November 2020 arXiv papers — page 29

Rustem Ozakar, Rafet Efe Gazanfer, Y. Sinan Hanay

In this work, we analyze the happiness levels of countries using an unbiased emotion detector, artificial intelligence (AI). To date, researchers proposed many factors that may affect happiness such as wealth, health and safety. Even though these factors all seem relevant, there is no clear consensus between sociologists on how to interpret these, and the mo

Parsa Sarosh, Shabir A. Parah, Romany F Mansur, G. M. Bhat

The emergence of COVID-19 has necessitated many efforts by the scientific community for its proper management. An urgent clinical reaction is required in the face of the unending devastation being caused by the pandemic. These efforts include technological innovations for improvement in screening, treatment, vaccine development, contact tracing and, survival

Wei Huang, Weitao Du, Richard Yi Da Xu, Chunrui Liu

Most theoretical studies explaining the regularization effect in deep learning have only focused on gradient descent with a sufficient small learning rate or even gradient flow (infinitesimal learning rate). Such researches, however, have neglected a reasonably large learning rate applied in most practical applications. In this work, we characterize the impl

Muna Al-Hawawreh, Elena Sitnikovas

While achieving security for Industrial Internet of Things (IIoT) is a critical and non-trivial task, more attention is required for brownfield IIoT systems. This is a consequence of long life cycles of their legacy devices which were initially designed without considering security and IoT connectivity, but they are now becoming more connected and integrated

Kosuke Miura, Yuki Izumida, Koji Okuda

We study the possibility of achieving the Carnot efficiency in a finite-power underdamped Brownian Carnot cycle. Recently, it was reported that the Carnot efficiency is achievable in a general class of finite-power Carnot cycles in the vanishing limit of the relaxation times. Thus, it may be interesting to clarify how the efficiency and power depend on the r

Matthieu Stigler, David Lobell

Index insurance has been promoted as a promising solution for reducing agricultural risk compared to traditional farm-based insurance. By linking payouts to a regional factor instead of individual loss, index insurance reduces monitoring costs, and alleviates the problems of moral hazard and adverse selection. Despite its theoretical appeal, demand for index

Jiarui Zhao, Zheng Yan, Meng Cheng, Zi Yang Meng

In recent years, new phases of matter that are beyond the Landau paradigm of symmetry breaking are mountaining, and to catch up with this fast development, new notions of global symmetry are introduced. Among them, the higher-form symmetry, whose symmetry charges are spatially extended, can be used to describe topologically ordered phases as the spontaneous

Takumi Fukunaga, Hiroyuki Kasai

This paper presents a proposal of a faster Wasserstein $k$-means algorithm for histogram data by reducing Wasserstein distance computations and exploiting sparse simplex projection. We shrink data samples, centroids, and the ground cost matrix, which leads to considerable reduction of the computations used to solve optimal transport problems without loss of

Ulrich J. Mohrhoff

The only acceptable reason why measurements are irreversible and outcomes definite is the intrinsic definiteness and irreversibility of human sensory experience. While QBists deserve credit for their spirited defense of this position, Niels Bohr urged it nearly a century ago, albeit in such elliptic ways that the core of his message has been lost or distorte

A Vamsi Krishna Reddy, K. Anki Reddy

We used the discrete element method (DEM) to study the flow dynamics of a mixture of dumbbells and discs for two silo cases: 1. orifice situated on the lateral wall and 2. multiple orifices placed on the base of a two-dimensional silo. The time-averaged flow fields of various parameters like velocity, area fraction, pressure, shear stress etc, obtained from

Chathurika P. Mediwaththe, Lachlan Blackhall

In this paper, the extent to which the integration of rooftop photovoltaic (PV) power with a community energy storage (CES) system can reduce energy cost and distribution network (DN) loss is explored. To this end, three energy trading systems (ETSs) are compared; first, an ETS where PV users exchange energy with the CES system in addition to the grid, secon

Yingying Li, Na Li

We consider online convex optimization with time-varying stage costs and additional switching costs. Since the switching costs introduce coupling across all stages, multi-step-ahead (long-term) predictions are incorporated to improve the online performance. However, longer-term predictions tend to suffer from lower quality. Thus, a critical question is: how

