Abstract: A method, computer program product, and system to generate a processor design via a deep neural network is provided. A processor selects an architecture search space and a hardware components space. A processor selects an initial deep neural network from the architecture search space. A processor determines an initial current chip design for executing the current deep neural network, wherein the initial chip design has a hardware performance metric for implementing the current deep neural network. A processor repeatedly executes an optimization method, the optimization method comprising modifying the chip design one or more times using components from the hardware components space and optimizing the current deep neural network by selecting a deep neural network from the architecture search space. A processor provides the optimized chip design and the specific deep neural network for performing the machine learning task.

Abstract: A neural network models fragmenting method, system, and computer program product include recursively factoring out common prefixes of models, constructing a hierarchy of decomposed model fragments based on the factoring, and grouping the constructed hierarchy for deployment.

Abstract: A flash translation layer method, system, and computer program product, include performing a virtualization of a meta-flash translation layer by: instantiating a range in a NAND chip comprising the number of free blocks using a meta-FTL to create a compatible range of blocks for a type of a feature and a flash characteristic of a translation table if a number of free blocks are available in the NAND chip and instantiating a second range in the NAND chip comprising a second number of free blocks using the meta-FTL to create a second compatible range of blocks for a second type of feature and a second flash characteristic of the translation table, and dynamically programming data on the fly based on an input requirement of a request into the range and the second range.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: A method and system are provided. The method includes generating, by a machine-based sentiment prediction generator, respective machine-determined sentiment predictions for each of a plurality of software patches using sentiment analysis. The method further includes setting, by a sentiment-based confidence value generator, a confidence value for each of the plurality of software patches based on the machine-determined sentiment predictions. The method also includes at least one of selecting and prioritizing, by a software patch selector and prioritizer, at least one of the plurality of software patches based on the respective confidence value therefor.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.

Abstract: A neural network models fragmenting method, system, and computer program product include recursively factoring out common prefixes of models, constructing a hierarchy of decomposed model fragments based on the factoring, and grouping the constructed hierarchy for deployment.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: A flash translation layer method, system, and computer program product, include performing a virtualization of a meta-flash translation layer by: instantiating a range in a NAND chip comprising the number of free blocks using a meta-FTL to create a compatible range of blocks for a type of a feature and a flash characteristic of a translation table if a number of free blocks are available in the NAND chip and instantiating a second range in the NAND chip comprising a second number of free blocks using the meta-FTL to create a second compatible range of blocks for a second type of feature and a second flash characteristic of the translation table, and dynamically programming data on the fly based on an input requirement of a request into the range and the second range.

Abstract: A flash translation layer method, system, and computer program product, include sending a request with a type of feature and a flash characteristic of a translation table for a Solid-State disk, receiving, via a meta-flash translation layer (meta-FTL), the request and checking for a number of free blocks in a NAND chip, and instantiating a range in the NAND chip including the number of free blocks using the meta-FTL to create a compatible range of blocks for the type of feature and the flash characteristic of the translation table if the checking returns a confirmation that the number of free blocks is available.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: Embodiments of the present invention provide systems and methods for the discovery of facets. A search query over sets of data (e.g., a set of documents) leads to search results. The search results are organized by facets. Responsive to receiving new queries, facets are dynamically extracted from the search results. Furthermore, user profiles are dynamically updated. The order of presentation of facets, as displayed in a graphical user interface, can be modified based on the extracted facets.

Abstract: A method includes embedding clothing with at least one sensor and at least one control unit; a power unit powering on the at least one sensor and the at least one control unit; the at least one sensor monitoring a sensed condition; the at least one control unit conducting a heat prediction based on the sensed condition; and the at least one control unit controlling threads within the clothing based on the heat prediction to actively adjust properties of the clothing.

Abstract: A method and system are provided. The method includes generating, by a machine-based sentiment prediction generator, respective machine-determined sentiment predictions for each of a plurality of software patches using sentiment analysis. The method further includes setting, by a sentiment-based confidence value generator, a confidence value for each of the plurality of software patches based on the machine-determined sentiment predictions. The method also includes at least one of selecting and prioritizing, by a software patch selector and prioritizer, at least one of the plurality of software patches based on the respective confidence value therefor.

Abstract: A method and system are provided. The method includes generating, by a machine-based sentiment prediction generator, respective machine-determined sentiment predictions for each of a plurality of software patches using sentiment analysis. The method further includes setting, by a sentiment-based confidence value generator, a confidence value for each of the plurality of software patches based on the machine-determined sentiment predictions. The method also includes at least one of selecting and prioritizing, by a software patch selector and prioritizer, at least one of the plurality of software patches based on the respective confidence value therefor.

Abstract: A method for diagnosing software crashes includes calculating, using vector space modeling, angles between vectors representing stack-traces from reports in a repository on one or more server computer systems, to define similarities in the reports, wherein reports are deemed similar where angles between their respective vectors are less than a threshold value. The method further includes grouping the reports into similar sets using a maximal cliques process and automatically diagnosing, in response to a received software crash, a first stack-trace extracted from a first report of the received software crash, to determine a potential solution to the received software crash.

Abstract: Embodiments of the present invention provide systems and methods for the discovery of facets. A search query over sets of data (e.g., a set of documents) leads to search results. The search results are organized by facets. Responsive to receiving new queries, facets are dynamically extracted from the search results. Furthermore, user profiles are dynamically updated. The order of presentation of facets, as displayed in a graphical user interface, can be modified based on the extracted facets.

Abstract: Embodiments of the present invention provide systems and methods for the discovery of facets. A search query over sets of data (e.g., a set of documents) leads to search results. The search results are organized by facets. Responsive to receiving new queries, facets are dynamically extracted from the search results. Furthermore, user profiles are dynamically updated. The order of presentation of facets, as displayed in a graphical user interface, can be modified based on the extracted facets.

Abstract: A method and system are provided. The method includes generating, by a machine-based sentiment prediction generator, respective machine-determined sentiment predictions for each of a plurality of software patches using sentiment analysis. The method further includes setting, by a sentiment-based confidence value generator, a confidence value for each of the plurality of software patches based on the machine-determined sentiment predictions. The method also includes at least one of selecting and prioritizing, by a software patch selector and prioritizer, at least one of the plurality of software patches based on the respective confidence value therefor.