The copula: a tool for simulating speckle dynamics

Use of a copula for generating a sequence of correlated speckle patterns is introduced. The chief characteristic of this algorithm is that it generates a continuous speckle sequence with a specified evolution of the correlation and does so with just two arrays of random numbers. Thus, physically realistic temporally varying speckle patterns with proper first- and second-order statistics are easily realized. We illustrate use of the algorithm for generating sequences with prescribed Gaussian, exponential, and equal-interval correlations and demonstrate how correlation times can be specified independently. This approach to generating sequences of random realizations with prescribed correlations should prove useful in modeling such phenomena as dynamic light scatter, flow-dependent laser speckle contrast, and propagation of spatial coherence.     

Multiple costs based decision making with back-propagation neural networks

The current research investigates a single cost for cost-sensitive neural networks (CNN) for decision making. This may not be feasible for real cost-sensitive decisions which involve multiple costs. We propose to modify the existing model, the traditional back-propagation neural networks (TNN), by extending the back-propagation error equation for multiple cost decisions. In this multiple-cost extension, all costs are normalized to be in the same interval (i.e. between 0 and 1) as the error estimation generated in the TNN. A comparative analysis of accuracy dependent on three outcomes for constant costs was performed: (1) TNN and CNN with one constant cost (CNN-1C), (2) TNN and CNN with two constant costs (CNN-2C), and (3) CNN-1C and CNN-2C. A similar analysis for accuracy was also made for non-constant costs; (1) TNN and CNN with one non-constant cost (CNN-1NC), (2) TNN and CNN with two non-constant costs (CNN-2NC), and (3) CNN-1NC and CNN-2NC. Furthermore, we compared the misclassification cost for CNNs for both constant and non-constant costs (CNN-1C vs. CNN-2C and CNN-1NC vs. CNN-2NC). Our findings demonstrate that there is a competitive behavior between the accuracy and misclassification cost in the proposed CNN model. To obtain a higher accuracy and lower misclassification cost, our results suggest merging all constant cost matrices into one constant cost matrix for decision making. For multiple non-constant cost matrices, our results suggest maintaining separate matrices to enhance the accuracy and reduce the misclassification cost.     

Extracting the optimal dimensionality for local tensor discriminant analysis

Supervised dimensionality reduction with tensor representation has attracted great interest in recent years. It has been successfully applied to problems with tensor data, such as image and video recognition tasks. However, in the tensor-based methods, how to select the suitable dimensions is a very important problem. Since the number of possible dimension combinations exponentially increases with respect to the order of tensor, manually selecting the suitable dimensions becomes an impossible task in the case of high-order tensor. In this paper, we aim at solving this important problem and propose an algorithm to extract the optimal dimensionality for local tensor discriminant analysis. Experimental results on a toy example and real-world data validate the effectiveness of the proposed method.     

The wabush mines fire

How could a five-million-dollar fire occur in a seemingly low hazard industry? The author discusses one such fire and examines the lessons learned from it. It takes careful study to determine if well-known fire protection principles are being violated.     

Improved diagnosis using enhanced fault dominance

Defect diagnosis can benefit from fault dominance relations to reduce the set of defect candidate sites. This paper presents new fault dominance collapsing operators that further reduce the set of candidates considered during the initial phase of diagnosis. In contrast to existing dominance-based methods which operate on pairs of faults, the proposed method operates on sets of faults. Fault-related entities are generated to guide the diagnosis process. The proposed collapsing operators can be used to accelerate effect-cause diagnosis. Experimental results demonstrate that the proposed method achieves a higher collapsing ratio than existing methods.     

WIPO Panels’ interpretation of the Uniform Dispute Resolution Policy (UDRP) three-prong test

The Uniform Dispute Resolution Policy (UDRP) is a unique process that resolves domain name disputes effectively and inexpensively. This paper, through an analysis of the UDRP three-prong test, revealed that even though the UDRP affords a great degree of discretion to the WIPO Panels deciding any given case, there is some consistency and predictability inherent in the UDRP process.     

Europe should stop taxing innovation

After nearly 48 years of failure to create the EU patent, language issues and the design of a centralised patent-litigation court still dominate headlines. But behind these issues there are high financial stakes and control power to play for. The recent EU Council deal on an ‘enhanced’ European patent system does not solve the above problems, and has not eliminated lingering governance issues. The risk for Europe is that a final patent agreement might be reached that does not cure the system of its major ills, and thus does not bring about any significant improvement for those that need it most: entrepreneurs and innovative companies starting out on the innovation process. The creation of an effective single EU patent requires (i) English-only post-grant translation, (ii) the end of nationally granted patents, (iii) phasing-out of the current ‘European patent’, (iv) lower fees for young innovative companies, and (v) a radical shake-up of the governance of the European Patent Office.