High-quality Training Datasets Research Articles

Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism

Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.

Ground-truth-free Deep Learning for 3-D Seismic Denoising and Reconstruction with Channel Attention Mechanism

Seismic denoising methods using supervised methods rely on a large number of high-quality paired training datasets to reach satisfactory performances. There are two ways to generate labels for network training: one is to simulate the synthetic data using the wave equation, and the other is to utilize denoised data obtained via conventional methods. However, using these labels will limit the networks' noise attenuation performance compared with using large volumes of noise-free data as labels. Here, we propose a ground-truth-free way for three-dimensional (3-D) seismic data processing. First, we use the 3-D patch scheme to divide the noisy seismic data into many fixed-size blocks and then flatten the obtained 3-D patches to expand the training set and capture more higher-order waveform characteristics from the input noisy data. Next, the obtained training dataset is sent into the proposed deep learning (DL) network, where the encoder blocks compress the feature map to extract the waveform features, and the decoder blocks reconstruct the denoised feature map. Notably, the convolutional bottleneck attention module (CBAM) and efficient channel attention (ECA) module are applied to guide the network to focus on signal fluctuation features with fewer network parameters. In addition, the concatenation mechanism is used to enable deep networks to reuse shallow-layer waveform features and mitigate overfitting during training. Finally, the unpatching scheme is used to reconstruct the denoised 3-D seismic data. Numerical experiments demonstrate that the proposed method outperforms benchmark approaches in terms of signal-to-noise ratio (SNR) improvement and useful signal preservation.

A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

Monitoring urbanization processes is important because they are often accompanied by intensive landscape pattern transitions and pluralistic socioeconomic changes. To effectively monitor urban expansion and support regional planning, it is essential to develop a fast, accurate and universal urban–rural classification model, especially identifying the dynamic spatial patterns of urban, urban–rural fringe and rural areas. Although deep learning can effectively detect land cover changes, its applications in urban–rural identification have received little attention due to a lack of high-quality training datasets. In this study, we develop a novel transferable full-resolution convolutional neural network (FR-Net) to identify urban-fringe-rural areas. A large-scale training dataset was constructed using field surveys and aerial photography, and a data cube was stacked by multiple typical socio-natural indicators. We took the Beijing-Tianjin-Hebei (BTH) urban agglomeration region in China as a case study and identified spatiotemporal evolutions of urban-fringe-rural areas from 2000 to 2020. The results indicated that over the past two decades, the urban–rural fringe expanded outward with urban areas, and both areas gradually increased, with an inverted U-shaped growth rate. Accurate identification of these fringes can benefit regional urban–rural planning and social governance. Based on the identification results, complex socio-ecological impacts of urbanization could be further explored. Testing demonstrated that the developed FR-Net model has high accuracy and robustness. Our developed open-source FR-Net model exhibits transferability and can be applied to multi-scale urbanized areas.

Parameterization and Performance Analysis of a Scalable, near Real-Time Packet Capturing Platform

The rapid evolution of technology has fostered an exponential rise in the number of individuals and devices interconnected via the Internet. This interconnectedness has prompted companies to expand their computing and communication infrastructures significantly to accommodate the escalating demands. However, this proliferation of connectivity has also opened new avenues for cyber threats, emphasizing the critical need for Intrusion Detection Systems (IDSs) to adapt and operate efficiently in this evolving landscape. In response, companies are increasingly seeking IDSs characterized by horizontal, modular, and elastic attributes, capable of dynamically scaling with the fluctuating volume of network data flows deemed essential for effective monitoring and threat detection. Yet, the task extends beyond mere data capture and storage; robust IDSs must integrate sophisticated components for data analysis and anomaly detection, ideally functioning in real-time or near real-time. While Machine Learning (ML) techniques present promising avenues for detecting and mitigating malicious activities, their efficacy hinges on the availability of high-quality training datasets, which in turn poses a significant challenge. This paper proposes a comprehensive solution in the form of an architecture and reference implementation for (near) real-time capture, storage, and analysis of network data within a 1 Gbps network environment. Performance benchmarks provided offer valuable insights for prototype optimization, demonstrating the capability of the proposed IDS architecture to meet objectives even under realistic operational scenarios.

Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg.

