Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

http://www.maths.qmul.ac.uk/%7Elatora/report_06.pdf

Complex networks: Structure and dynamics

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.

/pdf/community-detection-in-graphs-34rogm3r6p.pdf

Community detection in graphs

https://link.springer.com/content/pdf/10.2307%2F3343168.pdf

Development as Freedom

We present an outlook of the studies on correlations in the price time-series of stocks, discussing the construction and applications of “asset tree”. The topic discussed here should illustrate how the complex economic system (financial market) enrichens the list of existing dynamical systems that physicists have been studying for long.

An Outlook on Correlations in Stock Prices

The interdisciplinary field of econophysics has enjoyed recently a surge of activities especially with numerous agent-based models, which have led to a substantial development of this field. We review three main application areas of agent-based models in econophysics: order books, distributions of wealth in conservative economies, and minority games.

Agent-based models of economic interactions

We review some of the recent approaches and advances made by physicists in some selected problems in economics, given that this interdisciplinary field popularly called “Econophysics” is now two decades old. These approaches, mainly originating from statistical physics, have not been free of drawbacks and criticisms, but we intend to discuss these advancements and highlight some of the promising aspects of this research. We hope the readers will be able to judge the positive impact that these efforts have created; perhaps even improve the methods and the results or remove the shortcomings, and eventually strengthen the field with their inputs.

Physicists’ Approaches to a Few Economic Problems

The electroencephalograph (EEG) signal is one of the monitoring techniques to observe brain functionality. EEG is most preferable technology not just because of its non-invasive and cost effective quality, but also it can detect the cognitive activity of human. Brain Computer Interface (BCI), a direct pathway between the human brain and computer, is one of the most pragmatic applications of EEG signal. Mental Task Classification (MTC) based on EEG signals is a demanding BCI. Success of application depends on the efficient analysis of these signal. Empirical Mode Decomposition (EMD) is one of filter based heuristic technique which is used to analyze EEG signal in recent past. There are several variants of EMD algorithms which have their own merits and demerits. In this paper, we have explored three different EMD algorithms on EEG data for MTC-based BCI named as Empirical Mode Decomposition (EMD),Ensemble Empirical Mode Decomposition (EEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Features are extracted from EEG signal in two phases; in the first phase, the signal is decomposed into different oscillatory functions with the help of different EMD algorithm and in the second phase, eight different parameters (features) are calculated for the each function for compact representation. In this paper a new feature known as Hurst Exponent along with other feature have been investigated for mental task classification. These features are fed up into Support Vector Machine (SVM) classifier to classify the different mental tasks. We have formulated two different types of MTC, the first one is binary and second one is multi-MTC. The proposed work outperforms the existing work for both binary and multi mental tasks classification.

/pdf/performance-evaluation-of-empirical-mode-decomposition-38g5k50aih.pdf

Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

In the light of contemporary discussions of inter and trans disciplinarity, this paper approaches econophysics and sociophysics to seek a response to the question – whether these interdisciplinary fields could contribute to physics and economics Drawing upon the literature on history and philosophy of science, the paper argues that the two way traffic between physics and economics has a long history and this is likely to continue in the future

Anirban Chakraborti

Papers

An Outlook on Correlations in Stock Prices

Agent-based models of economic interactions

Physicists’ Approaches to a Few Economic Problems

Performance Evaluation of Empirical Mode Decomposition Algorithms for Mental Task Classification

Can an interdisciplinary field contribute to one of the parent disciplines from which it emerged