Abstract: The present disclosure describes methods and devices for generating a vector line drawing. A vector line drawing network may include a machine learning-based model that is trained to convert a raster image to a vector line drawing directly. The vector line drawing network may be trained end-to-end, using supervised learning, where only raster images are used as training data. A vector line drawing is generated stroke by stroke, over a series of time steps. In each time step, a dynamic drawing window is moved and scaled across the input raster image to sample a patch of the raster image, and a drawing stroke is predicted to draw a stroke in a corresponding patch in the canvas for the vector line drawing. The image patches are pasted in the canvas to assemble a final vector line drawing that corresponds to the input raster image.

Abstract: Approaches described and suggested herein relate to generating three-dimensional representations of objects to be used to render virtual reality and augmented reality effects on personal devices such as smartphones and personal computers, for example. An initial surface mesh of an object is obtained. A plurality of silhouette masks of the object taken from a plurality of viewpoints is also obtained. A plurality of depth maps are generated from the initial surface mesh. Specifically, the plurality of depth maps are taken from the same plurality of viewpoints from which the silhouette images are taken. A volume including the object is discretized into a plurality of voxels. Each voxel is then determined to be either inside the object or outside of the object based on the silhouette masks and the depth data. A final mesh is then generated from the voxels that are determined to be inside the object.

Abstract: The disclosure relates to an offline-online question answering system. In some aspects, the techniques described herein relate to a method including: receiving, by a processor, a query from a user; generating, by the processor, a query embedding representing the query; identifying, by the processor, at least one question corresponding to the query by comparing the query embedding to a plurality of embeddings of questions; and transmitting, by the processor, an answer corresponding to the at least one question to the user in response to the query.

Abstract: An apparatus, method, and computer-readable medium for rendering a 3D image using a neural network for implicit representation of the image. UV coordinates of a texture map corresponding to a point on the image are calculated using a texture neural network. The image is rendered by applying a color value located at the UV coordinates of the texture map to the point.

Abstract: Described is a system (and method) for providing multiple types of granular delete operations for a cloud-based object storage. The system may include a server that acts as an intermediary between a client device and an object storage that stores client data. The server may allow a client to perform a hard delete (or permanent) delete and a soft delete. The server may perform a specialized processing to synthetically create the soft delete feature using a permanent object delete operation provided by the object storage. The specialized processing may include manipulating retention periods associated with objects depending on whether the object is subject to a hard delete or a soft delete. As a result, the server may provide the ability for clients to perform a soft delete at an object level using existing storage level APIs that do not directly support such a feature.

Abstract: Described is a system for synchronizing expiration times for incremental backup data stored on a cloud-based object storage. More particularly, the system may provide a layer of intelligence when updating the expiration times associated with backup data to ensure that a full recovery of the client data to a point-in-time of any incremental backup may be performed. To provide such a capability, the system may maintain specialized metadata identifying expiration times for objects and a list of objects required to perform a full recovery to a point-in-time for each of the performed backups (e.g. full or incremental). The system may access this metadata to identify which objects stored by a previous backup are still referenced by a subsequent backup. Based on the identified objects, the system may synchronize object expiration times to ensure objects are not prematurely deleted from the object storage.

Abstract: The present disclosure describes methods and devices for generating a vector line drawing. A vector line drawing network may include a machine learning-based model that is trained to convert a raster image to a vector line drawing directly. The vector line drawing network may be trained end-to-end, using supervised learning, where only raster images are used as training data. A vector line drawing is generated stroke by stroke, over a series of time steps. In each time step, a dynamic drawing window is moved and scaled across the input raster image to sample a patch of the raster image, and a drawing stroke is predicted to draw a stroke in a corresponding patch in the canvas for the vector line drawing. The image patches are pasted in the canvas to assemble a final vector line drawing that corresponds to the input raster image.

Abstract: Embodiments herein disclose a method and electronic device for predicting multi-modal drawings. The method includes: receiving, by the electronic device, at least one of a text input and strokes of a drawing and determining, by the electronic device, features associated with the text input and features associated with the strokes of the drawing. The method includes classifying, by the electronic device, the features associated with the text input and the features associated with the strokes of the drawing into one of a dominant feature and a non-dominant feature and performing, by the electronic device, early concatenation or late concatenation of the features based on the classification; classifying, by the electronic device, the strokes of the drawing based on the concatenation into a category using a deep neural network (DNN) model; and predicting, by the electronic device, primary drawings corresponding to the category.

Abstract: Images of various views of objects can be captured. An object mesh structure can be created based at least in part on the object images. The object mesh structure represents the three-dimensional shape of the object. Alpha masks indicating which pixels are associated with the object can be used to refine the object mesh structure. A request can be made to view the object from an arbitrary viewpoint which differs from the viewpoints associated with the captured images. A subset of the captured images can be used to create a synthetic image. Different weights can be assigned to the captured image to render a synthetic image that represents the view from the arbitrary viewpoint selected. The input images for the synthetic image can be prefetched, or loaded into memory before the arbitrary view is requested. The images can also be cached for future use or to avoid reloading them for another synthetic image.

