There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Data Mining Practical Machine Learning Tools and Techniques

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Pattern recognition

One of the main themes which support text mining is text representation; that is, its task is to look for appropriate terms to transfer documents into numerical vectors. Recently, many efforts have been invested on this topic to enrich text representation using vector space model (VSM) to improve the performances of text mining techniques such as text classification and text clustering. The main concern in this paper is to investigate the effectiveness of using multi-words for text representation on the performances of text classification. Firstly, a practical method is proposed to implement the multi-word extraction from documents based on the syntactical structure. Secondly, two strategies as general concept representation and subtopic representation are presented to represent the documents using the extracted multi-words. In particular, the dynamic k-mismatch is proposed to determine the presence of a long multi-word which is a subtopic of the content of a document. Finally, we carried out a series of experiments on classifying the Reuters-21578 documents using the representations with multi-words. We used the performance of representation in individual words as the baseline, which has the largest dimension of feature set for representation without linguistic preprocessing. Moreover, linear kernel and non-linear polynomial kernel in support vector machines (SVM) are examined comparatively for classification to investigate the effect of kernel type on their performances. Index terms with low information gain (IG) are removed from the feature set at different percentages to observe the robustness of each classification method. Our experiments demonstrate that in multi-word representation, subtopic representation outperforms the general concept representation and the linear kernel outperforms the non-linear kernel of SVM in classifying the Reuters data. The effect of applying different representation strategies is greater than the effect of applying the different SVM kernels on classification performance. Furthermore, the representation using individual words outperforms any representation using multi-words. This is consistent with the major opinions concerning the role of linguistic preprocessing on documents' features when using SVM for text classification.

https://ccc.inaoep.mx/~villasen/bib/Text%20classification%20based%20on%20multi-word%20with%20support%20vector%20machine.pdf

Text classification based on multi-word with support vector machine

This paper investigates the free vibrations of the rotating pretwisted functionally graded (FG) composite cylindrical panels reinforced with the graphene platelets (GPLs) by considering the cantilever boundary conditions. The weight fraction of the graphene platelets in each ply may be different, which leads to the layer-wise functionally graded composite cylindrical panels reinforced with the GPLs. The effective Young's modulus is calculated by the modified Halpin-Tsai model. The effective Poisson's ratio and mass density are derived by the rule of the mixture. The strain-displacement relationship is acquired by the Green strain tensor. Based on the first-order shear deformation theory, Chebyshev-Ritz method is used to obtain the natural frequencies of the rotating pretwisted functionally graded composite cylindrical panel reinforced with the GPLs. The natural frequencies are discussed by considering different material and geometry parameters of the rotating pretwisted functionally graded composite cylindrical panel reinforced with the GPLs, such as the GPL distribution pattern, the GPL weight fraction, the geometries of the GPLs, the pretwisted angle, the presetting angle and the rotating speed. Several validations are carried out, the numerical results are in good agreement with the results of the literature and ANSYS.

Free vibration of rotating pretwisted functionally graded composite cylindrical panel reinforced with graphene platelets

This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.

DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking

Background: In most cases, bug resolution is a collaborative activity among developers in software development where each developer contributes his or her ideas on how to resolve the bug. Although only one developer is recorded as the actual fixer for the bug, the contribution of the developers who participated in the collaboration cannot be neglected.Aims: This paper proposes a new approach, called DRETOM (Developer REcommendation based on TOpic Models), to recommending developers for bug resolution in collaborative behavior.Method: The proposed approach models developers' interest in and expertise on bug resolving activities based on topic models that are built from their historical bug resolving records. Given a new bug report, DRETOM recommends a ranked list of developers who are potential to participate in and contribute to resolving the new bug according to these developers' interest in and expertise on resolving it.Results: Experimental results on Eclipse JDT and Mozilla Firefox projects show that DRETOM can achieve high recall up to 82% and 50% with top 5 and top 7 recommendations respectively.Conclusion: Developers' interest in bug resolving activities should be taken into consideration. On condition that the parameter θ of DRETOM is set properly with trials, the proposed approach is practically useful in terms of recall.

Wen Zhang

Papers

Text classification based on multi-word with support vector machine

Free vibration of rotating pretwisted functionally graded composite cylindrical panel reinforced with graphene platelets

DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking

DRETOM: developer recommendation based on topic models for bug resolution

Vibration characteristics of rotating pretwisted composite tapered blade with graphene coating layers