Disclosed are systems, apparatuses, and methods to determine, communicate, and/or respond to state information of at least one of a suspended load control apparatus, carrier, or load suspended by a cable from the carrier, wherein response to the state information may be to control at least one of the suspended load control apparatus, carrier, or load suspended by a cable from the carrier.

Disclosed are systems, apparatuses, and methods to determine, communicate, and/or respond to state information of at least one of a suspended load control apparatus, carrier, or load suspended by a cable from the carrier, wherein response to the state information may be to control at least one of the suspended load control apparatus, carrier, or load suspended by a cable from the carrier.

Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

Approximating a more complex multi-objective feed item scoring model using a less complex single objective feed item scoring model in a multistage feed ranking system of an online service. The disclosed techniques can facilitate multi-objective optimization for personalizing and ranking feeds including balancing personalizing a feed for viewer experience, downstream professional or social network effects, and upstream effects on content creators. The techniques can approximate the multi-objective model—that uses a rich set of machine learning features for scoring feed items at a second pass ranker in the ranking system—with the more lightweight, single objective model—that uses fewer machine learning features at a first pass ranker in the ranking system. The single objective model can more efficiently score a large set of feed items while maintaining much of the multi-objective model's richness and complexity and with high recall at the second pass ranking stage.

In an apparatus for identifying an obstacle around a vehicle, an acquirer is configured to acquire an image captured by a camera mounted to the vehicle. An extractor is configured to extract feature points of the image. A generator is configured to generate an optical flow that is a movement vector from each of the feature points of the image acquired before the current time to a corresponding feature point of the image acquired at the current time. A classifier configured to classify the optical flows into groups each corresponding to an object in the image based on pixel positions of the feature points. An identifier is configured to, for each of the groups that the optical flows are classified by the classifier into, identify whether an object corresponding to the group in the image is a stationary object or a moving object based on a degree of variability in lengths of the optical flows of the group.

In an apparatus for identifying an obstacle around a vehicle, an acquirer is configured to acquire an image captured by a camera mounted to the vehicle. An extractor is configured to extract feature points of the image. A generator is configured to generate an optical flow that is a movement vector from each of the feature points of the image acquired before the current time to a corresponding feature point of the image acquired at the current time. A classifier configured to classify the optical flows into groups each corresponding to an object in the image based on pixel positions of the feature points. An identifier is configured to, for each of the groups that the optical flows are classified by the classifier into, identify whether an object corresponding to the group in the image is a stationary object or a moving object based on a degree of variability in lengths of the optical flows of the group.

A modeling method and an apparatus are disclosed. The method includes: obtaining a first data set of a first indicator, and determining, based on the first data set, a second indicator similar to the first indicator; and determining a first model based on one or more second models associated with the second indicator. The first model is used to detect a status of the first indicator, and the status of the first indicator includes an abnormal state or a normal state. The second models are used to detect a status of the second indicator, and the status of the second indicator includes an abnormal state or a normal state.

A data sharing system for sharing datasets of data providers to data consumers and transferring incentives from the data consumers to the data providers in response to the data-sharing. The system comprises a multi-angle alliance guided data valuation module for fair allocation of the incentives between the data consumers. The system also comprises a flexible-scenario routed dataset comparison module for evaluating the data provided by the data providers via one of a plurality of evaluating routes. The system provides enhanced use of computer cloud and enables both data alliance and growing capacity of artificial intelligence (AI) supermodels for sustainable data sharing. Moreover, the system uses coreset based Shapley valuation method for efficient data valuation.

An electronic apparatus performs a method of recovering a complete and dense point cloud from a partial point cloud. The method includes: constructing a sparse but complete point cloud from the partial point cloud through a contrastive teacher-student neural network; and transforming the sparse but complete point cloud to the complete and dense point cloud. In some embodiments, the contrastive teacher-student neural network has a dual network structure comprising a teacher network and a student network both sharing the same architecture. The teacher network is a point cloud self-reconstruction network, and the student network is a point cloud completion network.

A method for receiving a full training data set including a plurality of individual training data set, dividing the plurality of individual training sets into N classes, where N is an integer greater than three, dividing the N classes into M full data classes and N-M partial data classes, performing training to obtain a trained fixed size machine learning (ML) classification model and a trained in-class confidence model, outputting a first set of prediction value(s) based on the performance of training, distributing each class of the N classes of individual training data sets to a different node of a distributed machine learning system; and outputting, from the nodes of the distributed machine learning system, a second set of prediction value(s) for each class of the N classes.