Abstract: Techniques in dynamic routing for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element enabled to execute programmed instructions using the data and a router enabled to route the wavelets via static routing, dynamic routing, or both. The routing is in accordance with a respective virtual channel specifier of each of the wavelets and controlled by routing configuration information of the router. The static techniques enable statically specifiable neuron connections. The dynamic techniques enable information from the wavelets to alter the routing configuration information during neural network processing.

Abstract: Systems and methods are provided for verifying attributes of users in connection with network interactions involving the users. An example computer-implemented method includes receiving an authentication request (AReq) in connection with an interaction related to an age-restricted product, where the AReq includes an account number for a user and an age verification signal. The method also includes, in response to the age verification signal, retrieving a birthdate of the user, determining an age of the user based on the birthdate of the user, and determining the age of the user satisfies a defined threshold. The method then includes, in response to the age of the user satisfying the defined threshold, causing a challenge question to be issued to the user, whereby authentication of the user is initiated through a mobile device of the user.

Abstract: Techniques in dynamic routing for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element enabled to execute programmed instructions using the data and a router enabled to route the wavelets via static routing, dynamic routing, or both. The routing is in accordance with a respective virtual channel specifier of each of the wavelets and controlled by routing configuration information of the router. The static techniques enable statically specifiable neuron connections. The dynamic techniques enable information from the wavelets to alter the routing configuration information during neural network processing.

Abstract: A system for managing data protection of virtual machines (VMs) hosted by hosts of data clusters includes a data protection manager. The data protection manager identifies a data protection event associated with at least one VM, obtains, in response the data protection event, data protection rules and a protection policy associated with the at least one VM, spawn, by a monitoring engine orchestrator, a monitoring engine to the data cluster, initiates performance of the data protection services for the at least one VM using a first storage of storages, obtains, after the spawning, monitoring information from the monitoring engine, makes a determination that a data protection rule event of the data protection rule events occurred using the monitoring information, and in response to the determination, initiates the performance of a corrective action of corrective actions based on the data protection rules using a second storage of the storages.

Abstract: Techniques in dynamic routing for advanced deep learning provide improvements in one or more of accuracy, performance, and energy efficiency. An array of processing elements comprising a portion of a neural network accelerator performs flow-based computations on wavelets of data. Each processing element comprises a compute element enabled to execute programmed instructions using the data and a router enabled to route the wavelets via static routing, dynamic routing, or both. The routing is in accordance with a respective virtual channel specifier of each of the wavelets and controlled by routing configuration information of the router. The static techniques enable statically specifiable neuron connections. The dynamic techniques enable information from the wavelets to alter the routing configuration information during neural network processing.

Abstract: Embodiments are described for a heuristic configuration selection process as part of or accessible by the backup management process. This processing component provides a method to automatically determine the configuration parameters needed to obtain optimal performance for a given backup/restore job. This process involves identifying key parameters that determine backup performance and suggest means to derive and incorporate those configurable parameters into the backup software automatically. Embodiments can be applied to stream based backups, or other types of backup software as well.

Abstract: A system for managing data protection of virtual machines (VMs) hosted by hosts of data clusters includes a data protection manager. The data protection manager identifies a data protection event associated with at least one VM, obtains, in response the data protection event, data protection rules and a protection policy associated with the at least one VM, spawn, by a monitoring engine orchestrator, a monitoring engine to the data cluster, initiates performance of the data protection services for the at least one VM using a first storage of storages, obtains, after the spawning, monitoring information from the monitoring engine, makes a determination that a data protection rule event of the data protection rule events occurred using the monitoring information, and in response to the determination, initiates the performance of a corrective action of corrective actions based on the data protection rules using a second storage of the storages.

Abstract: A method for backing up data is performed by a backup manager. The method includes obtaining, by the backup manager, a first request from a host, wherein the first request is associated with an application on the host; in response to the first request: obtaining backup policies; obtaining a tag associated with the application; making a first determination that the tag indicates that the application is a cloud native application; and in response to the first determination: associating the application with a cloud native backup policy, wherein the cloud native backup policy is one of the backup policies; and initiating backup generation of the application based on the cloud native backup policy.

Abstract: Methods, media, and systems directed to a platform for determining, processing, storing, and analyzing authentication data from various internal and external systems within an authentication ecosystem, and from other authentication systems providing authentication services.

