Dell Products Inc.

Emc IP Holding Company LLC

Anzhou Hou

Zhen Jia

Bin He

Zhenzhen Lin

Si Ruo Chen

Danqing Sha

Tianxiang Chen

Sanping Li

Victor Fong

Zhisong Liu

Chenxi Hu

YuHong Nie

Pengfei Wu

Zijia Wang

Kenneth Durazzo

Jinpeng Liu

Qiang Chen

Michael Robillard

Fucai Liu

Min Chao Zhao

Embodiments of the present disclosure relate to a method, a device, and a computer program product for generating a three-dimensional (3D) object model. The method includes generating a first 3D object model based on multiple two-dimensional (2D) images of an object in different views. The method further includes acquiring metadata related to the first 3D object model by searching for information related to the object in at least one of a database and the Internet. The method further includes generating a second 3D object model by combining the first 3D object model and the metadata. The method for generating a 3D object model according to the present disclosure can automatically generate customizable and editable 3D model metadata, thereby significantly reducing labor, saving costs, improving efficiency, and improving user experience.

Method, device, and computer program product for generating 3D object model utilizing multiple metadata components of different types

Embodiments of the present disclosure relate to a task processing method, a task processing device, and a computer program product. The method includes: receiving a task to be processed by a first neural network at a first computing node; determining that the first neural network failed to process the task; and sending the task to a second computing node for processing by a second neural network at the second computing node, wherein compared with the first neural network, the second neural network has at least one of the following: higher processing consumption and higher processing performance. With the technical solution of the present disclosure, a task can be processed quickly and accurately using a neural network with low processing consumption.

Task processing method, task processing device, and computer program product

Techniques are disclosed for multi-factor prediction of computing resources for algorithm execution. For example, a method comprises obtaining a set of factors associated with an algorithm configured to transform one or more two-dimensional images into one or more three-dimensional models. The method further comprises computing an estimated computing power value based on the set of factors. The method then comprises scheduling execution of the algorithm on one or more computing resources based on the estimated computing power value.

Multi-factor prediction of computing resources for two-dimensional to three-dimensional modeling acceleration

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a video. The method includes acquiring a video, where the video includes at least a current frame and a previous frame that are adjacent to each other. The method further includes determining, based on a first pixel value of a pixel in the current frame and a second pixel value of a corresponding pixel in the previous frame, whether the current frame has changed relative to the previous frame. The method further includes determining availability of the current frame for a computer vision task if it is determined that the current frame has changed relative to the previous frame. With the method, video data that needs to be processed is reduced, the task load of a computing device is lowered, system power consumption is improved, and data processing efficiency is improved.

Method, electronic device, and computer program product for processing video

Embodiments of the present disclosure relate to a method, a device, and a computer program product for executing computer programs. The method includes implementing multiple executions of a deep neural network that includes a hooking portion outside a trusted execution environment (TEE), wherein an operator in the hooking portion, when executed outside the TEE, invokes a corresponding execution operator in the TEE. During the period, the method includes determining, on the basis of the invocation of the corresponding execution operator in the TEE by the operator in the hooking portion, a computation graph corresponding to the hooking portion. The method further includes executing, on the basis of the computation graph, the execution operator corresponding to the hooking portion in the TEE during the execution of the deep neural network after the multiple executions.

Method, device, and computer program product for executing computer programs

An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Remote collaboration between users utilizing at least one of augmented reality and virtual reality computing devices

A method includes: receiving, by a repository server, requests for pulling images from a plurality of nodes, wherein an image requested to be pulled by a first node is a first image; detecting, by the repository server, that layers of a plurality of images requested to be pulled by the plurality of nodes all include a common layer; generating, by the repository server, a transmission path of data of the common layer, wherein the transmission path includes that the repository server transmits the data of the common layer to a second node, and the second node distributes the data of the common layer to other nodes; sending, by the repository server, setting information to the first node according to the transmission path; sending, by the repository server, the data of the common layer to the second node; and acquiring, by the first node, the data of the common layer from a third node according to the setting information.

