Abstract: A data processing system includes a vector data processing unit that includes a shared scheduler queue configured to store in a same queue, at least one entry that includes at least a mask type instruction and another entry that includes at least a vector type instruction. Shared pipeline control logic controls a vector data path or a mask data path, based a type of instruction picked from the same queue. In some examples, at least one mask type instruction and the at least one vector type instruction each include a source operand having a corresponding shared source register bit field that indexes into both a mask register file and a vector register file. The shared pipeline control logic uses a mask register file or a vector register file depending on whether bits of the shared source register bit field identify a mask source register or a vector source register.

Abstract: A method for operating an access control device having a processor, a memory, and an internet connection comprises receiving a credential from a user; checking, by comparing the credential to matching credential information that would indicate the user is authorized to enter stored in the memory of the access control device, if no matching credential information that indicates the user is authorized is found in the memory of the access control device, then checking, at a remote cloud server, a list of buffered credential information for the matching credential information that indicates the user is authorized, and if the matching credential information that indicates the user is authorized is found, sending an unlock signal from the remote cloud server to the access control device.

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.

Abstract: A system for drying hydrogen in a hydrogen production facility comprises an electrolyzer for producing a flow of hydrogen gas, a drying device configured to remove moisture from the flow of hydrogen gas, a compressor configured to receive the flow of hydrogen gas from the drying device, and a recirculation line connected to output of the compressor to recirculate at least a portion of the flow of hydrogen gas from the compressor to the drying device. A method of drying hydrogen in a hydrogen production facility comprises producing a flow of hydrogen gas with an electrolyzer, drying the flow of hydrogen gas with a drying device, compressing the flow of hydrogen gas from the drying device with a compressor, and recirculating at least a portion of the flow of hydrogen gas from the compressor to the drying device to maintain pressure distribution within the drying device.

Abstract: Digital pathology is a promising alternative to manual slide analysis, but improvements in imaging and analysis methods are needed to provide an analysis data set of images that can easily be handled, stored, and importantly, delivered from one data location to another. Methods and software are described herein to improve image collection and analysis.

Abstract: A power plant comprises a steam system, a first electrolyzer, a heat storage system, and a heat exchanger configured to exchange thermal energy between the steam system, the first electrolyzer and the heat storage system. A method of operating an electrolyzer in a combined cycle power plant comprises operating a steam system to convert water to steam, operating an electrolyzer in a standby mode, the electrolyzer configured to convert water and electricity to hydrogen and oxygen when the electrolyzer is in an operating mode, circulating water from the steam system through a heat exchanger, circulating a first heat transfer medium between the electrolyzer and the heat exchanger, and circulating a second heat transfer medium between the heat exchanger and a thermal storage container.

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Abstract: Methods, systems, and computer-readable media for multi-domain, multi-modal data segmentation, and automatically generating and refining hypotheses. The method receives data from a plurality of data sources; synthesizing the receive data; identifying trigger event data based on the synthesized data; generating an episode based on a segmentation of the synthesized data and trigger event data; and identifying at least one set of observational features associated with the episode based on the synthesized data and a relevancy metric. The method also includes iteratively generating a hypothesis based on the observational features using machine learning, predicting an outcome based on the hypothesis using machine learning, generating an outcome measure, and validating the hypothesis based on the outcome measure.

Abstract: Methods, systems, and computer-readable media for the generation of customizable insights using machine learning models. The method acquires data from one or more sources with different formats and data organization to extract events that each represent an interaction between a user and a service and classify the extracted events by adding labels to each of the extracted events. The method next projects the extracted events and generates the customized insights of events representing user interactions.

Abstract: Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

Abstract: Provided herein are compounds and pharmaceutical compositions useful in modulating kinase activity, and related diseases. Also provided herein are methods of treating an eye disease or disorder in a subject. Also provided herein are methods of reducing intraocular pressure in a subject. Also provided herein are methods of modulating kinase activity in a cell. Also provided herein are methods of making the compounds provided herein, and compounds useful for the preparation of the compounds provided herein.

Abstract: Methods, systems, and computer-readable media for generating a virtual based on location. The method acquires a request for a service based on a type of service and is associated with a user, the user's location, and user preferences. The method then acquires a set of service providers based on the type of service and the user's location who are filtered from a larger set of service providers using user preferences. The method in the next step acquires a machine learning model that is based on stored information associated with other users travel patterns and with service providers providing the service and the geographical information associated with the user's location. The method executed the identified machine learning model to aggregate a subset of service providers based on output from the machine learning model. The machine learning model is inputted the set of service providers, the user's location, the user's preferences, and the geographical information.

Abstract: Methods, systems, and computer-readable media for updating a machine learning model utilized in a search engine operation. The method identifies a set of search queries in stored search queries corresponding to a service and apply the identified set of search queries to the search engine to generate one or more search results for the service. Each search result has an assigned aggregate based on values of a set of parameters associated with the service. The method then analyzes the values of the set of parameters to determine a tradeoff point of each parameter to determine one or more weights to apply to the machine learning model based on the tradeoff points. The method stores the determined one or more weights and applies them to the machine learning model for a search query corresponding to the service.

Abstract: A method for providing and maintaining secure storage of target data includes, during a first time period in which a server provides a first mapping between user-specific cloaking sequence elements and hidden sequence elements, cloaking the target data using a first set of user-specific cloaking sequences and the first mapping, and storing the cloaked data in a persistent memory. The method further includes, during a later, second time period in which the server provides a different, second mapping between the user-specific cloaking sequence elements and the hidden sequence elements, re-cloaking the cloaked data using the first set of user-specific cloaking sequences and the second mapping, and storing the re-cloaked data in the persistent memory.

Abstract: Tubing comprising a tubing wall formed of an elongate thermoplastic ribbon helically wrapped and heat bonded to itself to form the tubing wall. The tubing may include one or more elongate conductors helically wrapped around and along the tubing wall. The tubing may include an elongate reinforcement rib helically wrapped around and along the tubing wall such that the tubing wall includes a first portion in which the elongate reinforcement rib covers the one or more elongate conductors and a second portion in which the one or more elongate conductors are uncovered by the elongate reinforcement rib and the elongate reinforcement rib wraps around the tubing wall.

Abstract: This invention relates to graphical user-interactive analysis of data, including in particular, mass spectrographic data analysis, as well as methods and software for generating and using such. One aspect provides user-customizable reports, including methods and apparatuses for generating customizable pivot tables and graphs specific to mass spectrographic data.

Abstract: Methods and apparatuses for the identification and/or characterization of properties of a macromolecule based on mass spectrometry data. Specifically, described herein are methods and apparatuses for converting peptide-level data into a pseudo-intact mass spectra. Also described herein are methods and apparatuses for converting peptide-level data into a pseudo-electropherogram. The methods may be well suited for analyzing proteins and protein complexes, including estimating properties of post-translational modifications of the proteins and protein complexes. Methods may include generating a theoretical graph or spectrum based on peptide-level mass spectrometry data. In some embodiments, the theoretical graph may be a theoretical intact mass spectrum or a theoretical charge distribution spectrum.

Abstract: Methods, systems, and computer-readable media for linking multiple data entities. The method collects a snapshot of data from one or more data sources and converts it into a canonical representation of records expressing relationships between data elements in the records. The method next cleans the records to generate output data of entities by grouping chunks of records using a machine learning model. The method next ingests the output data of entities to generate a versioned data store of the entities and optimizes versioned data store for real-time data lookup. The method then receives a request for data pertaining to a real-world entity and presenting relevant data from the versioned data store of entities.