Journal articles on the topic 'Dataset nested'
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Dinh, Thi Lan Anh, and Filipe Aires. "Nested leave-two-out cross-validation for the optimal crop yield model selection." Geoscientific Model Development 15, no. 9 (May 5, 2022): 3519–35. http://dx.doi.org/10.5194/gmd-15-3519-2022.
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Abstract. The use of statistical models to study the impact of weather on crop yield has not ceased to increase. Unfortunately, this type of application is characterized by datasets with a very limited number of samples (typically one sample per year). In general, statistical inference uses three datasets: the training dataset to optimize the model parameters, the validation dataset to select the best model, and the testing dataset to evaluate the model generalization ability. Splitting the overall database into three datasets is often impossible in crop yield modelling due to the limited number of samples. The leave-one-out cross-validation method, or simply leave one out (LOO), is often used to assess model performance or to select among competing models when the sample size is small. However, the model choice is typically made using only the testing dataset, which can be misleading by favouring unnecessarily complex models. The nested cross-validation approach was introduced in machine learning to avoid this problem by truly utilizing three datasets even with limited databases. In this study, we propose one particular implementation of the nested cross-validation, called the nested leave-two-out cross-validation method or simply the leave two out (LTO), to choose the best model with an optimal model selection (using the validation dataset) and estimate the true model quality (using the testing dataset). Two applications are considered: robusta coffee in Cu M'gar (Dak Lak, Vietnam) and grain maize over 96 French departments. In both cases, LOO is misleading by choosing models that are too complex; LTO indicates that simpler models actually perform better when a reliable generalization test is considered. The simple models obtained using the LTO approach have improved yield anomaly forecasting skills in both study crops. This LTO approach can also be used in seasonal forecasting applications. We suggest that the LTO method should become a standard procedure for statistical crop modelling.
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Sheikhaei, Mohammad Sadegh, Hasan Zafari, and Yuan Tian. "Joined Type Length Encoding for Nested Named Entity Recognition." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (May 31, 2022): 1–23. http://dx.doi.org/10.1145/3487057.
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In this article, we propose a new encoding scheme for named entity recognition (NER) called Joined Type-Length encoding (JoinedTL). Unlike most existing named entity encoding schemes, which focus on flat entities, JoinedTL can label nested named entities in a single sequence. JoinedTL uses a packed encoding to represent both type and span of a named entity, which not only results in less tagged tokens compared to existing encoding schemes, but also enables it to support nested NER. We evaluate the effectiveness of JoinedTL for nested NER on three nested NER datasets: GENIA in English, GermEval in German, and PerNest, our newly created nested NER dataset in Persian. We apply CharLSTM+WordLSTM+CRF, a three-layer sequence tagging model on three datasets encoded using JoinedTL and two existing nested NE encoding schemes, i.e., JoinedBIO and JoinedBILOU. Our experiment results show that CharLSTM+WordLSTM+CRF trained with JoinedTL encoded datasets can achieve competitive F1 scores as the ones trained with datasets encoded by two other encodings, but with 27%–48% less tagged tokens. To leverage the power of three different encodings, i.e., JoinedTL, JoinedBIO, and JoinedBILOU, we propose an encoding-based ensemble method for nested NER. Evaluation results show that the ensemble method achieves higher F1 scores on all datasets than the three models each trained using one of the three encodings. By using nested NE encodings including JoinedTL with CharLSTM+WordLSTM+CRF, we establish new state-of-the-art performance with an F1 score of 83.7 on PerNest, 74.9 on GENIA, and 70.5 on GermEval, surpassing two recent neural models specially designed for nested NER.
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Li, Zan, Hong Zhang, Zhengzhen Li, and Zuyue Ren. "Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation." Applied Sciences 12, no. 14 (July 15, 2022): 7149. http://dx.doi.org/10.3390/app12147149.
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Image segmentation is a basic technology in the field of image processing and computer vision. Medical image segmentation is an important application field of image segmentation and plays an increasingly important role in clinical diagnosis and treatment. Deep learning has made great progress in medical image segmentation. In this paper, we proposed Residual-Attention UNet++, which is an extension of the UNet++ model with a residual unit and attention mechanism. Firstly, the residual unit improves the degradation problem. Secondly, the attention mechanism can increase the weight of the target area and suppress the background area irrelevant to the segmentation task. Three medical image datasets such as skin cancer, cell nuclei, and coronary artery in angiography were used to validate the proposed model. The results showed that the Residual-Attention UNet++ achieved superior evaluation scores with an Intersection over Union (IoU) of 82.32%, and a dice coefficient of 88.59% with the skin cancer dataset, a dice coefficient of 85.91%, and an IoU of 87.74% with the cell nuclei dataset and a dice coefficient of 72.48%, and an IoU of 66.57% with the angiography dataset.
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Zhang, Jilong, Yajuan Zhang, Hongyang Zhang, Quan Zhang, Weihua Su, Shijie Guo, and Yuanquan Wang. "Segmentation of biventricle in cardiac cine MRI via nested capsule dense network." PeerJ Computer Science 8 (November 30, 2022): e1146. http://dx.doi.org/10.7717/peerj-cs.1146.
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Background Cardiac magnetic resonance image (MRI) has been widely used in diagnosis of cardiovascular diseases because of its noninvasive nature and high image quality. The evaluation standard of physiological indexes in cardiac diagnosis is essentially the accuracy of segmentation of left ventricle (LV) and right ventricle (RV) in cardiac MRI. The traditional symmetric single codec network structure such as U-Net tends to expand the number of channels to make up for lost information that results in the network looking cumbersome. Methods Instead of a single codec, we propose a multiple codecs structure based on the FC-DenseNet (FCD) model and capsule convolution-capsule deconvolution, named Nested Capsule Dense Network (NCDN). NCDN uses multiple codecs to achieve multi-resolution, which makes it possible to save more spatial information and improve the robustness of the model. Results The proposed model is tested on three datasets that include the York University Cardiac MRI dataset, Automated Cardiac Diagnosis Challenge (ACDC-2017), and the local dataset. The results show that the proposed NCDN outperforms most methods. In particular, we achieved nearly the most advanced accuracy performance in the ACDC-2017 segmentation challenge. This means that our method is a reliable segmentation method, which is conducive to the application of deep learning-based segmentation methods in the field of medical image segmentation.
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Fu, Yao, Chuanqi Tan, Mosha Chen, Songfang Huang, and Fei Huang. "Nested Named Entity Recognition with Partially-Observed TreeCRFs." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12839–47. http://dx.doi.org/10.1609/aaai.v35i14.17519.
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Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is difficult to detect entities with nested structures. In this work, we view nested NER as constituency parsing with partially-observed trees and model it with partially-observed TreeCRFs. Specifically, we view all labeled entity spans as observed nodes in a constituency tree, and other spans as latent nodes. With the TreeCRF we achieve a uniform way to jointly model the observed and the latent nodes. To compute the probability of partial trees with partial marginalization, we propose a variant of the Inside algorithm, the Masked Inside algorithm, that supports different inference operations for different nodes (evaluation for the observed, marginalization for the latent, and rejection for nodes incompatible with the observed) with efficient parallelized implementation, thus significantly speeding up training and inference. Experiments show that our approach achieves the state-of-the-art (SOTA) F1 scores on the ACE2004, ACE2005 dataset, and shows comparable performance to SOTA models on the GENIA dataset. We release the code at https://github.com/FranxYao/Partially-Observed-TreeCRFs.
