Abstract: The techniques described herein increase the throughput of a single VPN connection by creating multiple outbound and/or inbound Security Associations (SAs). For instance, two or more different SAs can encrypt outbound data packets to be sent over the VPN connection to a remote device. Moreover, two or more different SAs can decrypt inbound data packets received over the VPN connection from the remote device. Each of the SAs can be bound to a different processing core via the use of a Security Parameter Index (SPI) identifier. Consequently, inbound data packets communicated over a single VPN connection from a remote device to a physical host in a VPN gateway can be distributed amongst multiple processing cores for decryption purposes. Further, outbound data packets to be communicated over the single VPN connection from the physical host to the remote device can be distributed amongst multiple processing cores for encryption purposes.

Abstract: A technique enables coordination of unrelated software components to facilitate extensive recovery point management on a snapshot or recovery point through the use of a flexible tag structure. The tag is organized and arranged as a {key=value,[value] . . . } structure wherein the key denotes an operation that requires coordination between the unrelated software components and the value(s) denote multi-cardinality that provide parameters for coordination of the operation. The multi-cardinality aspect of the flexible tag structure provides a set of values associated with the key of the tag that enables a software component and/or protocol to insert its value(s) into the tag structure for its interpretation. The technique thus provides an extensible model where multiple components/protocols use the tag to coordinate operations on the RP by conveying certain meaning/interpretations of the tag and its values.

Abstract: Systems, devices, computer-implemented methods, and tangible non-transitory computer readable media for implementing an organizational management platform that manages organizational data for an organization. The system can receive, from a user device, a user request for a report. Additionally, the system can generate a logic plan based on the user request, the logic plan determining a plurality of models. Moreover, the system can generate a first query being expressed in a custom query language. Furthermore, the system can combine the first query with a second query to generate a unified query. Subsequently, the system can translate the unified query into a final query, wherein the final query is expressed in a data access language. The system can execute the final query to retrieve data from a database, where a report is generated based on the retrieved data.

Abstract: Techniques for automatically generating reports based on organizational data. For example, a computer system can include a database of organizational data associated with the organization. The organizational data includes a plurality of employee data objects and a plurality of previously generated report configuration files. The system can include a machine-learned model configured to generate a report configuration file based on a prompt. The machine-learned model can be trained using the plurality of previously generated report configuration files. The system can receive a user query requesting a report associated with the organizational data. Additionally, the system can process, using a natural language processing model, the user query and the employee data objects to generate a prompt. Moreover, the system can process, by the machine-learned model, the prompt and the organizational data to generate a report configuration file.

Abstract: Systems and methods take into account the criticality of workloads, the warranty needs of workloads, the warranty available time, and the lifetime of a workload to provide an optimal solution that ensures servers are used to highest extent. The warranty health of servers is computed and categorized as critical, warning, or healthy based on the number of days remaining in warranty. Workloads are tagged as short-term or long-term workloads. Workloads are also classified based on criticality. The quarantine mode for proactive high availability of servers is divided into multiple modes, including a long-time, critical-workload quarantine mode, a critical-workload quarantine mode, and a standard quarantine mode. Servers that are in quarantine mode are assigned new workloads based upon the warranty health, workload term, and workload criticality.

Abstract: A computer-implemented method for treating a medical condition via a digital therapeutic platform, the method including receiving one or more user inputs, generating a script at a script generator, based on the one or more user inputs, determining therapeutic digital content by applying the script as an input to a therapeutic machine learning platform, receiving the therapeutic digital content via at least one of a therapeutic content database or a content generator, identifying a user platform to output the therapeutic digital content, modifying the therapeutic digital content for output via the user platform, providing the therapeutic digital content via the user platform, receiving feedback based on user consumption of the therapeutic digital content via the user platform and adjusting at least one of the script generator or the therapeutic machine learning platform based on the feedback.

Abstract: A virtual private network (VPN) gateway selects a VPN connection, from multiple VPN connections established between a network VPN gateway and a remote VPN gateway, based on performance factors such as throughput. A system may measure throughput in megabytes per second (Mbps). More specifically, a VPN gateway (e.g., a remote VPN gateway or a network VPN gateway) can configure a routing preference that selects a VPN connection that is more performant based on a cryptographic algorithm that is used for the VPN connection. The VPN gateway can update the routing preference to select an alternative VPN connection when the performance of the VPN connection suffers.

Abstract: There is provided a method for reducing the amount of silica in a desert sand of a type that also comprises calcium carbonate, iron oxide and alumina components, to thereby produce a cement raw material feedstock, the method comprising: imparting a net negative electrical charge to a silica component and a net positive electrical charge to a calcium carbonate component of the desert sand through interparticle friction by a triboelectrostatic mechanism; and applying an electric field to the charged components of the desert sand by a triboelectrostatic mechanism; such that the negatively charged silica component is separated from a remainder of the components of the desert sand to thereby produce the cement raw material feedstock.

Abstract: The use of multiple horizon optimization for vehicle dynamics and powertrain control of a vehicle is provided. Long horizon optimization for a trip of the vehicle is performed, and an optimal value function is determined. Data is received from powertrain and/or connectivity features from one or more of components of the vehicle. Short horizon optimization for the trip is performed using a rollout algorithm, the optimal value function, and the received data. The operation of the vehicle is adjusted using results of the short horizon optimization.

Abstract: A technique creates a compact state of snapshot metadata and associated selected snapshots that are frequently used and maintained in memory of a node of a cluster to facilitate processing of workflow operations associated with a logical entity in a disaster recovery (DR) environment. The compact state represents a minimal subset of snapshot metadata that is frequently used to perform operations in accordance with the DR workflow operations. In addition, metadata associated with the progress of the DR workflow operations processed by the node is periodically consolidated within the compact state. Illustratively, the selected frequently used snapshots of the logical entity include (i) a recently created snapshot; (ii) one or more reference snapshots; (iii) a snapshot scheduled for replication; and (iv) any snapshot that is queued for a current or future-scheduled operation.

