Intel Corporation

Eli Lilly and Company

Agilent Technologies, Inc.

Chinese Academy of Sciences

Baoshan Iron & Steel Co., Ltd.

Sinocelltech Ltd.

Samsung Electronics Co., Ltd.

Ricoh Company, Ltd.

Boe Technology Group Co., Ltd.

Thomson Licensing

Ping Hu

Gang Wang

Anbang Yao

Dongqi Cai

Yurong Chen

Shandong Wang

Lin Xu

Yiwen Guo

Libin Wang

Wenhua Cheng

Yuqing Hou

Shuhui Chen

Liang Chen

Liu Yang

Wentao Wu

Wei Zhang

Jae-Hyun Kim

Wei Guo

Zhongyuan Sun

Young-su Moon

Kangying Cai

Cheng Du

Liyan Liu

Xin Fu

Yuan Liu

Wenfei Jiang

Zhongchao Shi

Gengyu Ma

Biao Tian

Richard P White

Jun Xiao

Yaojie Lu

William Hunter White

Jean Marie Defauw

Scott Dale Holmstrom

Zengyun An

Chongzhi Tang

Zhou Su

Qi Siegmundt-Pan

Fanny Hauser

Cathrin Sohns

Liangzhi Xie

Lin Pang

Maximilian Schneider

Chunxia Luo

Xiaoyan Suo

Thomas D Harrison

Xiangjun Tian

Zhao Kang

Zhonghua Zhang

Jie Yin

Xiaomeng Wang

Lei Zheng

Edward D Mroz

Yaoheng Liu

Rafael Mulero

Dianchao Liu

Wen Sun

Chuanguo Zhang

Jia Wei

Yangyuxuan Kang

Robert James Collins

Yannan Ma

Na He

Qian Tao

The present disclosure provides a bainite geological drilling pipe, comprising the following chemical elements in percentage by mass: 0.14-0.22% of C, 0.2-0.55% of Si, 2.1-2.9% of Mn, 0.01-0.04% of Nb, 0.015-0.04% of Al, 0.001-0.005% of B, 0<N≤0.007%, with the balance being Fe and inevitable impurities, wherein a content ratio of Al to N is Al/N≥3. In addition, the present disclosure further provides a manufacturing method for the bainite geological drilling pipe, comprising the following steps: (1) performing smelting and casting on molten steel to obtain a pipe blank; (2) performing heating, piercing, continuous rolling and sizing on the pipe blank to obtain a pipe body; and (3) performing two-stage air cooling on the pipe body; in first-stage air cooling, performing air circular blowing cooling on the outer surface of the pipe body, the temperature before cooling is greater than or equal to Ar/3+50° C., the cooling rate is 5-15° C./s, and cooling to a temperature range from Bs-100° C. to Bs-50° C.; in second-stage air cooling, performing natural air cooling on the pipe body, the cooling rate is 0.5-4° C./s.

High-strength and high-toughness bainite geological drilling pipe and manufacturing method therefor

The present invention relates to a multivalent HPV immunogenic composition for preventing human papillomavirus (HPV) related diseases or infections and uses thereof. Said multivalent HPV immunogenic composition comprises: HPV virus-like particles assembled from L1 proteins of HPV Types 6, 11, 16, 18, 31, 33, 45, 52, and 58; and one or more HPV virus-like particles assembled from L1 proteins of other pathogenic HPV types. In one embodiment, said one or more other pathogenic HPV types are selected from HPV Types 35, 39, 51, 56 and 59. In one embodiment, at least one of said HPV virus-like particles is a chimeric HPV virus-like particle, and said chimeric HPV virus-like particle comprises one or more chimeric HPV L1 proteins.

Polyvalent immunogenicity composition for human papillomavirus

Methods, apparatus, systems, and articles of manufacture for modifying a machine learning model are disclosed. An example apparatus includes a supervised branch inserter to insert a supervised branch into a machine learning model at an identified location, a first cluster generator to generate a first cluster of the inserted supervised branch using a first clustering technique, a second cluster generator to generate a second cluster of the inserted supervised branch using a second clustering technique, the second clustering technique different from the first clustering technique, a cluster joiner to join the first cluster and the second cluster to form a clustering block, the clustering block appended to an end of the supervised branch, and a propagation strategy executor to execute a propagation training strategy to modify a parameter of the machine learning model.

Methods and apparatus for modifying a machine learning model

Methods and systems for budgeted and simplified training of deep neural networks (DNNs) are disclosed. In one example, a trainer is to train a DNN using a plurality of training sub-images derived from a down-sampled training image. A tester is to test the trained DNN using a plurality of testing sub-images derived from a down-sampled testing image. In another example, in a recurrent deep Q-network (RDQN) having a local attention mechanism located between a convolutional neural network (CNN) and a long-short time memory (LSTM), a plurality of feature maps are generated by the CNN from an input image. Hard-attention is applied by the local attention mechanism to the generated plurality of feature maps by selecting a subset of the generated feature maps. Soft attention is applied by the local attention mechanism to the selected subset of generated feature maps by providing weights to the selected subset of generated feature maps in obtaining weighted feature maps. The weighted feature maps are stored in the LSTM. A Q value is calculated for different actions based on the weighted feature maps stored in the LSTM.

