Abstract: A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.

Abstract: A file system in a user space partition of virtual memory may be mounted by a computing device that runs a virtual machine which includes a set of storage disks. The file system in user space may then expose one or more virtual files associated with one or more storage disks that correspond to one or more loop devices configured to map files of the virtual machine to the one or more virtual files. The computing device may then receive a request to read a data block stored at the virtual machine and may identify a file and corresponding virtual file that stores the requested data block based on a set of metadata provided by the loop devices. The computing device may then determine the location of the data block stored at the virtual machine, and may read the data block from the determined location.

Abstract: Example solutions for video frame action detection use a gated history and include: receiving a video stream comprising a plurality of video frames; grouping the plurality of video frames into a set of present video frames and a set of historical video frames, the set of present video frames comprising a current video frame; determining a set of attention weights for the set of historical video frames, the set of attention weights indicating how informative a video frame is for predicting action in the current video frame; weighting the set of historical video frames with the set of attention weights to produce a set of weighted historical video frames; and based on at least the set of weighted historical video frames and the set of present video frames, generating an action prediction for the current video frame.

Abstract: Example solutions for video object segmentation (VOS) use a bilateral attention transformer in motion-appearance neighboring space, and perform a process that includes: receiving a video stream comprising a plurality of video frames in a sequence; receiving a first object mask for an initial video frame of the plurality of video frames; selecting a video frame of the plurality of video frames as a current query frame, the current query frame following, in the sequence, a reference frame of a reference frame set, wherein each reference frame has a corresponding object mask; using the current query frame and a video frame in the reference frame set, determining a bilateral attention; and using the bilateral attention, generating an object mask for the current query frame.

Abstract: A video streaming client is configured to check whether a target version of a desired video content is available for streaming from a video streaming server, the target version being encoded to a target value of an encoding attribute. The video streaming client obtains a data communication speed to the video streaming server, and determines that the data communication speed is sufficient to stream and display the target version of the desired video content. The target value is less than a maximum value of the encoding attribute which is decodable by the video streaming client. The video streaming client is configured to select to stream the target version of the desired video content even though the data communication speed is sufficient to stream a version of the desired video content without playback interruption when encoded using a value of the encoding attribute which is higher than the target value.

Abstract: A set of reconstruction elements useable to reconstruct a representation of a signal at a relatively high level of quality using data based on a representation of the signal at a relatively low level of quality is obtained. The representation at the relatively high level of quality is arranged as an array comprising at least first and second rows of signal elements. A reconstruction element is associated with a respective signal element in the set. A set of data elements is derived based on the set of reconstruction elements. At least one of the data elements is derived from at least two reconstruction elements associated with signal elements from the first row and a different number of reconstruction elements associated with signal elements from the second row.

Abstract: Example solutions for video frame action detection use a gated history and include: receiving a video stream comprising a plurality of video frames; grouping the plurality of video frames into a set of present video frames and a set of historical video frames, the set of present video frames comprising a current video frame; determining a set of attention weights for the set of historical video frames, the set of attention weights indicating how informative a video frame is for predicting action in the current video frame; weighting the set of historical video frames with the set of attention weights to produce a set of weighted historical video frames; and based on at least the set of weighted historical video frames and the set of present video frames, generating an action prediction for the current video frame.

Abstract: A medical telepresence system comprising: an interface to receive a plurality of data feeds from a live medical procedure, at least one data feed comprising a video signal capturing the live medical procedure; a hierarchical encoder to encode the plurality of data feeds using a first tier-based hierarchical data coding scheme, wherein encoded data from the hierarchical encoder is decodable by a first set of computing devices for viewing, the first set of computing devices being communicatively coupled to the hierarchical encoder using a first network connection; a transcoder to convert from the first tier-based hierarchical data coding scheme to a second tier-based hierarchical data coding scheme, wherein encoded data from the transcoder is receivable by a second set of computing devices for viewing, the second set of computing devices being communicatively coupled to the transcoder using a second network connection, the second network connection being of a lower quality than the first network connection; and

Abstract: A method for encoding a first stream of video data comprising a plurality of frames of video, the method, for one or more of the plurality of frames of video, comprising the steps of: encoding in a hierarchical arrangement a frame of the video data, the hierarchical arrangement comprising a base layer of video data and a first enhancement layer of video data, said first enhancement layer of video data comprising a plurality of sub-layers of enhancement data, such that when encoded: the base layer of video data comprises data which when decoded renders the frame at a first, base, level of quality; and each sub-layer of enhancement data comprises data which, when decoded with the base layer, render the frame at a higher level of quality than the base level of quality; and wherein the steps of encoding the sub-layers of enhancement data comprises: quantizing the enhancement data at a determined initial level of quantization thereby creating a set of quantized enhancement data; associating to each of the pluralit

Abstract: Disclosed solutions for improved machine learning (ML) employ knowledge balancing self-distillation with adaptive mutual information (AMI). Examples include: for a neural network (NN) having a plurality of modules, determining a task objective for at least a final module of the plurality of modules; for the NN, determining a balancing objective using at least an output of the final module and an output of a first intermediate module of the plurality of modules; determining an overall objective, wherein determining the overall objective comprises combining the task objective with the balancing objective; and adjusting weights of the NN to minimize the overall objective. Balancing information may combine mutual information (between an intermediate module output and the output of the final module) with self-information (for the intermediate module output) to produce AMI. Adjusting weights of the NN during training, using the AMI, results in knowledge balancing self-distillation.

