Disclosed are a method for accelerating early determination training. The method for accelerating early determination training includes a timestep splitting operation of splitting a timestep, a membrane potential measuring operation of measuring first and second membrane potentials for each splitted timestep during a current training process, a threshold value calculation operation of calculating a threshold value to be used in a subsequent training process based on the first and second membrane potentials, and when a difference between the first and second membrane potentials in the splitted timestep is greater than the threshold value, an early training termination operation of determining that the image does not have the training contribution and terminating training at the splitted timestep.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.

A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.

A method implemented by one or more computing systems includes accessing content viewing data associated with a first user account, wherein the first user account is associated with one or more client devices. The content viewing data includes temporal-based content viewing data. The method further includes determining, using one or more sequence models, a set of content viewing features based on the temporal-based content viewing data, and concatenating the content viewing features into a single computational array. The method further includes providing, through one or more dense layers of a deep-learning model, the single computational array to an output layer of the deep-learning model, and calculating, based on the output layer, one or more probabilities for one or more labels for the first user account. Each label includes a predicted attribute for the first user account.

The present invention relates to an Ising Machine utilizing a network of spin Hall nano-oscillators (SHNOs) suitable or computational tasks such as optimization problems. The spin Hall nano-oscillator based Ising machine is provided with a tuning nitarranged to effect the characteristics of at least one individual spin Hall nano-oscillators of the array; and a SHNO read-out unit arranged to detect and transfer a state of at least a one individual spin Hall nano-oscillators of the array.

A control circuit accesses a memory having time series acquisition history data for members of a predetermined group. That control circuit is configured to predict at least one future aggregation of items on a per-member basis by (1) using machine learning to predict specific items in the at least one future aggregation of items, wherein the machine learning uses a training corpus comprising, at least in part, the aforementioned time series acquisition history data, and (2) using matrix factorization to predict a quantity of at least some of the specific items in the at least one future aggregation of items.

Growth-transform (GT) neurons and their population models allow for independent control over spiking statistics and transient population dynamics while optimizing a physically plausible distributed energy functional involving continuous-valued neural variables. A backpropagation-less learning approach trains a GT network of spiking GT neurons by enforcing sparsity constraints on network spiking activity overall. Spike responses are generated because of constraint violations. Optimal parameters for a given task is learned using neurally relevant local learning rules and in an online manner. The GT network optimizes itself to encode the solution with as few spikes as possible and operate at a solution with the maximum dynamic range and away from saturation. Further, the framework is flexible enough to incorporate additional structural and connectivity constraints on the GT network. The framework formulation is used to design neuromorphic tinyML systems that are constrained in energy, resources, and network structure.

In one aspect the application relates to a computing system for providing data for modelling a human brain comprises a database including a plurality of datasets (or allow access to a plurality of datasets), each dataset including at least a dynamical model of the brain including at least one node and a neurodataset of a neuroimaging modality input. The at least one node include a representation of a local dynamic model and a parameter set of the local dynamic model.

This disclosure relates generally to time series forecasting, and, more particularly, to a system and method for online time series forecasting using spiking reservoir. Existing systems do not cater for efficient online time-series analysis and forecasting due to their memory and computation power requirements. System and method of the present disclosure convert a time series value F(t) at time ‘t’ to an encoded multivariate spike train and extracts temporal features from the encoded multivariate spike train by the excitatory neurons of a reservoir, predict a time series value Y(t + k) at time ‘t’ by performing a linear combination of extracted temporal features with read-out weights, compute an error for predicted time series value Y(t + k) with input time series value F(t + k), employs a FORCE learning on read-out weights using the error to reduce error in future forecasting. Feeding a feedback value back to the reservoir to optimize memory of the reservoir.