Emc IP Holding Company LLC

Pharmablock Sciences (Nanjing), Inc.

Crm Icbg (Wuxi) Co. Ltd.

Dell Products Inc.

Virginia Tech Intellectual Properties Inc

China Resources Powtech (Shanghai) Co., Ltd.

Pharmablock (Nanjing) R&d Co. Ltd

Jin Li

Jinpeng Liu

Zhen Jia

Yong You

WuiChak Wong

Christopher S MacLellan

Minmin Yang

Sanping Li

Guocheng Li

Bingyin Luo

Lei Zhang

Pengfei Wu

Jiacheng Ni

Danqing Sha

Mengze Liao

Dushan Boroyevich

Liu Liu

Wenbin Yang

Jinjun Liu

Jianmin Li

Xihan Wu

Yujie Wang

Shengsheng Lu

Linhai Xu

Huiyuang Ren

Xiaoyu Yang

Changquan Lin

Jingwei Zhu

Lisha Wang

Enge Zhang

Hongdao Chen

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for deploying a machine learning model. The method includes: acquiring a machine learning model in accordance with an open neural network exchange format; converting the machine learning model to an intermediate representation using a multi-level intermediate representation method; and deploying a computation associated with the machine learning model to at least one computing device using the intermediate representation.

Method, electronic device, and computer program product for deploying machine learning model

Provided are a compound represented by Formula (I), a stereoisomer thereof or a pharmaceutically acceptable salt thereof, and a use thereof in preparing a drug for diseases mediated by CBP and/or EP300.

CBP/EP300 inhibitor and use thereof

The present application provides a compound represented by formula (I), or a pharmaceutically acceptable salt, ester, prodrug, complex, solvate, hydrate, or isomer thereof; and a use thereof in preparing drugs used to treat EZH2-mediated disease.

EZH2 inhibitor and use thereof

A decoupled artificial intelligence model management architecture comprises a front-end component, a scheduler component and a back-end component configured to enable an open and dynamic system which accepts any models from any formats to be deployed on any target devices and to dynamically change scheduling of parallelism, parameter movements and computation executions across the target devices.

Decoupled architecture for artificial intelligence model management

Techniques are disclosed for managing artificial intelligence model partitions for execution in an information processing system with edge computing resources. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A computation graph is generated based on the intermediate representation. The computation graph is partitioned into a set of partitions. The method then schedules the set of partitions for respective execution on a set of computing devices in an edge computing environment, and causes deployment of the set of partitions respectively to the set of computing devices for execution in the edge computing environment.

Managing artificial intelligence model partitions for edge computing environment

A method for model adaptation, an electronic device, and a computer program product are disclosed. For example, the method comprises processing first input data by using a first machine learning model having first parameter set values, to obtain first feature information of the first input data, the first machine learning model having a capability of self-ordering and the first parameter set values being updated after the processing of the first input data; generating a first classification result for the first input data based on the first feature information by using a second machine learning model having second parameter set values; processing second input data by using the first machine learning model having the updated first parameter set values, to obtain second feature information of the second input data; and generating a second classification result for the second input data based on the second feature information by using the second machine learning model having the second parameter set values. As such, the machine learning model for classification can be adapted to changes in features of input data to provide better classification results.

Method for model adaptation, electronic device and computer program product

A first set of requirements of a first set of computing tasks for computing resources in a computing system is acquired respectively. Based on a determination that the requirement of a computing task in the first set of computing tasks for a computing resource satisfies a resource threshold condition, the computing task is divided into a plurality of sub-tasks. The resource threshold condition describes the threshold of a computing resource provided by a computing device in a plurality of computing devices in the computing system. A merging task for merging a plurality of sub-results of the plurality of sub-tasks into a result of the computing task is generated. Based on other computing tasks than the computing task in the set of computing tasks, the plurality of sub-tasks, and the merging task, a second set of computing tasks of the computing job is determined so as to process the computing job.

Method, device and computer program product for processing computing job

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. According to exemplary implementations of the present disclosure, a method for processing data includes: determining a factor associated with a first input of a deep learning model, wherein the factor affects the number of threads for executing the deep learning model; generating a plurality of first partial inputs by using the first input based on the factor, wherein each first partial input in the plurality of first partial inputs is a part of the first input; and performing an operation on the plurality of first partial inputs by using the deep learning model, and generating an output of the deep learning model. Thereby, the data processing performance can be improved, and the resource requirement for data processing is lowered.

