Abstract: A system logs life experiences using geographic cues. The system variously provides a comprehensive life-logging tool for recording a plurality of different types of life events. In one implementation, the system receives a user's GPS log files and multimedia content at a website. The system segments the GPS log files into geographic routes corresponding to user trips, and tags the multimedia content with geographic cues from the GPS log files. Then, the system indexes the geographic routes so that users can retrieve the geographic routes by browsing or by search techniques. The system displays animations of selected routes on a map, and displays the multimedia content at corresponding locations along the map route, as the route is replayed. The system also provides browsing and spatial and temporal techniques to search a person's travels and can provide graphical displays of the person's activity statistics.

Abstract: A bioreactor designed to produce N2O from organic nitrogen and/or reactive nitrogen in waste is coupled to a hardware reactor device in which the N2O is consumed in a gas phase chemical reaction, e.g., catalytic decomposition to form oxygen and nitrogen gas. Heat from the exothermic reaction may be used to generate power. The N2O may alternatively be used as an oxidant or co-oxidant in a combustion reaction, e.g., in the combustion of methane. The bioreactor may have various designs including a two-stage bioreactor, a hollow-fiber membrane bioreactor, or a sequencing batch reactor. The bioreactor may involve Fe(II)-mediated reduction of nitrite to nitrous oxide.

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: 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: Techniques describe constructing a location and activity recommendation model to identify relationships between locations and activities. To construct the model, the process obtains global positioning system (GPS) logs of geographical locations collected over time and identifies stay points representing locations visited by an individual user. The process also identifies points of interest in a region using a database and correlates a relationship between activity to activity by submitting queries to a search engine. The information gathered is used to fill locations and activities in a location-activity matrix. Recommendations may be made for a location and/or activity when given a user query, based on a user's present geographical location, or a prediction of a user's interest.

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: New multifunctional synthetic nanoparticles are adapted for water treatment, with environmentally-functional layers, optional capping layers, and synthetic antiferromagnetic cores. With high surface-to-volume ratio, these nanoparticles are very efficient in water treatment, including but not restricted to water disinfection, photo-catalytic degradation, contaminant adsorption, etc., in the context of drinking water or waste water treatment. Meanwhile, their magnetic cores are highly magnetically responsive and can be separated by 99% within 10 min using simply a permanent magnet. Moreover, once some non-degradable chemicals (like perfluorinated compounds) are absorbed to the particle surface, these chemicals can be further degraded by introducing hyperthermia or eddy current heating. These particles can be redispersed after the external magnetic field is removed, and can therefore be used in a regenerative treatment process, substantially reducing the cost while eliminating contaminated byproducts.

Abstract: Methods and systems for selecting advertisements to present to a user of a computing device are provided. An advertisement system selects advertisements to display to a user based on the serving area of candidate advertisements. The advertisement system selects those candidate advertisements whose serving area encompasses the user's current location. The advertisement system may also select candidate advertisements to present to a user based on a map area currently being displayed to the user. The advertisement system may filter the candidate advertisements based on the provider location being within the map area that is currently being displayed to the user.

Abstract: Techniques for reconstructing photo trip patterns from geo-tagged photos are described. Photo trip patterns are reconstructed by mining geo-tagged photos from the Web or a data storage and segmenting the photos based on at least the geographical identification information associated with the photos. Mining semantics of each photo trip pattern may also be performed using tags associated with the photos.

Abstract: Techniques describe determining a correlation between identified locations to recommend a location that may be of interest to an individual user. The process constructs a location model to identify locations. To construct the model, the process uses global positioning system (GPS) logs of geospatial locations collected over time and identifies trajectories representing trips of the individual user and extracts stay points from the trajectories. Each stay point represents a geographical region where the individual user stayed over a time threshold within a distance threshold. A location history is formulated for the individual user based on a sequence of the extracted stay points to identify locations. The process determines a correlation between identified locations. The process integrates travel experiences of individual users who have visited the locations in a weighted manner and identifies a common travel sequence which the individual users followed between the locations.

Abstract: Some implementations provide techniques and arrangements to generate a ranked set of possible locations of a user based on user information and a user location. Some examples may generate a first set of locations that are near a location associated with the user. Features may be extracted from information about the user and the first set of locations for use by a ranking model. A second set of locations may be generated by the ranking model based on the extracted features and the first set of locations. In some cases, the second set of locations may be a ranked set of one or more of the locations of the first set of locations.

