Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: Embodiments may include systems and methods to create and edit a representation of a worksite, to create various data objects, to classify such objects as various types of predefined “features” with attendant properties and layout constraints. As part of or in addition to classification, an embodiment may include systems and methods to create, associate, and edit intrinsic and extrinsic properties to these objects. A design engine may apply of design rules to the features described above to generate one or more solar collectors installation design alternatives, including generation of on-screen and/or paper representations of the physical layout or arrangement of the one or more design alternatives. Some embodiments may provide viewing, creating, and manipulating of multiple versions of a solar collector layout design for a particular installation worksite.

Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.

Abstract: Many computing systems process data organized in a matrix format. For example, artificial neural networks perform numerous computations on data organized into matrices using conventional matrix arithmetic operations. One such operation is the transpose operation. Techniques are introduced for storing a matrix in a compressed format that allows, for example, a transpose operation to be performed during decompression. Thus, by utilizing the introduced techniques, transformations of compressed matrices such transposition can be achieved in a more effective way. Parallel processing may also be used to more efficiently compress and/or decompress.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: Advertisement quality measures (e.g., predicted click through rates) are modified in accordance with a user's preferences with respect to domains to which the advertisements relate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: Advertisement quality measures (e.g., predicted click through rates) are modified in accordance with a user's preferences with respect to domains to which the advertisements relate.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing cell images using neural networks. One of the methods includes obtaining data comprising an input image of one or more biological cells illuminated with an optical microscopy technique; processing the data using a stained cell neural network; and processing the one or more stained cell images using a cell characteristic neural network, wherein the cell characteristic neural network has been configured through training to receive the one or more stained cell images and to process the one or more stained cell images to generate a cell characteristic output that characterizes features of the biological cells that are stained in the one or more stained cell images.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: A method of presenting computer-generated search result information can include receiving a search request from a client computer; identifying a plurality of search results responsive to the search request; ranking the plurality of search results using content in one or more web notebooks; and providing the ranked plurality of search results for presentation in the client computer. Using the content can include determining whether at least one of a title, a heading, clipped content, metadata or a user-annotation in at least one web notebook relates to the search request and, if so, increasing a ranking of at least one search result referenced by the at least one web notebook.

Abstract: A system receives a search query from a user and searches a corpus of documents using the received search query to determine a set of search results. The system provides the set of search results to the user and receives a request from the user to save one or more selected search results of the set of search results. The system saves the selected one or more results in a database in response to the request and uses the saved one or more results as indicators of search result quality for subsequent related searches.

Abstract: Advertisement quality measures (e.g., predicted click through rates) are modified in accordance with a user's preferences with respect to domains to which the advertisements relate.

Abstract: Systems and methods for optimizing traffic in a data network are provided. Network traffic may be optimized by associating costs with data links that connect network nodes. The method includes determining a set of lowest cost paths between a source network node and a destination network node. Paths that contain loops may be excluded from the set of lowest cost paths. Exploration of certain paths may also be delayed or prevented entirely.