Abstract: An epithelial detector and method for automatically identifying epithelial portions of a tissue sample, includes: staining the tissue sample with at least two dyes; applying a color transformation to a color image of the tissue sample to obtain one or more color channels; and applying a trained convolutional neural network to the color channels to obtain a decision for position in the tissue as to whether it is inside or outside an epithelial layer. Also, a method for training the convolutional neural network.

Abstract: There is provided a method for detecting optical signals comprising employing a photo diode to both directly detect a received optical signal and convert it into an electrical signal for recovery of data bit stream information in the received optical signal, the received optical signal being derived from a separation of two polarization multiplexed optical signals that were combined before being received.

Abstract: A method system for diagnosing a detected failure in a computer system, compares a failure signature of the detected failure to an archived failure signature contained in a database to determine if the archived failure signature matches the failure signature of the detected failure. If the archived failure signature matches the failure signature of the detected failure, an archived solution is applied to the computer system that resolves the detected failure, the archived solution corresponding to a solution used to resolve a previously detected computer system failure corresponding to the archived failure signature in the database that matches the detected failure.

Abstract: An automated method and system for analyzing a digital image of a biopsy to determine whether the biopsy is normal or abnormal, i.e., exhibits some type of disease such as, but not limited to, cancer. In the method and system, a classifier is trained to recognize well formed nuclei outlines from imperfect nuclei outlines in digital biopsy images. The trained classifier may then be used to filter nuclei outlines from one or more digital biopsy images to be analyzed, to obtain the well formed nuclei outlines. The well formed nuclei outlines may then be used to obtain statistics on the size or area of the nuclei for use in determining whether the biopsy is normal or abnormal.

Abstract: Disclosed is an improved technique for training a support vector machine using a distributed architecture. A training data set is divided into subsets, and the subsets are optimized in a first level of optimizations, with each optimization generating a support vector set. The support vector sets output from the first level optimizations are then combined and used as input to a second level of optimizations. This hierarchical processing continues for multiple levels, with the output of each prior level being fed into the next level of optimizations. In order to guarantee a global optimal solution, a final set of support vectors from a final level of optimization processing may be fed back into the first level of the optimization cascade so that the results may be processed along with each of the training data subsets.

Abstract: A computer-implemented method and system for predicting operation risks of a vehicle. The method and system obtains a training data stream of vehicular dynamic parameters and logging crash time instances; partitions the data stream into units representing dimension vectors, labels the units that overlap the crash time instances as most dangerous; labels the units, which are furthest from the units that are labeled as most dangerous, as most safe; propagates the most dangerous and the most safe labeling information of the labeled units to units which are not labeled; estimates parameters of a danger-level function using the labeled and unlabeled units; and applies the danger-level function to an actual data stream of vehicular dynamic parameters to predict the operation risks of the vehicle.

Abstract: Disclosed is a method for training a transductive support vector machine. The support vector machine is trained based on labeled training data and unlabeled test data. A non-convex objective function which optimizes a hyperplane classifier for classifying the unlabeled test data is decomposed into a convex function and a concave function. A local approximation of the concave function at a hyperplane is calculated, and the approximation of the concave function is combined with the convex function such that the result is a convex problem. The convex problem is then solved to determine an updated hyperplane. This method is performed iteratively until the solution converges.

Abstract: A method and system that automatically derives models between monitored quantities under non-faulty conditions so that subsequent faults can be detected as deviations from the derived models. The invention identifies unusual conditions for fault detection and isolation that is absent in rule-based systems.