Abstract: A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

Abstract: A method of identifying an epileptogenic zone of a subjects brain includes: receiving a plurality N of physiological brain signals that extend over a duration, each of the plurality N of physiological brain signals acquired from the subjects brain; calculating within a time window a state transition matrix based on at least a portion of each of the plurality N of physiological brain signals, wherein the state transition matrix is a linear time invariant model of a network of N nodes corresponding to the plurality N of physiological brain signals; calculating a minimum norm of a perturbation on the state transition matrix that causes the network to transition from a stable state to an unstable state; and assigning a fragility metric to each of the plurality N of physiological brain signals based on the minimum norm of the perturbation for that physiological brain signal.

Abstract: A method of identifying an epileptogenic zone of a brain includes receiving a plurality of electrical signals from a plurality of surgically implanted electrodes, calculating components of an adjacency matrix, calculating eigenvectors from the adjacency matrix, and selecting an eigenvector having a largest eigenvalue. The method includes assigning an integer rank to each component of the eigenvector, sliding the time window by a time increment and repeating the immediately preceding steps a plurality of times. The method includes normalizing each rank signal, extracting a multidimensional feature vector from each normalized signal, projecting each multidimensional feature vector onto a reduced dimensionality space, and receiving a plurality of training data points represented in the reduced dimensionality space.

Abstract: A method for selecting a target group of customers for promoting a product is based on data representing individual buying patterns for a pool of customers over a set of products a product to be promoted. A first subset of customers is selected whose buying patterns can form a behavioral benchmark for the selection of the target group of customers. A market model for the product set is constructed and a second subset of customers is selected who are considered to be potentially interested in a product to be promoted. The second subset of customers is scored according to their similarity to customer segments in the benchmark customer market model and a third subset is selected based upon their scores. Members of the third subset are matched to products according to product segments for which they have demonstrated similarity. Offer parameters indicating collaborative promotions can then be output.

Abstract: In the context of associations between sets of objects, such as between two categorical data sets, a system and method of performing associations and determining optimizations thereof is presented. Performing various modifications of original object sets, such as by aggregating, refining and permuting the elements of the original object sets yield modified object sets corresponding to the original object sets. Functions and metrics are performed on the object sets, such as by calculating pair association values and overall association values to evaluate the strength or adequacy of an association. A process which iteratively modifies and re-evaluates the resulting associations of modified object sets is described, optionally checking for consistency or convergence of the result, optionally onto an optimum result and optionally checking a stopping criterion for terminating the process.