Royal Holloway, University of London

About: Royal Holloway, University of London is a education organization based out in Egham, Surrey, United Kingdom. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7156 authors who have published 20961 publications receiving 851244 citations. The organization is also known as: Royal Holloway College & Royal Holloway and Bedford New College.

The relative antioxidant activities of plant-derived polyphenolic flavonoids

Abstract: The relative antioxidant activities, against radicals generated in the aqueous phase, of a range of plant-derived polyphenolic flavonoids, constituents of fruit, vegetables, tea and wine, have been assessed. The results show that compounds such as quercetin and cyanidin, with 3′,4′ dihydroxy substituents in the B ring and conjugation between the A and B rings, have antioxidant potentials four times that of Trolox, the vitamin E analogue. Removing the ortho-dihydroxy substitution, as in kaempferol, or the potential for electron deloculisation by reducing the 2.3 double bond in the C ring, as in catechin and epicatechin, decreases the antioxidant activity by more than 50%. but these structures are still more effective than α-tocopherol or ascorbate. The relative significance of the positions and extents of hydroxylation of the A and B rings to the total antioxidant activity of these plant polyphenols is demonstrated.

Anxiety and Performance: The Processing Efficiency Theory

Abstract: Anxiety often impairs performance of “difficult” tasks (especially under test conditions), but there are numerous exceptions. Theories of anxiety and performance need to address at least two major issues: (1) the complexity and apparent inconsistency of the findings; and (2) the conceptual definition of task difficulty. Some theorists (e.g. Humphreys & Revelle, 1984; Sarason, 1988) argue that anxiety causes worry, and worry always impairs performance on tasks with high attentional or short-term memory demands. According to the processing efficiency theory, worry has two main effects: (1) a reduction in the storage and processing capacity of the working memory system available for a concurrent task; and (2) an increment in on-task effort and activities designed to improve performance. There is a crucial distinction within the theory between performance effectiveness (= quality of performance) and processing efficiency (= performance effectiveness divided by effort). Anxiety characteristically impa...

Large Margin DAGs for Multiclass Classification

Abstract: We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an N-class problem, the DDAG contains N(N - 1)/2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motivates an algorithm, DAGSVM, which operates in a kernel-induced feature space and uses two-class maximal margin hyperplanes at each decision-node of the DDAG. The DAGSVM is substantially faster to train and evaluate than either the standard algorithm or Max Wins, while maintaining comparable accuracy to both of these algorithms.

Support Vector Method for Novelty Detection

Abstract: Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S equals some a priori specified ν between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. We provide a theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled data.