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Predicting beam transmission using 2-dimensional phase space projections of hadron accelerators

FRONTIERS IN PHYSICS 2022年 10卷

作者： Tran, Anthony Hao, Yue Mustapha, Brahim Marin, Jose L. Martinez L. Michigan State Univ Facil Rare Isotope Beams E Lansing MI 48824 USA Argonne Natl Lab Argonne IL USA

We present a method to compress the 2D transverse phase space projections from a hadron accelerator and use that information to predict the beam transmission. This method assumes that obtaining at least three projections of the 4D transverse phase space is possible and that an accurate simulation model is available for the beamline. Using a simulated model, we show that-a computer can train a convolutional autoencoder to reduce phase-space information which can later be used to predict the beam transmission. Finally, we argue that although using projections from a realistic nonlinear distribution produces less accurate results, the method still generalizes well.

关键词： virtual accelerator convolutional autoencoder neural network autoencoder beam transmission hadron accelerator phase space projections nonlinear field

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Parameter Flexible Wildfire Prediction Using Machine Learning Techniques: Forward and Inverse Modelling

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REMOTE SENSING 2022年第13期14卷 3228-3228页

作者： Cheng, Sibo Jin, Yufang Harrison, Sandy P. Quilodran-Casas, Cesar Prentice, Iain Colin Guo, Yi-Ke Arcucci, Rossella Imperial Coll London Data Sci Inst Dept Comp London SW7 2BX England Leverhulme Ctr Wildfires Environm & Soc London SW7 2AZ England Univ Calif Davis Dept Land Air & Water Resources Davis CA 95616 USA Imperial Coll London Georgina Mace Ctr Living Planet Dept Life Sci London SW7 2BX England Univ Reading Geog & Environm Sci Reading RG6 6EU Berks England Imperial Coll London Dept Earth Sci & Engn London SW7 2BX England

Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.

关键词： wildfire prediction machine learning reduced-order modelling convolutional autoencoder data assimilation latent assimilation parameter identification

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A snapshot neural ensemble method for cancer-type prediction based on copy number variations

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NEURAL COMPUTING & APPLICATIONS 2020年第19期32卷 15281-15299页

作者： Karim, Md Rezaul Rahman, Ashiqur Jares, Joao Bosco Decker, Stefan Beyan, Oya Fraunhofer Inst Appl Informat Technol FIT St Augustin Germany Rhein Westfal TH Aachen Aachen Germany

An accurate diagnosis and prognosis for cancer are specific to patients with particular cancer types and molecular traits, which needs to address carefully. The discovery of important biomarkers is becoming an important step toward understanding the molecular mechanisms of carcinogenesis in which genomics data and clinical outcomes need to be analyzed before making any clinical decision. Copy number variations (CNVs) are found to be associated with the risk of individual cancers and hence can be used to reveal genetic predispositions before cancer develops. In this paper, we collect the CNVs data about 8000 cancer patients covering 14 different cancer types from The Cancer Genome Atlas. Then, two different sparse representations of CNVs based on 578 oncogenes and 20,308 protein-coding genes, including genomic deletions and duplication across the samples, are prepared. Then, we train Conv-LSTM and convolutional autoencoder (CAE) networks using both representations and create snapshot models. While the Conv-LSTM can capture locally and globally important features, CAE can utilize unsupervised pretraining to initialize the weights in the subsequent convolutional layers against the sparsity. Model averaging ensemble (MAE) is then applied to combine the snapshot models in order to make a single prediction. Finally, we identify most significant CNVs biomarkers using guided-gradient class activation map plus (GradCAM++) and rank top genes for different cancer types. Results covering several experiments show fairly high prediction accuracies for the majority of cancer types. In particular, using protein-coding genes, Conv-LSTM and CAE networks can predict cancer types correctly at least 72.96% and 76.77% of the cases, respectively. Contrarily, using oncogenes gives moderately higher accuracies of 74.25% and 78.32%, whereas the snapshot model based on MAE shows overall 2.5% of accuracy improvement.

