Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Component Inventory Costs in an Assembly Problem with Uncertain Supplier Lead-Times

We present a generic study of inventory costs in a factory stockroom that supplies component parts to an assembly line. Specifically, we are concerned with the increase in component inventories due to uncertainty in supplier lead-times, and the fact that several different components must be present before assembly can begin. It is assumed that the suppliers of the various components are independent, that the suppliers' operations are in statistical equilibrium, and that the same amount of each type of component is demanded by the assembly line each time a new assembly cycle is scheduled to begin. We use, as a measure of inventory cost, the expected time for which an order of components must be held in the stockroom from the time it is delivered until the time it is consumed by the assembly line. Our work reveals the effects of supplier lead-time variability, the number of different types of components, and their desired service levels, on the inventory cost. In addition, under the assumptions that inventory holding costs and the cost of delaying assembly are linear in time, we study optimal ordering policies and present an interesting characterization that is independent of the supplier lead-time distributions.

Hydropower Project Design Incorporating Submergence Costs

Submergence of land is a major impact of large hydropower projects. Such projects are often also dogged by siltation, delays in construction and heavy debt burdens-factors that are not considered in the project planning exercise. A simple constrained optimization model for the benefit~ost analysis of large hydropower projects that considers these features is proposed. The model is then applied to two sites in India. Using the potential productivity of an energy plantation on the submergible land is suggested as a reasonable approach to estimating the opportunity cost of submergence. Optimum project dimensions are calculated for various scenarios. Results indicate that the inclusion of submergence cost may lead to a substanual reduction in net present value and hence in project viability. Parameters such as project lifespan, con$truction time, discount rate and external debt burden are also of significance. The designs proposed by the planners are found to be uneconomic, whIle even the optimal design may not be viable for more typical scenarios. The concept of energy opportunity cost is useful for preliminary screening; some projects may require more detailed calculations. The optimization approach helps identify significant trade-offs between energy generation and land availability.

In-Network Computation in Random Wireless Networks: A PAC Approach to Constant Refresh Rates with Lower Energy Costs

We propose a method to compute a probably approximately correct (PAC) normalized histogram of observations with a refresh rate of Theta(1) time units per histogram sample on a random geometric graph with noise-free links. The delay in computation is Theta(root n) time units. We further extend our approach to a network with noisy links. While the refresh rate remains Theta(1) time units per sample, the delay increases to Theta(root n log n). The number of transmissions in both cases is Theta(n) per histogram sample. The achieved Theta(1) refresh rate for PAC histogram computation is a significant improvement over the refresh rate of Theta(1/log n) for histogram computation in noiseless networks. We achieve this by operating in the supercritical thermodynamic regime where large pathways for communication build up, but the network may have more than one component. The largest component however will have an arbitrarily large fraction of nodes in order to enable approximate computation of the histogram to the desired level of accuracy. Operation in the supercritical thermodynamic regime also reduces energy consumption. A key step in the proof of our achievability result is the construction of a connected component having bounded degree and any desired fraction of nodes. This construction may also prove useful in other communication settings on the random geometric graph.

Discovering injective episodes with general partial orders

Frequent episode discovery is a popular framework for temporal pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Currently algorithms exist for episode discovery only when the associated partial order is total order (serial episode) or trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with unrestricted partial orders when the associated event-types are unique. These algorithms can be easily specialized to discover only serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that frequency alone is not a sufficient measure of interestingness in the context of partial order mining. We propose a new interestingness measure for episodes with unrestricted partial orders which, when used along with frequency, results in an efficient scheme of data mining. Simulations are presented to demonstrate the effectiveness of our algorithms.