Tengteng Wen, Zhuofeng Mo, Jingshan Li, Qi Liu

Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we proposed an odor labeling convolutional encoder-decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used fo

Ina Schmidt, Areti Papastavrou, Paul Steinmann

Continuum bone remodelling is an important tool for predicting the effects of mechanical stimuli on bone density evolution. While the modelling of only cancellous bone is considered in many studies based on continuum bone remodelling, this work presents an approach of modelling also cortical bone and the interaction of both bone types. The distinction betwee

Achintya kr. Sarkar, Zheng-Hua Tan

In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training deep neural networks with a self-supervi

Benjamin M. Armstrong, Kenji Bekki, Aaron D. Ludlow

We use the second Gaia data release to investigate the kinematics of 17 ultra-faint dwarf galaxies (UFDs) and 154 globular clusters (GCs) in the Milky Way, focusing on the differences between static and evolving models of the Galactic potential. An evolving potential modifies a satellite's orbit relative to its static equivalent, though the difference is sma

Nadia Kausar, Ijaz Ahmed, Ather M. W

The study aims to investigate the observability of pseudoscalar Higgs boson $A$ and neutral heavy CP even Higgs boson $H$, at different benchmark points, in the framework of type-I 2HDM. The study is done for $e^{-}e^{+}$ collisions at $\sqrt{s}$ = 1000 GeV centre of mass energy (c.o.m.) a possible scenario in future lepton collider. The associated productio

Shounak De, Suparna Roychowdhury, Roopkatha Banerjee

The dynamics of the planar circular restricted three-body problem with Kerr-like primaries in the context of a beyond-Newtonian approximation is studied. The beyond-Newtonian potential is developed by using the Fodor-Hoenselaers-Perj\'es procedure. An expansion in the Kerr potential is performed and terms up-to the first non-Newtonian contribution of both th

Mitsuhiko Horie, Hiroyuki Kasai

Multi-view data analysis has gained increasing popularity because multi-view data are frequently encountered in machine learning applications. A simple but promising approach for clustering of multi-view data is multi-view clustering (MVC), which has been developed extensively to classify given subjects into some clustered groups by learning latent common fe

Bing-Bing Wang, Xiao-Jun Bi, Kun Fang, Sujie Lin

We investigate the solar modulation effect with the long time cosmic ray proton and helium spectrum measured by AMS-02 on the time scale of a Bartels rotation (27 days) between May 2011 and May 2017. The time-span covers the negative heliospheric magnetic field polarity cycle, the polarity reversal period and the positive polarity cycle. The unprecedented ac

Jung-Woo Kim, Seong-Jin Lee, Jun-Yeong Jo, Semyung Wang

A three-dimensional acoustic Luneburg meta-lens has the advantage of refracting sound waves for all incident angles and focusing higher sound pressure compared to a two-dimensional lens. The lens made of plastic with a diameter of 120 mm was designed with thousands of lattice column-shaped meta-atoms to maintain its three-dimensional shape. The lens's three-

Eitaro Hamada, Yuki Fujii, Youichi Igarashi, Masahiro Ikeno

The COMET experiment at J-PARC aims to search for the neutrinoless transition of a muon to an electron. We have developed the readout electronics board called ROESTI for the COMET straw tube tracker. We plan to install the ROESTI in the gas manifold of the detector. The number of vacuum feedthroughs needs to be reduced due to space constraints and cost limit

Naofumi Akimoto, Akio Hayakawa, Andrew Shin, Takuya Narihira

We propose a novel reference-based video colorization framework with spatiotemporal correspondence. Reference-based methods colorize grayscale frames referencing a user input color frame. Existing methods suffer from the color leakage between objects and the emergence of average colors, derived from non-local semantic correspondence in space. To address this

Bowen Wang, Liangzhi Li, Manisha Verma, Yuta Nakashima

Few-shot learning (FSL) approaches are usually based on an assumption that the pre-trained knowledge can be obtained from base (seen) categories and can be well transferred to novel (unseen) categories. However, there is no guarantee, especially for the latter part. This issue leads to the unknown nature of the inference process in most FSL methods, which ha

