Abstract: A system may receive, from a client device, a user input indicating a product that is to be listed for sale via an online marketplace, and may transmit an instruction for the client device to capture a video of the product from a set of multiple perspectives including a reference perspective. The system may receive the video of the product, where the video includes a set of multiple image frames depicting the product from the set of multiple of perspectives. The system may extract a subset of image frames of the set of multiple of image frames that depict the product from one or more cardinal views, where the one or more cardinal views are determined relative to the reference perspective. The system may then generate an item listing for listing the product for sale via the online marketplace, where the item listing includes the subset of image frames.

Abstract: Disclosed herein are methods, systems, and computer-readable media for regenerating a region of an image with a machine learning model based on a text input. Disclosed embodiments involve accessing a digital input image. Disclosed embodiments involve generating a masked image by removing a masked region from the input image. Disclosed embodiments involve accessing a text input corresponding to an image enhancement prompt. Disclosed embodiments include providing at least one of the input image, the masked region, or the text input to a machine learning model configured to generate an enhanced image. Disclosed embodiments involve generating, with the machine learning model, the enhanced image based on at least one of the input image, the masked region, or the text input.

Abstract: Disclosed herein are methods, systems, and computer-readable media for generating an image corresponding to a text input. In an embodiment, operations may include accessing a text description and inputting the text description into a text encoder. The operations may include receiving, from the text encoder, a text embedding, and inputting at least one of the text description or the text embedding into a first sub-model configured to generate, based on at least one of the text description or the text embedding, a corresponding image embedding. The operations may include inputting at least one of the text description or the corresponding image embedding, generated by the first sub-model, into a second sub-model configured to generate, based on at least one of the text description or the corresponding image embedding, an output image. The operations may include making the output image, generated by the first second sub-model, accessible to a device.

Abstract: Techniques are disclosed relating to implementing a machine learning approach to cross-language translation and search. In certain embodiments, a method may include receiving a plurality of characters of a first language that are unsegmented and grouping the plurality of character into multiple groups. The method also includes determining a set of word tokens based on one or more transliterations of the multiple groups and one or more translations of the multiple groups to a second language. Further, the method includes generating one or more word token solution sets by querying an index file using the one or more word tokens. The method also includes determining whether the index file references an entity name corresponding to the plurality of characters of the first language based on comparing the one or more token solution sets with the index file.

Abstract: Various embodiments set forth one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform steps of determining at least one physical resource of a node in a cluster of nodes is under contention by virtual computing instances or a virtual computing instance cannot be placed on the cluster of nodes, determining a placement for one or more virtual computing instances on the cluster of nodes, each virtual computing instance having a virtual resource associated with a profile that is compatible with a profile associated with a physical resource of a node on which the virtual computing instance is placed, and generating and executing a plan to achieve the placement, the plan including at least one of migrating at least one virtual computing instance or reconfiguring a profile associated with at least one physical resource.

Abstract: Methods, systems and computer program products for computer cluster management. Multiple components are operatively interconnected to carry out operations for placing virtual machines onto a multi-tenant computing cluster, where the placement achieves adherence to a set of security requirements. Initially, data characterizing logical CPU resources of the multi-tenant computing cluster are gathered. Upon receipt of a request to place a virtual machine onto a node of the multi-tenant computing cluster, a set of security rules that are used to achieve the set of security requirements associated with the multi-tenant computing cluster are accessed. In accordance with the security rules the virtual machine is assigned to execute in a portion of the logical CPU resources. The virtual machine does not share logical CPU resources with any other tenant.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: Communication between VMs running in a clustered virtualization environment and a distributed storage system is managed. The distributed storage system consists of multiple storage/compute nodes. A hypervisor communicates with a controller in a local node on behalf of a VM. The controller will redirect a connection to a second controller on different node. The controller to which the connection is redirected is selected so as to distribute the load evenly across the cluster.

Abstract: Methods, systems and computer program products for computer cluster management. Multiple components are operatively interconnected to carry out operations for placing virtual machines onto a multi-tenant computing cluster, where the placement achieves adherence to a set of security requirements. Initially, data characterizing logical CPU resources of the multi-tenant computing cluster are gathered. Upon receipt of a request to place a virtual machine onto a node of the multi-tenant computing cluster, a set of security rules that are used to achieve the set of security requirements associated with the multi-tenant computing cluster are accessed. In accordance with the security rules the virtual machine is assigned to execute in a portion of the logical CPU resources. The virtual machine does not share logical CPU resources with any other tenant.