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Kulkarni, Rishikesh U., Catherine L. Wang, and Carolyn R. Bertozzi. "Analyzing nested experimental designs—A user-friendly resampling method to determine experimental significance." PLOS Computational Biology 18, no. 5 (May 2, 2022): e1010061. http://dx.doi.org/10.1371/journal.pcbi.1010061.
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While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. Resampling-based methods are a flexible strategy for performing these analyses but are difficult due to the lack of open-source software to automate test construction and execution. To address this, we present Hierarch, a Python package to perform hypothesis tests and compute confidence intervals on hierarchical experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest. Hierarch makes use of the Numba JIT compiler to reduce p-value computation times to under one second for typical datasets in biomedical research. Hierarch also enables researchers to construct user-defined resampling plans that take advantage of Hierarch’s Numba-accelerated functions.
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Liu, Wen, Yankui Sun, and Qingge Ji. "MDAN-UNet: Multi-Scale and Dual Attention Enhanced Nested U-Net Architecture for Segmentation of Optical Coherence Tomography Images." Algorithms 13, no. 3 (March 4, 2020): 60. http://dx.doi.org/10.3390/a13030060.
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Optical coherence tomography (OCT) is an optical high-resolution imaging technique for ophthalmic diagnosis. In this paper, we take advantages of multi-scale input, multi-scale side output and dual attention mechanism and present an enhanced nested U-Net architecture (MDAN-UNet), a new powerful fully convolutional network for automatic end-to-end segmentation of OCT images. We have evaluated two versions of MDAN-UNet (MDAN-UNet-16 and MDAN-UNet-32) on two publicly available benchmark datasets which are the Duke Diabetic Macular Edema (DME) dataset and the RETOUCH dataset, in comparison with other state-of-the-art segmentation methods. Our experiment demonstrates that MDAN-UNet-32 achieved the best performance, followed by MDAN-UNet-16 with smaller parameter, for multi-layer segmentation and multi-fluid segmentation respectively.
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Turanzas, J., M. Alonso, H. Amaris, J. Gutierrez, and S. Pastrana. "A nested decision tree for event detection in smart grids." Renewable Energy and Power Quality Journal 20 (September 2022): 353–58. http://dx.doi.org/10.24084/repqj20.308.
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Digitalization process experienced by traditional power networks towards smart grids extend the challenges faced by power grid operators to the field of cybersecurity. False data injection attacks, one of the most common cyberattacks in smart grids, could lead the power grid to sabotage itself. In this paper, an event detection algorithm for cyberattack in smart grids is developed based on a decision tree. In order to find the most accurate algorithm, two different decision trees with two different goals have been trained: one classifies the status of the network, corresponding to an event, and the other will classify the location where the event is detected. To train the decision trees, a dataset made by co-simulating a power network and a communication network has been used. The decision trees are going to be compared in different settings by changing the division criteria, the dataset used to train them and the misclassification cost. After looking at their performance independently, the best way to combine them into a single algorithm is presented.
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Jamali, A., and A. Abdul Rahman. "EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 283–89. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-283-2019.
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Abstract. For environmental monitoring, land-cover mapping, and urban planning, remote sensing is an effective method. In this paper, firstly, for land use land cover mapping, Landsat 8 OLI image classification based on six advanced mathematical algorithms of machine learning including Random Forest, Decision Table, DTNB, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge) and Simple Logistic is used. Then, results are compared in the terms of Overall Accuracy (OA), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for land use land cover (LULC) mapping. Based on the training and test datasets, Simple Logistic had the best performance in terms of OA, MAE and RMSE values of 99.9293, 0.0006 and 0.016 for training dataset and values of 99.9467, 0.0005 and 0.0153 for the test dataset.
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Hazard, Derek, Martin Schumacher, Mercedes Palomar-Martinez, Francisco Alvarez-Lerma, Pedro Olaechea-Astigarraga, and Martin Wolkewitz. "Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections." Infection Control & Hospital Epidemiology 39, no. 10 (August 30, 2018): 1196–201. http://dx.doi.org/10.1017/ice.2018.186.
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AbstractObjectiveCompeting risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.MethodsUsing data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.ResultsThe estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.ConclusionsUsing additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.
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Agrawal, Ankit, Sarsij Tripathi, Manu Vardhan, Vikas Sihag, Gaurav Choudhary, and Nicola Dragoni. "BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling." Applied Sciences 12, no. 3 (January 18, 2022): 976. http://dx.doi.org/10.3390/app12030976.
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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches.
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YILDIRIM, Hakan, Ülkü ÇELİKER, Sabiha GÜNGÖR KOBAT, Sengul DOGAN, Mehmet BAYĞIN, Orhan YAMAN, Türker TUNCER, and Murat ERDAĞ. "An automated diabetic retinopathy disorders detection model based on pretrained MobileNetv2 and nested patch division using fundus images." Journal of Health Sciences and Medicine 5, no. 6 (October 25, 2022): 1741–46. http://dx.doi.org/10.32322/jhsm.1184981.
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Aim: Fundus images are very important to diagnose some ophthalmologic disorders. Hence, fundus images have become a very important data source for machine-learning society. Our primary goal is to propose a new automated disorder classification model for diabetic retinopathy (DR) using the strength of deep learning. In this model, our proposed model suggests a treatment technique using fundus images. Material and Method: In this research, a new dataset was acquired and this dataset contains 1365 Fundus Fluorescein Angiography images with five classes. To detect these disorders automatically, we proposed a transfer learning-based feature engineering model. This feature engineering model uses pretrained MobileNetv2 and nested patch division to extract deep and exemplar features. The neighborhood component analysis (NCA) feature selection function has been applied to choose the top features. k nearest neighbors (kNN) classification function has been used to get results and we used 10-fold cross-validation (CV) to validate the results. Results: The proposed MobileNetv2 and nested patch-based image classification model attained 87.40% classification accuracy on the collected dataset. Conclusions: The calculated 87.40% classification accuracy for five classes has been demonstrated high classification accuracy of the proposed deep feature engineering model
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Bellala, G., A. Ganesan, R. Krishna, P. Saxman, C. Scott, M. Silveira, C. Given, and S. K. Bhavnani. "The Nested Structure of Cancer Symptoms." Methods of Information in Medicine 49, no. 06 (2010): 581–91. http://dx.doi.org/10.3414/me09-01-0083.
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Summary Objective: Although many cancer patients experience multiple concurrent symptoms, most studies have either focused on the analysis of single symptoms, or have used methods such as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations by first visually exploring the data to identify patterns in the co-occurrence of multiple symptoms, and then using those insights to select and develop quantitative measures to analyze and validate the results. Methods: We used networks to visualize how 665 cancer patients reported 18 symptoms, and then quantitatively analyzed the observed patterns using degree of symptom overlap between patients, degree of symptom clustering using network modularity, clustering of symptoms based on agglomerative hierarchical clustering, and degree of nestedness of the symptoms based on the most frequently co-occurring symptoms for different sizes of symptom sets. These results were validated by assessing the statistical significance of the quantitative measures through comparison with random networks of the same size and distribution. Results: The cancer symptoms tended to co-occur in a nested structure, where there was a small set of symptoms that co-occurred in many patients, and progressively larger sets of symptoms that co-occurred among a few patients. Conclusions: These results suggest that cancer symptoms co-occur in a nested pattern as opposed to distinct clusters, thereby demonstrating the value of exploratory network analyses to reveal complex relationships between patients and symptoms. The research also extends methods for exploring symptom co-occurrence, including methods for quantifying the degree of symptom overlap and for examining nested co-occurrence in co-occur-rence data. Finally, the analysis also suggested implications for the design of systems that assist in symptom assessment and management. The main limitation of the study was that only one dataset was considered, and future studies should attempt to replicate the results in new data.