N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation and splicing. Furthermore, it plays a critical role in the regulation of RNA degradation by primarily recruiting the YTHDF2 reader protein. However, the selective regulation of mRNA decay of the m6A-methylated mRNA through YTHDF2 binding is poorly understood. To improve our understanding, we developed m6A-BERT-Deg, a BERT model adapted for predicting YTHDF2-mediated degradation of m6A-methylated mRNAs. We meticulously assembled a high-quality training dataset by integrating multiple data sources for the HeLa cell line. To overcome the limitation of small training samples, we employed a pre-training-fine-tuning strategy by first performing a self-supervised pre-training of the model on 427760 unlabeled m6A site sequences. The test results demonstrated the importance of this pre-training strategy in enabling m6A-BERT-Deg to outperform other benchmark models. We further conducted a comprehensive model interpretation and revealed a surprising finding that the presence of co-factors in proximity to m6A sites may disrupt YTHDF2-mediated mRNA degradation, subsequently enhancing mRNA stability. We also extended our analyses to the HEK293 cell line, shedding light on the context-dependent YTHDF2-mediated mRNA degradation.

Wavelet-Improved Score-Based Generative Model for Medical Imaging.

The score-based generative model (SGM) has demonstrated remarkable performance in addressing challenging under-determined inverse problems in medical imaging. However, acquiring high-quality training datasets for these models remains a formidable task, especially in medical image reconstructions. Prevalent noise perturbations or artifacts in low-dose Computed Tomography (CT) or under-sampled Magnetic Resonance Imaging (MRI) hinder the accurate estimation of data distribution gradients, thereby compromising the overall performance of SGMs when trained with these data. To alleviate this issue, we propose a wavelet-improved denoising technique to cooperate with the SGMs, ensuring effective and stable training. Specifically, the proposed method integrates a wavelet sub-network and the standard SGM sub-network into a unified framework, effectively alleviating inaccurate distribution of the data distribution gradient and enhancing the overall stability. The mutual feedback mechanism between the wavelet sub-network and the SGM sub-network empowers the neural network to learn accurate scores even when handling noisy samples. This combination results in a framework that exhibits superior stability during the learning process, leading to the generation of more precise and reliable reconstructed images. During the reconstruction process, we further enhance the robustness and quality of the reconstructed images by incorporating regularization constraint. Our experiments, which encompass various scenarios of low-dose and sparse-view CT, as well as MRI with varying under-sampling rates and masks, demonstrate the effectiveness of the proposed method by significantly enhanced the quality of the reconstructed images. Especially, our method with noisy training samples achieves comparable results to those obtained using clean data. Our code at https://zenodo.org/record/8266123.

High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs

AbstractThe machine learning revolution presents geoscientists with exciting new opportunities for research, but is constrained by the need for large, high quality training data sets. Simultaneously, undergraduate and graduate program admissions have become increasingly competitive, pressuring high school and undergraduate students to differentiate themselves through involvement in research at earlier stages. Aligning these two interests provides mutually beneficial opportunities for both geoscientists and early‐stage students. We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. These partnerships enabled creation of a powerful new machine learning model that would have otherwise been infeasible. Student benefits varied with their commitment and proactiveness, ranging from exposure to geoscience research and a resume line item to strong letters of recommendation and ongoing connections with geoscience researchers at an elite university lab. Many students were attracted to the project solely out of interest in machine learning, so the opportunity reached students who would not otherwise have conducted research in geoscience. Still, without incentives for researchers to engage less‐privileged students, our experience suggests that mutually beneficial partnerships between researchers and early‐stage students may exacerbate issues of inequality and lack of diversity within the geosciences.

Transfer learning to leverage larger datasets for improved prediction of protein stability changes

Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations perturb protein stability are, therefore, of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here, we describe ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a recently released megascale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from ProteinMPNN, a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves state-of-the-art performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design.

Deep autoencoders and multivariate analysis for enhanced hypertension detection during the COVID-19 era

