Abstract: In some embodiments, a metaverse optimization and prioritization enabled cloud-based controller includes a metaverse traffic classification unit; and a metaverse optimization and prioritization unit, wherein based upon the identification and classification of data packets as metaverse data packets, the metaverse optimization and prioritization unit optimizes and prioritizes a metaverse client device and metaverse traffic associated with a metaverse optimization and prioritization enabled network. In some embodiments, the metaverse optimization and prioritization unit optimizes the metaverse client device based upon a quality of experience associated with the metaverse client device. In some embodiments, the metaverse optimization and prioritization unit prioritizes the metaverse traffic based on quality of service management features ascertained utilizing the metaverse optimization and prioritization enabled cloud-based controller.

Abstract: In one embodiment, a method includes receiving a token indicating a data chunk becomes available in a first circular buffer of a pre-determined size from a direct memory access component, determining that a computation is to be performed with data including the data chunk based on the token, and generating one or more addresses corresponding to one or more data chunks within the first circular buffer that are to be retrieved for the computation, where a generated address is subtracted by the pre-determined size of the first circular buffer when the generated address is greater than a pre-determined maximum associated with the first circular buffer, and where the generated address is added by the pre-determined size of the first circular buffer when the generated address is less than a pre-determined minimum associated with the first circular buffer.

Abstract: In particular embodiments, a computing system may access a plurality of images for pre-training a first machine-learning model that includes an encoder and a decoder. Using each image, the system may pre-train the model by dividing the image into a set a patches, selecting a first subset of the patches to be visible and a second subset of the patches to be masked during the pre-training, processing, using the encoder, the first subset of patches to generate corresponding first latent representations, processing, using the decoder, the first latent representations corresponding to the first subset of patches and mask tokens corresponding to the second subset of patches to generate reconstructed patches corresponding to the second subset of patches, the reconstructed patches and the first subset of patches being used to generate a reconstructed image, and updating the model based on comparisons between the image and the reconstructed image.

Abstract: Technology herein provides a method, apparatus and computer readable storage medium for use in video encoding. The technology performs operations including pruning interframe candidate modes, based on one or more criteria, to provide a reduced set of candidate modes for encoding a video block, wherein a candidate mode includes an interframe mode type, a set of reference frame types, and one or more dynamic reference list (DRL) candidates, and wherein pruning interframe candidate modes comprises excluding one or more interframe mode types, determining a rate distortion (RD) cost for each of the candidate modes in the reduced set of candidate modes, selecting a candidate mode from the reduced set of candidate modes, based on the lowest RD cost, as a selected interframe mode, and encoding the video block using the selected interframe mode.

Abstract: Aspects of the present disclosure are directed to an artificial intelligence (“AI”) application running in conjunction with an artificial reality (“XR”) space. The AI Builder responds to user commands, verbal or gestural, to build or edit spaces or objects in space. If the requested object is of a type recognized by the AI Builder, then the AI Builder builds the object from one or more stored templates. The new object's location is determined by the objects that already exist in the user's XR environment and on commands or gestures from the user. If the AI Builder does not recognize the requested object, the user can show an image to the AI Builder, and the AI builds a 3D object in the XR space according to that image. To ease collaboration among users, the AI Builder may present its user interface as a non-player character within the XR world.

Abstract: In one embodiment, a method includes rendering a first output image of an XR assistant avatar within a first environment associated with a first XR application for displays of a first extended-reality (XR) display device, wherein the XR assistant avatar has a first form according to a first rendering specification associated with the first XR application and the XR assistant avatar is interactable by a first user to access an assistant system, receiving an indication that the first user is switching from the first XR application to a second XR application, accessing a second rendering specification associated with the second XR application, and rendering a second output image of the XR assistant avatar within a second environment associated with the second XR application for displays of a second XR display device, wherein the XR assistant avatar is rendered to have a second form according to the second rendering specification.

Abstract: In particular embodiments, a computing system may receive a video comprising a plurality of image frames. The system may generate, for each image frame in the video, an initial depth map using a machine-learning model. The system may compute a misalignment error indicating depth misalignments in initial depth maps using a reprojection technique. The system may generate, for each image frame in the video, an optimized camera pose and a flexible deformation spline associated with the image frame to minimize the misalignment error. The system may generate, for each image frame in the video, a refined depth map by adjusting the initial depth map associated with the frame using the flexible deformation spline associated with the image frame.

Abstract: In one embodiment, a computer-implemented method includes receiving, through a user interface (UI) of an artificial-reality (AR) design tool, a selection of a configurable interface element to place the AR design tool and the UI into a configure phase to configure an AR effect. The computer-implemented method further includes receiving, through the UI of the AR design tool after the AR design tool and the UI are placed into the configure phase in response to selecting the configurable interface element, instructions to add a voice-command module to the AR effect. The computer-implemented method further includes configuring, while the AR design tool and the UI are placed into the configure phase, one or more parameters of the voice-command module. The computer-implemented method further includes generating the AR effect utilizing a particular voice command at runtime based on configured one or more parameters of the voice-command module.