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Pham, Minh Quang Nhat. "A Feature-Based Model for Nested Named-Entity Recognition at VLSP-2018 NER Evaluation Campaign." Journal of Computer Science and Cybernetics 34, no. 4 (January 30, 2019): 311–21. http://dx.doi.org/10.15625/1813-9663/34/4/13163.
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In this report, we describe our participant named-entity recognition system at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling problem using BIO encoding scheme. We applied a feature-based model which combines word, word-shape features, Brown-cluster-based features, and word-embedding-based features. We compare several methods to deal with nested entities in the dataset. We showed that combining tags of entities at all levels for training a sequence labeling model (joint-tag model) improved the accuracy of nested named-entity recognition.
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Cui, Hu, Haiwei Pan, and Kejia Zhang. "SCU-Net++: A Nested U-Net Based on Sharpening Filter and Channel Attention Mechanism." Wireless Communications and Mobile Computing 2022 (May 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/2848365.
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U-Net++ is one of the most prominent deep convolutional neural networks in the field of medical image segmentation after U-Net. However, the semantic gaps between the encoder and decoder subnets are still large, which will lead to fuzzy feature maps and even target regions of segmentation. To solve this problem, we propose an improved semantic segmentation model utilizing channel attention mechanism and Laplacian sharpening filter, SCU-Net++: dense skip connections are redesigned with sharpening filters to ease the semantic gaps, and channel attention modules are used to make the model pay more attention on the feature maps that are useful for our pixel-level classification task. Compared with U-Net++, the proposed model obtains a more competitive performance on the Pancreas Segmentation dataset and Liver Tumor Segmentation dataset, while increases a very small number of learnable parameters and thus almost does not make additional training and reasoning costs. The training of the proposed method is carried out in deep supervision mode, which alleviates the problem of gradient disappearance, and pruning mechanism can be activated to accelerate the reasoning speed.
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Yang, Ning, Sio Hang Pun, Mang I. Vai, Yifan Yang, and Qingliang Miao. "A Unified Knowledge Extraction Method Based on BERT and Handshaking Tagging Scheme." Applied Sciences 12, no. 13 (June 28, 2022): 6543. http://dx.doi.org/10.3390/app12136543.
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In the actual knowledge extraction system, different applications have different entity classes and relationship schema, so the generalization and migration ability of knowledge extraction are very important. By training a knowledge extraction model in the source domain and applying the model to an arbitrary target domain directly, open domain knowledge extraction technology becomes crucial to mitigate the generalization and migration ability issues. Traditional knowledge extraction models cannot be directly transferred to new domains and also cannot extract undefined relation types. In order to deal with the above issues, in this paper, we proposed an end-to-end Chinese open-domain knowledge extraction model, TPORE (Extract Open-domain Relations through Token Pair linking), which combined BERT with a handshaking tagging scheme. TPORE can alleviate the nested entities and nested relations issues. Additionally, a new loss function that conducts a pairwise comparison of target category score and non-target category score to automatically balance the weight was adopted, and the experiment results indicate that the loss function can bring speed and performance improvements. The extensive experiments demonstrate that the proposed method can significantly surpass strong baselines. Specifically, our approach can achieve new state-of-the-art Chinese open Relation Extraction (ORE) benchmarks (COER and SAOKE). In the COER dataset, F1 increased from 66.36% to 79.63%, and in the SpanSAOKE dataset, F1 increased from 46.0% to 54.91%. In the medical domain, our method can obtain close performance compared with the SOTA method in the CMeIE and CMeEE datasets.
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Lefebvre, Louis, Simon Ducatez, and Jean-Nicolas Audet. "Feeding innovations in a nested phylogeny of Neotropical passerines." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1690 (March 19, 2016): 20150188. http://dx.doi.org/10.1098/rstb.2015.0188.
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Several studies on cognition, molecular phylogenetics and taxonomic diversity independently suggest that Darwin's finches are part of a larger clade of speciose, flexible birds, the family Thraupidae , a member of the New World nine-primaried oscine superfamily Emberizoidea . Here, we first present a new, previously unpublished, dataset of feeding innovations covering the Neotropical region and compare the stem clades of Darwin's finches to other neotropical clades at the levels of the subfamily, family and superfamily/order. Both in terms of raw frequency as well as rates corrected for research effort and phylogeny, the family Thraupidae and superfamily Emberizoidea show high levels of innovation, supporting the idea that adaptive radiations are favoured when the ancestral stem species were flexible. Second, we discuss examples of innovation and problem-solving in two opportunistic and tame Emberizoid species, the Barbados bullfinch Loxigilla barbadensis and the Carib grackle Quiscalus lugubris fortirostris in Barbados. We review studies on these two species and argue that a comparison of L. barbadensis with its closest, but very shy and conservative local relative, the black-faced grassquit Tiaris bicolor , might provide key insights into the evolutionary divergence of cognition.
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Neri, Mattia, Juraj Parajka, and Elena Toth. "Importance of the informative content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density." Hydrology and Earth System Sciences 24, no. 11 (November 6, 2020): 5149–71. http://dx.doi.org/10.5194/hess-24-5149-2020.
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Abstract. The setup of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activities for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to estimate the model parameters based on the hydrometric information available in the region. The informative content of the dataset (i.e. which and how many gauged river stations are available) plays an essential role in the assessment of the best regionalisation method. This study analyses how the performances of regionalisation approaches are influenced by the “information richness” of the available regional dataset, i.e. the availability of potential donors, and in particular by the gauging density and by the presence of nested donor catchments, which are expected to be hydrologically very similar to the target section. The research is carried out over a densely gauged dataset covering the Austrian country, applying two rainfall-runoff models and different regionalisation approaches. The regionalisation techniques are first implemented using all the gauged basins in the dataset as potential donors and then re-applied, decreasing the informative content of the dataset. The effect of excluding nested basins and the status of “nestedness” is identified based on the position of the closing section along the river or the percentage of shared drainage area. Moreover, the impact of reducing station density on regionalisation performance is analysed. The results show that the predictive accuracy of parameter regionalisation techniques strongly depends on the informative content of the dataset of available donor catchments. The “output-averaging” approaches, which exploit the information of more than one donor basin and preserve the correlation structure of the parameter, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions. Moreover, with the use of an optimised set of catchment descriptors as a similarity measure, rather than the simple geographical distance, results are more robust to the deterioration of the informative content of the set of donors.
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Bukovsky, Melissa S., and David J. Karoly. "Precipitation Simulations Using WRF as a Nested Regional Climate Model." Journal of Applied Meteorology and Climatology 48, no. 10 (October 1, 2009): 2152–59. http://dx.doi.org/10.1175/2009jamc2186.1.
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Abstract This note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast–type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction–National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis.
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Yao, Fei, Jiansheng Wu, Weifeng Li, and Jian Peng. "Estimating Daily PM2.5 Concentrations in Beijing Using 750-M VIIRS IP AOD Retrievals and a Nested Spatiotemporal Statistical Model." Remote Sensing 11, no. 7 (April 8, 2019): 841. http://dx.doi.org/10.3390/rs11070841.