<abstract> <p>The incidence of hypertension has increased dramatically in both elderly and young populations. The incidence of hypertension also increased with the outbreak of the COVID-19 pandemic. To enhance hypertension detection accuracy, we proposed a multivariate outlier removal method based on the deep autoencoder (DAE) technique. The method was applied to the Korean National Health and Nutrition Examination Survey (KNHANES) database. Several studies have identified various risk factors for chronic hypertension. Chronic diseases are often multifactorial rather than isolated and have been associated with COVID-19. Therefore, it is necessary to study disease detection by considering complex factors. This study was divided into two main parts. The first module, data preprocessing, integrated external features for COVID-19 patients merged by region, age, and gender for the KHNANE-2020 and Kaggle datasets. We then performed multicollinearity (MC)-based feature selection for the KNHANES and integrated datasets. Notably, our MC analysis revealed that the "COVID-19 statement" feature, with a variance inflation factor (VIF) of 1.023 and a p-value < 0.01, is significant in predicting hypertension, underscoring the interrelation between COVID-19 and hypertension risk. The next module used a predictive analysis step to detect and predict hypertension based on an ordinal encoder (OE) transformation and multivariate outlier removal using a DAE from the KNHANES data. We compared each classification model's accuracy, F1 score, and area under the curve (AUC). The experimental results showed that the proposed XGBoost model achieved the best results, with an accuracy rate of 87.78% (86.49%–88.1%, 95% CI), an F1 score of 89.95%, and an AUC of 92.28% for the COVID-19 cases, and an accuracy rate of 87.72% (85.86%–89.69%, 95% CI), an F1 score of 89.94%, and an AUC of 92.23% for the non-COVID-19 cases with the DAE_OE model. We improved the prediction performance of the classifiers used in all experiments by developing a high-quality training dataset implementing the DAE and OE in our method. Moreover, we experimentally demonstrated how the steps of the proposed method improved performance. Our approach has potential applications beyond hypertension detection, including other diseases such as stroke and cardiovascular disease.</p> </abstract>

中国河南省2016–2021年尾矿库目标检测数据集

Tailings ponds are essential infrastructures in mining operations, but they also pose a significant risk source as potential sources of manmade debris flow with high potential energy. The object detection of tailings pond in remote sensing imagery is to accurately recognize and pinpoint the locations of them on the images. Compared with traditional methods, tailings pond detection in remote sensing imagery with the aid of deep learning has seen substantial improvements in accuracy, stability, and efficiency especially when trained on high quality training dataset. Based on years of China high-resolution satellite remote sensing images, through data processing, manual interpretation and annotation, image slicing and other steps, we have constructed a dataset of object detection of tailings ponds in Henan Province, China, available for public access. This dataset has the following characteristics: (1) the domestic high-resolution dataset comprising 1,183 slices and 1,728 object instances; (2) multi temporal dataset containing a total of four different years of sample data in 2016, 2018, 2020 and 2021; (3) objects labeled with oriented bounding box, with less image background interference. The dataset can be used for the technical research on the development of tailings pond detection models with deep learning, as well as for the automatic and intelligent detection of tailings pond, It is of great significance for promoting the development of automatic extraction technology and safety supervision of tailings ponds.

Mid-DeepLabv3+: A Novel Approach for Image Semantic Segmentation Applied to African Food Dietary Assessments.

Recent decades have witnessed the development of vision-based dietary assessment (VBDA) systems. These systems generally consist of three main stages: food image analysis, portion estimation, and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models and the availability of high-quality training datasets. Food image segmentation still faces various challenges, and most existing research focuses mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes with mixed-class food. This work focuses on the first stage of VBDAs, where we introduce two notable contributions. Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM after each extracted backbone feature layer. Secondly, we present CamerFood10, the first food image dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of the most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms benchmark convolutional neural network models for semantic image segmentation, with an mIoU (mean Intersection over Union) of 65.20%, representing a +10.74% improvement over DeepLabv3+ with the same backbone.

View Synthesis of Dynamic Scenes based on Deep 3D Mask Volume.

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Our algorithm addresses the temporal inconsistency of disocclusions by identifying the error-prone areas with a 3D mask volume, and replaces them with static background observed throughout the video. Our method enables manipulation in 3D space as opposed to simple 2D masks, We demonstrate better temporal stability than frame-by-frame static view synthesis methods, or those that use 2D masks. The resulting view synthesis videos show minimal flickering artifacts and allow for larger translational movements.

Simultaneous denoising and reconstruction of distributed acoustic sensing seismic data via a multicascade deep-learning method

Distributed acoustic sensing (DAS) has been moderately used in seismic exploration as a consequence of its advantages, such as low cost, high resolution, high sensitivity, and strong resistance to high temperatures. Nonetheless, seismic data recorded by the DAS system often are seriously contaminated by a massive amount of background noise and recording gaps, which also decrease the quality of DAS seismic data. Building on its success in numerous fields of data processing, convolutional neural networks (CNNs) also have been applied to seismic data denoising as well as reconstruction and achieved good performance compared with traditional methods. However, relatively few CNN-based studies have been done on the simultaneous denoising and reconstruction of seismic data, especially for the complex DAS seismic data with a low signal-to-noise ratio. In this study, we have developed a multicascade network structure in combination with a channel attention mechanism (MCA-Net) to achieve the simultaneous denoising and reconstruction of DAS seismic data by only using a unified trained model. Particularly, our MCA-Net consists of three branches of cascade networks that can extract abundant features from DAS seismic data with different resolutions. Furthermore, we deploy some channel attention modules and aggressive blocks to MCA-Net to fuse these aforementioned multiresolution features and thus improving the feature extraction ability of the whole network. Meanwhile, a high-quality training data set composed of paired clean complete and noisy incomplete patches is used to train MCA-Net in a supervised fashion. Some synthetic and real DAS seismic data are used to demonstrate the effectiveness of MCA-Net, and our method exhibits better denoising and reconstruction performance than some traditional methods and two existing CNN architectures.