Abstract: A technique enables recovery of failover data used to generate one or more High Frequency Snapshots (HFSs) at a source and replicated to a target for storage and recovery. The target is illustratively an intermediary repository embodied as a long-term storage service (LTSS) configured to organize and store the HFSs as recovery points (RPs) in an object store. The LTSS stores a HFS identifier (ID), a logical offset in an object of the object store storing data of the HFS, and a logical timestamp associated with each replicated HFS as a key of a segment descriptor in a key-value database configured to store metadata describing the failover data of the HFS stored as one or more objects in the object store. Upon recovery of the failover data, the technique enables identification of the HFS stored in the object store and creation of a HFS index metadata structure (B+ tree) to extract the identified HFS as a RP.

Abstract: A computer-implemented method for treating a medical condition via a digital therapeutic platform, the method including receiving one or more user inputs, generating a script at a script generator, based on the one or more user inputs, determining therapeutic digital content by applying the script as an input to a therapeutic machine learning platform, receiving the therapeutic digital content via at least one of a therapeutic content database or a content generator, identifying a user platform to output the therapeutic digital content, modifying the therapeutic digital content for output via the user platform, providing the therapeutic digital content via the user platform, receiving feedback based on user consumption of the therapeutic digital content via the user platform and adjusting at least one of the script generator or the therapeutic machine learning platform based on the feedback.

Abstract: A tribo-electrostatic separation process and system for the preparation of various food and feed products. A tribo-electric separation process and system for fractionating a feed mixture comprising at least two members of the group of proteins, starches, soluble and insoluble fibers. Namely, supplying a feed mixture comprising at least two of the group of proteins, starches, soluble and insoluble fibers to a tribo-electric separator and simultaneously charging and separating the feed mixture into at least two subfractions, with one of the subfractions enriched in one of protein, starch and fiber and having a composition different than the feed mixture.

Abstract: A discriminator of a training model is trained to discriminate between original training images without artificial subsurface data and modified training images with artificial subsurface data. A generator of the training model is trained to: replace portions of original training images with the artificial subsurface data to form the modified training images, and prevent the discriminator from discriminating between the original training images and the modified training images.

Abstract: Systems, methods, and articles for using an ensemble model to predict a number of impressions that media content programs will receive at a future time. The ensemble model is comprised of a plurality of machine learning models trained based on static and dynamic features obtained from historical impression data and media content program attributes. Static and dynamic features extracted from attributes of a media content program are applied to the ensemble model to generate a prediction of the number of impressions that the media content program will receive at a future time.

Abstract: A tribo-electrostatic separation process for beneficiation of bauxite minerals is disclosed. The process may include one or more steps of grinding, drying, de-agglomeration, air classification and electrostatic separation.

Abstract: A computing system includes computer-readable media to store instructions and processor(s) to execute the instructions to perform operations including: receiving a request for generation of a report having a plurality of data elements; identifying two or more different datasets that collectively contain the plurality of data elements; automatically generating one or more join configurations for combining the two or more different datasets based on a prioritization of data elements which define a join key for the one or more join configurations; executing the one or more join configurations to generate a combined dataset from the two or more different datasets; assigning unique identifier(s) to a portion of the combined dataset and expanding the combined dataset; performing an aggregation operation associated with the portion of the combined dataset by referencing the unique identifier(s); and generating the report based on the combined dataset and the aggregation operation.

Abstract: Systems and methods for generating digital content and selecting targets for presentation of digital content using artificial intelligence are disclosed. In one example, a nanosegment of customers can be selected as targets for digital content based on their propensity to accept offers for a given campaign. By incorporating a nanosegment-based classification of customers, there can be significantly reduced memory usage by the system, since the customer base will be reduced to a fewer number of groups, and each group more specifically targets traits for one cluster of customers. Attributes of the selected customers can be used to automatically design and generate digital content, such as personalized offers for distribution to the customers, including AI-generated taglines, content, and images. Feedback from each cycle of the campaign can be fed back into subsequent cycles to continuously improve performance and offer outcomes.

Abstract: Technologies for efficiently determining credit loss sensitivity to macroeconomic impacts include a compute device. The compute device includes circuitry configured to determine for each asset category in a set of multiple asset categories, a set of macroeconomic variables that affect a credit loss for the corresponding asset category. The circuitry is further configured to obtain data indicative of a change to be applied to a selected macroeconomic variable of the set of macroeconomic variables. Additionally, the circuitry is configured to calculate, for each asset category determined to be affected by the selected macroeconomic variable, an estimated credit loss resulting from the change in the selected macroeconomic variable while excluding from the calculation one or more asset categories from the set of multiple asset categories that have been determined to not be affected by the selected macroeconomic variable and present, in a user interface, the estimated credit loss.

Abstract: Distributed appending of transactions in data lakes is described. A first message is received, at a first ingestion node of a plurality of ingestion nodes, as part of a transaction. The first message identifies a transaction identifier (ID) and a portion of data for the transaction. The data of the first message is persisted in temporary storage. A count of messages for the transaction for the first ingestion node is determined. Based on at least the count of messages, it is determined that the first ingestion node has received a complete set of messages for the transaction for the first ingestion node. A metadata write request is transmitted, by the first ingestion node, to a coordinator. The metadata write request includes a self-describing reference to persisted data. The self-describing reference identifies the first ingestion node, location information of the persisted data, and a range of the first data.