Abstract: A network device, a method for the network device, and a network system comprising one or more such network devices is provided. The method for the network device involves receiving a connection request from a client device, the connection request including a MAC address and being generated using a PSK. A database is accessed to determine if a record associating the MAC address with a PSK exists. If such a record does exist, then authentication is attempted using the PSK identified using the record. If such a record does not exist, then a process for generating a new record for the database is performed. A non-transitory computer-readable storage medium comprising instructions for implementing the method is also provided.

Abstract: A communications framework can include a discussion tool operable within a process operations environment, where the discussion tool issues a request for communication with an expert and records contextual information of the process operations environment: a notification tool that, responsive to issuance of the request for communication, calls for issuance of a notification to an identified expert; and a recordation tool that calls for storage of communication information associated with communication with the identified expert to a database.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Compounds which are benzoxazolone derivatives are disclosed, including compounds of the following genus: The compounds possess anti-inflammasome properties and exhibit anti-fibrotic and anti-proliferative effects. They are useful in inhibiting the activation of NLRP3 or NLRC4 receptors, and in the treatment of a variety of neuroinflammatory disorders such as autoimmune diseases, type-2 diabetes, Cryopyrin-Associated Autoinflammatory Syndromes, Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, Multiple Sclerosis, and rheumatoid arthritis.

Abstract: Described herein are compounds having the following structure: or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof. Also disclosed are compositions containing the compounds, methods of inhibiting activity of DYRK1A in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Abstract: Described herein are compounds having the following structure: formula (I) or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof. Also disclosed are compositions containing the compounds, methods of inhibiting activity of DYRK1 A in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Disclosed herein are kinase inhibitor compounds having the structure (I) or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof, where R1, R2, X, L, Q, and Y are as defined herein. Also disclosed are compositions containing the kinase inhibitor compounds, methods of inhibiting activity of a kinase in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: The present invention is directed to kinase inhibitor compounds having the following structure: or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof, where R1, R2, X, n, R3, Y, Z, R4, R5, R6, and = are as defined herein. The present invention also relates to compositions containing the kinase inhibitor compounds, methods of inhibiting activity of a kinase in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.

Abstract: Disclosed herein are kinase inhibitor compounds having structure (I), or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof, where R1, R2, R3, R4, R5, R6, R7, X, Y, Z, and (AA) are as defined herein. Also disclosed are compositions containing the kinase inhibitor compounds, methods of inhibiting activity of a kinase in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Disclosed are kinase inhibitor compounds having the following structure: (I), or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof, where R1, R2, R3, R4, R5, R6, N—Ar, X, Y, Z, and AA are as defined herein. Also disclosed are compositions containing the kinase inhibitor compounds, methods of inhibiting activity of a kinase in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Described herein are compounds having the following structure: formula (I) or a stereoisomer, pharmaceutically acceptable salt, oxide, or solvate thereof. Also disclosed are compositions containing the compounds, methods of inhibiting activity of DYRK1 A in a cell, methods of increasing cell proliferation in a population of pancreatic beta cells, methods of treating a subject for a condition associated with insufficient insulin secretion, and methods of treating a subject for a neurological disorder.

Abstract: Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine.