Abstract: Latent representation based appearance modification for adversarial testing and training include obtaining a first latent representation of an actor, performing a modification of the first latent representation of an actor to obtain a second latent representation, and generating a 3D model from the second latent representation. The operations further include performing, by a simulator interacting with the virtual driver, a simulation of the virtual world having the 3D model of the actor and the autonomous system moving in the virtual world, evaluating the virtual driver interacting in the virtual world during the simulation to obtain an evaluation result, and outputting the evaluation result.

Abstract: Imitation and reinforcement learning for multi-agent simulation includes performing operations. The operations include obtaining a first real-world scenario of agents moving according to first trajectories and simulating the first real-world scenario in a virtual world to generate first simulated states. The simulating includes processing, by an agent model, the first simulated states for the agents to obtain second trajectories. For each of at least a subset of the agents, a difference between a first corresponding trajectory of the agent and a second corresponding trajectory of the agent is calculated and determining an imitation loss is determined based on the difference. The operations further include evaluating the second trajectories according to a reward function to generate a reinforcement learning loss, calculating a total loss as a combination of the imitation loss and the reinforcement learning loss, and updating the agent model using the total loss.

Abstract: A method includes generating a first sample including first raw parameter values of a first modifiable parameters by a probabilistic model and a kernel and executing a first test of a virtual driver of an autonomous system according to the first sample to generate a first evaluation result of multiple evaluation results. The method further includes updating the probabilistic model according to the first evaluation result and training the kernel using the first evaluation result. The method additionally includes generating a second sample including second raw parameter values of the parameters by the probabilistic model and the kernel and executing a second test of a virtual driver of an autonomous system according to the second sample to generate a second evaluation result of the evaluation results. The method further includes presenting the evaluation results.

Abstract: According to one aspect, a system for ultraviolet (UV) disinfection may include a switch and a lamp fixture. The lamp fixture may include a UV lamp and a UV controller. The switch may be configured to receive a command to engage in a UV disinfection mode. The UV controller may be configured to receive the command to engage in the UV disinfection mode from the switch via a switched line. The UV controller may activate the UV lamp based on the received command to engage in the UV disinfection mode.

Abstract: Systems and methods for training object detection models using adversarial examples are provided. A method includes obtaining a training scene and identifying a target object within the training scene. The method includes obtaining an adversarial object and generating a modified training scene based on the adversarial object, the target object, and the training scene. The modified training scene includes the training scene modified to include the adversarial object placed on the target object. The modified training scene is input to a machine-learned model configured to detect the training object. A detection score is determined based on whether the training object is detected, and the machine-learned model and the parameters of the adversarial object are trained based on the detection output. The machine-learned model is trained to maximize the detection output. The parameters of the adversarial object are trained to minimize the detection output.

Abstract: A workpiece is electrodischarge machined using a machining apparatus having a workpiece holder, an electrode holder having an electrode mounted thereto, and a power supply providing an electrical voltage and current. The workpiece is mounted in the workpiece holder, and the electrode is positioned adjacent to a machining location of the workpiece. The method further includes furnishing a source of a flow of conditioned water, directing the flow of conditioned water from the source into the machining location of the workpiece, without submerging the machining location in a liquid, and applying a voltage from the power supply such that an electrical current flows between the workpiece and the electrode holder so that metal is removed from the workpiece into the flow of conditioned water.