关键词： Cancer prediction Copy number variation Conv-LSTM network convolutional autoencoder Interpretability Snapshot ensemble

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A robust hybrid digital watermarking technique against a powerful CNN-based adversarial attack

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32769-32790页

作者： Sharma, Sai Shyam Chandrasekaran, V Sri Sathya Sai Inst Higher Learning Anantapur Andhra Pradesh India

Digital watermarking techniques are valuable tools to embed digital signatures on multimedia content to establish the legal ownership and authenticity claims by the owners. Firstly this paper investigates the robustness of popular transform domain-based digital image watermarking schemes such as DCT, SVD, DWT, and their hybrid combinations against known image processing type attacks such as image blurring, compression, noise addition, rotation and cropping. Then, an enhanced hybrid scheme using DWT and SVD methods is proposed and its improved performance is demonstrated in terms of the quality of the extracted watermarks measured in terms of PSNR, SSIM and NCC values. This paper then proposes a novel adversarial attack based on a powerful Deep convolutional Neural Network based autoencoder(CAE) scheme. The CAE is specifically chosen to exploit its intrinsic capability to represent the image content (spatial and structural) through lower dimensional projections in the intermediate layers. The CAE is trained and tested on the entire image repository of the CIFAR10 data set. Once CAE is trained on a class of images and the parameters are frozen, it will serve as a system to produce a perceptually close image for any unseen input image belonging to the same class. The power of the proposed adversarial attack scheme is shown in terms of the quality of extracted watermarks against popular water mark embedding schemes. Finally the proposed enhanced hybrid strategy of DWT+SVD is shown to be robust against the new form of attack and outperforms all other techniques measured in terms of its high quality watermark extraction.

关键词： Digital watermarking convolutional autoencoder Copyright protection Adversarial attacks Hybrid transforms

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A Learning-Based Framework for Identifying MicroRNA Regulatory Module

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 1598-1607页

作者： Yang, Yi Hunan Womens Univ Coll Informat Sci & Engn 160 Zhongyi First Rd Changsha 410004 Peoples R China

Accurate identification of microRNA regulatory modules can give insights to understand microRNA synergistical regulatory mechanism. However, the identification accuracy suffers from incomplete biological data. In this paper, we proposed a learning based framework called MicroRNA regulatory module dentification with convolutional autoencoders (MICA). Firstly, the framework applied convolutional autoencoders to extract significant features of microRNA and their target-genes. Then they were clustered into microRNA clusters and target-gene clusters. Finally, the two types of clusters were combined into modules by known microRNA-target interactions. Compared with three existing methods on three cancer data sets, the modules detected by the proposed method exhibited better overall performance. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： MicroRNA regulatory module convolutional autoencoder K-means MicroRNA-target interaction

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Identification of MicroRNA Regulatory Modules by Clustering MicroRNA-Target Interactions

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IEEE ACCESS 2020年 8卷 154133-154142页

作者： Yang, Yi Wan, Xuting Hunan Womens Univ Coll Informat Sci & Engn Changsha 410004 Peoples R China

Identification of microRNA regulatory modules can help decipher microRNA synergistic regulatory mechanism in the development and progression of complex diseases, especially cancers. Experimentally validated microRNA-target interactions provide strong direct evidence for the analysis of microRNA regulatory functions. We here developed a novel computational framework named CMIN to identify microRNA regulatory modules by performing link clustering on such experimentally verified microRNA-target interactions. CMIN runs in two main steps: it first utilizes convolutional autoencoders to extract high-level microRNA-target interaction features from the expression profile data, and then applied affinity propagation clustering algorithm to interaction feature to obtain overlapping microRNA-target clusters. Clusters with significant synergy correlations are considered as microRNA regulatory modules. We tested the proposed framework and other three existing methods on three types of cancer data sets from TCGA (The Cancer Genome Atlas). The results showed that the microRNA regulatory modules detected by CMIN exhibit stronger topological correlation and more functional enrichment compared with other methods. Availability: The supplementary files of CMIN are available at https://***/snryou/CMIN.

