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: Disclosed is a method for allocating resources for the low power backscatter devices in a sparse environment with a beamspace channel model in a symbiotic radio system. In other words, the method provides an adaptive power allocation mechanism to enhance the power of the beams incident on the backscatter devices for signal detection at the receiver in the beamspace channel model.

Abstract: Disclosed is a method for allocation of the secure pilots for integrated sensing and communication against potential security threats.

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 multi-stage transimpedance amplifier comprises a first gain stage cascaded with a second gain stage, the second gain stage's output connected to the first gain stage's inverting input and to the second gain stage's inverting input, a compensation network electrically connected between the first gain stage's output and the second gain stage's output, the first gain stage, the second gain stage, and the compensation network together implementing a transfer function having complex conjugate poles and a real-valued zero, the compensation network comprising a resistor electrically connected in series with a capacitor, the resistance of the resistor and the capacitance of the capacitor determining the positioning of the complex conjugate poles, and a third gain stage cascaded with the second gain stage for introducing an additional pole in the transfer function, the third gain stage's output connected to the second gain stage's non-inverting input.

Abstract: A method for determining a state of a transmission for a vehicle includes providing an input for a first generative model depending on a route information, a vehicle speed, a probabilistic variable, and an output of a second physical model, and determining an output of the first model characterizing the state of the transmission in response to the input for the first model. The first model includes a first layer trained to map input to an intermediate state and a second layer trained to map the intermediate state to the state of the transmission depending on the output of the second model. The method includes providing an input for the second physical model depending on at least one vehicle state and/or the route information, and determining an output of the second model in response to the input for the second model. The output of the second model characterizes limit(s) for the intermediate state.

Abstract: Explanation jobs may be performed for computer vision tasks. A request to execute an explanation job for a computer vision machine learning model may be received. The execution job may be performed, including extracting different features from the image, determining the respective relative importance values of the different features on inferences generated by the computer vision machine learning model. The result of the explanation job, including the generated heat maps may be provided.

Abstract: A feedback-based intervention system includes a data collector configured to collect profile data and life log data of a user, a user data quantification unit configured to extract at least one quantification factor through an evaluation process of the user based on the collected profile data, and to quantify behavior data of the collected life log data, which corresponds to the extracted quantification factor, an intervention information controller configured to determine a health status of the user based on the quantified behavior data, and to generate at least one piece of intervention information based on a determination result of the health status, and a communication unit configured to provide the generated intervention information to the user, wherein the intervention information controller receives feedback information of the user, corresponding to the provided intervention information, and adjusts a provision schedule of the intervention information based on the received feedback information.

Abstract: Techniques are described for implementing an out-of-band communication channel used to exchange control channel information in sub-carrier-based optical communication systems. In an example implementation, a transmitter includes a laser operable to supply an optical signal, a digital signal processor operable to supply first electrical signals based on first data input to the digital signal processor and second data input to the digital signal processor, digital-to-analog conversion circuitry operable to output second electrical signals based on the first electrical signals, modulator driver circuitry is operable to output third electrical signals based on the second electrical signals, and an optical modulator operable to supply first and second modulated optical signals based on the third electrical signals. The first modulated optical signal includes a plurality of optical subcarriers carrying user data. The plurality of optical subcarriers also being amplitude modulated to carry control information.

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: 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: 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 are disclosed for parallelizing service function chains.

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 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 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.