Abstract: A method for providing healthcare optimized cognitive insights comprising: receiving data from a plurality of data sources, at least some of the plurality of data sources comprising healthcare relevant data sources; processing the data from the plurality of data sources to provide cognitively processed insights; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed healthcare relevant insights to a destination.

Abstract: A cognitive information processing system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning operation applying the cognitive learning technique to generate a cognitive learning result; and, updating a destination based upon the learning result.

Abstract: A cognitive information processing system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive machine learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive machine learning operation applying the cognitive learning technique via a machine learning algorithm to generate a cognitive learning result; and, updating a destination based upon the learning result.

Abstract: A cognitive learning method comprising receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive machine learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive machine learning operation applying the cognitive learning technique via a machine learning algorithm to generate a cognitive learning result; and, updating a destination based upon the learning result.

Abstract: A cognitive information processing system comprising a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources to perform a cognitive learning operation, the processing being performed via a cognitive inference and learning system, the cognitive learning operation comprising a plurality of cognitive learning operation lifecycle phases, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; and, updating a destination based upon the cognitive learning result.

Abstract: Systems and/or methods for estimating color conversion components. A video coding device may receive a picture associated with a first color space. The picture may comprise a first component at a first sampling location, a second component at a second sampling location and the second component at a third sampling location. The video coding device may apply a first interpolation filter to the second component at the second sampling location and the second component at the third sampling location to determine the second component at the first sampling location. The second component at the first sampling location may be associated with the first color space. The video coding device may apply a color conversion model to the first component at the first sampling location and to the second component at the first sampling location to translate the first component at the first sampling location to a second color space.

Abstract: A data architecture for use within a cognitive information processing system environment comprising: a plurality of data sources, the plurality of data sources comprising a public data source and a private data source; and, a cognitive data management module, the cognitive data management module accessing information from the plurality of data sources and providing the information to an inference and learning system.

Abstract: Systems and methods are provided for adapting communication parameters to a variety of link conditions, traffic types and priorities. For example, WiFi transmission parameters (e.g. retry limit, AIFS, CW size, MCS order and/or CCA threshold) may be adapted to channel congestion levels, channel errors and/or traffic priority levels. Parameter adaptation may be coordinated across layers (e.g. between MAC and PHY layer parameters). Congestion levels may be detected, for example, using a smoothed queue size and/or channel busy time. Traffic may be transmitted using adapted parameters, such as reduced retry limits for a high congestion level and increased retry limits for priority traffic in response to channel error. Feedback may support parameter adaptation. For example, feedback may be provided by a receiver and/or within a sender, such as a sender MAC and/or PHY layer or a parameter adapter providing feedback (e.g. spoofed NACK packet) to a sender application, transport and/or network layer.

Abstract: Methods and apparatus are provided for performing one-dimensional (1D) transform and coefficient scanning. An encoder may apply 1D transform in either a horizontal or a vertical direction. The encoder may then determine a coefficient scan order based on the 1D transform direction. The scan order may be determined to be in a direction orthogonal to the 1D transform direction. The encoder may further flip the coefficients prior to scanning. The flipping may also be in a direction orthogonal to the 1D transform direction. A decoder may receive indications from the encoder with respect to the 1D transform, coefficient scanning, and/or coefficient flipping. The decoder may perform functions inverse to those performed by the encoder based on the indications.

Abstract: Systems and methods are described for generating and decoding a video data bit stream containing a high-level signaling lossless coding syntax element indicating that lossless coding is used. The high-level signaling syntax is one of a Video Parameter Set (VPS), Sequence Parameter Set (SPS), Picture Parameter Set (PPS), or slice segment header. The lossless coding syntax element may be used as a condition for generating one or more SPS, PPS and slice segment header syntax elements related to the quantization, transform, transform skip, transform skip rotation, and in-loop filtering processes.

Abstract: A method for providing cognitive insight via a cognitive information processing system comprising: encapsulating an operation for providing a desired cognitive insight; and, applying the operation to a target cognitive graph to generate a cognitive insight.

Abstract: Cross-plane filtering may be used to restore blurred edges and/or textures in one or both chroma planes using information from a corresponding luma plane. Adaptive cross-plane filters may be implemented. Cross-plane filter coefficients may be quantized and/or signaled such that overhead in a bitstream minimizes performance degradation. Cross-plane filtering may be applied to select regions of a video image (e.g., to edge areas). Cross-plane filters may be implemented in single-layer video coding systems and/or multi-layer video coding systems.