Patrick Copinger, Pablo Morales

Schwinger pair production is analyzed in a BPST instanton background and in its SL$(2,\mathbb{C})$ complex extension for complex scalar particles. A non-Abelian extension of the worldline instanton method is utilized, wherein Wong's equations in a coherent state picture adopted for SL$(2,\mathbb{C})$ are solved in Euclidean spacetime. While pair production i

Zhicheng Zhang, Xiaokun Liang, Wei Zhao, Lei Xing

Computed tomography (CT) has played a vital role in medical diagnosis, assessment, and therapy planning, etc. In clinical practice, concerns about the increase of X-ray radiation exposure attract more and more attention. To lower the X-ray radiation, low-dose CT is often used in certain scenarios, while it will induce the degradation of CT image quality. In

Takashi Uneyama

In polymer melts, the interaction between segments are considered to be screened and the ideal Gaussian chain statistics is recovered. The experimental fact that linear viscoelasticity of unentangled polymers can be well described by the Rouse model is naively considered as due to this screening effect. Although various theoretical models are based on the sc

Eckhard Platen, Stefan Tappe

We show that in a financial market given by semimartingales an arbitrage opportunity, provided it exists, can only be exploited through short selling. This finding provides a theoretical basis for differences in regulation for financial services providers that are allowed to go short and those without short sales. The privilege to be allowed to short sell gi

Takahiro S. Yamamoto, Takahiro Tanaka

The signal of continuous gravitational waves has a longer duration than the observation period. Even if the waveform in the source frame is monochromatic, we will observe the waveform with modulated frequencies due to the motion of the detector. If the source location is unknown, a lot of templates having different sky positions are required to demodulate th

Lin Yang, Audrey Moores, Tomislav Friščić, Nikolas Provatas

Mechanochemistry is becoming an established method for the sustainable, solid-phase synthesis of scores of nano-materials and molecules, ranging from active pharmaceutical ingredients to materials for cleantech. Yet we are still lacking a good model to rationalize experimental observations and develop a mechanistic understanding of the factors at play during

Yu-ichi Takamizu, Masayuki Umemura, Hidenobu Yajima, Makito Abe

Near infrared diffuse optical tomography (DOT) provides an imaging modality for the oxygenation of tissue. In this paper, we propose a novel machine learning algorithm based on time-domain radiative transfer equation. We use temporal profiles of absorption measure for a two-dimensional model of target tissue, which are calculated by solving time-domain radia

Ramesh Chandra Bhatt, Chun-Ming Liao, Lin-Xiu Ye, Ngo Trong Hai

The understanding of the characteristics of a magnetic layer in a different environment is crucial for any spintronics application. Before practical applications, thorough scrutiny of such devices is compulsory. Here we study such a potential Hall device of MgO-capped Hf/GdFeCo bilayer (FeCo-rich) for magnetization relaxation around nucleation fields at diff

Souradeep Sasmal, Ashutosh Rai, Sayan Gangopadhyay, Dipankar Home

A comprehensive treatment of the quantification of randomness certified device-independently by using the Hardy and Cabello-Liang-Li (CLL) nonlocality relations is provided in the two parties - two measurements per party - two outcomes per measurement (2-2-2) scenario. For the Hardy nonlocality, it is revealed that for a given amount of nonlocality signified

Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang

Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along e

Ian Laga, Le Bao, Xiaoyue Niu

Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the question, "How many X's do you know," where X is the population of interest (e.g. "How many female sex workers do you know?"

