Rare Radiative Bc→Ds1(2460)γB_{c}\rightarrow D_{s1}(2460)\gamma Transition in QCD

We investigate the radiative $B_{c} \to D_{s1} \gamma$ transition in the framework of QCD sum rules. In particular, we calculate the transition form factors responsible for this decay in both weak annihilation and electromagnetic penguin channels using the quark condensate, mixed and two-gluon condensate diagrams as well as propagation of the soft quark in the electromagnetic field as non-perturbative corrections. These form factors are then used to estimate the branching ratios of the channels under consideration. The total branching ratio of the $B_{c} \to D_{s1} \gamma$ transition is obtained to be in order of $10^{-5}$, and the dominant contribution comes from the weak annihilation channel.Comment: 24 Pages and 3 Figure

Bioinformatics tools in predictive ecology: Applications to fisheries

This article is made available throught the Brunel Open Access Publishing Fund - Copygith @ 2012 Tucker et al.There has been a huge effort in the advancement of analytical techniques for molecular biological data over the past decade. This has led to many novel algorithms that are specialized to deal with data associated with biological phenomena, such as gene expression and protein interactions. In contrast, ecological data analysis has remained focused to some degree on off-the-shelf statistical techniques though this is starting to change with the adoption of state-of-the-art methods, where few assumptions can be made about the data and a more explorative approach is required, for example, through the use of Bayesian networks. In this paper, some novel bioinformatics tools for microarray data are discussed along with their ‘crossover potential’ with an application to fisheries data. In particular, a focus is made on the development of models that identify functionally equivalent species in different fish communities with the aim of predicting functional collapse

Classifying oceanographic structures in the Amundsen Sea, Antarctica

Funding: The TARSAN project was funded by the U.S. National Science Foundation, Office of Polar Programs (Grant #1738992) and the UK Natural Environment Research Council (NERC, NE/S006591/1). I.R. was supported by the National Science Foundation’s Southern Ocean Carbon and Climate Observations and Modeling SOCCOM) project under NSF Award PLR-1425989, with additional support from NOAA and NASA.The remote and often ice‐covered Amundsen Sea Embayment in Antarctica is important for transporting relatively warm modified Circumpolar Deep Water (mCDW) to the Western Antarctic Ice Sheet, potentially accelerating its thinning and contribution to sea level rise. To investigate potential pathways and variability of mCDW, 3809 CTD profiles (instrumented seal and ship‐based data) are classified using a machine learning approach (Profile Classification Model). Five vertical regimes are identified, and areas of larger variability highlighted. Three spatial regimes are captured: Off‐Shelf, Eastern and Central Troughs. The on‐shelf profiles further show a separation between cold and warm modes. The variability is higher north of Burke Island and at the southern end of the Eastern Trough, which reflects the convergence of different mCDW pathways between the Eastern and the Central Trough. Finally, a clear but variable clockwise circulation is identified in Pine Island Bay.Publisher PDFPeer reviewe

A nonparametric Bayesian approach toward robot learning by demonstration

In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios

A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in L2, and the Fisher-Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the alpha-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Frechet derivative operators motivated by the geometry of the Hilbert sphere, and examine its properties. Through simulations and real-data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models

The importance of having two X chromosomes

Historically, it was thought that the number of X chromosomes plays little role in causing sex differences in traits. Recently, selected mouse models have been used increasingly to compare mice with the same type of gonad but with one versus two copies of the X chromosome. Study of these models demonstrates that mice with one X chromosome can be strikingly different from those with two X chromosomes, when the differences are not attributable to confounding group differences in gonadal hormones. The number of X chromosomes affects adiposity and metabolic disease, cardiovascular ischaemia/reperfusion injury and behaviour. The effects of X chromosome number are likely the result of inherent differences in expression of X genes that escape inactivation, and are therefore expressed from both X chromosomes in XX mice, resulting in a higher level of expression when two X chromosomes are present. The effects of X chromosome number contribute to sex differences in disease phenotypes, and may explain some features of X chromosome aneuploidies such as in Turner and Klinefelter syndromes

An Algorithmic Approach to Missing Data Problem in Modeling Human Aspects in Software Development

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/