Qualitative and Quantitative Comparisons between Spar and Semi-Submersible Platforms

Spars and semisubmersibles are two floating platforms commonly used in offshore deepwater exploration and production. However, currently lack of studies are available to comparatively evaluate the two platforms under similar cost range. This study compares the hydrodynamic motions of a truss spar and a semisubmersible under similar cost and wave environment. The platforms were numerically modelled using the radiation/diffraction software HydroSTAR. The response amplitude operators (RAO) were obtained for surge, heave and pitch respectively. The findings indicate the responses for the truss spar are generally lower than the semisubmersible, with the exception for the peak heave and pitch RAO. At 1000 m, the percentage difference of the peak surge RAO for spar and semisubmersible is 0.005%; heave RAO for semisubmersible is 34% of the spar’s value; and pitch RAO for semisubmersible is 79% of the spar’s value. Additionally, the semisubmersible achieved its peak RAO at a higher frequency for the heave and pitch. The findings proved that overall the spar’s dynamic responses are better for the majority of wave frequencies

Bayesian variable selection regression for genome-wide association studies and other large-scale problems

We consider applying Bayesian Variable Selection Regression, or BVSR, to genome-wide association studies and similar large-scale regression problems. Currently, typical genome-wide association studies measure hundreds of thousands, or millions, of genetic variants (SNPs), in thousands or tens of thousands of individuals, and attempt to identify regions harboring SNPs that affect some phenotype or outcome of interest. This goal can naturally be cast as a variable selection regression problem, with the SNPs as the covariates in the regression. Characteristic features of genome-wide association studies include the following: (i) a focus primarily on identifying relevant variables, rather than on prediction; and (ii) many relevant covariates may have tiny effects, making it effectively impossible to confidently identify the complete "correct" subset of variables. Taken together, these factors put a premium on having interpretable measures of confidence for individual covariates being included in the model, which we argue is a strength of BVSR compared with alternatives such as penalized regression methods. Here we focus primarily on analysis of quantitative phenotypes, and on appropriate prior specification for BVSR in this setting, emphasizing the idea of considering what the priors imply about the total proportion of variance in outcome explained by relevant covariates. We also emphasize the potential for BVSR to estimate this proportion of variance explained, and hence shed light on the issue of "missing heritability" in genome-wide association studies.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS455 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Practical Issues in Imputation-Based Association Mapping

Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption—specifically, that difficult-to-impute SNPs tend to have larger effects—and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate—their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html.</p

Association Between Homocysteine and Vascular Calcification Incidence, Prevalence, and Progression in the MESA Cohort.

Background While elevated homocysteine has been associated with calcification in several studies, its importance as a cardiovascular risk factor remains unclear. This study examines the relationship between homocysteine and vascular and valve calcification in the MESA (Multi-ethnic Study of Atherosclerosis) cohort. Methods and Results MESA participants with baseline homocysteine measurements and cardiac computed tomography scans were included (N=6789). Baseline and follow-up assessment of vascular (coronary artery [CAC], descending thoracic aorta [DTAC]) and valve (aortic valve [AVC], mitral annular [MAC]) calcification was performed. Prevalence ratio/relative risk regression was used to assess the relationship of homocysteine with prevalent and incident calcification, and multivariable logistic regression was used to assess associations between homocysteine and calcification progression. Elevated homocysteine was associated with greater relative risk of prevalent and incident CAC and incident DTAC. We also identified a strong association between elevated homocysteine and CAC and DTAC progression. Elevated homocysteine was found to confer a &gt;2-fold increased risk of severe CAC progression (defined as ΔCAC ≥100/year) and an ≈1.5-fold increased risk for severe DTAC progression (defined as ΔDTAC ≥100/year). Conclusions To our knowledge, this is the first study demonstrating an association between elevated homocysteine and both incidence and progression of coronary and extra-coronary vascular calcification. Our findings suggest a potential role for elevated homocysteine as a risk factor for severe vascular calcification progression. Future studies are warranted to further assess the utility of homocysteine as a biomarker for vascular calcification incidence and progression. Clinical Trial Registration https://www.clinicaltrials.gov/. Unique identifier: NCT00005487

