Abstract: A neuromodulation therapy is delivered via at least one electrode implanted subcutaneously and superficially to a fascia layer superficial to a nerve of a patient. In one example, an implantable medical device is deployed along a superficial surface of a deep fascia tissue layer superficial to a nerve of a patient. Electrical stimulation energy is delivered to the nerve through the deep fascia tissue layer via implantable medical device electrodes.

Abstract: A neuromodulation therapy is delivered via at least one electrode implanted subcutaneously and superficially to a fascia layer superficial to a nerve of a patient. In one example, an implantable medical device is deployed along a superficial surface of a deep fascia tissue layer superficial to a nerve of a patient. Electrical stimulation energy is delivered to the nerve through the deep fascia tissue layer via implantable medical device electrodes.

Abstract: A master computing node directs parallel structure learning with intelligent computational task distribution. The master computing node may determine what families are to be used to score neighbors in a neighbor scoring process, and determine if the families have scores in a score cache. Families to be scored for the score cache may be marked and distributed for calculation among nodes in the computing cluster. The score cache may be updated to include the scored families, and the cluster synchronized with the score cache data.

Abstract: A method and apparatus for performing matrix transformations including multiply-add operations and byte shuffle operations on packed data in a processor. In one embodiment, two rows of content byte elements are shuffled to generate a first and second packed data respectively including elements of a first two columns and of a second two columns. A third packed data including sums of products is generated from the first packed data and elements from two rows of a matrix by a multiply-add instruction. A fourth packed data including sums of products is generated from the second packed data and elements from two more rows of the matrix by another multiply-add instruction. Corresponding sums of products of the third and fourth packed data are then summed to generate two rows of a product matrix. Elements of the product matrix may be generated in an order that further facilitates a second matrix multiplication.

Abstract: Method, apparatus and system are described to perform a feature eliminating method based on a support vector machine. In some embodiments, a value for each feature in a group of features provided by a training data is determined. At least one feature is eliminated from the group by utilizing the value for each feature in the group. The value for each feature in the group is updated based upon a part of the training data that corresponds to the eliminated feature.

Abstract: A master computing node directs parallel structure learning with intelligent computational task distribution. The master computing node may determine what families are to be used to score neighbors in a neighbor scoring process, and determine if the families have scores in a score cache. Families to be scored for the score cache may be marked and distributed for calculation among nodes in the computing cluster. The score cache may be updated to include the scored families, and the cluster synchronized with the score cache data.

Abstract: Method, apparatus and system are described to perform a feature eliminating method based on a support vector machine. In some embodiments, a value for each feature in a group of features provided by a training data is determined. At least one feature is eliminated from the group by utilizing the value for each feature in the group. The value for each feature in the group is updated based upon a part of the training data that corresponds to the eliminated feature.

Abstract: A method and apparatus for performing matrix transformations including multiply-add operations and byte shuffle operations on packed data in a processor. In one embodiment, two rows of content byte elements are shuffled to generate a first and second packed data respectively including elements of a first two columns and of a second two columns. A third packed data including sums of products is generated from the first packed data and elements from two rows of a matrix by a multiply-add instruction. A fourth packed data including sums of products is generated from the second packed data and elements from two more rows of the matrix by another multiply-add instruction. Corresponding sums of products of the third and fourth packed data are then summed to generate two rows of a product matrix. Elements of the product matrix may be generated in an order that further facilitates a second matrix multiplication.