Chandra Thapa, M. A. P. Chamikara, Seyit A. Camtepe

In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL) recently attracted much attention due to its applications in health, finance, and the latest innovations such as industry 4.0 and smart vehicles. FL provides privacy-by-design. It trains a machine learning model collaboratively over several distributed clients (ranging

Jingzhi Hu, Hongliang Zhang, Kaigui Bian, Marco Di Renzo

Using RF signals for wireless sensing has gained increasing attention. However, due to the unwanted multi-path fading in uncontrollable radio environments, the accuracy of RF sensing is limited. Instead of passively adapting to the environment, in this paper, we consider the scenario where an intelligent metasurface is deployed for sensing the existence and

Fengqiao Luo

We establish and analyze a service center location model with a simple but novel decision-dependent demand induced from a maximum attraction principle. The model formulations are investigated in the distributionally-robust optimization framework. A statistical model that is based on the maximum attraction principle for estimating customer demand and utility

Evert Klaseboer, Qiang Sun, Derek Y. C. Chan

Analytical solutions in fluid dynamics can be used to elucidate the physics of complex flows and to serve as test cases for numerical models. In this work, we present the analytical solution for the acoustic boundary layer that develops around a rigid sphere executing small amplitude harmonic rectilinear motion in a compressible fluid. The mathematical frame

Michael P. Howard, Zachary M. Sherman, Adithya N Sreenivasan, Stephanie A. Valenzuela

Colloidal nanocrystal gels can be assembled using a difunctional "linker" molecule to mediate bonding between nanocrystals. The conditions for gelation and the structure of the gel are controlled macroscopically by the linker concentration and microscopically by the linker's molecular characteristics. Here, we demonstrate using a toy model for a colloid-link

Sen Lin, Li Yang, Zhezhi He, Deliang Fan

While deep learning has achieved phenomenal successes in many AI applications, its enormous model size and intensive computation requirements pose a formidable challenge to the deployment in resource-limited nodes. There has recently been an increasing interest in computationally-efficient learning methods, e.g., quantization, pruning and channel gating. How

Kentaro Iio, Xiaoyu Guo, Xiaoqiang "Jack" Kong, Kelly Rees

In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have encouraged and ordered citizens to practice social distancing, particularly by working and studying at home. Intuitively, only a subset of people have the ability to practice remote work. However, there has been little research on the disparity of mobility adaptation across dif

Aniruddha Rajendra Rao, Matthew Reimherr

This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state-of-the-art methods, which face major challenges in the fitting of more complex non-linear models. Currently, many of these models cannot be consistently estimated unless the number of observed points pe

Ye Yuan, Xueying Ding, Ziv Bar-Joseph

Causal inference from observation data is a core problem in many scientific fields. Here we present a general supervised deep learning framework that infers causal interactions by transforming the input vectors to an image-like representation for every pair of inputs. Given a training dataset we first construct a normalized empirical probability density dist

M. Kamba, H. Kiuchi, T. Yotsuya, K. Aikawa

We report on direct feedback cooling of single nanoparticles in an optical lattice to near their motional ground state. We find that the laser phase noise triggers severe heating of nanoparticles' motion along the optical lattice. When the laser phase noise is decreased by orders of magnitude, the heating rate is reduced and accordingly the occupation number

Yan Han, Chongyan Chen, Liyan Tang, Mingquan Lin

Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative fea

Eric Li, Jingyi Su, Hao Sheng, Lawrence Wai

Multiple-choice questions (MCQs) offer the most promising avenue for skill evaluation in the era of virtual education and job recruiting, where traditional performance-based alternatives such as projects and essays have become less viable, and grading resources are constrained. The automated generation of MCQs would allow assessment creation at scale. Recent

David Belanger, Ziyuan Gao, Sanjay Jain, Wei Li

Prior work of Gavryushkin, Khoussainov, Jain and Stephan investigated what algebraic structures can be realised in worlds given by a positive (= recursively enumerable) equivalence relation which partitions the natural numbers into infinitely many equivalence classes. The present work investigates the infinite one-one numbered recursively enumerable (r.e.) f

Vitaly Pronskikh

Monte-Carlo nuclear reaction and transport codes are widely used to devise accelerator-based nuclear physics experiments; at the same time, many experiments are performed to validate the Monte-Carlo codes, which can be used for the design of full-scale nuclear power applications or the design of new benchmark experiments. Dedicated model benchmark studies in

