Abstract: A method and a system for controlling an industrial machine generate multiple outcomes by optimizing an acausal coactive acquisition function configured to maximize a joint gain in the utility of the generated outcomes according to the trained utility model and a preference of the generated outcomes according to the trained preference outcome model. In response to determining a desired outcome based on the generated outcomes, the system determines the operating parameters of the industrial machine corresponding to the desired outcome; and controls the industrial machine according to the mapped operating parameters.

Abstract: A plasma system comprising a low temperature plasma source incident on a material sample and a plurality of sensors that measure a plurality of different characteristics of the plasma. The plasma system further comprising a feedback control system that receives measurements from the plurality of sensors and adjusts inputs to the low temperature plasma source to control one or more of physical, electrical, electromagnetic, chemical, or thermal characteristics of a plasma generated, at a sub-second rate.

Abstract: A system to collect sensor data from an interaction between a plasma and a material and use a machine learning system to characterize the material. A method and apparatus for characterizing and evaluating a material. The method includes in one embodiment applying a cold atmospheric plasma to an interface with the material, measuring a plurality of interactions between the plasma and the interface using a plurality of sensors to generate sensor data, and utilizing a trained machine learning model to analyze the sensor data to generate characterization of the bulk and/or surface material properties based on the interactions.

Abstract: The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.

Abstract: A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.

Abstract: The present disclosure provides a controller for controlling an ego vehicle in an uncertain environment. The controller is caused to acquire knowledge of the environment from measurements associated with sensors the ego. The measurements are based on a state of the ego vehicle and sensing instructions associated with controlling an operation of the sensors. The controller is further caused to estimate a state of the environment, including uncertainty of a state of the at least one moving object or obstacle in the environment. Further a sequence of control inputs is determined by solving a multivariable and a multistage stochastic constrained optimization of a model of the motion of the ego vehicle. The controller is then caused to control the ego vehicle and the sensors based on the sequence of control inputs and the sequence of sensing instructions.

Abstract: A controller of a machine determines jointly a sequence of control inputs defining a state trajectory of the machine and a desired knowledge of the environment by solving a multivariable constrained optimization of a model of dynamics of the machine relating the state trajectory with the sequence of control inputs subject to a constraint on admissible values of the states and the control inputs defined based on the desired knowledge of the surrounding environment represented by the state of the environment and the uncertainty of the state of the environment determined from the measurements of the environment. In such a manner, the controller performs joint but imbalance optimization of the control inputs and the sensing instructions to the sensor for learning the environment.

Abstract: Light to ultra-light cement compositions with modified rheological properties. The cement composition includes cement, hydrophobic nano-silica, at least one additive and a sufficient amount of water to make a cement slurry with high compressive strength, low porosity, low free water, and low fluid loss with a quick thickening time.

Abstract: Light to ultra-light cement compositions with modified rheological properties. The cement composition includes cement, hydrophobic nano-silica, at least one additive and a sufficient amount of water to make a cement slurry with high compressive strength, low porosity, low free water, and low fluid loss with a quick thickening time.

Abstract: In one embodiment, a method includes dynamically crawling an event-driven application in a plurality of client-tier environments; for each of the client-tier environments, generating a behavioral model of the event-driven application based on the dynamic crawling; for each of the behavioral models, determining one or more pairwise equivalences with respect to one or more other ones of the behavioral models; and, for each of the client-tier environments, determining one or more differences in an end-user experience of the event-driven application in the client-tier environment with respect to one or more other end-user experiences of the event-driven application in one or more other ones of the client-tier environments.

Abstract: Light to ultra-light cement compositions with modified rheological properties. The cement composition includes cement, hydrophobic nano-silica, at least one additive and a sufficient amount of water to make a cement slurry with high compressive strength, low porosity, low free water, and low fluid loss with a quick thickening time.

Abstract: Light to ultra-light cement compositions with modified rheological properties. The cement composition includes cement, hydrophobic nano-silica, at least one additive and a sufficient amount of water to make a cement slurry with high compressive strength, low porosity, low free water, and low fluid loss with a quick thickening time.

Abstract: Light to ultra-light cement compositions with modified rheological properties. The cement composition includes cement, hydrophobic nano-silica, at least one additive and a sufficient amount of water to make a cement slurry with high compressive strength, low porosity, low free water, and low fluid loss with a quick thickening time.

Abstract: In one embodiment, a method includes dynamically crawling an event-driven application in a plurality of client-tier environments; for each of the client-tier environments, generating a behavioral model of the event-driven application based on the dynamic crawling; for each of the behavioral models, determining one or more pairwise equivalences with respect to one or more other ones of the behavioral models; and, for each of the client-tier environments, determining one or more differences in an end-user experience of the event-driven application in the client-tier environment with respect to one or more other end-user experiences of the event-driven application in one or more other ones of the client-tier environments.