Abstract: Systems and methods are described for providing an interface and facilitating selection of sponsored content networks that provide sponsored content items. This may include providing, by a mediation server, a user interface to a publisher server, the user interface configured to provide access to data stored on the mediation server; receiving a metric associated with a rule for filtering content items associated with the publisher; applying the metric to a content network list associated with the publisher using the user interface to generate an updated content network list; and transmitting mediation code including the updated content network list to the publisher server, wherein the mediation code, when executed by a user device, (i) causes the user device to control display of content items according to the updated content network list and (ii) allows the user device to flag at least one content item for modifying the updated content network list.

Abstract: A cloud infrastructure orchestration service (CIOS) may be used to create a service plan and manifest (SPAM) that defines a deterministic order of releases for bootstrapping a service (e.g., provisioning and deploying resources of the service) to a cloud computing environment (e.g., to a data center). A corresponding manifest may be used to identify the configuration files and artifacts to be. The manifest may be used to validate the service plan. The CIOS may be configured to validate the SPAM. If compatible, the SPAM may be added to a SPAM set. A SPAM set (a collection of SPAMs corresponding to respective services) may be used to derive a version set (identifying configuration file and artifact versions) with which a directed acyclic graph may be generated. CIOS may bootstrap various services within the data center based at least in part on traversing the directed acyclic graph.

Abstract: A cloud infrastructure orchestration service may maintain a service plan and manifest (SPAM) corresponding to a service to be bootstrapped (e.g., provisioned and deployed) to a cloud computing environment (e.g., to a data center). The service plan may specify a deterministic order of releases for performing a process to fully bootstrap the service using one or more build milestones and one or more execution units, each execution unit specifying ordered steps for transitioning between build milestones Each step may reference one or more execution target checkpoint transitions, which in turn reference an alias of a configuration file that defines a release. A manifest may be used to identify the configuration files and artifacts to be used by the releases and to validate the service plan. A SPAM may be used to reduce/eliminate nondeterministic behavior of previous orchestration systems and to provide visualizations of the bootstrapping process at different granularities.

Abstract: A cloud-computing service (e.g., a “Puffin Service”) is described. The service may maintain service and skill catalogs corresponding to various services to be deployed to a region (e.g., during a region build). The service may host numerous user interfaces with which various service and skill metadata may be provided. In some embodiments, such data may include one or more dependencies between skills. The data managed by the cloud-computing service may be utilized to build a dependency graph. Navigation of the dependency graph may be performed via one or more user interfaces hosted by the cloud-computing service. An orchestration service (e.g., a Multi-Flock Orchestrator) may manage bootstrapping efforts for any suitable number of services during a region build based at least in part on dependencies between skills.

Abstract: A cloud infrastructure orchestration service (CIOS) may track build progress made by any suitable number of regional orchestrators. The cloud infrastructure orchestration system may include any suitable number of regional orchestrators, each regional orchestrator executing in an isolated hosting environment (e.g., a service cell isolated from other service cells). An orchestrator control plane may be configured to generate a build plan for bootstrapping a plurality of services within a data center, the build plan may be generated based at least in part on a service build definition of a plurality of service build definitions, the service build definition specifying a deterministic process for bootstrapping a service of the plurality of services. The orchestrator control plane may instruct a regional orchestrator to perform bootstrapping operations according to the build plan and may track the progress of the bootstrapping operations on an ongoing basis.

Abstract: A variety of testing environments and techniques are disclosed. An orchestrator control plane may generate a build plan comprising a plurality of ordered steps for bootstrapping one or more services. The build plan may be generated based at least in part on one or more service plans and manifests that individually specify a deterministic process for bootstrapping a service. The orchestrator control plan may instruct a region orchestrator executing within an isolated testing environment to execute a test build of the one or more services according to the build plan. The region orchestrator may execute, as part of executing the test build, a subset of steps from the plurality of ordered steps of the build plan utilizing resources of the isolated testing environment and in an order identified by the build plan. At any suitable time, the isolated testing environment may be reset to enable subsequent test build executions.

Abstract: A cloud infrastructure orchestration service (CIOS) may track build progress made by any suitable number of regional orchestrators. An orchestrator control plane may be configured to generate a region build plan for bootstrapping a plurality of services within a data center. The orchestrator control plane may instruct a region orchestrator to execute a build according to the build plan. The region orchestrator may be configured to update an execution state corresponding to the execution of the region build plan as it executes steps of the ordered steps of the region build plan. At any suitable time (e.g., when executing one of the steps fails), intervention data may be received with which a new region build plan may be generated. The new region build plan may be used for subsequent execution of the region build. This may enable run-time corrections to be made.

Abstract: Balancing content distribution between a machine learning model and a statistical model provides a baseline assurance in combination with the benefits of a well-trained machine learning model for content selection. In some implementations, a server receiving requests for a content item assigns a first proportion of the received requests to a first group and assigns remaining requests to a second group. The server uses a machine learning model to select variations of the requested content item for responding to requests assigned to the first group and uses a statistical model to select content variations for requests assigned to the second group. The server obtains performance information, e.g., acceptance rates for the different variations, and compares performance of the different models used for content selection. Audience share assigned to the machine learning model is increased when it outperforms the statistical model and decreased when it underperforms the statistical model.

