Abstract: A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.

Abstract: A processing system for processing objects is disclosed that includes a plurality of receiving stations for receiving a plurality of objects, each object being associated with prerecorded data, and a plurality of processing stations, each of which is in communication with at least one processing station. Each processing station includes perception means for perceiving data regarding an identity of any of an object or a bin of objects, and capture means for capturing characteristic data regarding an object to provide captured data. Each processing station further includes comparison means for comparing the captured data with the prerecorded data to provide comparison data, and a plurality of distribution stations, each of which is in communication with at least one processing station for receiving objects from the at least one processing station responsive to the comparison data.

Abstract: A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.

Abstract: A processing system for processing objects is disclosed that includes a plurality of receiving stations for receiving a plurality of objects, each object being associated with prerecorded data, and a plurality of processing stations, each of which is in communication with at least one processing station. Each processing station includes perception means for perceiving data regarding an identity of any of an object or a bin of objects, and capture means for capturing characteristic data regarding an object to provide captured data. Each processing station further includes comparison means for comparing the captured data with the prerecorded data to provide comparison data, and a plurality of distribution stations, each of which is in communication with at least one processing station for receiving objects from the at least one processing station responsive to the comparison data.

Abstract: A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.

Abstract: A processing system for processing objects is disclosed that includes a plurality of receiving stations for receiving a plurality of objects, each object being associated with prerecorded data, and a plurality of processing stations, each of which is in communication with at least one processing station. Each processing station includes perception means for perceiving data regarding an identity of any of an object or a bin of objects, and capture means for capturing characteristic data regarding an object to provide captured data. Each processing station further includes comparison means for comparing the captured data with the prerecorded data to provide comparison data, and a plurality of distribution stations, each of which is in communication with at least one processing station for receiving objects from the at least one processing station responsive to the comparison data.

Abstract: A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.

Abstract: A processing system for processing objects is disclosed that includes a plurality of receiving stations for receiving a plurality of objects, each object being associated with prerecorded data, and a plurality of processing stations, each of which is in communication with at least one processing station. Each processing station includes perception means for perceiving data regarding an identity of any of an object or a bin of objects, and capture means for capturing characteristic data regarding an object to provide captured data. Each processing station further includes comparison means for comparing the captured data with the prerecorded data to provide comparison data, and a plurality of distribution stations, each of which is in communication with at least one processing station for receiving objects from the at least one processing station responsive to the comparison data.

Abstract: A method of predicting progression of a cognitive state in a subject is disclosed including obtaining a neuroimage of the subject, acquiring a data sample from the neuroimage, selecting a time at which to predict progression of the cognitive state, and performing a calculation on the data sample by a transformation function to determine data associated with multiple cognitive metrics. A method of evaluating a cognitive state of a subject is also disclosed including providing a convolutional neural network, training the convolutional neural network with reference data to construct a transformation function, using the transformation function to predict multiple cognitive metrics from a subject data sample and a selected time, and determining the cognitive state of the subject from the predicted cognitive metrics. A cognitive evaluation system is also disclosed including a memory, a processor, and a cognitive state prediction component configured to program the processor with a transformation function.

Abstract: A method of predicting progression of a cognitive state in a subject is disclosed including obtaining a neuroimage of the subject, acquiring a data sample from the neuroimage, selecting a time at which to predict progression of the cognitive state, and performing a calculation on the data sample by a transformation function to determine data associated with multiple cognitive metrics. A method of evaluating a cognitive state of a subject is also disclosed including providing a convolutional neural network, training the convolutional neural network with reference data to construct a transformation function, using the transformation function to predict multiple cognitive metrics from a subject data sample and a selected time, and determining the cognitive state of the subject from the predicted cognitive metrics. A cognitive evaluation system is also disclosed including a memory, a processor, and a cognitive state prediction component configured to program the processor with a transformation function.

