Maximum A Posteriori Inference in Sum-Product Networks

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers

Investigation of Emissions and Fuel Economy for the Integrated Bus Information System

The primary objective of this study was to investigate emissions and fuel economy, and develop an Integrated Bus Information System (IBIS) for the Federal Transit Administration (FTA). IBIS included the development of transit fleet emissions models to assist transit agencies in evaluating the emissions implications of new transit vehicle procurements. Compared with existing models, the IBIS prediction model was intended to be less complicated but have sufficient accuracy to achieve its task as a vehicle procurement analysis tool.;Fuel economy (FE) and distance specific emissions (g/mile) were evaluated and predicted by the IBIS model, including carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOx), hydrocarbons (HC), and particulate matter (PM). Most data used in this study were based on chassis dynamometer testing conducted by West Virginia University (WVU), considering that chassis dynamometer test cycles could reflect the actual vehicle operations.;Many factors affect emissions and fuel economy, including vehicle parameters, fuel type, engine parameters, road conditions, ambient conditions and driving characteristics. Since driving characteristics significantly affected emissions and fuel economy, to determine the model inputs, correlation and regression studies between distance specific emissions, fuel economy and driving characteristics were performed. Results showed that average speed with idle (or average speed), percentage idle, stops per mile, standard deviation of vehicle speed, and kinetic intensity were the most influential parameters in driving characteristics and should be considered as the main driving parameters for the development of the predictive fleet emissions model.;A micro-trip based method was used throughout this research. A genetic algorithm (GA) was implemented to generate numerous new virtual cycles, to expand the cycle and emission database and to investigate transit operation characteristics encountered in the real-world. Then, the cycle generation method was applied to multiple representative buses tested with different types of fuel and powertrain technologies, to acquire the emissions and fuel economy data on over 350 newly generated virtual cycles. In addition, emissions testing was conducted over selected virtual cycles and validated the cycle generation method. It suggested that fuel consumption, CO2 and NOx emissions were not sensitive to microtrip history (sequence).;Based on this expanded dataset, multiple predictive backbone models were developed in certain model year (MY) groups for different fuel or propulsion system types (conventional and hybrid). The backbone models were validated with an additional dataset. For example, in terms of average percent errors, if using three cycle parameters as IBIS model inputs, emissions and FE of a MY 2008 60-foot CNG bus were predicted within 6% for FE, 6-8% for CO 2, 16-18% for CO, and 22-29% for HC. Emissions and FE of a MY 2008 40-foot hybrid bus were predicted within 7% for FE, 8-10% for CO2, and 7-17% for NOx. Multiple correction factors were developed to improve the models by introducing additional non-cycle parameters including vehicle weight, MY groups, and after-treatment technologies.;A case study compared the IBIS model with the Emission FACtors (EMFAC) model developed by California Air Resources Board (CARB). Comparison results agreed well for CO, NOx and PM for MY 2000 diesel buses and agreed well for CO for MY 2006 diesel buses. On average the IBIS model agreed well with the EMFAC model in terms of CO2 and fuel economy. In addition, both models showed that emissions and fuel economy did not change as the vehicle aged