Wei Gao, Shangwei Guo, Tianwei Zhang, Han Qiu

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threa

Joe Zhiyu Chen, Amol Upadhye, Yvonne Y. Y. Wong

We present in this work a novel and yet extremely simple method for incorporating the effects of massive neutrinos in cosmological $N$-body simulations. This so-called "SuperEasy linear response" approach is based upon analytical solutions to the collisionless Boltzmann equation in the clustering and free-streaming limits, which are then connected by a ratio

Joe Zhiyu Chen, Amol Upadhye, Yvonne Y. Y. Wong

A significant challenge for modelling the massive neutrino as a hot dark matter is its large velocity dispersion. In this work, we investigate and implement a multi-fluid perturbation theory that treats the cosmic neutrino population as a collection of fluids with a broad range of bulk velocities. These fluids respond linearly to the clustering of cold matte

Evan Carnahan, Natalie S. Wolfenbarger, Jacob S. Jordan, Marc A. Hesse

Ice shell dynamics are an important control on the habitability of icy ocean worlds. Here we present a systematic study evaluating the effect of temperature-dependent material properties on these dynamics. We review the published thermal conductivity data for ice, which demonstrates that the most commonly used conductivity model in planetary science represen

Steven V Sam, Andrew Snowden

We develop the idea of a supersymmetric monoidal supercategory, following ideas of Kapranov. Roughly, this is a monoidal category in which the objects and morphisms are ${\bf Z}/2$-graded, equipped with isomorphisms $X \otimes Y \to Y \otimes X$ of parity $\vert X \vert \vert Y \vert$ on homogeneous objects. There are two fundamental examples: the groupoid o

Bin Sheng

The feedback vertex set problem is one of the most studied parameterized problems. Several generalizations of the problem have been studied where one is to delete vertices to obtain graphs close to acyclic. In this paper, we give an FPT algorithm for the problem of deleting at most $k$ vertices to get an $r$-pseudoforest. A graph is an $r$-pseudoforest if we

Lingqi Kong, Xiangkun Kong, Shunliu Jiang, Yuanxin Lee

A water-based switchable frequency selective rasorber with polarization selectivity using the Great Wall structures is presented in this paper. The proposed structure comprises a container containing horizontal and vertical channels enabling dividable injection of water, and a cross-gap FSS. The novelty of the design lies in its switchability among four diff

Rongchang Xie, Chunyu Wang, Wenjun Zeng, Yizhou Wang

Semi-supervised learning aims to boost the accuracy of a model by exploring unlabeled images. The state-of-the-art methods are consistency-based which learn about unlabeled images by encouraging the model to give consistent predictions for images under different augmentations. However, when applied to pose estimation, the methods degenerate and predict every

Vitaly Pronskikh

The paper, drawing on the example of simulation codes used in nuclear physics and high-energy physics, seeks to highlight the ethical implications of discontinuing support for simulation codes and the loss of knowledge embodied in them. Predicated on the concept of trading zones and actor network models, the paper addresses the problem of extinction of simul

Bin Sheng, Changhong Lu

Let G be a simple undirected graph with no isolated vertex. A paired dominating set of G is a dominating set which induces a subgraph that has a perfect matching. The paired domination number of G, denoted by {\gamma}pr(G), is the size of its smallest paired dominating set. Goddard and Henning conjectured that {\gamma}pr(G) {\leq} 4n/7 holds for every graph

David O'Connell

In this paper we will introduce and develop a theory of adjunction spaces which allows the construction of non-Hausdorff topological manifolds from standard Hausdorff ones. This is done by gluing Hausdorff manifolds along homeomorphic open submanifolds whilst leaving the boundaries of these regions unidentified. In the case that these gluing regions have hom

Muhannad W. Gamaleldin, Alexander V. Babanin, Amin Chabchoub

The present study aims to extend the applicability of the static-boundary absorption method in phase-resolving CFD simulations outside the conventional shallow-water waves limit. Even though this method was originally formulated for shallow-water waves based on the conventional piston type wavemaker, extending its use to deeper water conditions provides a mo

Phuong Pham, Vivek Jain, Lukas Dauterman, Justin Ormont

As cloud services are growing and generating high revenues, the cost of downtime in these services is becoming significantly expensive. To reduce loss and service downtime, a critical primary step is to execute incident triage, the process of assigning a service incident to the correct responsible team, in a timely manner. An incorrect assignment risks addit

Florence Maas-Gariépy, Rebecca Patrias

We prove K-theoretic and shifted K-theoretic analogues of the bijection of Stanton and White between domino tableaux and pairs of semistandard tableaux. As a result, we obtain product formulas for pairs of stable Grothendieck polynomials and pairs of K-theoretic Q-Schur functions.