Abstract: Described is a system for rule-based re-tiering of backup data stored on a cloud-based object storage. More specifically, the system may re-tier objects based on one or more storage rules such that the system retains the ability to efficiently perform a full restore of backup data even when incremental backups are performed to a cloud-based object storage. To provide such a capability, the system may maintain a specialized metadata database that stores information indicating the backup time for each backup, and a list of objects required to perform a full restore to each of the backup times. Accordingly, when initiating a re-tiering based on one or more storage rules, the system may intelligently select candidate objects for re-tiering by leveraging the metadata database to ensure that objects that may still need to be referenced are not unnecessarily moved to a lower storage tier.

Abstract: Present disclosure relates to a method and a user equipment (UE) for enhancing multimodal input content. The UE obtains text-blocks associated with multimodal input and determines a plurality of features for each of the text-blocks using a natural language processing (NLP) model and a neural network (NN) model. Further, the UE identifies usage pattern of the plurality of features based on historic data associated with the usage of the plurality of features using NN model. Thereafter, the UE generates personalized text for each of the textblocks based on the plurality of features and usage pattern. The UE inserts personalized emoticons based on context of text-blocks and lingo of each word using user lingo database. Thus, the UE eliminates manual correction of texts and enhances the readability of message/text entered by user.

Abstract: A method for intelligently reading displayed contents by an electronic device is provided. The method includes obtaining a screen representation based on a plurality of contents displayed on a screen of the electronic device. The method includes extracting a plurality of insights comprising at least one of intent, importance, emotion, sound representation and information sequence of the plurality of contents from the plurality of contents based on the screen representation. The method includes generating audio emulating the extracted plurality of insights.

Abstract: Described is a system for intelligent re-tiering of backup data stored on a cloud-based object storage. More specifically, the system may re-tier objects such that the system retains the ability to efficiently perform a full restore of backup data even when incremental backups are performed to a cloud-based object storage. To provide such a capability, the system may maintain a specialized metadata database that stores information indicating the backup time for each backup, and a list of objects required to perform a full restore to each of the backup times. Accordingly, when using a threshold time (e.g. expiry) to select object candidates for re-tiering, the system may leverage the metadata database to ensure that objects that may still need to be referenced are not unnecessarily moved to a lower storage tier.

Abstract: A system including persistent storage for storing an object repository and a user data backup. The system also includes a manager that obtains fragmented user data associated with an object that is potentially duplicative of an object stored in the object repository; makes a determination that the object is duplicative based on a copy of the object stored in the object repository; in response to making the determination, adds the fragmented user data to a user data backup without adding the object to the backup; associates the fragmented user data in the user data backup with the copy of the object stored in the object repository; and stores the user data backup in the persistent storage.

Abstract: Example systems and methods provide input suggestions to a user to improve user experience on user devices. The input suggestions can be fill information from another app on device to the present app being used by user, information for performing a search (without the user having to copy-paste data or entering the data manually), responses to a message/notification received by the user, information/content/data to be shared between apps (without switching between apps), and emojis/GIFs that can be used by the user. The method includes analyzing one or more content of one or more screen displayed on device, generating at least one of a logical tree structure and a data mashup model of the one or more analyzed content for each screen, and providing a recommendation to a user. The recommendation can be a connected action or an input suggestion.

Abstract: A resource usage platform is disclosed. The platform performs preemptive container load balancing, auto scaling, and placement in a computing system. Resource usage data is collected from containers and used to train a model that generates inferences regarding resource usage. The resource usage operations are performed based on the inferences and on environment data such as available resources, service needs, and hardware requirements.

Abstract: A backup manager for managing backup services includes persistent storage and a backup analyzer. The persistent storage includes a backup data repository and protection policies. The backup analyzer identifies a new backup stored in backup storage; performs a backup compatibility analysis on the new backup to determine inter-backup compatibility of the identified new backup; updates the backup data repository based on the inter-backup compatibility to obtain an updated backup data repository; and modifies a backup schedule using the updated backup data repository to meet a requirement of a protection policy of the protection policies.

Abstract: A system for providing backup services for limited-access user data includes persistent storage for storing a user data visualization enhanced user data backup and a manager. The manager identifies a backup generation event for limited-access user data based on a protection policy; in response to identifying the backup generation event, obtains fragmented user data from an application that gates access to the limited-access user data; obtains organizational metadata associated with the fragmented user data from the application; makes a determination that the fragmented user data is associated with a user data visualization; in response to making the determination, obtains user data visualization metadata associated with the fragmented user data from the application; and generates the user data enhanced user data backup using the organizational metadata, the user data visualization metadata, and the fragmented user data.

Abstract: A method and system for capturing asset protection metadata pertinent to analytics. Specifically, the disclosed method and system entail aggregating and maintaining asset protection metadata—i.e., metadata descriptive of performed asset backup and recovery operations—in a central location. The asset protection metadata may include information relevant to backup and recovery analytics and reporting, while the maintaining said metadata in the central location facilitates access of the metadata by third-party analytics services.

Abstract: A method and system for asset protection threat detection and mitigation using interactive graphics. Specifically, the disclosed method and system entail discerning protection vulnerabilities exhibited by assets (or databases) based on maintained backup metadata. These protection vulnerabilities may subsequently be visualized as part of a projected graphical user interface, which may not only disclose the protection vulnerabilities to a user but also may enable the user to rectify the disclosed protection vulnerabilities through on-demand asset backup operations.