and generalized play, for use in first-order conservation laws with applications to adsorption-desorption hysteresis models. The model is easy to calibrate from sparse data, and offers rich secondary curves. We compare it with discrete regularized Preisach models. We also prove well-

Yu. E. Geints, A. A. Zemlyanov

Multiple filamentation in air of high-power ultrashort laser radiation with transverse intensity profile resembling a "corona" composed by incoherent combining of several annularly distributed independent top-hat sub-beams is theoretically studied. Through the numerical solution of time-averaged nonlinear Schrodinger equation, we study the spatio-angular dyn

Kwun Nam Hui

Zn-air battery (ZAB) has distinguished itself as new generation of energy storage device due to the high theoretical energy density and its performance relies on the oxygen reduction reaction (ORR) performance of the cathode catalysts. Single atomic Fe anchored N-doped carbon (Fe-N-C) has emerged as a promising ORR electrocatalyst because of the maximum util

Ryandhimas E. Zezario, Chiou-Shann Fuh, Hsin-Min Wang, Yu Tsao

Recent research on speech enhancement (SE) has seen the emergence of deep-learning-based methods. It is still a challenging task to determine the effective ways to increase the generalizability of SE under diverse test conditions. In this study, we combine zero-shot learning and ensemble learning to propose a zero-shot model selection (ZMOS) approach to incr

Peteris Zvejnieks, Mihails Birjukovs, Martins Klevs, Megumi Akashi

An efficient and versatile implementation of offline multiple hypothesis tracking with Algorithm X for optimal association search was developed using Python. The code is intended for scientific applications that do not require online processing. Directed graph framework is used and multiple scans with progressively increasing time window width are used for e

Shilei Ding, Lorenzo Baldrati, Andrew Ross, Zengyao Ren

Electrical manipulation of magnetism via spin-orbit torques (SOTs) promises efficient spintronic devices. In systems comprising magnetic insulators and heavy metals, SOTs have started to be investigated only recently, especially in systems with interfacial Dzyaloshinskii-Moriya interaction (iDMI). Here, we quantitatively study the SOT efficiency and iDMI in

Bo Lin, Bissan Ghaddar, Jatin Nathwani

The integration of electric vehicles (EVs) with the energy grid has become an important area of research due to the increasing EV penetration in today's transportation systems. Under appropriate management of EV charging and discharging, the grid can currently satisfy the energy requirements of a considerable number of EVs. Furthermore, EVs can help enhance

A. C. Herrera

We give a complete classification, up to isometric isomorphism and scaling, of $4$-dimensional metric Lie algebras $(\mathfrak{g},\langle \cdot,\cdot \rangle)$ that admit a non-zero parallel skew-symmetric endomorphism. In particular, we distinguish those metric Lie algebras that admit such an endomorphism which is not a multiple of a complex structure, and

Jiho Noh, Ramakanth Kavuluru

Information retrieval (IR) for precision medicine (PM) often involves looking for multiple pieces of evidence that characterize a patient case. This typically includes at least the name of a condition and a genetic variation that applies to the patient. Other factors such as demographic attributes, comorbidities, and social determinants may also be pertinent

Alvaro Restuccia, Francisco Tello-Ortiz

An anisotropic model describing gravity--vector gauge coupling at all energy scales is presented. The starting point is the 4+1 dimensional non--projectable Ho\v{r}ava--Lifshitz gravity theory subject to a geometrical restriction. Renormalizability arguments require all possible interactions in the potential up to terms with $z=4$ spatial derivatives on the

Kai-Cheng Yang, Francesco Pierri, Pik-Mai Hui, David Axelrod

The global spread of the novel coronavirus is affected by the spread of related misinformation -- the so-called COVID-19 Infodemic -- that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media

Bei Wei, Wu-Hao Cai, Chunling Ding, Guang-Wei Deng

We theoretically investigate the preparation of mid-infrared (MIR) spectrally-uncorrelated biphotons from a spontaneous parametric down-conversion process using doped LN crystals, including MgO doped LN, ZnO doped LN, and In2O3 doped ZnLN with doping ratio from 0 to 7 mol%. The tilt angle of the phase-matching function and the corresponding poling period are

Guidan Yao, Ahmed M. Bedewy, Ness B. Shroff

In this paper, we consider a status update system, in which update packets are sent to the destination via a wireless medium that allows for multiple rates, where a higher rate also naturally corresponds to a higher error probability. The data freshness is measured using age of information, which is defined as the age of the recent update at the destination.

