Abstract: Various information systems may benefit from the appropriate simplification of data structures and processes. For example, certain health information systems may benefit from dimension reduction of claims data. A method can include creating a tensor to organize data. The method can also include conditionally reducing the size of that tensor in order to more effectively apply machine learning.

Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: Various information systems may benefit from the appropriate simplification of data structures and processes. For example, certain health information systems may benefit from dimension reduction of claims data. A method can include creating a tensor to organize data. The method can also include conditionally reducing the size of that tensor in order to more effectively apply machine learning.

Abstract: Various health management systems may benefit from tools for appropriate identification of target patients. For example, various population health management approaches may benefit from systems and methods for optimal health assessment and optimal preventative program development. A method can include obtaining a simple risk measure score for a patient. The method can also include obtaining an evidence-based score for the patient. The method can further include comparing the simple risk measure score to the evidence-based score. The method can additionally include identifying the patient as a target for preventative medicine based on a discrepancy revealed by the comparison.

Abstract: Certain embodiments of the present invention relate generally to using machine learning or other automated techniques to among other things, identify, estimate, and/or predict patient health conditions. Furthermore, certain embodiments of the present invention are related to health interventions performed to reduce the risk of developing diseases and health conditions. This risk reduction improves the overall health of the individual and/or the population and helps reduce healthcare costs.

Abstract: Multiview videos are acquired of a scene with corresponding cameras arranged at poses, such that there is view overlap between any pair of cameras. V-frames are generated from the multiview videos. The V-frames are encoded using only spatial prediction. Then, the V-frames are inserted periodically in an encoded bit stream to provide random temporal access to the multiview videos. Additional view dependency information enables the decoding of a reduced number of frames prior to accessing randomly a target frame for a specified view and time, and decoding the target frame. The method also decodes multiview videos by maintaining a reference picture list for a current frame of a plurality of multiview videos, and predicting each current frame of the plurality of multiview videos according to reference pictures indexed by the associated reference picture list.

Abstract: A method randomly accesses multiview videos. Multiview videos are acquired of a scene with corresponding cameras arranged at poses, such that there is view overlap between any pair of cameras. V-frames are generated from the multiview videos. The V-frames are encoded using only spatial prediction. Then, the V-frames are inserted periodically in an encoded bit stream to provide random temporal access to the multiview videos. Additional view dependency information enables the decoding of a reduced number of frames prior to accessing randomly a target frame for a specified view and time, and decoding the target frame.

Abstract: Multiview videos are acquired of a scene with corresponding cameras arranged at poses, such that there is view overlap between any pair of cameras. V-frames are generated from the multiview videos. The V-frames are encoded using only spatial prediction. Then, the V-frames are inserted periodically in an encoded bit stream to provide random temporal access to the multiview videos. Additional view dependency information enables the decoding of a reduced number of frames prior to accessing randomly a target frame for a specified view and time, and decoding the target frame. The method also decodes multiview videos by maintaining a reference picture list for a current frame of a plurality of multiview videos, and predicting each current frame of the plurality of multiview videos according to reference pictures indexed by the associated reference picture list.

Abstract: Biometric parameters acquired from human forces, voices, fingerprints, and irises are used for user authentication and access control. Because the biometric parameters are continuous and vary from one reading to the next, syndrome codes are applied to determine biometric syndrome vectors. The biometric syndrome vectors can be stored securely while tolerating an inherent variability of biometric data. The stored biometric syndrome vector is decoded during user authentication using biometric parameters acquired at that time. The syndrome codes can also be used to encrypt and decrypt data.

Abstract: A system and method synthesizes multiview videos. Multiview videos are acquired of a scene with corresponding cameras arranged at a poses such that there is view overlap between any pair of cameras. A synthesized multiview video is generated from the acquired multiview videos for a virtual camera. A reference picture list is maintained for each current frame of each of the multiview videos and the synthesized video. The reference picture list indexes temporal reference pictures and spatial reference pictures of the acquired multiview videos and the synthesized reference pictures of the synthesized multiview video. Then, each current frame of the multiview videos is predicted according to reference pictures indexed by the associated reference picture list during encoding and decoding.

