International Business Machines Corporation

Huawei Technologies Co., Limited

Via Technologies, Inc.

Availink, Inc.

China Mobile Group Design Institute Co., Ltd.

State Grid Corporation of China

Hohai University

Beijing Xiaomi Pinecone Electronics Co., Ltd.

Sursen Corporation

Jiangsu Nanjing Power Supply Company

Guangdong Tongyu Communication Inc.

Beijing Sursen International Information Tech Co.

China Institute of Building Standard Design & Research

Peng Gao

Ning Duan

Xin Zhang

Wei Shan Dong

Zhi Hu Wang

Chang Li

Jian Ding

Chun Ma

Baohua Wang

Peng Ji

Wei Sun

Guo Qiang Hu

Jun Chi Yan

Jing Chang Huang

Shi Lei Zhang

Xu Liang Li

Shi Wan Zhao

Feng Gao

Zhi Jun Wang

Yu Huang

Yong Qin

Junchi Yan

Dejian Li

Peng Han

Xia Tian Zhang

Donglin Wang

Guoqiang Hu

Wen Han Luo

Hang Xiao

Bo Gao

Ying Di Zhang

Jian Li

Kai Yang

Shiwan Zhao

Kai Lin Li

Jie Yang

Dong Sheng Li

Shaw-Ben Shi

He Huang

Wei Zhou

Guo Tong Xie

Cheng Liu

Xu Bin Liu

Wei Ding

Yan Fang

Stephen Mingyu Chu

Shuo Yang

Zhao Cao

Yao Dong Qi

Rong Zeng Cao

Juan Li

Ren Jie Yao

Ting Yuan

Xiao Bo Li

Xiaolu Zhang

Xiu Li Li

Feng Juan Wang

Renjie Yao

Jian Dong Ding

Qian Zhao

Wentao Zhu

Wen Ting Mo

Juntan Zhang

Changwei Liu

Ningsheng Liu

Fengwen Sun

Jiwei He

Yunming Zhang

Bao Hua Wang

Xin Zang

Shutian Yin

Yuansong Liang

Yongzhi Lou

De Ouyang

Zhen Xiang Yang

Tao Bai

Yue Yu

Huan Yang

Wen Hao An

Xi Sun

Xinping Wang

Yanyan Mao

JingChang Huang

Chunning Wang

Kai Li

Yang Zheng

Kai Chen

Zhenhai Li

Yong Ding

Zhenxiao Liu

Yuan Jiang

Yifeng Wang

Weipeng Zhang

Hongzhong Ma

Guodu Cai

Zhiyong Wu

Gang Ning

Wenbin Luo

Yiming Wu

A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are unmatched to the stored route trajectory; obtaining candidate transition points from the unmatched points; aggregating the candidate transition points by applying a clustering algorithm; selecting a first cluster of points and a plurality of second clusters of points, determining a confidence level that the first cluster of points are transition points indicating a transition between the routes, classifying the first cluster of points as a first plurality of traveling points having a first direction in response to the confidence being below a threshold confidence and automatically adjusting the stored route trajectory to indicate that the first cluster of points are on the route trajectory.

Method and apparatus of data classification for routes in a digitized map

A method for detecting an audio signal, the method comprises: obtaining a speech segment and a non-speech segment of an audio signal to be detected, extracting a first audio feature of the speech segment and a second audio feature of the non-speech segment, detecting the first audio feature using a predetermined speech segment detection model to obtain a first detection score, detecting the second audio feature using a predetermined non-speech segment detection model to obtain a second detection score, and determining whether the audio signal belongs to a target audio based on the first detection score and the second detection score.

Method and device for detecting audio signal, and storage medium

Techniques facilitating attention based sequential image processing are provided. A system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an initialization component that can perform self-attention based training on a model that comprises context information associated with a sequence of images. Images of the sequence of images can be selected during the self-attention based training. The computer executable components can also comprise a localization component that can extract local information from the images selected during the self-attention based training based on the context information. In addition, the computer executable components can also comprise an integration component that can update the model based on an end-to-end integrated attention training framework comprising the context information and the local information.

