Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: In general, the disclosure describes techniques for personalizing a meeting summary according to the relevance of different meeting items within a meeting to different users. In some examples, a computing system for automatically providing personalized summaries of meetings comprises a memory configured to store information describing a meeting; and processing circuitry configured to receive a plurality of meeting item summaries of respective meeting items included in the transcript of the meeting; determine, by applying a model of meeting item relevance to the meeting item summaries, a corresponding relevance to a user of each of the meeting item summaries; and output respective indications of relevance to the user for one or more of the meeting item summaries to provide a personalized summary of the meeting to the user.

Abstract: In general, the disclosure describes techniques for personalizing a meeting summary according to the relevance of different meeting items within a meeting to different users. In some examples, a computing system for automatically providing personalized summaries of meetings comprises a memory configured to store information describing a meeting; and processing circuitry configured to receive a plurality of meeting item summaries of respective meeting items included in the transcript of the meeting; determine, by applying a model of meeting item relevance to the meeting item summaries, a corresponding relevance to a user of each of the meeting item summaries; and output respective indications of relevance to the user for one or more of the meeting item summaries to provide a personalized summary of the meeting to the user.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group and to provide improved scheduling and reports based on group characteristics.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: An apparatus includes an interface and a circuit. The interface may be configured to generate a read signal that carries read data from a memory channel. The circuit may be configured to (i) modify the read signal with a de-emphasis on each pull up of the read signal and a pre-emphasis on each pull down of the read signal and (ii) transfer the read signal as modified to a memory controller.

Abstract: The present disclosure extends to methods, systems, and computer program products for providing collaboration among users within a group for improved communication and scheduling.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: An apparatus includes an interface and a circuit. The interface may be configured to generate a read signal that carries read data from a memory channel. The circuit may be configured to (i) modify the read signal with a de-emphasis on each pull up of the read signal and a pre-emphasis on each pull down of the read signal and (ii) transfer the read signal as modified to a memory controller.

Abstract: The invention provides methods, compositions, uses of compositions, assays and kits for modulating brown adipose tissue (BAT) in animals and patients with obesity, insulin resistance and perturbed glucose homeostasis are disclosed. Accordingly, methods, compositions, uses of compositions, and kits are useful for the amelioration of pathological conditions characterized by storage of excess energy, insulin resistance and related metabolic syndromes often associated with obesity.

Abstract: An apparatus includes an interface and a circuit. The interface may be configured to generate a memory signal that carries read data from a memory channel. The circuit may be configured to modify a read signal that transfers the read data across a read line to a memory controller. A filter may delay the memory signal to generate a delayed signal. A driver generally amplifies the memory signal to generate the read signal. The driver may modify the read signal with a de-emphasis on each pull up of the memory signal and a pre-emphasis on each pull down of the memory signal based on the delayed signal.

Abstract: An apparatus includes an interface and a circuit. The interface may be configured to generate a memory signal that carries read data from a memory channel. The circuit may be configured to modify a read signal that transfers the read data across a read line to a memory controller. A filter may delay the memory signal to generate a delayed signal. A driver generally amplifies the memory signal to generate the read signal. The driver may modify the read signal with a de-emphasis on each pull up of the memory signal and a pre-emphasis on each pull down of the memory signal based on the delayed signal.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to (a) buffer write signals presented on a data bus connected between a memory channel and a memory controller, (b) buffer read signals presented on the data bus and (c) condition the write signals. The conditioning may be implemented by (i) converting the write signals to a first differential write signal on a first differential write line and a second differential write signal on a second differential write line and (ii) connecting (a) a negative impedance and (b) a combined resistive and capacitive load between the first and second differential write lines. The second circuit may be configured to (a) convert the first and the second differential write signals to a single-ended write signal and (b) present the single-ended write signal to the data bus.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.

Abstract: A system may include multiple personal data sources and a machine-implemented data extractor and correlator configured to retrieve personal data from at least one of the personal data sources. The data extractor and correlator may extract information from unstructured data within the retrieved personal data and correlate the extracted information with previously stored structured data to generate additional structured data. The system may also include a storage device configured to store the previously stored structured data and the additional structured data. A natural language query module may be configured to receive a natural language query from a user and provide a response to the natural language query based at least in part on one or both of the previously stored structured data and the additional structured data.

Abstract: A machine-implemented method for supporting a natural language user request against a user's personal data cloud can include a machine receiving the natural language user request from the user, determining a semantic interpretation of the natural language user request, querying a semantically-indexed, integrated knowledge store based on the semantic interpretation, and responding to the natural language user request by displaying results of the querying, wherein the results correspond to an item within the user's personal data cloud.