Abstract: Trajectory data representing tracked positions of a vehicle along a trajectory having a start and end point is accessed. The trajectory data may include spatio-temporal information about the vehicle at different points along the trajectory. The trajectory may be divided into segments based, at least in part, on knowledge of inferred-parking locations. The segments may be map-matched to corresponding road segments. Additionally, historical data representing spatio-temporal travel patterns of vehicles learned from historical trajectories of vehicles corresponding to the map-matched-road segments may also be accessed. A behavioral state of the vehicle for a segment or position within a segment may be inferred, based at least in part, on (i) the vehicle's spatio-temporal information corresponding to the segment or position within a segment, (ii) knowledge of the map-matched-road segment, and (iii) the historical data.

Abstract: One or more techniques and/or systems are disclosed for prioritizing one or more travel itineraries based on an itinerary query. Respective candidate itineraries from a set of candidate itineraries are ranked based on one or more ranking factors for the candidate itineraries, where the candidate itineraries were identified from a location-interest graph using the query. A desired number of the ranked candidate itineraries are re-ranked based on a one or more historical travel sequences, such that one or more prioritized travel itineraries can be identified in response to the itinerary query.

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 analyzing effectiveness of an urban area based on traffic patterns collected from route-oriented vehicles. A process collects sequences of global positioning system (GPS) points in logs and identifies geographical locations to represent the urban area where the route-oriented vehicles traveled. The process models traffic patterns by: partitioning the urban area into regions based at least in part on major roads, segmenting the GPS points from the logs into time slots, and identifying the GPS points associated with transporting a passenger in the route-oriented vehicles. The process models traffic patterns by projecting the identified GPS points onto the regions to construct transitions of the identified GPS points travelling between the regions. Then the process builds a matrix of the regions for each time slot in each day based on a number of the transitions. Each item in the matrix represents an effectiveness of a connection between two regions.

Abstract: Described is a technology in which an image retrieval system is updated incrementally as new image data becomes available. Updating is incrementally performed and only triggered when the new image data is large enough or diverse enough relative to the image data currently in use for image retrieval. Incremental updating updates the leaf nodes of a vocabulary tree based upon the new image data. Each leaf node's feature frequency is evaluated against upper and/or lower threshold values, to modify the nodes of the tree based on the feature frequency. Upon completion of the incremental updating, a server that performed the incremental updating is switched to an active state with respect to handling client queries for image retrieval, and another server that was actively handling client queries is switched to an inactive state, awaiting a subsequent incremental updating before switching back to active state.

Abstract: Functionality is described for generating a vocabulary from a source dataset of image items or other non-textual items. The vocabulary serves as a tool for retrieving items from a target dataset in response to queries. The vocabulary has at least one characteristic that allows it to be used to retrieve items from multiple different target datasets. A target dataset can have a different size than the source dataset and/or a different type than the source dataset. The enabling characteristic may correspond to a size of the source dataset above a prescribed minimum number of items and/or a size of the vocabulary above a prescribed minimum number of words.

Abstract: Described is using density to efficiently mine co-location patterns, such as closely located businesses frequently found together in business listing databases, geographic search logs, and/or GPS-based data. A data space of such information is geographically partitioned into a grid of cells, with dense cells scanned first. A dynamic upper bound of prevalence measure of co-location patterns is maintained during the scanning process. If the current upper bound is smaller than a threshold, the scanning is stopped, thereby significantly reducing the computation cost for processing many cells, while providing suitable results.

Abstract: Trajectory data representing tracked positions of a vehicle along a trajectory having a start and end point is accessed. The trajectory data may include spatio-temporal information about the vehicle at different points along the trajectory. The trajectory may be divided into segments based, at least in part, on knowledge of inferred-parking locations. The segments may be map-matched to corresponding road segments. Additionally, historical data representing spatio-temporal travel patterns of vehicles learned from historical trajectories of vehicles corresponding to the map-matched-road segments may also be accessed. A behavioral state of the vehicle for a segment or position within a segment may be inferred, based at least in part, on (i) the vehicle's spatio-temporal information corresponding to the segment or position within a segment, (ii) knowledge of the map-matched-road segment, and (iii) the historical data.

Abstract: Described is a technology by which raw GPS data is processed into segments of a trip, with a predicted mode of transportation (e.g., walking, car, bus, bicycling) determined for each segment. The determined transportation modes may be used to tag the GPS data with transportation mode information, and/or dynamically used. Segments are first characterized as walk segments or non-walk segments based on velocity and/or acceleration. Features corresponding to each of those walk segments or non-walk segments are extracted, and analyzed with an inference model to determine probabilities for the possible modes of transportation for each segment. Post-processing may be used to modify the probabilities based on transitioning considerations with respect to the transportation mode of an adjacent segment. The most probable transportation mode for each segment is selected.