Abstract: Among other disclosed subject matter, a computer-implemented method includes receiving a first data set associated with a first data provider. The first data set includes a first set of data attributes associated with a first set of users. The method includes receiving a second data set associated with a second different data provider. The second data set includes a second set of data attributes associated with a second set of users. The method includes generating user cluster information based at least in part on at least one common data attribute associated with the first set of users and the second set of users. The method includes providing the user cluster information to a data purchaser.

Abstract: In accordance with one embodiment, the assembly includes a chassis with a board mounted to it that has one or more electronic components. Spaced apart from the board is a bridge heat sink. A heat transfer block is positioned adjacent the electronic component of the board with the heat transfer block in thermal communication with the electronic component to afford transferring of heat (e.g., conductive transfer of heat) from the electronic component to the heat transfer block. An opposite end of the heat transfer block is adjacent the bridge heat sink. The bridge heat sink has at least a portion located externally from the chassis so that the bridge heat sink (which is in thermal communication with the heat transfer block) affords the transferring of heat from the heat transfer block to an environment external to the chassis.

Abstract: Embodiments of a system method and computer program product for selecting an advertisement and presenting it to a user are described. Products and services offered by various merchants are read using a merchant specific catalog and stored in a common format. Categories for such products and services are normalized and virtual categories are created using various product attributes. Visual creatives, termed as ad-templates are created to control the visual and interactive aspects of the ad, including ad-size, color, as well as product attributes that are displayed in the ad. Ad-templates may be constrained to specific products or product categories. A learning algorithm uses an adaptive sampling process to sample various products, product categories and ad-templates independently for different learning units such as individual users, groups of users determined by some demographics, individual web pages and groups of web pages grouped using various similarity criteria.