Degree Distribution of Competition-Induced Preferential Attachment Graphs

We introduce a family of one-dimensional geometric growth models, constructed iteratively by locally optimizing the tradeoffs between two competing metrics, and show that this family is equivalent to a family of preferential attachment random graph models with upper cutoffs. This is the first explanation of how preferential attachment can arise from a more basic underlying mechanism of local competition. We rigorously determine the degree distribution for the family of random graph models, showing that it obeys a power law up to a finite threshold and decays exponentially above this threshold. We also rigorously analyze a generalized version of our graph process, with two natural parameters, one corresponding to the cutoff and the other a ``fertility'' parameter. We prove that the general model has a power-law degree distribution up to a cutoff, and establish monotonicity of the power as a function of the two parameters. Limiting cases of the general model include the standard preferential attachment model without cutoff and the uniform attachment model.Comment: 24 pages, one figure. To appear in the journal: Combinatorics, Probability and Computing. Note, this is a long version, with complete proofs, of the paper "Competition-Induced Preferential Attachment" (cond-mat/0402268

Information Systems and Quality Management in Healthcare Organization: An Empirical Study

The paper explores current status of information systems, identifies gaps in the current information systems and assessment in healthcare organization. This paper is based on Critical Analysis of literature and a questionnaire is administered on administrative level employees of South Indian healthcare organizations. It has been identified that healthcare organization should have specific strategy and must implement measures derived from strategy. Data and information systems should be seen as business resources. The knowledge base of medical field is large and it is growing rapidly. Hence information system must be integrated across the enterprise. The results of the study determined the relationship between measurement, analysis and knowledge Management on performance. The Information system is the newest dimension among the MBNQA (Malcolm Baldrige National Quality Award) criteria. The Information system performance was assessed in terms of management relevant data and information. The outcomes suggest that there is a growing recognition of the administrators about the importance and use of information systems as a critical resource in healthcare organizations. From the study it is inferred that information system analysis continues to be a challenge. The higher utilization of technology, computerization and the Internet has resulted in dramatic change in the quality performance of the Healthcare Organizations. The paper provides an empirical evidence that information system has an impact on performance in the context of healthcare organizations. The information system is a key performance area of Quality management and it has received limited attention in improving quality performance including MBNQA. Finally, the study concludes that there is an immense scope for altering current information systems and it should be aligned with the quality management environment

Rho GTPases and signaling networks

The Rho GTPases form a subgroup of the Ras superfamily of 20- to 30-kD GTP-binding proteins that have been shown to regulate a wide spectrum of cellular functions. These proteins are ubiquitously expressed across the species, from yeast to man. The mammalian Rho-like GTPases comprise at least 10 distinct proteins: RhoA, B, C, D, and E; Rac1 and 2; RacE; Cdc42Hs, and TC10. A comparison of the amino acid sequences of the Rho proteins from various species has revealed that they are conserved in primary structure and are 50%–55% homologous to each other. Like all members of the Ras superfamily, the Rho GTPases function as molecular switches, cycling between an inactive GDP-bound state and an active GTP-bound state. Until recently, members of the Rho subfamily were believed to be involved primarily in the regulation of cytoskeletal organization in response to extracellular growth factors. However, research from a number of laboratories over the past few years has revealed that the Rho GTPases play crucial roles in diverse cellular events such as membrane trafficking, transcriptional regulation, cell growth control, and development. Consequently, a major challenge has been to unravel the underlying molecular mechanisms by which the Rho GTPases mediate these various activities. Many targets of the Rho GTPases have now been identified and further characterization of some of them has provided major insights toward our understanding of Rho GTPase function at the molecular level. This review aims to summarize the general established principles about the Rho GTPases and some of the more recent exciting findings, hinting at novel, unanticipated functions of the Rho GTPases

3-Ethyl-4-[(E)-(4-fluoro­benzyl­idene)amino]-1H-1,2,4-triazole-5(4H)-thione

In the title compound, C11H11FN4S, the dihedral angle between the 1,2,4-triazole ring and the benzene ring is 25.04 (12)° and an intra­moleuclar C—H⋯S inter­action leads to an S(6) ring. In the crystal, inversion dimers linked by pairs of N—H⋯S hydrogen bonds generate R 2 2(8) loops

Incremental online learning in high dimensions

this article, however, is problematic, as it requires a careful selection of initial ridge regression parameters to stabilize the highly rank-deficient full covariance matrix of the input data, and it is easy to create too much bias or too little numerical stabilization initially, which can trap the local distance metric adaptation in local minima.While the LWPR algorithm just computes about a factor 10 times longer for the 20D experiment in comparison to the 2D experiment, RFWR requires a 1000-fold increase of computation time, thus rendering this algorithm unsuitable for high-dimensional regression. In order to compare LWPR's results to other popular regression methods, we evaluated the 2D, 10D, and 20D cross data sets with gaussian process regression (GP) and support vector (SVM) regression in addition to our LWPR method. It should be noted that neither SVM nor GP methods is an incremental method, although they can be considered state-of-the-art for batch regression under relatively small numbers of training data and reasonable input dimensionality. The computational complexity of these methods is prohibitively high for real-time applications. The GP algorithm (Gibbs & MacKay, 1997) used a generic covariance function and optimized over the hyperparameters. The SVM regression was performed using a standard available package (Saunders et al., 1998) and optimized for kernel choices. Figure 6 compares the performance of LWPR and gaussian processes for the above-mentioned data sets using 100, 300, and 500 training data point