Abstract: The present disclosure relates to an operating table, the operating table comprising a table top, a table top support and a column with a column head, wherein the sealing device is bellows assembled below the column head, or an adhesive in a hole and/or window on the holder of the cable, or a sealing gasket between the receiver and the column head, or a shield sealingly attached to the column head below the gear. The present disclosure further relates to control equipment, a lifting device, an intelligent charger, a column guide system for an operating table, as well as an operating table including the same.

Abstract: Methods, devices, and compositions for use with spintronic devices such as magnetic random access memory (MRAM) and spin-logic devices are provided. Methods include manipulating magnetization states in spintronic devices and making a structure using spin transfer torque to induce magnetization reversal. A device described herein manipulates magnetization states in spintronic devices and includes a non-magnetic metal to generate spin current based on the giant spin Hall effect, a ferromagnetic thin film with perpendicular magnetic anisotropy, an oxide thin film, and an integrated magnetic sensor. The device does not require an insertion layer between a non-magnetic metal with giant spin Hall effect and a ferromagnetic thin film to achieve perpendicular magnetic anisotropy.

Abstract: An apparatus includes a patch library creator for creating a first patch library from an original version of the picture and a second patch library from a reconstructed version of the picture. Each of the libraries includes a plurality of high resolution replacement patches for replacing one or more pruned blocks during a recovery of a pruned version of the picture. A pruner generates the pruned version from the first patch library, and decides whether to respectively replace original blocks with pruned blocks responsive to a comparison between a first rate-distortion estimate and a second rate-distortion estimate. The estimates are based on both a distortion component and a bitrate component. A metadata generator generates metadata from the second patch library. The metadata is for recovering the pruned version. An encoder encodes the pruned version and the metadata.

Abstract: The techniques discussed herein discover three-dimensional (3-D) visual phrases for an object based on a 3-D model of the object. The techniques then describe the 3-D visual phrases. Once described, the techniques use the 3-D visual phrases to detect the object in an image (e.g., object recognition).

Abstract: Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user-generated content based on automated analysis of the user-generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques are described that facilitate automatically extracting snippets. These techniques include, for example, computer annotating travelogues with learned tags and images, performing topic learning to obtain an interest model, performing location matching based on the interest model, calculating geographic and semantic relevance scores, ranking snippets based on the geographic and semantic relevance scores, and searching snippets with a “location+context term” query.

Abstract: This document describes techniques that label text nodes of a seed site for each of a plurality of verticals. Once a seed site is labeled for a given vertical, the techniques extract features from the labeled text nodes of the seed site. The techniques learn vertical knowledge for the seed site based on the human labels and the extracted features, and adapt the learned vertical knowledge to a new web site to automatically and accurately identify attributes and extract attribute values targeted within a given vertical for structured web data extraction.

Abstract: The techniques discussed herein discover three-dimensional (3-D) visual phrases for an object based on a 3-D model of the object. The techniques then describe the 3-D visual phrases. Once described, the techniques use the 3-D visual phrases to detect the object in an image (e.g., object recognition).

Abstract: Method and apparatus for encoding a picture in a video sequence are disclosed. An apparatus includes a patch library creator for creating a first patch library from an original version of the picture and a second patch library from a reconstructed version of the picture. Each of the libraries includes a plurality of high resolution replacement patches for replacing one or more pruned blocks during a recovery of a pruned version of the picture. A pruner generates the pruned version from the first patch library, and decides whether to respectively replace original blocks with pruned blocks responsive to a comparison between a first rate-distortion estimate and a second rate-distortion estimate. The estimates are based on both a distortion component and a bitrate component. A metadata generator generates metadata from the second patch library. The rnetadata is for recovering the pruned version. An encoder encodes the pruned version and the metadata.

Abstract: Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user generated content based on automated analysis of the user generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques described herein include, for example, computer annotating travelogues with learned tags, performing topic learning to obtain an interest model, and performing location matching based on the interest model.

Abstract: This document describes techniques that label text nodes of a seed site for each of a plurality of verticals. Once a seed site is labeled for a given vertical, the techniques extract features from the labeled text nodes of the seed site. The techniques learn vertical knowledge for the seed site based on the human labels and the extracted features, and adapt the learned vertical knowledge to a new web site to automatically and accurately identify attributes and extract attribute values targeted within a given vertical for structured web data extraction.

Abstract: An embodiment of a system and method that inserts a virtual image into a sequence of video frames. The method includes capturing geometric characteristics of the sequence of video frames, employing the captured geometric characteristics to define an area of the video frames for insertion of a virtual image, registering a video camera to the captured geometric characteristics, identifying features in the sequence of video frames to identify the defined area of video frames for insertion of the virtual image, and inserting the virtual image in the defined area. Vanishing points are estimated to determine the geometric characteristics, and the virtual image is blended with the area of video frames prior to inserting the virtual image in the defined area.

Abstract: Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user generated content based on automated analysis of the user generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques described herein include, for example, computer annotating travelogues with learned tags, performing topic learning to obtain an interest model, and performing location matching based on the interest model.

Abstract: Described herein is a technology that facilitates efficient automated mining of topic-related aspects of user-generated content based on automated analysis of the user-generated content. Locations are automatically learned based on dividing documents into document segments, and decomposing the segments into local topics and global topics. Techniques are described that facilitate automatically extracting snippets. These techniques include, for example, computer annotating travelogues with learned tags and images, performing topic learning to obtain an interest model, performing location matching based on the interest model, calculating geographic and semantic relevance scores, ranking snippets based on the geographic and semantic relevance scores, and searching snippets with a “location+context term” query.