Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model.

Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Abstract: A sensor transformation attention network (STAN) model including sensors, attention modules, a merge module and a task-specific module is provided. The attention modules calculate attention scores of feature vectors corresponding to the input signals collected by the sensors. The merge module calculates attention values of the attention scores, and generates a merged transformation vector based on the attention values and the feature vectors. The task-specific module classifies the merged transformation vector.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: Methods and apparatus are provided for generating a graph neural network (GNN) model based on an entity-entity graph. The entity-entity graph comprising a plurality of entity nodes in which each entity node is connected to one or more entity nodes of the plurality of entity nodes by one or more corresponding relationship edges. The method comprising: generating an embedding based on data representative of the entity-entity graph for the GNN model, wherein the embedding comprises an attention weight assigned to each relationship edge of the entity-entity graph; and updating weights of the GNN model including the attention weights by minimising a loss function associated with at least the embedding; wherein the attention weights indicate the relevancy of each relationship edge between entity nodes of the entity-entity graph. The entity-entity graph may be filtered based on the attention weights of a trained GNN model.

Abstract: A method for operating an artificial neuron and an apparatus for performing the method are provided. The artificial neuron may calculate a change amount of an activation based on an input signal received via an input synapse, determine whether an event occurs in response to the calculated change amount of the activation, and transmit, to an output synapse, an output signal that corresponds to the event in response to an occurrence of the event.

Abstract: Systems and methods enable most valuable players (MVPs) of a gameplay session to play a bonus gameplay session where other players (non-MVP players) participate as player-spectators in online video games and, through a collective voting mechanism or through active interaction, determine the occurrence of certain events or contents of the bonus gameplay in real time. In this manner, non-MVP players are able to directly affect the course of the bonus gameplay.

Abstract: The invention disclosed herein relates to a video game system comprising a video game console and an object or toy used in connection with the video game console, wherein the object or toy has a rewritable data storage device, and wherein the video game console stores information in the rewritable data storage device in response to game play events occurring in a first gaming session. The stored information is subsequently retrieved by the video game console in a second gaming session, and the retrieved information is used to conduct the game play of the second gaming session.

Abstract: A recurrent neural network including an input layer, a hidden layer, and an output layer, wherein the hidden layer includes hybrid memory cell units, each of the hybrid memory cell units including a first memory cells of a first type, the first memory cells being configured to remember a first cell state value fed back to each of gates to determine a degree to which each of the gates is open or closed, and configured to continue to update the first cell state value, and a second memory cells of a second type, each second memory cell of the second memory cells including a first time gate configured to control a second cell state value of the second memory cell based on phase signals of an oscillatory frequency, and a second time gate configured to control an output value of the second memory cell based on the phase signals, and each second memory cell of the second memory cells being configured to remember the second cell state value.

Abstract: A sensor transformation attention network (STAN) model including sensors configured to collect input signals, attention modules configured to calculate attention scores of feature vectors corresponding to the input signals, a merge module configured to calculate attention values of the attention scores, and generate a merged transformation vector based on the attention values and the feature vectors, and a task-specific module configured to classify the merged transformation vector is provided.

Abstract: A memory cell unit and a recurrent neural network including memory cell units are provided. The memory cell unit includes a first time gate configured to control a cell state value of the memory cell unit, based on a phase signal of an oscillatory frequency, and a second time gate configured to control an output value of the memory cell unit, based on the phase signal.