Abstract: A graphical user interface receives natural language input from a user. A modular thread analytics exploration system uses context determination, dynamic context enrichment, and the natural language input to generate a solution recipe with a language model. The system prompt the language model with evaluation guides to improve the accuracy of the model output. The solution recipe includes steps (i) that are used to generate code and (ii) that are used to generate natural language explanations. The system generates code with a language model. The system processes the generated code in a sandbox and self-debugs the generated code as necessary. The output from the steps is presented in the graphical user interface.

Abstract: Techniques for handling natural language query processing are described. In some examples, semantic meanings of words are determined during the natural language query processing. These semantic meanings are generated from metadata associated with the query and are to be used by an entity linker to help the linker link candidates to columns.

Abstract: This disclosure describes techniques and architecture for enriching dataset metadata of datasets arranged in tabular form comprising rows and columns, wherein each column has a name. The dataset metadata is enriched with business semantics for natural language question answering. The techniques include one or more of generating one or more synonyms for each name; ranking the names with respect to a likelihood that a column includes possible data to be returned to a user in response to a received NLQ from the user; predicting a date granularity for each column; and predicting a semantic type to describe values in the columns.

Abstract: Techniques for handling natural language query processing are described. In some examples, entities are recognized during an entity recognition phase and then relations between those entities are determined. Those relations are fed to an entity linker to help the linker link candidate to columns and/or a intent representation generator to help parse multiple values and column pairs of a natural language query.

Abstract: Techniques for handling natural language query processing are described. In some examples, a sequence-to-sequence model is used to handle a natural language query. Post-processing of a result of the sequence-to-sequence model utilizes fine-grained information from an entity linker. In some examples, the sequence-to-sequence model and aspects of a natural language query pipeline are used to handle a natural language query.

Abstract: Techniques for predicting an answer to a question using a machine learning model are described. In some examples, the model predicts one or more answers to the question by: predicting at least two answers to the question using a first component of the question-answer model from a set of passages, generating, using a second component of the question-answer model, at least one question for each of the predicted at least two answers, and performing roundtrip predictions until each generated question only has one answer.

Abstract: Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Abstract: Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: Interactive assistances for executing natural language queries to data sets may be performed. A natural language query may be received. Candidate entity linkages may be determined between an entity recognized in the natural language query and columns in data sets. The candidate linkages may be ranked according to confidence scores which may be evaluated to detect ambiguity for an entity linkage. Candidate entity linkages may be provided to a user via an interface to select an entity linkage to use as part of completing the natural language query.

Abstract: Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Abstract: Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

Abstract: Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

Abstract: Combinations of Bruton's tyrosine kinase (Btk) inhibitors, e.g., 1-((R)-3-(4-amino-3-(4-phenoxyphenyl)-1H-pyrazolo[3,4-d]pyrimidin-1-yl)piperidin-1-yl)prop-2-en-1-one, with immunotherapy are provided. Also provided are methods of treating cancers, and autoimmune disorders by administering combinations of Bruton's tyrosine kinase (Btk) inhibitors, e.g., 1-((R)-3-(4-amino-3-(4-phenoxyphenyl)-1H-pyrazolo[3,4-d]pyrimidin-1-yl)piperidin-1-yl)prop-2-en-1-one, and an immune checkpoint inhibitor.

Abstract: Combinations of Bruton's tyrosine kinase (Btk) inhibitors, e.g., 1-((R)-3-(4-amino-3-(4-phenoxyphenyl)-1H-pyrazolo[3,4-d]pyrimidin-1-yl)piperidin-1-yl)prop-2-en-1-one, with immunotherapy are provided. Also provided are methods of treating cancers, and autoimmune disorders by administering combinations of Bruton's tyrosine kinase (Btk) inhibitors, e.g., 1-((R)-3-(4-amino-3-(4-phenoxyphenyl)-1H-pyrazolo[3,4-d]pyrimidin-1-yl)piperidin-1-yl)prop-2-en-1-one, and an immune checkpoint inhibitor.

Abstract: A device, system, and method are disclosed. In one embodiment the device receives a user key from a user application. The device then creates a management engine key by applying a management engine key creation algorithm to the user key. Then the device sends the management engine key to a remote server. Later, the device retrieves a server key from the remote server. The device next performs a hash combination of the user key, the management engine key, and the server key to create a super key. Once the super key has been created, the device authenticates the super key, and if the super key is valid, the device then sends a management engine certification to the user application.

Abstract: An isolated oligonucleotide comprising at least one ditag, wherein the ditag comprises two joined first and second sequence tags, wherein the first tag comprises the 5?-terminus sequence and the second tag comprises the 3?-terminus sequence of a nucleic acid molecule or a fragment thereof. The ditag analysis is useful for gene discovery and genome mapping.

Abstract: A device, system, and method are disclosed. In one embodiment the device receives a user key from a user application. The device then creates a management engine key by applying a management engine key creation algorithm to the user key. Then the device sends the management engine key to a remote server. Later, the device retrieves a server key from the remote server. The device next performs a hash combination of the user key, the management engine key, and the server key to create a super key. Once the super key has been created, the device authenticates the super key, and if the super key is valid, the device then sends a management engine certification to the user application.