Abstract: An apparatus for calibrating a rail scale is described. The apparatus is attached to a first trailer and a second trailer and the move from a first site to a rail scale site over highways and roads. The apparatus includes a calibration vehicle, bins of weights, and a crane. The crane positions the calibration vehicle, that is self-propelled, on rails and loads weights on the calibration vehicle. The combined weight of the calibration vehicle and weights has qualifying values that ensures a scale is in compliance rail scale calibration standards.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a hierarchical topic machine learning operation on the training data; generating a cognitive profile based upon the information generated by performing the hierarchical topic machine learning operation; and, generating a cognitive insight based upon the cognitive profile generated using the hierarchical topic machine learning operation.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system; performing a plurality of machine learning operations on the training data; generating a cognitive profile based upon the information generated by performing the plurality of machine learning operations; and, generating a cognitive insight based upon the profile generated using the plurality of machine learning operations.

Abstract: Inter-layer motion mapping information may be used to enable temporal motion vector prediction (TMVP) of an enhancement layer of a bitstream. For example, a reference picture and a motion vector (MV) of an inter-layer video block may be determined. The reference picture may be determined based on a collocated base layer video block. For example, the reference picture may be a collocated inter-layer reference picture of the reference picture of the collocated base layer video block. The MV may be determined based on a MV of the collocated base layer video block. For example, the MV may be determined by determining the MV of the collocated base layer video block and scaling the MV of the collocated base layer video block according to a spatial ratio between the base layer and the enhancement layer. TMVP may be performed on the enhancement layer picture using the MV of the inter-layer video block.

Abstract: A video coding system may perform inter-layer processing by simultaneously performing inverse tone mapping and color gamut conversion scalability processes on a base layer of a video signal. The video coding system may then perform upsampling on the processed base layer. The processed base layer may be used to code an enhancement layer. Bit depth may be considered for color gamut conversion modules. Luma and/or chroma bit depths may be aligned with respective larger or smaller bit depth values of luma and/or chroma.

Abstract: A method, system and computer-usable medium for providing cognitive insights comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching to provide enriched data; generating the cognitive session graph, the cognitive session graph being associated with a session, the cognitive session graph comprising at least some enriched data; and, processing the cognitive session graph to provide a cognitive insight, the cognitive insight being related to the session.

Abstract: Systems and methods are described for enabling a client device to request video streams with different bit depth remappings for different viewing conditions. In an embodiment, information indicating the availability of additional remapped profiles is sent in a manifest file. Alternative bit-depth remappings may be optimized for different regions of interest in the image or video content, or for different viewing conditions, such as different display technologies and different ambient illumination. Some embodiments based on the DASH protocol perform multiple depth mappings at the encoder and also perform ABR-encoding for distribution. The manifest file contains information indicating additional remapping profiles. The remapping profiles are associated with different transformation functions used to convert from a higher bit-depth to a lower bit-depth.

Abstract: Inter-layer motion mapping information may be used to enable temporal motion vector prediction (TMVP) of an enhancement layer of a bitstream. For example, a reference picture and a motion vector (MV) of an inter-layer video block may be determined. The reference picture may be determined based on a collocated base layer video block. For example, the reference picture may be a collocated inter-layer reference picture of the reference picture of the collocated base layer video block. The MV may be determined based on a MV of the collocated base layer video block. For example, the MV may be determined by determining the MV of the collocated base layer video block and scaling the MV of the collocated base layer video block according to a spatial ratio between the base layer and the enhancement layer. TMVP may be performed on the enhancement layer picture using the MV of the inter-layer video block.

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: 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 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: Intra planar approach(es) may be used to predict a pixel(s) in a current block. The current block may be associated with a reconstructed left reference line, a reconstructed top reference line, and an non-reconstructed reference line to be predicted. The reconstructed reference lines may have been decoded and may be available. The non-reconstructed reference lines to be predicted may include an non-reconstructed right and/or an non-reconstructed bottom reference lines. A pivot reference pixel may be identified and may be located on an extension of the reconstructed left and/or top reference lines. A reference pixel may be determined and may be located on the reconstructed top and/or left reference lines. Pixels on the non-reconstructed reference line(s) may be predicted based on the pivot reference pixel and the reference pixel. Pixels of the current block may be predicted using the predicted pixels on the right and the bottom reference lines.

