Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: Embodiments relate to extracting various portions of log messages. Syntax, order and/or level information that differentiates header and detail message is identified. This syntax, order and/or level information is used to automatically associate enhance each of one or more detail log entries with information that provides context. This approach can facilitate efficient transmission of detail-information log entries that reduces redundant information, while still supporting flexible approaches for providing information that can enhance detail-information log entries.

Abstract: A method is described for iteratively training a policy model, such as a neural network, of a computer-implemented action selection system to control an agent interacting with an environment to perform a task subject to one or more constraints. The task has a reward associated with performance of the task. Each constraint limits to a corresponding threshold the expected value of the total of a corresponding constraint function which if the future actions of the agent are chosen according to the policy model, and each constraint is associated with a corresponding multiplier variable. In each iteration, a mixed reward function is generated based on values for the multiplier variables generated in the preceding iteration, and estimates of the rewards and the values of constraint reward functions if the actions are chosen based on the policy model generated in the preceding iteration.

Abstract: The actions of an agent in an environment are selected using a policy model neural network which implements a policy model defining, for any observed state of the environment characterized by an observation received by the policy model neural network, a state-action distribution over the set of possible actions the agent can perform. The policy model neural network is jointly trained with a cost model neural network which, upon receiving an observation characterizing the environment, outputs a reward vector. The reward vector comprises a corresponding reward value for every possible action. The training involves a sequence of iterations, in each of which (a) a cost model is derived based on the state-action distribution of a candidate policy model defined in one or more previous iterations, and subsequently (b) a candidate policy model is obtained based on reward vector(s) defined by the cost model obtained in the iteration.

Abstract: In general, trace and debug logic should not be affected by all functional or destructive resets of a processing system. However, certain events, such as power supply related events may be utilized to reset the trace and debug logic since the trace and debug logic may cease correct operation if the provided power supply is insufficient. In addition, it may be beneficial for a debugger to initiate requests to reset trace and debug logic. Further, fault triggers from critical path monitors may be candidates as a source of reset for the trace and debug circuitry. For example, when critical path monitors trigger a fault, the fault may be from the logic associated with either trace and debug logic or the logic which is being debugged or traced. As such, in some instances both trace and debug circuitry and the processing system may be inoperable and may need to be reset.

Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: There is provided a computer-implemented method for updating a search distribution of an evolutionary strategies optimizer using an optimizer neural network comprising one or more attention blocks. The method comprises receiving a plurality of candidate solutions, one or more parameters defining the search distribution that the plurality of candidate solutions are sampled from, and fitness score data indicating a fitness of each respective candidate solution of the plurality of candidate solutions. The method further comprises processing, by the one or more attention neural network blocks, the fitness score data using an attention mechanism to generate respective recombination weights corresponding to each respective candidate solution. The method further comprises updating the one or more parameters defining the search distribution based upon the recombination weights applied to the plurality of candidate solutions.

Abstract: In one aspect there is provided a method for training a neural network system by reinforcement learning. The neural network system may be configured to receive an input observation characterizing a state of an environment interacted with by an agent and to select and output an action in accordance with a policy aiming to satisfy an objective. The method may comprise obtaining a policy set comprising one or more policies for satisfying the objective and determining a new policy based on the one or more policies. The determining may include one or more optimization steps that aim to maximize a diversity of the new policy relative to the policy set under the condition that the new policy satisfies a minimum performance criterion based on an expected return that would be obtained by following the new policy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using an action selection neural network that performs in-context reinforcement learning when controlling an agent on a new task.

Abstract: Techniques are disclosed for aggregating received data from a data stream. Data is received from a particular stream partition to which a device is subscribed, and subsets of the data, from the particular stream partition, are aggregated based on respective keys associated with the subsets of the data. The device determines whether one or more subsets of data, associated with a particular key, meet at least one processing criteria, such as a threshold amount of data, and refraining from processing the aggregated data when the processing criteria is not met. Once additional subset(s) of data associated with the particular key are received, they are aggregated with the one or more subsets of data. When the processing criteria is satisfied, the device processes the aggregated subsets of data associated with the particular key.