Qualitative and Quantitative Comparisons between Spar and Semi-Submersible Platforms

Spars and semisubmersibles are two floating platforms commonly used in offshore deepwater exploration and production. However, currently lack of studies are available to comparatively evaluate the two platforms under similar cost range. This study compares the hydrodynamic motions of a truss spar and a semisubmersible under similar cost and wave environment. The platforms were numerically modelled using the radiation/diffraction software HydroSTAR. The response amplitude operators (RAO) were obtained for surge, heave and pitch respectively. The findings indicate the responses for the truss spar are generally lower than the semisubmersible, with the exception for the peak heave and pitch RAO. At 1000 m, the percentage difference of the peak surge RAO for spar and semisubmersible is 0.005%; heave RAO for semisubmersible is 34% of the spar’s value; and pitch RAO for semisubmersible is 79% of the spar’s value. Additionally, the semisubmersible achieved its peak RAO at a higher frequency for the heave and pitch. The findings proved that overall the spar’s dynamic responses are better for the majority of wave frequencies

Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion

There is a growing interest in developing automated agents that can work alongside humans. In addition to completing the assigned task, such an agent will undoubtedly be expected to behave in a manner that is preferred by the human. This requires the human to communicate their preferences to the agent. To achieve this, the current approaches either require the users to specify the reward function or the preference is interactively learned from queries that ask the user to compare trajectories. The former approach can be challenging if the internal representation used by the agent is inscrutable to the human while the latter is unnecessarily cumbersome for the user if their preference can be specified more easily in symbolic terms. In this work, we propose PRESCA (PREference Specification through Concept Acquisition), a system that allows users to specify their preferences in terms of concepts that they understand. PRESCA maintains a set of such concepts in a shared vocabulary. If the relevant concept is not in the shared vocabulary, then it is learned. To make learning a new concept more efficient, PRESCA leverages causal associations between the target concept and concepts that are already known. Additionally, the effort of learning the new concept is amortized by adding the concept to the shared vocabulary for supporting preference specification in future interactions. We evaluate PRESCA by using it on a Minecraft environment and show that it can be effectively used to make the agent align with the user's preference

Analysis of Unsupervised and Noise-Robust Speaker-Adaptive HMM-Based Speech Synthesis Systems toward a Unified ASR and TTS Framework

For the 2009 Blizzard Challenge we have built an unsupervised version of the HTS-2008 speaker-adaptive HMM-based speech synthesis system for English, and a noise robust version of the systems for Mandarin. They are designed from a multidisciplinary application point of view in that we attempt to integrate the components of the TTS system with other technologies such as ASR. All the average voice models are trained exclusively from recognized, publicly available, ASR databases. Multi-pass LVCSR and confidence scores calculated from confusion network are used for the unsupervised systems, and noisy data recorded in cars or public spaces is used for the noise robust system. We believe the developed systems form solid benchmarks and provide good connections to ASR fields. This paper describes the development of the systems and reports the results and analysis of their evaluation

The deposition of metal nanoparticles on carbon surfaces: the role of specific functional groups

The enormous complexity of a typical heterogeneous catalyst makes understanding the development and properties of any active nanoparticles present extremely challenging. In the case of carbon based catalysts that difficulty is compounded by the variability of the carbon powders used. We have previously developed a strategy that addresses these problems by mimicking the catalyst preparation conditions very closely but using highly ordered pyrolytic graphite crystals (HOPG) as a model surface. This enables us to examine the effects of specific functional groups on nanoparticle formation. We report here an extension of our work characterising functional groups on the HOPG surface, using XPS and AFM to explore the deposition of gold from aqueous solution onto HOPG surfaces treated in a variety of ways to alter the surface functionality. The structure and oxidation state of the resulting nanoparticles depend critically on the nature of the functional groups present and offers some insight into the development of catalysts based on these materials. Hydroxyls are identified as key functional species, reducing gold ions to their metallic state whilst being oxidised themselves to carbonyls. Carbonyls meanwhile promote the nucleation of Au3+, creating a network of islands at the HOPG surface. The results have relevance not only to catalysts using activated carbons but also the new generation of materials based on graphene and carbon nanotubes