Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: An acoustic absorber for a gas turbine engine includes a back sheet, a face sheet spaced apart from the back sheet and defining a plurality of perforations, and a core layer positioned between the back sheet and the face sheet and comprising a plurality of cells. The core layer comprises an outer wall extending between the back sheet and the face sheet to define an outer boundary of at least one cell of the plurality of cells and an inner wall positioned within the outer boundary to divide the at least one cell of the plurality of cells into an outer damping volume and an inner damping volume, the inner damping volume being at least partially surrounded by the outer damping volume.

Abstract: A system and method for scheduling tasks associated with changing a personality of a ticketing interface. One or more processors generate interaction scores for each of the plurality of user devices based on receiving interactions between the ticketing engine and a plurality of user devices. The system further generate interaction patterns for each of the plurality of user devices that include a relation between the interaction scores generated for each of the plurality of user devices with the interactions from the plurality of user devices. The system further classify each of the plurality of user devices based on the generated interaction patterns to identify whether a user device from the plurality of user devices is a fraudulent or a non-fraudulent user device and modify interface of the ticketing engine based on the classification of each of the plurality of user devices.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Some embodiments relate to a method for performing a battery power-based control of an in-call experience based on shared battery power information. Some embodiments relate to a coating of a headset that has a first emissivity over an ultraviolet band and a near-infrared band, a second emissivity over a visible band, and a third emissivity over a mid-to-far infrared band. Some embodiments relate to an aggregate coating of a headset. A thin films is applied to a surface of the headset, and a paint coating is applied to a surface of the thin film to form the aggregate coating. Some embodiments relate to a method for a high-definition multimedia interface derived network timing for distributed audio-video synchronization. Some embodiments relate to a battery containment structure with a metal chassis having surfaces coated with electrical insulators configured to receive a battery, and a lid coupled to the metal chassis.

Abstract: Disclosed are embodiments for information barriers that are conditional on the type of information being communicated. Information barrier polices provided by the disclosed embodiments selectively allow communication between accounts or groups based on characteristics of the content of the communication. For example, communication between a marketing department and an engineering department may be conditional on the communication not including any sensitive information. The determination of whether the communication includes sensitive information is further designed to provide good performance even in environments that maintain substantial portions of data in an offsite or cloud environment, where latencies associated with searching large datastores can be prohibitive.

Abstract: A method for controlling transactional processing system having transactions that include multiple tasks, a throughput limit a transaction processing time limit includes allocating a plurality of threads to be used by multiple tasks to achieve a throughput approximating the throughput limit. The method assigns the multiple tasks to the plurality of threads and assigns respectively different processing delays to the plurality of threads. The processing delays span an interval less than the transaction processing time limit. The method processes the multiple tasks within the transaction processing time limit by executing the plurality of threads at times determined by the respective processing delays.

Abstract: A heat engine including a liner extended along a first dimension and a second dimension, wherein the liner includes a plurality of cavities formed by a honeycomb structure. Each cavity forms a volume extended from an opening at a fluid contact side of the liner and along a third dimension substantially orthogonal to the first dimension and the second dimension. The opening at the fluid contact side includes a cross sectional area of each respective volume of the plurality of cavities. The plurality of cavities is non-uniform with respect to the cross sectional area at the opening along the first dimension, the second dimension, or both.

Abstract: An apparatus is disclosed. The apparatus comprises a system on chip (SOC), including a plurality of hardware components and a processor to launch a secure execution environment to verify integrity of the plurality of hardware components using an expected integrity measurement generated based on properties of the plurality of hardware components.

Abstract: An apparatus is disclosed. The apparatus comprises a processor to generate a launch control policy (LCP) comprising a plurality of measured launch environment (MLE) elements associated with one or more libraries of a software application and a cryptographic processor to deploy the LCP to generate root of trust (ROT) measurements, wherein the processor to perform launch verification to execute the one or more libraries upon execution and to verify integrity of the one or more libraries based on the associated MLEs and the ROT measurements.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an inferred subscription level based at least in part on the type associated with each notification; classifying the notifications based on the inferred subscription of each notification; and sending one or more of the notifications to the user. Each of the sent notifications has an inferred subscription level higher than a pre-determined threshold subscription level.

Abstract: Methods and apparatus for selection of radio access technology (RAT) based on device usage patterns are provided. A User Equipment (UE) obtains information relating to one or more Quality of Service (QoS) metrics for communication of data by the UE. The UE designates a Radio Access Technology (RAT) from a plurality of available RATs as a preferred RAT for the communication, based on the obtained information.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: In one embodiment, a method includes receiving a number of notifications of activity relevant to a user. Each notification has an associated type. The method also includes calculating an interest of each activity to the user based at least in part on the type of each notification; ranking the notifications based at least in part on the calculated interest; and sending one or more of the notifications to the user. Each of the sent notifications has a ranking higher than a pre-determined threshold ranking.

Abstract: A method for controlling transactional processing system having transactions that include multiple tasks, a throughput limit a transaction processing time limit includes allocating a plurality of threads to be used by multiple tasks to achieve a throughput approximating the throughput limit. The method assigns the multiple tasks to the plurality of threads and assigns respectively different processing delays to the plurality of threads. The processing delays span an interval less than the transaction processing time limit. The method processes the multiple tasks within the transaction processing time limit by executing the plurality of threads at times determined by the respective processing delays.

Abstract: Disclosed are embodiments for information barriers that are conditional on the type of information being communicated. Information barrier polices provided by the disclosed embodiments selectively allow communication between accounts or groups based on characteristics of the content of the communication. For example, communication between a marketing department and an engineering department may be conditional on the communication not including any sensitive information. The determination of whether the communication includes sensitive information is further designed to provide good performance even in environments that maintain substantial portions of data in an offsite or cloud environment, where latencies associated with searching large datastores can be prohibitive.