Methods and systems for budgeted and simplified training of deep neural networks

The present invention relates to chimeric papilloma virus L1 proteins and polynucleotides encoding thereof, and also to HPV virus-like particles and the preparation methods thereof. Said chimeric papilloma virus L1 protein comprises an N-terminal fragment derived from L1 protein of the first papilloma virus type, said N-terminal fragment maintains the immunogenicity of the L1 protein of the corresponding type of HPV; and a C-terminal fragment derived from L1 protein of the second papilloma virus type, said L1 protein of the second papilloma virus type has a better expression level and a better solubility compared to the L1 proteins of other HPV types; wherein said chimeric papilloma virus L1 proteins have the immunogenicity of the L1 proteins of the corresponding HPV types. Said chimeric papilloma virus L proteins have better expression amount and solubility for mass production of vaccines.

Chimeric papilloma virus L1 protein

Methods and systems are disclosed for boosting deep neural networks for deep learning. In one example, in a deep neural network including a first shallow network and a second shallow network, a first training sample is processed by the first shallow network using equal weights. A loss for the first shallow network is determined based on the processed training sample using equal weights. Weights for the second shallow network are adjusted based on the determined loss for the first shallow network. A second training sample is processed by the second shallow network using the adjusted weights. In another example, in a deep neural network including a first weak network and a second weak network, a first subset of training samples is processed by the first weak network using initialized weights. A classification error for the first weak network on the first subset of training samples is determined. The second weak network is boosted using the determined classification error of the first weak network with adjusted weights. A second subset of training samples is processed by the second weak network using the adjusted weights.

Methods and systems for boosting deep neural networks for deep learning

Described herein are hardware acceleration of random number generation for machine learning and deep learning applications. An apparatus () includes a uniform random number generator (URNG) circuit () to generate uniform random numbers and an adder circuit () that is coupled to the URNG circuit (). The adder circuit hardware () accelerates generation of Gaussian random numbers for machine learning.

Systems and methods for generating gaussian random numbers with hardware acceleration

Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.

Methods and systems using camera devices for deep channel and convolutional neural network images and formats

Methods and systems are disclosed using improved training and learning for deep neural networks. In one example, a deep neural network includes a plurality of layers, and each layer has a plurality of nodes. The nodes of each L layer in the plurality of layers are randomly connected to nodes of an L+layer. The nodes of each L+layer are connected to nodes in a subsequent L layer in a one-to-one manner. Parameters related to the nodes of each L layer are fixed. Parameters related to the nodes of each L+layers are updated. In another example, inputs for the input layer and labels for the output layer of a deep neural network are determined related to a first sample. A similarity between different pairs of inputs and labels is estimated using a Gaussian regression process.

Methods and systems using improved training and learning for deep neural networks

Methods and systems for advanced and augmented training of deep neural networks (DNNs) using synthetic data and innovative generative networks. A method includes training a DNN using synthetic data, training a plurality of DNNs using context data, associating features of the DNNs trained using context data with features of the DNN trained with synthetic data, and generating an augmented DNN using the associated features.

Methods and systems for advanced and augmented training of deep neural networks using synthetic data and innovative generative networks

Disclosed are a protein molecular marker Dkk-3 protein associated with age-related muscle atrophy and the use thereof in the diagnosis of age-related muscle atrophy. The expression level of the Dkk-3 protein in amyotrophic cells is significantly higher than that in normal myocytes or tissues, and thus the Dkk-3 protein can be used as an effective marker for the detection of age-related muscle atrophy.

Use of molecular marker Dkk-3 associated with muscle atrophy

Described herein are advanced artificial intelligence agents for modeling physical interactions. An apparatus to provide an active artificial intelligence (AI) agent includes at least one database to store physical interaction data and compute cluster coupled to the at least one database. The compute cluster automatically obtains physical interaction data from a data collection module without manual interaction, stores the physical interaction data in the at least one database, and automatically trains diverse sets of machine learning program units to simulate physical interactions with each individual program unit having a different model based on the applied physical interaction data.

Advanced artificial intelligence agent for modeling physical interactions

Techniques are provided for neural network based, human attribute recognition, guided by anatomical key-points and statistic correlation models. Attributes include characteristics that can be visibly identified or inferred from an image, such as gender, hairstyle, clothing style, etc. A methodology implementing the techniques according to an embodiment includes applying an attribute feature extraction (AFE) convolutional neural network (CNN) to an image of a human to generate attribute feature maps based on the image. The method further includes applying a key-point guided proposal (KPG) CNN to the image of the human to generate proposed hierarchical regions of the image based on associated anatomical key-points. The method further includes generating recognition probabilities for the human attributes using a CNN combination layer that incorporates the attribute feature maps, the proposed hierarchical regions, and statistical correlation models (SCMs) which provide correlations between the features of the attribute feature maps and the proposed hierarchical regions.

Key-point guided human attribute recognition using statistic correlation models

Growing community of inventors

Beijing, China

Ping Hu