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Journal articles on the topic 'Dataset nested'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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 (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 numb

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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 (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

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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 (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

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Adama, Samassi, Brou Konan Marcellin, Kouame Appoh, and Toure Kidjegbo Augustin. "A BIDIRECTIONAL ENCODER-DECODER MODEL WITH ATTENTIONMECHANISM FOR NESTED NAMED ENTITY RECOGNITION." International Journal of Advanced Research 12, no. 03 (2024): 382–94. http://dx.doi.org/10.21474/ijar01/18405.

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Named entity recognition is a fundamental task for several natural language processing applications. It consists in identifying mentions of named entities in a text, then classifying them according to predefined entity types. Most labeling methods for this task use a label to recognize flat named entities because they belong to a single entity type. Therefore, they cannot recognize named entities that belong to multiple entity types.In this work, we concatenated all the labels of a word of a named entity into a joint in order to recognize flat or nested named entities. Then, we proposed a bidi

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Yang, Hongjian, Qinghao Zhang, and Hyuk-Chul Kwon. "PNER: Applying the Pipeline Method to Resolve Nested Issues in Named Entity Recognition." Applied Sciences 14, no. 5 (2024): 1717. http://dx.doi.org/10.3390/app14051717.

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Named entity recognition (NER) in natural language processing encompasses three primary types: flat, nested, and discontinuous. While the flat type often garners attention from researchers, nested NER poses a significant challenge. Current approaches to addressing nested NER involve sequence labeling methods with merged label layers, cascaded models, and those rooted in reading comprehension. Among these, sequence labeling with merged label layers stands out for its simplicity and ease of implementation. Yet, highlighted issues persist within this method, prompting our aim to enhance its effic

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Mu, Jichong, Jihong Ouyang, Yachen Yao, and Zongxiao Ren. "Span-Prototype Graph Based on Graph Attention Network for Nested Named Entity Recognition." Electronics 12, no. 23 (2023): 4753. http://dx.doi.org/10.3390/electronics12234753.

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Named entity recognition, a fundamental task in natural language processing, faces challenges related to the sequence labeling framework widely used when dealing with nested entities. The span-based method transforms nested named entity recognition into span classification tasks, which makes it an efficient way to deal with overlapping entities. However, too much overlap among spans may confuse the model, leading to inaccurate classification performance. Moreover, the entity mentioned in the training dataset contains rich information about entities, which are not fully utilized. So, in this pa

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Liza, Mst Zinia Afroz, Md Al-Imran, Md Morshed Bin Shiraj, Tozam Hossain, Md Masum Murshed, and Nasima Akhter. "Exploring the Lazy Witness Complex for Efficient Persistent Homology in Large-Scale Data." Tensor: Pure and Applied Mathematics Journal 5, no. 2 (2025): 79–92. https://doi.org/10.30598/tensorvol5iss2pp79-92.

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In this paper, topological data analysis (TDA) techniques have been explored, with a focus on the selection of the Witness Complex and Persistent Homology of some nested families of Lazy Witness Complex as approximations for analyzing complex datasets. The Witness Complex was chosen for its efficiency and scalability, as it constructs a simplicial complex using landmark points, reducing computational load compared to methods like the Vietoris-Rips and Čech complexes. This makes it suitable for large, high-dimensional datasets, accurately representing the dataset's intrinsic geometry even with

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Alharbi, Shuaa S., Athbah A. AlRugaibah, Haifa F. Alhasson, and Rehan Ullah Khan. "Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models." Applied Sciences 13, no. 23 (2023): 12771. http://dx.doi.org/10.3390/app132312771.

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Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries l

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Paul, Sandip, Deepak Mishra, and Senthil Kumar Marimuthu. "Nested DWT–Based CNN Architecture for Monocular Depth Estimation." Sensors 23, no. 6 (2023): 3066. http://dx.doi.org/10.3390/s23063066.

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Applications such as medical diagnosis, navigation, robotics, etc., require 3D images. Recently, deep learning networks have been extensively applied to estimate depth. Depth prediction from 2D images poses a problem that is both ill–posed and non–linear. Such networks are computationally and time–wise expensive as they have dense configurations. Further, the network performance depends on the trained model configuration, the loss functions used, and the dataset applied for training. We propose a moderately dense encoder–decoder network based on discrete wavelet decomposition and trainable coe

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Wang, Xiajun, Cheng Peng, Qifeng Li, et al. "A Chinese Nested Named Entity Recognition Model for Chicken Disease Based on Multiple Fine-Grained Feature Fusion and Efficient Global Pointer." Applied Sciences 14, no. 18 (2024): 8495. http://dx.doi.org/10.3390/app14188495.