Abstract: Systems, methods, and instrumentalities are disclosed to perform rate adaptation in a wireless transmit/receive unit (WTRU). The WTRU may receive an encoded data stream, which may be encoded according to a Dynamic Adaptive HTTP Streaming (DASH) standard. The WTRU may request and/or receive the data stream from a content server. The WTRU may monitor and/or receive a cross-layer parameter, such as a physical layer parameter, a RRC layer parameter, and/or a MAC layer parameter (e.g., a CQI, a PRB allocation, a MRM, or the like). The WTRU may perform rate adaption based on the cross-layer parameter. For example, the WTRU may set the CE bit of an Explicit Congestion Notification (ECN) field based on the cross-layer parameter. The WTRU may determine to request the data stream encoded at a different rate based on the cross-layer parameter, the CE bit, and/or a prediction based on the cross-layer parameter.

Abstract: Systems and methods for improving efficiency in three-dimensional (3D) look-up table (LUT) coding and/or reducing table size of a 3D LUT may be provided. For example, octants associated with the 3D LUT may be provided for color space segmentation and coding may be performed on an octree associated with the octants where coding may include encoding nodes of the octree associated with the octants and corresponding vertices of the 3D LUT belonging to the nodes. The 3D LUT may also be signaled (e.g., based on a sequence and/or picture level).

Abstract: A video block of a current picture may be coded in an intra block copy (IBC) mode. Weighted prediction may be disabled for the IBC-coded screen content video block. Fractional block vectors may be used for the chroma components of the IBC-coded video block. An interpolation filter may be utilized to generate chroma prediction samples for the video block. A decoded version of the current reference picture may be added to both reference picture list L0 and reference picture list L1 that are associated with the IBC-coded video block. When constrained intra prediction is applied, reference samples that may be used to predict an intra-coded video block may be limited to those in intra-coded neighboring blocks. The range of IBC searches may be restricted by imposing a maximum absolute value for block vectors.

Abstract: Conversion between different projection formats of a 360-degree video may be performed in a uniform way. The geometric characteristics of the different projection formats may be considered when applying 3D-to-2D and 2D-to-3D mapping. Parameters reflective of the geometric characteristics of the different projection formats may be determined and used in the mapping and/or conversion. The parameters may include a normal vector that is perpendicular to a projection plane, a reference point in the projection plane, and/or unit vectors defined in the projection plane. An architecture with consolidated modules for handling the various projection formats may be provided.

Abstract: A method for receiving a plurality of types of data within a cognitive information processing system environment comprising: receiving data from a plurality of data sources, the plurality of data sources comprising a public data source and a private data source; accessing information from the plurality of data sources via a cognitive data management module; and, providing the information to an inference and learning system.

Abstract: Systems and methods are provided for adapting communication parameters to a variety of link conditions, traffic types and priorities. For example, WiFi transmission parameters (e.g. retry limit, AIFS, CW size, MCS order and/or CCA threshold) may be adapted to channel congestion levels, channel errors and/or traffic priority levels. Parameter adaptation may be coordinated across layers (e.g. between MAC and PHY layer parameters). Congestion levels may be detected, for example, using a smoothed queue size and/or channel busy time. Traffic may be transmitted using adapted parameters, such as reduced retry limits for a high congestion level and increased retry limits for priority traffic in response to channel error. Feedback may support parameter adaptation. For example, feedback may be provided by a receiver and/or within a sender, such as a sender MAC and/or PHY layer or a parameter adapter providing feedback (e.g. spoofed NACK packet) to a sender application, transport and/or network layer.

Abstract: Processing video data may include capturing the video data with multiple cameras and stitching the video data together to obtain a 360-degree video. A frame-packed picture may be provided based on the captured and stitched video data. A current sample location may be identified in the frame-packed picture. Whether a neighboring sample location is located outside of a content boundary of the frame-packed picture may be determined. When the neighboring sample location is located outside of the content boundary, a padding sample location may be derived based on at least one circular characteristic of the 360-degree video content and the projection geometry. The 360-degree video content may be processed based on the padding sample location.