Abstract: Aspects of the disclosure provide for eliminating or reducing uniquification of blocks in a chip-level graph of a computer chip, to reduce the size of the graph while still encoding block-specific information. For each group of blocks in the graph generated from a multiply-instantiated block (MIB), a block in the group is selected as a base block. The physical position of the base block is encoded in a reduced graph, and the physical positions of the remaining blocks are encoded as a linear transformation of the physical position of the base block across the face of the chip. Each group of blocks instantiated from the same MIB is represented as a single instance. The reduced graph can be fed into a device configured to perform a circuit component placement process, to identify the placement of circuit components for blocks in the chip in accordance with one or more objectives.

Abstract: In some implementations, the disclosed systems and methods can detect an interaction with respect to a set of virtual objects, which can start with a particular gesture, and take an action with respect to one or more virtual objects based on a further interaction (e.g., holding the gesture for a particular amount of time, moving the gesture in a particular direction, releasing the gesture, etc.). In some implementations, the disclosed systems and methods can automatically review a 3D video to determine a depicted user or avatar movement pattern (e.g., dance moves, repair procedure, playing an instrument, etc.). In some implementations, the disclosed systems and methods can allow the gesture to included a flat hand with the user's thumb next to the palm, with the gesture toward the user's face.

Abstract: The present teaching relates to method system, and medium for generating knowledge for a chat bot. Training data are used to learn and generating knowledge and are received with at least some labeled training seeds and unlabeled conversation data. The training data are parsed and various linguistic elements are extracted therefrom. Such linguistic elements are then used to perform automated learning in accordance with at least one label used in labeling the training seeds. Based on the automated learning, at least one model associated with the at least one label is generated from the training data.

Abstract: The present teaching relates to developing a virtual agent. In one example, a plurality of graphical objects is presented to a user via a bot design programming interface. Each of the plurality of graphical objects represents a module corresponding to an action to be performed by the virtual agent. One or more inputs from the user are received, via the bot design programming interface, for selecting a set of graphical objects from the plurality of graphical objects. The one or more inputs provide information of a first order of the set of graphical objects. A plurality of modules represented by the set of graphical objects is identified. Based on the one or more inputs, a second order of the plurality of modules is determined based on the first order. The plurality of modules is integrated in the second order to generate a customized virtual agent for executing an associated task according to the second order.