Subtyping psychological distress in the population: a semi-parametric network approach

Aims The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. Methods A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. Results A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). Conclusion Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

A Parametric Network Approach for Concepts Hierarchy Generation in Text Corpus

The article presents a preflow approach for the parametric maximum flow problem, derived from the rules of constructing concepts hierarchy in text corpus. Just as generating a taxonomy can be equivalently reduced to ranking concepts within a text corpus according to a defined criterion, the proposed preflow bipush-relabel algorithm computes the maximum flow - the optimum ow that respects certain ranking constraints. The parametric preflow algorithm for generating two level concepts hierarchy in text corpus works in a parametric bipartite association network and, on each step, the maximum possible amount of ow is pushed along conditional augmenting two-arcs directed paths in the parametric residual network, for the maximum interval of the parameter values. The obtained parametric maximum ow generates concepts hierarchies (taxonomies) in text corpus for different degrees of association values described by the parameter values.

Principle and Application of Complex Surfaces Expand

Based on triangulation plane parametric method, the in-depth research of the complex surface flattening system has been attempted. The required database system of complex surface flattening algorithm is established. An attached LSCM algorithm is realized. To be continued, with a proposed initial parametric network, the auxiliary program is constructed with higher accuracy. A friendly interface in VB was finished and a complex surface flattening system is realized with Solidworks secondary development technology based on SolidworksAPI interfacing.

A Contribution to the System-Theoretic Approach to Bandwidth Estimation

The network calculus provides a theoretical background for description of traffic in computer networks. Using this tool in explanation of the so-called pathchirp method of measuring the available bandwidth, the validity and range of application of some relationships exploited are verified in this paper. The derivations are carried out in a wider context than that considered in a recent paper by Liebeherr et al. published in IEEE/ACM Transactions on Networking on network bandwidth estimation, providing thereby new insights and outcomes. These results, summarized in a table, show a means of bounding the service curve, depending upon its convexity or non-convexity property assumed and upon the linearity or non-linearity of a network considered. Moreover, it is shown here that the nonlinear network example analyzed by Liebeherr et al. can be viewed equivalently as a linear parametric network. For this network, the behaviour of the cross traffic is considered in a more detail, too.