Abstract: A system includes at least one article of personal protective equipment, one or more industrial devices, an industrial controller device configured to output industrial controller data indicative of one or more attributes of the one or more industrial devices, and a computing device. The computing devices is configured to determine a set of one or more safety rules corresponding to a work environment at the first time and determine a change in an attribute of the one or more attributes of the one or more industrial devices. The computing device is further configured to identify an updated set of one or more worker safety rules corresponding to the work environment at the second time and determine whether the status of at least one article of personal protective equipment satisfies the updated set of one or more worker safety rules.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system organizes fact data to be aggregated into sliding time window features and observation data associated with the fact data into a set of partitions based on a join key. Next, the system sorts the fact data and the observation data within the set of partitions by the join key and timestamps associated with the fact data and the observation data. For each observation record in the observation data, the system aggregates fact records in the sorted fact data that share a value of the join key with the observation record and that fall within a first time window associated with the observation record to produce a sliding time window feature. The system then stores the sliding time window feature in association with the observation record.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system organizes fact data to be aggregated into sliding time window features and observation data associated with the fact data into a set of partitions based on a join key. Next, the system sorts the fact data and the observation data within the set of partitions by the join key and timestamps associated with the fact data and the observation data. For each observation record in the observation data, the system aggregates fact records in the sorted fact data that share a value of the join key with the observation record and that fall within a first time window associated with the observation record to produce a sliding time window feature. The system then stores the sliding time window feature in association with the observation record.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

Abstract: The disclosed embodiments provide a system for sharing features in a feature management framework. During operation, the system creates a repository of feature configurations for a set of features that are accessed across multiple environments. Next, the system identifies dependencies of the repository. The system then copies shared feature configurations from other repositories represented by the dependencies. Finally, the system combines the shared feature configurations with existing feature configurations in the repository for use in retrieving feature values for one or more machine learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features. Next, the system obtains, from the feature configurations, an anchor containing metadata for accessing a first feature in an environment and a feature derivation for generating a second feature from the first feature. The system then uses the anchor to retrieve feature values of the first feature from the environment and uses the feature derivation to generate additional feature values of the second feature from the feature values of the first feature. Finally, the system provides the additional feature values for use with one or more machine learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features and a command for inspecting a data set that is produced using the feature configurations. Next, the system obtains, from the feature configurations, one or more anchors containing metadata for accessing the set of features in an environment and a join configuration for joining a feature with one or more additional features. The system then uses the anchors to retrieve feature values of the features and zips the feature values according to the join configuration without matching entity keys associated with the feature values. Finally, the system outputs the zipped feature values in response to the command.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system selects a set of entity keys associated with reference feature values used with one or more machine learning models, wherein the reference feature values are generated in a first environment. Next, the system matches the set of entity keys to feature values from a second environment. The system then compares the feature values and the reference feature values to assess a consistency of a feature across the first and second environments. Finally, the system outputs a result of the assessed consistency for use in managing the feature in the first and second environments.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of values and a set of reference values for one or more features used with one or more statistical models. Next, the system applies a hypothesis test to the set of values and the set of reference values to assess a distribution-level consistency in the one or more features. The system then outputs the distribution-level consistency for use in monitoring the distribution of the one or more features. Finally, the system includes, with the outputted distribution-level consistency, one or more factors that contribute to the distribution-level consistency.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

Abstract: A device sample is screened for defects using its power spectrum in response to a dynamic stimulus. The device sample receives a time-varying electrical signal. The power spectrum of the device sample is measured at one of the pins of the device sample. A defect in the device sample can be identified based on results of comparing the power spectrum with one or more power spectra of the device that have a known defect status.

Abstract: Detection and deterrence of device tampering and subversion may be achieved by including a cryptographic fingerprint unit within a hardware device for authenticating a binding of the hardware device and a physical structure. The cryptographic fingerprint unit includes an internal physically unclonable function (“PUF”) circuit disposed in or on the hardware device, which generate an internal PUF value. Binding logic is coupled to receive the internal PUF value, as well as an external PUF value associated with the physical structure, and generates a binding PUF value, which represents the binding of the hardware device and the physical structure. The cryptographic fingerprint unit also includes a cryptographic unit that uses the binding PUF value to allow a challenger to authenticate the binding.

Abstract: An electrode array for neural stimulation is disclosed which has particular applications for use in a retinal prosthesis. The electrode array can be formed as a hermetically-sealed two-part ceramic package which includes an electronic circuit such as a demultiplexer circuit encapsulated therein. A relatively large number (up to 1000 or more) of individually-addressable electrodes are provided on a curved surface of a ceramic base portion the electrode array, while a much smaller number of electrical connections are provided on a ceramic lid of the electrode array. The base and lid can be attached using a metal-to-metal seal formed by laser brazing. Electrical connections to the electrode array can be provided by a flexible ribbon cable which can also be used to secure the electrode array in place.