Abstract: A video coding device may be configured to perform directional Bi-directional optical flow (BDOF) refinement on a coding unit (CU). The device may determine the direction in which to perform directional BDOF refinement. The device may calculate the vertical direction gradient difference and the horizontal direction gradient difference for the CU. The vertical direction gradient difference may indicate the difference between the vertical gradients for a first reference picture and the vertical gradients for a second reference picture. The horizontal direction gradient difference may indicate the difference between the horizontal gradients for the first reference picture and the horizontal gradients for the second reference picture. The video coding device may determine the direction in which to perform directional BDOF refinement based on the vertical direction gradient difference and the horizontal direction gradient difference.

Abstract: A system, method, and/or instrumentality may be provided for coding a 360-degree video. A picture of the 360-degree video may be received. The picture may include one or more faces associated with one or more projection formats. A first projection format indication may be received that indicates a first projection format may be associated with a first face. A second projection format indication may be received that indicates a second projection format may be associated with a second face. Based on the first projection format, a first transform function associated with the first face may be determined. Based on the second projection format, a second transform function associated with the second face may be determined. At least one decoding process may be performed on the first face using the first transform function and/or at least one decoding process may be performed on the second face using the second transform function.

Abstract: In an intra-block copy video encoding method, an encoder performs a hash-based search to identify a selected set of candidate blocks for prediction of an input video block. For each of the candidate blocks in the selected set, the encoder determines a correlation between, on the one hand, luma and chroma components of the input video block and, on the other hand, luma and chroma components of the respective candidate blocks. A predictor block is selected based on the correlation and is used to encode the input video block. In different embodiments, the correlation may be the negative of the sum of absolute differences of the components, may include a Jaccard similarity measure between respective pixels, or may be based on a Hamming distance between two high precision hash values of the input video block and the candidate block.

Abstract: Systems, methods, and instrumentalities are disclosed for client centric service quality control. A first viewport of a 360 degree video may be determined. The 360 degree video may comprise one or more of an equirectangular, a cube-map, a cylindrical, a pyramidal, and/or a spherical projection mapping. The first viewport may be associated with a spatial region of the 360 degree video. An adjacent area that extends around the spatial region may be determined. A second viewport of the 360 degree video may be determined. A bitstream associated with the 360 degree video may be received. One or more enhanced regions may be included in the bitstream. The one or more enhanced regions may correspond to the first and/or second viewport. A high coding bitrate may be associated with the first viewport and/or the second viewport.

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.

Abstract: A method, system and computer readable medium for generating a cognitive insight comprising: receiving data, the data comprising a plurality of examples, each of the plurality of examples comprising an input object and a desired output value, at least some of the plurality of examples being based upon feedback from a user; performing a machine learning operation on the data, the machine learning operation comprising performing an augmented gamma belief network operation, the augmented gamma belief network operation producing an inferred function based upon the data; and, generating a cognitive insight based upon the cognitive profile generated using the inferred function generated by the augmented gamma belief network operation.

Abstract: Quality-based optimizations of a delivery process of streaming content may be enabled. The optimization may take the form of quality-based switching. To enable quality-based switching in a streaming client, the client may have access to information about the quality of an encoded segment and/or sub-segment. Quality-related information may include any number of added quality metrics relating to an encoded segment and/or sub-segment of an encoded video stream. The addition of quality-related information may be accomplished by including the quality-related information in a manifest file, including the quality-related information in segment indices stored in a segment index file, and/or providing additional files with quality-related segment information and providing a link to the information from an MPD file. Upon receiving the quality-related information, the client may request and receive a stream that has a lower bitrate, thereby saving bandwidth while retaining quality of the streaming content.