Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Abstract: Disclosed herein are glass pharmaceutical vials having sidewalls of reduced thickness. In embodiments, the glass pharmaceutical vial may include a glass body comprising a sidewall enclosing an interior volume. An outer diameter D of the glass body is equal to a diameter d1 of a glass vial of size X as defined by ISO 8362-1, wherein X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1. However, the sidewall of the glass pharmaceutical vial comprises an average wall thickness Ti that is less than or equal to 0.85*s1, wherein s1 is a wall thickness of the glass vial of size X as defined by ISO 8362-1 and X is one of 2R, 3R, 4R, 6R, 8R, 10R, 15R, 20R, 25R, 30R, 50R, and 100R as defined by ISO 8362-1.

Abstract: Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.

Abstract: Techniques are provided for using machine learning techniques to associate skills with different content. In one technique, multiple classifications models are trained. Each classification model corresponds to a different skill and is trained based on textual embeddings of a plurality of content items and labels indicating whether each content item is associated with the skill that corresponds to that classification model. A particular content item embedding is generated based on text from a particular content item. The particular content item embedding is applied to the classification models to generate multiple results. One or more results of the multiple results are identified that indicate that one or more corresponding skills are associated with the particular content item. For each result of the one or more results, skill tagging data are stored that associate the particular content item with a particular skill that corresponds to that result.

Abstract: Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Abstract: The video recommendation system provided with an on-line connection system generates on-line video recommendations using collaborative filtering for clusters of member profiles. The recommendation system clusters member profiles using member profile information as clustering criteria. The video recommendations are then generated for a given cluster, based on aggregation of video viewing history recorded for the member profiles that are in the given cluster, using the video similarity matrix. In order to produce video recommendations for a particular member profile, the recommendation system first determines cluster membership for the member profile, retrieves recommendations generated for that cluster, and provides recommendations to the associated member. A user interface including references to one or more recommended videos is rendered on a display device of a viewer.

Abstract: The recommendation system provided with an on-line connection system identifies on-line recommendations of videos and generates a user interface (UI) by including into the resulting presentation selected recommendations of videos. The recommendations of videos presented in the UI are organized into groups that are topically coherent, where each group is decorated with a context annotation—an explanation of why the recommendations in a given carousel are relevant for a member. Each video that is being evaluated by the recommendation system with respect to a subject member profile is assigned an annotation that is selected from a plurality of potentially applicable annotations.

Abstract: Techniques for implementing a feature generation pipeline for machine learning are provided. In one technique, multiple jobs are executed, each of which computes a different set of feature values for a different feature of multiple features associated with videos. A feature registry is stored that lists each of the multiple features. After the jobs are executed and the feature registry is stored, a model specification is received that indicates a set of features for a model. For each feature in a subset of the set of features, a location is identified in storage where a value for said each feature is found and the value for that feature is retrieved from the location. A feature vector is created that comprises, for each feature in the set of features, the value that corresponds to that feature. The feature vector is used to train the model or as input to the model.

Abstract: The video recommendation system provided with an on-line connection system generates on-line video recommendations using collaborative filtering for clusters of member profiles. The recommendation system clusters member profiles using member profile information as clustering criteria. The video recommendations are then generated for a given cluster, based on aggregation of video viewing history recorded for the member profiles that are in the given cluster, using the video similarity matrix. In order to produce video recommendations for a particular member profile, the recommendation system first determines cluster membership for the member profile, retrieves recommendations generated for that cluster, and provides recommendations to the associated member. A user interface including references to one or more recommended videos is rendered on a display device of a viewer.

Abstract: Techniques are provided for using machine learning techniques to associate skills with different content. In one technique, multiple classifications models are trained. Each classification model corresponds to a different skill and is trained based on textual embeddings of a plurality of content items and labels indicating whether each content item is associated with the skill that corresponds to that classification model. A particular content item embedding is generated based on text from a particular content item. The particular content item embedding is applied to the classification models to generate multiple results. One or more results of the results are identified that indicate that one or more corresponding skills are associated with the particular content item. For each result of the one or more results, skill tagging data is stored that associates the particular content item with a particular skill that corresponds to that result.

Abstract: The recommendation system provided with an on-line connection system identifies on-line recommendations of videos and generates a user interface (UI) by including into the resulting presentation selected recommendations of videos. The recommendations of videos presented in the UI are organized into groups that are topically coherent, where each group is decorated with a context annotation—an explanation of why the recommendations in a given carousel are relevant for a member. Each video that is being evaluated by the recommendation system with respect to a subject member profile is assigned an annotation that is selected from a plurality of potentially applicable annotations.

Abstract: A system and method for detection of duplicate bug reports. A receiver is configured to receive a first bug report and a word matrix. An extractor extracts keywords from the first bug report for creating a first search string. A comparator compares each of the keywords from the first search string with the word matrix for identifying dissimilar duplicate words. The duplicate bug detector further includes an expander to expand the first search string by including the dissimilar duplicate words for creating the second search string and a searcher to search a bug repository with the first search string and the second search string for identifying similar duplicate bug reports and dissimilar duplicate bug reports.

Abstract: A system and method for detection of duplicate bug reports. A receiver is configured to receive a first bug report and a word matrix. An extractor extracts keywords from the first bug report for creating a first search string. A comparator compares each of the keywords from the first search string with the word matrix for identifying dissimilar duplicate words. The duplicate bug detector further includes an expander to expand the first search string by including the dissimilar duplicate words for creating the second search string and a searcher to search a bug repository with the first search string and the second search string for identifying similar duplicate bug reports and dissimilar duplicate bug reports.