An experimental evaluation of error seeding as a program validation technique

A previously reported experiment in error seeding as a program validation technique is summarized. The experiment was designed to test the validity of three assumptions on which the alleged effectiveness of error seeding is based. Errors were seeded into 17 functionally identical but independently programmed Pascal programs in such a way as to produce 408 programs, each with one seeded error. Using mean time to failure as a metric, results indicated that it is possible to generate seeded errors that are arbitrarily but not equally difficult to locate. Examination of indigenous errors demonstrated that these are also arbitrarily difficult to locate. These two results support the assumption that seeded and indigenous errors are approximately equally difficult to locate. However, the assumption that, for each type of error, all errors are equally difficult to locate was not borne out. Finally, since a seeded error occasionally corrected an indigenous error, the assumption that errors do not interfere with each other was proven wrong. Error seeding can be made useful by taking these results into account in modifying the underlying model

Faster K-Means Cluster Estimation

There has been considerable work on improving popular clustering algorithm `K-means' in terms of mean squared error (MSE) and speed, both. However, most of the k-means variants tend to compute distance of each data point to each cluster centroid for every iteration. We propose a fast heuristic to overcome this bottleneck with only marginal increase in MSE. We observe that across all iterations of K-means, a data point changes its membership only among a small subset of clusters. Our heuristic predicts such clusters for each data point by looking at nearby clusters after the first iteration of k-means. We augment well known variants of k-means with our heuristic to demonstrate effectiveness of our heuristic. For various synthetic and real-world datasets, our heuristic achieves speed-up of up-to 3 times when compared to efficient variants of k-means.Comment: 6 pages, Accepted at ECIR 201

Porcine bone scaffolds adsorb growth factors secreted by MSCs and improve bone tissue repair

An ideal tissue-engineered bone graft should have both excellent pro-osteogenesis and pro-angiogenesis properties to rapidly realize the bone regeneration in vivo . To meet this goal, in this work a porcine bone scaffold was successfully used as a Trojan horse to store growth factors produced by mesenchymal stem cells (MSCs). This new scaffold showed a time-dependent release of bioactive growth factors, such as vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (bFGF), in vitro . The biological effect of the growth factors-adsorbed scaffold on the in vitro commitment of MSCs into osteogenic and endothelial cell phenotypes has been evaluated. In addition, we have investigated the activity of growth factor-impregnated granules in the repair of critical-size defects in rat calvaria by means of histological, immunohistochemical, and molecular biology analyses. Based on the results of our work bone tissue formation and markers for bone and vascularization were significantly increased by the growth factor-enriched bone granules after implantation. This suggests that the controlled release of active growth factors from porcine bone granules can enhance and promote bone regeneratio

Contextual Centrality: Going Beyond Network Structures

Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate $p$ and the largest eigenvalue $\lambda_1$ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when $p \lambda_1 < 1$. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns