A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation

The capacity of microscopic traffic simulation to estimate the environmental and road safety impacts opens the possibility to address the Network Design Problem from a new multi-objective point of view. Computation time, however, has hindered the use of this tool. The aim of this thesis was to find a continuous optimization method that would require only a very limited number of evaluations, and thus reduce the computation time. For this purpose, the most recent optimization literature was studied and two algorithms were selected: PAL and SMS-EGO. Both these algorithms rely on Gaussian process meta-models, but they are distinct with respect to the assumptions, criteria and methods used. They were then compared on a real-world case-study with NSGA-II, a genetic algorithm considered as state-of-the-art. Within the very limited computational budget allowed, SMS-EGO was found to outperform PAL and NSGA-II in the three configurations studied. However, the computational time required was still too important to allow for large scale optimization. To further accelerate the optimization process, three main adjustments were proposed, based on variable noise modeling, gradient-based optimization and conditional updates of the meta-models. Considering 20 runs for each optimization process, only variable noise modeling exhibited a statistically significant positive impact. The two other modifications also accelerated the optimization process on average, but high variability in the results led to p-values in the order of 0.15. Overall, the proposed optimization methodology represents a useful tool for transportation researchers to solve multi-objective optimization problems of limited scale

Automatic \u3csup\u3e13\u3c/sup\u3eC Chemical Shift Reference Correction of Protein NMR Spectral Data Using Data Mining and Bayesian Statistical Modeling

Nuclear magnetic resonance (NMR) is a highly versatile analytical technique for studying molecular configuration, conformation, and dynamics, especially of biomacromolecules such as proteins. However, due to the intrinsic properties of NMR experiments, results from the NMR instruments require a refencing step before the down-the-line analysis. Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamentally limits and even prevents effective study of biomacromolecules via NMR. There is no available method that can rereference carbon chemical shifts from protein NMR without secondary experimental information such as structure or resonance assignment. To solve this problem, we constructed a Bayesian probabilistic framework that circumvents the limitations of previous reference correction methods that required protein resonance assignment and/or three-dimensional protein structure. Our algorithm named Bayesian Model Optimized Reference Correction (BaMORC) can detect and correct 13C chemical shift referencing errors before the protein resonance assignment step of analysis and without a three-dimensional structure. By combining the BaMORC methodology with a new intra-peaklist grouping algorithm, we created a combined method called Unassigned BaMORC that utilizes only unassigned experimental peak lists and the amino acid sequence. Unassigned BaMORC kept all experimental three-dimensional HN(CO)CACB-type peak lists tested within ± 0.4 ppm of the correct 13C reference value. On a much larger unassigned chemical shift test set, the base method kept 13C chemical shift referencing errors to within ± 0.45 ppm at a 90% confidence interval. With chemical shift assignments, Assigned BaMORC can detect and correct 13C chemical shift referencing errors to within ± 0.22 at a 90% confidence interval. Therefore, Unassigned BaMORC can correct 13C chemical shift referencing errors when it will have the most impact, right before protein resonance assignment and other downstream analyses are started. After assignment, chemical shift reference correction can be further refined with Assigned BaMORC. To further support a broader usage of these new methods, we also created a software package with web-based interface for the NMR community. This software will allow non-NMR experts to detect and correct 13C referencing errors at critical early data analysis steps, lowering the bar of NMR expertise required for effective protein NMR analysis

An evaluation of the key factors that influence a South African-based firm to implement environmental management

Master of Science in Engineering - EngineeringInternational research indicates that the practice of environmental management may lead to profitability and competitive advantage for the firm. But this theory has not been tested in South Africa. This lack of empirical evidence led the researcher to the primary research question: does environmental management increase a firm’s profitability in South African-based firms? The secondary objective of the study is to determine which factors cause South African-based firms to implement environmental management strategies. Based on a comprehensive literature review, this study delineates the concepts environmental management and profitability and examines the causal relationship between the two factors. Data is collected from firms operating in ten sectors in South Africa using a cross-sectional online mail survey. A proposed research model and hypotheses are tested using confirmatory factor analysis and path analysis with latent variables. The SAS System is used for statistical analysis. The test of the structural model supports the proposed hypothesis that environmental management increases competitive advantage in South African-based firms. Environmental management, however, is limited to the minimization of natural resource consumption and competitive advantage is determined by the strength of the firm’s relationships with its stakeholders. In turn, top management positively influences the strength of these relationships. South African firms follow a strategy of pollution control as opposed pollution prevention. One of the main contributing factors to compliance with regulation in environmental management (as opposed to innovation) is the lack the technical skill and knowledge in both government and the manufacturing sector

