Abstract: Technologies are provided for generating optimized sets of client rendering options for renderable content items (such as images, videos, text, and audio). Rendering optimization parameters for a renderable content item can be determined by assessing the performance of processing requests and rendering content items using different file formats, quality settings, and/or other settings for the renderable content items. The rendering optimization parameters can then be used in combination with a rendering specification that is defined by a developer (such as an application developer, web page designer, or the like) in order to create an optimized set of client rendering options. The set of client rendering options can then be transmitted to one or more client computing devices. A client computing device that receives the set of client rendering options can select one or more of the options in order to render the renderable content item.

Abstract: One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: Systems and methods are disclosed for parallelizing service function chains.

Abstract: A determination is made that an explanatory data set for a common set of predictions generated by a machine learning model for records containing text tokens is to be provided. Respective groups of related tokens are identified from the text attributes of the records, and record-level prediction influence scores are generated for the token groups. An aggregate prediction influence score is generated for at least some of the token groups from the record-level scores, and an explanatory data set based on the aggregate scores is presented.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user's performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: Systems, computer-readable media, and methods are disclosed for parallel data processing for service function chains with network functions spanning multiple servers. An example system includes a first server hosting a first network function of a service function chain, a second server hosting a second network function of the service function chain, a mirror function deployed in a first switch to replicate a plurality of packets received by the system and to send respective copies of the plurality of packets to the first network function and to at least one of the second network function and a third network function of the service function chain, and a merge function deployed in a second switch to merge respective outputs of the first network function and the at least one of the second network function and the third network function.

Abstract: Systems and methods for performing impairment compensation in point-to-multi-point communication systems are described. In a data snapshot mode, a hub node can send instructions to each communication node connected to the hub node to send a data snapshot of data being received and processed by the communication nodes at a particular time. In a trench line mode, a hub node sends instructions to each communication node to send trench line data back to the hub node. The hub node uses the data snapshot or trench line data to determine how to tune filter coefficients in the hub node to perform impairment compensation and improve performance of the communication system.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: A membrane surface monitoring system (MSM) and membrane surface monitoring cell for direct and unambiguous detection of membrane surface fouling and mineral scaling. The system includes a membrane surface monitoring system cell, a control valve, a retentate flow meter/transmitter and a controller. The MSM cell has a visually-observable membrane, an edge-lit light guide, an edge illumination light source, a retentate module, and a permeate module. A pressurized inlet stream is fed into the MSM cell. The feed contacts a membrane sheet, leading to membrane-based separation operation to produce retentate and permeate streams. The MSM cell integrates surface illumination and imaging components to allow direct real-time visualization and spectral imaging of the membrane surface in real time. The pressure on the feed-side of the MSM cells is approximately that of the membrane plant element being monitored such that the plant control system can adjust plant operating conditions.

Abstract: Systems and methods for data ingestion in real time are described herein. In an embodiment, a server computer receives a message comprising one or more client events from a storage device which publishes the message in response to storing the one or more client events. The server computer stores the one or more client events as raw event strings which are then parsed into parsed event strings. Identity resolution methods are performed on the parsed event strings. Feature groups are then identified in the parsed event strings and used to generate aggregation keys which are used to aggregate the feature groups prior to storing aggregated data in one or more aggregation tables.

Abstract: Provided are an in-memory management system and method using user traffic. According to embodiments thereof, traffic bandwidth is monitored for an ERP user who has left an LAN coverage area, by using an OpenFlow-based SDN WAN, and loading or unloading of a table partition set, an SQL Plan cache, and an SQL view cache in an in-memory is managed depending on a result of monitoring, so that the in-memory can be efficiently managed. The user traffic for the ERP user and the user can be monitored through the local network, and loading or unloading of table partitions of the in-memory can be managed on the basis of the monitored user traffic so that the in-memory can be managed regardless of Internet access. Data transmission rate and security can be enhanced because distributed communication is performed over an LAN and a WAN for each user and each ERP user.

Abstract: Techniques for prioritizing resources of various users, associated with a device, when responding to a user input received from the device are described. When a user input is received from a device, a system may generate a resource list for a group profile (e.g., a household profile) and each user profile (including any guest user profile) associated with the device. Each resource list may include the catalogs of resources (e.g., songs of a playlist, contacts of a contact list, etc.) of the group profile or user profile. The system may also generate a weight matrix including a respective weight for each catalog of each resource list. Various processing components (e.g., an automatic speech recognition component, a natural language understanding component, and an entity resolution component) may process using the resource lists and the weight matrix to determine an output responsive to the user input.

Abstract: Various embodiments are disclosed herein that generally relate to a three-dimensional (3D) hernia plug device and a method of manufacturing thereof. In at least one embodiment, there is disclosed a three-dimensional (3D) hernia plug, comprising: a waist portion extending, along a longitudinal extension axis, between a first waist end and a second waist end; one or more first overhangs coupled to the first waist end; and one or more second overhangs coupled to the second waist end, wherein the hernia plug is configured to translate between a pre-activation state and a post-activation state.

Abstract: Apparatus and computer-implemented method, including: presetting data points which include pairs of mutually assigned input and output of a Gaussian process; determining a positive semi-definite kernel matrix from inputs predetermined by the data points; determining an inverse of the kernel matrix depending on an estimation for an inverse of a 1-Lipschitz mapping of the kernel matrix; presetting an input for the Gaussian process; determining a prediction for an expected value of the Gaussian process, and/or a prediction for a variance of the Gaussian process; determining a probable output variable of a sensor and/or a control variable for a machine depending on at least one of the predictions.

Abstract: Systems and methods for computing a causal uplift in performance of an output action for one or more treatment actions in parallel are described herein. In an embodiment, a server computer receives interaction data for a particular period of time which identifies a plurality of users and a plurality of actions that were performed by each user of the plurality of users through a particular graphical user interface during the particular period of time. The server computer uses the interaction data to generate a feature matrix of actions for each user, and a set of confounding variables included to minimize spurious correlations. The feature matrix is then used to train a machine learning system, using data identifying a user’s performance or non-performance of each action as inputs and data identifying performance or non-performance of a target output action as the output.

Abstract: A particular method includes storing a manifest file and a parameters file at a data store. The manifest file and the parameters file are associated with a software deployment package. An application associated with the software deployment package is published to a first remote computing device and to a second remote computing device. The method includes receiving first parameter values from the first remote computing device and receiving second parameter values from the second remote computing device. The first parameter values are stored in a first region of the data store that is specific to the first computing device. The second parameter values are stored in a second region of the data store that is specific to the second computing device.

Abstract: Systems, computer-readable media, and methods are disclosed for parallel data processing for service function chains with network functions spanning multiple servers. An example system includes a first server hosting a first network function of a service function chain, a second server hosting a second network function of the service function chain, a mirror function deployed in a first switch to replicate a plurality of packets received by the system and to send respective copies of the plurality of packets to the first network function and to at least one of the second network function and a third network function of the service function chain, and a merge function deployed in a second switch to merge respective outputs of the first network function and the at least one of the second network function and the third network function.

Abstract: A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.