Michele Dall'Arno, Francesco Buscemi, Takeshi Koshiba

The guesswork of a quantum ensemble quantifies the minimum number of guesses needed in average to correctly guess the state of the ensemble, when only one state can be queried at a time. Here, we derive analytical solutions of the guesswork problem subject to a finite set of conditions, including the analytical solution for any qubit ensemble with uniform pr

Dingtong Yang, Navjyoth J. S. Sarma, Michael Hyland, R. Jayakrishnan

Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling framework of a regional system of EV fast-c

V. Dike, M. R. Morris, R. M. Rich, M. O. Lewis

Using a subsample of the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey of stellar SiO masers, we explore the prevalence and characteristics of $^{28}$SiO $J=1-0, v=0$ emission. We identify 90 detections of maser, thermal, or composite $^{28}$SiO $J=1-0, v=0$ emission out of approximately 13,000 candidate spectra from the NSF's Karl G. Jansky Very

Minsu Kim, Seongjun Kim, Jemin Lee

In this paper, we analyze the impact of a friendly unmanned aerial vehicle (UAV) jammer on UAV communications in the presence of multiple eavesdroppers. We first present channel components determined by the line-of-sight (LoS) probability between the friendly UAV jammer and the ground device, and introduce different channel fadings for LoS and non-line-of-si

Hideaki Iiduka, Hiroyuki Sakai

This paper considers a stochastic optimization problem over the fixed point sets of quasinonexpansive mappings on Riemannian manifolds. The problem enables us to consider Riemannian hierarchical optimization problems over complicated sets, such as the intersection of many closed convex sets, the set of all minimizers of a nonsmooth convex function, and the i

Mayank Agrawal, Sharon C. Glotzer

Materials that respond to external stimuli by expanding or contracting provide a transduction route that integrates sensing and actuation powered directly by the stimuli. This motivates us to build colloidal scale robots using these materials that can morph into arbitrary configurations. For intelligent use of global stimuli in robotic systems, computation a

Bushra Sabir, Faheem Ullah, M. Ali Babar, Raj Gaire

Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of know

Wei-Kuo Chen, Arnab Sen

For $p\geq 1$ and $(g_{ij})_{1\leq i,j\leq n}$ being a matrix of i.i.d. standard Gaussian entries, we study the $n$-limit of the $\ell_p$-Gaussian-Grothendieck problem defined as \begin{align*}\max\Bigl\{\sum_{i,j=1}^n g_{ij}x_ix_j: x\in \mathbb{R}^n,\sum_{i=1}^n |x_i|^p=1\Bigr\}.\end{align*} The case $p=2$ corresponds to the top eigenvalue of the Gaussian O

Nethra Viswanathan

Educational recommender systems have become a necessity in the recent years due to overload of available educational resource which makes it difficult for an individual to manually hunt for the required resource on the internet. E-learning recommender systems simplify the tedious task of gathering the right web pages and web documents from the scattered worl

H. P. Freund, P. J. M. van der Slot

Free-electron lasers (FELs) over virtually the entire electromagnetic spectrum from microwaves through ultraviolet through hard x-rays that are either seeded or start from noise. FELs can produce a variety of different optical polarizations of the output radiation ranging from linear through elliptic to circular polarization depending upon the characteristic

