Abstract: A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.

Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.

Abstract: A method of cleaning a component within a turbine that includes disassembling the turbine engine to provide a flow path to an interior passageway of the component from an access point. The component has coked hydrocarbons formed thereon. The method further includes discharging a flow of cleaning solution towards the interior passageway from the access point, wherein the cleaning solution is configured to remove the coked hydrocarbons from the component.

Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.

Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Abstract: A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.

Abstract: GABAA receptor mediated hypersomnia can be treated by administering a GABAA receptor antagonist (e.g., flumazenil; clarithromycin; picrotoxin; bicuculline; cicutoxin; and oenanthotoxin). In some embodiments, the GABAA receptor antagonist is flumazenil or clarithromycin. The GABAA receptor mediated hypersomnia includes shift work sleep disorder, obstructive sleep apnea/hypopnea syndrome, narcolepsy, excessive sleepiness, hypersomnia (e.g., idiopathic hypersomnia; recurrent hypersomnia; endozepine related recurrent stupor; and amphetamine resistant hypersomnia), and excessive sleepiness associated with shift work sleep disorder, obstructive sleep apnea/hypopnea syndrome, and hypersomnia (e.g., idiopathic hypersomnia; recurrent hypersomnia; endozepine related recurrent stupor; and amphetamine resistant hypersomnia.

Abstract: GABAA receptor mediated hypersomnia can be treated by administering a GABAA receptor antagonist (e.g., flumazenil; clarithromycin; picrotoxin; bicuculline; cicutoxin; and oenanthotoxin). In some embodiments, the GABAA receptor antagonist is flumazenil or clarithromycin. The GABAA receptor mediated hypersomnia includes shift work sleep disorder, obstructive sleep apnea/hypopnea syndrome, narcolepsy, excessive sleepiness, hypersomnia (e.g., idiopathic hypersomnia; recurrent hypersonmia; endozepine related recurrent stupor; and amphetamine resistant hypersonmia), and excessive sleepiness associated with shift work sleep disorder, obstructive sleep apnea/hypopnea syndrome, and hypersomnia (e.g., idiopathic hypersomnia; recurrent hypersomnia; endozepine related recurrent stupor; and amphetamine resistant hypersomnia.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system.

Abstract: A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.

Abstract: A system and a method generate a recommendation on a mobile device. The system and the method may use a time, a location, a venue and/or an event to generate the recommendation. Further, the system and the method may use an event database to determine current interests of the user. Still further, the system and the method for generating a recommendation on a mobile device may use a transactional history of the user and/or behavior of other users to generate the recommendation. The system and the method may recommend, for example, digital media, news and event information, editorial content and/or physical or digital merchandise. As a result, the system and the method may generate a recommendation that corresponds to the current interests of the user.

Abstract: A system and a method generate a recommendation on a mobile device. The system and the method may use a time, a location, a venue and/or an event to generate the recommendation. Further, the system and the method may use an event database to determine current interests of the user. Still further, the system and the method for generating a recommendation on a mobile device may use a transactional history of the user and/or behavior of other users to generate the recommendation. The system and the method may recommend, for example, digital media, news and event information, editorial content and/or physical or digital merchandise. As a result, the system and the method may generate a recommendation that corresponds to the current interests of the user.

Abstract: The present invention relates to a cushioning device for supporting a large animal such as a cow or a horse. The cushioning device comprises a top surface, a bottom surface, said top surface and bottom surface forming a chamber, and an amount of a gelatinous filling material disposed within the chamber. The present invention relates also to methods of transporting and installing a cushioning device for supporting a large animal. The method comprises transporting the cushioning device and a compound capable of creating a gel upon contact with a liquid may be delivered separately to the site. At the remote site the compound may be disposed within the chamber and the chamber may then be filled with an amount of liquid to create the gel within the cushioning device. The compound may also be delivered to the site already disposed within the chamber.

Abstract: The present invention describes a method for detection of human papillomavirus (HPV) types and a kit for detection of said HPV types.

Abstract: A spray coating system includes a rotating spray head and a transfer assembly. The spray head is coupled with a spray nozzle that directs spraying of a fluid multi-component product onto an interior surface of a structure. The spray head rotates around a longitudinal axis in order to cause the spray nozzle to also rotate around the longitudinal axis while spraying the multi-component product onto the interior surface of the structure. The transfer assembly is fluidly coupled with the spray nozzle and supplies plural different fluids that form the multi-component product to the spray nozzle. The transfer assembly supplies the different fluids that form the multi-component product to the spray nozzle without mixing the different fluids with each other prior to the different fluids being disposed proximate to the at least one spray nozzle.