Emc IP Holding Company LLC

Rohm and Haas Company

Dow Global Technolgoies LLC

Boe Technology Group Co., Ltd.

Beijing Didi Infinity Technology and Development Co., Ltd.

International Business Machines Corporation

Honeywell International Inc.

3M Innovative Properties Company

Dell Products Inc.

Dow Silicones Corporation

Shanghai Imilab Technology Co., Ltd.

Midland Global Technologies LLC

Wei Cui

Kun Wang

Jianming Xu

Junping Zhao

Qingwei Zhang

Zhen Qian

Ling Li

Tao Hu

Jian Gao

Tao Wang

Sanping Li

Chen Gong

Xiangting Dong

Lester Zhang

Lingyu Zhang

Longlan Cui

Yu Zhang

Peng Liu

Lin Yang

Seiji Fujino

Li Sun

Wei Zhou

James Charles Bohling

Fei Fei Li

Yan Wu

Lin Wang

Zhihua Liu

Hui Liu

Li Cao

Kevin Xu

Huan Chen

WeiFeng Zhang

Layne Lin Peng

Zhan Peng Huo

Junyu Chen

Dong Yun

Rui Hong

Jing Song

Juan Li

Mohamed Sohail

Siyuan Jiang

Baoqing Zheng

Han Liu

Shijing Cheng

Xiaohu Wang

Zhilin Hu

Denny Dengyu Wang

Kaijie Qin

Haiyang Lu

Jintao Liu

Yunfei Lan

Mingcong Sun

Xiangxin Bao

Ping Yang

Yaming Wang

Xiaolei Zhang

Wenyue Fu

Qing Wu

Zhi Juan Gong

Han Su

Jingchao Zhang

Daoshu Lin

Jia Tang

Jinyuan Zhang

Kuan Shi

Patrick Glaze

Jinfei Wang

Cuilan Chang

Zhongyu Cao

Zhou Jin

David Gerald Speece, Jr

Yaguang Zhao

Zheng Guan

Yangbiao Liu

Qianqian Li

Han Lv

Changkun Piao

Ren Jie Feng

Zhong Hao Wang

David G Speece

Lijing Zhang

Brown Zan Liu

Xiaolin Deng

Yong Ye

Danping Ma

Xuzhi Zhu

Jun Yin

In a method for improving converter and compiler operator granularity, a processor extracts an operator granularity from an artificial intelligence framework and an original model. A processor receives device characteristics from a target device. A processor outputs a converter granularity level to a converter based on the operator granularity and the device characteristics. A processor outputs a compiler granularity level to a compiler based on the operator granularity and the device characteristics.

Optimizing operator granularity in compiling and converting artificial intelligence (AI) models

A storage stable aqueous coating composition comprising a novel combination of an anionic acrylic polymer with a small amount of a specific acetoacetamide functional compound and providing coating films made therefrom with formaldehyde abatement property, yellowing resistance, and scrub resistance.

Aqueous coating composition and process for preparing the same

The present invention provides an aqueous dispersion and an aqueous coating composition comprising the aqueous dispersion that provides coatings with improved durable water and ink repellency properties without compromising early water repellency.

Aqueous coating composition

The present invention provides an aqueous dispersion as a biobased binder and an aqueous coating composition comprising such aqueous dispersion and showing balanced stain resistance, freeze-thaw stability, and anti-clogging properties.

Aqueous dispersion and aqueous coating composition

An aqueous dispersion comprising a multistage polymer comprising a first polymer and a second polymer that comprises structural units of an ethylenically unsaturated siloxane monomer, the multistage polymer comprising, by weight based on the weight of the multistage polymer, 4.5% or more of structural units of the ethylenically unsaturated siloxane monomer, and the coagulum content of the aqueous dispersion is less than 2,000 ppm after filtration through a 44 micron sieve.

Aqueous polymer dispersion and process of making the same

A resource management method comprises: in response to receiving, from an application operating on a client, a resource allocation request indicating an amount of dedicated processing resources required by the application, acquiring a mapping between a group of physical dedicated processing resources provided by a group of servers and a group of logical dedicated processing resources, the group of physical dedicated processing resources being divided into the group of logical dedicated processing resources; determining allocation statuses of the group of logical dedicated processing resources; determining, based at least on the mapping and the allocation statuses, a first amount of logical dedicated processing resources to be allocated to the application from the group of logical dedicated processing resources; and indicating the first amount of logical dedicated processing resources to the application, to allow the application to utilize physical dedicated processing resources provided by at least one of the group of servers.

