Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Disclosed embodiments may include a method for prioritizing messages. The system may include receiving one or more messages comprising message data and application sender data. From the message data and application sender data, the system may determine a ranking of importance of the one or more messages, and then determine whether a first message is urgent. If the first message is urgent, the system may send the first message to the user device. If the first message is not urgent, the system may determine a set time for the first message to be sent and send the first message to the user device at the set time.

Abstract: A flow cell subassembly includes a carriage assembly coupled to a flow cell body to selectively engage a nozzle with a base of a cuvette. The carriage assembly includes a linear bearing slidingly engaged with the flow cell body, a tiltable carriage plate coupled to the linear bearing, and a nozzle mount coupled to the tiltable carriage plate. The nozzle mount receives a nozzle assembly with the nozzle. Set screws can adjust pitch angle of the tiltable carriage plate with the linear bearing to adjust engagement between the nozzle and cuvette in a first dimension. To adjust engagement between the nozzle and cuvette in a second dimension, axial play in bolts/screws through the tiltable carriage plate into threaded holes of the linear bearing allow for yaw angle adjustments.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: One or more group-specific aggregate losses, one or more group-agnostic aggregate losses, and a joint loss are computed. A regularizer loss is computed based on the one or more group-specific aggregate losses and the one or more group-agnostic aggregate losses. One or more group-specific models are trained based on the one or more group-specific aggregate losses. A feature extractor is updated based on the regularizer loss and a joint classifier is updated based on the joint loss.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

Abstract: Examples described herein provide a computer-implemented method that includes training, by one or more processing devices, a first neural network for classification based on training data in accordance with a first learning objective, the first neural network producing an intermediate feature function and a final feature function as outputs. The computer-implemented method further includes training, by the one or more processing devices, a second neural network for classification based on the intermediate feature function and the final feature function and further based at least in part on target task samples in accordance with a second learning objective. Training the second neural network includes computing maximal correlation functions of each of the intermediate feature function, the final feature function, and the target task samples.

Abstract: The present disclosure provides compositions and methods, as well as combinations, kits, and systems that include the compositions and methods, for amplification, detection, characterization, assessment, profiling and/or measurement of nucleic acids in samples, particularly biological samples. Compositions and methods provided herein include combinations of microbial species target-specific nucleic acid primers for selective amplification and/or combinations of primers for amplification of nucleic acids from a large group of taxonomically related microorganisms. In one aspect, amplified nucleic acids obtained using the compositions and methods can be used in various processes including nucleic acid sequencing and used to detect the presence of microbial species and assess microbial populations in a variety of samples.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: A computer-implemented data processing system includes a data storage device and a processor and program logic stored in memory and executable by the processor. The program logic may include account management logic coupled to the data storage device and configured to manage accounts respectively associated with a plurality of users, the account management logic including account processing logic configured to process transactions for the accounts and store account data related to the accounts in the data storage device. The program logic may further include interface logic coupled to the account management logic and the data storage device, the interface logic configured to connect the data processing system to computing systems associated with the plurality of users by way of the Internet, the interface logic providing the plurality of users with web access to an on-line banking area of a website of a financial institution.

Abstract: A parking system is described. The system includes a parking tower. The parking tower includes at least two levels and a vertical passageway. A vehicle gripping system is provided. The vehicle gripping system includes a 5 vertical lift frame vertically movable in the passageway. The vehicle gripping system also includes a horizontal slide frame slidably coupled to the vertical lift frame and horizontally movable relative to the vertical lift frame and across each level. A sliding wheel gripper is slidably coupled to and extending downwards from the horizontal slide frame, for releasably gripping a vehicle 10 by gripping the wheels of the vehicle. The sliding wheel gripper is longitudinally slidable along the horizontal slide frame between a release position and a gripping position.

Abstract: Examples described herein provide a computer-implemented method that includes training, by one or more processing devices, a first neural network for classification based on training data in accordance with a first learning objection, the first neural network producing an intermediate feature function and a final feature function as outputs. The computer-implemented method further includes training, by the one or more processing devices, a second neural network for classification based on the intermediate feature function and the final feature function and further based at least in part on target task samples in accordance with a second learning objective. Training the second neural network includes computing maximal correlation functions of each of the intermediate feature function, the final feature function, and the target task samples.

Abstract: A method, comprising amplifying a nucleic acid sequence of interest in a sample comprising genomic DNA of a subject; amplifying a reference nucleic acid sequence in the sample; quantifying the amplified sequence of interest relative to the amplified reference sequence; and determining a copy number of the sequence of interest from the relative quantified amplified sequence of interest. The reference sequence may have at least 80% sequence identity to at least one of SEQ ID NO:1-38, such as SEQ ID NO:1-13. Also disclosed are kits and compositions, each comprising a first probe which specifically hybridizes to at least a portion of at least one reference sequence. Also disclosed is a system configured to perform the above method.

Abstract: Aspects discussed herein relate to the storage of data in graph databases and detecting fraudulent behavior in the stored data. Fraud detection systems may use graph databases to store data, allowing for querying the graph database to obtain data using a variety of graph semantics such as nodes, edges, and properties. Graph databases in accordance with embodiments of the invention may include account nodes and attribute nodes, where nodes of the same type are not directly linked to each other. When a particular node is updated, an updated node may be created with a higher version number than the existing node. Each node may include an indication of the node being associated with fraudulent activity. Fraud indicators may be calculated based on the relationships between the nodes and fraud indicators for the nodes.

Abstract: A method, comprising amplifying a nucleic acid sequence of interest in a sample comprising genomic DNA of a subject; amplifying a reference nucleic acid sequence in the sample; quantifying the amplified sequence of interest relative to the amplified reference sequence; and determining a copy number of the sequence of interest from the relative quantified amplified sequence of interest. The reference sequence may have at least 80% sequence identity to at least one of SEQ ID NO:1-38, such as SEQ ID NO:1-13. Also disclosed are kits and compositions, each comprising a first probe which specifically hybridizes to at least a portion of at least one reference sequence. Also disclosed is a system configured to perform the above method.

Abstract: A computer-implemented data processing system includes a data storage system, and a processor and program logic stored in memory and executable by the processor. The program logic includes logic configured to manage accounts respectively associated with a plurality of users, process transactions for the accounts and store account data related to the accounts in the data storage device, access the account data stored in the storage device and determine projected retirement spending amounts for a user based on account data associated with the user, determine a projected required savings amount based on the projected retirement spending amounts, and establish a connection with one of the users and provide the user with a user interface, the user interface including retirement planning data, the retirement planning data including an indication of the user's progress in accumulating the projected required savings amount.