Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: The disclosed technology relates to a network agent for reporting to a network policy system. A network agent includes an agent enforcer and an agent controller. The agent enforcer is configured to implementing network policies on the system, access data associated with the implementation of the network policies on the system, and transmit, via an interprocess communication, the data to the agent controller. The agent controller is configured to generate a report including the data and transmit the report to a network policy system.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: An exercise recommendation system determines recommended weights for users to perform exercises with. The exercise recommendation system accesses a plurality of exercise pairs, each labeled with performance statistics of users who performed the exercises. Each exercise in an exercise pair is associated with a weight. The exercise recommendation system trains a machine learning model on the plurality of exercise pairs to determine a weight to recommend to a user for a first exercise based on performance statistics of the user associated with one or more second exercises, which are each in an exercise pair with the first exercise. The exercise recommendation system retrieves performance statistics of a target user including weights for exercises previously performed by the target user. The exercise recommendation system applies the machine learning model to the performance statistics to determine a target weight to recommend and modifies a user interface to include the target weight.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: In some implementations, a verification system may receive, from a user device, a request to validate an account. The verification system may initiate a microdeposit using an electronic rail, and the microdeposit may be associated with a human-readable description. The verification system may receive, from the user device, an indication of the human-readable description associated with the microdeposit. The verification system may therefore validate the account based on the indication.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system receives a profile of a user and a level of variance selected by the user. The profile includes a history of exercises the user has performed, available gym equipment, and exercise goals. The exercise recommendation system inputs the profile to a machine learning model configured to rank a set of exercises for a user to perform. The exercise recommendation system modifies the ranking of exercises based on the level of variance selected by the user. Modification of the ranking is greater for a first level of variance than for a second level of variance less than the first level of variance. The exercise recommendation system generates a workout plan for display within a user interface to the user based on the modified ranking.

Abstract: The disclosed technology relates to a network agent for reporting to a network policy system. A network agent includes an agent enforcer and an agent controller. The agent enforcer is configured to implementing network policies on the system, access data associated with the implementation of the network policies on the system, and transmit, via an interprocess communication, the data to the agent controller. The agent controller is configured to generate a report including the data and transmit the report to a network policy system.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system receives a profile of a user and a level of variance selected by the user. The profile includes a history of exercises the user has performed, available gym equipment, and exercise goals. The exercise recommendation system inputs the profile to a machine learning model configured to rank a set of exercises for a user to perform. The exercise recommendation system modifies the ranking of exercises based on the level of variance selected by the user. Modification of the ranking is greater for a first level of variance than for a second level of variance less than the first level of variance. The exercise recommendation system generates a workout plan for display within a user interface to the user based on the modified ranking.

Abstract: An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.

Abstract: An exercise recommendation system determines a current capability of a user. The exercise recommendation system accesses an exercise history for a user. The exercise history comprises an exercise performed by the user and a capability of the user each time the user performed the exercise. The exercise recommendation system partitions the exercise history into a plurality of time periods, and, for each time period, computes an aggregate capability of the user for the exercise during the time period. The exercise recommendation system calculates a moving average capability of the user for the exercise based on the aggregate capabilities and determines a current capability of the user for the exercise based on the moving average capability. The current capability of the user may be discounted at least in part based on how recently the user performed the exercise.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: An exercise recommendation system determines workout plans for users. The exercise recommendation system trains a machine-learned model configured to rank a set of exercises, and the ranking of exercises can be modified based on feedback from a user, for instance requesting that an exercise be recommended more frequently, less frequently, or never. The exercise recommendation system can also implement a machine-learned model configured to predict a measure of strength for the user, and can, in response to determining that the measure of strength of the user has decreased or plateaued over time, modify a workout for a user based on a muscle or muscle group associated with the measure of strength. Likewise, the exercise recommendation system can modify a workout in response to a predicted measure of strength being less than an actual measure of strength, for instance to include exercises targeting muscles associated with the measure of strength.

Abstract: Adaptive matching is provided, which is used to automatically match a block size of a transactional file system with an IO size of a client. An example method includes creating a share domain, wherein the share domain is created on a first file system, and a block size of the first file system is a first block size. The method further includes determining a block size of the share domain as a second block size, wherein the second block size is not equal to the first block size. If a block size of a second file system is the second block size, the share domain is migrated from the first file system to the second file system. By implementing the present application, it is possible to simplify user operations, improve operational convenience, and help to reduce storage space fragments and indirect blocks, thereby further improving the performance of a storage system.

Abstract: A virtualization platform for Network Functions Virtualization (NFV) is provided. The virtualization platform may include a host processor coupled to an acceleration coprocessor. The acceleration coprocessor may be a reconfigurable integrated circuit to help provide improved flexibility and agility for the NFV. The coprocessor may include multiple virtual function hardware acceleration modules each of which is configured to perform a respective accelerator function. A virtual machine running on the host processor may wish to perform multiple accelerator functions in succession at the coprocessor on a given data. In one suitable arrangement, intermediate data output by each of the accelerator functions may be fed back to the host processor. In another suitable arrangement, the successive function calls may be chained together so that only the final resulting data is fed back to the host processor.

Abstract: A virtualization platform for Network Functions Virtualization (NFV) is provided. The virtualization platform may include a host processor coupled to an acceleration coprocessor. The acceleration coprocessor may be a reconfigurable integrated circuit to help provide improved flexibility and agility for the NFV. The coprocessor may include multiple virtual function hardware acceleration modules each of which is configured to perform a respective accelerator function. A virtual machine running on the host processor may wish to perform multiple accelerator functions in succession at the coprocessor on a given data. In one suitable arrangement, intermediate data output by each of the accelerator functions may be fed back to the host processor. In another suitable arrangement, the successive function calls may be chained together so that only the final resulting data is fed back to the host processor.

Abstract: Adaptive matching is provided, which is used to automatically match a block size of a transactional file system with an IO size of a client. An example method includes creating a share domain, wherein the share domain is created on a first file system, and a block size of the first file system is a first block size. The method further includes determining a block size of the share domain as a second block size, wherein the second block size is not equal to the first block size. If a block size of a second file system is the second block size, the share domain is migrated from the first file system to the second file system. By implementing the present application, it is possible to simplify user operations, improve operational convenience, and help to reduce storage space fragments and indirect blocks, thereby further improving the performance of a storage system.

Abstract: A virtualization platform for Network Functions Virtualization (NFV) is provided. The virtualization platform may include a host processor coupled to an acceleration coprocessor. The acceleration coprocessor may be a reconfigurable integrated circuit to help provide improved flexibility and agility for the NFV. The coprocessor may include multiple virtual function hardware acceleration modules each of which is configured to perform a respective accelerator function. A virtual machine running on the host processor may wish to perform multiple accelerator functions in succession at the coprocessor on a given data. In one suitable arrangement, intermediate data output by each of the accelerator functions may be fed back to the host processor. In another suitable arrangement, the successive function calls may be chained together so that only the final resulting data is fed back to the host processor.

Abstract: A virtualization platform for Network Functions Virtualization (NFV) is provided. The virtualization platform may include a host processor coupled to an acceleration coprocessor. The acceleration coprocessor may be a reconfigurable integrated circuit to help provide improved flexibility and agility for the NFV. The coprocessor may include multiple virtual function hardware acceleration modules each of which is configured to perform a respective accelerator function. A virtual machine running on the host processor may wish to perform multiple accelerator functions in succession at the coprocessor on a given data. In one suitable arrangement, intermediate data output by each of the accelerator functions may be fed back to the host processor. In another suitable arrangement, the successive function calls may be chained together so that only the final resulting data is fed back to the host processor.