Composing And Performing Complex Systems: From The Quantum To The Cosmological

Leonardo ◽  
2021 ◽  
pp. 1-7
Author(s):  
JoAnn Kuchera-Morin
Keyword(s):  
Large Scale ◽  
Orchestral Music ◽  
Data Sets ◽  
Complex Data ◽  
Instrumental Ensemble ◽  
Complex Data Sets ◽  
Temporal Structures ◽  
Multi Media ◽  
Spatio Temporal ◽  
And Performance

Abstract This paper discusses the creation and development of a large distributed immersive multimedia computation system and environment based on the discipline of orchestral music composition, concert hall design, and performance. Just as the orchestra evolved through mechanical engineering to become a large distributed multi-user instrument whose information can be transmitted either by a client-server model as in orchestra-conductor, or a client-to-client model, as in an instrumental ensemble, large-scale distributed multi-media computational platforms can be modeled in the same way, facilitating the users as performers of the system. Multiple researchers can mine large, complex data sets to uncover important relationships in their spatio-temporal structures.

scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

scholarly journals Historical drought patterns over Canada and their teleconnections with large-scale climate signals

2018 ◽  
Vol 22 (6) ◽  
pp. 3105-3124 ◽  
Author(s):  
Zilefac Elvis Asong ◽  
Howard Simon Wheater ◽  
Barrie Bonsal ◽  
Saman Razavi ◽  
Sopan Kurkute
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Kendall Test ◽  
Wavelet Coherence ◽  
Climate Indices ◽  
Data Sets ◽  
Canadian Prairies ◽  
Agriculture Industry ◽  
Spatio Temporal

Abstract. Drought is a recurring extreme climate event and among the most costly natural disasters in the world. This is particularly true over Canada, where drought is both a frequent and damaging phenomenon with impacts on regional water resources, agriculture, industry, aquatic ecosystems, and health. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. This study provides a comprehensive analysis of historical droughts over the whole of Canada, including the role of large-scale teleconnections. Drought events are characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) over various temporal scales (1, 3, 6, and 12 consecutive months, 6 months from April to September, and 12 months from October to September) applied to different gridded monthly data sets for the period 1950–2013. The Mann–Kendall test, rotated empirical orthogonal function, continuous wavelet transform, and wavelet coherence analyses are used, respectively, to investigate the trend, spatio-temporal patterns, periodicity, and teleconnectivity of drought events. Results indicate that southern (northern) parts of the country experienced significant trends towards drier (wetter) conditions although substantial variability exists. Two spatially well-defined regions with different temporal evolution of droughts were identified – the Canadian Prairies and northern central Canada. The analyses also revealed the presence of a dominant periodicity of between 8 and 32 months in the Prairie region and between 8 and 40 months in the northern central region. These cycles of low-frequency variability are found to be associated principally with the Pacific–North American (PNA) and Multivariate El Niño/Southern Oscillation Index (MEI) relative to other considered large-scale climate indices. This study is the first of its kind to identify dominant periodicities in drought variability over the whole of Canada in terms of when the drought events occur, their duration, and how often they occur.

scholarly journals Using Hadoop Technology to Overcome Big Data Problems by Choosing Proposed Cost-efficient Scheduler Algorithm for Heterogeneous Hadoop System (BD3)

2020 ◽  
pp. 58-84
Author(s):  
Abou_el_ela Abdou Hussein
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Storage ◽  
Database Management System ◽  
Data Sets ◽  
Complex Data ◽  
Daily Data ◽  
Complex Data Sets ◽  
Cost Efficient ◽  
Hadoop System

Day by day advanced web technologies have led to tremendous growth amount of daily data generated volumes. This mountain of huge and spread data sets leads to phenomenon that called big data which is a collection of massive, heterogeneous, unstructured, enormous and complex data sets. Big Data life cycle could be represented as, Collecting (capture), storing, distribute, manipulating, interpreting, analyzing, investigate and visualizing big data. Traditional techniques as Relational Database Management System (RDBMS) couldn’t handle big data because it has its own limitations, so Advancement in computing architecture is required to handle both the data storage requisites and the weighty processing needed to analyze huge volumes and variety of data economically. There are many technologies manipulating a big data, one of them is hadoop. Hadoop could be understand as an open source spread data processing that is one of the prominent and well known solutions to overcome handling big data problem. Apache Hadoop was based on Google File System and Map Reduce programming paradigm. Through this paper we dived to search for all big data characteristics starting from first three V's that have been extended during time through researches to be more than fifty six V's and making comparisons between researchers to reach to best representation and the precise clarification of all big data V’s characteristics. We highlight the challenges that face big data processing and how to overcome these challenges using Hadoop and its use in processing big data sets as a solution for resolving various problems in a distributed cloud based environment. This paper mainly focuses on different components of hadoop like Hive, Pig, and Hbase, etc. Also we institutes absolute description of Hadoop Pros and cons and improvements to face hadoop problems by choosing proposed Cost-efficient Scheduler Algorithm for heterogeneous Hadoop system.

