Abstract: Methods, systems, apparatuses, and computer program products are described for providing content recommendations to a requestor. In implementations, user event records associated with media items and content types may be stored, for instance, in an event catalog. A set of user scores may be generated based on a user-content score model and the user event records, where each generated user score represents an affinity between a user and a corresponding content type of the plurality of content types. A set of title scores may also be generated for a media item based on a title-content score model, where each generated title score represents an affinity between the media item and a corresponding content type. In response to a request, the sets of scores may be combined to generate a set of content recommendations to provide to the requestor.

Abstract: An insulator that has particular application for enclosing a switching device, such as a vacuum interrupter. The insulator includes a body having a top portion and a bottom portion, and a plurality of ring-shaped sheds extending from the body between the top portion and the bottom portion. The sheds are asymmetrical in an axial direction such that an axial dimension of the sheds at one side towards the front of the switching device is shorter than an axial dimension of the sheds at an opposite side towards the rear of the switching device. The axial dimension of the sheds uniformly increases from the one side to the opposite side.

Abstract: A machine tool management system connects an external server and a large number of NC devices controlling the external server and respective machine tools through a network. The system collects several kinds of signal data from the NC device of each machine tool to the external server. In the system, the external server stores a maintenance period model and a refinement algorithm and obtains a next maintenance period from the maintenance period model.

Abstract: Methods, systems, apparatuses, and computer program products are provided for a two-phase technique for generating content recommendations. In a first phase, a baseline recommender is configured to generate a baseline content recommendation using one or more content recommendation models, such as a Smart Adaptive Recommendations (SAR) model, Factorization Machine (FM) or Matrix Factorization (MF) models, collaborative filtering models, and/or any other machine-learning models or techniques. In a second phase, a personalized recommender implements a vector combiner configured to combine profile vectors, content vectors, and the baseline content recommendations to generate combined user vectors. A model generator may train a machine-learning model using the combined user vectors and training data comprising actual interaction behavior of the users, which may be then applied to identify a content recommendation for a particular user.

Abstract: A machine tool management system connects an external server and a large number of NC devices controlling the external server and respective machine tools through a network. The system collects several kinds of signal data from the NC device of each machine tool to the external server. In the system, the external server collects an estimated thermal distortion from the NC device and compares the estimated thermal distortion with pre-stored data to determine whether the tool is machining favorably.

Abstract: Methods, systems, apparatuses, and computer program products are provided for a two-phase technique for generating content recommendations. In a first phase, a baseline recommender is configured to generate a baseline content recommendation using one or more content recommendation models, such as a Smart Adaptive Recommendations (SAR) model, Factorization Machine (FM) or Matrix Factorization (MF) models, collaborative filtering models, and/or any other machine-learning models or techniques. In a second phase, a personalized recommender implements a vector combiner configured to combine profile vectors, content vectors, and the baseline content recommendations to generate combined user vectors. A model generator may train a machine-learning model using the combined user vectors and training data comprising actual interaction behavior of the users, which may be then applied to identify a content recommendation for a particular user.

Abstract: There is provided a thermal displacement correction apparatus for a machine tool which automatically determines necessity/unnecessity of actual measurement of the machine tool, the apparatus including: a thermal displacement correction unit that predicts a thermal displacement amount from an operation of a machine or a temperature of each portion of the machine and calculates a thermal displacement correction amount for correcting thermal displacement by adding, to a position command value of a feed axis, the thermal displacement correction amount for cancelling the thermal displacement amount thus predicted; and a thermal displacement correction amount adjustment unit that calculates an adjustment value for adjusting the thermal displacement correction amount on the basis of the thermal displacement correction amount, wherein a change amount ?E from the start of processing is obtained, ?E is compared with designated Em, and when ?E?Em, actual measurement is performed with measurement means.