Abstract: The disclosure herein describes preparing and using a cross-attention model for action recognition using pre-trained encoders and novel class fine-tuning. Training video data is transformed into augmented training video segments, which are used to train an appearance encoder and an action encoder. The appearance encoder is trained to encode video segments based on spatial semantics and the action encoder is trained to encode video segments based on spatio-temporal semantics. A set of hard-mined training episodes are generated using the trained encoders. The cross-attention module is then trained for action-appearance aligned classification using the hard-mined training episodes. Then, support video segments are obtained, wherein each support video segment is associated with video classes. The cross-attention module is fine-tuned using the obtained support video segments and the associated video classes.

Abstract: To enable a private enterprise to subscribe to wireless service provided at a site of the private enterprise using a private radio access network (RAN), one embodiment is directed to a hyperscale network configured to: obtain subscription information for providing the wireless; determine initial configurations for physical entities deployed at the site; store the determined initial configurations in vendors clouds; deploy, in the hyperscale network, virtual entities needed to provide the wireless service; instruct the private enterprise to cause the physical entities deployed at the site to perform initial bootstrap processes whereby the physical entities download the determined initial configurations for use thereby in communicating with at least one of the virtual entities; and provide final configurations to the physical entities and the virtual entities for use thereby in configuring the physical entities and the virtual entities to provide the wireless service at the site using the RAN.

Abstract: A method for encoding a first stream of video data comprising a plurality of frames of video, the method, for one or more of the plurality of frames of video, comprising the steps of: encoding in a hierarchical arrangement a frame of the video data, the hierarchical arrangement comprising a base layer of video data and a first enhancement layer of video data, said first enhancement layer of video data comprising a plurality of sub-layers of enhancement data, such that when encoded: the base layer of video data comprises data which when decoded renders the frame at a first, base, level of quality; and each sub-layer of enhancement data comprises data which, when decoded with the base layer, render the frame at a higher level of quality than the base level of quality; and wherein the steps of encoding the sub-layers of enhancement data comprises: quantizing the enhancement data at a determined initial level of quantization thereby creating a set of quantized enhancement data; associating to each of the pluralit

Abstract: Providing a task-aware recommendation of hyperparameter configurations for a neural network architecture. First, a joint space of tasks and hyperparameter configurations are constructed using a plurality of tasks (each of which corresponds to a dataset) and a plurality of hyperparameter configurations. The joint space is used as training data to train and optimize a performance prediction network, such that for a given unseen task corresponding to one of the plurality of tasks and a given hyperparameter configuration corresponding to one of the plurality of hyperparameter configurations, the performance prediction network is configured to predict performance that is to be achieved for the unseen task using the hyperparameter configuration.

Abstract: A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.

Abstract: A method of buffering, at a decoder, a segment (320?) of an encoded data stream (300?), the segment (320?) being arranged in hierarchical layers comprising a base layer segment (320-0) and an enhancement layer segment (320-1?), the base layer segment (320-0) being decodable to a base level of reproduction quality (LOQ#6), and the enhancement layer segment (320-1?), together with the base layer segment (320-0), being decodable to an enhanced level of reproduction quality (LOQ#1), the method comprising the steps of: receiving the encoded data stream (300?) for a prescribed time period so as to buffer the base layer segment (320-0) and as much of the enhancement layer segment (320-1?) as possible in the prescribed time period (P); and sending the buffered base layer segment (320-0) and what is received of the enhancement layer segment (320-1?) to a decoder for decoding and output.

Abstract: Generating a contributor-based power abstract for a device, including: identifying a clock power component for each of a plurality of clock gating domains, identifying a switching characteristic for each of the clock gating domains, combining the switching characteristics for all of the clock gating domains into a domain combination list, performing a per-case simulation based at least on the domain combination list, calculating an effective capacitance for each of the clock gating domains based at least on the per-case simulation, and generating a power abstract for each of the clock gating domains based at least on the effective capacitance.

Abstract: A decoder device receives data useable to generate data for representing a data signal at a first level of quality. The decoder device receives enhancement data useable to generate data for representing the data signal at a second, higher level of quality based on the data for representing the data signal at the first level of quality. The decoder device generates data for representing a target region of the data signal at a target level of quality using a selected portion of the received enhancement data. The selected portion is associated with the target region. The target level of quality is higher than the first level of quality. The decoder device generates data for representing a further region of the data signal at a level of quality that is lower than the target level of quality.

Abstract: A simultaneous multithreading processor and related method of operating are disclosed. The method comprises dispatching portions of a first instruction to be executed by a respective plurality of execution units of the processor; receiving, at an instruction completion table of the processor, respective finish reports responsive to execution of the portions of the first instruction; determining, using the received finish reports, that all of the portions of the first instruction have been executed; and updating the instruction completion table to indicate that the first instruction is ready for completion.

Abstract: A set of reconstruction elements useable to reconstruct a representation of a signal at a relatively high level of quality using data based on a representation of the signal at a relatively low level of quality is obtained. The representation at the relatively high level of quality is arranged as an array comprising at least first and second rows of signal elements. A reconstruction element is associated with a respective signal element in the set. A set of data elements is derived based on the set of reconstruction elements. At least one of the data elements is derived from at least two reconstruction elements associated with signal elements from the first row and a different number of reconstruction elements associated with signal elements from the second row.