A Real-Time Log Analyzer Based on MongoDB

2014 ◽  
Vol 571-572 ◽  
pp. 497-501 ◽  
Author(s):  
Qi Lv ◽  
Wei Xie
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Performance Comparison ◽  
Log Analysis ◽  
Data Sets ◽  
Time Data ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Implementation Approach ◽  
And Performance

Real-time log analysis on large scale data is important for applications. Specifically, real-time refers to UI latency within 100ms. Therefore, techniques which efficiently support real-time analysis over large log data sets are desired. MongoDB provides well query performance, aggregation frameworks, and distributed architecture which is suitable for real-time data query and massive log analysis. In this paper, a novel implementation approach for an event driven file log analyzer is presented, and performance comparison of query, scan and aggregation operations over MongoDB, HBase and MySQL is analyzed. Our experimental results show that HBase performs best balanced in all operations, while MongoDB provides less than 10ms query speed in some operations which is most suitable for real-time applications.

Alternative Clustering

2017 ◽  
pp. 1-12 ◽  
Author(s):  
Avinash Navlani ◽  
V. B. Gupta
Keyword(s):  
Research Problem ◽  
Research Community ◽  
Data Sets ◽  
Complex Data ◽  
High Quality ◽  
Data Set ◽  
Alternative Clustering ◽  
Complex Data Sets ◽  
Data Objects ◽  
Community Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

Science Communication with Dinosaurs

2012 ◽  
pp. 587-611
Author(s):  
Phillip L. Manning ◽  
Peter L. Falkingham
Keyword(s):  
High Performance Computing ◽  
Public Engagement ◽  
Science Communication ◽  
High Performance ◽  
Computational Science ◽  
Data Sets ◽  
Complex Data ◽  
Multidisciplinary Research ◽  
Complex Data Sets ◽  
Performance Computing

Dinosaurs successfully conjure images of lost worlds and forgotten lives. Our understanding of these iconic, extinct animals now comes from many disciplines, not just the science of palaeontology. In recent years palaeontology has benefited from the application of new and existing techniques from physics, biology, chemistry, engineering, but especially computational science. The application of computers in palaeontology is highlighted in this chapter as a key area of development in studying fossils. The advances in high performance computing (HPC) have greatly aided and abetted multiple disciplines and technologies that are now feeding paleontological research, especially when dealing with large and complex data sets. We also give examples of how such multidisciplinary research can be used to communicate not only specific discoveries in palaeontology, but also the methods and ideas, from interrelated disciplines to wider audiences. Dinosaurs represent a useful vehicle that can help enable wider public engagement, communicating complex science in digestible chunks.

Anomaly Detection for Inferring Social Structure

2010 ◽  
pp. 1797-1803
Author(s):  
Lisa Friedland
Keyword(s):  
Data Analysis ◽  
Anomaly Detection ◽  
Social Structure ◽  
Small Groups ◽  
Analysis Data ◽  
Data Sets ◽  
Complex Data ◽  
Detection Approach ◽  
Complex Data Sets ◽  
Data Points

In traditional data analysis, data points lie in a Cartesian space, and an analyst asks certain questions: (1) What distribution can I fit to the data? (2) Which points are outliers? (3) Are there distinct clusters or substructure? Today, data mining treats richer and richer types of data. Social networks encode information about people and their communities; relational data sets incorporate multiple types of entities and links; and temporal information describes the dynamics of these systems. With such semantically complex data sets, a greater variety of patterns can be described and views constructed of the data. This article describes a specific social structure that may be present in such data sources and presents a framework for detecting it. The goal is to identify tribes, or small groups of individuals that intentionally coordinate their behavior—individuals with enough in common that they are unlikely to be acting independently. While this task can only be conceived of in a domain of interacting entities, the solution techniques return to the traditional data analysis questions. In order to find hidden structure (3), we use an anomaly detection approach: develop a model to describe the data (1), then identify outliers (2).

Introduction to Big Data and Business Analytics

2022 ◽  
pp. 67-76
Author(s):  
Dineshkumar Bhagwandas Vaghela
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sets ◽  
Complex Data ◽  
Business Analytics ◽  
Database Applications ◽  
Complex Data Sets ◽  
History Of ◽  
Rapid Generation

The term big data has come due to rapid generation of data in various organizations. In big data, the big is the buzzword. Here the data are so large and complex that the traditional database applications are not able to process (i.e., they are inadequate to deal with such volume of data). Usually the big data are described by 5Vs (volume, velocity, variety, variability, veracity). The big data can be structured, semi-structured, or unstructured. Big data analytics is the process to uncover hidden patterns, unknown correlations, predict the future values from large and complex data sets. In this chapter, the following topics will be covered more in detail. History of big data and business analytics, big data analytics technologies and tools, and big data analytics uses and challenges.

Bayesian Modelling for Machine Learning

2005 ◽  
pp. 236-242
Author(s):  
Paul Rippon ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Bayesian Learning ◽  
Data Sets ◽  
Complex Data ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Complex Data Sets ◽  
Complex Phenomena ◽  
Learning Data

Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating parameters of a process model based on data, modelling and analysis of complex phenomena using multiple sources of information, posterior probabilistic expectation, and so on. In all of these guises, it has exploded in popularity over recent years.

scholarly journals A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation

2020 ◽  
Vol 223 (3) ◽  
pp. 1837-1863
Author(s):  
M C Manassero ◽  
J C Afonso ◽  
F Zyserman ◽  
S Zlotnik ◽  
I Fomin
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Forward Problem ◽  
Parallel Structure ◽  
Data Sets ◽  
Magnetotelluric Data ◽  
Reduced Order ◽  
Mcmc Algorithms ◽  
Simulation Based ◽  
And Performance

SUMMARY Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.

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