Abstract: Methods, systems, apparatuses, and computer program products are described for providing content recommendations to a requestor. In implementations, user event records associated with media items and content types may be stored, for instance, in an event catalog. A set of user scores may be generated based on a user-content score model and the user event records, where each generated user score represents an affinity between a user and a corresponding content type of the plurality of content types. A set of title scores may also be generated for a media item based on a title-content score model, where each generated title score represents an affinity between the media item and a corresponding content type. In response to a request, the sets of scores may be combined to generate a set of content recommendations to provide to the requestor.

Abstract: A technique for training a machine learning model can use time-series data sampled from a population. The training includes creating a training set comprising feature vectors and corresponding labels generated using the time-series data. In some embodiments, for example, the feature vectors can include time-based features generated from the time-series data that preserves time information contained in the time-series data. The labels can be generated using data within a fixed period of time in the time-series data relative to a cut-off date. In some embodiments, the data used to create the training set can use a moving window sampling of the population to account for seasonal effects in the time-series data, where the cut-off date for generating the label varies from one sample to the next.

Abstract: Aspects of the technology described here provide systems, methods, computer-storage media, and the like, for identifying one or more influential users. A confidence level is associated with users based on one or more validation metrics. An influence ability score is computed based on weighted values indicated a number of connections and strength of the connections. The confidence level and the influence ability score are then utilized to compute an overall influential score used to rank users according to influence.

Abstract: Aspects provided herein are relevant to systems, methods, and techniques for classifying relationships between people (e.g., users of a platform or ecosystem) based on relationship data. In an example, the relationship data can be provided as input into a two-layer classification framework in which the first layer acts a filter for the second layer. The framework can identify relationships such as a self-relationship (e.g., two different accounts on the platform are operated by the same person), a non-self, family-member relationship (e.g., two users are different people but part of the same family), and a non-family-member relationship (e.g., the two users are different people and not part of the same family, such as coworkers or roommates).

Abstract: A thermal displacement correction apparatus for a machine tool first determines the coefficient k in E=a+k|F| where F is a thermal displacement correction amount and E is an adjustment value (first step). Next, in actual processing, a is set if a has not been set yet (second step). After a and the coefficient k are determined in advance, thermal displacement correction unit is enabled and an operation of a machining program is started. The thermal displacement correction amount F is calculated, the adjustment value E is calculated based on E=a+k|F|, a thermal displacement correction amount F? after adjustment (=E×F) is calculated, and F? is sent to the thermal displacement correction unit.

Abstract: There is provided a thermal displacement correction apparatus for a machine tool which automatically determines necessity/unnecessity of actual measurement of the machine tool, the apparatus including: a thermal displacement correction unit that predicts a thermal displacement amount from an operation of a machine or a temperature of each portion of the machine and calculates a thermal displacement correction amount for correcting thermal displacement by adding, to a position command value of a feed axis, the thermal displacement correction amount for cancelling the thermal displacement amount thus predicted; and a thermal displacement correction amount adjustment unit that calculates an adjustment value for adjusting the thermal displacement correction amount on the basis of the thermal displacement correction amount, wherein a change amount ?E from the start of processing is obtained, ?E is compared with designated Em, and when ?E?Em, actual measurement is performed with measurement means.

Abstract: A machine tool management system connects an external server and a large number of NC devices controlling the external server and respective machine tools through a network. The system collects several kinds of signal data from the NC device of each machine tool to the external server. In the system, the external server collects an estimated thermal distortion from the NC device and compares the estimated thermal distortion with pre-stored data to determine whether the tool is machining favorably.

Abstract: A thermal displacement correction apparatus for a machine tool first determines the coefficient k in E=a+k|F| where F is a thermal displacement correction amount and E is an adjustment value (first step). Next, in actual processing, a is set if a has not been set yet (second step). After a and the coefficient k are determined in advance, thermal displacement correction unit is enabled and an operation of a machining program is started. The thermal displacement correction amount F is calculated, the adjustment value E is calculated based on E=a+k|F|, a thermal displacement correction amount F? after adjustment (=E×F) is calculated, and F? is sent to the thermal displacement correction unit.