Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: A method is disclosed for obtaining an image biomarker that quantifies the quality of the trabecular structure of bones. The method includes: retrieving high-resolution CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) trabecular images from an image database; pre-processing and post-processing the high-resolution CT and/or MRI trabecular images and obtaining the unique image biomarker “QTS”. Pre-processing may include: calculating the region of interest “ROI”; calculating the bone fraction map; eliminating the partial volume effect; and, binarizing. Post-processing may include: skeletonisation and extraction of morphological and structural characteristics. Lastly, the unique image biomarker “QTS” is defined as: QTS=0.7137*Comp1+0.2863*Comp2.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: An elevator car (2, 2?), includes a first safety brake (8, 8?), including a first electronic safety actuator (512, 612), the first safety brake (8, 8?) positioned on a first side of the elevator car (2, 2?) at a first height (20, 20?); and a second safety brake (10, 10?), including a second electronic safety actuator (512, 612), the second safety brake (10, 10?) positioned on a second side of the elevator car (2, 2?) at a second height (22, 22?); the first height is different to the second height.

Abstract: An elevator car (2, 2?), includes a first safety brake (8, 8?), including a first electronic safety actuator (512, 612), the first safety brake (8, 8?) positioned on a first side of the elevator car (2, 2?) at a first height (20, 20?); and a second safety brake (10, 10?), including a second electronic safety actuator (512, 612), the second safety brake (10, 10?) positioned on a second side of the elevator car (2, 2?) at a second height (22, 22?); the first height is different to the second height.

Abstract: Magnet assemblies of electromechanical assemblies for elevator systems are described. The magnet assemblies include a magnet, at least one rail engagement block, and an encapsulating body encapsulating the magnet and the at least one rail engagement block, wherein the encapsulating body is formed from a non-magnetic material. A target extension is formed from the material of the encapsulating body and extends away from the magnet and the at least one rail engagement block. A proximity switch target is held within the target extension for detection by a proximity switch.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Magnet assemblies of electromechanical assemblies for elevator systems are described. The magnet assemblies include a magnet, at least one rail engagement block, and an encapsulating body encapsulating the magnet and the at least one rail engagement block, wherein the encapsulating body is formed from a non-magnetic material. A target extension is formed from the material of the encapsulating body and extends away from the magnet and the at least one rail engagement block. A proximity switch target is held within the target extension for detection by a proximity switch.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: The present invention relates to a method and a system for the segmentation of white matter hyperintensities (WMHs) present in magnetic resonance brain images, comprising: providing an array of trained convolutional neural networks (CNNs) with a magnetic resonance brain image; determining, for each of the CNNs and for each voxel, the probability that the given voxel corresponds to a pathological hyperintensity; calculating the average of all the probabilities determined for each voxel; comparing the averaged probabilities for each voxel with a threshold; generating an image mask with the voxels that exceed the threshold.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: A method is disclosed for obtaining an image biomarker that quantifies the quality of the trabecular structure of bones. The method includes: retrieving high-resolution CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) trabecular images from an image database; pre-processing and post-processing the high-resolution CT and/or MRI trabecular images and obtaining the unique image biomarker “QTS”. Pre-processing may include: calculating the region of interest “ROI”; calculating the bone fraction map; eliminating the partial volume effect; and, binarizing. Post-processing may include: skeletonisation and extraction of morphological and structural characteristics. Lastly, the unique image biomarker “QTS” is defined as: QTS=0.7137*Comp1+0.2863*Comp2.

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: Machine learning, artificial intelligence, and other computer-implemented methods are used to identify various semantically important chunks in documents, automatically label them with appropriate datatypes and semantic roles, and use this enhanced information to assist authors and to support downstream processes. Chunk locations, datatypes, and semantic roles can often be automatically determined from what is here called “context”, to wit, the combination of their formatting, structure, and content; those of adjacent or nearby content; overall patterns of occurrence in a document, and similarities of all these things across documents (mainly but not exclusively among documents in the same document set).

Abstract: An elevator safety gear assembly includes a base plate having a guiding component. Also included is a first engagement member operatively coupled to the base plate and configured to be positioned on a first side of a guide rail. Further included is a second engagement member operatively coupled to the base plate and configured to be positioned on a second side of a guide rail. Yet further included is a connector operatively coupled to the first engagement member and the second engagement member for symmetric movement of the first engagement member and the second engagement member relative to the guide rail, the connector having a guiding element disposed in engagement with the guiding component of the base plate.