Abstract: In one embodiment, a method for accessing an instruction that is to be executed by a control agent within a computing system may include decompressing the instruction by replacing each of one or more zero-symbol run-length fields in the instruction with as many continuous zero symbols as its corresponding value and removing one or more non-zero-symbol run-length fields from the instruction. The method may also include determining that the instruction is spatial-delta-encoded based on a compression data header associated with the instruction, performing spatial-delta decoding on the instruction in response to the determination by orderly determining a spatial-delta-decoded value of each bit in the instruction, and causing the instruction to be sent to the control agent.

Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for selectively rendering augmented reality content based on predictions regarding a user's ability to visually process the augmented reality content. For instance, the disclosed systems can identify eye tracking information for a user at an initial time. Moreover, the disclosed systems can predict a change in an ability of the user to visually process an augmented reality element at a future time based on the eye tracking information. Additionally, the disclosed systems can selectively render the augmented reality element at the future time based on the predicted change in the ability of the user to visually process the augmented reality element.

Abstract: In one embodiment, a method includes accessing a first document, accessing a plurality of second documents, calculating a relevance score for each of the plurality of second documents indicating a degree of relevance of the second document to the first document using an encoder of a machine-learning model, selecting a subset of the second documents based on their corresponding relevance scores, generating a target document by using the machine-learning model to process the subset of second documents and their corresponding relevance scores, and updating parameters of the machine-learning model based on a comparison between the first document and the generated target document.

Abstract: A system and method for generating task-specific text by processing multimodal inputs using machine-learning models is provided. The method may include accessing first sets of tokens associated with a desired task and one or more modalities associated with a context of the desired task. The method may further include determining a second set of tokens for each of the one or more modalities using a classifier network associated with the modality. The method may further include generating a number of embedding vectors by mapping the first sets of tokens and the second set of tokens associated with each of the one or more modalities to an embedding space. The method may further include producing a sequence of words addressing the desired task by processing the number of embedding vectors with an encoder-decoder network.

Abstract: Methods, systems, and apparatuses for disguising or otherwise hiding big data access patterns and frequencies may include a replica being directly obtained from a key and a replica index, based on applying a pseudorandom permutation or pseudorandom function. Data object access frequencies may be quantized so that the ratios of quantized data object access frequencies are rational numbers of integers, which may allow for a uniform distribution of data object accesses. Fake accesses may be implemented by simulating an access schedule produced by a cryptographic primitive and by performing a greedy mapping between the real data object accesses and simulated data object accesses.

Abstract: The disclosed systems for suspending cable (e.g., fiber optic cable) from an overhead powerline may include a payload subsystem for housing and dispensing a cable along an overhead powerline, a rotation subsystem for winging the cable from the payload subsystem around the powerline, an extension subsystem for moving the payload subsystem to avoid obstacles, an obstacle detection subsystem for automatically detecting obstacles encountered along the powerline, a drive subsystem for driving the system along the powerline, and at least one processor for controlling the payload subsystem, rotation subsystem, extension subsystem, obstacle detection subsystem, and drive subsystem in a manner that avoids obstacles as the system moves along the powerline. Various other related systems, devices, components, and methods are also disclosed.

Abstract: Methods, systems, and storage media for augmenting a video are disclosed. Exemplary implementations may: receive a selection of an effect; receive user-generated content comprising video data and audio data; detect a characteristic of the audio data comprising at least a volume and/or a pitch of the audio data during a period of time; determine a series of numeric values based on the characteristic of the audio data during the period of time, individual numeric values of the series of numeric values being correlated with an amplitude of the volume and/or pitch at a discrete point within the period of time; and augment at least one of the video data and/or the audio data to include the effect based on the series of numeric values at discrete points in time within the period of time.

Abstract: The present disclosure is directed toward systems and methods for optimizing video conferences. For instance, systems and methods described herein optimize both the transmission and display of one or more video conference data streams. Systems and methods described herein optimize the transmission and display of one or more video conference data streams by identifying a context associated with the one or more video conference data streams and optimizing the one or more video conference data streams based on the identified context.

Abstract: According to examples, a system for implementing image modification functions via use of variable scanning orders is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to partition an image into a plurality of image blocks, identify one or more image blocks of the plurality of image blocks associated with a region of interest (ROI), and scan the one or more image blocks in an image modification order. The processor, when executing the instructions, may then arrange the one or more image blocks according to the image modification order to form a modified image including the region of interest (ROI) and crop the region of interest (ROI) in the modified image to form a new image.

Abstract: A semiconductor design that uses high refractive index material between low refractive index material. This structure may act as an optical waveguide.

Abstract: A system for facilitating RDMA transmit flow scheduling and pacing is disclosed. The system may determine a set of groups. The groups may be associated with a range of transmission rates. The system may determine a transmission rate of a queue pair. The system may assign the QP to a first group of the set of groups. The transmission rate of the QP may be within the range of transmission rates of the first group. The system may determine an available QP of at least one group from the set of groups. The system may schedule transmission, by an arbiter, of the available QP. The system may transmit, by the arbiter, a message associated with the available QP.