Abstract: Techniques for detecting outliers in data and determining spatiotemporal causal interactions in the data are discussed. A process collects global positioning system (GPS) points in logs and identifies geographical locations to represent the area where the service vehicles travelled with a passenger. The process models traffic patterns by: partitioning the area into regions, segmenting the GPS points from the logs into time bins, and identifying the GPS points associated with transporting the passenger. The process projects the identified GPS points onto the regions to construct links connecting GPS points located in two or more regions. Furthermore, the process builds a three-dimensional unit cube to represent features of each link. The points farthest away from a center of data cluster are detected as outliers, which represent abnormal traffic patterns. The process constructs outlier trees to evaluate relationships of the outliers and determines the spatiotemporal causal interactions in the data.

Abstract: A microbial battery is provided. At the anode, microbial activity provides electrons to an external circuit. The cathode is a solid state composition capable of receiving the electrons from the external circuit and changing from an oxidized cathode composition to a reduced cathode composition. Thus, no external source of oxygen is needed at the cathode, unlike conventional microbial fuel cells. The cathode can be removed from the microbial battery, re-oxidized in a separate oxidation process, and then replaced in the microbial battery. This regeneration of the cathode amounts to recharging the microbial battery.

Abstract: Described herein is a mixing entropy battery including a cationic electrode for sodium ion exchange and an anionic electrode for chloride ion exchange, where the cationic electrode includes at least one Prussian Blue material, and where the mixing entropy battery is configured to convert salinity gradient into electricity.

Abstract: Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.

Abstract: Techniques for determining a status of a user are described. A mobile device equipped with sensors may collect sensor data pertaining to transportation modes of the user, tracking locations of the user, identifying environmental noise levels surrounding the user, or speech being spoken in proximity to the user. Features of the collected sensor readings are then used to infer activities the user may be performing. Based at least in part on the multiple inferred activities, a status of the user is determined.

Abstract: Described is a technology by which a user hums, sings or otherwise plays a user-provided rendition of a ringtone (or ringback tone) through a mobile telephone to a ringtone search service (e.g., a WAP, interactive voice response or SMS-based search platform). The service matches features of the user's rendition against features of actual ringtones to determine one or more matching candidate ringtones for downloading. Features may include pitch contours (up or down), pitch intervals and durations of notes. Matching candidates may be ranked based on the determined similarity, possibly in conjunction with weighting criterion such as the popularity of the ringtone and/or the importance of the matched part. The candidate set may be augmented with other ringtones independent of the matching, such as the most popular ones downloaded by other users, ringtones from similar artists, and so forth.

Abstract: Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.

Abstract: Described is a technology by which a user hums, sings or otherwise plays a user-provided rendition of a ringtone (or ringback tone) through a mobile telephone to a ringtone search service (e.g., a WAP, interactive voice response or SMS-based search platform). The service matches features of the user's rendition against features of actual ringtones to determine one or more matching candidate ringtones for downloading. Features may include pitch contours (up or down), pitch intervals and durations of notes. Matching candidates may be ranked based on the determined similarity, possibly in conjunction with weighting criterion such as the popularity of the ringtone and/or the importance of the matched part. The candidate set may be augmented with other ringtones independent of the matching, such as the most popular ones downloaded by other users, ringtones from similar artists, and so forth.

Abstract: Webpage content may be identified and stored for later review by capturing at least part of an image of the webpage content, and sending the image to a remote device. The remote device may recognize text included in the image and may form a plurality of text groups based on the text. The remote device may also generate a plurality of searches using the text. The remote device may also generate a content item using content that is available online or through a private network, and that is identified in one of the searches. The content item may then be stored and made available for subsequent review.

Abstract: Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.

Abstract: The use of data from multiple data source provides inferred air quality indices with respect to a particular pollutant for multiple areas without the addition of air quality monitor stations to those areas. Labeled air quality index data for a pollutant in a region may be obtained from one or more air quality monitor stations. Spatial features for the region may be extracted from spatially-related data for the region. The spatially-related data may include information on fixed infrastructures in the region. Likewise, temporal features for the region may be extracted from temporally-related data for the region that changes over time. A co-training based learning framework may be further applied to co-train a spatial classifier and a temporal classifier based at least on the labeled air quality index data, the spatial features for the region, and the temporal features for the region.

