Abstract: There is provided a system and method of time series (TS) prediction. The method includes providing a machine learning (ML) network trained to perform TS prediction with respect to one or more components, the ML network configured with a set of hyperparameters including one or more hyperparameters associated with each component, the ML network comprising one or more ML modules operatively connected to an output layer, each ML module configured to represent a respective component in accordance with a given model characterized by the one or more hyperparameters associated therewith, where values of the hyperparameters associated with each component are automatically optimized during training of the ML network; and in response to a user's request for TS prediction, using the trained ML network to perform TS prediction, giving rise to a prediction result comprising an overall predicted TS, and one or more decomposed TS corresponding to respective components.

Abstract: There is provided a system and method of data prediction. The method includes obtaining a hierarchical data structure comprising a plurality of layers, each including one or more nodes; obtaining one or more machine learning (ML) models each corresponding to a respective node of at least some of the nodes in at least a given layer, in response to a user's request of prediction related to a given node in the given layer; generating a prediction result using a given ML model corresponding to the given node; upon receiving the user's feedback, selecting one or more configuration parameters of the given ML model related to the feedback; updating the selected configuration parameters according to additional factors in the feedback, and re-training the given ML model to obtain a re-trained ML model,—and using the re-trained ML model to generate an updated prediction result to be sent to the user.

Abstract: There is provided a system and method of data prediction. The method includes obtaining a hierarchical data structure comprising a plurality of layers, each including one or more nodes; obtaining one or more machine learning (ML) models each corresponding to a respective node of at least some of the nodes in at least a given layer, in response to a user's request of prediction related to a given node in the given layer; generating a prediction result using a given ML model corresponding to the given node; upon receiving the user's feedback, selecting one or more configuration parameters of the given ML model related to the feedback; updating the selected configuration parameters according to additional factors in the feedback, and re-training the given ML model to obtain a re-trained ML model—and using the re-trained ML model to generate an updated prediction result to be sent to the user.

Abstract: A method of deploying machine learning-based models may include identifying a profile of a target execution environment to implement a machine learning-based model in communication with a cloud infrastructure. The method may further include identifying, using the profile, a software module implementing the model. The method may further include causing the software module to be uploaded from a code repository associated with the cloud infrastructure to the target execution environment.

Abstract: A system for delivering content desired by a specific user among a plurality of users directly to a user client of the specific user via the Internet is disclosed. The system comprises at least one server including a memory. The at least one server is designed and configured for (a) receiving a user profile from the specific user, the user profile defining user preferences with respect to a plurality of criteria; (b) storing a plurality of content items in the memory, each of the items having a content profile with respect to the plurality of criteria; (c) assembling a packet of content items, each of the items in the packet being characterized by a correspondence between the user profile and the content profile; and (d) sending the packet to the user client of the specific user.

Abstract: A roller cam follower has a transverse bearing shaft that is restrained against substantial lateral motion in a follower body supporting the shaft which rotatably carries a cam follower roller. The follower roller is rotatably mounted in a recess between opposite sides of the body on the transverse bearing shaft, which is supported in shaft bores through the sides of the body. One or more retainers are pressed onto the shaft within the recess and are operatively engageable with the sides of the body to limit axial motion of the shaft in said shaft bores. Various embodiments of retainers are disclosed as are applications to various types of roller cam followers, such as valve lifters, finger followers and rocker arms.

Abstract: A cam follower roller bearing shaft is restrained against lateral motion in shaft support bores of the follower body by plugging the bore ends with retainer plugs in or adjacent the ends of the bores. Soft metal or plastic bearing materials may be pressed or molded in the bore ends or in recesses in the follower body at the ends of the bores. Other forms of mechanical retention are disclosed. A fixture for pressing in plugs cut from plastic strips is also disclosed. Applications of the plugs in roller valve lifters, roller finger followers and rocker arm roller followers are disclosed.