Huawei Technologies Co., Limited

Miics & Partners Inc.

Shenzhen Sibionics Technology Co., Ltd.

Shenzhen Sibright Technology Co., Ltd.

Interface Optoelectronics (shenzhen) Co., Ltd.

Hon Hai Precision Industry Co., Ltd.

Fu Tai Hua Industry (shenzhen) Co., Ltd.

Gree Electric Appliances, Inc. of Zhuhai

Shenzhen Angel Drinking Water Industrial Group Corporation

Yangtze Memory Technologies Co., Ltd.

Tcl China Star Optoelectronics Technology Co., Ltd.

Interface Technology (chengdu) Co., Ltd.

Miics & Partners (shenzhen) Co., Ltd.

Bgi Genomics Co., Limited

Zte Corporation

Juan Wang

Yu Zhao

Bin Xia

Yujing Bai

Yinghui Yu

Rui Wang

Zhigang Hu

Xiaoxin Li

Tai-Shan Zhu

Feng Yu

Song Zhu

Tao Jiang

Guoping Li

Jun-Liang Zhang

Ning Deng

Zhou Chen

Xiaojian Liu

Sheng Wang

Zhiyong Feng

Xiang Li

Jing Lei

Hai-Qian Ge

Yinqing Jiang

Kai Zhao

Qiang Gao

Xiaodong Luo

Guolin Liu

Xun Shi

Bo Zhao

Xianquan Liu

Yu Zhou

Haoping Guo

Dongjun Sun

Tang Zhou

Cui Pan

Jinyu Tong

Fanghui Bao

Chong Tang

Yiqin Tong

Linfeng Yang

Xiaoqiong Du

Lin Qi

Chengzhao Jin

Liang Wan

Chenguang Wang

Chien-Chih Kuo

Yusong Liu

Charger

Provided in the present invention are a splint nucleic acid molecule for cyclizing a single-stranded nucleic acid molecule and an application therefor. The splint nucleic acid, molecule is composed of a 5' terminal fragment and a 3′ terminal fragment, the 5′ terminal fragment being adapted to forming a first complementary region with a 5′ terminal of the single-stranded nucleic acid molecule, and the 3′ terminal fragment being suited to forming a second complementary region with a 3′ terminal of the single-stranded nucleic acid molecule, the length of the first complementary region and the second complementary region being different.

Splint nucleic acid molecule for cyclizing single-stranded nucleic acid molecule and use thereof

Lens protective film

Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus () configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus () configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Retinopathy recognition system

Watch frame

A structure for performing analysis includes a first opening formed on a back side of a substrate and passing through the substrate, a second opening connected with a bottom of the first opening and penetrating into a first dielectric layer formed on a front side of the substrate, a first conductive layer formed on a sidewall of the second opening and a contact element in the first dielectric layer, and a second conductive layer formed on a second dielectric layer. The first conductive layer contacts the second conductive layer electrically.

Methods and structures for semiconductor device testing

Embodiments of this application provide a terminal configuration method and an apparatus. When determining that a SIM card in a terminal is powered on, a wireless communications module reads SIM card information such as some or all of an international mobile subscriber identity (IMSI), a service provider name (SPN), or a global identifier ID (GID) in the SIM card from the SIM card. After that, the SIM card information obtained by the wireless communications module is sent to an application processor. The application processor obtains an operator parameter based on the SIM card information reported by the wireless communications module, and configures the terminal. Therefore, at a moment at which the SIM card is powered on, the wireless communications module obtains the SIM card information from the SIM card, so that a timing for the UE to obtain the IMSI is advanced.

Terminal configuration method and apparatus

A display panel and a light board are provided. The display panel includes a back board and a plurality of light boards, each of the plurality of light boards includes light beads and a first substrate and a second substrate that are disposed in a stack. The first substrate includes a first portion and the second substrate includes a second portion that exceeds the first substrate. The first portion of one of the plurality of light boards is stacked with the second portion of an adjacent light board to splice two of the plurality of light boards, and a distance between two columns of the plurality of light beads adjacent to each other and on an outermost side of the two light boards is within a preset range.

Display panel and light board

A full-effect reverse osmosis membrane element includes a central tube and a reverse osmosis membrane assembly wound around the central tube. The reverse osmosis membrane assembly includes a pair of reverse osmosis membranes attached at front surfaces thereof and wound around the central tube. A water feed channel has a water inlet formed at front edges of the reverse osmosis membranes and a water outlet formed at rear edges of the reverse osmosis membranes. A water division structure is arranged in the water feed channel, and includes a first water division band forming a preset space with the central tube when the reverse osmosis membranes are in an unfolded state. The first water division band divides the water feed channel into a first flow channel having a gradually decreasing cross-section and a second flow channel in communication with the first flow channel.

Full-effect reverse osmosis membrane element, and water purifier having same

Mobile phone case

Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network () configured to generate a first advanced feature set from the target fundus image, a second neural network () configured to generate a second advanced feature set from the reference fundus image, a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network () configured to generate, according to the feature combination set, a judgmental result of lesions.

System for recognizing diabetic retinopathy

A full-effective reverse osmosis membrane element and a water purification machine are provided. The reverse osmosis membrane element includes a reverse osmosis membrane () and a central tube () provided with several water-inlet holes (). The reverse osmosis membrane () is wound around the central tube () and covers each water-inlet hole () and forms a raw water flow channel. Two sides of the reverse osmosis membrane () are sealed by first and second water division bands () (). The first water division band () and the central tube () form a first concentrated water-outlet (). The first water division band () and the reverse osmosis membrane () form a second concentrated water-outlet (). The second water division band () and the reverse osmosis membrane () form a first raw water-inlet (). The second water division band () and the central tube () form a second raw water-inlet ().

Full-effective reverse osmosis membrane element and water purification machine

The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network () configured to generate a first advanced feature set from the target retinal fundus image; a second neural network () configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module () configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network () configured to generate, according to the feature combination set, a diagnosis result. By using a target retinal fundus image and a reference retinal fundus image as independent input information, the artificial neural network may simulate a doctor, determining lesions on the target retinal fundus image using other retinal fundus images from the same person as a reference, thereby enhancing the diagnosis accuracy.

Artificial neural network and system for identifying lesion in retinal fundus image

Diabetic retinopathy recognition system based on fundus image

Growing community of inventors

Shenzhen, China

Juan Wang