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Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy. / Singapore-MIT Alliance (SMA)

Improved numerical reservoir models are constructed when all available diverse data sources are accounted for to the maximum extent possible. Integrating various diverse data is not a simple problem because data show different precision and relevance to the primary variables being modeled, nonlinear relations and different qualities. Previous approaches rely on a strong Gaussian assumption or the combination of the source-specific probabilities that are individually calibrated from each data source. This dissertation develops different approaches to integrate diverse earth science data. First approach is based on combining probability. Each of diverse data is calibrated to generate individual conditional probabilities, and they are combined by a combination model. Some existing models are reviewed and a combination model is proposed with a new weighting scheme. Weakness of the probability combination schemes (PCS) is addressed. Alternative to the PCS, this dissertation develops a multivariate analysis technique. The method models the multivariate distributions without a parametric distribution assumption and without ad-hoc probability combination procedures. The method accounts for nonlinear features and different types of the data. Once the multivariate distribution is modeled, the marginal distribution constraints are evaluated. A sequential iteration algorithm is proposed for the evaluation. The algorithm compares the extracted marginal distributions from the modeled multivariate distribution with the known marginal distributions and corrects the multivariate distribution. Ultimately, the corrected distribution satisfies all axioms of probability distribution functions as well as the complex features among the given data. The methodology is applied to several applications including: (1) integration of continuous data for a categorical attribute modeling, (2) integration of continuous and a discrete geologic map for categorical attribute modeling, (3) integration of continuous data for a continuous attribute modeling. Results are evaluated based on the defined criteria such as the fairness of the estimated probability or probability distribution and reasonable reproduction of input statistics. / Mining Engineering

WICKRAMARATHNE, T. L. (M.S., Electrical and Computer Engineering) A Belief Theoretic Approach for Automated Collaborative Filtering (May 2008) Abstract of a thesis at the University of Miami. Thesis supervised by Professor Kamal Premaratne. No. of pages in text. (84) Automated Collaborative Filtering (ACF) is one of the most successful strategies available for recommender systems. Application of ACF in more sensitive and critical applications however has been hampered by the absence of better mechanisms to accommodate imperfections (ambiguities and uncertainties in ratings, missing ratings, etc.) that are inherent in user preference ratings and propagate such imperfections throughout the decision making process. Thus one is compelled to make various "assumptions" regarding the user preferences giving rise to predictions that lack sufficient integrity. With its Dempster-Shafer belief theoretic basis, CoFiDS, the automated Collaborative Filtering algorithm proposed in this thesis, can (a) represent a wide variety of data imperfections; (b) propagate the partial knowledge that such data imperfections generate throughout the decision-making process; and (c) conveniently incorporate contextual information from multiple sources. The "soft" predictions that CoFiDS generates provide substantial exibility to the domain expert. Depending on the associated DS theoretic belief-plausibility measures, the domain expert can either render a "hard" decision or narrow down the possible set of predictions to as smaller set as necessary. With its capability to accommodate data imperfections, CoFiDS widens the applicability of ACF, from the more popular domains, such as movie and book recommendations, to more sensitive and critical problem domains, such as medical expert support systems, homeland security and surveillance, etc. We use a benchmark movie dataset and a synthetic dataset to validate CoFiDS and compare it to several existing ACF systems.

Demand uncertainty has been recognized as one factor that may cause price dispersion in perfectly competitive markets with costly and perishable capacity. With the persistence of the degree of price dispersion in increasingly competitive markets, demand uncertainty has become more important for us to understand the phenomenon of fare inequality. This dissertation consists of three related studies on this topic. In the first study, Prescott (1975) model is extends by incorporating the heterogeneity of customers' reservation values. The model shows that the equilibrium price dispersion also depends on the mix of customers and their reservation values. With customer segmentation based on reservation values, the equilibrium price dispersion is more efficient than what can be achieved without segmentation. In the airline industry context, the model implies that different prices can exist simultaneously in the market and carriers would provide more seats if they can segment their travelers. This sheds light on an alternative motivation for airlines to require Saturday night stay over other than the practice of price discrimination. In the second study, a price simulation in the airline industry is conducted. The stochastic demand for coach class, nonstop, air travel service on the observed routs is calculated. Then a market price schedule based on Prescott's model is simulated by using nonparametric method. The comparison between the simulated price distribution and the actual price distribution provides evidence that on average more than 60 percent of the fare inequality on the observed routes can be accounted for by cost variation due to demand uncertainty under the condition of perfect competition. At last, an empirical model is specified to explore the relationship between route demand uncertainty and carrier price dispersion in U.S. air travel markets. The results demonstrate that the effect of route demand uncertainty on carrier price dispersion varies with the market structure. In monopoly market, the route demand uncertainty has no effect on carrier price dispersion. While in duopoly and competitive markets, the increase of route demand uncertainty is associated with the decrease of the carrier price dispersion. Furthermore, the negative relationship is magnified when the market becomes more competitive.

