Abstract: In general, various embodiments of the present disclosure provide systems, methods, apparatuses, technologies, and/or the like for loading, storing, and/or dispensing objects in an automated and/or semi-automated fashion. For example, various embodiments provide a storage system comprising: a frame comprising a shelf; a plurality of doors attached to the frame along a dispensing side, wherein each door can be opened and closed independently through operation of a corresponding release mechanism; a plurality of dividers adjustable to different positions along a length of the shelf to thereby define storage compartments that vary in size along the length of the shelf for storing objects of different sizes; and a controller that, for each storage compartment, is configured to cause operation of the corresponding release mechanism for a series of the plurality of doors corresponding to a dimension of the storage compartment in unison to release an object stored in the storage compartment.

Abstract: Systems and methods of the present disclosure are directed to a method for generating a machine-learned multitask model configured to perform tasks. The method can include obtaining a machine-learned multitask search model comprising candidate nodes. The method can include obtaining tasks and machine-learned task controller models associated with the tasks. As an example, for a task, the method can include using the task controller model to route a subset of the candidate nodes in a machine-learned task submodel for the corresponding task. The method can include inputting task input data to the task submodel to obtain a task output. The method can include generating, using the task output, a feedback value based on an objective function. The method can include adjusting parameters of the task controller model based on the feedback value.

Abstract: The present disclosure is directed to object and/or character recognition for use in applications such as computer vision. Advantages of the present disclosure include lightweight functionality that can be used on devices such as smart phones. Aspects of the present disclosure include a sequential architecture where a lightweight machine-learned model can receive an image, detect whether an object is present in one or more regions of the image, and generate an output based on the detection. This output can be applied as a filter to remove image data that can be neglected for more memory intensive machine-learned models applied downstream.

Abstract: The invention encompasses pharmaceutical compositions comprising an antibody and methods of formulating antibodies. The heavy and/or a light chain variable region of the antibody may have certain characteristics. In some embodiments, the antibody may comprise an N-glycan site in a heavy and/or light chain variable region. The compositions may comprise a buffering agent and, optionally, a sugar and/or a salt.