Chance constrained uncertain classification via robust optimization

This paper studies the problem of constructing robust classifiers when the training is plagued with uncertainty. The problem is posed as a Chance-Constrained Program (CCP) which ensures that the uncertain data points are classified correctly with high probability. Unfortunately such a CCP turns out to be intractable. The key novelty is in employing Bernstein bounding schemes to relax the CCP as a convex second order cone program whose solution is guaranteed to satisfy the probabilistic constraint. Prior to this work, only the Chebyshev based relaxations were exploited in learning algorithms. Bernstein bounds employ richer partial information and hence can be far less conservative than Chebyshev bounds. Due to this efficient modeling of uncertainty, the resulting classifiers achieve higher classification margins and hence better generalization. Methodologies for classifying uncertain test data points and error measures for evaluating classifiers robust to uncertain data are discussed. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle data uncertainty and outperform state-of-the-art in many cases

Physical, biochemical, and immunological characterization of a thermostable amidase from Klebsiella pneumoniae NCTR 1.

An amidase capable of degrading acrylamide and aliphatic amides was purified to apparent homogeneity from Klebsiella pneumoniae NCTR 1. The enzyme is a monomer with an apparent molecular weight of 62,000. The pH and temperature optima of the enzyme were 7.0 and 65 degrees C, respectively. The purified amidase contained 11 5,5-dithiobis(2-nitrobenzoate) (DTNB)-titratable sulfhydryl (SH) groups. In the native enzyme 1.0 SH group readily reacted with DTNB with no detectable loss of activity. Titration of the next 3.0 SH groups with DTNB resulted in a loss of activity of more than 70%. The remaining seven inaccessible SH groups could be titrated only in the presence of 8 M guanidine hydrochloride. Titration of SH groups was strongly inhibited by carboxymethylation and KMnO4, suggesting the presence of SH groups at the active site(s). Inductively coupled plasma-atomic emission spectrometry analysis indicated that the native amidase contains 0.33 mol of cobalt and 0.33 mol of iron per mol of the native enzyme. Polyclonal antiserum against K. pneumoniae amidase was raised in rabbits, and immunochemical comparisons were made with amidases from Rhodococcus sp., Mycobacterium smegmatis, Pseudomonas chlororaphis B23, and Methylophilus methylotrophus. The antiserum immunoprecipitated and immunoreacted with the amidases of K. pneumoniae and P. chlororaphis B23. The antiserum failed to immunoreact or immunoprecipitate with other amidases

Allocative efficiency of rural water supply - a globally flexible SGM cost frontier

This contribution investigates the efficiency of water suppliers in rural areas of East and West Germany. A non-radial measure of input specific allocative inefficiency is used to reduce the distributional dependency with respect to the inefficiency parameters. It is based on the demand system of a flexible cost function for the variable inputs labour, energy and chemicals modelled by applying a modified symmetric generalized McFadden functional form. Concavity restrictions, as required by economic theory, are imposed. The analysis reveals that efforts towards increasing suppliers' allocative efficiency should focus on the relatively inefficient usage of the input chemicals. The input specific allocative model specification was found to be superior to the overall allocative specification