Weijun Wang Phones & Addresses
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Baldwin Park, CA
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Rosemead, CA
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San Gabriel, CA
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Alhambra, CA
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Arcadia, CA
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Monrovia, CA
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Long Beach, CA
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Baldwin Park, CA
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Cerritos, CA
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Los Angeles, CA
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West Covina, CA
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1139 W Duarte Rd APT I, Arcadia, CA 91007 (626) 676-4644
Work
Position:
Building and Grounds Cleaning and Maintenance Occupations
Business Records
Name / Title
Company / Classification
Phones & Addresses
President
GAIN STAR CORP
5710 Atlantic Ave, Long Beach, CA 90805
Publications
Us Patents
Intraarterial (Ia) Application Of Low Dose Of Ethyl Alcohol Enables Blood-Brain Barrier (Bbb) Impermeable Therapeutics Brain Entry
View pageUS Patent:
20220287986, Sep 15, 2022
Filed:
Mar 8, 2022
Appl. No.:
17/689969
Inventors:
Weijun WANG - Cypress CA, US
International Classification:
A61K 31/045
A61K 45/06
A61M 37/00
A61K 9/00
A61K 45/06
A61M 37/00
A61K 9/00
Abstract:
A method of inducing blood-brain barrier (BBB) opening of for delivery of a non-BBB permeable substance to a subject includes the steps of: administering an ethyl alcohol at 0.015%-5% v/v into a blood stream of the subject via intra-arterial injection, intracardiac injection, or intra-arterial injection guided by ultrasound, wherein said ethyl alcohol reaches a brain of the subject to induce opening of blood-brain barrier so that the non-BBB permeable substance is capable of penetrating the blood-brain barrier to reach the brain of the subject. The non-BBB substance includes a therapeutic agent, a diagnostic agent, or a prophylactic agent. The use of low concentration of ethyl alcohol can induce a temporary BBB opening within 240 minutes after administration so that the non-BBB substance can be deliver to the brain during the time frame of BBB opening. Since ethyl alcohol is water soluble, the non-BBB substance can also be administered concurrently.
Efficient Convolutional Neural Networks And Techniques To Reduce Associated Computational Costs
View pageUS Patent:
20190347537, Nov 14, 2019
Filed:
Jul 29, 2019
Appl. No.:
16/524410
Inventors:
- Mountain View CA, US
Bo Chen - Pasadena CA, US
Dmitry Kalenichenko - Los Angeles CA, US
Tobias Christoph Weyand - Venice CA, US
Menglong Zhu - Los Angeles CA, US
Marco Andreetto - Pasadena CA, US
Weijun Wang - Los Angeles CA, US
Bo Chen - Pasadena CA, US
Dmitry Kalenichenko - Los Angeles CA, US
Tobias Christoph Weyand - Venice CA, US
Menglong Zhu - Los Angeles CA, US
Marco Andreetto - Pasadena CA, US
Weijun Wang - Los Angeles CA, US
International Classification:
G06N 3/04
G06T 7/32
G06N 3/08
G06T 7/32
G06N 3/08
Abstract:
The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.
Efficient Convolutional Neural Networks And Techniques To Reduce Associated Computational Costs
View pageUS Patent:
20180137406, May 17, 2018
Filed:
Sep 18, 2017
Appl. No.:
15/707064
Inventors:
- Mountain View CA, US
Bo Chen - Pasadena CA, US
Dmitry Kalenichenko - Los Angeles CA, US
Tobias Christoph Weyand - Venice CA, US
Menglong Zhu - Los Angeles CA, US
Marco Andreetto - Pasadena CA, US
Weijun Wang - Los Angeles CA, US
Bo Chen - Pasadena CA, US
Dmitry Kalenichenko - Los Angeles CA, US
Tobias Christoph Weyand - Venice CA, US
Menglong Zhu - Los Angeles CA, US
Marco Andreetto - Pasadena CA, US
Weijun Wang - Los Angeles CA, US
International Classification:
G06N 3/04
G06N 3/08
G06T 7/32
G06N 3/08
G06T 7/32
Abstract:
The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.