Abstract: An apparatus includes a capacitive touch sensor. The capacitive touch sensor is to provide first capacitive node measurements for capacitive touch detection responsive to touch at a respective one of multiple touch points within a capacitive touch-sensitive area of the capacitive touch sensor. The first capacitive node measurements indicate a reduction in capacitance at one or more first capacitive nodes of the capacitive touch sensor. The capacitive touch sensor is to also provide second capacitive node measurements for force detection responsive to touch surface depression at a respective one of one or more force regions of the capacitive touch sensor. The second capacitive node measurements indicate an increase in capacitance at one or more second capacitive nodes of the capacitive touch sensor. In one or more examples, the one or more force regions are vertically-displaceable, sectioned portions of one or more vertically-stacked layers of the capacitive touch sensor.

Abstract: The present invention relates in general to the field of parallel data processing, and more particularly to machine learning and classification of extremely large volumes of unstructured gene sequence data using Collaborative Analytics Gene Sequence Classification Learning Systems and Methods.

Abstract: A computer-implemented method, implemented, at least in part, by hardware in combination with software, the method includes (A) obtaining text from a document; (B) parsing said text using at least one parallel sentence parsing process to obtain sentence data from said text; (C) parsing said sentence data using at least one parallel noun parsing process to obtain text data from said sentence data; (D) scoring said text data using at least one term scorer process and a known word list to obtain scored terms corresponding to said text data; and (E) determining known word scores corresponding to said text data, using said known word list, wherein said known word scores comprise base scores and category penetration scores; wherein steps (B), (C), (D), and (E) operate in parallel for at least some of the text from the document.

Abstract: The present invention relates in general to the field of parallel data processing, and more particularly to machine learning and classification of extremely large volumes of unstructured gene sequence data using Collaborative Analytics Gene Sequence Classification Learning Systems and Methods.

Abstract: A computer-implemented method, implemented, at least in part, by hardware in combination with software, the method includes (A) obtaining text from a document; (B) parsing said text using at least one parallel sentence parsing process to obtain sentence data from said text; (C) parsing said sentence data using at least one parallel noun parsing process to obtain text data from said sentence data; (D) scoring said text data using at least one term scorer process and a known word list to obtain scored terms corresponding to said text data; and (E) determining known word scores corresponding to said text data, using said known word list, wherein said known word scores comprise base scores and category penetration scores; wherein steps (B), (C), (D), and (E) operate in parallel for at least some of the text from the document.