Qinyan Huang, Weiwen Zhou, Minjie Wan, Xin Chen

Infrared (IR) image segmentation is essential in many urban defence applications, such as pedestrian surveillance, vehicle counting, security monitoring, etc. Active contour model (ACM) is one of the most widely used image segmentation tools at present, but the existing methods only utilize the local or global single feature information of image to minimize

Lunjun Zhang, Ge Yang, Bradly C. Stadie

Planning - the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems - is a hallmark of human intelligence. While deep reinforcement learning (RL) has shown great promise for solving relatively straightforward control tasks, it remains an open problem how to best incorporate planning into existing deep RL p

Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li

With the advent of Neural Radiance Fields (NeRF), neural networks can now render novel views of a 3D scene with quality that fools the human eye. Yet, generating these images is very computationally intensive, limiting their applicability in practical scenarios. In this paper, we propose a technique based on spatial decomposition capable of mitigating this i

Masanari Shimada, Norihiro Oyama

The relationship between glasses and gels has been intensely debated for decades; however, the transition between these two phases remains elusive. To investigate a gel formation process in the zero-temperature limit and its relation to the glass phase, we conducted numerical experiments on athermal quasistatic decompression. During decompression, the system

Park Jae Whan, Lee Jinwon, Yeom Han Woong

Domain walls in correlated charge density wave compounds such as 1T-TaS2 can have distinct localized states which govern physical properties and functionalities of emerging quantum phases. However, detailed atomic and electronic structures of domain walls have largely been elusive. We identify using scanning tunneling microscope and density functional theory

Anupriya, Daniel J. Graham, Prateek Bansal, Daniel Hörcher

Congestion; operational delays due to a vicious circle of passenger-congestion and train-queuing; is an escalating problem for metro systems because it has negative consequences from passenger discomfort to eventual mode-shifts. Congestion arises due to large volumes of passenger boardings and alightings at bottleneck stations, which may lead to increased st

Woohyeon Shim, Minsu Cho

We present a novel discriminator for GANs that improves realness and diversity of generated samples by learning a structured hypersphere embedding space using spherical circles. The proposed discriminator learns to populate realistic samples around the longest spherical circle, i.e., a great circle, while pushing unrealistic samples toward the poles perpendi

Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color bleeding, haze, etc.) and this diversity in appearance makes flare removal challenging. Existing analytical solutions make strong assumptions about the artifact's geometry or brightne

Raúl O. Chametla, Frédéric S. Masset

We evaluate the thermal torques exerted on low-mass planets embedded in gaseous protoplanetary discs with thermal diffusion, by means of high-resolution three-dimensional hydrodynamics simulations. We confirm that thermal torques essentially depend on the offset between the planet and its corotation, and find a good agreement with analytic estimates when thi

Heng Fan, Haibin Ling

High quality object proposals are crucial in visual tracking algorithms that utilize region proposal network (RPN). Refinement of these proposals, typically by box regression and classification in parallel, has been popularly adopted to boost tracking performance. However, it still meets problems when dealing with complex and dynamic background. Thus motivat

Luca D'Alessio, Mehrtash Babadi

We introduce CellSegmenter, a structured deep generative model and an amortized inference framework for unsupervised representation learning and instance segmentation tasks. The proposed inference algorithm is convolutional and parallelized, without any recurrent mechanisms, and is able to resolve object-object occlusion while simultaneously treating distant

Ellery Wulczyn, Kunal Nagpal, Matthew Symonds, Melissa Moran

Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether A.I. grading translates to better prognostication. I

