Abstract: In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set.

Abstract: Systems and methods described herein relate to the efficient handling of data purge requests in the context of a distributed storage system. A plurality of data purge requests is stored in a first data structure. The data purge requests may be grouped into batches that are processed at least partially in parallel. A first data purge request from the plurality of data purge requests is successfully processed, and is moved from the first data structure to a second data structure. Processing of a second data purge request from the plurality of data purge requests is unsuccessful. The second data purge request is retained in the first data structure. Purge status data is generated based on the first data purge request being in the second data structure and the second data purge request being in the first data structure. The purge status data may be presented at a user device.

Abstract: Systems and methods described herein relate to the efficient handling of data purge requests in the context of a distributed storage system. A plurality of data purge requests is stored in a first data structure. The data purge requests may be grouped into batches that are processed at least partially in parallel. A first data purge request from the plurality of data purge requests is successfully processed, and is moved from the first data structure to a second data structure. Processing of a second data purge request from the plurality of data purge requests is unsuccessful. The second data purge request is retained in the first data structure. Purge status data is generated based on the first data purge request being in the second data structure and the second data purge request being in the first data structure. The purge status data may be presented at a user device.

Abstract: Provided is an inductor winding method and an inductor winding device. The inductor winding method comprises steps of: A. dividing turns of coil of each winding of the inductor into a first winding and a second winding based on a preset ratio; B. winding the first winding on one of multiple magnetic columns, and winding the second winding on another one of the multiple magnetic columns which is different from the magnetic column on which the first winding is wound; and C. performing step A and step B cyclically until all the windings of the inductor are wound. With a coupling inductor having interleaving-wound structure, power frequency magnetic fluxes generated by magnetic lines in magnetic columns counteract one another, thereby solving the problem of high magnetic flux density in a magnetic core while achieving certain leakage inductance.

Abstract: Provided is an inductor winding method and an inductor winding device. The inductor winding method comprises steps of: A. dividing turns of coil of each winding of the inductor into a first winding and a second winding based on a preset ratio; B. winding the first winding on one of multiple magnetic columns, and winding the second winding on another one of the multiple magnetic columns which is different from the magnetic column on which the first winding is wound; and C. performing step A and step B cyclically until all the windings of the inductor are wound. With a coupling inductor having interleaving-wound structure, power frequency magnetic fluxes generated by magnetic lines in magnetic columns counteract one another, thereby solving the problem of high magnetic flux density in a magnetic core while achieving certain leakage inductance.