Abstract: A reamer has a basic tool body made of hard metal (12) and a plate-like cutting insert (18) inserted into a recess (28) at the front. The cutting insert (18) is at least partially made of polycrystalline diamond, its cutting edges (20) situated axially offset relative to the cutting edges (30) on the basic tool body (12), thus engaging with the workpiece later.

Abstract: A reamer has a basic tool body made of hard metal (12) and a plate-like cutting insert (18) inserted into a recess (28) at the front. The cutting insert (18) is at least partially made of polycrystalline diamond, its cutting edges (20) situated axially offset relative to the cutting edges (30) on the basic tool body (12), thus engaging with the workpiece later.

Abstract: An active learning record matching system and method for producing a record matching package that is used to identify pairs of duplicate records. Embodiments of the system and method allow a precision threshold to be specified and then generate a learned record matching package having precision greater than this threshold and a recall close to the best possible recall. Embodiments of the system and method use a blocking technique to restrict the space of record matching packages considered and scale to large inputs. The learning method considers several record matching packages, estimates the precision and recall of the packages, and identifies the package with maximum recall having precision greater than equal to the given precision threshold. A human domain expert labels a sample of record pairs in the output of the package as matches or non-matches and this labeling is used to estimate the precision of the package.

Abstract: The subject disclosure is directed towards personalizing content (e.g., advertisement) delivery to a mobile device such as a smartphone, without violating user privacy. A user decides how much context information (from the device's sensor readings and/or other data) to share with an advertisement server. Based on this limited, partial context information, the server selects a subset of advertisements from those available and sends them to the client. The client then picks the most relevant one based on richer, more granular context data, e.g., more (or even all) of the device's sensor readings and possibly other non-revealed information such as user preference data. The optimization of selecting the most relevant advertisement to display is done jointly by the user and the server, with the server selecting a subset of advertisements based upon partial context, and the client selecting from the subset based upon full context.

Abstract: An active learning record matching system and method for producing a record matching package that is used to identify pairs of duplicate records. Embodiments of the system and method allow a precision threshold to be specified and then generate a learned record matching package having precision greater than this threshold and a recall close to the best possible recall. Embodiments of the system and method use a blocking technique to restrict the space of record matching packages considered and scale to large inputs. The learning method considers several record matching packages, estimates the precision and recall of the packages, and identifies the package with maximum recall having precision greater than equal to the given precision threshold. A human domain expert labels a sample of record pairs in the output of the package as matches or non-matches and this labeling is used to estimate the precision of the package.