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Satellite-retrieved aerosol optical depth (AOD) data have been widely used to predict PM2.5 concentrations. Most of their spatial resolutions (~1 km or greater), however, are too coarse to support PM2.5-related studies at fine scales (e.g., urban-scale PM2.5 exposure assessments). Space-time regression models have been widely developed and applied to predict PM2.5 concentrations from satellite-retrieved AOD. Their accuracies, however, are not satisfactory particularly on days that lack a model dataset. The present study aimed to evaluate the effectiveness of recent high-resolution (i.e., ~750 m at nadir) AOD obtained from the Visible Infrared Imaging Radiometer Suite instrument (VIIRS) Intermediate Product (IP) in estimating PM2.5 concentrations with a newly developed nested spatiotemporal statistical model. The nested spatiotemporal statistical model consisted of two parts: a nested time fixed effects regression (TFER) model and a series of geographically weighted regression (GWR) models. The TFER model, containing daily, weekly, or monthly intercepts, used the VIIRS IP AOD as the main predictor alongside several auxiliary variables to predict daily PM2.5 concentrations. Meanwhile, the series of GWR models used the VIIRS IP AOD as the independent variable to correct residuals from the first-stage nested TFER model. The average spatiotemporal coverage of the VIIRS IP AOD was approximately 16.12%. The sample-based ten-fold cross validation goodness of fit (R2) for the first-stage TFER models with daily, weekly, and monthly intercepts were 0.81, 0.66, and 0.45, respectively. The second-stage GWR models further captured the spatial heterogeneities of the PM2.5-AOD relationships. The nested spatiotemporal statistical model produced more daily PM2.5 estimates and improved the accuracies of summer, autumn, and annual PM2.5 estimates. This study contributes to the knowledge of how well VIIRS IP AOD can predict PM2.5 concentrations at urban scales and offers strategies for improving the coverage and accuracy of daily PM2.5 estimates on days that lack a model dataset.
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Ma, Chunyong, Anni Wang, Ge Chen, and Chi Xu. "Hand joints-based gesture recognition for noisy dataset using nested interval unscented Kalman filter with LSTM network." Visual Computer 34, no. 6-8 (May 11, 2018): 1053–63. http://dx.doi.org/10.1007/s00371-018-1556-0.
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Zhang, Ningshan, Kyle Schmaus, and Patrick O. Perry. "Fitting a deeply nested hierarchical model to a large book review dataset using a moment-based estimator." Annals of Applied Statistics 13, no. 4 (December 2019): 2260–88. http://dx.doi.org/10.1214/19-aoas1251.
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Wu, X., R. Zurita-Milla, M. J. Kraak, and E. Izquierdo-Verdiguier. "CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1387–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1387-2017.
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As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.
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Lin, Chih-Wei, Mengxiang Lin, and Yu Hong. "Aerial and Optical Images-Based Plant Species Segmentation Using Enhancing Nested Downsampling Features." Forests 12, no. 12 (December 3, 2021): 1695. http://dx.doi.org/10.3390/f12121695.
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Plant species, structural combination, and spatial distribution in different regions should be adapted to local conditions, and the reasonable arrangement can bring the best ecological effect. Therefore, it is essential to understand the classification and distribution of plant species. This paper proposed an end-to-end network with Enhancing Nested Downsampling features (END-Net) to solve complex and challenging plant species segmentation tasks. There are two meaningful operations in the proposed network: (1) A compact and complete encoder–decoder structure nests in the down-sampling process; it makes each downsampling block obtain the equal feature size of input and output to get more in-depth plant species information. (2) The downsampling process of the encoder–decoder framework adopts a novel pixel-based enhance module. The enhanced module adaptively enhances each pixel’s features with the designed learnable variable map, which is as large as the corresponding feature map and has n×n variables; it can capture and enhance each pixel’s information flexibly effectively. In the experiments, our END-Net compared with eleven state-of-the-art semantic segmentation architectures on the self-collected dataset, it has the best PA (Pixel Accuracy) score and FWloU (Frequency Weighted Intersection over Union) accuracy and achieves 84.52% and 74.96%, respectively. END-Net is a lightweight model with excellent performance; it is practical in complex vegetation distribution with aerial and optical images. END-Net has the following merits: (1) The proposed enhancing module utilizes the learnable variable map to enhance features of each pixel adaptively. (2) We nest a tiny encoder–decoder module into the downsampling block to obtain the in-depth plant species features with the same scale in- and out-features. (3) We embed the enhancing module into the nested model to enhance and extract distinct plant species features. (4) We construct a specific plant dataset that collects the optical images-based plant picture captured by drone with sixteen species.
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Pavelchek, Cole, Andrew P. Michelson, Amit Walia, Amanda Ortmann, Jacques Herzog, Craig A. Buchman, and Matthew A. Shew. "Imputation of missing values for cochlear implant candidate audiometric data and potential applications." PLOS ONE 18, no. 2 (February 6, 2023): e0281337. http://dx.doi.org/10.1371/journal.pone.0281337.
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Objective Assess the real-world performance of popular imputation algorithms on cochlear implant (CI) candidate audiometric data. Methods 7,451 audiograms from patients undergoing CI candidacy evaluation were pooled from 32 institutions with complete case analysis yielding 1,304 audiograms. Imputation model performance was assessed with nested cross-validation on randomly generated sparse datasets with various amounts of missing data, distributions of sparsity, and dataset sizes. A threshold for safe imputation was defined as root mean square error (RMSE) <10dB. Models included univariate imputation, interpolation, multiple imputation by chained equations (MICE), k-nearest neighbors, gradient boosted trees, and neural networks. Results Greater quantities of missing data were associated with worse performance. Sparsity in audiometric data is not uniformly distributed, as inter-octave frequencies are less commonly tested. With 3–8 missing features per instance, a real-world sparsity distribution was associated with significantly better performance compared to other sparsity distributions (Δ RMSE 0.3 dB– 5.8 dB, non-overlapping 99% confidence intervals). With a real-world sparsity distribution, models were able to safely impute up to 6 missing datapoints in an 11-frequency audiogram. MICE consistently outperformed other models across all metrics and sparsity distributions (p < 0.01, Wilcoxon rank sum test). With sparsity capped at 6 missing features per audiogram but otherwise equivalent to the raw dataset, MICE imputed with RMSE of 7.83 dB [95% CI 7.81–7.86]. Imputing up to 6 missing features captures 99.3% of the audiograms in our dataset, allowing for a 5.7-fold increase in dataset size (1,304 to 7,399 audiograms) as compared with complete case analysis. Conclusion Precision medicine will inevitably play an integral role in the future of hearing healthcare. These methods are data dependent, and rigorously validated imputation models are a key tool for maximizing datasets. Using the largest CI audiogram dataset to-date, we demonstrate that in a real-world scenario MICE can safely impute missing data for the vast majority (>99%) of audiograms with RMSE well below a clinically significant threshold of 10dB. Evaluation across a range of dataset sizes and sparsity distributions suggests a high degree of generalizability to future applications.
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Kong, Lei, Xiao Tang, Jiang Zhu, Zifa Wang, Jianjun Li, Huangjian Wu, Qizhong Wu, et al. "A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC." Earth System Science Data 13, no. 2 (February 23, 2021): 529–70. http://dx.doi.org/10.5194/essd-13-529-2021.
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Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.
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Pirkl, Martin, Elisabeth Hand, Dieter Kube, and Rainer Spang. "Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models." Bioinformatics 32, no. 6 (November 17, 2015): 893–900. http://dx.doi.org/10.1093/bioinformatics/btv680.
