Abstract: Methods and systems for automated generation of an advanced planning tool (APT) for an advanced planning system (APS) are described. One or more input questions for an operations plan are received. A generic APT contract that is relevant to a given one input question is retrieved. The generic APT contract is customized by mapping parameters defined in the data schema associated with the operations plan to placeholders in the generic APT contract. Generic APT code corresponding to the generic APT contract is customized by replacing placeholders in the generic APT code with parameters defined in the data schema, in accordance with the mapping. The customized APT code is deployed to obtain the executable customized APT. Verification of the customized APT is performed by automatically performing test executions of the customized APT using test inputs automatically generated using the data schema. The customized APT contract is outputted to a user device.

Abstract: A data processing method and apparatus are described. The data processing apparatus obtains an input tensor corresponding to input data. The data processing apparatus determines M1 first-type tensor blocks and M2 second-type tensor blocks. P processing units in the data processing apparatus process the M tensor blocks concurrently. In a first time period, all of the tensor blocks that are processed concurrently by the P processing units are first-type tensor blocks. In a second time period, all of the tensor blocks that are processed concurrently by the P processing units are second-type tensor blocks.

Abstract: Methods and systems for generating a symbolic model from a markup document, and instantiating a model instance from a symbolic model are described. A markup document containing human language content and mathematical content is parsed into a symbolic model that contains only symbolic code representing an optimization problem. The markup document is parsed to extract a markup declaration, the markup declaration is then processed to a math content span, any metadata entity and any relationship between any metadata entity and the math content span. The math content span is processed into a math content parse tree. The math content parse tree is converted into symbolic code of the symbolic model using any relationship between the metadata entity and the math content span. The symbolic model can be instantiated using data definitions.

Abstract: A mathematical model solving method includes obtaining a problem description from a user; extracting a first feature in the problem description using a first artificial intelligence (AI) model; processing the first feature to obtain target data, where the target data includes a target model obtained by optimizing the problem description based on the first feature and/or a solving approach generated based on the first feature; and calculating, based on the target data, a value of each decision variable that meets the constraint in the problem description.

Abstract: A data processing method and apparatus are described. The data processing apparatus obtains an input tensor corresponding to input data. The data processing apparatus determines M1 first-type tensor blocks and M2 second-type tensor blocks. P processing units in the data processing apparatus process the M tensor blocks concurrently. In a first time period, all of the tensor blocks that are processed concurrently by the P processing units are first-type tensor blocks. In a second time period, all of the tensor blocks that are processed concurrently by the P processing units are second-type tensor blocks.

Abstract: A data processing method and apparatus are described. The data processing apparatus obtains an input tensor corresponding to input data. The data processing apparatus determines M1 first-type tensor blocks and M2 second-type tensor blocks. P processing units in the data processing apparatus process the M tensor blocks concurrently. In a first time period, all of the tensor blocks that are processed concurrently by the P processing units are first-type tensor blocks. In a second time period, all of the tensor blocks that are processed concurrently by the P processing units are second-type tensor blocks.

Abstract: The present disclosure provides a displacement detection circuit of a maglev rotor system and a displacement self-sensing system thereof.

Abstract: The present disclosure relates to operations research and optimization methods, apparatuses, and computing devices One example method includes obtaining a hyperparameter of an operations research and optimization algorithm based on a feature of data of a current application scenario and a hyperparameter inference model. Optimization calculation is performed on the data of the current application scenario, according to the operations research and optimization algorithm and based on the obtained hyperparameter of the operations research and optimization algorithm, to obtain a calculation result, where the hyperparameter inference model is obtained through dynamic training based on training data obtained in a historical application scenario and training data obtained in the current application scenario.

Abstract: The present disclosure provides a displacement detection circuit of a maglev rotor system and a displacement self-sensing system thereof.

Abstract: A data processing method and apparatus are described. The data processing apparatus obtains an input tensor corresponding to input data. The data processing apparatus determines M1 first-type tensor blocks and M2 second-type tensor blocks. P processing units in the data processing apparatus process the M tensor blocks concurrently. In a first time period, all of the tensor blocks that are processed concurrently by the P processing units are first-type tensor blocks. In a second time period, all of the tensor blocks that are processed concurrently by the P processing units are second-type tensor blocks.