Unconstrained Cross-Sectional Shape Optimisation of Cold-Formed Steel Beams and Beam-Columns

This paper is focused on optimising the cross-sectional shapes of simply-supported, singly-symmetric and open-section cold-formed steel (CFS) beams and beam-columns without manufacturing or assembly constraints. A previously developed Genetic Algorithm (GA) is used in this study. Fully restrained and unrestrained beams against lateral deflection and twist, as well as unrestrained beam-columns are optimised, of which the nominal member capacities are determined by the Direct Strength Method (DSM). The optimised cross-sectional shapes are presented and the evolution of the unrestrained cross-sectional shapes for various combinations of axial load and bending moment is analysed and discussed

Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Termites mitigate the effects of drought in tropical rainforest

Acknowledgments: This work was supported by the South East Asia Rainforest Research Partnership (SEARRP) with permission from the Maliau Basin Management Committee. We thank G. Reynolds, U. Jami, and L. Kruitbos for coordinating fieldwork; S. Both and U. Kritzler for help in establishing the experimental plots; R. Walsh for providing rainfall data; and A. Zanne and A. Cheesman for discussions on experimental design. We thank J. Nash from Bayer Southeast Asia Pte-Ltd, Singapore, for donating Premise 200SC and Agenda 10SC. We thank J. Rees, A. Tagliabue, M. Begon, R. Williams, W. Cheng, C. Dahlsjö, R. Kitching, and J. Barlow for comments on the manuscript. Finally, we thank all our field assistants: R. Binti Manber, A. Jupri, F. John, Y. Binti Suffian, E. Bin Esing, D. Bin Paul, Z. Bin Angau, A. Allbanah Bin Anchun, N. Angau, D. Ku Shamirah Binti Pg Bakar, E. Binti Nahun, R. Rusili, A. Bin Rantau, R. Bin Sahamin, A. Mastor, M. Adzim Bin Rahili, M. Azuan, H. Nasir, and N. Fazzli. Funding: This publication is a contribution from the UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (NERC grant NE/L000016/1). Author contributions: C.L.P., H.M.G., L.A.A., P.E., and T.A.E. conceived and designed the experiment; C.L.P., P.E., and T.A.E. established the experimental plots; H.M.G., L.A.A., and P.E. collected the data; H.M.G., L.A.A., P.E., and R.K.D. analyzed the data; C.S.V., F.H., and H.S.T. carried out laboratory analysis; H.M.G. and L.A.A. led the writing of the manuscript with significant input from C.L.P., P.E., R.K.D., and Y.A.T. Competing interests: None declared. Data and materials availability: Data have been deposited in the NERC Environmental Information Data Centre (37).Peer reviewedPostprin

Crystallization and preliminary X-ray diffraction studies of FHA domains of Dun1 and Rad53 protein kinases

Forkhead-associated (FHA) domains are modular protein–protein interaction domains of ~130 amino acids present in numerous signalling proteins. FHA-domain-dependent protein interactions are regulated by phosphorylation of target proteins and FHA domains may be multifunctional phosphopeptide-recognition modules. FHA domains of the budding yeast cell-cycle checkpoint protein kinases Dun1p and Rad53p have been crystallized. Crystals of the Dun1-FHA domain exhibit the symmetry of the space group P6122 or P6522, with unit-cell parameters a = b = 127.3, c = 386.3 Å; diffraction data have been collected to 3.1 Å resolution on a synchrotron source. Crystals of the N-terminal FHA domain (FHA1) of Rad53p diffract to 4.0 Å resolution on a laboratory X-ray source and have Laue-group symmetry 4/mmm, with unit-cell parameters a = b = 61.7, c = 104.3 Å

Location Dependent Dirichlet Processes

Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arising from space and time. In this paper, we propose location dependent Dirichlet processes (LDDP) which incorporate nonparametric Gaussian processes in the DP modeling framework to model such dependencies. We develop the LDDP in the context of mixture modeling, and develop a mean field variational inference algorithm for this mixture model. The effectiveness of the proposed modeling framework is shown on an image segmentation task