Phycobiliprotein Lyases

Phycobilins are light harvesting pigments of cyanobacteria and red algae. In cyanobacteria, four phycobiliproteins are organized in phycobilisomes: phycocyanin (PC), allophycocyanin (APC), and often also phycoerythrocyanin (PEC) or phycoerythrin (PE). Their phycobilin chromophores, linear tetrapyrroles, are generally bound to the apoprotein at conserved positions by cysteinyl thioether linkages. A final step in phycobiliprotein biosynthesis is the post-translational phycobilin addition to the various biliproteins. In vivo, the correct attachment of most chromophores is catalyzed by binding-site and chromophore-specific lyases. Only two such lyases, which both belong to the E/F-type were known at the beginning of this work. Two additional types, S/(U)-type and T-type lyase, have been characterized during this work. In addition, the correct structures of the products from all three lyase types have been verified, and evidence was obtained for the reaction mechanisms. This characterization relied on two methodological advances. The first is the use of a multi-plasmidic expression system for reconstitution of phycobiliproteins in E. coli. After cloning of apophycobiliprotein genes, phycobilin biosynthesis genes and (putative) lyase genes from several cyanobacteria, various phycobiliproteins could be biosynthesized in the heterologous E. coli system using dual plasmids containing the respective genes. This heterologous system produces higher yields than the in vitro reconstitution, it is nearly devoid of spontaneous binding, better reproducible, and more easily controlled. The second methodological advance is the consequent use of a combination of chromatographic, electrophoretic and spectroscopic tools that allowed a full characterization of the structure and binding sites of attached chromophores. This included, besides optical spectroscopy, in particular mass and magnetic resonance (1H-NMR) spectroscopy. Using the unmodified genes coding for both subunits of PEC, as well as their cystein mutants, three lyases were identified for the three binding site. Besides the already known isomerizing lyase, PecE/PecF, for Cys-84 of α-PEC, these are the two new lyases, CpcT (all5339) for Cys-153 of β-PEC, and CpcS (alr0617) for Cys-82 of β-PEC. The spectroscopic analysis proved that the chromophores (PCB and PVB)are correctly attached to these three binding sites. Similarly, three lyases were identified for the three binding sites of CPC. The well known heterodimeric lyase (CpcE/CpcF) catalyzes the covalent attachment of PCB to αC84 of CPC, CpcS catalyses the site-selective attachment of PCB to cysteine-β84 in CpcB; and CpcT for cysteine-β155 of CpcB. CpcE/F is specific for CpcA, while CpcS and CpcT can react with both CpcB and PecB. We also tested the lyase activity of the deoxyhyposyl-hydroxylase (DOHH) from the malaria parasite, Plasmodium falciparum. This enzyme has Heat-like repeats that are characteristic for the E/F-type lyases, but it had not chromophore-attaching activity. The substrate specificity of the new lyase, CpcS (coded by alr0617), was further tested with APC subunits; It is very unspecific with regard to the acceptor protein and attaches PCB to ApcA1, ApcB, ApcD ApcF, as well as to the product of an additional gene, apcA2; of unknown function that is highly homologous to apcA1 coding for the APC α-subunit. Obviously, this lyase has a much broader substrate specificity than the E/F-type lyases, but it has high site-specificity, attaching the chromophore exclusively to the Cys-84 (consensus sequence) binding site of the APC subunits. CpcS from Anabaena PCC7120 is a relatively simple system, it acts as a monomer, and does not require any cofactors. CpcS binds PCB rapidly (<1s) and relatively strongly, but probably non-covalently. The chromophore is bound in an extended conformation similar to that in phycobiliproteins, but only poorly fluorescent. The extended conformation is supported by binding studies with a conformationally locked chromophore, 15Za-PCB, which also binds rapidly and non-covalently to CpcS and gives a product with similar spectral properties as PCB-CpcS. Upon addition of apo-biliproteins to the PCB-CpcS (or 15Za-PCB-CpcS) complex, the chromophore is transferred to the latter much more slowly (~1 hr), indicating that chromophorylated CpcS is an intermediate in the enzymatic reaction. There are distinct differences in the absorption, extinction coefficient in acidic methanol and pKa of the free 15Za-PCB, compared with that of free PCB, which are probably due to a shift in the pK-values by about 1 pH unit. Nucleophilic addition products of PCB were characterized that are formed spontaneously or by the lyases, and gave first indications for a mechanistic model for the lyases. The first nucleophile was imidazole, which is a model for histidine. Two imidazole-PCB adducts were prepared and the structures determined by MS and NMR spectroscopy. Surprisingly, the chromophore is isomerized in this reaction to a 2,22 H –bilin termed iso-phycocyanobilin (iPCB). CpcS not only can promote covalent binding of PCB to imidazole, but also catalyses the transfer of the chromophore of the formed iPCB-imidazole to the cysteine84 of acceptor apoprotein, CpcB. During this transfer reaction, the chromophore is re-isomerized to PCB, to yield CpcB-C84-PCB. It indicates that chromophorylation by CpcS might then involve a histidine-bound intermediate; this could be a model for the reaction catalyzed by CpcS. The second nucleophile was mercaptoethanol, as a model for cysteine. In the ME and PCB reaction system, two isomers each of isomeric PVB-ME and iPCB-ME were obtained in a non-enzymatic reaction. The chromophore of the two complexes can be transferred to cysteine-84 of CpcB, yielding CpcB-C84-PCB and CpcB-C84-PVB. In the presence of the lyase, CpcS, only the iPCB adducts are formed. It indicates autocatalytical chromophorylation might then involve a thiol-chromophore intermediate; this could be a model for the chromophorylation reaction. At the same, we propose a possible generalized catalytic mechanism for the non-isomerizing heterodimeric lyase, CpcE/CpcF, and its isomerizing homolog, PecE/PecF

