Abstract: Systems and methods directed to providing recommended charts are provided. More specifically, a selection of data arranged in a plurality of data series may be received and classified into series data types. Based on the series data type for each data series of the plurality of data series, a plurality of recommended charts visually describing the data may be automatically provided to a user interface, wherein each chart of the plurality of recommended charts is a different chart type visually describing the data. To provide the plurality of recommended charts, best practices and/or one or more machine learning models may be utilized. In some instances, the charts provided in the user interface may automatically change or otherwise updated based on a different selection of data and/or an assignment of a different data series type to a data series.

Abstract: Methods and compositions that affect yield and other agronomic characteristics in plants are disclosed. Methods of transgenic modulation and marker-assisted breeding methods improve grain size and grain yield in rice are also disclosed. Increased expression of BG1 results in increased grain size and yield.

Abstract: Embodiments create and label contextual slices from observation data and aggregate slices into a hierarchical storyline for a user. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged in groups at one or more hierarchical levels. A storyline is created through a process of data collection, slicing, labeling, and aggregating. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the raw context data into a consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the slices. Aggregation identifies groups of slices that correspond to a single semantic concept.

Abstract: Methods and compositions that affect yield and other agronomic characteristics in plants are disclosed. Methods of transgenic modulation and marker-assisted breeding methods improve grain size and grain yield in rice are also disclosed. Increased expression of BG1 results in increased grain size and yield.

Abstract: Embodiments create and label contextual slices from observation data and aggregate slices into a hierarchical storyline for a user. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time that are arranged in groups at one or more hierarchical levels. A storyline is created through a process of data collection, slicing, labeling, and aggregating. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the raw context data into a consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the slices. Aggregation identifies groups of slices that correspond to a single semantic concept.

Abstract: A user's context history is used to help select contextual information to provide to the user. Context data describing the user's current context is received and a plurality of information items corresponding to the user's current context are identified from a contextual information corpus. A personalized user behavior model for the user is applied to determine the likelihood that each of the identified information items will be of value to the user. One or more of the information items are selected based on the corresponding likelihoods and the selected information items are provided for presentation to the user.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: Embodiments create and label context slices from observation data that together define a storyline of a user's movements. A context is a (possibly partial) specification of what a user was doing in the dimensions of time, place, and activity. Contexts can vary in their specificity, their semantic content, and their likelihood. A storyline is composed of a time-ordered sequence of contexts that partition a given span of time. A storyline is created through a process of data collection, slicing and labeling. Raw context data can be collected from a variety of observation sources with various error characteristics. Slicing refines the chaotic collection of contexts produced by data collection into a single consistent storyline composed of a sequence of contexts representing homogeneous time intervals. Labeling adds more specific and semantically meaningful data (e.g., geography, venue, activity) to the storyline produced by slicing.

Abstract: A user's context history is used to help select contextual information to provide to the user. Context data describing the user's current context is received and a plurality of information items corresponding to the user's current context are identified from a contextual information corpus. A personalized user behavior model for the user is applied to determine the likelihood that each of the identified information items will be of value to the user. One or more of the information items are selected based on the corresponding likelihoods and the selected information items are provided for presentation to the user.