Attention based sequential image processing

A computer-implemented method is presented for inferring passenger routes in a subway system. The method includes identifying, via an imaging device, a target passenger within the subway system, employing an exiting flow extractor to determine passenger exiting waves, employing an exiting wave identifier to mark an exiting wave of the passenger exiting waves including the target passenger, employing a supporting evidence backtracer to determine an entrance gate for each of the passengers in the marked exiting wave including the target passenger, determining a route probability for each of the passengers in the marked exiting wave including the target passenger via voting or distribution estimation processing, and employing a most probable route inferer to infer a route of the target passenger based on an aggregate route probability for all the passengers in the marked exiting wave.

Passenger travel route inferencing in a subway system

Embodiments of the present invention may be directed toward a method, a system, and a computer program product of adaptive calibration of sensors through cognitive learning. In an exemplary embodiment, the method, the system, and the computer program product include (1) in response to receiving a data from at least one calibration sensor and data from an itinerant sensor, comparing the data from the at least one calibration sensor and the data from the itinerant sensor, (2) in response to the comparing, determining, by one or more processors, the accuracy of the itinerant sensor, (3) generating, by the one or more processors, one or more calibration parameters based on the determining and based on a machine learning associated with preexisting sensor information, and (4) executing, by the one or more processors, the one or more calibration parameters.

Adaptive calibration of sensors through cognitive learning

Systems and methods for training a neural network model are disclosed. In the method, training data is obtained by a deep neural network (DNN) first, the deep neural network comprising at least one hidden layer. Then features of the training data are obtained from a specified hidden layer of the at least one hidden layer, the specified hidden layer being connected respectively to a supervised classification network for classification tasks and an autoencoder based reconstruction network for reconstruction tasks. And at last the DNN, the supervised classification network and the reconstruction network are trained as a whole based on the obtained features, the training being guided by the classification tasks and the reconstruction tasks.

Feature extraction using multi-task learning

Display screen or portion thereof with transitional graphical user interface

Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Deep image classification of medical images

Techniques for neural network training are provided. One computer-implemented method comprises: obtaining, by an electronic device operatively coupled to a processing unit, based on a similarity between two images, a first image and a second image. The computer-implemented method also comprises training, by the electronic device, a neural network based on the first image and the second image such that a distance between a first vector and a second vector generated respectively from the first image and the second image in the trained neural network is associated with the similarity.

Neural network training

A method to train a machine learning model for in-vehicle air quality control in a knowledge-based system, executed by one or more computer processors, includes collecting data related to in-vehicle air quality from a plurality of probe cars where the data is collected by various on-board systems in each probe car. The method includes correlating the data related to in-vehicle air quality from each probe car with air quality measurements from each probe car, where the correlation is used to update the machine learning model. The method includes determining a situation when an in-vehicle air quality measurement of the air quality measurements is above a pre-determined in-vehicle air quality level and determining instructions for actions by one or more of the one or more on-board systems in each of the probe cars to maintain an in-vehicle air quality level at or below the pre-determined in-vehicle air quality level.

Controlling in-vehicle air quality

Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Neural network computing systems for predicting vehicle requests

An interconnection unit includes a first connector configured to be coupled to an electronic device. There is a second connector configured to be coupled to a power station and to provide a path to the electronic device via the first connector. There is a low pass filter coupled between the first connector and the second connector and configured to allow the electronic device to receive power from the power station while maintaining data security of the electronic device.

Safe charging interface

A method and system of detecting abnormalities captured in a medical image are provided. A medical image is received having one or more lesions and one or more interferences. One or more convolution layers are applied to the medical image to create one or more feature maps, respectively. A region proposal network (RPN) is applied to each of the one or more feature maps to create a bounding box around each lesion and interference. For each bounding box, an object in the bounding box is classified as a lesion, an interference, or a background. Each object that is incorrectly classified as a lesion but determined to be an interference, is stored as a hard-negative example to be part of a training set for a next application of the RPN.

Negative sample enhanced object detection machine

The present disclosure relates to a method and system for generating a road network. An embodiment of the present invention provides a method comprising: identifying a target area to be refined in a road network skeleton; and refining the target area using at least one trajectory associated with the target area to generate the road network. Another embodiment of the present invention provides a corresponding system.

Generating a road network from location data

Growing community of inventors

Beijing, China

Peng Gao