Abstract: A lighting and monitoring module is disclosed herein. The lighting and monitoring module includes a module body including a front surface and a back surface, the module body defining a space between the front surface and the back surface, a connector disposed within the space defined by the module body, a light disposed within the space defined by the module body, the light coupled to the connector, a first component disposed within the space defined by the module body, the first component coupled to the connector, and a second component disposed within the space defined by the module body, the second component coupled to the connector.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: The invention discloses a method for preparation of spray dried solid dispersions (SDD) comprising an active pharmaceutical ingredient (API) and a dispersion polymer (DISPPOL), wherein the spray drying is done with a supersaturated solution of API in a solvent mixture comprising two solvents, this supersaturated solution further comprising DISPPOL.

Abstract: A lighting and monitoring module is disclosed herein. The lighting and monitoring module includes a module body including a front surface and a back surface, the module body defining a space between the front surface and the back surface, a connector disposed within the space defined by the module body, a light disposed within the space defined by the module body, the light coupled to the connector, a first component disposed within the space defined by the module body, the first component coupled to the connector, and a second component disposed within the space defined by the module body, the second component coupled to the connector.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: A computer-implemented technique is described herein for assisting a user in advancing a task objective. The technique uses a suggestion-generating system (SGS) to provide one or more suggestions to a user in response to at least a last-submitted query provided by the user. The SGS may correspond to a classification-type or generative-type neural network. The SGS uses a machine-trained model that is trained using a multi-task training framework based on plural groups of training examples, which, in turn, are produced using different respective example-generating methods. One such example-generating method constructs a training example from queries in a search session. It operates by identifying the task-related intent the queries, and then identifying at least one sequence of queries in the search session that exhibits a coherent task-related intent. A training example is constructed based on queries in such a sequence.

Abstract: The invention discloses a method for preparation of spray dried solid dispersions (SDD) comprising an active pharmaceutical ingredient (API) and a dispersion polymer (DISPPOL), wherein the spray drying is done with a supersaturated solution of API in a solvent mixture comprising two solvents, one is acetic acid, this supersaturated solution further comprising DISPPOL.

Abstract: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: The present concepts relate to a differentiable user-item co-clustering (“DUICC”) model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters—groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations.

Abstract: A system including at least one object and a computing system. The computing system includes a tracking system configured to detect the object. The tracking system may include a camera or other recording device. The computing system determines at least one attribute of the object based on input from the tracking system.

Abstract: Disclosed are techniques for extracting, identifying, and consuming imprecise temporal elements (“ITEs”). A user input may be received from a client device. A prediction may be generated of one or more time intervals to which the user input refers based upon an ITE model. The user input may be associated with the prediction, and provided to the client device.

Abstract: Provided herein are oral dosage forms comprising a) a core tablet comprising (i) a drug layer comprising apremilast and hypromellose acetate succinate (HPMCAS) in an amorphous solid dispersion; and (ii) a swellable layer comprising one or more swellable polymers; and b) a coating layer disposed on the core tablet, wherein the oral dosage form surface comprises at least one drug release orifice. The disclosed oral dosage forms provide once-a-day dosing of apremilast and are suitable for treating diseases or disorders ameliorated by inhibiting phosphodiesterase subtype IV (PDE4).

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: This document relates to natural language processing using a framework such as a neural network. One example method involves obtaining a first document and a second document and propagating attention from the first document to the second document. The example method also involves producing contextualized semantic representations of individual words in the second document based at least on the propagating. The contextualized semantic representations can provide a basis for performing one or more natural language processing operations.

Abstract: The present concepts relate to a differentiable user-item co-clustering (“DUICC”) model for recommendation and co-clustering. Users' interaction with items (e.g., content) may be centered around information co-clusters—groups of items and users that exhibit common consumption behavior. The DUICC model may learn fine-grained co-cluster structures of items and users based on their interaction data. The DUICC model can then leverage the learned latent co-cluster structures to calculate preference stores of the items for a user. The top scoring items may be presented to the user as recommendations.