scholarly journals Visual Features with Spatio-Temporal-Based Fusion Model for Cross-Dataset Vehicle Re-Identification

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1083 ◽  
Author(s):  
Zakria ◽  
Jianhua Deng ◽  
Jingye Cai ◽  
Muhammad Umar Aftab ◽  
Muhammad Saddam Khokhar ◽  
...  
Keyword(s):  
Neural Network ◽  
Temporal Patterns ◽  
Extensive Study ◽  
Background Information ◽  
Training Dataset ◽  
Visual Features ◽  
Surveillance Network ◽  
Significant Difference ◽  
Spatio Temporal ◽  
The Impact

Vehicle re-identification (Re-Id) is the key module in an intelligent transportation system (ITS). Due to its versatile applicability in metropolitan cities, this task has received increasing attention these days. It aims to identify whether the specific vehicle has already appeared over the surveillance network or not. Mostly, the vehicle Re-Id method are evaluated on a single dataset, in which training and testing of the model is performed on the same dataset. However in practice, this negatively effects model generalization ability due to biased datasets along with the significant difference between training and testing data; hence, the model becomes weak in a practical environment. To demonstrate this issue, we have empirically shown that the current vehicle Re-Id datasets are usually strongly biased. In this regard, we also conduct an extensive study on the cross and the same dataset to examine the impact on the performance of the vehicle Re-Id system, considering existing methods. To address the problem, in this paper, we have proposed an approach with augmentation of the training dataset to reduce the influence of pose, angle, camera color response, and background information in vehicle images; whereas, spatio-temporal patterns of unlabelled target datasets are learned by transferring siamese neural network classifiers trained on a source-labelled dataset. We finally calculate the composite similarity score of spatio-temporal patterns with siamese neural-network-based classifier visual features. Extensive experiments on multiple datasets are examined and results suggest that the proposed approach has the ability to generalize adequately.

scholarly journals Encoding Health Records into Pathway Representations for Deep Learning

2021 ◽  
Author(s):  
Marco Luca Sbodio ◽  
Natasha Mulligan ◽  
Stefanie Speichert ◽  
Vanessa Lopez ◽  
Joao Bettencourt-Silva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Source Code ◽  
Training Dataset ◽  
Health Records ◽  
Learning Tasks ◽  
Patient Pathways ◽  
Computational Resources ◽  
The Impact

There is a growing trend in building deep learning patient representations from health records to obtain a comprehensive view of a patient’s data for machine learning tasks. This paper proposes a reproducible approach to generate patient pathways from health records and to transform them into a machine-processable image-like structure useful for deep learning tasks. Based on this approach, we generated over a million pathways from FAIR synthetic health records and used them to train a convolutional neural network. Our initial experiments show the accuracy of the CNN on a prediction task is comparable or better than other autoencoders trained on the same data, while requiring significantly less computational resources for training. We also assess the impact of the size of the training dataset on autoencoders performances. The source code for generating pathways from health records is provided as open source.

scholarly journals Camera-based spatial assessment of perfusion upon stimuli

2019 ◽  
Vol 5 (1) ◽  
pp. 105-108 ◽  
Author(s):  
Vincent Fleischhauer ◽  
Nora Ruprecht ◽  
Sebastian Zaunseder
Keyword(s):  
Time Delay ◽  
Correlation Coefficients ◽  
Temporal Patterns ◽  
Spatial Assessment ◽  
Pairwise Correlation ◽  
Map Generation ◽  
Pulse Waves ◽  
Phase Map ◽  
Spatio Temporal ◽  
The Impact

AbstractImaging photoplethysmography allows to capture spatio-temporal patterns related to the perfusion. One such approach is based on the analysis of the time delay between pulse waves at different locations by so-called phase maps. There are different ways to establish such maps. However, neither a comparison between existing methods has been published nor has the impact of different stimuli been sufficiently examined until today. In this work, we compare three previously published approaches for the generation of phase maps and investigate the impact of two physiological stimuli on such maps. Our results show pairwise correlation coefficients between the different approaches of phase map generation from r = 0.65 to r = 0.82, indicating substantial differences between maps. The different maps reflect our physiological expectation in varying degrees. Particularly for a weaker (distant) stimulation refinements are needed to reveal characteristic changes.

scholarly journals A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

2005 ◽  
Vol 5 (2) ◽  
pp. 451-459 ◽  
Author(s):  
C. Jiménez ◽  
P. Eriksson ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Single Channel ◽  
Weighting Function ◽  
Traditional Approach ◽  
Training Dataset ◽  
The Neural Network ◽  
Radiosonde Station ◽  
Neural Network Algorithm ◽  
The Impact

Abstract. A neural network algorithm inverting selected channels from the Advance Microwave Sounding Unit instruments AMSU-A and AMSU-B was applied to retrieve layer averaged relative humidity. The neural network was trained with a global synthetic dataset representing clear-sky conditions. A precision of around 6% was obtained when retrieving global simulated radiances, the precision deteriorated less than 1% when real mid-latitude AMSU radiances were inverted and compared with co-located data from a radiosonde station. The 6% precision outperforms by 1% the reported precision estimate from a linear single-channel regression between radiance and weighting function averaged relative humidity, the more traditional approach to exploit AMSU data. Added advantages are not only a better precision; the AMSU-B humidity information is more optimally exploited by including temperature information from AMSU-A channels; and the layer averaged humidity is a more physical quantity than the weighted humidity, for comparison with other datasets. The training dataset proved adequate for inverting real radiances from a mid-latitude site, but it is limited by not considering the impact of clouds or surface emissivity changes, and further work is needed in this direction for further validation of the precision estimates.

scholarly journals A practical demonstration on AMSU retrieval precision for upper tropospheric humidity by a non-linear multi-channel regression method

2004 ◽  
Vol 4 (6) ◽  
pp. 7487-7511 ◽  
Author(s):  
C. Jiménez ◽  
P. Eriksson ◽  
V. O. John ◽  
S. A. Buehler
Keyword(s):  
Neural Network ◽  
Relative Humidity ◽  
Single Channel ◽  
Weighting Function ◽  
Traditional Approach ◽  
Training Dataset ◽  
The Neural Network ◽  
Radiosonde Station ◽  
Neural Network Algorithm ◽  
The Impact

Abstract. A neural network algorithm inverting selected channels from the AMSU-A and AMSU-B instruments was applied to retrieve layer averaged relative humidity. The neural network was trained with a global synthetic dataset representing clear-sky conditions. A precision of around 6% was obtained when retrieving global simulated radiances, the precision deteriorated less than 1% when real mid-latitude AMSU radiances were inverted and compared with co-located data from a radiosonde station. The 6% precision outperforms by 1% the reported precision estimate from a linear single-channel regression between radiance and weighting function averaged relative humidity, the more traditional approach to exploit AMSU data. Added advantages are not only a better precision; the AMSU-B humidity information is more optimally exploited by including temperature information from AMSU-A channels; and the layer averaged humidity is a more physical quantity than the weighted humidity, for comparison with other datasets. The training dataset proved adequate for inverting real radiances from a mid-latitude site, but it is limited by not considering the impact of clouds or surface emissivity changes, and further work is needed in this direction for further validation of the precision estimates.

scholarly journals Using Mobility Data to Understand and Forecast COVID19 Dynamics

2020 ◽  
Author(s):  
Lijing Wang ◽  
Xue Ben ◽  
Aniruddha Adiga ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Spatial Interaction ◽  
Human Mobility ◽  
State Level ◽  
Interaction Patterns ◽  
Disease Dynamics ◽  
Mobility Data ◽  
Spatio Temporal ◽  
The Impact

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

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