Yiming Qian, Hao Zhang, Yasutaka Furukawa

This paper presents Roof-GAN, a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. Given the number of primitives, the generator produces a structured roof model as a graph, which consists of 1) primitive geometry as raster images at each node, encoding f

Patrick A. Lee

We consider the transport properties of a model of fermions scattered by a critical bosonic mode. The mode is overdamped and scattering is mainly in the forward direction. Such a mode appears at the quantum critical point for a electronic nematic phase transition, and in gauge theories for a U(1) spin liquid. It leads to a short fermion life-time, violating

Jingkai Wang

The Event Horizon Telescope's image of the M87 black hole provides an exciting opportunity to study black hole physics. Since a black hole's event horizon absorbs all electromagnetic waves, it is difficult to actively probe the horizon's existence. However, with the help of a family of extremely compact, horizon-less objects, named gravastars", whose externa

Yang Huang, Qingzheng Li, Huawei Zhang, Xinyi Li

In this letter, we report the discovery of an intriguing HVS (J1443+1453) candidate that is probably from the Sagittarius Dwarf Spheroidal galaxy (Sgr dSph). The star is an old and very metal-poor low-mass main-sequence turn-off star (age $\sim14.0$ Gyr and [Fe/H] $= -2.23$ dex) and has a total velocity of $559.01^{+135.07}_{-87.40}$ km s$^{-1}$ in the Galac

Donny Dwiputra, Freddy P. Zen

Environment-assisted quantum transport (ENAQT) is a unique situation where environmental noise can, counterintuitively, enhance the transport of an open quantum system. In this paper, we investigate how the presence of a one-dimensional single-particle mobility edge can generate strong ENAQT. For this purpose, we study the energy current of a generalized Aub

John Gathergood, Fabian Gunzinger, Benedict Guttman-Kenney, Edika Quispe-Torreblanca

We show the recovery in consumer spending in the United Kingdom through the second half of 2020 is unevenly distributed across regions. We utilise Fable Data: a real-time source of consumption data that is a highly correlated, leading indicator of Bank of England and Office for National Statistics data. The UK's recovery is heavily weighted towards the "home

Brian Reily, Terran Mott, Hao Zhang

Effective multi-robot teams require the ability to move to goals in complex environments in order to address real-world applications such as search and rescue. Multi-robot teams should be able to operate in a completely decentralized manner, with individual robot team members being capable of acting without explicit communication between neighbors. In this p

Brian Reily, Terran Mott, Hao Zhang

We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor covera

Huai Chen, Jieyu Li, Renzhen Wang, Yijie Huang

Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structures. However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distin

Tunazzina Islam, Dan Goldwasser

Leveraging social media data to understand people's lifestyle choices is an exciting domain to explore but requires a multiview formulation of the data. In this paper, we propose a joint embedding model based on the fusion of neural networks with attention mechanism by incorporating social and textual information of users to understand their activities and m

Brian Reily, Hao Zhang

Sensor coverage is the critical multi-robot problem of maximizing the detection of events in an environment through the deployment of multiple robots. Large multi-robot systems are often composed of simple robots that are typically not equipped with a complete set of sensors, so teams with comprehensive sensing abilities are required to properly cover an are

Nguyen Ngoc Luan, Do Sang Kim, Nguyen Dong Yen

We consider the conic linear program given by a closed convex cone in an Euclidean space and a matrix, where vector on the right-hand-side of the constraint system and the vector defining the objective function are subject to change. Using the strict feasibility condition, we prove the locally Lipschitz continuity and obtain some differentiability properties

Tiantu Xu, Kaiwen Shen, Yang Fu, Humphrey Shi

Object re-identification (ReID) is a key application of city-scale cameras. While classic ReID tasks are often considered as image retrieval, we treat them as spatiotemporal queries for locations and times in which the target object appeared. Spatiotemporal reID is challenged by the accuracy limitation in computer vision algorithms and the colossal videos fr