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Abstract Motivation: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. Results: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. Availability and implementation: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. Contact: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de Supplementary information: Supplementary data are available at Bioinformatics online.
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Zhang, Yan, Weiguo Gong, Jingxi Sun, and Weihong Li. "Web-Net: A Novel Nest Networks with Ultra-Hierarchical Sampling for Building Extraction from Aerial Imageries." Remote Sensing 11, no. 16 (August 14, 2019): 1897. http://dx.doi.org/10.3390/rs11161897.
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How to efficiently utilize vast amounts of easily accessed aerial imageries is a critical challenge for researchers with the proliferation of high-resolution remote sensing sensors and platforms. Recently, the rapid development of deep neural networks (DNN) has been a focus in remote sensing, and the networks have achieved remarkable progress in image classification and segmentation tasks. However, the current DNN models inevitably lose the local cues during the downsampling operation. Additionally, even with skip connections, the upsampling methods cannot properly recover the structural information, such as the edge intersections, parallelism, and symmetry. In this paper, we propose the Web-Net, which is a nested network architecture with hierarchical dense connections, to handle these issues. We design the Ultra-Hierarchical Sampling (UHS) block to absorb and fuse the inter-level feature maps to propagate the feature maps among different levels. The position-wise downsampling/upsampling methods in the UHS iteratively change the shape of the inputs while preserving the number of their parameters, so that the low-level local cues and high-level semantic cues are properly preserved. We verify the effectiveness of the proposed Web-Net in the Inria Aerial Dataset and WHU Dataset. The results of the proposed Web-Net achieve an overall accuracy of 96.97% and an IoU (Intersection over Union) of 80.10% on the Inria Aerial Dataset, which surpasses the state-of-the-art SegNet 1.8% and 9.96%, respectively; the results on the WHU Dataset also support the effectiveness of the proposed Web-Net. Additionally, benefitting from the nested network architecture and the UHS block, the extracted buildings on the prediction maps are obviously sharper and more accurately identified, and even the building areas that are covered by shadows can also be correctly extracted. The verified results indicate that the proposed Web-Net is both effective and efficient for building extraction from high-resolution remote sensing images.
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Amo de Paz, Guillermo, H. Thorsten Lumbsch, Paloma Cubas, John A. Elix, and Ana Crespo. "The genus Karoowia (Parmeliaceae, Ascomycota) includes unrelated clades nested within Xanthoparmelia." Australian Systematic Botany 23, no. 3 (2010): 173. http://dx.doi.org/10.1071/sb09055.
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Thallus morphology has traditionally played a major role in the classification of lichenised fungi. We have used a combined dataset of nuITS, nuLSU and mtSSU rDNA sequences to evaluate the phylogenetic relationships between the subcrustose genus Karoowia and the mostly foliose genus Xanthoparmelia. Our phylogenetic analyses using maximum parsimony, maximum likelihood and a Bayesian approach show that Karoowia species do not form a monophyletic group but cluster in different clades nested within Xanthoparmelia. The monophyly of Karoowia either as a separate clade from Xanthoparmelia, or nested within Xanthoparmelia is significantly rejected using alternative hypothesis testing. These results suggest that the usefulness of the phenotypic features used to define Karoowia has been overestimated because the subcrustose growth form has evolved independently in several clades within Xanthoparmelia. Other characters used to circumscribe Karoowia, such as the presence of cylindrical conidia, also occur in Xanthoparmelia, and the differences in rhizine morphology are minimal. Consequently, we propose to reduce Karoowia to synonymy with Xanthoparmelia. The enlarged genus is characterised by the presence of Xanthoparmelia-type lichenan in the hyphal cell walls and the presence of an arachiform vacuolar body in the ascospores. Fifteen new combinations in Xanthoparmelia and the new name Xanthoparmelia mucinae for Karoowia squamatica are made.
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Ren, Jiansi, Ruoxiang Wang, Gang Liu, Yuanni Wang, and Wei Wu. "An SVM-Based Nested Sliding Window Approach for Spectral–Spatial Classification of Hyperspectral Images." Remote Sensing 13, no. 1 (December 31, 2020): 114. http://dx.doi.org/10.3390/rs13010114.
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This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood window constructed with the target pixel as the centre contains relevant pixels that are spatially adjacent to the target pixel. In the neighbourhood window, a nested sliding sub-window contains the target pixel and a part of the relevant pixels. The optimal sub-window position is determined according to the average value of the Pearson correlation coefficients of the target pixel and the relevant pixels, and the target pixel can be reconstructed by using the pixels and the corresponding correlation coefficients in the optimal sub-window. By combining NSW with Principal Component Analysis (PCA) and Support Vector Machine (SVM), a classification model, namely NSW-PCA-SVM, is obtained. This paper conducts experiments on three public datasets, and verifies the effectiveness of the proposed model by comparing with two basic models, i.e., SVM and PCA-SVM, and six state-of-the-art models, i.e., CDCT-WF-SVM, CDCT-2DCT-SVM, SDWT-2DWT-SVM, SDWT-WF-SVM, SDWT-2DCT-SVM and Two-Stage. The proposed approach has the following advantages in overall accuracy (OA)—take the experimental results on the Indian Pines dataset as an example: (1) Compared with SVM (OA = 53.29%) and PCA-SVM (OA = 58.44%), NSW-PCA-SVM (OA = 91.40%) effectively utilizes the spatial information of HSI and improves the classification accuracy. (2) The performance of the proposed model is mainly determined by two parameters, i.e., the window size in NSW and the number of principal components in PCA. The two parameters can be adjusted independently, making parameter adjustment more convenient. (3) When the sample size of the training set is small (20 samples per class), the proposed NSW-PCA-SVM approach achieves 2.38–18.40% advantages in OA over the six state-of-the-art models.
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Paul, Sourav, and Danilo Calliari. "Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata." Journal of the Marine Biological Association of the United Kingdom 99, no. 5 (December 18, 2018): 1059–64. http://dx.doi.org/10.1017/s002531541800108x.
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AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.
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Maussion, F., D. Scherer, R. Finkelnburg, J. Richters, W. Yang, and T. Yao. "WRF simulation of a precipitation event over the Tibetan Plateau, China – an assessment using remote sensing and ground observations." Hydrology and Earth System Sciences Discussions 7, no. 3 (June 16, 2010): 3551–89. http://dx.doi.org/10.5194/hessd-7-3551-2010.
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Abstract. Meteorological observations over the Tibetan Plateau are scarce, and precipitation estimations over this remote region are difficult. Numerical weather prediction models can be used to retrieve precipitation fields at a higher spatial and temporal resolution than the commonly used gridded precipitation products. In this paper, the Weather Research and Forecasting (WRF) model capacity in retrieving rain- and snowfall during a single event is evaluated. The simulated event is the tropical cyclone RASHMI (22–28 October 2008). The simulations are conducted with three nested domains, with a mesh size of 30, 10, and 2 km. The output of the model in each resolution is compared to the Tropical Rainfall Measuring Mission (TRMM) dataset for precipitation and to the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset for snow. TRMM and WRF precipitation products are then compared to ground based measurements: both datasets agree on the spatial repartition of precipitation, but differ on the retrieval of strong precipitation events. The results suggest an overall improvement from WRF over TRMM with respect to ground based measurements. In a second part, various physical parameterizations schemes of the model are compared. Their impact on WRF precipitation output is small, this suggests that model errors during the event may have other causes.