Abstract: Techniques for providing a route based on route-oriented vehicle trajectories are described. This disclosure describes receiving GPS logs and extracting route-oriented vehicle trajectory content from the GPS log data to pertain to a single trip. Next, the process maps each route-oriented vehicle trajectory to a corresponding road segment to construct a landmark graph. A landmark is a road segment frequently visited by route-oriented vehicles. The process includes receiving a user query with a starting point and a destination point; searching the landmark graph for a sequence of landmarks with corresponding transition times and a least amount of travel time. Then the process identifies and connects sets of road segments between each pair of consecutive landmarks, and displays a route to a user with a nearest landmark to the starting point, other landmarks along the route, and another nearest landmark to the destination point.

Abstract: Disclosed herein are techniques and systems for discovering functional groups in an area, such as an urban area. A process includes segmenting a map of the area into sections, and inferring, for each section, a distribution of functions according to a topic model framework which considers mobility patters of users and points of interest (POIs) in the section. The topic model framework regards the section as a document, each function as a topic, the mobility patterns as words, and a POI feature vector for the section as metadata. The process may further include clustering the sections based at least in part on a similarity of the distribution of functions between each of the sections to obtain functional groups, estimating a functionality intensity for each of the functional groups, and annotating each of the functional groups.

Abstract: Disclosed herein are techniques and systems for discovering functional groups in an area, such as an urban area. A process includes segmenting a map of the area into sections, and inferring, for each section, a distribution of functions according to a topic model framework which considers mobility patters of users and points of interest (POIs) in the section. The topic model framework regards the section as a document, each function as a topic, the mobility patterns as words, and a POI feature vector for the section as metadata. The process may further include clustering the sections based at least in part on a similarity of the distribution of functions between each of the sections to obtain functional groups, estimating a functionality intensity for each of the functional groups, and annotating each of the functional groups.

Abstract: A microbial battery is provided. At the anode, microbial activity provides electrons to an external circuit. The cathode is a solid state composition capable of receiving the electrons from the external circuit and changing from an oxidized cathode composition to a reduced cathode composition. Thus, no external source of oxygen is needed at the cathode, unlike conventional microbial fuel cells. The cathode can be removed from the microbial battery, re-oxidized in a separate oxidation process, and then replaced in the microbial battery. This regeneration of the cathode amounts to recharging the microbial battery.

Abstract: Described is a technology by which co-located query patterns are mined from a data space such as a geographic search log. An overall data space (basic) approach and/or a lattice-based approach may be used when mining. The data space contains objects, each comprising associated type and location information. The location information is used to determine the distance between different two or more types of objects, e.g., pairs. The frequency of occurrence of those pairs within the data space determines whether that pairing of object types indicates a co-located pattern. Also described is partitioning the data space into regions, including for the purpose of categorizing a co-located pattern as a local pattern or a global pattern based on how that co-located pattern is distributed among the regions.

Abstract: Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.

Abstract: Disclosed herein are one or more embodiments that arrange a plurality of location entities into a hierarchy of location descriptors. One or more of the disclosed embodiments may determine whether one of the location entities is a spatial outlier based at least in part on presence of one or more other location entities within a predetermined distance of the one location entity. Also, the other location entities and the one location entity may share a location descriptor.

Abstract: Techniques for detecting outliers in data and determining spatiotemporal causal interactions in the data are discussed. A process collects global positioning system (GPS) points in logs and identifies geographical locations to represent the area where the service vehicles travelled with a passenger. The process models traffic patterns by: partitioning the area into regions, segmenting the GPS points from the logs into time bins, and identifying the GPS points associated with transporting the passenger. The process projects the identified GPS points onto the regions to construct links connecting GPS points located in two or more regions. Furthermore, the process builds a three-dimensional unit cube to represent features of each link. The points farthest away from a center of data cluster are detected as outliers, which represent abnormal traffic patterns. The process constructs outlier trees to evaluate relationships of the outliers and determines the spatiotemporal causal interactions in the data.

Abstract: Techniques for searching and providing geographical regions are described. The process searches and recommends points of interests based on a user-specified region. Points of interests include spatial objects (e.g., buildings, landmarks, rivers, parks) and their distributions in a geographical region. The process searches and recommends points of interests by partitioning a spatial map into grids to identify representative categories located in each of the grids. In response to the user-specified region, a set of geographical candidates containing the representative categories is retrieved. The process determines whether the user-specified region and the set of geographical candidates include similar or common representative categories and similar or common spatial distributions of the representative categories. Then the process provides the top ranked set of geographical candidates that have similar content information.