Abstract: An apparatus for static weight-bearing assessment of foot and lower limb abnormalities, the apparatus comprising: a platform configured to support at least part of a patient's foot; and an actuator arrangement configured to control a displacement of the platform.

Abstract: Training data for a machine learning model is generated. Generating the training data includes generating first training input, the first training input including first contextual information associated with a first user device of multiple user devices associated with a user account. The first contextual information identifies first location information corresponding to the first user device. A first target output for the first training input is generated. The first target output identifies an indication of a preference associated with the user account to cancel a first transmission of one or more media items to the first user device. The training data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

Abstract: Systems and methods are described for providing an interface and facilitating selection of sponsored content networks that provide sponsored content items. This may include providing, by a mediation server, a user interface to a publisher server, the user interface configured to provide access to data stored on the mediation server; receiving a metric associated with a rule for filtering content items associated with the publisher; applying the metric to a content network list associated with the publisher using the user interface to generate an updated content network list; and transmitting mediation code including the updated content network list to the publisher server, wherein the mediation code, when executed by a user device, (i) causes the user device to control display of content items according to the updated content network list and (ii) allows the user device to flag at least one content item for modifying the updated content network list.

Abstract: Systems and methods are described for providing an interface and implementing rules and metrics received from the interface regarding the selection of sponsored content networks that provide sponsored content items. This may include providing mediation code to a publisher for inclusion in publisher content provided to a user device, the mediation code associated with a table of sponsored content networks, receiving an image of a sponsored content item and a sponsored content network identifier from the user device, analyzing the image of the sponsored content item, the analysis generating extracted image data from the sponsored content item, categorizing the sponsored content item based on the extracted image data and the sponsored content network identifier, receiving an instruction to filter the sponsored content networks exceeding a metric based on a category, and updating the table of sponsored content networks to remove a particular sponsored content network.

Abstract: Training data for a machine learning model is generated. Generating the training data includes generating first training input, the first training input including first contextual information associated with a first user device of multiple user devices associated with a user account. The first contextual information identifies first location information corresponding to the first user device. A first target output for the first training input is generated. The first target output identifies an indication of a preference associated with the user account to cancel a first transmission of one or more media items to the first user device. The training data is provided to train the machine learning model on (i) a set of training inputs including the first training input, and (ii) a set of target outputs including the first target output.

Abstract: Systems and methods for dynamic sequence-based adjustment of prompt generation are provided. The system can receive a first interaction and a second interaction via a client device and identify a first sequence based on the first interaction and the second interaction. The system can map the first sequence to a node data structure and identify a node in the node data structure that matches the first sequence. The system can generate an adjusted parameter for a first digital component object responsive to a match with an attribute of the node in the node data structure. The system can execute a real-time digital component selection process among a plurality of digital component objects including the first digital component object to select the first digital component object. The system can transmit a prompt with the first digital component object to a client device to cause the client device to present the prompt.

Abstract: A system and method are disclosed for training a machine learning model using information pertaining to transmissions of one or more media items to user devices associated with a user account. Generating training data for the machine learning model includes generating first contextual information associated with a first user device and generating a first target output that identifies an indication of a preference of a user preference to cancel the first transmission. The method includes providing the training data to train the machine learning model.

Abstract: Systems and methods of reducing latency in online network traffic are provided. The system can include a processor and a memory couple to the processor. The memory stores computer-executable instructions, which when executed by the processor, cause the processor to identify an indication of an interaction with a content item presented within a content slot of an information resource, generate a click identifier that uniquely identifies the interaction, transmit a first request comprising a click resource link including the click identifier to a click server, and transmit a second request to access the landing page of the content item independent of transmitting the first request. The second request comprises a generated resource link that identifies a resource identifier of the landing page of the content item and the click identifier.

Abstract: A method includes receiving, at a user device, a plurality of signals pushed to the user device from a plurality of store devices located in a plurality of physical storefronts. Each signal includes a store identifier identifying the associated physical storefront. Store visit data is generated for each signal, the store visit data including the store identifiers and time data representing times associated with visits to the physical storefronts. Store visit data is aggregated for the plurality of signals pushed to the user device. Aggregated store visit data is transmitted to an analytics server configured to combine aggregated store visit data from a plurality of user devices and generate analytics data from the combined aggregated store visit data. At least a portion of the time data is removed from the aggregated store visit data before transmission to the analytics server.

Abstract: An apparatus for static weight-bearing assessment of foot and lower limb abnormalities, the apparatus comprising: a platform configured to support at least part of a patient's foot; and an actuator arrangement configured to control a displacement of the platform.

Abstract: A system and method are disclosed for training a machine learning model using information pertaining to transmissions of one or more media items to user devices associated with a user account. Generating training data for the machine learning model includes generating first contextual information associated with a first user device and generating a first target output that identifies an indication of a preference of a user preference to cancel the first transmission. The method includes providing the training data to train the machine learning model.

Abstract: A method and system include a SDP controller in a SDP receiving a request from a first device for communicating with a second device. The second device is in the SDP. A firewall includes a connection table configured to map an identity of the first device and second device, and a filter table is configured to open and close ports. The SDP controller authenticates the first device, and provides the identity of the first device to the second device. The second device transmits a first packet to the first device. The firewall maps in the connection table, the identity of the first device in association with the second device. The first device transmits a second packet to the second device. The firewall determines that the identity of the first device is in the connection table for communicating with the second device and forwards the second packet to the second device.