A method includes determining a change to storage parameters associated with storage of data objects in a storage network, where a data segment of the data objects is dispersed storage error encoded into a set of encoded data slices based on dispersed storage error encoding parameters, and where the set of encoded data slices is stored in the set of storage units. The method also includes determining a storage modification process for the set of encoded data slices based on the change to the storage parameters. The method also includes executing the storage modification process such that the set of encoded data slices are stored in the storage network in accordance with the changed storage parameters.

Techniques for auto weight scaling a bounded weight range of RPU devices with the size of the array during ANN training are provided. In one aspect, a method of ANN training includes: initializing weight values w.sub.init in the array to a random value, wherein the array represents a weight matrix W with m rows and n columns; calculating a scaling factor β based on a size of the weight matrix W; providing digital inputs x to the array; dividing the digital inputs x by a noise and bound management factor α to obtain adjusted digital inputs x′; performing a matrix-vector multiplication of the adjusted digital inputs x′ with the array to obtain digital outputs y′; multiplying the digital outputs y′ by the noise and bound management factor α; and multiplying the digital outputs y′ by the scaling factor β to provide digital outputs y of the array.

Methods and devices for simplifying data collected from assets are provided. An example method involves obtaining raw data from a data source at an asset, determining whether a data logging trigger is satisfied, and, when satisfied, performing a dataset simplification algorithm on the raw data to generate a simplified set of data in which interpolation error is limited by an upper bound that is fixed across the simplified set of data.

Methods and systems are provided for searching information in a distributed data processing system. A system for processing a semantic search query where the system may include a memory and a processor coupled to the memory being configured to, receive a structured search query, process the structured search query to deconstruct into query elements, identify a set of connected elements that define a data source associated with the received structured search query based on a processed query element, process the query elements to determine one or more command data element types associated with the received structured search query, and process data associated with the defined data source according to a command data element type to develop a semantic search query resultant data set.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive structural analysis on document data objects that are associated with an ontology graph. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations on document data objects that are associated with an ontology graph using document embeddings that are generated by graph-embedding-based paragraph vector machine learning models.

Described herein are example implementations for the automatic detection and handling of personal identifiable information (PII) in electronic records. In some aspects, a system receives one or more computer readable logs of information for one or more computer services, with each log including a string of characters. The system performs one or more string search algorithm based operations on the entirety of the one or more strings of the one or more computer readable logs to identify a range of the one or more strings to be searched for PII that is less than the entirety of the one or more strings. The system also performs one or more regular expression algorithm based operations on the range of the one or more strings to identify one or more instances of PII. The system generates and outputs an indication of the one or more instances of the PII that are identified.

The disclosed technology provides solutions for finding samples from image data that are similar to failure cases, by constructing N-dimensional vectors of the failure cases. The vectors of failure cases are compared to other image data, with the objective of identifying groups of images that can be labeled. The labeled images are then used to retrain a model. Systems and machine-readable media are also provided.

A system and methods for designing single-stage hardware sorting blocks, and further using the single-stage hardware sorting blocks to reduce the number of stages in multistage sorting processes, or to define multiway merge sorting networks.

A computer-implemented method for content-agnostic referencing of a binary data file, the method comprising: determining a length of the binary data file, the length comprising the number of bits of the binary data file; for the determined length, generating all permutations of data of the determined length; locating an index within the generated permutations, wherein the index is the starting position of the binary data file within the generated permutations; and using the length and the index to indicate the binary data file.

Described herein are technologies that provide an element of security related to file system operations. Individual nodes in a file system, such as a directory or a file, can be associated with information that describes how to handle letter case when a file name included in a file system operation request is used to locate a file in the file system. For example, a case sensitive designation associated with a directory can require a case sensitive match between a file name included in a request and a file name included in the directory, in order to perform the requested file system operation. In another example, a case preferring designation associated with a directory first checks for a case sensitive match between file names. If a case sensitive match does not exist, then a case insensitive match between the file names can be used to perform the requested file system operation.