A method of updating a trained cardinality estimation model includes receiving a cardinality estimation model with cardinality labels and detecting a drift in underlying data or predicates of the cardinality estimation model. The type of the detected drift is determined and new test queries that mimic test queries for the detected drift are synthesized. A portion of the synthesized test queries is selected to reduce annotation cost and used to update the cardinality estimation model.

A method and a system for generating a dynamic addictive neural circuit based on weakly supervised contrastive learning are disclosed. The method includes: based on a convolutional neural network, reducing a dimensionality of voxels of multiple groups of fMRI to attributes of brain region nodes, and generating multiple groups of dynamic brain connection maps containing time series based on the attributes of the brain region nodes; extracting spatio-temporal features of brain connections in the dynamic brain connection maps; inputting the spatio-temporal features into an abnormal connection detection network, calculating an abnormal probability of brain connections based on contrastive learning, and obtaining the brain connection with a highest abnormal probability at each time point; and generating the dynamic addictive neural circuit based on neuroscientific prior knowledge and the brain connection with the greatest probability of abnormality.

A method and a system for generating a dynamic addictive neural circuit based on weakly supervised contrastive learning are disclosed. The method includes: based on a convolutional neural network, reducing a dimensionality of voxels of multiple groups of fMRI to attributes of brain region nodes, and generating multiple groups of dynamic brain connection maps containing time series based on the attributes of the brain region nodes; extracting spatio-temporal features of brain connections in the dynamic brain connection maps; inputting the spatio-temporal features into an abnormal connection detection network, calculating an abnormal probability of brain connections based on contrastive learning, and obtaining the brain connection with a highest abnormal probability at each time point; and generating the dynamic addictive neural circuit based on neuroscientific prior knowledge and the brain connection with the greatest probability of abnormality.

Systems and methods for calculating external bone loss for alignment of pre-diseased joints comprising: generating a three-dimensional (“3D”) computer model of an operative area from at least two two-dimensional (“2D”) radiographic images, wherein at least a first radiographic image is captured at a first position, and wherein at least a second radiographic image is captured at a second position, and wherein the first position is different than the second position; identifying an area of bone loss on the 3D computer model; and applying a surface adjustment algorithm to calculate an external missing bone surface fitting the area of bone loss.

A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing brain emulation neural networks using compressed matrix representations. One of the methods includes obtaining a network input; and processing the network input using a neural network to generate a network output, comprising: processing the network input using an input subnetwork of the neural network to generate an embedding of the network input; and processing the embedding of the network input using a brain emulation subnetwork of the neural network, wherein the brain emulation subnetwork has a brain emulation neural network architecture that represents synaptic connectivity between a plurality of biological neurons in a brain of a biological organism, the processing comprising: obtaining a compressed matrix representation of a sparse matrix of brain emulation parameters; and applying the compressed matrix representation to the embedding of the network input to generate a brain emulation subnetwork output.

Technology for inspection for detecting a defect of a printed matter using machine logic informed by machine learning. Some embodiments of the present invention may include one, or more, of the following features: (i) generates defect datasets; (ii) generates defect libraries; (iii) uses the generated defect libraries for deep learning training; and (iv) uses machine learning to detect defects using computer code (for example, a *.jpg format file) corresponding to an image of a piece of printed matter instead of using a visual image (that is, an image of the type that is created when a person takes a picture using a traditional film camera).

Adversarial patches can be inserted into sample pictures by an adversarial image generator to realistically depict adversarial images. The adversarial image generator can be utilized to train an adversarial patch generator by inserting generated patches into sample pictures, and submitting the resulting adversarial images to object detection models. This way, the adversarial patch generator can be trained to generate patches capable of defeating object detection models.

A method and device for harmony-aware audio-driven motion synthesis are provided. The method includes determining a plurality of testing meter units according to an input audio, each testing meter unit corresponding to an input audio sequence of the input audio, obtaining an auditory input corresponding to each testing meter unit, obtaining an initial pose of each testing meter unit as a visual input based on a visual motion sequence synthesized for a previous testing meter unit, and automatically generating a harmony-aware motion sequence corresponding to the input audio using a generator of a generative adversarial network (GAN) model. The GAN model is trained by incorporating a hybrid loss function. The hybrid loss function includes a multi-space pose loss, a harmony loss, and a GAN loss. The harmony loss is determined according to beat consistencies of audio-visual beat pairs.

Methods, systems, and computer readable media for network traffic generation using machine learning. An example method includes collecting first traffic from a production data center environment. At least a portion of the first traffic comprises live computer network traffic transiting the production data center environment. The method includes collecting second traffic from an emulated data center testbed device. At least a portion of the second traffic comprises testbed traffic that transits an emulated data center switching fabric of the emulated data center testbed device. The method includes training a traffic generation inference engine using the first traffic and the second traffic.

The method includes generating, using the traffic generation inference engine, test traffic to test or stimulate a network system under test (SUT).