Abstract: A backup storage includes a persistent storage and a backup manager. The persistent storage stores backups of entities and a global application data map. The backup manager obtains a restoration request for restoration of an application hosted by an entity of the entities; makes a determination to accelerate the restoration based, at least in part, on the application; in response to the determination: identifies a virtual disk level backup of the entity that is associated with the application using the global application data map; obtains the virtual disk level backup from the backups of the entities; and restores the application using the virtual disk level backup.

Abstract: Embodiments are described for a heuristic configuration selection process as part of or accessible by the backup management process. This processing component provides a method to automatically determine the configuration parameters needed to obtain optimal performance for a given backup/restore job. This process involves identifying key parameters that determine backup performance and suggest means to derive and incorporate those configurable parameters into the backup software automatically. Embodiments can be applied to stream based backups, or other types of backup software as well.

Abstract: A backup storage includes persistent storage and a backup manager. The persistent storage stores backups of entities and an entity list that lists the entities. The backup manager obtains a restoration availability request from a user; filters the entity list based on an identity of the user to obtain an available entity list; identifies, based on user input obtain based on the available entity list, an entity of the entities; and restores the entity using the backups.

Abstract: Embodiments are described for a heuristic configuration selection process as part of or accessible by the backup management process. This processing component provides a method to automatically determine the configuration parameters needed to obtain optimal performance for a given backup/restore job. This process involves identifying key parameters that determine backup performance and suggest means to derive and incorporate those configurable parameters into the backup software automatically. Embodiments can be applied to stream based backups, or other types of backup software as well.

Abstract: The invention described herein pertains to the diagnosis, imaging, and/or treatment of pathogenic cell populations. In particular, the invention described herein pertains to the diagnosis, imaging, and/or treatment of diseases caused by PSMA expressing cells, such as prostate cancer cells, using compounds capable of targeting PSMA expressing cells.

Abstract: A backup storage includes a persistent storage and a backup manager. The persistent storage stores backups of entities and a global application data map. The backup manager obtains a restoration request for restoration of an application hosted by an entity of the entities; makes a determination to accelerate the restoration based, at least in part, on the application; in response to the determination: identifies a virtual disk level backup of the entity that is associated with the application using the global application data map; obtains the virtual disk level backup from the backups of the entities; and restores the application using the virtual disk level backup.

Abstract: A backup storage includes persistent storage and a backup manager. The persistent storage stores backups of entities and an entity list that lists the entities. The backup manager obtains a restoration availability request from a user; filters the entity list based on an identity of the user to obtain an available entity list; identifies, based on user input obtain based on the available entity list, an entity of the entities; and restores the entity using the backups.

Abstract: A backup storage includes a persistent storage for storing backups of entities and a backup data map. The backup storage also includes a backup manager that obtains a search request for data; obtains at least two data maps associated with at least two of the entities; generates the backup data map using the at least two data maps; searches the backups for the data using the backup data map to identify a copy of the data; and provides the copy of the data in response to the search request.

Abstract: A backup storage includes a persistent storage and a backup manager. The persistent storage stores virtual machine level backups and application level backups. The backup manager obtains a request for a point in time restoration of an application; identifies, based on the application, a virtual machine level backup of the virtual machine level backups that is associated with: a virtual machine that hosted an instance of the application and a first point in time prior to a restoration point in time specified by the request for the point in time restoration; identifies, based on the application, an application level backup of the application level backups that is associated with the restoration point in time; generates a point in time backup using: the identified virtual machine level backup, and the identified application level backup; and services the request using the point in time backup.

Abstract: Embodiments are described for a heuristic configuration selection process as part of or accessible by the backup management process. This processing component provides a method to automatically determine the configuration parameters needed to obtain optimal performance for a given backup/restore job. This process involves identifying key parameters that determine backup performance and suggest means to derive and incorporate those configurable parameters into the backup software automatically. Embodiments can be applied to stream based backups, or other types of backup software as well.

Abstract: The disclosure provided herein pertains to CCK2R-drug conjugates. In particular, the disclosure pertains to CCK2R-drug conjugates that target the delivery of drugs to a mammalian recipient. Also described are methods of making and using CCK2R-drug conjugates.