Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating latencies associated with the encoding of digital assets. Instead of waiting for codebook generation to complete in order to encode a digital asset for storage, embodiments described herein describe a shifting codebook generation and employment technique that significantly mitigates any latencies typically associated with encoding schemes. As a digital asset is received, a single codebook is trained based on each portion of the digital asset, or in some instances along with each portion of other digital assets being received. The single codebook is employed to encode subsequent portion(s) of the digital asset as it is received. The process continues until an end of the digital asset is reached or another command to terminate the encoding process is received. To encode an initial portion of the digital asset, a bootstrap codebook can be employed.

Abstract: Techniques and systems are provided for generating a video from texture images, and for reconstructing the texture images from the video. For example, a texture image can be divided into a number of tiles, and the number of tiles can be sorted into a sequence of ordered tiles. The sequence of ordered tiles can be provided to a video coder for generating a coded video. The number of tiles can be encoded based on the sequence of ordered tiles. The encoded video including the encoded sequence of ordered tiles can be decoded. At least a portion of the decoded video can include the number of tiles sorted into a sequence of ordered tiles. A data file associated with at least the portion of the decoded video can be used to reconstruct the texture image using the tiles.

Abstract: Technologies are provided for automated identification of system devices to be disabled in a computing system and the disablement of the system devices during bootup of the computing system. In some embodiments, the computing system can execute a firmware configured to perform a bootup process of the computing system. The computing system includes multiple system devices. The firmware can generate program code for identifying a system device for disablement. The firmware can send the program code to a controller device curing the bootup process, where execution of the program code by the controller device generates data identifying one or several specific system devices to be disabled in the computing system. The firmware can then access such data from the controller device. Using the data, the firmware can determine that a specific system device to be disabled. The firmware can then disable that particular system device on a next bootup process.

Abstract: Techniques are disclosed for the improvement of vector quantization (VQ) codebook generation. The improved codebooks may be used for compression in cloud-based video applications. VQ achieves compression by vectorizing input video streams, matching those vectors to codebook vector entries, and replacing them with indexes of the matched codebook vectors along with residual vectors to represent the difference between the input stream vector and the codebook vector. The combination of index and residual is generally smaller than the input stream vector which they collectively encode, thus providing compression. The improved codebook may be generated from training video streams by grouping together similar types of data (e.g., image data, motion data, control data) from the video stream to generate longer vectors having higher dimensions and greater structure. This improves the ability of VQ to remove redundancy and thus increase compression efficiency.

Abstract: Generating images and videos depicting a human subject wearing textually defined attire is described. An image generation system receives a two-dimensional reference image depicting a person and a textual description describing target clothing in which the person is to be depicted as wearing. To maintain a personal identity of the person, the image generation system implements a generative model, trained using both discriminator loss and perceptual quality loss, which is configured to generate images from text. In some implementations, the image generation system is configured to train the generative model to output visually realistic images depicting the human subject in the target clothing. The image generation system is further configured to apply the trained generative model to process individual frames of a reference video depicting a person and output frames depicting the person wearing textually described target clothing.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can initially train a machine-learning-logo classifier using synthetic training images and incrementally apply the machine-learning-logo classifier to identify logo images to replace the synthetic training images as training data. By incrementally applying the machine-learning-logo classifier to determine one or both of logo scores and positions for logos within candidate logo images, the disclosed systems can select logo images and corresponding annotations indicating positions for ground-truth logos. In some embodiments, the disclosed systems can further augment the iterative training of a machine-learning-logo classifier to include user curation and removal of incorrectly detected logos from candidate images, thereby avoiding the risk of model drift across training iterations.

Abstract: Techniques are disclosed for improving media content effectiveness. A methodology implementing the techniques according to an embodiment includes generating an intermediate representation (IR) of provided media content, the IR specifying editable elements of the content and maintaining a result of cumulative edits to those elements. The method also includes editing the elements of the IR to generate a set of candidate IR variations. The method further includes creating a set of candidate media contents based on the candidate IR variations, evaluating the candidate media contents to generate effectiveness scores, and pruning the set of candidate IR variations to retain a threshold number of the candidate IR variations as surviving IR variations associated with the highest effectiveness scores. The process iterates until either an effectiveness score exceeds a threshold value, the incremental improvement at each iteration falls below a desired value, or a maximum number of iterations have been performed.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.

Abstract: Techniques and systems are provided for generating a video from texture images, and for reconstructing the texture images from the video. For example, a texture image can be divided into a number of tiles, and the number of tiles can be sorted into a sequence of ordered tiles. The sequence of ordered tiles can be provided to a video coder for generating a coded video. The number of tiles can be encoded based on the sequence of ordered tiles. The encoded video including the encoded sequence of ordered tiles can be decoded. At least a portion of the decoded video can include the number of tiles sorted into a sequence of ordered tiles. A data file associated with at least the portion of the decoded video can be used to reconstruct the texture image using the tiles.

Abstract: Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoint; the estimate of the target value; and to an updated summation of intermediate calculated terms generated by application of the streaming mode ALS optimization to previously received datapoints associated with prior parameter updates of the FM.

Abstract: Residual vectors are compressed in a lossless compression scheme suitable for cloud DVR video content applications. Thus, a cloud DVR service provider can take many copies of the same file stored in the cloud and save storage space by compressing those copies while still maintaining their status as distinct copies, one per user. Vector quantization is used for compressing already-compressed video streams (e.g., MPEG streams). As vector quantization is a lossy compression scheme, the residual vector has to be stored to regenerate the original video stream at the decoding (playback) node. Entropy coding schemes like Arithmetic or Huffman coding can be used to compress the residual vectors. Additional strategies can be implemented to further optimize this residual compression. In some embodiments, the techniques operate to provide a 25-50% improvement in compression. Storage space is thus more efficiently used and video transmission may be faster in some cases.