Abstract: Techniques are disclosed for partitioning received data for use in a data stream. Data is received during a period of time at a device, such as a data partitioner. The device a) determines that a first subset of the data is for a first system component, b) a second subset of the data is for a second system component, and c) a third subset of the data is for the first system component. Also, determines that a first stream partition is mapped to the first system component and a second stream partition is mapped to the second system component. Based on these determinations, the device assigns the first and third subsets of the data to the first stream partition and the second subset of the data to the second stream partition. Thereafter, the device transmits a stream that includes the first stream partition and the second stream partition.

Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated wi

Abstract: A reinforcement learning system, method, and computer program code for controlling an agent to perform a plurality of tasks while interacting with an environment. The system learns options, where an option comprises a sequence of primitive actions performed by the agent under control of an option policy neural network. In implementations the system discovers options which are useful for multiple different tasks by meta-learning rewards for training the option policy neural network whilst the agent is interacting with the environment.

Abstract: Systems and methods include reception of a set of data including continuous features and a discrete feature, each continuous feature associated with a plurality of values and the discrete feature associated with a plurality of discrete values, determine, for each continuous feature, a relationship factor representing a relationship between the discrete feature and the continuous feature based on the plurality of values associated with the continuous feature and the plurality of discrete values, identify one of the continuous features associated with a largest one of the determined relationship factors, generate, for each of the other features, a correlation factor representing a correlation between the continuous feature and the identified continuous feature, determine, for each of the continuous features other than the identified continuous feature, a composite relationship score based on the relationship factor and the correlation factor associated with the feature, and present a visualization associated wi

Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: There is described methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a reinforcement learning system. The reinforcement learning system comprises an agent configured to perform actions based upon a policy and an intrinsic reward system configured to generate intrinsic reward values for the agent based upon the actions taken by the agent. The method comprises training the reinforcement learning system based upon a plurality of tasks. The training comprises updating the agent's policy based upon the intrinsic reward values generated by the intrinsic reward system and updating the intrinsic reward system based upon an extrinsic reward value obtained based upon the task being performed by the agent. The training further comprises re-initializing the agent's policy when an expiration criterion associated with the agent is met.

Abstract: The present invention is related to a motorcycle LED light system for lighting the ground underneath and around a motorcycle to make the motorcycle more visible, comprising one or more high intensity LED lights situated on or near the rear of the motorcycle and in a direction so that the LED light is projected toward the ground behind and in front of the rear wheel of the motorcycle.

Abstract: A scan chain collects scan chain data from testing of a functional circuit and outputs a scan chain signal containing the scan chain data. A voltage monitor circuit operates to compare a supply voltage against a threshold and assert a reset signal when the supply voltage crosses the threshold. The reset signal resets a flip flop circuit whose output signal controls operation of a logic circuit that blocks passage of the scan chain signal to an integrated circuit probe pad and instead applies a constant logic signal to the probe pad indicating a voltage monitoring error.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems, including a method for generating executable components. One method includes identifying a user request to create a new function based pre-existing algorithms, the new function to be used in an application used by a user; providing a set of available algorithms from an algorithm library; receiving a selection by a user of an algorithm from the available algorithms; providing a set of available parameters associated with the selected algorithm; receiving an election by the user of one or more parameters from the set of available parameters; generating an executable component in response to receiving the selection of the algorithm and the election of the one or more parameters, the executable component performing the selected algorithm using at least the elected one or more parameters; and storing the executable component for subsequent execution in response to the requested new function.

Abstract: A scan chain collects scan chain data from testing of a functional circuit and outputs a scan chain signal containing the scan chain data. A voltage monitor circuit operates to compare a supply voltage against a threshold and assert a reset signal when the supply voltage crosses the threshold. The reset signal resets a flip flop circuit whose output signal controls operation of a logic circuit that blocks passage of the scan chain signal to an integrated circuit probe pad and instead applies a constant logic signal to the probe pad indicating a voltage monitoring error.