This application provides an in-vehicle user positioning method, an in-vehicle interaction method, a vehicle-mounted apparatus, and a vehicle. In an example, the in-vehicle user positioning method includes: obtaining a sound signal collected by an in-vehicle microphone; in response to that a first voice command is recognized from the sound signal, determining a first user who sends the first voice command; and determining an in-vehicle location of the first user based on a mapping relationship between an in-vehicle user and an in-vehicle location.

A UE may train a NN, based on a blockage of a beam transmission, to indicate one or more beam weights in association with the blockage of the beam transmission. The UE may store, in an ML database, information indicative of at least one of the trained NN or the one or more beam weights indicated via the trained NN, such that the UE may communicate, to an ML server, the information via the trained NN. The ML server may train the NN, based on a TL/FL procedure for the one or more beam weights associated with the at least one blockage, to indicate one or more TL/FL beam weights in association with the at least one blockage, and communicate, to at least one UE, information indicative of at least one of the trained NN or the one or more TL/FL beam weights indicated via the trained NN.

An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

Systems and methods for correction of sigma-delta analog-to-digital converters (ADCs) using neural networks are described. In an illustrative, non-limiting embodiment, a device may include: an ADC; a filter coupled to the ADC, where the filter is configured to receive an output from the ADC and to produce a filtered output; and a neural network coupled to the filter, where the neural network is configured to receive the filtered output and to produce a corrected output.

Techniques are generally described for causal impact estimation using machine learning. A first machine learning model is trained using non-treatment variables during training. A second machine learning model uses learned weights from the first machine learning model for non-treatment variables and is trained on one or more treatment variables. The second machine learning model estimates outcomes based on the presence or absence of an event represented by the treatment variable. Selection bias is reduced by warm-starting the second machine learning model with non-treatment variable weights learned during training of the first machine learning model.

The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

A method of processing data related to a machine learning model, executed by a computer including a memory including a memory area and a processor, includes: compressing the data in a course of calculation of a first calculation process, to generate compressed data; storing the generated compressed data in the memory area; and executing a second calculation process by using the compressed data stored in the memory area.

Systems, methods, and protocols for developing invasive brain computer interface (iBCI) decoders non-invasively by using emulated brain data are provided. A human operator can interact in real-time with control algorithms designed for iBCI. An operator can provide input to one or more computer models (e.g., via body gestures), and this process can generate emulated brain signals that would otherwise require invasive brain electrodes to obtain.

A data processing method includes: obtaining to-be-processed data and a target neural network model, where the target neural network model includes a first transformer layer, the first transformer layer includes a first residual branch and a second residual branch, the first residual branch includes a first attention head, and the second residual branch includes a target feed-forward network (FFN) layer; and performing target task related processing on the to-be-processed data based on the target neural network model, to obtain a data processing result, where the target neural network model is for performing a target operation on an output of the first attention head and a first weight value to obtain an output of the first residual branch, and/or the target neural network model is for performing a target operation on an output of the target FFN and a second weight value to obtain an output of the second residual branch.