Localized time accurate sampling of nonequilibrium and unsteady hypersonic flows: methods and horizons

AbstractModern “non-intrusive” optical methods are providing revolutionary capabilities for diagnostics of hypersonic flow fields. They generate accurate information on the performance of ground test facilities and provide local time accurate measurements of near-wall and off-body flow fields surrounding hypersonic test articles. They can follow the true molecular motion of the flow and detect nonequilibrium states and gas mixtures. They can be used to capture a wide range of turbulent scales and can produce highly accurate velocity, temperature and density measurements as well as time-frozen images that provide intuitive understanding of flow phenomena. Recent review articles address many of these methods and their applications. The methods highlighted in this review are those that have been enabled or greatly improved by new, versatile laser systems, particularly including kHz rate femtosecond lasers and MHz rate pulse burst lasers. Although these methods can be applied to combusting environments, the focus of this review is on external high Mach number flows surrounding test articles and wind tunnel core flow properties. The high repetition rates enable rapid time evolving flows to be analyzed and enable the collection of large data sets necessary for statistical analysis. Future capabilities based on the use of atomic vapor filters and on frequency tunable, injection locked MHz rate lasers are promising.

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Graph representation learning: a survey

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2020.13 ◽

2020 ◽

Vol 9 ◽

Author(s):

Fenxiao Chen ◽

Yun-Cheng Wang ◽

Bin Wang ◽

C.-C. Jay Kuo

Keyword(s):

Graph Embedding ◽

Large Data ◽

Representation Learning ◽

Graph Representation ◽

Data Sets ◽

Graph Data ◽

Graph Properties ◽

Wide Range ◽

Regular Lattices ◽

Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

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0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds

SLEEP ◽

10.1093/sleep/zsaa056.060 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A24-A26

Author(s):

J Hammarlund ◽

R Anafi

Keyword(s):

Machine Learning ◽

Network Architecture ◽

Large Data ◽

Data Sets ◽

Sample Collection ◽

Circadian Phase ◽

Confounding Variables ◽

Wide Range ◽

Fully Connected ◽

Genome Scale

Abstract Introduction We recently used unsupervised machine learning to order genome scale data along a circadian cycle. CYCLOPS (Anafi et al PNAS 2017) encodes high dimensional genomic data onto an ellipse and offers the potential to identify circadian patterns in large data-sets. This approach requires many samples from a wide range of circadian phases. Individual data-sets often lack sufficient samples. Composite expression repositories vastly increase the available data. However, these agglomerated datasets also introduce technical (e.g. processing site) and biological (e.g. age or disease) confounders that may hamper circadian ordering. Methods Using the FLUX machine learning library we expanded the CYCLOPS network. We incorporated additional encoding and decoding layers that model the influence of labeled confounding variables. These layers feed into a fully connected autoencoder with a circular bottleneck, encoding the estimated phase of each sample. The expanded network simultaneously estimates the influence of confounding variables along with circadian phase. We compared the performance of the original and expanded networks using both real and simulated expression data. In a first test, we used time-labeled data from a single-center describing human cortical samples obtained at autopsy. To generate a second, idealized processing center, we introduced gene specific biases in expression along with a bias in sample collection time. In a second test, we combined human lung biopsy data from two medical centers. Results The performance of the original CYCLOPS network degraded with the introduction of increasing, non-circadian confounds. The expanded network was able to more accurately assess circadian phase over a wider range of confounding influences. Conclusion The addition of labeled confounding variables into the network architecture improves circadian data ordering. The use of the expanded network should facilitate the application of CYCLOPS to multi-center data and expand the data available for circadian analysis. Support This work was supported by the National Cancer Institute (1R01CA227485-01)

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Galaxy formation and evolution using multi-wavelength, multi-resolution imaging data in the Virtual Observatory

Proceedings of the International Astronomical Union ◽

10.1017/s1743921307011908 ◽

2006 ◽

Vol 2 (14) ◽

pp. 592-592

Author(s):

Paresh Prema ◽

Nicholas A. Walton ◽

Richard G. McMahon

Keyword(s):

Galaxy Formation ◽

High Redshift ◽

Large Data ◽

Electromagnetic Spectrum ◽

Data Sets ◽

Virtual Observatory ◽

X Rays ◽

Imaging Data ◽

Wide Range ◽

Multi Wavelength

Observational astronomy is entering an exciting new era with large surveys delivering deep multi-wavelength data over a wide range of the electromagnetic spectrum. The last ten years has seen a growth in the study of high redshift galaxies discovered with the method pioneered by Steidel et al. (1995) used to identify galaxies above z>1. The technique is designed to take advantage of the multi-wavelength data now available for astronomers that can extend from X-rays to radio wavelength. The technique is fast becoming a useful way to study large samples of objects at these high redshifts and we are currently designing and implementing an automated technique to study these samples of objects. However, large surveys produce large data sets that have now reached terabytes (e.g. for the Sloan Digital Sky Survey, <http://www.sdss.org>) in size and petabytes over the next 10yr (e.g., LSST, <http://www.lsst.org>). The Virtual Observatory is now providing a means to deal with this issue and users are now able to access many data sets in a quicker more useful form.

