Abstract: Virality cause determination includes receiving a plurality of historical feedback items. It further includes bucketing a first feedback item in the plurality of historical feedback items into a first partition of feedback items, and bucketing a second feedback item in the plurality of historical feedback items into a second partition of feedback items. The second partition of feedback items is a baseline partition of feedback items. It further includes determining a set of candidate patterns present in the first partition of feedback items. It further includes determining whether a candidate pattern in the set of candidate patterns is disproportionately associated with the first partition of feedback items relative to the baseline partition of feedback items.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: Virality cause determination includes receiving a plurality of historical feedback items. It further includes bucketing a first feedback item in the plurality of historical feedback items into a first partition of feedback items, and bucketing a second feedback item in the plurality of historical feedback items into a second partition of feedback items. The second partition of feedback items is a baseline partition of feedback items. It further includes determining a set of candidate patterns present in the first partition of feedback items. It further includes determining whether a candidate pattern in the set of candidate patterns is disproportionately associated with the first partition of feedback items relative to the baseline partition of feedback items.

Abstract: In some aspects, a device of a vehicle may obtain information relating to an environment in which the vehicle is located. The device may determine using a machine learning model, a driving context of the vehicle based at least in part on the information relating to the environment, and a set of hyperparameters for a model, that is used to determine a driving behavior for the vehicle, based at least in part on the driving context. The device may determine, using the model configured with the set of hyperparameters, the driving behavior for the vehicle. The device may cause autonomous operation of the vehicle in accordance with the driving behavior. Numerous other aspects are described.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: Impact-based strength and weakness determination includes receiving a plurality of industry-wide feedback items, the industry-wide feedback items pertaining to a plurality of entities associated with an industry. It further includes, based at least in part on an evaluation of the plurality of industry-wide feedback items, generating an industry-wide reputation scoring model usable to determine an expected reputation score for a typical entity in the industry based at least in part on a combination of one or more reputation score components. Generating the industry-wide reputation scoring model comprises determining (1) a baseline reputation score, and (2) an expected impact of a reputation score component on reputation scoring for the typical entity in the industry.

Abstract: A liquid electrophotographic (LEP) printing device that includes a photo-imaging plate (PIP) to receive a liquid printing fluid, the liquid printing fluid including a pigment incorporated into a resin, a charge conductor, and a carrier liquid, and a transfer roller to transfer the liquid printing fluid from the PIP to a fabric substrate while wet.

Abstract: A liquid electrophotographic (LEP) printing device that includes a photo-imaging plate (PIP) to receive a liquid printing fluid, the liquid printing fluid including a pigment incorporated into a resin, a charge conductor, and a carrier liquid, and a transfer roller to transfer the liquid printing fluid from the PIP to a fabric substrate while wet.

Abstract: A platform-as-a-service infrastructure and application lifecycle manager is configured to implement a common services model to deploy selected services from a common set of services to service domains hosted on multiple different cloud platforms by abstracting dependence on availability of various additional supporting services, such as services that are platform-specific. The platform-as-a-service infrastructure and application lifecycle manager may also manage a lifecycle of available services, such as managing upgrades and/or patches to services.

Abstract: Systems and methods described herein generally relate to compute node resource scheduling. AI inference services described herein may receive a request to execute a machine learning model in a clustered edge system. To determine which hardware resource comprising computing nodes of the clustered edge system on which to execute the machine learning model, AI inference services may compare the computational workload of the machine learning model, with the computational abilities and functions of the hardware resources. In examples, the comparison is based on a scheduling algorithm, including an identification stage to identify candidate hardware resources capable of executing the machine learning model, and a scoring stage to select the best candidate hardware resource for executing the machine learning model. A scheduler may assign the machine learning model to the selected hardware resource for execution by the AI inference services.

Abstract: A liquid electrophotographic (LEP) printing device that includes a photo-imaging plate (PIP) to receive a liquid printing fluid, the liquid printing fluid including a pigment incorporated into a resin, a charge conductor, and a carrier liquid, and a transfer roller to transfer the liquid printing fluid from the PIP to a fabric substrate while wet.

Abstract: In one example, a method for drying a developer unit of a liquid electrophotographic printer. After printing is complete, development voltage biases are applied to the developer unit while a liquid marking agent flows to the developer unit. The liquid marking agent flow to the developer unit is stopped. Drying voltage biases, lower than the development voltage biases, are applied to the developer unit. Idle voltage biases are applied to the developer unit when dry.

Abstract: In one example, a method for drying a developer unit of a liquid electrophotographic printer. After printing is complete, development voltage biases are applied to the developer unit while a liquid marking agent flows to the developer unit. The liquid marking agent flow to the developer unit is stopped. Drying voltage biases, lower than the development voltage biases, are applied to the developer unit. Idle voltage biases are applied to the developer unit when dry.

Abstract: Certain aspects of the present disclosure relate to methods and apparatus for providing UDP receive offloading using the communications systems. For example, a method generally includes grouping, into a container, the datasets from the same source and destination arriving at a receiver based on datasets metadata, and ungrouping the datasets from the container once network layer processing on the container is completed and datasets are ready to traverse to a next destination.

Abstract: In one example, a method for drying a developer unit of a liquid electrophotographic printer. After printing is complete, development voltage biases are applied to the developer unit while a liquid marking agent flows to the developer unit. The liquid marking agent flow to the developer unit is stopped. Drying voltage biases, lower than the development voltage biases, are applied to the developer unit. Idle voltage biases are applied to the developer unit when dry.

Abstract: In one example, a method for drying a developer unit of a liquid electrophotographic printer. After printing is complete, development voltage biases are applied to the developer unit while a liquid marking agent flows to the developer unit. The liquid marking agent flow to the developer unit is stopped. Drying voltage biases, lower than the development voltage biases, are applied to the developer unit. Idle voltage biases are applied to the developer unit when dry.

Abstract: Techniques described herein convert mobile traffic between different types of VPN protocols, including IP and Transport. In an embodiment, a security proxy associated with a server receives a packet associated with a client app on a device, the packet including a source identifier and a destination identifier. The security proxy reassigns a tunnel identifier as the source and a node identifier as the destination, then stores a correlation of the tunnel identifier, the source identifier, and the destination identifier. The security proxy forwards the packet to the node inside the security proxy, and determines the destination identifier based on the correlation. The node then forwards the packet to the destination. This allows for multiple devices to use a same source identifier, e.g., same IP address. In some embodiments, a secure connection is established and/or the device and server are mutually authenticated prior to the processing of the packets.