Abstract: Embodiments according to examples aspects of the present disclosure provide for an example computer-implemented method. The example method can include obtaining a data structure configured for rendering a plurality of content containers on a user device, the plurality of content containers providing a collapsed configuration and an expanded configuration. The example method can include rendering, based on a first rate parameter descriptive of a user input associated with the user device, one or more of the plurality of content containers according to the collapsed configuration. The example method can include rendering, based on a second rate parameter, at least one of the plurality of content containers according to the expanded configuration.

Abstract: A computing machine receives a representation of a machine learning model, a representation of a first data segment, and a representation of a second data segment. The computing machine computes an output difference between an output of the machine learning model applied to the first data segment and an output of the machine learning model applied to the second data segment. The computing machine determines a set of reasons for the computed output difference based on a set of metrics defining distance between feature importance distributions, the set of reasons identifying a set of features from a feature vector of the machine learning model along with a relative contribution of each feature to the computed output difference. The computing machine provides an output representing the set of reasons.

Abstract: Embodiments according to examples aspects of the present disclosure provide for an example computer-implemented method. The example method can include obtaining a data structure configured for rendering a plurality of content containers on a user device, the plurality of content containers providing a collapsed configuration and an expanded configuration. The example method can include rendering, based on a first rate parameter descriptive of a user input associated with the user device, one or more of the plurality of content containers according to the collapsed configuration. The example method can include rendering, based on a second rate parameter, at least one of the plurality of content containers according to the expanded configuration.

Abstract: A game cloud server, a method of operating a cloud server, and a method of playing a game on a game cloud server are disclosed. In one example, the game cloud server includes: (1) one or more processing units that virtually supports different gaming applications according to a gaming operating mode, and (2) an operating mode selector that is coupled to the one or more processing units and has (2A) a virtual fusing register that selects the gaming operating mode for executing the different virtually supported gaming applications, and (2B) a security processor that enables a secure virtual fusing based on documented security files authorizing selection of the gaming operating mode, separately from executing an operating system of the one or more processing units, wherein the gaming operating mode is a reconfigurable operating mode selectable from at least one signed license file of the documented security files.

Abstract: In some implementations, a computing machine accesses an artificial intelligence model and a dataset for the artificial intelligence model, the dataset comprising at least one datapoint. The computing machine identifies a feature group used by the artificial intelligence model, the feature group comprising at least two features having a similarity with one another exceeding a similarity threshold, wherein the feature group comprises a subset of the features used by the artificial intelligence model. The computing machine determines an overall influence value for the feature group on an output of the artificial intelligence model applied to the dataset. The computing machine provides an output representing the overall influence value.

Abstract: A computing machine receives a representation of a machine learning model, a representation of a first data segment, and a representation of a second data segment. The computing machine computes an output difference between an output of the machine learning model applied to the first data segment and an output of the machine learning model applied to the second data segment. The computing machine determines a set of reasons for the computed output difference based on a set of metrics defining distance between feature importance distributions, the set of reasons identifying a set of features from a feature vector of the machine learning model along with a relative contribution of each feature to the computed output difference. The computing machine provides an output representing the set of reasons.

Abstract: A game cloud server, a method of operating a cloud server, and a method of playing a game on a game cloud server are disclosed. In one example, the game cloud server includes: (1) one or more processing units that virtually supports different gaming applications according to a gaming operating mode, and (2) an operating mode selector that is coupled to the one or more processing units and has (2A) a virtual fusing register that selects the gaming operating mode for executing the different virtually supported gaming applications, and (2B) a security processor that enables a secure virtual fusing based on documented security files authorizing selection of the gaming operating mode, separately from executing an operating system of the one or more processing units, wherein the gaming operating mode is a reconfigurable operating mode selectable from at least one signed license file of the documented security files.

Abstract: A secure reconfigurable operating mode system includes a hardware device having multiple operating modes and an operating mode selector that is coupled to the hardware device. The operating mode selector has a virtual fusing register that selects an operating mode for the hardware device and a security processor that enables a secure virtual fusing based on documented security files authorizing selection of the operating mode. A method of secure hardware device operating mode reconfiguration is also provided.

Abstract: Described herein are methods for dynamic redirection of metabolic flux in a cell from central metabolism towards production of heterologous products.

Abstract: A secure reconfigurable operating mode system includes a hardware device having multiple operating modes and an operating mode selector that is coupled to the hardware device. The operating mode selector has a virtual fusing register that selects an operating mode for the hardware device and a security processor that enables a secure virtual fusing based on documented security files authorizing selection of the operating mode. A method of secure hardware device operating mode reconfiguration is also provided.

Abstract: A system, process, and computer program product are provided for identifying a faulty processing unit. A shader program that configures a plurality of processing units to generate data is executed and the data is compared with verification data to produce a test result. The test result is examined to identify a faulty processing unit of the plurality of processing units, where a unique identifier corresponding to each processing unit is encoded into the data generated by the respective processing unit.

Abstract: Systems and methods that decompress block compressed texture data may decompress the texture data while simplifying computations to reduce die area while maintaining the required accuracy. Reducing the die area permits more texture data to be decompressed in the same die area compared with a more accurate decompression, thereby increasing texture decompression throughput. Computations are simplified by combining denominators for linear interpolation with format conversion to decompress texture data components compressed using conventional block compression formats.

Abstract: Systems and methods that decompress block compressed texture data may decompress the texture data while simplifying computations to reduce die area while maintaining the required accuracy. Reducing the die area permits more texture data to be decompressed in the same die area compared with a more accurate decompression, thereby increasing texture decompression throughput. Computations are simplified by combining denominators for linear interpolation with format conversion to decompress texture data components compressed using conventional block compression formats.