Understanding Large-Scale Structure in Massive Data Sets

2008 ◽  
Author(s):  
Amy J. Braverman
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets

Neural Network for Big Data Sets

2022 ◽  
pp. 41-67
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

The Large Scale Structure Peak as a Comoving Standard Ruler

2005 ◽  
Vol 201 ◽  
pp. 388-391
Author(s):  
Boudewijn F. Roukema ◽  
Gary A. Mamon
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Data Sets ◽  
Density Parameter ◽  
Observable Universe ◽  
Standard Candle ◽  
Type Ia ◽  
Density Perturbations ◽  
Empirical Standard

Estimates of the curvature parameters Ω0 (density parameter) and Δ0 (cosmological constant) can be made geometrically by use of either a standard candle or a standard ruler. Just as supernovae of Type Ia appear to provide a good empirical standard candle, it now appears observationally justified to use the peak in the power spectrum of density perturbations at L ≍ 130±10h-1 Mpc as an empirical standard rod. It will be shown that voids of this size are traced by quasars in a homogeneous catalogue near the South Galactic Pole at z ˜ 2 and that the large scale structure peak of the catalogue constrains the value of Ω0 to 0.1 < Ω0 < 0.45 (68% confidence), independently of Δ0. Combination with the supernovae Ia data is sufficient to show that the observable Universe is almost flat. In other words, the combination of a standard ruler and a standard candle detected in two presently available data sets is sufficient to show that the Universe is nearly flat, independently of any microwave background data or any other data analyses.

GENERALIZED PARTICLE MODEL USED FOR DATA CLUSTERING

2006 ◽  
Vol 20 (07) ◽  
pp. 1001-1028 ◽  
Author(s):  
DIANXUN SHUAI ◽  
XUE FANGLIANG
Keyword(s):  
Data Clustering ◽  
Large Scale ◽  
Hardware Implementation ◽  
Learning Ability ◽  
High Dimensional ◽  
Particle Model ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Clustering Approach

Data clustering has been widely used in many areas, such as data mining, statistics, machine learning and so on. A variety of clustering approaches have been proposed so far, but most of them are not qualified to quickly cluster a large-scale high-dimensional database. This paper is devoted to a novel data clustering approach based on a generalized particle model (GPM). The GPM transforms the data clustering process into a stochastic process over the configuration space on a GPM array. The proposed approach is characterized by the self-organizing clustering and many advantages in terms of the insensitivity to noise, quality robustness to clustered data, suitability for high-dimensional and massive data sets, learning ability, openness and easier hardware implementation with the VLSI systolic technology. The analysis and simulations have shown the effectiveness and good performance of the proposed GPM approach to data clustering.

Neural Network for Big Data Sets

2019 ◽  
pp. 271-303
Author(s):  
Vo Ngoc Phu ◽  
Vo Thi Ngoc Tran
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Computer Science ◽  
Large Scale ◽  
Massive Data ◽  
Data Sets ◽  
Massive Data Sets ◽  
Large Scale Data ◽  
Commercial Applications ◽  
Novel Model

Machine learning (ML), neural network (NN), evolutionary algorithm (EA), fuzzy systems (FSs), as well as computer science have been very famous and very significant for many years. They have been applied to many different areas. They have contributed much to developments of many large-scale corporations, massive organizations, etc. Lots of information and massive data sets (MDSs) have been generated from these big corporations, organizations, etc. These big data sets (BDSs) have been the challenges of many commercial applications, researches, etc. Therefore, there have been many algorithms of the ML, the NN, the EA, the FSs, as well as computer science which have been developed to handle these massive data sets successfully. To support for this process, the authors have displayed all the possible algorithms of the NN for the large-scale data sets (LSDSs) successfully in this chapter. Finally, they have presented a novel model of the NN for the BDS in a sequential environment (SE) and a distributed network environment (DNE).

scholarly journals Quantifying the Cosmic Web using the Shapefinder diagonistic

2014 ◽  
Vol 11 (S308) ◽  
pp. 250-253
Author(s):  
Prakash Sarkar
Keyword(s):  
Gaussian Curvature ◽  
Mean Curvature ◽  
Large Scale ◽  
Large Scale Structure ◽  
Discrete Data ◽  
Scale Structure ◽  
Data Sets ◽  
Polyhedral Surface ◽  
Minkowski Functionals ◽  
And Topology

AbstractOne of the most successful method in quantifying the structures in the Cosmic Web is the Minkowski Functionals. In 3D, there are four minkowski Functionals: Area, Volume, Integrated Mean Curvature and the Integrated Gaussian Curvature. For defining the Minkowski Functionals one should define a surface. We have developed a method based on Marching cube 33 algorithm to generate a surface from a discrete data sets. Next we calculate the Minkowski Functionals and Shapefinder from the triangulated polyhedral surface. Applying this methodology to different data sets , we obtain interesting results related to geometry, morphology and topology of the large scale structure

Zeitstrukturen im Jazz

2014 ◽  
Vol 59 (1) ◽  
pp. 79-92
Author(s):  
Alexander Becker
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Time Frame ◽  
Scale Structure ◽  
Classical Form ◽  
Jazz Music ◽  
The Moment

Wie erlebt der Hörer Jazz? Bei dieser Frage geht es unter anderem um die Art und Weise, wie Jazz die Zeit des Hörens gestaltet. Ein an klassischer Musik geschultes Ohr erwartet von musikalischer Zeitgestaltung, den zeitlichen Rahmen, der durch Anfang und Ende gesetzt ist, von innen heraus zu strukturieren und neu zu konstituieren. Doch das ist keine Erwartung, die dem Jazz gerecht wird. Im Jazz wird der Moment nicht im Hinblick auf ein Ziel gestaltet, das von einer übergeordneten Struktur bereitgestellt wird, sondern so, dass er den Bewegungsimpuls zum nächsten Moment weiterträgt. Wie wirkt sich dieses Prinzip der Zeitgestaltung auf die musikalische Form im Großen aus? Der Aufsatz untersucht diese Frage anhand von Beispielen, an denen sich der Weg der Transformation von einer klassischen zu einer dem Jazz angemessenen Form gut nachverfolgen lässt.<br><br>How do listeners experience Jazz? This is a question also about how Jazz music organizes the listening time. A classically educated listener expects a piece of music to structure, unify and thereby re-constitute the externally given time frame. Such an expectation is foreign to Jazz music which doesn’t relate the moment to a goal provided by a large scale structure. Rather, one moment is carried on to the next, preserving the stimulus potentially ad infinitum. How does such an organization of time affect the large scale form? The paper tries to answer this question by analyzing two examples which permit to trace the transformation of a classical form into a form germane to Jazz music.

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