An “Intropy Driven Gibbsian Phase Rule†For Communities And Markets

Abstract: It appears that a phase rule, which hints at how communities or social grouping can be viewed as “phases†similar to that in Thermodynamics, is plausible. Trade potential m , similar to Gibbs Chemical Potential , resulting from the â€Intropy†(or negative entropy) gradients, associated with physical, mental ,social or spiritual “ Quality†or “Worthâ€, of individuals,groups,products and services cause Trade/Social Transactions. A “phase rule†for communities that coexist in such a market is derived and hypothetical “phase diagrams†are explained.Experimentation in micro scales are a possibility to build appropriate “phase diagramsâ€

Life history of prawns: a review of recent studies with special reference to Indian species

The paper contains a brief review of the studies on the life histories of Indian species of prawns chiefly belonging to the family Penaeidae. References to similar work carried out outside India are furnished where significant variations have been observed. The three main larval stages viz., Nauplius, Protozoea and Zoea (Mysis) and their important characteristics, including modes of locomotion, are described. The post-larval development of one species that has been studied in detail (Metapenaeus dobsoni) is indicated in outline. Some aspects of the bionomics of these prawns, especially breeding and migration, are also briefly dealt with in view of their relevance in their life cycle. An outline of the life histories of some Palaemonid prawns of both fresh water and marine habitats is added at the end and the need for well- planned investigations in regard to species of such economic value as Palaemon carcinus (Macrobrachium rosenbergii) is indicated

Learning with Symmetric Label Noise: The Importance of Being Unhinged

Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2010] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the SLN-robustness of the unhinged loss