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He, Chu, Shenglin Li, Dehui Xiong, Peizhang Fang, and Mingsheng Liao. "Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance." Remote Sensing 12, no. 9 (May 8, 2020): 1501. http://dx.doi.org/10.3390/rs12091501.
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Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.
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Dutcă. "The Variation Driven by Differences between Species and between Sites in Allometric Biomass Models." Forests 10, no. 11 (November 4, 2019): 976. http://dx.doi.org/10.3390/f10110976.
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Background and Objectives: It is commonly assumed that allometric biomass models are species-specific and site-specific. However, the magnitude of species and site dependency in these models is not well-known. This study aims to investigate the variation in allometric models (i.e., aboveground biomass predicted by diameter at breast height and tree height) that has originated from the differences between tree species and between sites, thereby contributing to a better understanding of species and site-specificity issue in these models. Materials and Methods: The study is based on two large biomass datasets of 4921 and 5199 trees, from Eurasia and Canada. Using a nested ANOVA model on relative aboveground biomass residuals (with species and site as random effects), the proportion of variance explained by species or site was assessed by means of Variance Partition Coefficient (VPC). Results: The proportion of variance explained by species (VPCspecies = 42.56%, SE = 6.10% for Dataset 1 and VPCspecies = 47.54%, SE = 6.07% for Dataset 2) was larger than that explained by site (VPCsite = 20.08%, SE = 3.35% for Dataset 1 and VPCsite = 8.27%, SE = 1.38% for Dataset 2). The proportion of variance explained by site decreased by 24%–44% and the proportion of variance explained by species changed only slightly, when height is included in the allometric biomass models (i.e., models based on diameter at breast height alone, compared to models based on diameter at breast height and tree height). Conclusions: Allometric biomass models were more species-specific than they were site-specific. Therefore, the species (i.e., differences between species) seems to be a more important driver of variability in allometric models compared to site (i.e., differences between sites). Including height in allometric biomass models helped reduce the dependency of these models, on sites only.
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Yuan, Yiqing, Chang Liu, Zhongwei Sun, and Xuesong He. "Baseline Survey of China Social Work Longitudinal Study 2019: Design and Implementation." Research on Social Work Practice 31, no. 5 (January 18, 2021): 513–19. http://dx.doi.org/10.1177/1049731520984536.
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Purpose: The China Social Work Longitudinal Study (CSWLS) aims to provide the first national longitudinal multilevel dataset that contains comprehensive domains to monitor the development of social work in China. This study presents the design and implementation of the baseline survey in 2019. Methods: The CSWLS includes three scholarly themes of professionalization, governmentality, and institutionalization, which are reflected in comprehensive questionnaires on social workers and social work agencies. The CSWLS is a longitudinal panel study that has multilevel datasets of social workers nested within agencies that are selected by a multistage sampling strategy. Results: The baseline survey collected 979 agency questionnaires and 5,965 social worker questionnaires in 56 cities, holding the validity rates of 98.59% and 99.92% respectively. The sample had certain national representativeness when comparing to existed national data in China. Conclusions: The CSWLS will become a critical tool for government officials, professional leaders, and academic researchers.
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LIN, WAN-ROU, PI-HAN WANG, and SUNG-YUAN HSIEH. "Scytinopogon cryptomerioides (Hydnodontaceae), a new species from Taiwan." Phytotaxa 552, no. 1 (June 21, 2022): 73–83. http://dx.doi.org/10.11646/phytotaxa.552.1.6.
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Scytinopogon cryptomerioides (Trechisporales, Basidiomycota) is described as a new species collected from Cryptomeria japonica plantations in Taiwan. This new species is characterized by a white basidiome with dichotomous and flattened branches, clamped hypha, ampullate septa, clavate basidia with four sterigmata, and colorless, ellipsoid, echinulate or verrucose spores with acute warts or spines. Based on the phylogeny constructed from a combined dataset of ITS-nrLSU sequences, S. cryptomerioides nested within the Trechispora clade, sister to Trechispora copiosa.
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Wahlberg, Niklas, Jana Maresova, Leidys Murillo-Ramos, Steve Collins, and Li-Wei Wu. "The phylogenetic positions of Bhagadatta Moore, 1898, Kumothales Overlaet, 1940 and Harmilla Aurivillius, 1892 (Lepidoptera, Nymphalidae, Limenitidinae) based on molecular data." Nota Lepidopterologica 43 (May 29, 2020): 167–71. http://dx.doi.org/10.3897/nl.43.50307.
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We sequenced multiple genes from the enigmatic genera Bhagadatta Moore, 1898, Kumothales Overlaet, 1940 and Harmilla Aurivillius, 1892 (Nymphalidae, Limenitidinae) and analysed them together with a large published dataset. We find that Bhagadatta is sister to the genera Cymothoe Hübner, 1819+Harma Doubleday, 1848, and that Kumothales is sister to these three. Harmilla is nested within the genus Euriphene Boisduval, 1847. We thus transfer Kumothales and Bhagadatta to the tribe Cymothoini, and we synonymise Harmillasyn. nov. with Euriphene.
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Inatsu, Masaru, Tsubasa Nakayama, Yoshie Maeda, and Hirotaka Matsuda. "Dynamical Downscaling for Assessment of the Climate in Ghana." Journal of Disaster Research 9, no. 4 (August 1, 2014): 412–21. http://dx.doi.org/10.20965/jdr.2014.p0412.
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Dynamical downscaling (DDS), in which a regional atmospheric model (RAM) experiment nested into coarser-resolution data provides a spatio-temporal fine dataset for a particular region, was performed to assess the present climate in Ghana. The DDS successfully evaluated realistic seasonal march and inter-annual variability in rainfall, in comparison with gauge and satellite observation. The DDS also indicated that land-lake and land-sea circulation interacted with the West African monsoon likely characterized the local climate in Ghana.
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Niu, Yue, Hongjie Zhang, and Jing Li. "A Nested Chinese Restaurant Topic Model for Short Texts with Document Embeddings." Applied Sciences 11, no. 18 (September 18, 2021): 8708. http://dx.doi.org/10.3390/app11188708.
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In recent years, short texts have become a kind of prevalent text on the internet. Due to the short length of each text, conventional topic models for short texts suffer from the sparsity of word co-occurrence information. Researchers have proposed different kinds of customized topic models for short texts by providing additional word co-occurrence information. However, these models cannot incorporate sufficient semantic word co-occurrence information and may bring additional noisy information. To address these issues, we propose a self-aggregated topic model incorporating document embeddings. Aggregating short texts into long documents according to document embeddings can provide sufficient word co-occurrence information and avoid incorporating non-semantic word co-occurrence information. However, document embeddings of short texts contain a lot of noisy information resulting from the sparsity of word co-occurrence information. So we discard noisy information by changing the document embeddings into global and local semantic information. The global semantic information is the similarity probability distribution on the entire dataset and the local semantic information is the distances of similar short texts. Then we adopt a nested Chinese restaurant process to incorporate these two kinds of information. Finally, we compare our model to several state-of-the-art models on four real-world short texts corpus. The experiment results show that our model achieves better performances in terms of topic coherence and classification accuracy.
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Muse, Abdisalam Hassan, Samuel Mwalili, Oscar Ngesa, Christophe Chesneau, Afrah Al-Bossly, and Mahmoud El-Morshedy. "Bayesian and Frequentist Approaches for a Tractable Parametric General Class of Hazard-Based Regression Models: An Application to Oncology Data." Mathematics 10, no. 20 (October 15, 2022): 3813. http://dx.doi.org/10.3390/math10203813.
