Texas Instruments Corporation

Google Inc.

Ambarella International LP

East China University of Science and Technology

Shanghai Zhenhua Heavy Industries Company Limited

Yao Lu

Wenjun Li

Jian Liang

Chen Feng

Anelia Angelova

Wei Dai

Juan Li

Zhao Chen

Soeren Pirk

Qing Yi

Ariel Gordon

Vincent Michael Casser

Lei Luo

Long Zhao

Jianguo Shan

Zijian Zhao

Weihong Zhu

Anthony Brohan

Jan Dlabal

Yutao Zhang

Zhiqian Guo

Xiuyan Zhao

Chenxu Yan

Meiling Zhao

Yunfu Yan

Jun Yu

Dinggen Xiong

Maohua Fu

A luminescent probe and its preparation method and application are provided. The luminescent probe has a steric hindrance group Rof aliphatic hydrocarbon structure such as adamantane or norborneol, a detection group Rof nitrobenzyl and its derivative structure, an electron-withdrawing group Rcontaining cyano group and an electron-donating group methoxy group. In the presence of HSA or BSA, the detection group is cut off to form a parent structure that exposes atomic oxygen anions and is activated under external light irradiation, the luminescent probe can be used in solution or cells, when detecting HSA or BSA, the luminescent probe has obvious chemiluminescence characteristics, which can sensitively distinguish HSA and BSA, quantitatively analyze HSA and BSA, and determine the mixing ratio of HSA and BSA at the same time, and the luminescent probe has been successfully used for cell fluorescence imaging.

Luminescent probe and its preparation method and application

A circuit includes a control circuit having a first control circuit input, a second control circuit input, a first control circuit output, and a second control circuit output, and a first transistor having a first current terminal, a second current terminal, and a control terminal, the control terminal coupled to the first control circuit output, the first current terminal coupled to the first control circuit input and to a second transistor, and the second current terminal adapted to be coupled to the second transistor, a logic circuit having a first logic input, a second logic input, and a logic output, the first logic input coupled to the second control circuit output and a switch having a first switch terminal, a second switch terminal, and a switch control terminal, the switch control terminal coupled to the logic output and the first switch terminal coupled to the second current terminal.

Voltage converter with average input current control and input-to-output isolation

A voltage converter having a voltage input and a voltage output, the voltage converter including a first and second transistors and an average current control circuit. The first transistor has a first control input, a first current terminal, and a second current terminal. The first current terminal is adapted to be coupled to a switch node. The second transistor has a second control input, a third current terminal, and a fourth current terminal. The third current terminal is adapted to be coupled to an inductor. The average current control circuit is coupled to the third current terminal and the fourth current terminal. The average current control circuit is configured to determine an average current level of current flowing through the second transistor and to control a voltage on the first control input of the first terminal based on the determined average current level.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network using consistency measures. One of the methods includes processing a particular training example from a mediator training data set using a first neural network to generate a first output for a first machine learning task; processing the particular training example in the mediator training data set using each of one or more second neural networks, wherein each second neural network is configured to generate a second output for a respective second machine learning task; determining, for each second machine learning task, a consistency target output for the first machine learning task; determining, for each second machine learning task, an error between the first output and the consistency target output corresponding to the second machine learning task; and generating a parameter update for the first neural network from the determined errors.

Training neural networks using consistency measures

An apparatus includes a first image sensor, a second image sensor, a first light source associated with the first image sensor, a second light source associated with the second image sensor, and a control circuit. The control circuit may be configured to control the first light source and the second light source to prevent interference between the first and the second light sources affecting images captured by the first and the second image sensors.

940nm LED flash synchronization for DMS and OMS

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Shanghai, China

Yao Lu