scholarly journals AbacusSummit: A Massive Set of High-Accuracy, High-Resolution N-Body Simulations

2021 ◽  
Author(s):  
Nina A Maksimova ◽  
Lehman H Garrison ◽  
Daniel J Eisenstein ◽  
Boryana Hadzhiyska ◽  
Sownak Bose ◽  
...  
Keyword(s):  
High Resolution ◽  
High Accuracy ◽  
Covariance Estimation ◽  
Particle Mass ◽  
Data Set ◽  
The Public ◽  
Oak Ridge ◽  
The Core ◽  
Public Data ◽  
Data Release

Abstract We present the public data release of the AbacusSummit cosmological N-body simulation suite, produced with the Abacus N-body code on the Summit supercomputer of the Oak Ridge Leadership Computing Facility. Abacus achieves $\mathcal {O}\left(10^{-5}\right)$ median fractional force error at superlative speeds, calculating 70M particle updates per second per node at early times, and 45M particle updates per second per node at late times. The simulation suite totals roughly 60 trillion particles, the core of which is a set of 139 simulations with particle mass 2 × 109 h−1 M⊙ in box size 2 h−1 Gpc. The suite spans 97 cosmological models, including Planck 2018, previous flagship simulation cosmologies, and a linear derivative and cosmic emulator grid. A sub-suite of 1883 boxes of size 500 h−1 Mpc is available for covariance estimation. AbacusSummit data products span 33 epochs from z = 8 to 0.1 and include lightcones, full particle snapshots, halo catalogs, and particle subsets sampled consistently across redshift. AbacusSummit is the largest high-accuracy cosmological N-body data set produced to date.

scholarly journals CNVScope: Visually Exploring Copy Number Aberrations in Cancer Genomes

2019 ◽  
Vol 18 ◽  
pp. 117693511989029
Author(s):  
James LT Dalgleish ◽  
Yonghong Wang ◽  
Jack Zhu ◽  
Paul S Meltzer
Keyword(s):  
Copy Number ◽  
High Performance ◽  
Data Sets ◽  
Data Set ◽  
The Public ◽  
Public Data ◽  
Analysis Package ◽  
Cis And Trans ◽  
High Performance Computing Cluster ◽  
Shiny Application

Motivation: DNA copy number (CN) data are a fast-growing source of information used in basic and translational cancer research. Most CN segmentation data are presented without regard to the relationship between chromosomal regions. We offer both a toolkit to help scientists without programming experience visually explore the CN interactome and a package that constructs CN interactomes from publicly available data sets. Results: The CNVScope visualization, based on a publicly available neuroblastoma CN data set, clearly displays a distinct CN interaction in the region of the MYCN, a canonical frequent amplicon target in this cancer. Exploration of the data rapidly identified cis and trans events, including a strong anticorrelation between 11q loss and17q gain with the region of 11q loss bounded by the cell cycle regulator CCND1. Availability: The shiny application is readily available for use at http://cnvscope.nci.nih.gov/ , and the package can be downloaded from CRAN ( https://cran.r-project.org/package=CNVScope ), where help pages and vignettes are located. A newer version is available on the GitHub site ( https://github.com/jamesdalg/CNVScope/ ), which features an animated tutorial. The CNVScope package can be locally installed using instructions on the GitHub site for Windows and Macintosh systems. This CN analysis package also runs on a linux high-performance computing cluster, with options for multinode and multiprocessor analysis of CN variant data. The shiny application can be started using a single command (which will automatically install the public data package).

scholarly journals Stellar velocity dispersions and emission line properties of SDSS-III/BOSS galaxies

2012 ◽  
Vol 8 (S295) ◽  
pp. 129-132
Author(s):  
D. Thomas ◽  
O. Steele ◽  
C. Maraston ◽  
J. Johansson ◽  
A. Beifiori ◽  
...  
Keyword(s):  
Emission Line ◽  
Velocity Dispersion ◽  
Target Selection ◽  
Emission Lines ◽  
Clear Correlation ◽  
Data Set ◽  
Star Forming ◽  
Public Data ◽  
Data Release ◽  
Stellar Velocity

AbstractWe perform a spectroscopic analysis of 492,450 galaxy spectra from the first two years of observations of the Sloan Digital Sky Survey-III/Baryonic Oscillation Spectroscopic Survey (BOSS) collaboration. This data set has been released in the ninth SDSS data release, the first public data release of BOSS spectra. We show that the typical signal-to-noise ratio of BOSS spectra is sufficient to measure stellar velocity dispersion and emission line fluxes for individual objects. The typical velocity dispersion of a BOSS galaxy is 240 km/s, with an accuracy of better than 30 per cent for 93 per cent of BOSS galaxies. The distribution in velocity dispersion is redshift independent between redshifts 0.15 and 0.7, which reflects the survey design targeting massive galaxies with an approximately uniform mass distribution in this redshift interval. The majority of BOSS galaxies lack detectable emission lines. We analyse the emission line properties and present diagnostic diagrams using the emission lines [OII], Hβ, [OIII], Halpha, and [NII] (detected in about 4 per cent of the galaxies). We show that the emission line properties are strongly redshift dependent and that there is a clear correlation between observed frame colours and emission line properties. Within in the low-z sample around 0.15 < z < 0.3, half of the emission-line galaxies have LINER-like emission line ratios, followed by Seyfert-AGN dominated spectra, and only a small fraction of a few per cent are purely star forming galaxies. AGN and LINER-like objects, instead, are less prevalent in the high-z sample around 0.4 < z < 0.7, where more than half of the emission line objects are star forming. This is a pure selection effect caused by the non-detection of weak Hβ emission lines in the BOSS spectra. Finally, we show that star forming, AGN and emission line free galaxies are well separated in the g - r vs r - i target selection diagram.