关键词： MicroRNA regulatory module MicroRNA-target interaction convolutional autoencoder affinity propagation link clustering

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Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020年 175卷 105557-105557页

作者： Li, Jiating Oswald, Cody Graef, George L. Shi, Yeyin Univ Nebraska Dept Biol Syst Engn Lincoln NE 68583 USA Univ Nebraska Dept Agron & Hort Lincoln NE 68583 USA

Iron deficiency chlorosis (IDC) is a major yield-limiting factor for soybean production in the mid-western USA. The most practical solution in mitigating losses due to IDC is the development and characterization of IDC tolerant varieties. Leveraging the advanced technique of unmanned aircraft system (UAS) and the thriving deep learning methodology, a convolutional neural network (CNN) could be trained to assist breeders with IDC resistance selection. However, a known difficulty in IDC screening is that the symptoms often vary across diverse genetic backgrounds and spatial or temporal soil heterogeneities. A robust CNN model is desired to mitigate such difficulty. While high robustness usually relies on a sufficiently large labeled training data, the available labeled samples in most breeding programs are normally not enough. Under this limitation, it is critical to find an alternative way to train a robust model. The solution proposed in this study was to apply unsupervised pretraining on the unlabeled aerial images that are much easier to obtain by the UAS. Specifically, a convolutional autoencoder (CAE) was pre-trained on unlabeled sub-images clipped from aerial RGB images;then, the pretrained weights were reused to initialize the CNN model that was trained on labeled plot-wise sub-images clipped from stitched RGB maps. To WA the robustness of this CAE initialized model (CAE1 -CNN), two baseline models were equally trained: the first was CAE2-CNN, where the CAE2 was pre-trained with three times of unlabeled data as that of CAE1, by adding wniter wheat and sorghum aerial images;the second was Ran-CNN where the CNN was randomly initialized. Three conditions were considered for testing model robustness: different soybean trials, field locations and vegetative growth stages. Results revealed that both the CAE1-CNN and the CAE2-CNN had relatively better robustness than the Ran-CNN model, i.e., higher R-2 and lower RMSE values, especially on different soybean trials and

关键词： Plant stress phenotyping Remote sensing convolutional neural network convolutional autoencoder Unlabeled data

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Vis-NIR hyperspectral imaging combined with incremental learning for open world maize seed varieties identification

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 199卷

作者： Zhang, Liu Wang, Dong Liu, Jincun An, Dong China Agr Univ Natl Innovat Ctr Digital Fishery Beijing 100083 Peoples R China China Agr Univ Key Lab Smart Farming Technol Aquat Anim & Livesto Minist Agr & Rural Affairs Beijing 100083 Peoples R China Beijing Engn & Technol Res Ctr Internet Things Agr Beijing 100083 Peoples R China China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China China Agr Univ Coll Agron & Biotechnol Beijing 100193 Peoples R China China Agr Univ Natl Maize Improvement Ctr Beijing 100193 Peoples R China

There are many maize seed varieties circulating on the Chinese market, some varieties will be added and eliminated by the government every year, and fake varieties cannot be exhaustively listed. Furthermore, the updating of some varieties requires retraining the entire model, which is time-consuming and laborious. This poses a huge challenge for the authenticity identification of varieties and online model updating. Herein, hyperspectral imaging (HSI) combined with incremental learning (IL) was used to solve this problem. A novel radial basis function-biomimetic pattern recognition (RBF-BPR) model for IL was proposed and compared with one-class support vector machine (OCSVM) and BPR models under two test schemes. Hyperspectral images of five varieties of maize seeds were collected, and convolutional autoencoder (CAE) was used to extract features to remove redundant information and improve the generalization ability of models. A unique hybrid model was designed for each variety respectively, and the IL process was simulated. In two test schemes, the overall correct acceptance rate (CAR) for the known varieties and the overall correct rejection rate (CRR) for the unknown varieties of CAE-RBF-BPR model both reached 100%, which was superior to CAE-OCSVM and CAE-BPR models. Especially after RBF was used to map data, the performance of RBF-BPR model had a qualitative improvement compared with the original BPR model. In summary, the proposed method can realize IL without accessing the old classes data, while meeting the requirements of identifying known varieties and rejecting unknown varieties. In addition, if some varieties are eliminated by the government in the future, the corresponding models can also be removed form the whole system. The combination of such method and HSI has a broad application prospect in the identification of maize seed varieties, thus avoiding the trouble of retraining entire model when some maize seed varieties need to be updated.