Atul C. Thakur, Richard C. Remsing

Liquid hydrocarbons are often modeled with fixed, symmetric, atom-centered charge distributions and Lennard-Jones interaction potentials that reproduce many properties of the bulk liquid. While useful for a wide variety of applications, such models cannot capture dielectric effects important in solvation, self-assembly, and reactivity. The dielectric constan

Beatriz A. Asfora, Jacopo Banfi, Mark Campbell

In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a moving target within a given deadline. We prove this problem to be NP-hard, and present the first set of Mixed-Integer Linear Programming (MILP) models to tackle the MESPP problem. Our

Ana C. M. Brito, Filipi N. Silva, Diego R. Amancio

Understanding the dynamics of authors is relevant to predict and quantify performance in science. While the relationship between recent and future citation counts is well-known, many relationships between scholarly metrics at the author-level remain unknown. In this context, we performed an analysis of author-level metrics extracted from subsequent periods,

Ery Arias-Castro, Phong Alain Chau

We start by considering the problem of estimating intrinsic distances on a smooth submanifold. We show that minimax optimality can be obtained via a reconstruction of the surface, and discuss the use of a particular mesh construction -- the tangential Delaunay complex -- for that purpose. We then turn to manifold learning and argue that a variant of Isomap w

Jiajun Zhang, Jingkun Zhao, Terry D. Oswalt, Xilong Liang

We construct a sample of nearly 30,000 main-sequence stars with 4500K $<T\rm_{eff}<$ 5000K and stellar ages estimated by the chromospheric activity$-$age relation. This sample is used to determine the age distribution in the $R-Z$ plane of the Galaxy, where $R$ is the projected Galactocentric distance in the disk midplane and $Z$ is the height above the disk

Debabrata Sinha

We study a Josephson junction involving an inversion-asymmetric Weyl semimetal in presence of time-reversal symmetric (TRS) or time-reversal symmetry broken tilt in the Weyl spectra. We reveal that both types of tilts in the Weyl nodes lead to a Josephson $0$-$\pi$ transition and a zero bias valley/chiral supercurrent. Strikingly, the TRS tilt gives rise to

Karin de Langis, Michael Fulton, Junaed Sattar

With the end goal of selecting and using diver detection models to support human-robot collaboration capabilities such as diver following, we thoroughly analyze a large set of deep neural networks for diver detection. We begin by producing a dataset of approximately 105,000 annotated images of divers sourced from videos -- one of the largest and most varied

Chenghao Feng, Wenqian Shen, Xinyu Gao, Jianping An

Hybrid analog/digital precoding in millimeter-wave (mmWave) multi-input multi-ouput (MIMO) systems is capable of achieving the near-optimal full-digital performance at reduced hardware cost and power consumption compared to its full-RF digital counterpart. However, having numerous phase shifters is still costly, especially when the phase shifters are of high

Shaozhu Xiao, Yinxiang Li, Yong Li, Xiufu Yang

The prediction of topological states in rare earth monopnictide compounds has attracted renewed interest. Extreme magnetoresistance (XMR) has also been observed in several nonmagnetic rare earth monopnictide compounds. The origin of XMR in these compounds could be attributed to several mechanisms, such as topologically nontrivial electronic structures and el

Zhi-Cheng Shi, Ye-Hong Chen, Wei Qin, Yan Xia

In this work, we derive exact solutions of a dynamical equation, which can represent all two-level Hermitian systems driven by periodic $N$-step driving fields. For different physical parameters, this dynamical equation displays various phenomena for periodic $N$-step driven systems. The time-dependent transition probability can be expressed by a general for

Yang-Yang Lyu, Xiaoyu Ma, Jing Xu, Yong-Lei Wang

The ability to control the potential landscape in a medium of interacting particles could lead to intriguing collective behavior and innovative functionalities. Here, we utilize spatially reconfigurable magnetic potentials of a pinwheel artificial spin ice structure to tailor the motion of superconducting vortices. The reconstituted chain structures of the m

