Melvin Yamamoto - Fremont CA, US Clifford Oostman - Quilcene WA, US John Sze - Saratoga CA, US Keith Pearson - Campbell CA, US Philip Trenholme - Santa Cruz CA, US Dan Liu - Sunnyvale CA, US Chi Yu - Saratoga CA, US
Assignee:
Affymetrix, INC. - Santa Clara CA
International Classification:
C12Q 1/68 H01L 29/15 C12M 1/34 H01L 31/0256
US Classification:
435006000, 435287200, 257076000
Abstract:
The present invention provides methods to process multiple sensors by providing a sensor plate and HT plates. In a preferred embodiment of the invention, methods for assembling microarray pegs and microarray plates are described for high throughput microarray processing.
Melvin Yamamoto - Fremont CA, US Clifford Oostman - Quilcene WA, US John Sze - Saratoga CA, US Keith Pearson - Campbell CA, US Philip Trenholme - Santa Cruz CA, US Dan Liu - Sunnyvale CA, US Chi Yu - Saratoga CA, US
Assignee:
Affymetrix, INC. - Santa Clara CA
International Classification:
C12M 1/34 H01L 21/00
US Classification:
435287200, 438001000
Abstract:
The present invention provides methods to process multiple sensors by providing a sensor plate and HT plates. In a preferred embodiment of the invention, methods for assembling microarray pegs and microarray plates are described for high throughput microarray processing.
Automated Assembly Device For Assembly Of Components Of A Disc Drive
Dan Liu - Bloomington MN Michael W. Pfeiffer - Richfield MN
Assignee:
Seagate Technology LLC - Scotts Valley CA
International Classification:
B11B 5127
US Classification:
2960303
Abstract:
An assembly device for assembling components of a disc drive, the assembly device being adapted to assembly components of a disc drive supported at a disc drive station. The assembly members are operably supported for operation between a load position and an install position aligned with the disc drive station. Components are engaged by the assembly members and transported to the disc drive station. At the disc drive station, the components are aligned for assembly in the disc drive.
Techniques To Enable Stateful Decompression On Hardware Decompression Acceleration Engines
- San Jose CA, US Mark Vernon - Park City UT, US Dan Liu - San Jose CA, US Jinchao Lyu - San Jose CA, US Yang Liu - Milpitas CA, US
International Classification:
G06F 9/50 G06F 9/54 H03M 7/30
Abstract:
A hardware decompression acceleration engine including: an input buffer for receiving to-be-decompressed data from a software layer of a host computer; a decompression processing unit coupled to the input buffer for decompressing the to-be-decompressed data, the decompression processing unit further receiving first and second flags from the software layer of the host computer, wherein the first flag is indicative of a location of the to-be-decompressed data in a to-be-decompressed data block and the second flag is indicative of a presence of an intermediate state; and an output buffer for storing decompressed data from the decompression processing unit.
- Redmond WA, US Dan Liu - Santa Clara CA, US Qi Guo - Sunnyvale CA, US
International Classification:
G06F 16/2457 G06F 17/18 G06F 17/15 G06N 3/04
Abstract:
In an example embodiment, a platform is provided that utilizes information available to a computer system to feed a neural network. The neural network is trained to determine both the probability that a searcher would select a given potential search result if it was presented to him or her and the probability that a subject of the potential search result would respond to a communication from the searcher. These probabilities are combined to produce a single score that can be used to determine whether to present the searcher with the potential search result and, if so, how high to rank the potential search result among other search results. During the training process, a rescaling transformation for each input feature is learned and applied to the values for the input features.
Two-Stage Training With Non-Randomized And Randomized Data
- Redmond WA, US Dan Liu - Santa Clara CA, US Qi Guo - Sunnyvale CA, US Wenxiang Chen - Sunnyvale CA, US Xiaoyi Zhang - Sunnyvale CA, US Lester Gilbert Cottle - Sunnyvale CA, US Xuebin Yan - Sunnyvale CA, US Yu Gong - Santa Clara CA, US Haitong Tian - San Jose CA, US Siyao Sun - Mountain View CA, US Pei-Lun Liao - Sunnyvale CA, US
International Classification:
G06F 16/9538 G06N 20/00 G06F 17/27 G06N 3/04
Abstract:
In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.
Name / Title
Company / Classification
Phones & Addresses
Dan Liu President
COMPUVIEW MICROSYSTEMS INTERNATIONAL INC
828 Balboa Ln, San Mateo, CA 94404 15160 Nautique Way, Houston, TX 77047
Dan Liu, a researcher at the Center for Polar and Deep Ocean Development at Shanghai Communications University, told the CSIS panel that the Philippine claims were some kind of political mask and questioned how the experts could evaluate the reefs unless they visited them personally something ne
Pamela Macapagal (1995-1999), George Rolfo (1984-1987), Sean Kinney (1995-1999), Betty Lax (1973-1975), Teres Rodney (1991-1995), E Dan Liu (1990-1994)