Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method assessing tissue morphology using machine learning includes a step of training a machine learnable device to predict the status of a diagnostic feature in stained tissue samples. The machine learnable device is trained with a characterized set of digital images of stained tissue samples. Each digital image of the characterized set has a known status for the diagnostic feature and an extracted feature map provides values for a extracted feature over an associated 2-dimensional grid of spatial locations. A step of inputting the set of extracted feature maps is inputted into the machine learnable device to form associations therein between the set of extracted feature maps to and the known status for the diagnostic feature to form a trained machine learnable device. The status for the diagnostic feature of a stained tissue sample of unknown status for the diagnostic feature is predicted from the trained machine learnable device.

Abstract: A method of transferring data from a server via a web application by receiving a request from a user operating in a disparate security domain for data on a data store. Generating a labeled view of the data requested from the data store, wherein the label-data relationship can be trusted at a level commensurate to the trust level of the operating system. Next, determining if the data is authorized by a security policy with a policy design engine; and then transmitting the data to the user if the data is authorized. Data can also be transferred by receiving a data flow from the user for writing to the data store. Next, the data flow can be inspected for disallowed content, and a determination is made if the data flow is authorized. If the data flow is authorized, mediating the data flow between the user and the data store with a trusted monitor.

Abstract: A surface acoustic wave (SAW) tapped delay line, including a piezoelectric substrate having a pair of signal launching transducers disposed thereon for providing surface acoustic waves in the substrate in response to electrical signals applied thereto, and including a plurality of transducer taps disposed in an interaction region of the substrate for providing a plurality of sensed electrical signals in response to the surface acoustic waves propagating through the interaction region, the plurality of sensed electrical signals including signals at the frequency of each of the acoustic waves, is combined with a plurality of nonlinear frequency mixers, each responsive to a corresponding one of the transducer taps, and each providing signal manifestations of the sum and difference frequency of the surface acoustic waves in a selected one of two phases which are displaced by 180.degree.

Abstract: A surface acoustic wave (SAW) tapped delay line, including a piezoelectric substrate having a pair of signal launching transducers disposed thereon for providing surface acoustic waves in the substrate in response to electrical signals applied thereto, and including a plurality of transducer taps disposed in an interaction region of the substrate for providing a plurality of sensed electrical signals in response to the surface acoustic waves propagating through the interaction region, the plurality of sensed electrical signals including signals at the frequency of each of the acoustic waves, is combined with a plurality of nonlinear frequency mixers, each responsive to a corresponding one of the transducer taps, and each providing signal manifestations of the sum and different frequencies of the surface acoustic waves in a selected one of two phases which are displaced by 180.degree.