关键词： Hyperspectral imaging Maize seed Incremental learning convolutional autoencoder Biomimetic pattern recognition

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A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction

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COMPUTERS & FLUIDS 2022年 233卷 1页

作者： Gupta, Rachit Jaiman, Rajeev Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada

In this work, we present a hybrid partitioned deep learning framework for the reduced-order modeling of moving interfaces and predicting fluid-structure interaction. Using the discretized Navier-Stokes in the arbitrary Lagrangian-Eulerian reference frame, we generate the full-order flow snapshots and point cloud displacements as target physical data for the learning and inference of coupled fluid-structure dynamics. The hybrid operation of this methodology comes by combining two separate data-driven models for fluid and solid subdomains via deep learning-based reduced-order models (DL-ROMs). The proposed multi-level framework comprises the partitioned data-driven drivers for unsteady flow and the moving point cloud displacements. At the fluid-structure interface, the force information is exchanged synchronously between the two partitioned subdomain solvers. The first component of our proposed framework relies on the proper orthogonal decomposition-based recurrent neural network (POD-RNN) as a DL-ROM procedure to infer the point cloud with a moving interface. This model utilizes the POD basis modes to reduce dimensionality and evolve them in time via long short-term memory-based recurrent neural networks (LSTM-RNNs). The second component employs the convolution-based recurrent autoencoder network (CRAN) as a self-supervised DL-ROM procedure to infer the nonlinear flow dynamics at static Eulerian probes. We introduce these probes as spatially structured query nodes in the moving point cloud to treat the Lagrangian-to-Eulerian conflict together with convenience in training the CRAN driver. To determine these Eulerian probes, we construct a novel snapshot-field transfer and load recovery algorithm. They are chosen in such a way that the two components (i.e., POD-RNN and CRAN) are constrained at the interface to recover the bulk force quantities. These DL-ROM-based data-driven drivers rely on the LSTM-RNNs to evolve the low-dimensional states. A popular prototypical flui

关键词： Fluid-structure interaction Deep learning-based reduced-order model Proper orthogonal decomposition convolutional autoencoder Long short-term memory network Digital twin

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Unsupervised noise-robust feature extraction for aerial image classification

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Science China(Technological Sciences) 2020年第8期63卷 1406-1415页

作者： LIANG Ye LU Shuai WENG Rui HAN ChengZhe LIU Ming School of Astronautics Harbin Institute of TechnologyHarbin 150001China Academy of Art Harbin University of Science and TechnologyHarbin 150001China Academy of Software and Microelectronics Harbin University of Science and TechnologyHarbin 150001China

The rich data provided by satellites and unmanned aerial vehicles bring opportunities to directly model aerial image features by extracting their spatial and structural *** convolutional autoencoders(CAEs)have been attained a remarkable performance in ideal aerial image feature extraction,they are still challenging to extract information from noisy images which are generated from capture and *** this paper,a novel CAE-based noise-robust unsupervised learning method is proposed for extracting high-level features accurately from aerial images and mitigating the effect of *** from conventional CAEs,the proposed method introduces the noise-robust module between the encoder and the ***,several pooling layers in CAEs are replaced by convolutional layers with stride=*** performance of feature extraction is evaluated by the prediction accuracy and the accuracy loss in image classification experiments.A 5-classes aerial optical scene and a 9-classes hyperspectral image(HSI)data set are utilized for optical image and HSI feature extraction,*** features extracted from aerial images are utilized for image classification by a linear support vector machine(SVM)*** results indicate that the proposed method improves the classification accuracy for noisy images(Gaussian noise 2Dσ=0.1,3Dσ=60)in both optical images(2D 87.5%)and HSIs(3D 85.6%)compared with the traditional CAE(2D 78.6%,3D 84.2%).The accuracy loss in classification experiments increases with the increment of *** with the traditional CAE(2D 15.7%,3D 11.8%),the proposed method shows the lower classification accuracy loss in experiments(2D 0.3%,3D 6.3%).The proposed unsupervised noise-robust feature extraction method attains desirable classification accuracy in ideal input and enhances the feature extraction capability from noisy input.

关键词： aerial image classification convolutional autoencoder feature extraction noise-robust

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