Abstract: Systems, devices, and techniques are disclosed for verification of backup data across a data pipeline. Records from a first storage may be received at a first end of a data pipeline. The records may be hashed to generate first hashes. A first hash tree may be generated from the first hashes. The records may be received at a second end of the data pipeline. Bits of bitmaps that correspond to the records may be set. The records may be hashed to generate second hashes. The records may be stored in a second storage. A second hash tree may be generated form the second hashes. Using the bitmaps, whether all of the records or any duplicate records were received may be determined. The first hash tree and the second hash tree may be compared to determine if any of the records stored in the second storage are corrupt.

Abstract: Various embodiments of the present disclosure relate generally to providing services to users via communication channels. More specifically, various embodiments of the present disclosure relate to systems and methods for modifying, updating, and/or changing communication channel interactions based on the tracking or listening for events within other communication channels.

Abstract: A concentrate vapor delivery system places a viscous concentrate at an optimal location for vaporization while simultaneously protecting the heating elements that enable vaporization from damage. Concentrate is positioned proximate to the heating source on a delivery apparatus whereby heat from the heating source liquifies the concentrate. Upon liquification the concentrate flows along the delivery device toward the heating source and into an environment of higher temperature, eventually vaporizing thereby releasing the desired vapor effect.

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

Abstract: A system for detecting a biological target in a sample, including a sensor comprised of single-walled nanotubes coating a sample substrate, where the sensor includes a sensing electrode and a control electrode, and an analyzer configured to accept the sensor including a controller configured to measure a resistance of the sensing electrode and a resistance of the control electrode and compare the resistance of the sensing electrode to the resistance of the control electrode. Additionally, a method of detecting a biological target with the system including placing a sample into an analyzer, inserting a sensor made of single-walled carbon nanotubes into the analyzer, wherein the sensor includes a sensing electrode and a control electrode, submerging the sensing electrode into the sample, submerging the sensing electrode into a washing solution, measuring the resistance of the sensing electrode and the control electrode, and comparing the resistances.

Abstract: A concentrate vapor delivery system places a viscous concentrate at an optimal location for vaporization while simultaneously protecting the heating elements that enable vaporization from damage. Concentrate is positioned proximate to the heating source on a delivery apparatus whereby heat from the heating source liquefies the concentrate. Upon liquification the concentrate flows along the delivery device toward the heating source and into an environment of higher temperature, eventually vaporizing thereby releasing the desired vapor effect.

Abstract: In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

Abstract: In various examples, physical sensor data may be generated by a vehicle in a real-world environment. The physical sensor data may be used to train deep neural networks (DNNs). The DNNs may then be tested in a simulated environment—in some examples using hardware configured for installation in a vehicle to execute an autonomous driving software stack—to control a virtual vehicle in the simulated environment or to otherwise test, verify, or validate the outputs of the DNNs. Prior to use by the DNNs, virtual sensor data generated by virtual sensors within the simulated environment may be encoded to a format consistent with the format of the physical sensor data generated by the vehicle.

Abstract: A packaging system for fragile items comprising an outer box, a tray insert and a plurality of inner boxes. The system protects items during distribution, and easily convert into a display system for the items at the point of retail sale. Each item is contained in its own inner box, which includes one or more viewing windows to allow a purchaser easily to view the items. The inner boxes fit removably within paired holes in the tray insert, which provides multiple points of contact to hold the boxes in place, yet allows flexibility to protect the items from impact forces and to allow easy access by a purchaser. The tray insert fits within the outer box, which provides impact and debris protection during transit, and converts to a display mode. Both the outer box and each inner box may be marked with a code to allow inventory tracking.

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: Various embodiments of the present disclosure relate generally to providing services to users via communication channels. More specifically, various embodiments of the present disclosure relate to systems and methods for modifying, updating, and/or changing communication channel interactions based on the tracking or listening for events within other communication channels.

Abstract: Methods, systems, and computer-readable media for generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

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 generating a statistically covaried machine learning model for performance measurement of service providers. The method receives a configuration file that includes one or more parameters associated with a plurality of individuals and parses it to generate and executing the database query on input data to generate sets of tabulated data of individuals of the plurality of individuals. The method next determines one or more measures of service providers listed in the configuration file using two or more tabulated data of individuals from the sets of tabulated data of individuals. The method finally generates a covaried machine learning model by training a machine learning model by statistically covarying measures and using them as training data.

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: A method and apparatus for perforating a wellbore by firing one or more projectiles into a casing and formation using a single source of energy.

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 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: A packaging system for fragile items comprising an outer box, a tray insert and a plurality of inner boxes. The system protects items during distribution, and easily convert into a display system for the items at the point of retail sale. Each item is contained in its own inner box, which includes one or more viewing windows to allow a purchaser easily to view the items. The inner boxes fit removably within paired holes in the tray insert, which provides multiple points of contact to hold the boxes in place, yet allows flexibility to protect the items from impact forces and to allow easy access by a purchaser. The tray insert fits within the outer box, which provides impact and debris protection during transit, and converts to a display mode. Both the outer box and each inner box may be marked with a code to allow inventory tracking.

Abstract: A customer logs into a desired retailer through a Virtual Reality (VR)/Augmented Reality (AR) cross-retailer application (app). Preferences and/or a profile are obtained from the retailer via an Application Programming Interface (API). A customized VR/AR setting is rendered within the app based on the preferences and/or profile and a VR/AR session is initiated with the app. Consumable items and ingredients of each item are rendered as VR/AR selectable objects during the session for interaction by the customer and a custom item is made with selected ingredients. The customer purchases the item within the session and an order is placed with the retailer through the API. In an embodiment, an avatar of the customer and avatars of other customer placing orders with the retailer are rendered on a virtual floor or superimposed on an actual floor of the customer's physical environment within the app for social interaction and social network features.