Abstract: An image reconstruction method comprises reconstructing an image using an iterative reconstruction method and computing a projection used in the reconstructing by summing ray increments between neighboring planes (P0, P1, P2, . . . ) parallel with a detector face (42) with stationary incremental blurring associated with each ray increment computed based on a distance between the neighboring parallel planes. A non-stationary blurring kernel may also be convolved at a plane closest to the detector face to generate a projection incorporating shift-variant blurring.

Abstract: Techniques for estimating, in real time, the likelihood that any particular member of a social network service is a third degree connection of another member are described. Consistent with some embodiments, various member profile attributes of a member are used as a sort of proxy for determining the likelihood or probability that the member is a third degree connection of another member. For example, in some instances, the number of first-degree connections a member has is used to derive a probability score indicating the likelihood that the member is a third-degree connection of another member, such as a person performing a people-search. Once derived, the probability score for each member may be used in various applications, such as a people-search engine, to boost or increase a ranking score assigned to each search result and used to order the search results when presented to the user who has performed the search.

Abstract: A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity.

Abstract: When imaging a compact structure, such as a calcium deposit in a patient's heart, a slow scan (e.g., less than approximately 6 rpm) CT data acquisition is performed, wherein data is continuously but sparsely acquired during around a 360° revolution around the patient. Arc segments are defined that equate to one heart cycle (e.g., heartbeat) given the patient's heart rate and the speed of the CT gantry. Electrocardiogram signal data is used to identify sets of acquired projection data that correspond to each of a plurality of heart cycle phases during which the heart is relatively still. A sparse reconstruction algorithm is executed on the identified sets of sparse projection data to generate images for each heart cycle phase from the scan data acquired for that phase across all heart cycles.

Abstract: An image reconstruction method comprises reconstructing an image using an iterative reconstruction method and computing a projection used in the reconstructing by summing ray increments between neighboring planes (P0, P1, P2, . . . ) parallel with a detector face (42) with stationary incremental blurring associated with each ray increment computed based on a distance between the neighboring parallel planes. A non-stationary blurring kernel may also be convolved at a plane closest to the detector face to generate a projection incorporating shift-variant blurring.

Abstract: A method for generating search results based on a search term and displaying the search results in one or more groupings. The method includes receiving a search term entered by a user, selecting one or more search result groupings based on the search term entered by the user and at least in part on a bid amount associated with the search term, providing search results based on the search term entered by the user for each search result grouping, and displaying the search groupings to the user.

Abstract: Users who visit a network site typically visit other sites in the same session. The surfing pattern or “trail” through the pages may be significant once aggregated, because it outlines the underlining relationship between those pages or sites. Once such user navigational trail data is available, the relationship between network sites on such user trails may used to make various decisions regarding advertising and keyword management. Additionally, the relative popularity of each trail may be used to rank or compute relative weights for the various user trails. Such information may then aid in keyword suggestion and advertising decisions.

Abstract: Users that visit a network site typically visit other sites in the same session. The surfing pattern or “trail” through the sites is significant because it may outline the underlining relationship between those sites. Once such data is available, the relationship between sites on the same user trails may be used to determine the placement of advertisements for sites by publishers. Additionally, the popularity or volume of traffic that navigates along a particular user trail may be used to affect the ranking/ordering of advertisements placed on a certain publisher site. An advertising broker may determine advertising pricing in part based on navigational path or user trail information. Thus, placing advertising on sites that are closer in proximity to the advertised site may cost more than placing advertising on sites not as close in proximity to the advertised site.