Abstract: A process for obtaining vanadium component in the form of vanadium oxide from gasifier slag is disclosed. The process comprises pulverizing the slag to obtain pulverized slag, which is blended with water and an alkali salt to obtain a slurry. The slurry is dried and then roasted in the presence of air to obtain a roasted slag. The roasted slag is leached to obtain a first filtrate comprising the vanadium component. The first filtrate is reacted with a magnesium salt to remove a silica component in the form of a precipitate. The silica free second filtrate is reacted with an ammonium salt to obtain ammonium metavanadate, which is further calcined to obtain the significant amount of vanadium pentoxide (V2O5).

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating an association between a graphic element and text for a messaging thread and providing an overlay including the graphic element based on providing a message to the messaging thread including the text. In particular, in one or more embodiments, the disclosed systems receive user selection of the text and the graphic element and provide the association between the text and the element to various client devices associated with the messaging thread. Accordingly, the disclosed systems provide instructions for each client device associated with the messaging thread to generate and provide an overlay for the messaging thread.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Systems, methods, client devices, and non-transitory computer-readable media are disclosed for utilizing an augmented reality (AR) data channel to enable shared augmented reality video calls which facilitate the sharing of and interaction with AR elements during video calls. For example, the disclosed systems can establish a video call between client devices that include a video (and audio) data channel and an AR data channel. Moreover, in one or more embodiments, the disclosed systems enable one of the client devices to transmit AR data (e.g., AR element identifiers, AR elements, object vectors, participant identifiers) through an AR data channel to cause the other client device to render an AR element on a video captured by the other client device during a video call. Indeed, the disclosed systems can enable AR environments, AR effects, AR-based activities, and/or individual AR elements during a video call utilizing an AR data channel.

Abstract: Presented herein are methods, non-transitory computer readable media, and devices triggering a metadata recovery process within a network storage system, which include: dividing metadata into metadata segments, wherein each of the metadata segments is tasked to perform a specific file system operation function, validating each of the metadata segments during the specific file system operation function; upon failure to validate at least one of the metadata segments, triggering an automatic repair process while maintaining the operation function tasked to the metadata segment, and upon finalizing the automatic repair process, resuming the specific file system operation function tasked to the metadata segment.

Abstract: Systems and methods for supporting dynamic disk growth within a virtual storage appliance are provided. According to one embodiment, a portion of a logical size of respective hyperscale disks provided by a hyperscaler are provisioned for use by a virtual storage system as backing for respective file system disks. To accommodate growth, block numbers for the file system disks are pre-allocated within a sparse space of a contiguous sequence of block numbers corresponding to a number of blocks represented by the logical size. Metadata is maintained for the file system disks regarding a range of the pre-allocated block numbers that are available for use. Responsive to a triggering condition, the provisioned portion of a hyperscale disk is increased and subsequently, responsive to detecting a change in a size of the hyperscale disk by the virtual storage system, a size of the corresponding file system disk is updated within the metadata.

Abstract: An artificial intelligence (AI) system is disclosed. The AI system provides an AI system lane processing chain, at least one AI processing block, a local memory, a hardware sequencer, and a lane composer. Each of the at least one AI processing block, the local memory coupled to the AI system lane processing chain, the hardware sequencer coupled to the AI system lane processing chain, and the lane composer is coupled to the AI system lane processing chain. The AI system lane processing chain is dynamically created by the lane composer.

Abstract: A system and method for determining sub-activities in videos and segmenting the videos is disclosed. The method includes extracting one or more batches from one or more videos and extracting one or more features from set of frames associated with the one or more batches. The method further includes generating a set of predicted codes and determining a cross-entropy loss, temporal coherence loss and a final loss. Further, the method includes categorizing the set of frames into one or more predefined clusters and generating one or more segmented videos based on the categorized set of frames, the determined final loss, and the set of predicted codes by using s activity determination-based ML model. The method includes outputting the generated one or more segmented videos on user interface screen of one or more electronic devices associated with one or more users.

Abstract: A system and method for learning human activities from video demonstrations using video augmentation is disclosed. The method includes receiving original videos from one or more data sources. The method includes processing the received original videos using one or more video augmentation techniques to generate a set of augmented videos. Further, the method includes generating a set of training videos by combining the received original videos with the generated set of augmented videos. Also, the method includes generating a deep learning model for the received original videos based on the generated set of training videos. Further, the method includes learning the one or more human activities performed in the received original videos by deploying the generated deep learning model. The method includes outputting the learnt one or more human activities performed in the original videos.

