121 research outputs found
Laboratory Evaluation of Dibenz (b,t)-1,4-0xazepine for the Protection of Nylon Tapes against Rodents Attack
The efficacy of dibenz (b,f)-1 ,4-oxazepine (CR), a potent sensory irritant and deltarnethrin a wellknown insecticide, in providing protection to the multi-element nylon tapes, used as aircraft arresters at airports have been evaluated. The results obtained indicate that 5 per cent CR-admixed UV resistant nylon tapes got adequate protection against attacks from wild type laboratory bred Rattus rattus for up to 160 days. CR treatment was found to be water wash resistant against 7, 30 and 60 days protectionoffered by 3, 4 and 5 per cent deltarnethrin, respectively
XV. Notice of certain meteorological phænomena observed at Swansea : To the editors of the Philosophical Magazine and Journal
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Continuous Percolation with Discontinuities
Complex networks are a highly useful tool for modeling a vast number of different real world structures. Percolation describes the transition to extensive connectedness upon the gradual addition of links. Whether single links may explosively change macroscopic connectivity in networks where, according to certain rules, links are added competitively has been debated intensely in the past three years. In a recent article [ O. Riordan and L. Warnke Science 333 322 (2011)], O. Riordan and L. Warnke conclude that (i) any rule based on picking a fixed number of random vertices gives a continuous transition, and (ii) that explosive percolation is continuous. In contrast, we show that it is equally true that certain percolation processes based on picking a fixed number of random vertices are discontinuous, and we resolve this apparent paradox. We identify and analyze a process that is continuous in the sense defined by Riordan and Warnke but still exhibits infinitely many discontinuous jumps in an arbitrary vicinity of the transition point: a Devil’s staircase. We demonstrate analytically that continuity at the first connectivity transition and discontinuity of the percolation process are compatible for certain competitive percolation systems
An ISA algorithm with unknown group sizes successfully identifies biologically meaningful clusters in metabolomics data.
Independent Subspace Analysis (ISA) denotes the task of linearly separating multivariate observations into statistically independent multi-dimensional sources, where dependencies only exist within these subspaces but not between them. So far ISA algorithms have mostly been described in the context of known group sizes. Here, we extend a previously proposed ISA algorithm based on joint block di-agonalization of 4-th order cumulant matrices to separate subspaces of unknown sizes. Further automated interpretation of the demixed sources then requires a means of recovering the subspace structure within them, and we propose two distinct methods for this. We then apply the method to a novel application field, namely clustering of metabolites, which seems to be well-fit to the ISA model. We are able to successfully identify dependencies between metabolites that could not be recovered using conventional methods
An ISA Algorithm With Unknown Group Sizes Identifies Meaningful Clusters in Metabolomics Data
Independent Subspace Analysis (ISA) denotes the task of linearly separating multivariate observations into statistically independent multi-dimensional sources, where dependencies only exist within these subspaces but not between them. So far ISA algorithms have mostly been described in the context of known group sizes. Here, we extend a previously proposed ISA algorithm based on joint block diagonalization of 4-th order cumulant matrices to separate subspaces of unknown sizes. Further automated interpretation of the demixed sources then requires a means of recovering the subspace structure within them, and we propose two distinct methods for this. We then apply the method to a novel application field, namely clustering of metabolites, which seems to be well-fit to the ISA model. We are able to successfully identify dependencies between metabolites that could not be recovered using conventional methods
Independent Subspace Analysis Is Unique, Given Irreducibility
Independent Subspace Analysis (ISA) is a generalization of ICA. It tries to find a basis in which a given random vector can be decomposed into groups of mutually independent random vectors. Since the first introduction of ISA, various algorithms to solve this problem have been introduced, however a general proof of the uniqueness of ISA decompositions remained an open question. In this contribution we address this question and sketch a proof for the separability of ISA. The key condition for separability is to require the subspaces to be not further decomposable (irreducible). Based on a decomposition into irreducible components, we formulate a general model for ISA without restrictions on the group sizes. The validity of the uniqueness result is illustrated on a toy example. Moreover, an extension of ISA to subspace extraction is introduced and its indeterminacies are discussed
Repression of the interferon signal transduction pathway by the adenovirus E1A oncogene.
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