The spatial sign covariance matrix and its application for robust correlation estimation

8 pages, 2 figures, to be published in the conference proceedings of 11th international conference "Computer Data Analysis & Modeling 2016"

A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection

Peer reviewed

Enzymatic Macrocyclization of 1,2,3-Triazole Peptide Mimetics

Funded by European Research Council. Grant Number: 339367 UK Biotechnology and Biological Sciences Research Council. Grant Number: K015508/1 The Wellcome Trust. Grant Number: 094476 EPSRC Acknowledgements This work was supported by the European Research Council (339367), UK Biotechnology and Biological Sciences Research Council (K015508/1), The Wellcome Trust (TripleTOF 5600 mass spectrometer (094476), the MALDI TOF-TOF Analyser (079272AIA), 700 NMR) and the EPSRC UK National Mass Spectrometry Facility at Swansea University. J.H.N. is a Royal Society Wolfson Merit Award Holder and 1000 talent scholar at Sichuan University.

Economic Impacts of Rules-based vs Risk-based Cybersecurity Regulations in Critical Infrastructure Providers (Bulk Electricity Providers)

Economics of Cybersecurity Part 2. SPSI-2015-01-0024.

New lower bounds for the topological complexity of aspherical spaces

Date of Acceptance: 5/04/2015 15 pages, 4 figures

Host-Imposed Copper Poisoning Impacts Fungal Micronutrient Acquisition during Systemic Candida albicans Infections

This work was supported by the European Research Council (http://erc.europa.eu/: STRIFE Advanced Grant ERC-2009-AdG-249793). A.J.P.B. was also supported by the UK Biotechnology and Biological Research Council (www.bbsrc.ac.uk: Research Grants BB/F00513X/1, BB/K017365/1), the UK Medical Research Council (www.mrc.ac.uk: Programme Grant MR/M026663/1; Centre Grant MR/ N006364/1), and the Wellcome Trust (www.wellcome.ac.uk: Strategic Award 097377)

The impact of model detail on power grid resilience measures

Peer reviewed

Argumentation schemes for policy-driven planning

Postprint

Argumentation strategies for plan resourcing

Postprint

Identification of the mRNA targets of tRNA-­specific regulation using genome-­wide simulation of translation

FUNDING Biotechnology and Biological Sciences Research Council (BBSRC) [BB/I020926/1 to I.S.]; BBSRC PhD studentship award [C103817D to I.S. and M.C.R.]; Scottish Universities Life Science Alliance PhD studentship award (to M.C.R. and I.S.]. Funding for open access charge: BBSRC. Conflict of interest statement. None declared.

Tilted excitation implies odd periodic resonances

This work was supported by the Brazilian agencies FAPESP and CNPq. MSB also acknowledges the Engineering and Physical Sciences Research Council grant Ref. EP/I032606/1. GID thanks Felipe A. C. Pereira for fruitful discussions.

Electrically Tunable Selective Reflection of Light from Ultraviolet to Visible and Infrared by Heliconical Cholesterics

Acknowledgements This work was supported by NSF DMR-1410378 and DMR-1121288. We thank V. Borshch for helping with preparation of illustrations, to Y. K. Kim for the help in experiments, V. A. Belyakov and S. V. Shiyanovskii for useful discussions.

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.

Delimited Massively Parallel Algorithm Based on Rules Elimination for Application of Active Rules in Transition P Systems

In the field of Transition P systems implementation, it has been determined that it is very important to determine in advance how long takes evolution rules application in membranes. Moreover, to have time estimations of rules application in membranes makes possible to take important decisions related to hardware / software architectures design. The work presented here introduces an algorithm for applying active evolution rules in Transition P systems, which is based on active rules elimination. The algorithm complies the requisites of being nondeterministic, massively parallel, and what is more important, it is time delimited because it is only dependant on the number of membrane evolution rules.