Abstract: A glass container for storing pharmaceutical formulations may include a glass body formed from a Type IA or Type IB glass composition according to ASTM Standard E438-92(2011). The glass body may include a wall portion with an inner surface and an outer surface, a heel portion and a floor portion, wherein the inner surface of the glass container is formed by the inner surface of the glass body. The glass body may include at least a class A2 base resistance or better according to ISO 695, at least a type HGB2 hydrolytic resistance or better according to ISO 719 and Type 1 chemical durability according to USP <660>. The glass container does not comprise a boron-rich layer on the inner surface of the glass body in as formed condition.

Abstract: A coated glass pharmaceutical package includes a glass body having a Type 1 chemical durability according to USP 660, at least a class A2 base resistance or better according to ISO 695, and at least a type HGB2 hydrolytic resistance or better according to ISO 719, the glass body having an interior surface and an exterior surface and a wall extending therebetween. A lubricous coating having a thickness of less than or equal to 90 nm may be positioned on at least a portion of the exterior surface of the glass body but not on any portion of the interior surface. The portion of the coated glass package with the lubricous coating comprises a coefficient of friction that is at least 20% less than an uncoated glass package and the coefficient of friction does not increase by more than 30% after undergoing a depyrogenation cycle including exposure to a temperature of 250° C. for a time period of 30 minutes.

Abstract: According to embodiments, a coated pharmaceutical container may include a pharmaceutical container comprising an interior surface and an exterior surface, wherein the pharmaceutical container may include a glass composition that has Class HGA1 hydrolytic resistance when tested according to the ISO 720 testing standard. The coated pharmaceutical container may further include a coating bonded to at least a portion of the exterior surface but not on any portion of the interior surface. The coating may have a coefficient of friction less than or equal to 0.7, and the coated pharmaceutical container may be thermally stable after heating at a temperature of at least 260° C. for a time period of 30 minutes.

Abstract: Disclosed is a data fabric service system that can be implemented in a distributed computer network, such as a data intake and query system. The data index and query system can receive a search query and define a search scheme for applying the search query on distributed data storage systems including internal data storage and external data storage. The data index and query system may provide a portion of the search scheme to a search service of the data fabric service system, which can cause worker nodes of the data fabric service system to perform various functions—including applying the search query to the external data storage based on the portion of the search scheme in order to obtain search results.

Abstract: Three-dimensional (3D) visualization of multi-layer networks include receiving data associated with a network, wherein the network includes interconnected network elements that operate at a plurality of levels that include any of network layers and encapsulations; displaying a plurality of three-dimensional icons each represent one of a plurality of network elements of the interconnected network elements; and displaying links between the plurality of three-dimensional icons each link representing one of a network layer connection and an encapsulation connection.

Abstract: There is provided a non-transitory storage medium and a system for generating an abstractive summary of a document using an abstractive machine learning algorithm (MLA). A document including a plurality of text sequences is received. An extractive summary of the document is generated, the extractive summary including a set of summary text sequences which is a subset of the plurality of text sequences. The abstractive MLA generates, based on the set of summary text sequences and at least a portion of the plurality of text sequences, an abstractive summary of the document including a set of abstractive text sequences, at least one abstractive text sequence not being included in the plurality of text sequences.

Abstract: There is provided a method and a system for training an extractive machine learning algorithm (MLA) to generate extractive summaries of text documents. Reference documents and associated extractive summaries are received. The extractive MLA is then trained to generate an extractive summary, where the training includes, for a given reference document, encoding, using a sentence encoder, a plurality of reference sentences to obtain an associated plurality of sentence representations, encoding, using a document encoder, the associated plurality of sentence representations to obtain a document representation, extracting, using a decoder and based on the associated plurality of sentence representations and the document representation, a first reference sentence of the plurality of reference sentences to obtain a first extracted sentence. A given parameter is updated based on the first extracted sentence and the given reference document summary.

Abstract: There is provided a method and system for training and using a transformer language model (TLM) part of a recommendation engine. Natural language discussions about a category of items are received, the discussions comprising tags each indicative of a respective item belonging to the category of item. Information is received for each respective item. Based on the natural language discussions, the tags and the information about the respective item, the TLM is trained to: upon receipt of a user input, determine whether a given item should be recommended based on the user input, if the given item should be recommended, retrieving given information about the given item and generating a response to the user input, the response to the user input comprising the given item to be recommended and the given information, and output the response to the user input. The response is generated in natural language format.

Abstract: There is provided a method and a system for training an extractive machine learning algorithm (MLA) to generate extractive summaries of text documents. Reference documents and associated extractive summaries are received. The extractive MLA is then trained to generate an extractive summary, where the training includes, for a given reference document, encoding, using a sentence encoder, a plurality of reference sentences to obtain an associated plurality of sentence representations, encoding, using a document encoder, the associated plurality of sentence representations to obtain a document representation, extracting, using a decoder and based on the associated plurality of sentence representations and the document representation, a first reference sentence of the plurality of reference sentences to obtain a first extracted sentence. A given parameter is updated based on the first extracted sentence and the given reference document summary.

Abstract: Arbitrary input sounds are analyzed and the coefficients of a low-dimensional representation, such as LPC or MFCC, are determined as a measure of the timbre of the sounds. The coefficients can be employed in different ways to control output events, such as the generation of synthesized sounds. In one approach, the individual coefficients are mapped to the control parameters of a sound synthesizer, to enable highly complex output sounds to be generated in response to simple input sounds. In another approach, pattern recognition techniques are employed with respect to the coefficients, to classify the input sounds. Each classification is mapped to a control parameter, to cause different events to occur in response to the respective input sounds. In one example, the sounds of different musical instruments are generated in dependence upon the classification of the input sounds. These analysis techniques have low latency, and thereby allow events to be controlled without perceptible delay.