Abstract: Provided herein are energy storage device electrode films comprising multiple active layers, and methods of forming such multiple active layer energy storage device electrode films. Each active layer may be a self-supporting active layer comprising a binder and an active material. The binder and/or active material may be the same or different as any other active layer. The active layers may be stacked to form an electrode film, and the electrode film may be laminated with a current collector to form an electrode.

Abstract: Provided herein are dry process electrode films, and energy storage devices incorporating the same, including a microparticulate non-fibrillizable binder having certain particle sizes. The electrode films exhibit improved mechanical and processing characteristics. Also provided are methods for processing such microparticulate non-fibrillizable electrode film binders, and for incorporating the microparticulate non-fibrillizable binders in electrode films.

Abstract: Numerous examples are disclosed of verification circuitry and associated methods in an artificial neural network. In one example, a system comprises a vector-by-matrix multiplication array comprising a plurality of non-volatile memory cells arranged in rows and columns, the non-volatile memory cells respectively capable of storing one of N possible levels corresponding to one of N possible currents, and a plurality of output blocks to receive current from respective columns of the vector-by-matrix multiplication array and generate voltages during a verify operation of the vector-by-matrix multiplication and generate digital outputs during a read operation of the vector-by-matrix multiplication.

Abstract: Methods for analyzing data are disclosed. One disclosed method includes defining an intent language model for domain specific meaning behind historical enterprise data produced during operation of an enterprise; applying the historical enterprise data to build the intent language model; extracting intent element features of interest from the enterprise data to generate domain specific intent metadata; and storing the domain specific intent metadata into a database. The historical enterprise data includes findings and observations by one or more human experts in one or more service records and data associated with a problem.

Abstract: Systems and methods are described herein to improve data-driven decision-making in an enterprise by discovering intent applicable to enterprise domain.

Abstract: A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.