J. Gordon Robertson

One of the basic parameters of a CCD camera is its gain, i.e. the number of detected electrons per output Analogue to Digital Unit (ADU). This is normally determined by finding the statistical variances from a series of flat-field exposures with nearly constant levels over substantial areas, and making use of the fact that photon (Poisson) noise has variance

Brian Reily, Hao Zhang

Collaborative multi-robot perception provides multiple views of an environment, offering varying perspectives to collaboratively understand the environment even when individual robots have poor points of view or when occlusions are caused by obstacles. These multiple observations must be intelligently fused for accurate recognition, and relevant observations

Gustavo Perez, Matteo Messa, Daniela Calzetti, Subhransu Maji

We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic Ultraviolet Survey). StarcNet (STAR Cluster classification NETwork) is a multi-scale convolutional neural network (CNN) which

Alma Rocío Sagaceta-Mejía, Julián Alberto Fresán-Figueroa, Ehyter M. Martín-González

In this paper we propose a model to study the appropriation of knowledge of one student in a non-collaborative online class. We formulate a stochastic model based on the quality of the teacher's class and the affinity of the student to understand the sessions, under the assumption that previous sessions have some influence in the understanding of the next se

Jacob Moran, Devon Finlay, Mikhail Tikhonov

Expression level is known to be a strong determinant of a protein's rate of evolution. But the converse can also be true: evolutionary dynamics can affect expression levels of proteins. Having implications in both directions fosters the possibility of a feedback loop, where higher expressed systems are more likely to improve and be expressed even higher, whi

Qingyi Pan, Wenbo Hu, Jun Zhu

Time series forecasting is an important yet challenging task. Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models. Previous interpretation methods, including the methods for general neural networks and attention-based methods, mainly consider the i

Sayantan Choudhury, Satyaki Chowdhury, Nitin Gupta, Anurag Mishara

Recently in various theoretical works, path-breaking progress has been made in recovering the well-known Page Curve of an evaporating black hole with Quantum Extremal Islands, proposed to solve the long-standing black hole information loss problem related to the unitarity issue. Motivated by this concept, in this paper, we study cosmological circuit complexi

Gastón Andrés García, Cristian Vay

Based on previous results on the classification of finite-dimensional Nichols algebras over dihedral groups and the characterization of simple modules of Drinfeld doubles, we compute the irreducible characters of the Drinfeld doubles of bosonizations of finite-dimensional Nichols algebras over the dihedral groups $\mathbb{D}_{4t}$ with $t\geq 3$. To this end

Mauricio Delbracio, Ignacio Garcia-Dorado, Sungjoon Choi, Damien Kelly

We present a highly efficient blind restoration method to remove mild blur in natural images. Contrary to the mainstream, we focus on removing slight blur that is often present, damaging image quality and commonly generated by small out-of-focus, lens blur, or slight camera motion. The proposed algorithm first estimates image blur and then compensates for it

Yeong Hwan Ko, Nasrin Razmjooei, Hafez Hemmati, Robert Magnusson

Resonant periodic nanostructures provide perfect reflection across small or large spectral bandwidths depending on the choice of materials and design parameters. This effect has been known for decades, observed theoretically and experimentally via one-dimensional and two-dimensional structures commonly known as resonant gratings, metamaterials, and metasurfa

Ananya Renuka Balakrishna, Richard D. James

Magnetic coercivity is often viewed to be lower in alloys with negligible (or zero) values of the anisotropy constant. However, this explains little about the dramatic drop in coercivity in FeNi alloys at a non-zero anisotropy value. Here, we develop a theoretical and computational tool to investigate the fundamental interplay between material constants that

Jingze Zhu

In this paper, we prove that the translating solitons of the mean curvature flow in $\mathbb{R}^4$ which arise as blow up limit of embedded, mean convex mean curvature flow must have $SO(2)$ symmetry.