Image-Based Ship Detection Using Deep Variational Information Bottleneck.

Image-based ship detection is a critical function in maritime security. However, lacking high-quality training datasets makes it challenging to train a robust supervision deep learning model. Conventional methods use data augmentation to increase training samples. This approach is not robust because the data augmentation may not present a complex background or occlusion well. This paper proposes to use an information bottleneck and a reparameterization trick to address the challenge. The information bottleneck learns features that focus only on the object and eliminate all backgrounds. It helps to avoid background variance. In addition, the reparameterization introduces uncertainty during the training phase. It helps to learn more robust detectors. Comprehensive experiments show that the proposed method outperforms conventional methods on Seaship datasets, especially when the number of training samples is small. In addition, this paper discusses how to integrate the information bottleneck and the reparameterization into well-known object detection frameworks efficiently.

Embracing limited and imperfect training datasets: opportunities and challenges in plant disease recognition using deep learning.

Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Consequently, the practical application of current deep learning-based methods in real-world scenarios is hindered by the scarcity of high-quality datasets. In this paper, we argue that embracing poor datasets is viable and aims to explicitly define the challenges associated with using these datasets. To delve into this topic, we analyze the characteristics of high-quality datasets, namely, large-scale images and desired annotation, and contrast them with the limited and imperfect nature of poor datasets. Challenges arise when the training datasets deviate from these characteristics. To provide a comprehensive understanding, we propose a novel and informative taxonomy that categorizes these challenges. Furthermore, we offer a brief overview of existing studies and approaches that address these challenges. We point out that our paper sheds light on the importance of embracing poor datasets, enhances the understanding of the associated challenges, and contributes to the ambitious objective of deploying deep learning in real-world applications. To facilitate the progress, we finally describe several outstanding questions and point out potential future directions. Although our primary focus is on plant disease recognition, we emphasize that the principles of embracing and analyzing poor datasets are applicable to a wider range of domains, including agriculture. Our project is public available at https://github.com/xml94/EmbracingLimitedImperfectTrainingDatasets.

A quantile-regression physics-informed deep learning for car-following model

Car-following models have been extensively studied using either theory-driven models or data-driven models. On the other hand, theory-driven models have low accuracy, whereas data-driven models have poor interpretability and require a high-quality training dataset. Furthermore, the majority of studies focused on predicting short-term trajectory based on a single vehicle. This paper aims to combine physics knowledge with deep learning and stochasticity to completely capture the genuine regularities of car-following behaviors, leveraging the benefits of both physics-based (interpretability) and deep learning-based (data-efficient and generalizability) models. We design a quantile-regression physics-informed deep learning car-following model (QRPIDL-CF). The physics-informed computational graph is integrated with a long short-term memory (LSTM) architecture (abbreviated as PIDL). By incorporating the quantile regression to the loss function of the PIDL model, stochastic behaviors can be produced without prior assumptions of drivers. To validate the proposed model, data from a 25-vehicle platoon experiment was used. The prediction results prove that given the first vehicle’s trajectory and other vehicles’ initial states, it can preserve the high precision in learning car-following behaviors and introduce small disturbances into car-following in a vehicle-platoon just the same as the real driving process. The trajectory prediction of the whole platoon shows that the proposed hybrid QRPIDL-CF model can better perform the macro-traffic behavior of car-following than every single model from the hybrid QRPIDL-CF model.

Development of taxonomy for classifying defect patterns on wafer bin map using Bin2Vec and clustering methods

A wafer consists of several chips, and serial electrical tests are conducted for each chip to investigate whether the chip is defective. A bin indicates the test results for each chip with information on which tests the chip failed. A wafer bin map (WBM) shows the locations and bins of the defects on the wafer. WBMs showing spatial patterns of defects usually result from assignable causes in the wafer fabrication process; hence, they should be classified in advance. The existing defect-pattern taxonomies do not consider bins, although useful information can be obtained from them. We propose a taxonomy that consists of the shape, size, location, and bin dimensions. The bin dimension is developed using Bin2Vec method, which determines RGB (red-green-blue) code for each bin according to the spatial similarity between bins. Three levels of the bin dimension are defined by analyzing a large number of WBMs using Bin2Vec and clustering methods. Compared with the existing taxonomies, the proposed taxonomy has the advantage of identifying major bins of defect patterns, new defect patterns, and non-critical defect patterns. A high-quality training dataset was obtained using the proposed taxonomy; consequently, a defect pattern classification model with satisfactory classification performance could be obtained.