Mathematical Analysis in Investment Theory: Applications to the Nigerian Stock Market

This thesis intends to optimise a portfolio of assets from the Nigerian Stock Exchange (NSE) using mathematical analysis in the investment theory to model the Nigerian financial market data better. In this work, we analysed the 82 stocks which were consistently traded in the NSE throughout 4years from August 2009 to August 2013. We attempt to maximise the expected return and minimise the variance of the portfolio by using Markowitz's portfolio selection model and a three-objective linear programming model allocating different percentages of weight to different assets to obtain an optimal/feasible portfolio of the financial sector of the NSM. The mean and the standard deviation served as constraints in the three-objective model used, and we constructed portfolios with the aims of maximising the returns and the Sharpe ratio and minimising the Standard Deviation (Variance) respectively. In another development, we use Random Matrix Theory (RMT) to analyse the Eigen-structure of the empirical correlations, apply the Marchenko-Pastur distribution of eigenvalues of a purely random matrix to investigate the presence of investment-pertinent information contained in the empirical correlation matrix of the selected stocks. We use a hypothesised standard normal distribution of eigenvector components from RMT to assess deviations of the empirical eigenvectors to this distribution for different eigenvalues. We also use the Inverse Participation Ratio to measure the deviation of eigenvectors of the empirical correlation matrix from RMT results. These preliminary results on the dynamics of asset price correlations in the NSE are essential for improving risk-return trade-offs associated with Markowitz's portfolio optimisation in the stock exchange, which we achieve by cleaning up the correlation matrix. Since the variance-covariance method underestimates risk, we employ Monte-Carlo simulations to estimate Value-at-Risk (VaR) and copula for a portfolio of 9 stocks of NSE. The result compared with historical simulation and variance-covariance data. Finally, with the outcome of our simulation and analysis, we were able to select the assets that form the optimal portfolio and the weights allocation to each stock. We were able to provide advice to the investors and market practitioners on how best to invest in the sector of NSE. We propose to measure the extent of closeness or otherwise in selected sectors of the NSE and the Johannesburg Stock Exchange (JSE) in our future work

Stein-Like Estimation and Inference.

The dissertation addresses three issues in the use of Stein-like estimators of the classical normal linear regression model. The St. Louis equation is used to generate out-of-sample forecasts using least squares. These forecasts are compared to those produced by restricted least squares, pretest, and members of a general family of minimax shrinkage estimators using the root-mean-square error criterion. Bootstrap confidence intervals and ellipsoids are constructed which are centered at least squares and James-Stein estimators and their coverage probability and size is explored in a Monte Carlo experiment. A Stein-like estimator of the probit regression model is suggested and its quadratic risk properties are explored in a Monte Carlo experiment

Learning discriminative models with incomplete data

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 115-121).Many practical problems in pattern recognition require making inferences using multiple modalities, e.g. sensor data from video, audio, physiological changes etc. Often in real-world scenarios there can be incompleteness in the training data. There can be missing channels due to sensor failures in multi-sensory data and many data points in the training set might be unlabeled. Further, instead of having exact labels we might have easy to obtain coarse labels that correlate with the task. Also, there can be labeling errors, for example human annotation can lead to incorrect labels in the training data. The discriminative paradigm of classification aims to model the classification boundary directly by conditioning on the data points; however, discriminative models cannot easily handle incompleteness since the distribution of the observations is never explicitly modeled. We present a unified Bayesian framework that extends the discriminative paradigm to handle four different kinds of incompleteness. First, a solution based on a mixture of Gaussian processes is proposed for achieving sensor fusion under the problematic conditions of missing channels. Second, the framework addresses incompleteness resulting from partially labeled data using input dependent regularization.(cont.) Third, we introduce the located hidden random field (LHRF) that learns finer level labels when only some easy to obtain coarse information is available. Finally the proposed framework can handle incorrect labels, the fourth case of incompleteness. One of the advantages of the framework is that we can use different models for different kinds of label errors, providing a way to encode prior knowledge about the process. The proposed extensions are built on top of Gaussian process classification and result in a modular framework where each component is capable of handling different kinds of incompleteness. These modules can be combined in many different ways, resulting in many different algorithms within one unified framework. We demonstrate the effectiveness of the framework on a variety of problems such as multi-sensor affect recognition, image classification and object detection and segmentation.by Ashish Kapoor.Ph.D