Method, server, and system for image deployment

A method, an electronic device, and a computer program product for generating a three-dimensional scene are provided in embodiments of the present disclosure. The method may include obtaining source image features from a plurality of two-dimensional source images associated with the three-dimensional scene to be generated. The method may further include obtaining editing features from an editing instruction input by a user for the three-dimensional scene, each of the editing features respectively forming a feature pair with each of the source image features. Furthermore, the method may include updating the source image features by maximizing a correlation coefficient of each of the feature pairs, and generating the three-dimensional scene based at least on the updated source image features. Embodiments of the present disclosure can realize arbitrary editing of a three-dimensional scene, thus enhancing the experience of human-computer interaction.

Method, electronic device, and computer program product for generating three-dimensional scene

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data processing. The method for data processing includes: generating, based on a category of a target data content segment, a target tag for the target data content segment; acquiring a reference tag set, a reference tag in the reference tag set being generated based on a category of a historical data content segment; and determining redundancy of the target data content segment based on comparison between the target tag and the reference tag set. In this way, the distribution of a large amount of redundant data from an edge server to a terminal device can be avoided, thereby saving bandwidth, improving delay, and further providing a better user experience for AR/VR content experience.

Method, electronic device, and computer program product for data processing

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for cross-regional data searching. The method includes acquiring a data identifier of target data in response to receiving a searching request for the target data at a first node. The method includes determining, based on the data identifier, a second node storing metadata of the target data, wherein both the first node and the second node are located in a first region, and the metadata includes the data identifier. The method further includes determining, based on the metadata, a third node storing the target data, wherein the third node is located in a second region different from the first region.

Method, electronic device, and computer program product for cross-regional data searching

Techniques are disclosed for processing unit virtualization with scalable over-provisioning in an information processing system. For example, the method accesses a data structure that maps a correspondence between a plurality of virtualized processing units and a plurality of abstracted processing units, wherein the plurality of abstracted processing units are configured to decouple an allocation decision from the plurality of virtualized processing units, and further wherein at least one of the virtualized processing units is mapped to multiple ones of the abstracted processing units. The method allocates one or more virtualized processing units to execute a given application by allocating one or more abstracted processing units identified from the data structure. The method also enables migration of one or more virtualized processing units across the system. Examples of processing units with which scalable over-provisioning functionality can be applied include, but are not limited to, accelerators such as GPUs.

Processing unit virtualization with scalable over-provisioning in an information processing system

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data storage. The method includes determining, based on attribute information of each of a set of nodes in a distributed storage system, a hash value of each of the set of nodes; determining a node identifier of each of the set of nodes based on geographic location information of each of the set of nodes and the hash value, with the node identifier being used to indicate a logical location of a node in the distributed storage system; and determining, in response to a processing request from a first node in the set of nodes for first data, a target node in the set of nodes based on the node identifier of each of the set of nodes. The target node is used to process the processing request for the first data.

Method, device, and computer program product for data storage

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for looking up data. This method includes: acquiring, at a first node, a data identifier for data to be looked up, wherein the first node is located in a system having a layered structure, and the data identifier comprises layer information related to a layer in the layered structure. The method further includes: determining a distance between the data identifier and a node identifier for the first node. The method further includes: determining, based on the distance, a second node that can be used to acquire metadata for the data for acquiring the metadata, wherein the distance is related to positions of the first node and the second node in the layered structure. The method further includes: determining, based on the metadata, a third node storing the data for acquiring the data.

Method, electronic device, and computer program product for looking up data

The present disclosure relates to a method, a device, and a program product for managing a computing system. In a method, a current state and a plurality of historical states of a computing device in a computing system are acquired, the plurality of historical states respectively describing historical states of the computing device in the computing system at a plurality of historical time points. In response to determining that the current state matches a scheduling type for scheduling the computing device, the plurality of historical states are searched for a historical state matching the current state. A historical scheduling policy associated with the historical state is determined. Based on the historical scheduling policy, a computing task to be executed by the computing device is allocated to at least one other computing device in the computing system.

Method, device, and program product for managing computing system

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for implementing a blockchain system on a switch. The method includes establishing a first blockchain node and a second blockchain node on the switch, where the first blockchain node includes a first ingress port and a first egress port, and the second blockchain node includes a second ingress port and a second egress port. The method further includes receiving a broadcast or multicast request from a user device through a user port of the switch. The method further includes sending the broadcast or multicast request to the first ingress port of the first blockchain node and the second ingress port of the second blockchain node. Through the embodiments of the present disclosure, a blockchain system can be implemented on a switch, so as to reduce the latency of the blockchain system.

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Shanghai, China

Anzhou Hou