Heading Direction Estimation Using Deep Learning with Automatic Large-scale Data Acquisition

AbstractSARS-CoV-2 drive through screening centers (DTSC) have been implemented worldwide as a fast and secure way of mass screening. We use DTSCs as a platform for the acquisition of multimodal datasets that are needed for the development of remote screening methods. Our acquisition setup consists of an array of thermal, infrared and RGB cameras as well as microphones and we apply methods from computer vision and computer audition for the contactless estimation of physiological parameters. We have recorded a multimodal dataset of DTSC participants in Germany for the development of remote screening methods and symptom identification. Acquisition in the early stages of a pandemic and in regions with high infection rates can facilitate and speed up the identification of infection specific symptoms and large scale data acquisition at DTSC is possible without disturbing the flow of operation.

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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Nature Biotechnology ◽

10.1038/s41587-021-01094-0 ◽

2021 ◽

Author(s):

Noah F. Greenwald ◽

Geneva Miller ◽

Erick Moen ◽

Alex Kong ◽

Adam Kagel ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Cell Segmentation ◽

Whole Cell ◽

Data Annotation ◽

Large Scale Data ◽

Level Performance ◽

Scale Data

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Adaptation of a software development methodology to the implementation of a large-scale data acquisition and control system

10.2514/6.1983-2412 ◽

1983 ◽

Author(s):

G. MADRID ◽

P. WESTMORELAND

Keyword(s):

Control System ◽

Software Development ◽

Data Acquisition ◽

Large Scale ◽

Large Scale Data ◽

Development Methodology ◽

Software Development Methodology ◽

And Control ◽

Scale Data

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Deep Learning Method for RNA Secondary Structure Prediction with Pseudoknots Based on Large-Scale Data

Journal of Healthcare Engineering ◽

10.1155/2021/6699996 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Bowen Shen ◽

Hao Zhang ◽

Cong Li ◽

Tianheng Zhao ◽

Yuanning Liu

Keyword(s):

Deep Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Large Scale ◽

Secondary Structure Prediction ◽

Learning Methods ◽

Rna Secondary Structure Prediction ◽

Large Scale Data ◽

Scale Data

Traditional machine learning methods are widely used in the field of RNA secondary structure prediction and have achieved good results. However, with the emergence of large-scale data, deep learning methods have more advantages than traditional machine learning methods. As the number of network layers increases in deep learning, there will often be problems such as increased parameters and overfitting. We used two deep learning models, GoogLeNet and TCN, to predict RNA secondary results. And from the perspective of the depth and width of the network, improvements are made based on the neural network model, which can effectively improve the computational efficiency while extracting more feature information. We process the existing real RNA data through experiments, use deep learning models to extract useful features from a large amount of RNA sequence data and structure data, and then predict the extracted features to obtain each base’s pairing probability. The characteristics of RNA secondary structure and dynamic programming methods are used to process the base prediction results, and the structure with the largest sum of the probability of each base pairing is obtained, and this structure will be used as the optimal RNA secondary structure. We, respectively, evaluated GoogLeNet and TCN models based on 5sRNA, tRNA data, and tmRNA data, and compared them with other standard prediction algorithms. The sensitivity and specificity of the GoogLeNet model on the 5sRNA and tRNA data sets are about 16% higher than the best prediction results in other algorithms. The sensitivity and specificity of the GoogLeNet model on the tmRNA dataset are about 9% higher than the best prediction results in other algorithms. As deep learning algorithms’ performance is related to the size of the data set, as the scale of RNA data continues to expand, the prediction accuracy of deep learning methods for RNA secondary structure will continue to improve.

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