Abstract: A method processes a multiview videos of a scene, in which each video is acquired by a corresponding camera arranged at a particular pose, and in which a view of each camera overlaps with the view of at least one other camera. Side information for synthesizing a particular view of the multiview video is obtained in either an encoder or decoder. A synthesized multiview video is synthesized from the of multiview videos and the side information. A reference picture list is maintained for each current frame of each of the multiview videos, the reference picture indexes temporal reference pictures and spatial reference pictures of the acquired multiview videos and the synthesized reference pictures of the synthesized multiview video. Each current frame of the multiview videos is predicted according to reference pictures indexed by the associated reference picture list.

Abstract: A method randomly accesses multiview videos. Multiview videos are acquired of a scene with corresponding cameras arranged at poses, such that there is view overlap between any pair of cameras. V-frames are generated from the multiview videos. The V-frames are encoded using only spatial prediction. Then, the V-frames are inserted periodically in an encoded bitstream to provide random temporal access to the multiview videos.

Abstract: First biometric parameters are acquired from a user. Input data are encrypted according to the biometric parameters to produce ciphertext. The biometric parameters are encoded using a syndrome encoder to produce a syndrome code. The ciphertext and the syndrome code are associated with each other and stored in a computer readable media so that only the same user can subsequently decrypt the cipher text.

Abstract: A system and method manages multiview videos. A reference picture list is maintained for each current frame of multiple multiview videos. The reference picture list indexes temporal reference pictures, spatial reference pictures and synthesized reference pictures of the multiview videos. Then, each current frame of the multiview videos is predicted according to reference pictures indexed by the associated reference picture list during encoding and decoding.

Abstract: A method decomposes multiview video acquired of a scene by multiple cameras. Each multiview video includes a sequence of frames, and each camera provides a different view of the scene. A prediction mode is selected from a temporal prediction mode, a spatial prediction mode, and a view interpolation prediction mode. The multiview videos are then decomposed into low band frames, high band frames, and side information according to the selected prediction mode.

Abstract: Embodiments of the invention describe a compound conditional source coding method and system for communicating source data over a network. Length-n random uncompressed source data are drawn according to a distribution px(x), and serves as input data to an encoder. A set P of candidate side-information vectors is also input to the encoder. The encoder encodes the source data, utilizing the set of the candidate side-information vectors, to produce an encoded message. The message is transmitted to a decoder. The decoder decodes the received message to produce a source estimate, using selected side-information vector and an index of the selected side-information vector in the set of the candidate side-information vectors.

Abstract: A method converts an input image of noisy wrapped phases to an output image of absolute unwrapped phases. The noisy wrapped phases in the input image are represented as a set of re-wrapped phases and a set of phase shifts. The set of re-wrapped phases are partitioned into a first group and a second group. Integer differences between the phase shifts are optimized while holding the re-wrapped phases fixed. Then, the first group of re-wrapped phases are optimized, while holding the integer differences between the phase shifts, and the second group of re-wrapped phases fixed. The integer differences between the phase shifts are optimized again, while holding the re-wrapped phases fixed. Then, the second group of re-wrapped phases are optimized, while holding the integer differences between the phase shifts, and the first group of re-wrapped phases fixed. The optimizing steps are repeated until the re-wrapped phase converge.

Abstract: Described are techniques for performing lossy encoding. Source data and quality data are received by an encoder. The encoder maps the source data into a compressed representation having a level of distortion in accordance with the quality information. The compressed representation may be decoded without using the quality information.

Abstract: A method converts an input image of noisy wrapped phases to an output image of absolute unwrapped phases. The noisy wrapped phases in the input image are represented as a set of re-wrapped phases and a set of phase shifts. The set of re-wrapped phases are partitioned into a first group and a second group. Integer differences between the phase shifts are optimized while holding the re-wrapped phases fixed. Then, the first group of re-wrapped phases are optimized, while holding the integer differences between the phase shifts, and the second group of re-wrapped phases fixed. The integer differences between the phase shifts are optimized again, while holding the re-wrapped phases fixed. Then, the second group of re-wrapped phases are optimized, while holding the integer differences between the phase shifts, and the first group of re-wrapped phases fixed. The optimizing steps are repeated until the re-wrapped phase converge.

Abstract: Biometric parameters acquired from human forces, voices, fingerprints, and irises are used for user authentication and access control. Because the biometric parameters are continuous and vary from one reading to the next, syndrome codes are applied to determine biometric syndrome vectors. The biometric syndrome vectors can be stored securely while tolerating an inherent variability of biometric data. The stored biometric syndrome vector is decoded during user authentication using biometric parameters acquired at that time. The syndrome codes can also be used to encrypt and decrypt data.