Optimized partitioning of a wavelength distributed data interfacering network

A fiber optic ring network, such as fiber distributed data interface (FDDI), can be operated over multiple wavelengths on its existing fiber plant consisting of point-to-point fiber links. Using wavelength division multiplexing (WDM) technology, FDDI nodes can be partitioned to operate over multiple subnetworks, with each subnetwork operating independently on a different wavelength, and inter-subnetwork traffic forwarding performed by a bridge. For this multiwavelength version of FDDI, which we refer to as wavelength distributed data interface (WDDI), we examine the necessary upgrades to the architecture of a FDDI node, including its possibility to serve as a bridge. The main motivation behind this study is that, as network traffic scales beyond (the single-wavelength) FDDI's information-carrying capacity, its multiwavelength version, WDDI, can gracefully accommodate such traffic growth. A number of design choices exist in constructing a good WDDI network. Specifically, we investigate algorithms using which, based on prevailing traffic conditions, partitioning of nodes into subnetworks can be performed in an optimized fashion. Our algorithms partition the nodes into subrings, such that the total traffic flow in the network and/or the network-wide average packet delay is minimized     

Selective monitoring of peptidase activities with synthetic polypeptide substrates and polyion-sensitive membrane electrode detection

A novel method to monitor specific peptidase activities in biological samples as complex as undiluted plasma/blood is described. The approach is based on the design of synthetic polypeptide substrates in which di- or triarginine sequences are linked to each other via one or more other amino acids recognized specifically by the peptidase to be determined. Detection of chymotrypsin and renin activities using synthetic substrates P4 (F-R-R-R-F-V-R-R-F-NH2) and P5 (R-R-R-L-L-R-R-L-L-R-R-R), respectively, serves to demonstrate the principles of this new assay system. A polyion-sensitive membrane electrode, prepared by doping polymer films with dinonylnaphthalene-sulfonate (DNNS), is shown to exhibit significant nonequilibrium electromotive force (EMF) responses toward these and other polycationic substrates at microgram/milliliter levels under physiological conditions. The same electrode, however, exhibits much smaller total EMF response toward the shorter fragments of the synthetic peptides generated by peptidase activity; hence, the addition of peptidase to a solution containing the synthetic substrate yields a change in electrode EMF response, the rate of which is proportional to the activity of peptidase present. Other synthetic polycationic peptides as well as natural polycationic peptides (e.g., protamine) that lack specific cleavage sites for chymotrypsin and renin, yet are detected by the DNNS-based membrane electrode, do not elicit any significant change in EMF response in the presence of the peptidases, confirming the feasibility and utility of the proposed bioanalytical method.     

Animal model for the study of methanol toxicity: comparison of folate-reduced rat responses with published monkey data

The postpartum period is an ideal time to begin contraception, as women are more highly motivated to adopt contraception at this time and it is convenient for both patients and service providers. For intrauterine device (IUD) contraception, this period offers other advantages, such as ease of insertion and minimal adverse impacts on breastfeeding. Among early studies, most postpartum insertions were performed anywhere from a few hours to seven days or more after delivery, and retention of the IUD in the uterus was poor. Since the 1970s, immediate postplacental insertion (IPPI), i.e., IUD insertion performed within 10 minutes after placental delivery, has been advocated, and fairly, low expulsion rates have been reported. Up to now, IPPI has not been widely accepted in clinics because its expulsion rate still appears to be higher than that of interval insertion. In order to further study IPPI and perfect this contraceptive technique, it is essential to comprehensively review IPPI results and compare the Chinese experience with that of the rest of the world.     

Mixing matrix estimation using discriminative clustering for blind source separation

Mixing matrix estimation in instantaneous blind source separation (BSS) can be performed by exploiting the sparsity and disjoint orthogonality of source signals. As a result, approaches for estimating the unknown mixing process typically employ clustering algorithms on the mixtures in a parametric domain, where the signals can be sparsely represented. In this paper, we propose two algorithms to perform discriminative clustering of the mixture signals for estimating the mixing matrix. For the case of overdetermined BSS, we develop an algorithm to perform linear discriminant analysis based on similarity measures and combine it with K-hyperline clustering. Furthermore, we propose to perform discriminative clustering in a high-dimensional feature space obtained by an implicit mapping, using the kernel trick, for the case of underdetermined source separation. Using simulations on synthetic data, we demonstrate the improvements in mixing matrix estimation performance obtained using the proposed algorithms in comparison to other clustering methods. Finally we perform mixing matrix estimation from speech mixtures, by clustering single source points in the time-frequency domain, and show that the proposed algorithms achieve higher signal to interference ratio when compared to other baseline algorithms.     

Internet messaging: An examination of the impact of attitudinal,normative, and control belief systems

While instant messaging (IM) has been very popular among teenagers and college students for social and recreation communication, its use in corporate settings has been limited. We examined the factors that influence the intention to use and actual use of IM. A research model was developed based on diffusion of innovation and Socio-psychological theories. The model examined the influence of seven variables in three belief structures (the attitudinal, normative, and control). The model was empirically validated using a field survey of 349 respondents. The results indicated that five of the seven variables explained about 41% of the variance in the intention to use IM. The five significant variables were: utilitarian beliefs, hedonic beliefs, subjective norms, critical mass, and ease of use. Affiliation motivation was not significant and social belief had a negative impact on intention. As expected, intention to use had a significant influence on actual usage.     

Diverse community: Demand differentiation in P2P live streaming

Peer-to-Peer (P2P) technology has become an attractive approach for enabling large-scale video streaming applications, but the factor of users’ subjective preferences is not paid enough attention in such networks. As users have various demand on video qualities, we can provide them with video streams at different resolutions without impairing their satisfaction. The adaptive streaming rate technique is a promising method. However, in providing adaptive streaming rate services, P2P live streaming design faces the following challenge: how to provide all users with uninterrupted video with their desired qualities in case that their demand dynamically changes? To shed more light on this problem, we first derive a model and formulate the problem as a resource demand vs supply problem. Then we present a framework to address the challenge via efficient bandwidth allocation and group cooperation. Through comprehensive simulations, we evaluate the effectiveness of the proposed framework, and conclude that it effectively helps existing solutions, such as Partial Participation Scheme (PPS), achieve better performance.     

Optimality and stability of the K-hyperline clustering algorithm

K-hyperline clustering is an iterative algorithm based on singular value decomposition and it has been successfully used in sparse component analysis. In this paper, we prove that the algorithm converges to a locally optimal solution for a given set of training data, based on Lloyd’s optimality conditions. Furthermore, the local optimality is shown by developing an Expectation-Maximization procedure for learning dictionaries to be used in sparse representations and by deriving the clustering algorithm as its special case. The cluster centroids obtained from the algorithm are proved to tessellate the space into convex Voronoi regions. The stability of clustering is shown by posing the problem as an empirical risk minimization procedure over a function class. It is proved that, under certain conditions, the cluster centroids learned from two sets of i.i.d. training samples drawn from the same probability space become arbitrarily close to each other, as the number of training samples increase asymptotically.