Abstract: A system for object processing is disclosed. The system includes a framework of processes that enable reliable deployment of artificial intelligence-based policies in a warehouse setting to improve the speed, reliability, and accuracy of the system. The system harnesses a vast number of picks to provide data points to machine learning techniques. These machine learning techniques use the data to refine or reinforce in-use policies to optimize the speed and successful transfer of objects within the system. For example, objects in the system are identified at a supply location, a predetermined set of information regarding object is retrieved and combined with a set of object information and processing parameters determined by the system. The combined information is then used to determine routing of the object according to an initial policy. This policy is then observed, altered, tested, and re-implemented in an altered form.

Abstract: A processing system for processing objects is disclosed that includes a plurality of receiving stations for receiving a plurality of objects, each object being associated with prerecorded data, and a plurality of processing stations, each of which is in communication with at least one processing station. Each processing station includes perception means for perceiving data regarding an identity of any of an object or a bin of objects, and capture means for capturing characteristic data regarding an object to provide captured data. Each processing station further includes comparison means for comparing the captured data with the prerecorded data to provide comparison data, and a plurality of distribution stations, each of which is in communication with at least one processing station for receiving objects from the at least one processing station responsive to the comparison data.

Abstract: The present invention provides metformin-containing dental materials that can be widely applied in a variety of dental applications. The metformin-containing dental materials of the invention include metformin-containing dental primers, metformin-containing dental adhesives, metformin-containing dental resins, metformin-containing dental composites, metformin-containing dental bonding systems and the like, as well as metformin-containing dental cements, metformin-containing dental sealants, metformin-containing dental bases and metformin-containing dental liners.

Abstract: A method of predicting progression of a cognitive state in a subject is disclosed including obtaining a neuroimage of the subject, acquiring a data sample from the neuroimage, selecting a time at which to predict progression of the cognitive state, and performing a calculation on the data sample by a transformation function to determine data associated with multiple cognitive metrics. A method of evaluating a cognitive state of a subject is also disclosed including providing a convolutional neural network, training the convolutional neural network with reference data to construct a transformation function, using the transformation function to predict multiple cognitive metrics from a subject data sample and a selected time, and determining the cognitive state of the subject from the predicted cognitive metrics. A cognitive evaluation system is also disclosed including a memory, a processor, and a cognitive state prediction component configured to program the processor with a transformation function.

Abstract: Tetrahydrotricyclopentadienes are isomerized to a low pour point, high energy missile fuel. Aluminum trichloride is the catalyst and an inert chlorinated hydrocarbon solvent is present. The mole ratio of AlCl3 to the diene is in the range between from about 0.005 to about 1.0. The isomerization temperature is in the range from between about ?20° C. to about 25° C.

Abstract: A linear fluid impulse engine enables efficient conversion of the kinetic energy of large volume flows of fluid at low velocity into useful work. The linear fluid impulse engine is suited for ease of manufacture and installation, is tolerant of suspended abrasives in the fluid, and is fish-friendly. The linear fluid impulse engine uses symmetrically curved blades that are mounted to continuous power transmission belts that revolve on a pair of axles. The blades move in substantially linear paths on both the upstream and downstream sides of the inter-axle plane, which extends between the axes of the shafts. A linear cascade of stationary mid-plane guidevanes, located between the two sets of moving blade cascades, acts as a row of nozzles, accelerating the fluid so that it interacts with the downstream blade cascade with the proper velocity characteristics of an impulse device.

Abstract: 1.

Abstract: Oxygen compounds are removed, e.g., by extraction, from a coal liquid prior to its hydrogenation. As a result, compared to hydrogenation of such a non-treated coal liquid, the rate of nitrogen removal is increased.

Abstract: Tetrahydrodimethyldicyclopentadiene is catalytically isomerized to a liquid isomeric mixture having a suitable low temperature viscosity making it suitable as a missile fuel. The catalyst comprises an acidic alumina. Hydrogen is also present and in an amount sufficient to maintain the isomerization activity of the alumina. The elevated temperature is sufficient to cause isomerization.

Abstract: Hydrogenolysis of a saturated endo-endo dihydronorbornadiene hexacyclic dimer involves the use of a catalytic amount of a Group VIII metal and the presence of hydrogen. The resulting pentacyclic isomers can be used as a diluent for depressing the freezing point of the saturated hexacyclic dimer which is a component of a high density missile fuel.