Forecasting the equity risk premium: The role of technical indicators

Ministry of Education, Singapore under its Academic Research Funding Tier

A Multilevel Analysis of Neighborhood Socioeconomic Effect on Preterm Births in Georgia, USA

This study estimates the neighborhood socioeconomic status (SES) effect on the risk of preterm birth (PTB) using multilevel regression (MLR) models. Birth data retrieved from year 2000 and 2010 Georgia Vital Records were linked to their respective census tracts. Principle component analysis (PCA) was performed on nine selected census variables and the first two principal components (Fac1 and Fac2) were used to represent the neighborhood-level SES in the MLR models. Two-level random intercept MLR models were specified using 122,744 and 112,578 live and singleton births at the individual level and 1613 and 1952 census tracts at the neighborhood level, for 2000 and 2010, respectively. After adjustment for individual level factors, Fac1, which represents disadvantaged SES, respectively generated an Odds Ratio of 1.056 (95% CI: 1.031-1.081) and 1.080 (95% CI: 1.056-1.105) for these two years, showing a modest but statistically significant effect on PTB. After adjusting for individual level factors and the census tract level factors, Intra-class correlation (ICC) was 1.2% and 1.4%, for year 2000 and 2010, respectively. The two IOR-80% intervals, 0.73-1.52 (year 2000) and 0.73-1.59 (year 2010) suggest large unexplained between census tract variation. The Median Odds Ratio (MOR) value of 1.21(year 2000) and 1.23 (year 2010) revealed that the un-modeled neighborhood effect was smaller than two individual-level predictor variables, race, and tobacco use but larger than the fixed effect of census tract-level predicting variable, Fac1 and all the other individual level factors. Overall, better census tract level SES was found to have a modest protective effect for PTB risk and the effects of the two examined years were similar. Large unexplained between census tract heterogeneity warrants more sophisticated MLR models to further investigate the PTB risk factors and their interactions at both individual and neighborhood levels

Spatial Associations of Lung Cancer Rates and Socioeconomic, Health, and Environmental Factors in Georgia

According to CDC, Lung and Bronchus Cancer ranks the highest by the rate of cancer deaths among different types of cancers in the United States with the rate of 31.8 per 100 thousand people, and also for Georgia with the rate of 33.4 per 100 thousand people. Thus, to reduce the death rate of lung cancer, it is quite important and urgent to understand its risk factors. Smoking and inhaling radon are among the top risk factors of lung cancer for individuals. The socioeconomic, health, and environmental characteristics of communities might be also related to the likelihood of getting lung cancer for the residents in the communities, but their associations are not well understood. The overall objective of this study is to analyze the spatial associations of lung cancer rates andsocioeconomic, health, and environmental factors at county-level in Georgia using GIS (Geographic Information System) and statistical analyses. GIS is used to map and compare the spatial patterns in lungcancer rate, socioeconomic, health, and environmental factors by counties. GIS-based hot spot analysis is used to identify the spatial clusters of the lung cancer rate. Statistical analyses, especially correlation analysis, are used to quantify and compare the associations of the lung cancer rate with each of the studied socioeconomic, health, and environmental factors. The lung cancer rates between male and female, and among specific age groups are also compared. This study is expected to reveal the spatial patterns and hot spots of the lung cancer rate and its associations with risk factors across counties in Georgia. It will contribute to a better understanding of the associations of lung cancer rate with the health, socioeconomic, and environmental conditions of communities and provide useful information of health policy making