Semileptonic Form Factors

I report the current status of the heavy-light decay constants, the bag parameters and the semileptonic form factors. I compare the heavy-light decay constants with Wilson-Wilson and clover-clover fermions. Systematic errors such as scale setting and renormalization factors are also discussed. 1/M dependences for the heavy-light semileptonic form factors near $q^2 = q^2_max$ with clover-clover and NRQCD-Wilson fermions are found to be small.Comment: 12 pgs. 15 figures. Talk presented at LATTICE9

Tuneable diode laser spectroscopy correction factor investigation on ammonia measurement

Current diesel engine aftertreatment systems, such as Selective Catalyst Reduction (SCR) use ammonia (NH3) to reduce Nitrogen Oxides (NOx) into Nitrogen (N2) and water (H2O). However, if the reaction between NH3 and NOx is unbalanced, it can lead either NH3 or NOx being released into the environment. As NH3 is classified as a dangerous compound in the environment, its accurate measurement is essential. Tuneable Diode Laser (TDL) spectroscopy is one of the methods used to measure raw emissions inside engine exhaust pipes, especially NH3. This instrument requires a real-time exhaust temperature, pressure and other interference compounds in order to adjust itself to reduce the error in NH3 readings. Most researchers believed that exhaust temperature and pressure were the most influential factors in TDL when measuring NH3 inside exhaust pipes. The aim of this paper was to quantify these interference effects on TDL when undertaking NH3 measurement. Surprisingly, the results show that pressure was the least influential factor when compared to temperature, H2O, CO2 and O2 when undertaking NH3 measurement using TDL

Perioperative renal failure in pediatric heart transplant recipients: Outcome and risk factors

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90192/1/j.1399-3046.2010.01445.x.pd

The continuum limit of fBf_B from the lattice in the static approximation

We present an analysis of the continuum extrapolation of $f_B$ in the static approximation from lattice data. The method described here aims to uncover the systematic effects which enter in this extrapolation and has not been described before. Our conclusions are that we see statistical evidence for scaling of $f_B^{stat}$ for inverse lattice spacings \gtap 2 GeV but not for \ltap 2 GeV. We observe a lack of {\em asymptotic} scaling for a variety of quantities, including $f_B^{stat}$, at all energy scales considered. This can be associated with finite lattice spacing systematics. Once these effects are taken into account, we obtain a value of 230(35) MeV for $f_B^{stat}$ in the continuum where the error represents uncertainties due to both the statistics and the continuum extrapolation. In this method there is no error due to uncertainties in the renormalization constant connecting the lattice and continuum effective theories.Comment: 33 pages, latex text file and postscript figures all uuencoded into a single file, ROME preprint 94/104

Weighted ergodic theorems for Banach-Kantorovich lattice Lp(∇^,μ^)L_{p}(\hat{\nabla},\hat{\mu})

In the present paper we prove weighted ergodic theorems and multiparameter weighted ergodic theorems for positive contractions acting on $L_p(\hat{\nabla},\hat{\mu})$. Our main tool is the use of methods of measurable bundles of Banach-Kantorovich lattices.Comment: 11 page

Bulk Segregant Analysis Using Single Nucleotide Polymorphism Microarrays

Bulk segregant analysis (BSA) using microarrays, and extreme array mapping (XAM) have recently been used to rapidly identify genomic regions associated with phenotypes in multiple species. These experiments, however, require the identification of single feature polymorphisms (SFP) between the cross parents for each new combination of genotypes, which raises the cost of experiments. The availability of the genomic polymorphism data in Arabidopsis thaliana, coupled with the efficient designs of Single Nucleotide Polymorphism (SNP) genotyping arrays removes the requirement for SFP detection and lowers the per array cost, thereby lowering the overall cost per experiment. To demonstrate that these approaches would be functional on SNP arrays and determine confidence intervals, we analyzed hybridizations of natural accessions to the Arabidopsis ATSNPTILE array and simulated BSA or XAM given a variety of gene models, populations, and bulk selection parameters. Our results show a striking degree of correlation between the genotyping output of both methods, which suggests that the benefit of SFP genotyping in context of BSA can be had with the cheaper, more efficient SNP arrays. As a final proof of concept, we hybridized the DNA from bulks of an F2 mapping population of a Sulfur and Selenium ionomics mutant to both the Arabidopsis ATTILE1R and ATSNPTILE arrays, which produced almost identical results. We have produced R scripts that prompt the user for the required parameters and perform the BSA analysis using the ATSNPTILE1 array and have provided them as supplemental data files

Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View

Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way toward in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behavior optimization. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilizing CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community, at https://github.com/LCAS/engagement_detector, as a tool to measure engagement in a variety of settings