Daniel C. Elton

Artificial intelligence has made great strides since the deep learning revolution, but AI systems still struggle to extrapolate outside of their training data and adapt to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes predict the

Matheus de Oliveira Souza, Pablo Martin Rodriguez

A $M/M/1$ queue with catastrophes is a modified $M/M/1$ queue model for which, according to the times of a Poisson process, catastrophes occur leaving the system empty. In this work, we study a fractional $M/M/1$ queue with catastrophes, which is formulated by considering fractional derivatives in the Kolmogorov's Forward Equations of the original Markov pro

R. Flores-Calderón, J. Fujioka, A. Espinosa-Cerón

In this paper we study the soliton dynamics of a high-density Bose-Einstein condensate (BEC) subject to a time-oscillating trap. The behavior of the BEC is described with a modified Gross-Pitaevskii equation (mGPE) which takes into account three-body losses, atomic feeding and quantum fluctuations (up to a novel high-density term). A variational approximatio

Mahendra DC, Ding-Fu Shao, Vincent D. -H. Hou, P. Quarterman

High spin-orbit torques (SOTs) generated by topological materials and heavy metals interfaced with a ferromagnetic layer show promise for next generation magnetic memory and logic devices. SOTs generated from the in-plane spin polarization along y-axis originated by the spin Hall and Edelstein effects can switch magnetization collinear with the spin polariza

Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu

Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been screened in existing materials databases, discovering new 2D materials remains to be challenging. Herein we propose a dee

Chris R. Serrano, Pape M. Sylla, Michael A. Warren

Testing remains the primary method to evaluate the accuracy of neural network perception systems. Prior work on the formal verification of neural network perception models has been limited to notions of local adversarial robustness for classification with respect to individual image inputs. In this work, we propose a notion of global correctness for neural n

Nguyen Hong Ha, Phan Viet Cuong, Le Tuan Anh, Ho Thi Thao

This article illustrates the development of a software named GoldEnvSim for simulation of the dispersion of radionuclides in the atmosphere. The software is written in JavaFX programming language to couple the Weather Research and Forecasting (WRF) model and the FLEXPART-WRF model. The highlight function of this software is to provide convenience for users t

Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding

We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images. It is based on the hypothesis that images' distinct source features can be preserved and extracted after going through state-of-the-art deepfake generation processes. We introduce a novel representation learning approach, called pair-

Jawher Jerray, Laurent Fribourg

A method is given for generating a bounded invariant of a differential system with a given set of initial conditions around a point $x_0$. This invariant has the form of a tube centered on the Euler approximate solution starting at $x_0$, which has for radius an upper bound on the distance between the approximate solution and the exact ones. The method consi

Henrik Christensen, Maria Gini, Odest Chadwicke Jenkins, Holly Yanco

Robotics has the potential to magnify the skilled workforce of the nation by complementing our workforce with automation: teams of people and robots will be able to do more than either could alone. The economic engine of the U.S. runs on the productivity of our people. The rise of automation offers new opportunities to enhance the work of our citizens and dr

Taylor A. Hutchison, Josh Walawender, Shui Hung Kwok

We present a recent evaluation and updates applied to the Multi-Object Spectrometer For Infra-Red Exploration (MOSFIRE) on the Keck I telescope. Over the course of significantly long integrations, when MOSFIRE sits on one mask for $>$4 hours, a slight drift in mask stars has been measured. While this does not affect all science-cases done with MOSFIRE, the d

R. Flores-Calderón, A. Martín-Ruiz

We show that under the effect of an external electric field and a gradient of chemical potential, a topological electric current can be induced in Weyl semimetals without inversion and mirror symmetries. We derive analytic expressions for the nonlinear conductivity tensor and show that it is nearly quantized for small tilting when the Fermi levels are close

Matthias Nadler, Fabian Schär

In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well

Antonio Ferreiro

We propose a suitable vacuum state for a quantized Dirac field interacting with a classical scalar field in a Friedman-Robertson-Walker-Lemaitre spacetime. This state is constructed from an extended version of the adiabatic expansion of the associated Fourier modes of the field. We compute the full renormalized stress-energy tensor. For any given initial tim