Terabyte-scale supervised 3D training and benchmarking dataset of the mouse kidney

The performance of machine learning algorithms, when used for segmenting 3D biomedical images, does not reach the level expected based on results achieved with 2D photos. This may be explained by the comparative lack of high-volume, high-quality training datasets, which require state-of-the-art imaging facilities, domain experts for annotation and large computational and personal resources. The HR-Kidney dataset presented in this work bridges this gap by providing 1.7 TB of artefact-corrected synchrotron radiation-based X-ray phase-contrast microtomography images of whole mouse kidneys and validated segmentations of 33 729 glomeruli, which corresponds to a one to two orders of magnitude increase over currently available biomedical datasets. The image sets also contain the underlying raw data, threshold- and morphology-based semi-automatic segmentations of renal vasculature and uriniferous tubules, as well as true 3D manual annotations. We therewith provide a broad basis for the scientific community to build upon and expand in the fields of image processing, data augmentation and machine learning, in particular unsupervised and semi-supervised learning investigations, as well as transfer learning and generative adversarial networks.

An optimized GATK4 pipeline for Plasmodium falciparum whole genome sequencing variant calling and analysis

BackgroundAccurate variant calls from whole genome sequencing (WGS) of Plasmodium falciparum infections are crucial in malaria population genomics. Here a falciparum variant calling pipeline based on GATK version 4 (GATK4) was optimized and applied to 6626 public Illumina WGS samples.MethodsControl WGS and accurate PacBio assemblies of 10 laboratory strains were leveraged to optimize parameters that control the heterozygosity, local assembly region size, ploidy, mapping and base quality in both GATK HaplotypeCaller and GenotypeGVCFs. From these controls, a high-quality training dataset was generated to recalibrate the raw variant data.ResultsOn current high-quality samples (read length = 250 bp, insert size = 405–524 bp), the optimized pipeline shows improved sensitivity (86.6 ± 1.7% for SNPs and 82.2 ± 5.9% for indels) compared to the default GATK4 pipeline (77.7 ± 1.3% for SNPs; and 73.1 ± 5.1% for indels, adjusted P < 0.001) and previous variant calling with GATK version 3 (GATK3, 70.3 ± 3.0% for SNPs and 59.7 ± 5.8% for indels, adjusted P < 0.001). Its sensitivity on simulated mixed infection samples (80.8 ± 6.1% for SNPs and 78.3 ± 5.1% for indels) was again improved relative to default GATK4 (68.8 ± 6.0% for SNPs and 38.9 ± 0.7% for indels, adjusted, adjusted P < 0.001). Precision was high and comparable across all pipelines on each type of data tested. The resulting combination of high-quality SNPs and indels increases the resolution of local population population structure detection in sub-Saharan Africa. Finally, increasing ploidy improves the detection of drug resistance mutations and estimation of complexity of infection.ConclusionsOverall, this study provides an optimized falciparum GATK4 pipeline resource for variant calling which should help improve genomic studies of malaria.

Automated Pavement Crack Detection with Deep Learning Methods: What Are the Main Factors and How to Improve the Performance?

Recently, deep learning (DL) methods have been intensively studied in pavement distress detection research. To build an effective yet robust DL-based distress detection model, both the training data and DL model structure need to be carefully prepared. This paper discusses the main influencing factors that affect the performance of DL models in pavement distress detection and aims to explore possible ways to improve the model performance. An open-source pavement image dataset and three promising object detection methods were selected for model development and evaluation. A series of experiments were conducted with different settings of model structures and data manipulation. According to the experiment results, selection of detection methods, data size, and annotation consistency have significant impact on detection performance. DL models YOLO and Faster R-CNN were found to yield the same level of performance, while CenterNet notably underperformed on the test dataset. Distress classes with a larger number of instances were found to be able to yield better performances. With the new annotation data carefully marked up and verified by experienced pavement raters, the accuracy score using the F1 index was found to be 0.84, which was a very sound improvement compared with the 0.7 of the original annotation. Data augmentation methods were found to be able to significantly improve the detection accuracy. Based on the experiment results, we believe the focus should be placed on the development of high-quality training datasets and thin object detection methods in the future.