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In this study, we consider a general, flexible, parametric hazard-based regression model for censored lifetime data with covariates and term it the “general hazard (GH)” regression model. Some well-known models, such as the accelerated failure time (AFT), and the proportional hazard (PH) models, as well as the accelerated hazard (AH) model accounting for crossed survival curves, are sub-classes of this general hazard model. In the proposed class of hazard-based regression models, a covariate’s effect is identified as having two distinct components, namely a relative hazard ratio and a time-scale change on hazard progression. The new approach is more adaptive to modelling lifetime data and could give more accurate survival forecasts. The nested structure that includes the AFT, AH, and PH models in the general hazard model may offer a numerical tool for identifying which of them is most appropriate for a certain dataset. In this study, we propose a method for applying these various parametric hazard-based regression models that is based on a tractable parametric distribution for the baseline hazard, known as the generalized log-logistic (GLL) distribution. This distribution is closed under all the PH, AH, and AFT frameworks and can incorporate all of the basic hazard rate shapes of interest in practice, such as decreasing, constant, increasing, V-shaped, unimodal, and J-shaped hazard rates. The Bayesian and frequentist approaches were used to estimate the model parameters. Comprehensive simulation studies were used to evaluate the performance of the proposed model’s estimators and its nested structure. A right-censored cancer dataset is used to illustrate the application of the proposed approach. The proposed model performs well on both real and simulation datasets, demonstrating the importance of developing a flexible parametric general class of hazard-based regression models with both time-independent and time-dependent covariates for evaluating the hazard function and hazard ratio over time.
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Stanković, Ranka, Mihailo Škorić, and Branislava Šandrih Todorović. "Parallel Bidirectionally Pretrained Taggers as Feature Generators." Applied Sciences 12, no. 10 (May 16, 2022): 5028. http://dx.doi.org/10.3390/app12105028.
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In a setting where multiple automatic annotation approaches coexist and advance separately but none completely solve a specific problem, the key might be in their combination and integration. This paper outlines a scalable architecture for Part-of-Speech tagging using multiple standalone annotation systems as feature generators for a stacked classifier. It also explores automatic resource expansion via dataset augmentation and bidirectional training in order to increase the number of taggers and to maximize the impact of the composite system, which is especially viable for low-resource languages. We demonstrate the approach on a preannotated dataset for Serbian using nested cross-validation to test and compare standalone and composite taggers. Based on the results, we conclude that given a limited training dataset, there is a payoff from cutting a percentage of the initial training set and using it to fine-tune a machine-learning-based stacked classifier, especially if it is trained bidirectionally. Moreover, we found a measurable impact on the usage of multiple tagsets to scale-up the architecture further through transfer learning methods.
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Collier, Emily, and Thomas Mölg. "BAYWRF: a high-resolution present-day climatological atmospheric dataset for Bavaria." Earth System Science Data 12, no. 4 (December 2, 2020): 3097–112. http://dx.doi.org/10.5194/essd-12-3097-2020.
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Abstract. Climate impact assessments require information about climate change at regional and ideally also local scales. In dendroecological studies, this information has traditionally been obtained using statistical methods, which preclude the linkage of local climate changes to large-scale drivers in a process-based way. As part of recent efforts to investigate the impact of climate change on forest ecosystems in Bavaria, Germany, we developed a high-resolution atmospheric modelling dataset, BAYWRF, for this region over the thirty-year period of September 1987 to August 2018. The atmospheric model employed in this study, the Weather Research and Forecasting (WRF) model, was configured with two nested domains of 7.5 and 1.5 km grid spacing centred over Bavaria and forced at the outer lateral boundaries by ERA5 reanalysis data. Using an extensive network of observational data, we evaluate (i) the impact of using grid analysis nudging for a single-year simulation of the period of September 2017 to August 2018 and (ii) the full BAYWRF dataset generated using nudging. The evaluation shows that the model represents variability in near-surface meteorological conditions generally well, although there are both seasonal and spatial biases in the dataset that interested users should take into account. BAYWRF provides a unique and valuable tool for investigating climate change in Bavaria with high interdisciplinary relevance. Data from the finest-resolution WRF domain are available for download at daily temporal resolution from a public repository at the Open Science Framework (Collier, 2020; https://doi.org/10.17605/OSF.IO/AQ58B).
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Kaplan, Nils Hinrich, Ernestine Sohrt, Theresa Blume, and Markus Weiler. "Monitoring ephemeral, intermittent and perennial streamflow: a dataset from 182 sites in the Attert catchment, Luxembourg." Earth System Science Data 11, no. 3 (September 4, 2019): 1363–74. http://dx.doi.org/10.5194/essd-11-1363-2019.
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Abstract. The temporal and spatial dynamics of streamflow presence and absence is considered vital information to many hydrological and ecological studies. Measuring the duration of active streamflow and dry periods in the channel allows us to classify the degree of intermittency of streams. We used different sensing techniques including time-lapse imagery, electric conductivity and stage measurements to generate a combined dataset of presence and absence of streamflow within various nested sub-catchments in the Attert catchment, Luxembourg. The first sites of observation were established in 2013 and successively extended to a total number of 182 in 2016 as part of the project Catchments As Organized Systems (CAOS). Temporal resolution ranged from 5 to 15 min intervals. Each single dataset was carefully processed and quality controlled before the time interval was homogenised to 30 min. The dataset provides valuable information of the dynamics of a meso-scale stream network in space and time. This can be used to test and evaluate hydrologic models but also for the assessment of the intermittent stream ecosystem in the Attert basin. The dataset presented in this paper is available at the online repository of the German Research Center for Geosciences (GFZ, https://doi.org/10.5880/FIDGEO.2019.010, Kaplan et al., 2019).
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Agnolin, Federico L. "Reappraisal on the Phylogenetic Relationships of the Enigmatic Flightless Bird (Brontornis burmeisteri) Moreno and Mercerat, 1891." Diversity 13, no. 2 (February 20, 2021): 90. http://dx.doi.org/10.3390/d13020090.
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The fossil record of birds in South America is still very patchy. One of the most remarkable birds found in Miocene deposits from Patagonia is Brontornis burmeisteri Moreno and Mercerat, 1891. This giant flightless bird is known by multiple incomplete specimens that represent a few portions of the skeleton, mainly hindlimb bones. Since the XIX century, Brontornis was considered as belonging to or closely related to phorusrhacoid birds. In contrast to previous work, by the end of 2000 decade it was proposed that Brontornis belongs to Galloanserae. This proposal was recently contested based on a large dataset including both phorusrhacoids and galloanserine birds, that concluded Brontornis was nested among cariamiform birds, and probably belonged to phorusrhacoids. The aim of the present contribution is to re-evaluate the phylogenetic affinities of Brontornis. Based on modified previous datasets, it is concluded that Brontornis does belong to Galloanserae, and that it represents a member of a largely unknown radiation of giant graviportal birds from South America.
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Bamber, J. L., and R. A. Bindschadler. "An improved elevation dataset for climate and ice-sheet modelling: validation with satellite imagery." Annals of Glaciology 25 (1997): 439–44. http://dx.doi.org/10.3189/s0260305500014427.