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A Hidden Markov Model for Investigating Recent Positive Selection through Haplotype Structure

10.1101/011247 ◽

2014 ◽

Author(s):

Hua Chen ◽

Jody Hey ◽

Montgomery Slatkin

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Positive Selection ◽

Genetic Model ◽

Hidden Markov ◽

Large Data ◽

Data Sets ◽

Recent Positive Selection ◽

A Genome ◽

Wide Range

Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients.

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An Atom-Probe Tomography Primer

MRS Bulletin ◽

10.1557/mrs2009.194 ◽

2009 ◽

Vol 34 (10) ◽

pp. 717-724 ◽

Cited By ~ 105

Author(s):

David N. Seidman ◽

Krystyna Stiller

Keyword(s):

Atom Probe Tomography ◽

Organic Materials ◽

Ion Beam ◽

Large Data ◽

Laser Pulses ◽

Atom Probe ◽

Femtosecond Laser Pulses ◽

Large Data Sets ◽

Data Sets ◽

Wide Range

AbstractAtom-probe tomography (APT) is in the midst of a dynamic renaissance as a result of the development of well-engineered commercial instruments that are both robust and ergonomic and capable of collecting large data sets, hundreds of millions of atoms, in short time periods compared to their predecessor instruments. An APT setup involves a field-ion microscope coupled directly to a special time-of-flight (TOF) mass spectrometer that permits one to determine the mass-to-charge states of individual field-evaporated ions plus theirx-,y-, andz-coordinates in a specimen in direct space with subnanoscale resolution. The three-dimensional (3D) data sets acquired are analyzed using increasingly sophisticated software programs that utilize high-end workstations, which permit one to handle continuously increasing large data sets. APT has the unique ability to dissect a lattice, with subnanometer-scale spatial resolution, using either voltage or laser pulses, on an atom-by-atom and atomic plane-by-plane basis and to reconstruct it in 3D with the chemical identity of each detected atom identified by TOF mass spectrometry. Employing pico- or femtosecond laser pulses using visible (green or blue light) to ultraviolet light makes the analysis of metallic, semiconducting, ceramic, and organic materials practical to different degrees of success. The utilization of dual-beam focused ion-beam microscopy for the preparation of microtip specimens from multilayer and surface films, semiconductor devices, and for producing site-specific specimens greatly extends the capabilities of APT to a wider range of scientific and engineering problems than could previously be studied for a wide range of materials: metals, semiconductors, ceramics, biominerals, and organic materials.

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How to detect high-performing individuals and groups: decision similarity predicts accuracy

10.31234/osf.io/ryvnw ◽

2019 ◽

Cited By ~ 1

Author(s):

Ralf Kurvers ◽

Stefan Michael Herzog ◽

Ralph Hertwig ◽

Jens Krause ◽

Mehdi Moussaid ◽

...

Keyword(s):

Real World ◽

Large Data ◽

Cancer Diagnostics ◽

Data Sets ◽

Past Performance ◽

High Stakes ◽

Binary Decision ◽

High Performing ◽

Novel Approach ◽

Wide Range

Distinguishing between high- and low-performing individuals and groups is of prime importance in a wide range of high-stakes contexts. While this is straightforward when accurate records of past performance exist, such records are unavailable in most real-world contexts. Focusing on the class of binary decision problems, we use a combined theoretical and empirical approach to develop and test a novel approach to this important problem. First, we employ a general mathematical argument and numerical simulations to show that the similarity of an individual’s decisions to others is a powerful predictor of that individual’s decision accuracy. Second, testing this prediction with several large data sets on breast and skin cancer diagnostics, geopolitical forecasting, and a general knowledge task, we find that decision similarity robustly permits the identification of high-performing individuals and groups. Our findings offer an intriguingly simple, yet broadly applicable, heuristic for improving real-world decision-making systems.

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An Examination of Student Success within Canadian Higher Education: Fifty Years of Findings and Recommendations for the Future

Canadian Journal of Higher Education ◽

10.47678/cjhe.vi0.189127 ◽

2021 ◽

pp. 13-32

Author(s):

Jason R. Holmes

Keyword(s):

Higher Education ◽

Academic Success ◽

Student Success ◽

Large Data ◽

Future Research ◽

Data Sets ◽

College Performance ◽

Arts And Humanities ◽

Canadian Higher Education ◽

Wide Range

This article explores the student success literature published within the Canadian Journal of Higher Education (CJHE) over the last fifty years. Sixty articles were thematically organized into seven component measures of student success to present consistent themes that have persisted within the CJHE from inception in 1971 to 2020. Analysis demonstrates that there has been a disproportionate interest in some aspects of student success such as post-college performance, while other areas such as educational attainment and student engagement have lagged considerably behind in focus. Scholars have presentedongoing concerns supported by a wide range of data regarding the underemployment of graduates from Arts and Humanities, the sparse professorial landscape and the underutilization of Canadian PhD graduates in the workforce, debate on student competence and skill measurement, and the lack of large data sets on student persistence. Results suggest that a continuous effort is required to understand and support student success in a variety of formats—both within the academy and out in the workforce. Thus, this article concludes with a discussion and recommendations for future research avenues in the field of academic success and various subfields that may be of interest to higher education scholars and those who support student success.