This project presents the development and design of a comprehensive interface coupled with a geodatabase (ArcGISwat 2003), for the Soil and Water Assessment Tool (SWAT). SWAT is a hydrologically distributed, lumped parameter model that runs on a continuous time step. The quantity and extensive detail of the spatial and hydrologic data, involved in the input and output, both make SWAT highly complex. A new interface, that will manage the input/output (I/O) process, is being developed using the Geodatabase object model and concepts from hydrological data models such as ArcHydro. It also incorporates uncertainty analysis on the process of modeling. This interface aims to further direct communication and integration with other hydrologic models, consequently increasing efficiency and diminishing modeling time. A case study is presented in order to demonstrate a common watershed-modeling task, which utilizes SWAT and ArcGIS-SWAT2003.

This dissertation analyzes the basis for and magnitudes of discount factors based on the characteristics of greenhouse gas emission (GHGE) offsets that are applied to the GHGE reduction projects, concentrating on agricultural projects. Theoretical approaches to discount factors, estimation and incorporation of discount factors procedures are developed. Discount factors would be imposed by credit purchasers due to noncompliance with regulatory program of the credits with GHG program including consideration of shortfall penalties and limited durations. Discount factors are proposed for (i) additionality, (ii) leakage, (iii) permanence, and (iv) uncertainty. Additionality arise when the region where an AO project is being proposed would have substantial adoption of the AO practice in the absence of GHG programs (business as usual GHGE offset). Leakage arises when the effect of a program is offset by an induced increase in economic activity and accompanying emissions elsewhere. The leakage effect depends on demand and supply elasticities. Permanence reflects the saturation and volatility characteristics of carbon sequestration. Carbon is stored in a volatile form and can be released quickly to the atmosphere when an AO practice is discontinued. The permanence discount depends on the project design including practice continuation after the program and the dynamic rate of offset. Also, consideration of multiple offsets is important. Uncertainty arises due to the stochastic nature of project quantity. The uncertainty discount tends to be smaller the larger the size of the offset contract due to aggregation over space and time. The magnitude of these discounts is investigated in Southeast Texas rice discontinuation study. The additionality and the leakage discounts are found to play an important role in case of rice lands conversion to other crops but less so for pasture conversions and yet less for forest conversions. The permanence discount is important when converting to other crops and short rotation forestry. When all discounts are considered, rice lands conversion to forest yields claimable credits amounting to 52.8% ~ 77.5% of the total offset. When converting rice lands to pasture, the claimable credits 45.1% ~ 64.2%, while a conversion of rice lands to other crops yields claimable credits 38.9% ~ 40.4%.

Environmental models are important tools; however uncertainty is pervasive in the modeling process.   Current research has shown that under­standing and representing these uncertainties is critical when decisions are expected to be made from the modeling results.  One critical question has become: how focused should uncertainty intervals be with consideration of characteristics of uncertain input data, model equation representations, and output observations?   This thesis delves into this issue with applied research in four independent studies.  These studies developed a diverse array of simply-structured process models (catchment hydrology, soil carbon dynamics, wetland P cycling, stream rating); employed field data observations with wide ranging characteristics (e.g., spatial variability, suspected systematic error); and explored several variations of probabilistic and non-probabilistic uncertainty schemes for model calibrations.  A key focus has been on how the design of various schemes impacted the resulting uncertainty intervals, and more importantly the ability to justify conclusions.  In general, some uncertainty in uncertainty (u2) resulted in all studies, in various degrees.  Subjectivity was intrinsic in the non-probabilistic results.  One study illustrated that such subjectivity could be partly mitigated using a “limits of acceptability” scheme with posterior validation of errors.  u2 was also a factor from probabilistic calibration algorithms, as residual errors were not wholly stochastic.  Overall however, u2 was not a deterrent to drawing conclusions from each study. One insight on the value of data for modeling was that there can be substantial redundant information in some hydrological time series.  Several process insights resulted: there can be substantial fractions of relatively inert soil carbon in agricultural systems; the lowest achievable outflow phosphorus concentration in an engineered wetland seemed partly controlled by rapid turnover and decomposition of the specific vegetation in that system.  Additionally, consideration of uncertainties in a stage-discharge rating model enabled more confident detection of change in long-term river flow patterns. / <p>QC 20121105</p>

Introduction: The world is changing rapidly and the uncertainty that characterizes today´s society makes companies preparations for the future crucial. Purpose: To examine how CEO:s in small companies look at the future and what kind of preparations they make in order to meet it. Hereby, our ambition is to find out whether there is a difference between companies working in industries characterized by different degrees of stability. Mode of procedure: The purpose of this thesis has been fulfilled by interviews with four CEO:s from industries characterized by different degrees of stability. Result: The companies working in relatively unstable environments tended to be relatively more flexible and have an emergent strategy formation process, whereas companies working in relatively stable environments displayed relatively less flexibility and tended to have a planning strategy formation process.