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Recent studies by several groups have indicated that the performance of general circulation models (GCMs) over the ice sheets is severely limited by the relatively low resolution of the models at the margins, where surface slopes are greatest. To provide accurate energy-budget estimates, resolutions of better than 0.5° are desirable, requiring nested or multiple gridding and accurate, high-resolution boundary conditions. Here we present a new, high-resolution (5 km) digital elevation model for the Antarctic ice sheet, derived from radar-altimeter data obtained from the geodetic phase of the satellite, ERS-1. These data have been combined with the revised ice-thickness grid reported in Bamber and Huybrechts (1996) to produce a bed- and surface-elevation dataset for use in regional and global climate and paleo-climaie modelling applications. The real level of spatial detail in the datasets has been examined with the aid of Landsat Thematic Mapper data. Imagery around Ice Stream D, West Antarctica, shows that the revised ice-thickness grid is accurately geolocated, and contains valuable fine-scale topographic detail beyond that available from the cartographic version of the data (Drewry, 1983). The surface topography in the region of the Ross Ice Shelf has been used to illustrate the level of detail in both the vertical and horizontal resolution of (he surface dataset. Laudsat data has also been used to examine features in the surface-elevation data. In particular, the location of the grounding zone, for Ice Streams D and E, derived from the two data sources shows good agreement. The results of this validation underscore the utility of the new datasets for high-resolution modelling, and highlight the limitations of the Folio maps for such applications.
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Bamber, J. L., and R. A. Bindschadler. "An improved elevation dataset for climate and ice-sheet modelling: validation with satellite imagery." Annals of Glaciology 25 (1997): 439–44. http://dx.doi.org/10.1017/s0260305500014427.
Full textAbstract:
Recent studies by several groups have indicated that the performance of general circulation models (GCMs) over the ice sheets is severely limited by the relatively low resolution of the models at the margins, where surface slopes are greatest. To provide accurate energy-budget estimates, resolutions of better than 0.5° are desirable, requiring nested or multiple gridding and accurate, high-resolution boundary conditions. Here we present a new, high-resolution (5 km) digital elevation model for the Antarctic ice sheet, derived from radar-altimeter data obtained from the geodetic phase of the satellite, ERS-1. These data have been combined with the revised ice-thickness grid reported in Bamber and Huybrechts (1996) to produce a bed- and surface-elevation dataset for use in regional and global climate and paleo-climaie modelling applications. The real level of spatial detail in the datasets has been examined with the aid of Landsat Thematic Mapper data. Imagery around Ice Stream D, West Antarctica, shows that the revised ice-thickness grid is accurately geolocated, and contains valuable fine-scale topographic detail beyond that available from the cartographic version of the data (Drewry, 1983). The surface topography in the region of the Ross Ice Shelf has been used to illustrate the level of detail in both the vertical and horizontal resolution of (he surface dataset. Laudsat data has also been used to examine features in the surface-elevation data. In particular, the location of the grounding zone, for Ice Streams D and E, derived from the two data sources shows good agreement. The results of this validation underscore the utility of the new datasets for high-resolution modelling, and highlight the limitations of the Folio maps for such applications.
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RUIZ, NICOLAS. "MEASURING THE JOINT DISTRIBUTION OF HOUSEHOLD’S INCOME, CONSUMPTION AND WEALTH USING NESTED GENERALIZED MEAN." Singapore Economic Review 63, no. 03 (June 2018): 759–85. http://dx.doi.org/10.1142/s0217590815501040.
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Following recommendations from the Stiglitz et al. (2009) Commission, this paper proposes the use of a new methodology to measure the joint distribution of households’ income, consumption and wealth. Based on a multi-dimensional extension of the generalized mean framework, the paper justifies the application of this methodology based on a set of standard and acknowledged properties. The derived multi-dimensional index has an intuitive structure, which allows evaluating the overall material conditions of households under different perspectives and with varying sensitivity to distributional issues. Under its general form, the index encompasses a class of sub-indices that impose various restrictions on its parameters; the paper discusses the extent to which different restrictions on parameters affect the multi-dimensional assessments of various population groups, and provides some empirical illustrations using those different specifications. The question addressed by the multi-dimensional measure presented here is whether the joint consideration of household income, consumption and wealth modifies substantially the picture of material living standards of different individuals and groups relative to the one provided by income alone. Based on the dataset used here, the paper provides strong evidence on the importance of such a multi-dimensional assessment.
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Jackson, Joni, Natalia V. Lewis, Gene S. Feder, Penny Whiting, Timothy Jones, John Macleod, and Maria Theresa Redaniel. "Exposure to domestic violence and abuse and consultations for emergency contraception: nested case-control study in a UK primary care dataset." British Journal of General Practice 69, no. 680 (December 3, 2018): e199-e207. http://dx.doi.org/10.3399/bjgp18x700277.
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BackgroundEvidence of an association between exposure to domestic violence and abuse (DVA) and use of emergency contraception (EC) is lacking in the UK.AimTo quantify the association between exposure to DVA and consultations for EC in general practice.Design and settingNested case-control study in UK general practice.MethodUsing the Clinical Practice Research Datalink, the authors identified all women all women aged 15–49 years registered with a GP between 1 January 2011 and 31 December 2016. Cases with consultations for EC (n = 43 570) were each matched on age and GP against four controls with no consultations for EC (n = 174 280). The authors calculated odds ratios (ORs) and 95% confidence intervals (CIs) for the association between exposure to DVA in the previous year and consultations for EC. Covariates included age, ethnicity, socioeconomic status, pregnancy, children, alcohol misuse, and depression.ResultsWomen exposed to DVA were 2.06 times more likely to have a consultation for EC than unexposed women (95% CI = 1.64 to 2.61). Women aged 25–39 years with exposure to DVA were 2.8 times more likely to have a consultation for EC, compared with unexposed women (95% CI = 2.08 to 3.75). The authors found some evidence of an independent effect of exposure to DVA on the number of consultations for EC (OR 1.48, 95% CI = 0.99 to 2.21).ConclusionA request for EC in general practice can indicate possible exposure to DVA. Primary care consultation for EC is a relevant context for identifying and responding to DVA as recommended by the World Health Organization and National Institute for Health and Care Excellence guidelines. DVA training for providers of EC should include this new evidence.
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ELDREDGE, K. TARO. "A new myrmecophilous genus of Falagriini from Colorado, USA (Coleoptera: Staphylinidae: Aleocharinae)." Zootaxa 5165, no. 4 (July 18, 2022): 575–90. http://dx.doi.org/10.11646/zootaxa.5165.4.8.
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A new genus and species, Myrmecoagria hoebekei is described as the first known myrmecophilous representative of Falagriini in North America. The new species is believed to be associated with Myrmica ants based on an associated host specimen and label data. Ahn & Ashe’s (1995) dataset is modified to investigate the placement of Myrmecoagria and evolution of myrmecophily within Falagriini. Sceptobiini, a long suspected close relative of Falagriini was included in the analysis and was recovered nested within Falagriini. Myrmecoagria was recovered distant to Myrmecopora, a genus with anatomical similarities.
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Turan, Semen Son. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies." Information Management and Business Review 6, no. 6 (December 30, 2014): 317–28. http://dx.doi.org/10.22610/imbr.v6i6.1130.
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This study analyzes the relationship of the volatility ofstock returns and internet search volume (ISV). The dataset consists of 10 Turkish companies listed on the BIST-100 Index of Borsa Istanbul, and encompasses the period between January 2004 - September 2013. The GARCH (1,1) model is applied with two alternative mean specifications. The use of the novel exogenous variable ISV as proxy for investor sentimentis complemented through the inclusion of trading volume.Results show that as the GARCH (1,1) model becomes increasingly nested, volatility persistence declines with however no case of a vanishing G(ARCH) effect.