Abstract: Systems and methods are provided for learning item types of items listed in an electronic repository, and for training a machine learning model to predict the item type of a given input item. For example, a machine learning model may be obtained or accessed that has been previously trained to classify an input item to a browse node. Vector representations of individual items assigned to different browse nodes may be obtained from an intermediate layer of the previously trained machine learning model, and a vector representation of individual browse nodes may then be generated based on the vector representations of individual items assigned to that browse node. A clustering algorithm may be applied to the browse node vector representations in order to identify clusters of similar browse nodes, where individual clusters may represent different unique item types.

Abstract: Assigning pins to macro-blocks of an Integrated Circuit (IC) chip is described. The macro-block pin assignments are automatically placed using Constraint Programming (CP) techniques to position the pins on the periphery of a macro-block. Bus-ordering and pin-spacing constraints are performed considering all the nets forming the IC design simultaneously. The CP formulation includes modeling detailed and discrete constraints required to achieve an optimal pin-assignment. A stochastic CSP solver is used to define the cost function on search points giving full assignments to all the variables. The macro-block pins are ultimately moved to computed locations.

Abstract: Assigning pins to macro-blocks of an Integrated Circuit (IC) chip is described. The macro-block pin assignments are automatically placed using Constraint Programming (CP) techniques to position the pins on the periphery of a macro-block. Bus-ordering and pin-spacing constraints are performed considering all the nets forming the IC design simultaneously. The CP formulation includes modeling detailed and discrete constraints required to achieve an optimal pin-assignment. A stochastic CSP solver is used to define the cost function on search points giving full assignments to all the variables. The macro-block pins are ultimately moved to computed locations.

Abstract: A truck configuration satisfying a truck configuration problem is automatically determined. The truck configuration problem is transformed to a target function, outputting values indicative of configurations satisfying the truck configuration problem. Stochastic local search methods are applied on the target function to determine the truck configuration. Preprocessing may be performed to improve efficiency, performance or the like of the stochastic local search methods. The truck configuration problem may be obtained from several sources, which may be independent of one another.