scholarly journals Cloud-enabled Fiji for reproducible, integrated and modular image processing

2021 ◽  
Author(s):  
Ling-Hong Hung ◽  
Evan Straw ◽  
Shishir Reddy ◽  
Zachary Colburn ◽  
Ka Yee Yeung
Keyword(s):  
Focal Adhesion ◽  
3D Imaging ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Cloud Platform ◽  
Biomedical Image ◽  
Complex Image ◽  
Different Types ◽  
Computational Resources

Biomedical image analyses can require many steps processing different types of data. Analysis of increasingly large data sets often exceeds the capacity of local computational resources. We present an easy-to-use and modular cloud platform that allows biomedical researchers to reproducibly execute and share complex analytical workflows to process large image datasets. The workflows and the platform are encapsulated in software containers to ensure reproducibility and facilitate installation of even the most complicated workflows. The platform is both graphical and interactive allowing users to use the viewer of their choice to adjust the image pre-processing and analysis steps to iteratively improve the final results. We demonstrate the utility of our platform via two use cases in focal adhesion and 3D imaging analyses. In particular, our focal adhesion workflow demonstrates integration of Fiji with Jupyter Notebooks. Our 3D imaging use case applies Fiji/BigStitcher to big datasets on the cloud. The accessibility and modularity of the cloud platform democratizes the application and development of complex image analysis workflows.

scholarly journals Haplotag: software for haplotype-based genotyping-by-sequencing analysis

2015 ◽  
Author(s):  
Nicholas A Tinker ◽  
Wubishet A Bekele ◽  
Jiro Hattori
Keyword(s):  
Molecular Breeding ◽  
Genotyping By Sequencing ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Sequencing Analysis ◽  
The Novel ◽  
Short Read Sequencing ◽  
Genomic Complexity ◽  
Computational Resources

Genotyping-by-sequencing (GBS) and related methods are based on high-throughput short-read sequencing of genomic complexity reductions followed by discovery of SNPs within sequence tags. This provides a powerful and economical approach to whole-genome genotyping, facilitating applications in genomics, diversity analysis, and molecular breeding. However, due to the complexity of analysing large data sets, applications of GBS may require substantial time, expertise and computational resources. Haplotag, the novel GBS software described here, is freely available and operates with minimal user-investment on widely-available computer platforms. Haplotag is unique in fulfilling the following set of criteria: (1) operates without a reference genome; (2) can be used in a polyploid species; (3) provides a discovery mode and a production mode; (4) discovers polymorphisms based on a model of tag-level haplotypes within sequenced tags; (5) reports SNPs as well as haplotype-based genotypes; (6) provides an intuitive visual passport for each inferred locus. Haplotag is optimized for use in a self-pollinating plant species.

scholarly journals A survey of deep meta-learning

2021 ◽  
Author(s):  
Mike Huisman ◽  
Jan N. van Rijn ◽  
Aske Plaat
Keyword(s):  
Deep Neural Networks ◽  
Theoretical Foundation ◽  
Large Data ◽  
Large Data Sets ◽  
Performance Evaluations ◽  
Data Sets ◽  
Computational Costs ◽  
New Concepts ◽  
Meta Learning ◽  
Computational Resources

AbstractDeep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into (i) metric-, (ii) model-, and (iii) optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.

Orbit Taxonomy in an Electromagnetic Binary System of Two Magnetic Dipoles

2020 ◽  
Vol 30 (05) ◽  
pp. 2030011
Author(s):  
Euaggelos E. Zotos
Keyword(s):  
Binary System ◽  
Magnetic Moments ◽  
Large Data ◽  
Large Data Sets ◽  
Orbital Dynamics ◽  
Data Sets ◽  
Jacobi Constant ◽  
Magnetic Dipoles ◽  
Different Types ◽  
Starting Conditions

We elucidate the orbital dynamics of a binary system of two magnetic dipoles, by utilizing the grid classification method. Our target is to unveil how the total energy (expressed through the Jacobi constant), as well as the ratio of the magnetic moments affect the character of the trajectories of the test particle. By integrating numerically large data sets of starting conditions of trajectories in different types of 2D maps, we manage to reveal the basins corresponding to bounded, close encounter and escape motion, along with the respective time scales of the phenomena.

scholarly journals The augmented radiologist: artificial intelligence in the practice of radiology

2021 ◽  
Author(s):  
Erich Sorantin ◽  
Michael G. Grasser ◽  
Ariane Hemmelmayr ◽  
Sebastian Tschauner ◽  
Franko Hrzic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Content Knowledge ◽  
Large Data ◽  
Large Data Sets ◽  
Human Intelligence ◽  
Data Sets ◽  
Great Expectations ◽  
Training Samples ◽  
Different Types ◽  
Generic Tasks

AbstractIn medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can “see” more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics — thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled “explainable AI,” which should enable a balance/cooperation between AI and human intelligence — thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

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