Adaptive fault-tolerant routing in hypercube multicomputers

A connected hypercube with faulty links and/or nodes is called an injured hypercube. To enable any non-faulty node to communicate with any other non-faulty node in an injured hypercube, the information on component failures has to be made available to non-faulty nodes so as to route messages around the faulty components. A distributed adaptive fault tolerant routing scheme is proposed for an injured hypercube in which each node is required to know only the condition of its own links. Despite its simplicity, this scheme is shown to be capable of routing messages successfully in an injured hypercube as long as the number of faulty components is less than n. Moreover, it is proved that this scheme routes messages via shortest paths with a rather high probabiltiy and the expected length of a resulting path is very close to that of a shortest path. Since the assumption that the number of faulty components is less than n in an n-dimensional hypercube might limit the usefulness of the above scheme, a routing scheme is introduced based on depth-first search which works in the presence of an arbitrary number of faulty components. Due to the insufficient information on faulty components, the paths chosen by the above scheme may not always be the shortest. To guarantee that all messages be routed via shortest paths, it is proposed that every mode be equipped with more information than that on its own links. The effects of this additional information on routing efficiency are analyzed, and the additional information to be kept at each node for the shortest path routing is determined. Several examples and remarks are also given to illustrate the results

The Combustion of Linear Droplet Arrays in a Coaxial Convective Flow.

As approximations for spray-combustion processes, a series of increasingly sophisticated numerical models has been developed to simulate the combustion of linear droplet arrays in a co-axial, convective flow. Common to all of the models is an embedded grid, developed to increase computational accuracy. The first and simplest model is potential flow model (for Re $\to$ $\infty$). The flow is assumed to be ideal and infinitely-fast kinetics (flame sheet assumption) represent the combustion. The results show that the instantaneous droplet burning rates are increased as the droplet spacing is increased, and the burning rates of droplets tend asymptotically to smaller values as the number of droplets in the array is increased. A second model, a Stokes flow model (for Re $\u3c$ 1), is developed by using the Stokes approximation for the flow field. The results show that, owing to the lack of strong convective flow, the temperature and species contours can penetrate deeper into the flow. The third model extends the analysis to heat transfer for linear arrays of spheres at any Reynolds number, i.e. the model is based on the steady-state Navier-Stokes equations, but no combustion is considered. A more accurate flow pattern around the arrays is obtained and the predicted heat transfer and drag agree well with the experimental data in the literature. The fourth model introduces combustion into the third model through a finite-rate, one-step chemical reaction approximation. The model predicts a thick flame layer, rather than a flame sheet. For large Reynolds numbers, the results show that the downstream droplets have a higher burning rate than the leading droplets. For small Reynolds numbers, the model predicts behavior similar to that predicted with the potential and Stokes flow models. Finally, an unsteady-state model, based on the full Navier-Stokes equations, is used to study the variation in burning behavior with time. The reduction in droplet size, velocity, and spacing is included. The results show that even when the droplet spacing is significantly reduced (from 14 to 6 radii) the burning behavior of droplets is not affected

Scale-Adaptive Group Optimization for Social Activity Planning

Studies have shown that each person is more inclined to enjoy a group activity when 1) she is interested in the activity, and 2) many friends with the same interest join it as well. Nevertheless, even with the interest and social tightness information available in online social networks, nowadays many social group activities still need to be coordinated manually. In this paper, therefore, we first formulate a new problem, named Participant Selection for Group Activity (PSGA), to decide the group size and select proper participants so that the sum of personal interests and social tightness of the participants in the group is maximized, while the activity cost is also carefully examined. To solve the problem, we design a new randomized algorithm, named Budget-Aware Randomized Group Selection (BARGS), to optimally allocate the computation budgets for effective selection of the group size and participants, and we prove that BARGS can acquire the solution with a guaranteed performance bound. The proposed algorithm was implemented in Facebook, and experimental results demonstrate that social groups generated by the proposed algorithm significantly outperform the baseline solutions.Comment: 20 pages. arXiv admin note: substantial text overlap with arXiv:1305.150

Maximizing Friend-Making Likelihood for Social Activity Organization

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by modeling both existing friendships and the likelihood of new friend making. To find a set of attendees for socialization activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships and the objective function on the likelihood of friend making. We prove that HMGF is NP-Hard, and no approximation algorithm exists unless P = NP. We then propose an error-bounded approximation algorithm to efficiently obtain the solutions very close to the optimal solutions. We conduct a user study to validate our problem formulation and per- form extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm

Analisis Perilaku Konsumtif Anak Kos Pada Mahasiswa UMS

This study aims to determine the general description of consumer behavior of boarding students at the Muhammadiyah University of Surakarta. This type of research is qualitative research. The design used in this research is phenomenology. The subject of this research is students of Muhammadiyah University of Surakarta who live in boarding house. The object of this research is the boarding place around UMS campus. The results of this study states that the behavior of students who live dikos consumptive, both luxury and simple kos in buying goods or products behave rationally by looking at its benefits compared with brands, advertisements, and promotions. Factors that influence student consumptive behavior are the motivation and culture of the living environment

Tax policy and innovation performance: Evidence from enactment of the Alternative Simplified Credit

We examine how direct tax incentives affect firm innovation performance using a new U.S. R&D tax credit regime enacted in 2007, the Alternative Simplified Credit (ASC). A difference-in- differences analysis indicates that innovation performance is poorer for ASC users than for firms using the original R&D tax credit method following the ASC enactment. The results are stronger for firms with poorer governance and greater innovation diversity. ASC users suffer from poorer profitability and lower valuations. The findings remain robust to self-selection bias and various robustness checks. Our evidence favors a dark-side view of R&D tax credit effects under the ASC