Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: The present disclosure provides a method for remote control of computer game by a gamepad, including: remotely accessing, by the client-side, the virtual cloud desktop provided by the server-side, and sending message to the server-side when detecting gamepads; creating or enabling by the virtual cloud desktop server-side, a corresponding number of virtual game devices; capturing, by the client-side, the operation instruction data of the gamepads, and sending the operation instruction data to the server-side; causing each virtual game device to output a KeyCode value corresponding to each operation instruction data by the server-side, such that the virtual cloud desktop server runs the computer game according to each KeyCode value and the relative movement value of the joystick; sending the data of the running results of the computer game on the virtual cloud desktop to the client-side for display.

Abstract: The present disclosure provides a method for remote control of computer game by a gamepad, including: remotely accessing, by the client-side, the virtual cloud desktop provided by the server-side, and sending message to the server-side when detecting gamepads; creating or enabling by the virtual cloud desktop server-side, a corresponding number of virtual game devices; capturing, by the client-side, the operation instruction data of the gamepads, and sending the operation instruction data to the server-side; causing each virtual game device to output a KeyCode value corresponding to each operation instruction data by the server-side, such that the virtual cloud desktop server runs the computer game according to each KeyCode value and the relative movement value of the joystick; sending the data of the running results of the computer game on the virtual cloud desktop to the client-side for display.

Abstract: A computer system is provided for monitoring and detecting changes in a data generating processes, which may be under a multi-dimensional and unsupervised setting. A target dataset is split into paired subgroups by a separator and one or more detectors are applied to detect changes, anomalies, inconsistencies, and the like between the paired subgroups. Metrics may be generated by the detector(s), which are then passed to an evaluating system.

Abstract: A computer system is provided that is programmed to select feature sets from a large number of features. Features for a set are selected based on metagradient information returned from a machine learning process that has been performed on an earlier selected feature set. The process can iterate until a selected feature set converges or otherwise meets or exceeds a given threshold.