Abstract: There are provided systems and methods comprising obtaining design data of each of a plurality of given overlay targets comprising a plurality of stacked layers, using at least part of the design data to simulate image data of each given overlay target that would have been acquired by an electron beam examination system, using the image data to determine, before actual manufacturing of each given overlay target, second data informative of estimated probability that each given overlay target, upon being manufactured according to the design data, provides measurement data in an overlay measurement process meeting a measurement quality criterion, and using the second data of each given overlay target to select at least one optimal overlay target among the plurality of different overlay targets, wherein the at least one optimal overlay target is usable to be actually manufactured on the semiconductor specimen.

Abstract: Documents needing to be analyzed for various reasons, such as financial crimes, are ranked by examining the topicality and sentiment present in each document for a given subject of interest. In one approach a given document is classified to determine its category, and entity recognition is used to identify the subject of interest. Passages from the document that relate to the entity are grouped and analyzed for sentiment to generate a sentiment score. Documents are then ranked based on the sentiment scores. In another approach, a classification probability score is computed for each passage representing a likelihood that the passage relates to a category of interest, and the document is ranked based on the sentiment scores and the classification probability scores. The category classification uses an ensemble of natural language text classifiers. One of the classifiers is a naïve Bayes classifier with feature vectors generated using Word2Vec modeling.

Abstract: Documents needing to be analyzed for various reasons, such as financial crimes, are ranked by examining the topicality and sentiment present in each document for a given subject of interest. In one approach a given document is classified to determine its category, and entity recognition is used to identify the subject of interest. Passages from the document that relate to the entity are grouped and analyzed for sentiment to generate a sentiment score. Documents are then ranked based on the sentiment scores. In another approach, a classification probability score is computed for each passage representing a likelihood that the passage relates to a category of interest, and the document is ranked based on the sentiment scores and the classification probability scores. The category classification uses an ensemble of natural language text classifiers. One of the classifiers is a naïve Bayes classifier with feature vectors generated using Word2Vec modeling.

Abstract: Documents needing to be analyzed for various reasons, such as financial crimes, are ranked by examining the topicality and sentiment present in each document for a given subject of interest. In one approach a given document is classified to determine its category, and entity recognition is used to identify the subject of interest. Passages from the document that relate to the entity are grouped and analyzed for sentiment to generate a sentiment score. Documents are then ranked based on the sentiment scores. In another approach, a classification probability score is computed for each passage representing a likelihood that the passage relates to a category of interest, and the document is ranked based on the sentiment scores and the classification probability scores. The category classification uses an ensemble of natural language text classifiers. One of the classifiers is a naïve Bayes classifier with feature vectors generated using Word2Vec modeling.

Abstract: Documents needing to be analyzed for various reasons, such as financial crimes, are ranked by examining the topicality and sentiment present in each document for a given subject of interest. In one approach a given document is classified to determine its category, and entity recognition is used to identify the subject of interest. Passages from the document that relate to the entity are grouped and analyzed for sentiment to generate a sentiment score. Documents are then ranked based on the sentiment scores. In another approach, a classification probability score is computed for each passage representing a likelihood that the passage relates to a category of interest, and the document is ranked based on the sentiment scores and the classification probability scores. The category classification uses an ensemble of natural language text classifiers. One of the classifiers is a naïve Bayes classifier with feature vectors generated using Word2Vec modeling.

Abstract: The present invention provides a radiation detector for detecting both the intensity and direction of one or more sources of radiation comprising a radiation sensor, an inverse collimator that shields the sensor from at least a portion of the incident radiation originating from the direction in which the inverse collimator is pointed and a means for pointing the inverse collimator in different directions.

Abstract: The present invention provides a radiation detector for detecting both the intensity and direction of one or more sources of radiation comprising a radiation sensor, an inverse collimator that shields the sensor from at least a portion of the incident radiation originating from the direction in which the inverse collimator is pointed and a means for pointing the inverse collimator in different directions.