The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

The Power of Online Learning in Stochastic Network Optimization

In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics {\it a priori}. We are interested in understanding how information and learning can be efficiently incorporated into system control techniques, and what are the fundamental benefits of doing so. We propose two \emph{Online Learning-Aided Control} techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the past system information in current system control via a learning procedure called \emph{dual learning}. We prove strong performance guarantees of the proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time. $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that simultaneously possess explicit near-optimal delay guarantee and sub-linear convergence time. Simulation results also confirm the superior performance of the proposed algorithms in practice. To the best of our knowledge, our attempt is the first to explicitly incorporate online learning into stochastic network optimization and to demonstrate its power in both theory and practice

A spatial decision support system for designing solid waste collection routes in rural counties

Because of the increasing need to develop optimized routes for solid waste collection in rural counties, there has been a lot of research on exploring arc routing problems and their efficient solutions. The complex nature of solving arc routing problems lends itself to a GIS-based spatial decision support system. Such a system could combine user knowledge of a problem with heuristic algorithms to obtain arc routing solutions. This thesis presents SWRoute, which is such a system. Developing SWRoute requires three major steps: building a suitable geographic data base, determining and implementing the necessary heuristic algorithmic techniques, and setting up a GIS framework that allows users to manipulate the data inputs and the algorithm outputs. Taken together, these three components form a spatial decision support system for designing solid waste collection routes. The area of study is Hamblen County in East Tennessee. A database of roads in the county was developed using TIGER and other map sources. The demands for solid waste collection were obtained from the Hamblen County solid waste region. SWRoute also uses two modeling algorithms. The first is a heuristic algorithm for generating solutions to the rural arc routing problem. The second algorithm is used to develop lower bounds on candidate solutions. The lower bounds help determine the quality of the heuristic solutions. The SWRoute interface has tools which allow the user to partition a base road network into subnetworks and create a seed node set. The GIS interface is also useful for generating and reporting routes. With the aid of these tools, the user can study and manipulate solutions generated from the algorithms. The results derived from the Hamblen County example indicate how a GIS-based spatial decision support system can help solve complex problems facing rural U.S. counties

An Exploratory Data Analysis Approach for Land Use-Transportation Interaction: The Design and Implementation of Transland Spatio-Temporal Data Model

Land use and transportation interaction is a complex and dynamic process. Many models have been used to study this interaction during the last several decades. Empirical studies suggest that land use and transportation patterns can be highly variable between geographic areas and at different spatial and temporal scales. Identifying these changes presents a major challenge. When we recognize that long-term changes could be affected by other factors such as population growth, economic development, and policy decisions, the challenge becomes even more overwhelming. Most existing land use and transportation interaction models are based on some prior theories and use mathematical or simulation approaches to study the problem. However, the literature also suggests that little consensus regarding the conclusions can be drawn from empirical studies that apply these models. There is a clear research need to develop alternative methods that will allow us to examine the land use and transportation patterns in more flexible ways and to help us identify potential improvements to the existing models. This dissertation presents a spatio-temporal data model that offers exploratory data analysis capabilities to interactively examine the land use and transportation interaction at use-specified spatial and temporal scales. The spatio-temporal patterns and the summary statistics derived from this interactive exploratory analysis process can be used to help us evaluate the hypotheses and modify the structures used in the existing models. The results also can suggest additional analyses for a better understanding of land use and transportation interaction. This dissertation first introduces a conceptual framework for the spatio-temporal data model. Then, based on a systematic method for explorations of various data sets relevant to land use and transportation interaction, this dissertation details procedures of designing and implementing the spatio-temporal data model. Finally, the dissertation describes procedures of creating tools for generating the proposed spatio-temporal data model from existing snapshot GIS data sets and illustrate its use by means of exploratory data analysis. Use of the spatio-temporal data model in this dissertation study makes it feasible to analyze spatio-temporal interaction patterns in a more effective and efficient way than the conventional snapshot GIS approach. Extending Sinton’s measurement framework into a spatio-temporal conceptual interaction framework, on the other hand, provides a systematic means of exploring land use and transportation interaction. Preliminary experiments of data collected for Dade County (Miami), Florida suggest that the spatio-temporal exploratory data analysis implemented for this dissertation can help transportation planners identify and visualize interaction patterns of land use and transportation by controlling the spatial, attribute, and temporal components. Although the identified interaction patterns do not necessarily lead to rules that can be applied to different areas, they do provide useful information for transportation modelers to re-evaluate the current model structure to validate the existing model parameter

Site-specific selection reveals selective constraints and functionality of tumor somatic mtDNA mutations.

BACKGROUND: Previous studies have indicated that tumor mitochondrial DNA (mtDNA) mutations are primarily shaped by relaxed negative selection, which is contradictory to the critical roles of mtDNA mutations in tumorigenesis. Therefore, we hypothesized that site-specific selection may influence tumor mtDNA mutations. METHODS: To test our hypothesis, we developed the largest collection of tumor mtDNA mutations to date and evaluated how natural selection shaped mtDNA mutation patterns. RESULTS: Our data demonstrated that both positive and negative selections acted on specific positions or functional units of tumor mtDNAs, although the landscape of these mutations was consistent with the relaxation of negative selection. In particular, mutation rate (mutation number in a region/region bp length) in complex V and tRNA coding regions, especially in ATP8 within complex V and in loop and variable regions within tRNA, were significantly lower than those in other regions. While the mutation rate of most codons and amino acids were consistent with the expectation under neutrality, several codons and amino acids had significantly different rates. Moreover, the mutations under selection were enriched for changes that are predicted to be deleterious, further supporting the evolutionary constraints on these regions. CONCLUSION: These results indicate the existence of site-specific selection and imply the important role of the mtDNA mutations at some specific sites in tumor development

File-level defect prediction: Unsupervised vs. supervised models

NSFC Progra

Interaction of functional NPC1 gene Polymorphism with smoking on coronary heart disease

<p>Abstract</p> <p>Background</p> <p>The protein of Niemann-pick type C1 gene (<it>NPC1</it>) is known to facilitate the egress of cholesterol and other lipids from late endosomes and lysosomes to other cellular compartments. This study aims to investigate whether the genetic variation in <it>NPC1 </it>is associated with risk of coronary heart disease (CHD) and to detect whether <it>NPC1 </it>might interact with smoking on the risk of CHD.</p> <p>Methods</p> <p>We performed a case-control study, including 873 patients with coronary heart disease (CHD) and 864 subjects without CHD as control. Polymorphisms of <it>NPC1 </it>gene were genotyped by polymerase chain reaction (PCR) -restriction fragment length polymorphism (RFLP).</p> <p>Results</p> <p>A tag-SNP rs1805081 (+644A > G) in <it>NPC1 </it>was identified. The G allele of the +644 locus showed reduced risk of CHD than wild-type genotype in Chinese population (recessive model GG vs. AG+AA: odds ratio [OR] 0.647, 95% CI 0.428 to 0.980, <it>P </it>= 0.039; additive model GG vs. AG vs. AA: OR 0.847, 95% CI 0.718 to 0.998, <it>P </it>= 0.0471). Moreover in smokers, the G-allele carriers had reduced risk of CHD compared with A-allele carries (OR 0.552, 95% CI 0.311 to 0.979, <it>P </it>= 0.0421).</p> <p>Conclusions</p> <p>The results of the present study suggest that <it>NPC1 </it>variants seem to be contributors to coronary heart disease occurrence in Chinese population. Moreover, in smokers, <it>NPC1 </it>variants seem to confer protection to coronary heart disease.</p