Abstract: Methods for preparing the Bruton's Tyrosine Kinase (“BTK”) inhibitor compound 2-{3?-hydroxymethyl-1-methyl-5-[5-((S)-2-methyl-4-oxetan-3-yl-piperazin-1-yl)-pyridin-2-ylamino]-6-oxo-1,6-dihydro-[3,4?]bipyridinyl-2?-yl}-7,7-dimethyl-3,4,7,8-tetrahydro-2H,6H-cyclopenta[4,5]pyrrolo[1,2-a]pyrazin-1-one are provided.

Abstract: The disclosure describes various aspects of a system with scalable and programmable coherent waveform generators. A network and digital-to-analog conversion (DAC) cards used by the network are described where each DAC card has a clock divider/replicator device with an input SYNC pin, a digital logic component, and one or more DAC components, and each output of the DAC components is used to control optical beams for a separate qubit of a quantum information processing (QIP) system. The network also includes a first distribution network to provide a clock signal to the clock divider/replicator device in the DAC cards, and a second distribution network to provide a start signal to the DAC cards, where the start signal is used by the digital logic component in the DAC card to assert the input SYNC pin when the start signal is asserted unless it is masked by the digital logic component.

Abstract: The invention relates in particular to a process for the preparation of a compound of formula (I) wherein R1 and R2 are as defined in the description and in the claims.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.