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

The present invention provides PRMT5 inhibitors of Formula (I), wherein Ris a non-hydrogen monovalent group; W is a direct bond or &#x2014;NH&#x2014;; T, U, and V are independently of each other selected from C and N; Ris H or a halo; m is 1 or 2; X is a carbon, a nitrogen, or an oxygen; Y is C or N; Z is a direct bond or a carbon; Ris H, a non-hydrogen monovalent group, an oxo group, a bivalent spiro ring-forming group, or a bivalent bridge-forming group; n is 1 or 2; and Formula (II) stands for a single bond or a double bond. Pharmaceutical products comprising the PRMT5 inhibitors and use thereof in treating proliferative disorders such as cancer, metabolic disorders, blood disorders, autoimmune diseases, and inflammatory diseases are also provided. 

Inhibitors of protein arginine methyltransferase 5 (PRMT5), pharmaceutical products thereof, and methods thereof

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for application migration. The method includes determining an application used by a terminal device if it is determined that the terminal device moves from a first cell provided by a first network device to a second cell provided by a second network device. The method also includes determining a migration strategy for the application, the migration strategy describing the migration of components of the application in a predetermined scenario corresponding to the application. The method also includes determining migration information of components in an initial edge node associated with the first network device to a target edge node associated with the second network device based on the migration strategy, the migration information at least indicating whether the components in the initial edge node are to be migrated to the target edge node.

Method, electronic device, and computer program product for application migration

Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

Scheduling artificial intelligence model partitions based on reversed computation graph

A task scheduling method comprises the steps of: in response to the reception of a request for processing a plurality of task sets, creating a current to-be-scheduled task queue in a task processing system based on priorities of the plurality of task sets and tasks in the plurality of task sets, where a plurality of to-be-scheduled tasks in the current to-be-scheduled task queue are scheduled in the same round of scheduling; allocating computing resources used for scheduling the plurality of to-be-scheduled tasks; and enabling the plurality of to-be-scheduled tasks in the current to-be-scheduled task queue to be scheduled by using the computing resources. In this manner, a plurality of tasks with different priorities and quotas can be scheduled according to SLA levels of users, and the efficiency and flexibility of parallel services of cloud computing deep learning models are improved by using a run-time load-balancing scheduling solution.

Method,electronic device and computer program product for scheduling computer resources in a task processing environment

Methods, systems, and computer program products for deploying an application. One method includes acquiring a computational graph corresponding to an application. The computational graph includes a plurality of nodes corresponding to operations performed by the application, the nodes including at least one input node for exchanging data with at least one terminal device and at least one computational node for processing data. The at least one input node is deployed to a first group of edge nodes co-located with a first base station serving the at least one terminal device, and the at least one computational node is deployed to a second group of edge nodes co-located with an area data center. Illustrative embodiments of the present disclosure enable an input node to be as close to a user as possible, thereby achieving low response delays while meeting the requirements of a computational node for storage and computational resources.

Method, system, and computer program product for deploying application

Embodiments include a method, an electronic device, and a computer program product for storage management. According to one embodiment of the present disclosure, a method for storage management includes: determining at a kernel of a storage server whether a first system call related to a target file is initiated by a thread executed in the storage server, wherein the target file is a file targeted by an accessing operation executed by a client terminal with respect to a storage device; determining a first connection and a second connection that are associated with the target file if the first system call is initiated, wherein the first connection is a connection between the client terminal and the storage server, and the second connection is a connection between the storage server and the storage device; and causing the client terminal to access the target file via the first connection and the second connection.

Method, electronic device, and computer program product for storage management

A method includes: if it is determined that a currently available resource of a task processing system cannot satisfy a resource requirement of a to-be-executed current task set of a plurality of task sets, scheduling, for execution, one or more tasks in the current task set that can be supported by the currently available resource; creating, based on one or more tasks that have not been scheduled in the current task set, a to-be-scheduled task set for a next round of scheduling; determining, in a next task set of the plurality of task sets that follows the current task set, a subset of tasks that are independent of the one or more tasks in the to-be-scheduled task set, where the next task set is dependent at least in part on the current task set; and moving the subset from the next task set into the to-be-scheduled task set.

Growing community of inventors

Shanghai, China

Jin Li