This chapter describes the possibility of simulating physics in the classical approximation, a thing which is usually described by local differential equations. But the physical world is quantum mechanical, and therefore the proper problem is the simulation of quantum physics. A computer which will give the same probabilities as the quantum system does. The present theory of physics allows space to go down into infinitesimal distances, wavelengths to get infinitely great, terms to be summed in infinite order, and so forth; and therefore, if this proposition is right, physical law is wrong. Quantum theory and quantizing is a very specific type of theory. The chapter talks about the possibility that there is to be an exact simulation, that the computer will do exactly the same as nature. There are interesting philosophical questions about reasoning, and relationship, observation, and measurement and so on, which computers have stimulated people to think about anew, with new types of thinking.

/pdf/simulating-physics-with-computers-11bt3yutby.pdf

Simulating physics with computers

This article is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individuallyq We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the e1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.

/pdf/robust-principal-component-analysis-59kf1xh72d.pdf

Robust principal component analysis

The Message Passing Interface Forum (MPIF), with participation from over 40 organizations, has been meeting since November 1992 to discuss and define a set of library standards for message passing MPIF is not sanctioned or supported by any official standards organization The goal of the Message Passing Interface, simply stated, is to develop a widely used standard for writing message-passing programs As such the interface should establish a practical, portable, efficient and flexible standard for message passing , This is the final report, Version 10, of the Message Passing Interface Forum This document contains all the technical features proposed for the interface This copy of the draft was processed by LATEX on April 21, 1994 , Please send comments on MPI to mpi-comments@csutkedu Your comment will be forwarded to MPIF committee members who will attempt to respond

MPI: A Message-Passing Interface Standard

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

We have studied the three quark system in a relativized version of the quark potential model with chromodynamics. With parameters consistent with those of an analogous study of meson spectroscopy we obtain a successful description of the spectrum of baryons. The model naturally explains the apparent absence of spin‐orbit interactions in baryons.

Baryons in a relativized quark model with chromodynamics

From the Publisher:
Grid computing is applying the resources of many computers in a network to a single problem at the same time 
Grid computing appears to be a promising trend for three reasons: 
(1) Its ability to make more cost-effective use of a given amount of computer resources, 
(2) As a way to solve problems that can't be approached without an enormous amount of computing power 
(3) Because it suggests that the resources of many computers can be cooperatively and perhaps synergistically harnessed and managed as a collaboration toward a common objective. 
A number of corporations, professional groups, university consortiums, and other groups have developed or are developing frameworks and software for managing grid computing projects. The European Community (EU) is sponsoring a project for a grid for high-energy physics, earth observation, and biology applications. In the United States, the National Technology Grid is prototyping a computational grid for infrastructure and an access grid for people. Sun Microsystems offers Grid Engine software. Described as a distributed resource management tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. 


"the Grid" is a very hot topic generating broad interest from research and industry (e.g. IBM, Platform, Avaki, Entropia, Sun, HP) Grid architecture enables very popular e-Science projects like the Genome project which demand global interaction and networking In recent surveys over 500f Chief Information Officers are expected to use Grid technology this year 
Grid Computing: Features contributions from the major players in the field Covers all aspects of grid technology from motivation to applications Provides an extensive state-of-the-art guide in grid computing 
This is essential reading for researchers in Computing and Engineering, physicists, statisticians, engineers and mathematicians and IT policy makers.

Grid Computing: Making the Global Infrastructure a Reality

From the Publisher:
From 1983 to 1986, the legendary physicist and teacher Richard Feynman gave a course at Caltech called "Potentialities and Limitations of Computing Machines." Although the lectures are over ten years old, most of the material is timeless and presents a "Feynmanesque" overview of many standard and some not-so-standard topics in computer science. These include compatibility, Turing machines (or as Feynman said, "Mr. Turing's machines"), information theory, Shannon's Theorem, reversible computation, the thermodynamics of computation, the quantum limits to computation, and the physics of VLSI devices. Taken together, these lectures represent a unique exploration of the fundamental limitations of digital computers. Feynman's philosophy of learning and discovery comes through strongly in these lectures. He constantly points out the benefits of playing around with concepts and working out solutions to problems on your own - before looking at the back of the book for the answers. As Feynman says in the lectures: "If you keep proving stuff that others have done, getting confidence, increasing the complexities of your solutions - for the fun of it - then one day you'll turn around and discover that nobody actually did that one! And that's the way to become a computer scientist."

Feynman Lectures on Computation

This paper previews the imminent flood of scientific data expected from the next generation of experiments, simulations, sensors and satellites. In order to be exploited by search engines and data mining software tools, such experimental data needs to be annotated with relevant metadata giving information as to provenance, content, conditions and so on. The case for automating the process of going from raw data to information to knowledge is briefly discussed. The paper argues the case for creating new types of digital libraries for scientific data with the same sort of management services as conventional digital libraries in addition to other data-specific services. Some likely implications of both the Open Archives Initiative and e-Science data for the future role for university libraries are briefly mentioned. A substantial subset of this e-Science data needs to archived and curated for long-term preservation. Some of the issues involved in the digital preservation of both scientific data and of the programs needed to interpret the data are reviewed. Finally, the implications of this wealth of e-Science data for the Grid middleware infrastructure are highlighted. * Postal address: EPSRC, Polaris House, North Star Avenue, Swindon SN2 1 ET, UK + On secondment from the Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK

/pdf/the-data-deluge-an-e-science-perspective-3386zi3mo3.pdf

The Data Deluge: An e-Science Perspective

We are now seeing governments and funding agencies looking at ways to increase the value and pace of scientific research through increased or open access to both data and publications. In this point of view article, we wish to look at another aspect of these twin revolutions, namely, how to enable developers, designers and researchers to build intuitive,multimodal, user-centric, scientific applications that can aid and enable scientific research.

The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View]

/pdf/gauge-theories-in-particle-physics-28caca1vtq.pdf

Anthony J. G. Hey

Papers

Grid Computing: Making the Global Infrastructure a Reality

Feynman Lectures on Computation

The Data Deluge: An e-Science Perspective

The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View]

Gauge theories in particle physics