Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to execute skills and/or behaviors using cached trajectories or plans. In some embodiments, the processor can be configured to execute skills requiring navigation and manipulation behaviors.

Abstract: The present disclosure provides, among other things, a recombinant adeno-associated virus (rAAV) vector comprising an AAV8 or AAV9 capsid and a codon-optimized sequence encoding a human iduronate-2-sulfatase (I2S) enzyme. The disclosure also provides a method of treating a subject having Hunter syndrome (MPS II), comprising administering to the subject in need thereof a recombinant adeno-associated virus (rAAV) vector comprising an AAV8 or AAV9 capsid, and a promoter operably linked to a nucleic acid sequence that encodes iduronate-2-sulfatase (I2S), and wherein administering results in an increase in I2S enzymatic activity in the subject.

Abstract: In some implementations, a classification system may receive credentials associated with a data source and may receive, from the data source and using the credentials, a set of structured data including input events and output events. The classification system may filter the set of structured data by applying a first set of rules to generate a filtered set of structured data and may convert the filtered set of structured data to one or more numerical vectors, where a vector space associated with the one or more numerical vectors is infinite-dimensional. The classification system may further cluster the one or more numerical vectors using a first machine learning model to generate one or more clusters. Accordingly, the classification system may determine one or more classifications based on the set of structured data, each of the one or more classifications being associated with a corresponding frequency and a corresponding category.

Abstract: Embodiments of the present disclosure relate to a denticle array. The denticle array includes a substrate and a plurality of denticles coupled to the substrate. Each denticle of the plurality of denticles includes an upper portion and a lower portion. The upper portion includes an upper body. The lower portion includes a lower body. The upper body includes a first prong extending from a front end of the denticle to a rear end of the denticle, a second prong extending from the front end of the denticle to a rear end of the denticle, and a third prong extending from the front end of the denticle to a rear end of the denticle. A first ridge separates the first prong and second prong. A second ridge separates the first prong from the third prong. The first prong has a length greater than the second prong and the third prong.