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An Analysis of Data Processing for Big Data Analytics

Journal of Computing and Natural Science ◽

10.53759/181x/jcns202101019 ◽

2021 ◽

pp. 130-138

Author(s):

Steve Blair ◽

Jon Cotter

Keyword(s):

Big Data ◽

Deep Learning ◽

Data Analytics ◽

High Performance ◽

Big Data Analytics ◽

Large Data ◽

Data Availability ◽

Large Data Sets ◽

Data Sets ◽

Wide Range

The need for high-performance Data Mining (DM) algorithms is being driven by the exponentially increasing data availability such as images, audio and video from a variety of domains, including social networks and the Internet of Things (IoT). Deep learning is an emerging field of pattern recognition and Machine Learning (ML) study right now. It offers computer simulations of numerous nonlinear processing layers of neurons that may be used to learn and interpret data at higher degrees of abstractions. Deep learning models, which may be used in cloud technology and huge computational systems, can inherently capture complex structures of large data sets. Heterogeneousness is one of the most prominent characteristics of large data sets, and Heterogeneous Computing (HC) causes issues with system integration and Advanced Analytics. This article presents HC processing techniques, Big Data Analytics (BDA), large dataset instruments, and some classic ML and DM methodologies. The use of deep learning to Data Analytics is investigated. The benefits of integrating BDA, deep learning, HPC (High Performance Computing), and HC are highlighted. Data Analytics and coping with a wide range of data are discussed.

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Modeling Location Choice of Secondary Activities with a Social Network of Cooperative Agents

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105193500116 ◽

2005 ◽

Vol 1935 (1) ◽

pp. 141-146 ◽

Cited By ~ 9

Author(s):

F. Marchal ◽

K. Nagel

Keyword(s):

Social Network ◽

Travel Behavior ◽

Large Data ◽

Accurate Information ◽

Small Subset ◽

Random Utility ◽

Data Sets ◽

Cooperative Agents ◽

Spatial Environment ◽

Utility Models

Activity-based models in transportation science focus on the description of human trips and activities. Modeling the spatial decision for so-called secondary activities is addressed in this paper. Given both home and work locations, where do individuals perform activities such as shopping and leisure? Simulation of these decisions using random utility models requires a full enumeration of possible outcomes. For large data sets, it becomes computationally unfeasible because of the combinatorial complexity. To overcome that limitation, a model is proposed in which agents have limited, accurate information about a small subset of the overall spatial environment. Agents are interconnected by a social network through which they can exchange information. This approach has several advantages compared with the explicit simulation of a standard random utility model: ( a) it computes plausible choice sets in reasonable computing times, ( b) it can be extended easily to integrate further empirical evidence about travel behavior, and ( c) it provides a useful framework to study the propagation of any newly available information. This paper emphasizes the computational efficiency of the approach for real-world examples.

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CLASS: Accurate and Efficient Splice Variant Annotation from RNA-seq Reads

10.1101/011718 ◽

2014 ◽

Author(s):

Li Song ◽

Sarven Sabunciyan ◽

Liliana D Florea

Keyword(s):

Alternative Splicing ◽

Splice Variants ◽

Transcriptome Assembly ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Rna Seq ◽

Wide Range ◽

Splice Graph ◽

Selection Algorithms

Next generation sequencing of cellular RNA is making it possible to characterize genes and alternative splicing in unprecedented detail. However, designing bioinformatics tools to capture splicing variation accurately has proven difficult. Current programs find major isoforms of a gene but miss finer splicing variations, or are sensitive but highly imprecise. We present CLASS, a novel open source tool for accurate genome-guided transcriptome assembly from RNA-seq reads. CLASS employs a splice graph to represent a gene and its splice variants, combined with a linear program to determine an accurate set of exons and efficient splice graph-based transcript selection algorithms. When compared against reference programs, CLASS had the best overall accuracy and could detect up to twice as many splicing events with precision similar to the best reference program. Notably, it was the only tool that produced consistently reliable transcript models for a wide range of applications and sequencing strategies, including very large data sets and ribosomal RNA-depleted samples. Lightweight and multi-threaded, CLASS required <3GB RAM and less than one day to analyze a 350 million read set, and is an excellent choice for transcriptomics studies, from clinical RNA sequencing, to alternative splicing analyses, and to the annotation of new genomes.

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