Techniques for generating multiple-resolutions of time series data are described. An input irregular time series having a plurality of data points is obtained, each data point of the plurality of data points including a timestamp and a feature vector. Based on the input irregular time series, multiple variant time series are generated. A data point in one of the variant time series is based in part on a combination of at least two data points of the input irregular time series. The multiple variant time series can then be used for machine learning tasks such as training a machine learning model or using a machine learning model to infer an output.

A method of training a neural network for classifying an image into one of a plurality of classes, the method comprising: extracting, from the neural network, a plurality of subclass center vectors for each class; inputting an image into the neural network, wherein the image is associated with a predetermined class; generating, using the neural network, an embedding vector corresponding to the input image; determining a similarity score between the embedding vector and each of the plurality of subclass center vectors; updating parameters of the neural network in dependence on a plurality of the similarity scores using an objective function; extracting a plurality of updated parameters from the neural network; and updating each subclass center vector in dependence on the extracted updated parameters.

Among other things, we describe techniques for detecting objects in the environment surrounding a vehicle. A computer system is configured to receive a set of measurements from a sensor of a vehicle. The set of measurements includes a plurality of data points that represent a plurality of objects in a 3D space surrounding the vehicle. The system divides the 3D space into a plurality of pillars. The system then assigns each data point of the plurality of data points to a pillar in the plurality of pillars. The system generates a pseudo-image based on the plurality of pillars. The pseudo-image includes, for each pillar of the plurality of pillars, a corresponding feature representation of data points assigned to the pillar. The system detects the plurality of objects based on an analysis of the pseudo-image. The system then operates the vehicle based upon the detecting of the objects.

The present disclosure provides a method and apparatus for evaluating a matching degree based on artificial intelligence, a device and a storage medium, wherein the method comprises: respectively obtaining word expressions of words in a query and word expressions of words in a title; respectively obtaining context-based word expressions of words in the query and context-based word expressions of words in the title according to the word expressions; generating matching features according to obtained information; determining a matching degree score between the query and the title according to the matching features. The solution of the present disclosure may be applied to improve the accuracy of the evaluation result.

The present disclosure provides a method and apparatus for evaluating a matching degree of multi-domain information based on artificial intelligence, a device and a medium. The method comprises: respectively obtaining valid words in a query, and valid words in each information domain in at least two information domains in a to-be-queried document; respectively obtaining word expressions of valid words in the query and word expressions of valid words in said each information domain in at least two information domains in the to-be-queried document; based on the word expressions, respectively obtaining context-based word expressions of valid words in the query and context-based word expressions of valid words in said each information domain; generating matching features corresponding to said each information domain according to the obtained information; determining a matching degree score between the query and the to-be-queried document according to the matching features corresponding to said each information domain.

A feature extraction method of fruit spectrum includes taking a vector of each wavelength point in spectrum of samples as source data, and acquiring a sorting of all vectors by processing the source data by SPA; according to the sorting of the vectors, acquiring distribution points of each sample on a coordinate system; acquiring classification results of the samples by destructive analysis, and acquiring a number of first sample categories; acquiring a first Euclidean distance between the first sample categories; according to a sorting of the wavelength points, acquiring distribution points of each sample on the coordinate system; acquiring a number of second sample categories; acquiring a second Euclidean distance between the second sample categories; determining whether the first Euclidean distance is less than the second Euclidean distance; determine a (M+2)-th vector to be valid or invalid based on a comparison result.

Methods, apparatus, systems, and articles of manufacture to translation between sign language and spoken language are disclosed. An example apparatus includes processor circuitry to at least one of instantiate or execute machine readable instructions to identify a plurality of candidate signs across different frames in video; associate a respective gloss to respective ones of the candidate signs; associate a respective confidence score with the respective glosses; identify overlapping frames of the candidate signs; select one or more of the candidate signs as performed signs based on the respective confidence scores and overlapping frames; and convert the performed signs to audio data.

Fitness tracking devices and methods of operating the same. The fitness tracking device includes a sensor circuit to generate sensor data; a processor coupled to the sensor circuit; and a memory coupled to the processor and storing processor-executable instructions that, when executed, configure the processor to: buffer sensor data associated with motion of the user limb; generate an exercise prediction based on a prediction model and the sensor data, the prediction model defined by one or more oscillating signal profiles to identify genus predictions for respective limb movement types about at least one sensor axis, wherein the exercise prediction is generated based on a combination of an identified genus prediction associated with the generated sensor data and environment data associated with motion of the user limb; and transmit a signal representing the exercise prediction for display on a user interface.

In an embodiment, an image-capture system, includes an image-capture device and computing circuitry. The image-capture device is configured to capture an image of a region of space that includes an object. And the computing circuitry is coupled to the image-capture device and is configured to detect a representation of the object in the image, to determine a representation of a boundary of the detected representation, to provide image information corresponding to the detected representation to an image-analysis system, to receive, from the image-analysis system, an identifier of a category to which the object belongs, and a descriptor of the object, and to generate a representation of a list that includes the identifier and the descriptor.

Training and/or using a machine learning model for performing robotic tasks is disclosed herein. In many implementations, an environment-conditioned action sequence prediction model is used to determine a set of actions as well as a corresponding particular order for the actions for the robot to perform to complete the task. In many implementations, each action in the set of actions has a corresponding action network used to control the robot in performing the action.