scholarly journals Dynamics based on analysis of public data for spreading of disease

2021 ◽  
Author(s):  
Leonardo S. Lima
Keyword(s):  
Planck Equation ◽  
The Novel ◽  
Epidemic Spreading ◽  
Data Set ◽  
Public Health Agencies ◽  
The Public ◽  
Public Data ◽  
Health Agencies ◽  
Novel Coronavirus ◽  
Official Data

Abstract The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supported by the public health agencies in countries as Brazil, EUA and India is investigated. We perform the numerical analysis using the stochastic differential equation in Itô’s calculus (SDE) for the estimating of novel cases daily as well as analytical calculations solving the correspondent Fokker-Planck equation for the density probability distribution of novel cases, P(N(t); t). Our results display that the model based in the Itô diffusion fits well to the results due to uncertain in the official data and to the number of tests realized in the populations of each country.

An end-to-End deep context gate convolutional visual odometry system based on lightweight attention mechanism

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yan Xu ◽  
Hong Qin ◽  
Jiani Huang ◽  
Yanyun Wang
Keyword(s):  
Visual Odometry ◽  
Attention Mechanism ◽  
Learning Ability ◽  
Global Features ◽  
Data Set ◽  
Content Type ◽  
Generalization Ability ◽  
The Public ◽  
Public Data ◽  
End To End

Purpose Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability. Design/methodology/approach Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system. Findings The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively. Originality/value The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals THE ESSENCE OF DEEP LEARNING

2021 ◽  
Author(s):  
wei wang
Keyword(s):  
Deep Learning ◽  
Data Set ◽  
The Public ◽  
Public Data

Using the public data set Cifar-10.

scholarly journals Fall Detection with CNN-Casual LSTM Network

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 403
Author(s):  
Jiang Wu ◽  
Jiale Wang ◽  
Ao Zhan ◽  
Chengyu Wu
Keyword(s):  
Fall Detection ◽  
Detection Algorithm ◽  
Personal Privacy ◽  
Data Set ◽  
The Public ◽  
The Neural Network ◽  
Public Data ◽  
Spatio Temporal ◽  
Axis Rotation ◽  
Lstm Network

Falls are one of the main causes of elderly injuries. If the faller can be found in time, further injury can be effectively avoided. In order to protect personal privacy and improve the accuracy of fall detection, this paper proposes a fall detection algorithm using the CNN-Casual LSTM network based on three-axis acceleration and three-axis rotation angular velocity sensors. The neural network in this system includes an encoding layer, a decoding layer, and a ResNet18 classifier. Furthermore, the encoding layer includes three layers of CNN and three layers of Casual LSTM. The decoding layer includes three layers of deconvolution and three layers of Casual LSTM. The decoding layer maps spatio-temporal information to a hidden variable output that is more conducive relative to the work of the classification network, which is classified by ResNet18. Moreover, we used the public data set SisFall to evaluate the performance of the algorithm. The results of the experiments show that the algorithm has high accuracy up to 99.79%.

scholarly journals SlideAugment: A Simple Data Processing Method to Enhance Human Activity Recognition Accuracy Based on WiFi

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2181
Author(s):  
Junyan Li ◽  
Kang Yin ◽  
Chengpei Tang
Keyword(s):  
Activity Recognition ◽  
Data Augmentation ◽  
Recognition Accuracy ◽  
Original Data ◽  
Data Sets ◽  
Data Set ◽  
The Public ◽  
Data Processing Method ◽  
Public Data ◽  
Multiple Samples

Currently, there are various works presented in the literature regarding the activity recognition based on WiFi. We observe that existing public data sets do not have enough data. In this work, we present a data augmentation method called window slicing. By slicing the original data, we get multiple samples for one raw datum. As a result, the size of the data set can be increased. On the basis of the experiments performed on a public data set and our collected data set, we observe that the proposed method assists in improving the results. It is notable that, on the public data set, the activity recognition accuracy improves from 88.13% to 97.12%. Similarly, the recognition accuracy is also improved for the data set collected in this work. Although the proposed method is simple, it effectively enhances the recognition accuracy. It is a general channel state information (CSI) data augmentation method. In addition, the proposed method demonstrates good interpretability.

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