Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

Techniques are disclosed that enable automating user interface input by generating a sequence of actions to perform a task utilizing a multi-agent reinforcement learning framework. Various implementations process an intent associated with received user interface input using a holistic reinforcement policy network to select a software reinforcement learning policy network. The sequence of actions can be generated by processing the intent, as well as a sequence of software client state data, using the selected software reinforcement learning policy network. The sequence of actions are utilized to control the software client corresponding to the selected software reinforcement learning policy network.

A method may include obtaining a document and using a first prediction model to generate text block scores for text blocks in the document, where a first text block of the text blocks is associated with a first text block score of the plurality of text block scores. The method also includes updating, in response to the first text block score for the first text block failing to satisfy a criterion, a modified version of the document with an indicator to set the first text block as a hidden text block in a presentation of the modified version. The method also includes generating a summarization of the first text block based on the words in the first text block and updating the modified version of the document to include the summarization. The method also includes providing the modified version of the document to a user device.

A method may include obtaining a document and using a first prediction model to generate text block scores for text blocks in the document, where a first text block of the text blocks is associated with a first text block score of the plurality of text block scores. The method also includes updating, in response to the first text block score for the first text block failing to satisfy a criterion, a modified version of the document with an indicator to set the first text block as a hidden text block in a presentation of the modified version. The method also includes generating a summarization of the first text block based on the words in the first text block and updating the modified version of the document to include the summarization. The method also includes providing the modified version of the document to a user device.

The technology of this application relates to an entropy encoding method that includes obtaining base layer information of a to-be-encoded picture block, where the base layer information corresponds to M samples in the picture block, and M is a positive integer, obtaining K elements corresponding to enhancement layer information of the picture block, where the enhancement layer information corresponds to N samples in the picture block, both K and N are positive integers, and N≥M, inputting the base layer information into a neural network to obtain K groups of probability values, where the K groups of probability values correspond to the K elements, and any group of probability values is for representing probabilities of a plurality of candidate values of a corresponding element, and performing entropy encoding on the K elements based on the K groups of probability values.

Systems and methods for scoring and visualizing the effects of variants in biological sequences. Variants may include substitutions, insertions and deletions. The method comprises encoding biological sequences as vector sequences and then operating a neural network in the forward-propagation mode and possibly in the back-propagation mode to compute variant scores. Variant scores are determined by normalizing the gradients. Variant scores may be used to select a subset of variants, which are then used to produce modified vector sequences which are analyzed by the neural network operating in forward-propagation mode, to determine improved variant scores. The variant scores may be visualized using black and white, greyscale or colored elements that are arranged in blocks with dimensions corresponding to different possible symbols and the length of the sequence. These blocks are aligned with the biological sequence, which is illustrated by a symbol sequence arranged in a line.

An autonomous personal companion executing a method including capturing data related to user behavior. Patterns of user behavior are identified in the data and classified using predefined patterns associated with corresponding predefined tags to generate a collected set of one or more tags. The collected set is compared to sets of predefined tags of a plurality of scenarios, each to one or more predefined patterns of user behavior and a corresponding set of predefined tags. A weight is assigned to each of the sets of predefined tags, wherein each weight defines a corresponding match quality between the collected set of tags and a corresponding set of predefined tags. The sets of predefined tags are sorted by weight in descending order. A matched scenario is selected for the collected set of tags that is associated with a matched set of predefined tags having a corresponding weight having the highest match quality.

A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.

Neuromorphic methods, systems and devices are provided. The embodiment may include a neuromorphic device which may comprise a crossbar array structure and an analog circuit. The crossbar array structure may include N input lines and M output lines interconnected at junctions via N×M electronic devices, which, in preferred embodiments, include, each, a memristive device. The input lines may comprise N.sub.1 first input lines and N.sub.2 second input lines. The first input lines may be connected to the M output lines via N.sub.1×M first devices of said electronic devices. Similarly, the second input lines may be connected to the M output lines via N.sub.2×M second devices of said electronic devices. The analog circuit may be configured to program the electronic devices so as for the first devices to store synaptic weights and the second devices to store neuronal states.

Disclosed is a method for recognizing a marine object based on hyperspectral data including collecting target hyperspectral data; preprocessing the target hyperspectral data; and detecting and identifying an object included in the target hyperspectral data based on a marine object detection and identification model, trained through learning of the detection and identification of the marine object. According to the present invention, the preprocessing and processing of the hyperspectral data collected in real time according to a communication state may be performed in the sky or on the ground.