Polymer thin film structures for ultra-low cost biosensing

Reflectance change due to binding of molecules on thin film structures has been exploited for bio-molecular sensing by several groups due to its potential in the development of sensitive, low cost, easy to fabricate, large area sensors with high multiplexing capabilities. However, all of these sensing platforms have been developed using traditional semiconductor materials and processing techniques, which are expensive. This article presents a method to fabricate disposable thin film reflectance biosensors using polymers, such as polycarbonate, which are 2-3 orders of magnitude cheaper than conventional semiconductor and dielectric materials and can be processed by alternate low cost methods, leading to significant reduction in consumable costs associated with diagnostic biosensing. (C) 2011 Elsevier GmbH. All rights reserved.

Two orders of magnitude increase in metal piezoresistor sensitivity through nanoscale inhomogenization

Metal-based piezoresistive sensing devices could find a much wider applicability if their sensitivity to mechanical strain could be substantially improved. Here, we report a simple method to enhance the strain sensitivity of metal films by over two orders of magnitude and demonstrate it on specially designed microcantilevers. By locally inhomogenizing thin gold films using controlled electromigration, we have achieved a logarithmic divergence in the strain sensitivity with progressive microstructural modification. The enhancement in strain sensitivity could be explained using non-universal tunneling-percolation transport. We find that the Johnson noise limited signal-to-noise ratio is an order of magnitude better than silicon piezoresistors. This method creates a robust platform for engineering low resistance, high gauge factor metallic piezoresistors that may have profound impact on micro and nanoscale self-sensing technology. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4761817]

Costs and benefits of the presence of leopards to the sport-hunting industry and local communities in Niassa national reserve, mozambique

Sport hunting is often proposed as a tool to support the conservation of large carnivores. However, it is challenging to provide tangible economic benefits from this activity as an incentive for local people to conserve carnivores. We assessed economic gains from sport hunting and poaching of leopards (Panthera pardus), costs of leopard depredation of livestock, and attitudes of people toward leopards in Niassa National Reserve, Mozambique. We sent questionnaires to hunting concessionaires (n = 8) to investigate the economic value of and the relative importance of leopards relative to other key trophy-hunted species. We asked villagers (n = 158) the number of and prices for leopards poached in the reserve and the number of goats depredated by leopard. Leopards were the mainstay of the hunting industry; a single animal was worth approximately U.S.$24,000. Most safari revenues are retained at national and international levels, but poached leopard are illegally traded locally for small amounts ($83). Leopards depredated 11 goats over 2 years in 2 of 4 surveyed villages resulting in losses of $440 to 6 households. People in these households had negative attitudes toward leopards. Although leopard sport hunting generates larger gross revenues than poaching, illegal hunting provides higher economic benefits for households involved in the activity. Sport-hunting revenues did not compensate for the economic losses of livestock at the household level. On the basis of our results, we propose that poaching be reduced by increasing the costs of apprehension and that the economic benefits from leopard sport hunting be used to improve community livelihoods and provide incentives not to poach.

Statistical significance of episodes with general partial orders

Frequent episode discovery is one of the methods used for temporal pattern discovery in sequential data. An episode is a partially ordered set of nodes with each node associated with an event type. For more than a decade, algorithms existed for episode discovery only when the associated partial order is total (serial episode) or trivial (parallel episode). Recently, the literature has seen algorithms for discovering episodes with general partial orders. In frequent pattern mining, the threshold beyond which a pattern is inferred to be interesting is typically user-defined and arbitrary. One way of addressing this issue in the pattern mining literature has been based on the framework of statistical hypothesis testing. This paper presents a method of assessing statistical significance of episode patterns with general partial orders. A method is proposed to calculate thresholds, on the non-overlapped frequency, beyond which an episode pattern would be inferred to be statistically significant. The method is first explained for the case of injective episodes with general partial orders. An injective episode is one where event-types are not allowed to repeat. Later it is pointed out how the method can be extended to the class of all episodes. The significance threshold calculations for general partial order episodes proposed here also generalize the existing significance results for serial episodes. Through simulations studies, the usefulness of these statistical thresholds in pruning uninteresting patterns is illustrated. (C) 2014 Elsevier Inc. All rights reserved.