Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: The present disclosure describes a blockchain interoperability system. The system includes one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively, receive a request for a destination token on a destination blockchain, determine, based on one or more data structures, a first blockchain on which an intermediate token can be converted into the destination token, convert, on the first blockchain, the intermediate token into the destination token, and bridge the destination token from the first blockchain to the destination blockchain.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: The present disclosure describes a blockchain interoperability system. The system includes one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively, receive a request for a destination token on a destination blockchain, determine, based on one or more data structures, a first blockchain on which an intermediate token can be converted into the destination token, convert, on the first blockchain, the intermediate token into the destination token, and bridge the destination token from the first blockchain to the destination blockchain.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: The present disclosure describes a blockchain interoperability system. The system includes one or more memories and one or more processors communicatively coupled to the one or more memories. The one or more processors, individually or collectively, receive a request for a destination token on a destination blockchain, determine, based on one or more data structures, a first blockchain on which an intermediate token can be converted into the destination token, convert, on the first blockchain, the intermediate token into the destination token, and bridge the destination token from the first blockchain to the destination blockchain.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: Techniques are provided for computing affinity for protein-protein interaction. 3D structure models of the first and second protein parts are generated using a trained first deep learning model. A 3D structure model of a protein-protein complex comprising the first and the second protein parts is generated using a trained second deep learning model. A low energy score state is determined for the 3D structure models of each of the first and second protein parts, and the protein-protein complex. A relax algorithm applied to amino acid side chain and backbone 3D structure models determines a low energy score state for the 3D structure models. Based on the low energy score states, an energy score is generated for the 3D structure models, and a score difference is determined between the energy scores, where the score difference defines a binding affinity score.

Abstract: A method of automated database element processing includes training a wide machine learning model with historical feature vector inputs to generate a wide ranked element output. The method includes training a deep machine learning model with the historical feature vector inputs to generate a deep ranked element output. The method includes generating a set of inputs specific to an individual entity, obtaining a set of current article database elements, and creating a feature vector input according to the set of inputs and the set of current article database elements. The method includes processing the feature vector input with the wide machine learning model to generate a wide ranked element list, processing the feature vector input with the deep machine learning model to generate a deep ranked element list, and merging database elements of the wide and deep ranked element lists to generate a ranked element recommendation output.

Abstract: The present invention includes an apparatus and method for blood pressure trend determination of a subject comprising: a housing; at least one of a photoplethysmographic (PPG) sensor or a magnetic sensor in or on the housing and adapted to be worn by the subject, wherein the PPG/magnetic sensor uses a pulse oximeter to measure changes in skin light absorption; a processor for receiving a signal from the at least one of the PPG sensor or the magnetic sensor measurements, wherein the processor comprises a non-transitory computer readable medium having instruction stored thereon, wherein the instructions, when executed by the processor, cause the processor to: measure a trend analysis results to determine the blood pressure trend over a time period; and an input/output device that at least one of stores, displays, or transmits blood pressure trend of the subject.

Abstract: A technique for enhancing the image quality in intravascular ultrasound imaging increases contrast between blood and vessel wall processes image data using a point-wise t-statistic technique. Data from an ultrasound transducer is digitized and stored in a memory buffer [500]. For each point in the image, a t-statistic value is derived from signal amplitude values for the same point at a sequence of previous frames [502]. An image is then generated and displayed using the t-statistic values for the intensity of each point [504]. The improvement in contrast ratio as compared to averaging techniques is most significant at highly oblique angles when contrast ratio is particularly poor in the unprocessed signal.

Abstract: A needle connecting apparatus for connecting a needle to a syringe and for covering the needle for disposal includes a connector housing. A first mounting point on the connector housing mounts the housing to a syringe body. A second mounting point on the connector housing mounts base of a needle to the housing. A rigid needle cover is movably mounted to the connector housing for exposing a needle mounted to the connector housing when in a first position and for covering a needle mounted to the connector housing when in a second position.