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 disclosure provides a method and an apparatus for identifying a falling object based on a LiDAR system. The method includes obtaining a point cloud data set of a LiDAR system; identifying a dynamic point cloud cluster set based on the point cloud data at a first moment and a second moment, where the dynamic point cloud cluster set includes at least one dynamic point cloud cluster; and enabling a tracking and determination process in response to identifying the dynamic point cloud cluster set where, for each dynamic point cloud cluster, a data set of a dynamic point cloud cluster at each current moment following the second moment is updated in real time, and it is determined that an object represented by the dynamic point cloud cluster is a falling object in response to determining that the data meets a law of free fall.

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: 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: 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 anti-counterfeiting pattern having an optically variable structure, comprising lithographic lines and gravure blind embossed relief lines printed on a carrier. The lithographic lines are a set of lithographic curve lines having a curvature, the widths of the lithographic curve lines changing from thick to thin, or thin to thick. The gravure blind embossed relief lines are a set of curve lines corresponding to the lithographic lines, and having the same curvature. The relief lines are overprinted on the lithographic lines with a width variation identical to that of said lithographic lines. Accurate overprinting of the two printing types forms a curved relief structure wherein the width of the lines changes continuously. When a printed product is rotated and observed, a continuously variable optical effect will be seen, which is visual, readily to identify, anti-copying and difficult to forgery.

Abstract: An anti-counterfeiting pattern having an optically variable structure, comprising lithographic lines and gravure blind embossed relief lines printed on a carrier. The lithographic lines are a set of lithographic curve lines having a curvature, the widths of the lithographic curve lines changing from thick to thin, or thin to thick. The gravure blind embossed relief lines are a set of curve lines corresponding to the lithographic lines, and having the same curvature. The relief lines are overprinted on the lithographic lines with a width variation identical to that of said lithographic lines. Accurate overprinting of the two printing types forms a curved relief structure wherein the width of the lines changes continuously. When a printed product is rotated and observed, a continuously variable optical effect will be seen, which is visual, readily to identify, anti-copying and difficult to forgery.

Abstract: An enhanced dynamic optically variable anti-counterfeiting effect and a preparation method thereof, comprises a full-page colored print form and a colored print form having high narrow stereoscopic lines, the colored print form having high narrow stereoscopic lines is overprinted on the full-page colored print form; the colored print form having high narrow stereoscopic lines has a shape of combined lines, comprising a curve line, a straight line, a dot matrix or a combination thereof; the interval of the lines is 100-300% times of the width of the lines, and the height of the lines is 10-100% times of the width of the lines; the color of the colored print form having high narrow stereoscopic lines is a complementary color of the color of the full-page colored print form in a color gamut space, or is visually significant different with the color of the full-page colored print form.

Abstract: An anti-counterfeiting pattern having an optically variable structure, comprising lithographic lines and gravure blind embossed relief lines printed on a carrier. The lithographic lines are a set of lithographic curve lines having a curvature, the widths of the lithographic curve lines changing from thick to thin, or thin to thick. The gravure blind embossed relief lines are a set of curve lines corresponding to the lithographic lines, and having the same curvature. The relief lines are overprinted on the lithographic lines with a width variation identical to that of said lithographic lines. Accurate overprinting of the two printing types forms a curved relief structure wherein the width of the lines changes continuously. When a printed product is rotated and observed, a continuously variable optical effect will be seen, which is visual, readily to identify, anti-copying and difficult to forgery.