Abstract: A system and method include migrating virtual machines (VMs) between compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, a plurality of virtual machines hosted on compute only (CO) nodes in the virtual computing system. The management processor then identifies hyper converged (HC) nodes having virtual disks hosting data for the plurality of virtual machines hosted on CO nodes. When a virtual machine (VM) in the plurality of virtual machines is migrating, the management processor biases the VM to migrate to a first HC node hosting a virtual disk assigned to host data for the VM.

Abstract: A system and method include assigning virtual machines (VMs) to any one of compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, input-output patterns for a plurality of virtual disks in the virtual computing system; ranking, by the management processor, the plurality of virtual machines according to the identified input-output patterns; assigning, by the management processor, the plurality of virtual machines to any one of CO and HC nodes that optimize performance of the virtual computing system; monitoring, by the management processor, performance of the plurality of virtual machines; and determining, by the management processor, a change in behavior of one of the plurality of virtual machines.

Abstract: A system and method include classifying and assigning virtual disks accessed from compute only nodes. The method determines, by a management processor of a virtual computing system, characteristics for a plurality of virtual disks hosted on a plurality of hyper converged nodes in a cluster of nodes in the virtual computing system. The method further classifies, by the management processor, each of the plurality of virtual disks based on the determined characteristics and identifies, by the management processor, one of the plurality of virtual disks to host data for a virtual machine on a compute only node based on the classification to spread out input-output demand in the cluster, reducing probability of input-output bottlenecks and increasing cluster-wide storage throughput. The method also assigns, by the management processor, the identified virtual disk to host data for the virtual machine located on the compute only node.

Abstract: Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured.

Abstract: A system and method include assigning virtual machines (VMs) to any one of compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, input-output patterns for a plurality of virtual disks in the virtual computing system; ranking, by the management processor, the plurality of virtual machines according to the identified input-output patterns; assigning, by the management processor, the plurality of virtual machines to any one of CO and HC nodes that optimize performance of the virtual computing system; monitoring, by the management processor, performance of the plurality of virtual machines; and determining, by the management processor, a change in behavior of one of the plurality of virtual machines.

Abstract: A system and method include migrating virtual machines (VMs) between compute only (CO) and hyper converged (HC) nodes. The method includes identifying, by a management processor of a virtual computing system, a plurality of virtual machines hosted on compute only (CO) nodes in the virtual computing system. The management processor then identifies hyper converged (HC) nodes having virtual disks hosting data for the plurality of virtual machines hosted on CO nodes. When a virtual machine (VM) in the plurality of virtual machines is migrating, the management processor biases the VM to migrate to a first HC node hosting a virtual disk assigned to host data for the VM.

Abstract: The present disclosure provides a method in a data processing system that includes at least one processor and at least one memory. The at least one memory includes instructions executed by the at least one processor to implement a driving encounter recognition system. The method includes receiving information, from one or more sensors coupled to a first vehicle, determining first trajectory information associated with the first vehicle and second trajectory information associated with a second vehicle, extracting a feature vector, providing the feature vector to a trained classifier, the classifier trained using unsupervised learning based on a plurality of feature vectors, and receiving, from the trained classifier, a classification of the current driving encounter in order to facilitate the first vehicle to perform a maneuver based on the current driving encounter.

Abstract: Systems for distributed resource system management. A first computing system operates in a first computing environment. A predictive model is trained in the first computing environment to form a trained resource performance predictive model that comprises a set of trained model parameters to capture at least computing and storage IO parameters that are responsive to execution of one or more workloads that consume computing and storage resources in the first computing environment. When the trained resource performance predictive model is deployed to a second computing environment, various computing system configuration differences, and/or workload differences and/or other differences between the first computing environment and the second computing environment are detected and measured.

Abstract: Communication between VMs running in a clustered virtualization environment and a distributed storage system is managed. The distributed storage system consists of multiple storage/compute nodes. A hypervisor communicates with a controller in a local node on behalf of a VM. The controller will redirect a connection to a second controller on different node. The controller to which the connection is redirected is selected so as to distribute the load evenly across the cluster.