Method, device and computer program product for managing dedicated processing resources

A method, device and computer program product for deep learning are provided. According to one example, a parameter related to a deep learning model for a training dataset allocated to a server is obtained at a client; a transmission state of the parameter is determined, the transmission state indicating whether the parameter has been transmitted to the server; and information associated with the parameter to be sent to the server is determined based on the transmission state to update the deep learning model. Therefore, the performance of deep learning may be improved, and the network load of deep leaning may be reduced.

Method, device, and computer program product for deep learning

Techniques for optimizing a remote call are provided. For example, a method includes obtaining a command for calling a remote dedicated processing resource and determining whether the command satisfies an optimization condition based on a type of the command. The method further comprises in response to determining that the command satisfies the optimization condition, ceasing to transmit the command to the remote dedicated processing resource. Accordingly, in the case that the remote dedicated processing resource needs to be called, the number of remote call commands transmitted to the remote dedicated processing resource can be reduced by optimizing the remote call commands, thereby improving the execution efficiency effectively.

Method, device and computer program product for optimizing remote call

The present invention provides a high PVC aqueous coating composition that provides coatings with improved beading effect and good stain resistance.

The present invention provides an aqueous paint composition comprising by dry weight based on total dry weight of the pigment composition, from 11% to 35% of a polymeric duller of (co)polymeric particles having an average diameter of from 1 to 20 &#x3bc;m, and from 25% to 70% of titanium dioxide particles; wherein from 50% to 100% of the titanium dioxide particles are encapsulated by polymer shell of (co)polymeric particles.

Low sheen paint composition with effective opacity

Systems and methods are provided for implementing a self-adaptive batch dataset partitioning control process which is utilized in conjunction with a distributed deep learning model training process to optimize load balancing among a set of accelerator resources. An iterative batch size tuning process is configured to determine an optimal job partition ratio for partitioning mini-batch datasets into sub-batch datasets for processing by a set of hybrid accelerator resources, wherein the sub-batch datasets are partitioned into optimal batch sizes for processing by respective accelerator resources to minimize a time for completing the deep learning model training process.

Self-adaptive batch dataset partitioning for distributed deep learning using hybrid set of accelerators

A data processing method includes: obtaining a first subset and at least a second subset in a training dataset for learning, the first subset and the at least a second subset having a same size; determining a set of substrings based on data strings in the first subset and the at least a second subset, the substrings being suffix substrings of the data strings and being sorted in a lexicographical order; and determining a grain for deduplication of the training dataset from a set of longest common prefix (CLP) lengths of adjacent substrings in the set of substrings, for use in the deduplication. Thereby, different grains of duplicating procedures for different training datasets can be predicted automatically, and universality and flexibility of GPUaaS can be achieved. In addition, the deduplication rate can be improved, network resource waste can be reduced and system efficiency can be enhanced.

Data processing method, electronic device and computer readable storage method for deduplication of a training dataset

In one example, this disclosure is directed to a system configured to detect inclement weather in the travel path of a vehicle, determine a recommended maneuver to avoid the inclement weather, and determine the feasibility of the recommended maneuver with respect to any nearby vehicles.

Connected weather radar

The present invention provides an aqueous polymer composition and an aqueous coating composition comprising such aqueous polymer composition that shows balanced stain resistance, freeze-thaw stability, and anti-clogging properties.

Aqueous polymer composition

An intelligent door control system and method is provided. The intelligent door control method includes the following steps: receiving an ambient temperature inside and outside a door body of the intelligent door through a first sensor and a second sensor which are respectively installed inside and outside the door body of the intelligent door; comparing the ambient temperature inside the door with a preset first temperature threshold, and comparing the ambient temperature outside the door with a preset second temperature threshold; automatically controlling the intelligent door according to whether there is a living body inside the intelligent door, and comparison results of the ambient temperatures inside and outside the door body with the first and second temperature thresholds respectively.

Intelligent door control system and method

Growing community of inventors

Beijing, China

Wei Cui