Abstract: Aspects of the present disclosure are presented for an AI system featuring specially designed AI hardware that incorporates security features to provide iron clad trust and security to run AI applications/solution models. Presented herein are various security features for AI processing, including: a trust and integrity verifier of data during operation of an AI solution model; identity and trust establishment between an entity and the AI solution model; secure isolation for a virtual AI multilane system; a real-time attack detection and prevention mechanism; and built in detection mechanisms related to rogue security attack elements insertion during manufacturing. Aspects also include security to implement an AI network interconnecting multiple user devices in an AI environment.

Abstract: Aspects of the present disclosure provide for a novel system and method where users can promote, subscribe, license, train collaboratively, and/or trade AI models in real-time over an AI trusted model network that represents a group of trusted users across platform agnostic ways. The system and methods described herein may effectively provide a marketplace to collaboratively develop and reliably disseminate developed AI models to other users who may have similar needs. The system may include an AI user device server (AI-UDS), an AI trusted model network (AI-TMN), an AI secure marketplace system (AI-SMPS), an AI collaborative training platform (AI-CTP), an AI secure transaction fulfillment system (AI-STFS), and an AI model license subscription management platform (AI-LSMP).

Abstract: A system and method for correlating video frames in a computing environment. The method includes receiving first video data and second video data from one or more data sources. The method further includes encoding the received first video data and the second video data using machine learning network. Further, the method includes generating first embedding video data and second embedding video data corresponding to the received first video data and the received second video data. Additionally, the method includes determining a contrastive IDM temporal regularization value for the first video data and the second video data. The method further includes determining temporal alignment loss between the first video data and the second video data. Also, the method includes determining correlated video frames between the first video data and the second video databased on the determined temporal alignment loss and the determined contrastive IDM temporal regularization value.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Techniques are provided for storage tier verification checks. A determination is made that a mount operation of an aggregate of a set of volumes stored within a multi-tier storage environment has completed. A first metafile and a second metafile are maintained to track information related to the storage of objects of a volume of the aggregate within a remote object store that is a tier of the multi-tier storage environment. A distributed verification is performed between the first metafile and the second metafile to identify an inconsistency. Accordingly, the first metafile and the second metafile are reconciled to address the inconsistency so that storage information within the first metafile and the second metafile are consistent.

Abstract: Aspects of the present disclosure are presented for a power management system of a multilane AI system architecture. The system may include an orchestrator configured to control power and other operations of a lane. An uber orchestrator manages the overall system, and may know all of the multilane systems within the AI virtual multilane system that need to be active at a given frequency and power envelope for given price, and performance constraints. The orchestrator of each lane knows the compute/logic blocks that need to be active for a given AI app model AI processing chain execution. The orchestrator may be configured to send commands to turn off power to certain components that are not utilized in performing an AI execution sequence, deactivate operation to the lane when its functions are completed, and also modulate the clock frequency of a lane to fit the computation demands while minimizing power usage.

Abstract: Dry liquid concentrates allow carbon nanotubes to be dispersed in various mediums under standard composite processing conditions. The incorporation of carbon nanotubes can enhance the physical properties of the resulting material, such as, for example, improving the anti-corrosive properties of thermosets if present, and/or improving electrical properties of transitional metal oxide composites.

Abstract: Dry liquid concentrates allow carbon nanotubes to be dispersed in rubber formulations under standard rubber processing conditions. The incorporation of carbon nanotubes can enhance the physical properties of the resulting rubber material in many ways, including creating a more resilient rubber which resists abrasion, tearing, and chipping.

Abstract: Dry liquid concentrates allow carbon nanotubes to be dispersed in rubber formulations under standard rubber processing conditions. The incorporation of carbon nanotubes can enhance the physical properties of the resulting rubber material in many ways, including creating a more resilient rubber which resists abrasion, tearing, and chipping.

Abstract: A system for building computational models of a goal-driven task from demonstration is disclosed. A task recording subsystem receives a recorded video file or recorded sensor data representative of an expert demonstration for a task. An instructor authoring tool generates one or more sub-activity proposals; enables an instructor to specify one or more sub-activity labels upon modification of the one or more sub-activity proposals into one or more sub-tasks. A task learning subsystem learns the one or more sub-tasks represented in the demonstration of the task; builds an activity model to predict and locate the task being performed in the recorded video file. A task evaluation subsystem evaluates a live video representative of the task; generates at least one performance description statistics; identifies a type of activity step executed by the one or more actors; provides an activity guidance feedback in real-time to the one or more actors.