Konstantin Ottnad

Excited state contributions represent a formidable challenge for hadron structure calculations in lattice QCD. For physical systems that exhibit an exponential signal-to-noise problem they often hinder the extraction of ground state matrix elements, introducing a major source of systematic error in lattice calculations of such quantities. The development of

Sicheng Zhao, Xuanbai Chen, Xiangyu Yue, Chuang Lin

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UDA) studies

Minh N. H. Nguyen, Nguyen H. Tran, Yan Kyaw Tun, Zhu Han

Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices. In this paper, we study a new model of multiple federated learning services at the multi-access edge computing server. Accordingly, the sharing of CPU resources among learning services at each mobile device for

Chandra Maddila, Sai Surya Upadrasta, Chetan Bansal, Nachiappan Nagappan

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests towards c

Tetsuro Habe

We theoretically investigate the electronic states in monolayer NbSe$_2$ and develop continuous models to describe these states in Fermi pockets. In $1H$-type metallic transition-metal dichalcogenides(TMDCs), the Femi surface consists of three pockets enclosing the $\Gamma$, $K$, and $K'$ points. We reveal that the conventional effective model used for semic

Alexander Partin, Thomas Brettin, Yvonne A. Evrard, Yitan Zhu

Motivated by the size of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating data, a common question is whether the proposed predictors can further improve the generalization performance with more training data.

Sunipa Dev

High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining. These representations have different degrees of interpretability, with efficient distributed representations coming at the cost of the loss of feature to dimension mapping. This imp

Feng Rong, Shichao Yang

Let $D$ be a bounded domain in $\mathbb{C}^n$, $n\ge 1$. In this paper, we study two biholomorphic invariants on $D$, the Fridman invariant $e_D(z)$ and the squeezing function $s_D(z)$. More specifically, we study the following two questions about the \textit{quotient invariant} $m_D(z)=s_D(z)/e_D(z)$: 1) If $m_D(z_0)=1$ for some $z_0\in D$, is $D$ biholomor

Marcus E. Lower, Simon Johnston, Ryan M. Shannon, Matthew Bailes

Radio-loud magnetars display a wide variety of radio-pulse phenomenology seldom seen among the population of rotation-powered pulsars. Spectropolarimetry of the radio pulses from these objects has the potential to place constraints on their magnetic topology and unveil clues about the magnetar radio emission mechanism. Here we report on eight observations of

Zhiwei Fan, Dmitry V. Skryabin

We report a method to control - disrupt and restore, a regime of the unidirectional soliton generation in a bidirectionally pumped ring microresonator. This control, i.e., the soliton blockade, is achieved by tuning pump frequency of the counterrotating field. The blockade effect is correlated with the emergence of a dark-bright nonlinear resonance of the cw

Wei Wang, Chao Zhang, Xiaopei Wu

Accent recognition with deep learning framework is a similar work to deep speaker identification, they're both expected to give the input speech an identifiable representation. Compared with the individual-level features learned by speaker identification network, the deep accent recognition work throws a more challenging point that forging group-level accent

Michael Muratov, Abdulwasay Mehar, Wan Song Lee, Michael Szpakowicz

This report demonstrates several methods used to make a self-driving vehicle using a supervised learning algorithm and a forward-facing RGBD camera. The project originally involved research in creating an adversarial attack on the vehicle's model, but due to difficulties with the initial training of the car, the plans were discarded in favor of completing th

Makoto Naka, Yukitoshi Motome, Hitoshi Seo

We theoretically show that materials with perovskite-type crystal structures provide a platform for spin current generation, taking advantage of a mechanism requiring neither the spin-orbit coupling nor a ferromagnetic moment, but is based on spin-split band structures in certain kinds of collinear antiferromagnetic states. By investigating a multiband Hubba

Feige Wang, Xiaohui Fan, Jinyi Yang, Chiara Mazzucchelli

X-ray emission from quasars has been detected up to redshift $z=7.5$, although only limited to a few objects at $z>6.5$. In this work, we present new Chandra observations of five $z>6.5$ quasars. By combining with archival Chandra observations of six additional $z>6.5$ quasars, we perform a systematic analysis on the X-ray properties of these earliest accret