Kinetic Monte Carlo simulation of faceted islands in heteroepitaxy using multi-state lattice model

A solid-on-solid model is generalized to study the formation of Ge pyramid islands bounded by (105) facets on Si(100) substrates in two dimensions. Each atomic column is not only characterized by the local surface height but also by two deformation state variables dictating the local surface tilt and vertical extension. These deformations phenomenologically model surface reconstructions in (105) facets and enable the formation of islands which better resemble faceted pyramids. We demonstrate the model by application to a kinetic limited growth regime. We observe significantly reduced growth rates after faceting and a continuous nucleation of new islands until overcrowding occurs.Comment: 7 pages, 5 figure

Applying genetic programming to learn spatial differences between textures using a translation invariant representation

This paper describes an approach to evolving texture feature extraction programs using tree based genetic programming. The programs are evolved from a learning set of 13 textures selected from the Brodatz database. In the evolutionary phase, texture images are first "binarised" to 256 grey levels. An encoding of the positions of the black pixels is used as the input to the evolved programs. A separate feature extraction program is evolved for each of the 256 grey levels. Fitness is measured by applying the evolved program to all of the images in the learning set, using one dimensional clustering on the outputs and then using the separation between the clusters as the fitness value. On two benchmark problems using the evolved programs for feature extraction and a nearest neighbour classifier, the evolved features gave test accuracies of 74.6% and 66.2% respectively for a 13 Brodatz and a 15 Vistex texture problem. This is better than a number of human derived methods on the same problems

BCFA: Bespoke Control Flow Analysis for CFA at Scale

Many data-driven software engineering tasks such as discovering programming patterns, mining API specifications, etc., perform source code analysis over control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be expensive and performance of the analysis heavily depends on the underlying CFG traversal strategy. State-of-the-art analysis frameworks use a fixed traversal strategy. We argue that a single traversal strategy does not fit all kinds of analyses and CFGs and propose bespoke control flow analysis (BCFA). Given a control flow analysis (CFA) and a large number of CFGs, BCFA selects the most efficient traversal strategy for each CFG. BCFA extracts a set of properties of the CFA by analyzing the code of the CFA and combines it with properties of the CFG, such as branching factor and cyclicity, for selecting the optimal traversal strategy. We have implemented BCFA in Boa, and evaluated BCFA using a set of representative static analyses that mainly involve traversing CFGs and two large datasets containing 287 thousand and 162 million CFGs. Our results show that BCFA can speedup the large scale analyses by 1%-28%. Further, BCFA has low overheads; less than 0.2%, and low misprediction rate; less than 0.01%.Comment: 12 page

Abelian Landau-Pomeranchuk-Migdal effects

It is shown that the high-energy expansion of the scattering amplitude calculated from Feynman diagrams factorizes in such a way that it can be reduced to the eikonalized form up to the terms of inverse power in energy in accordance with results obtained by solving the Klein-Gordon equation. Therefore the two approaches when applied to the suppression of the emission of soft photons by fast charged particles in dense matter should give rise to the same results. A particular limit of thin targets is briefly discussed.Comment: 14 pages, LATEX, 1 Fig. ps, submitted to Mod. Phys. Lett.

On Random Walks with a General Moving Barrier

Random walks with a general, nonlinear barrier have found recent applications ranging from reionization topology to refinements in the excursion set theory of halos. Here, we derive the first-crossing distribution of random walks with a moving barrier of an arbitrary shape. Such a distribution is shown to satisfy an integral equation that can be solved by a simple matrix inversion, without the need for Monte Carlo simulations, making this useful for exploring a large parameter space. We discuss examples in which common analytic approximations fail, a failure which can be remedied using the method described here.Comment: 6 pages, 7 figures, submitted to Ap

Improving an interactive simulator for computer systems with learning objects

In the 21st century, learning is a crucial activity through which people can assimilate or acquire new knowledge. However, many existing e-Iearning systems contain complicated knowledge structure that hinders the reuse or sharing of knowledge. In a previous project awarded by the Microsoft Research Asia, we successfully developed an interactive simulator to facilitate the learning of essential concepts related to computer systems through live animations. Here, we propose to integrate learning objects and relevant technologies into our interactive simulator to illustrate the underlying knowledge structure and, more importantly, facilitate the sharing and reuse of relevant concepts. Through adopting the IEEE learning object metadata (LOM) standard, our simulator can easily exchange relevant learning objects with other e-Iearning systems. The system design and prototype implementation of our LOM-based simulator is considered in this paper to evaluate how general and experienced users can benefit from our LOM-based simulator in various ways. © 2010 IEEE.published_or_final_versionThe 2nd International Conference on Education Technology and Computer (ICETC 2010), Shanghai, China, 22-24 June 2010. In Proceedings of the International Conference on Education Technology and Computer, 2010, v. 3, p. 16-2

Toward a complete e-learning system framework for semantic analysis, concept clustering and learning path optimization

Most online e-learning systems often demand the pre-requisite requirements between course modules and/or some relationship measures between involved concepts to be explicitly inputed by the course instructors so that an optimizer can be ultimately used to find an optimal learning sequence of involved concepts or modules for each individual learner after considering his/her past performance, learner's profile, learning style, etc. However, relying solely on the course instructor's input on the relationship among the involved concepts can be imprecise possibly due to the individual biases by human experts. Furthermore, the decision will become more complicated when various instructors hold conflicting views on the relationship among the involved concepts that may hinder any reasonable deduction. Therefore, we propose in this paper a complete system framework that can perform an explicit semantic analysis on the course materials, possibly aided by the relevant Wiki articles for any missing information about the involved concepts, to formulate the individual concepts, and followed by a heuristic-based concept clustering algorithm to group relevant concepts before finding their relationship measures. Lastly, an evolutionary optimizer will be used to return the optimal learning sequence after considering multiple experts' recommended learning sequences possibly containing conflicting views. To demonstrate the feasibility of our prototype, we implemented a prototype of the proposed e-learning system framework. Our empirical evaluation clearly revealed the possible advantages of our proposal with many possible directions for future investigation. © 2012 IEEE.published_or_final_versio

Building an interactive simulator on a cloud computing platform to enhance students' understanding of computer systems

Cloud computing technologies have been widely adopted to improve the competitiveness and efficiency of core operations in many enterprises through additional computational resources and/or storage as provided on the underlying cloud platforms. Yet there are relatively few studies on how cloud computing may enhance students' understanding of a specific subject in e-learning systems. In a research project awarded by the Microsoft Research Asia, we successfully developed an interactive simulator aimed to enhance the students' understanding of essential concepts related to computer systems through live animations on a cloud computing platform. Essentially, we propose to integrate the latest technologies of cloud computing and learning objects into an efficient, flexible and interactive simulator to deliver powerful computing services for dynamic simulations of various computer systems specified as 'reactive' models of learning objects on the cloud storage. More importantly, through adopting the IEEE learning object metadata standard to represent each key concept/component in different computer systems, our proposed simulator can readily facilitate the sharing and reuse of relevant concepts for future e-learning applications. The system design and prototype implementation of our cloud-based interactive simulator is carefully considered with a thorough evaluation plan to investigate on how learners may benefit from our interactive simulator in various ways. And there are many directions for future extensions. © 2013 IEEE.published_or_final_versio