Abstract: Embodiments of a system and method for low-latency content streaming are described. In various embodiments, multiple data fragments may be sequentially generated. Each data fragment may represent a distinct portion of media content generated from a live content source. Each data fragment may include multiple sub-portions. Furthermore, for each data fragment, generating that fragment may include sequentially generating each sub-portion of that fragment. Embodiments may include, responsive to receiving a request for a particular data fragment from a client during the generation of a particular sub-portion of that particular data fragment, providing the particular sub-portion to the client subsequent to that particular sub-portion being generated and prior to the generation of that particular data fragment being completed in order to reduce playback latency at the client relative to the live content source.

Abstract: A baseboard management controller (BMC) may comprise a processor, a non-volatile memory and a volatile memory. The non-volatile memory comprises firmware categorized into a plurality of independently updatable service modules. Each of the independently updatable service modules is stored on a read-write partition of the non-volatile memory and comprises at least one of an application, a library and a driver. The BMC comprises an update agent that performs an update process. In the update process, a BMC update package, which comprises an update service module for updating an existing service module stored in one of the plurality of RW partitions, is stored in the volatile memory. The existing service module stored in the RW partition is replaced with the update service module.

Abstract: A baseboard management controller (BMC) may be configured to enable a communication interface from the BMC to a host processor on a host computing device and provide input to the host processor via the communication interface. The input causes at least one diagnostic operation to be performed on the host computing device. The BMC may collect host information in response to the diagnostic operation(s) being performed. The BMC may report the host information to another entity and/or store the host information in persistent memory within the BMC. In some embodiments, the input may be provided to the host processor in response to receiving a signal from a fabric controller. In some embodiments, the input may be provided to the host processor in response to detecting an anomaly associated with the host computing device. The BMC may take at least one action to mitigate the anomaly.

Abstract: Automatic item placement recommendation is described. An item placement configuration system receives an item for which a recommended placement is to be generated and identifies an entity associated with the item. The item placement configuration system then identifies a multi-domain taxonomy that describes relationships between different entities based on items associated with the different entities published among different domains. A representation of the entity associated with the item to be placed is then identified within the multi-domain taxonomy, along with a representation of at least one similar entity. Upon identifying a similar entity, historic item placement metrics for the similar entity are leveraged to generate a placement recommendation for the received item. In some implementations, the placement recommendation is output with a visual indication of a similar entity and associated performance metrics that were considered in generating the recommended placement.

Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for mitigating delays typically experienced when training codebooks during the encoding process. Instead of training a codebook based on a single digital asset, multiple digital assets determined to have asset characteristics in common can be grouped together to form a group of digital assets, from which a single codebook can be trained. The group of digital assets together form a codebook training set, such that each digital asset therein can be analyzed, in parallel, to expeditiously train a single codebook. A codebook trained in this manner can be employed to encode other digital assets sharing the asset characteristics as those in the codebook training set.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can initially train a machine-learning-logo classifier using synthetic training images and incrementally apply the machine-learning-logo classifier to identify logo images to replace the synthetic training images as training data. By incrementally applying the machine-learning-logo classifier to determine one or both of logo scores and positions for logos within candidate logo images, the disclosed systems can select logo images and corresponding annotations indicating positions for ground-truth logos. In some embodiments, the disclosed systems can further augment the iterative training of a machine-learning-logo classifier to include user curation and removal of incorrectly detected logos from candidate images, thereby avoiding the risk of model drift across training iterations.

Abstract: In various implementations, a server is configured to execute instructions stored in storage that when executed perform operations that include receiving a hypertext transfer protocol (HTTP) request to stream a video segment of multimedia content to a client device. The video segment is of a video sub-stream of the multimedia content. The operations further include sending the video segment and an audio segment to the client device based on the HTTP request for the video segment. The sending pushes the video segment and/or the audio segment to the client device. The audio segment is of an audio sub-stream of the multimedia content. A plurality of segment sets may be pushed based on the HTTP request for the video segment. Each segment set can include an additional video segment and an additional audio segment that correspond to at least partially concurrent portions of the multimedia content.

Abstract: In implementations of trajectory-based viewport prediction for 360-degree videos, a video system obtains trajectories of angles of users who have previously viewed a 360-degree video. The angles are used to determine viewports of the 360-degree video, and may include trajectories for a yaw angle, a pitch angle, and a roll angle of a user recorded as the user views the 360-degree video. The video system clusters the trajectories of angles into trajectory clusters, and for each trajectory cluster determines a trend trajectory. When a new user views the 360-degree video, the video system compares trajectories of angles of the new user to the trend trajectories, and selects trend trajectories for a yaw angle, a pitch angle, and a roll angle for the user. Using the selected trend trajectories, the video system predicts viewports of the 360-degree video for the user for future times.

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that determine multiple personas corresponding to a user account for digital content and train a persona classifier to predict a given persona (from among the multiple personas) for content requests associated with the user account. By using the persona classifier, the disclosed methods, non-transitory computer readable media, and systems accurately detect a given persona for a content request upon initiation of the request. Based on determining the given persona, in some implementations, the methods, non-transitory computer readable media, and systems generate a digital-content recommendation for presentation on a client device associated with the user account.