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Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevalence of nested entities and domain-specific named entities, coupled with a scarcity of labeled data. To address these challenges, we compiled a corpus from 50 books on chicken diseases, covering 28 different disease types. Utilizing this corpus, we constructed the CDNER dataset and developed a nested NER model, MFGFF-BiLSTM-EGP. This model integrate

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Zhang, Jilong, Yajuan Zhang, Hongyang Zhang, et al. "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

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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 (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 partia

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Yu, Jiayi, Yuliang Lu, Yongheng Zhang, Yi Xie, Mingjie Cheng, and Guozheng Yang. "A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition." Electronics 13, no. 21 (2024): 4329. http://dx.doi.org/10.3390/electronics13214329.

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In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limi

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Li, Weijun, Jintong Liu, Yuxiao Gao, Xinyong Zhang, and Jianlai Gu. "Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer." Applied System Innovation 7, no. 1 (2024): 8. http://dx.doi.org/10.3390/asi7010008.

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The task of named entity recognition (NER) is to identify entities in the text and predict their categories. In real-life scenarios, the context of the text is often complex, and there may exist nested entities within an entity. This kind of entity is called a nested entity, and the task of recognizing entities with nested structures is referred to as nested named entity recognition. Most existing NER models can only handle flat entities, and there has been limited research progress in Chinese nested named entity recognition, resulting in relatively few models in this direction. General NER mo

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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 (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 ot

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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 (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 resamp

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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, an

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Tahkola, Mikko, and Zou Guangrong. "ATSC-NEX: Automated Time Series Classification With Sequential Model-Based Optimization and Nested Cross-Validation." IEEE Access 10 (April 11, 2022): 39299–312. https://doi.org/10.1109/ACCESS.2022.3166525.

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New methods to perform time series classification arise frequently and multiple state-of-the-art approaches achieve high performance on benchmark datasets with respect to accuracy and computation time. However, often the modeling procedures do not include proper validation but rather rely only on either external test dataset or one-level cross-validation. ATSC-NEX is an automated procedure that employs sequential model-based optimization together with nested cross-validation to build an accurate and properly validated time series classification model. It aims to find an optimal pipeline config

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Wang, Ziming, Xirong Xu, Xinzi Li, Haochen Li, Xiaopeng Wei, and Degen Huang. "An Improved Nested Named-Entity Recognition Model for Subject Recognition Task under Knowledge Base Question Answering." Applied Sciences 13, no. 20 (2023): 11249. http://dx.doi.org/10.3390/app132011249.

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In the subject recognition (SR) task under Knowledge Base Question Answering (KBQA), a common method is by training and employing a general flat Named-Entity Recognition (NER) model. However, it is not effective and robust enough in the case that the recognized entity could not be strictly matched to any subjects in the Knowledge Base (KB). Compared to flat NER models, nested NER models show more flexibility and robustness in general NER tasks, whereas it is difficult to employ a nested NER model directly in an SR task. In this paper, we take advantage of features of a nested NER model and pro

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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 tra

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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 (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

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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 (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-learni

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Listopad, Stanislav, Christophe Magnan, Le Z. Day, et al. "Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning." PLOS Digital Health 3, no. 2 (2024): e0000447. http://dx.doi.org/10.1371/journal.pdig.0000447.

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Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcr

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YILDIRIM, Hakan, Ülkü ÇELİKER, Sabiha GÜNGÖR KOBAT, et al. "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 (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

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Wylie, Korey P., and Jason R. Tregellas. "Rootlets Hierarchical Principal Component Analysis for Revealing Nested Dependencies in Hierarchical Data." Mathematics 13, no. 1 (2024): 72. https://doi.org/10.3390/math13010072.

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Hierarchical clustering analysis (HCA) is a widely used unsupervised learning method. Limitations of HCA, however, include imposing an artificial hierarchy onto non-hierarchical data and fixed two-way mergers at every level. To address this, the current work describes a novel rootlets hierarchical principal component analysis (hPCA). This method extends typical hPCA using multivariate statistics to construct adaptive multiway mergers and Riemannian geometry to visualize nested dependencies. The rootlets hPCA algorithm and its projection onto the Poincaré disk are presented as examples of this

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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 (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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Wen, Lei. "SC-UneXt: Nested UNeXt Architecture based on Medical Image Segmentation." Frontiers in Computing and Intelligent Systems 6, no. 2 (2023): 30–34. http://dx.doi.org/10.54097/fcis.v6i2.07.

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UNet and its various variants are commonly used methods in medical image segmentation tasks; however, many network parameters, complex calculations, and slow usage are problems that need to be overcome. These problems hinder the specific application of fast image segmentation in real-time tasks. At the same time, the lesion area has problems such as small size, irregular shape, and blurred edges, which makes the network feature extraction difficult and the segmentation accuracy needs to be improved. At the same time, medical image segmentation provides a variety of effective methods for the ac

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Bellala, G., A. Ganesan, R. Krishna, et al. "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 symp

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Jaksik, Roman, Kamila Szumała, Khanh Ngoc Dinh, and Jarosław Śmieja. "Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival." International Journal of Molecular Sciences 25, no. 7 (2024): 3661. http://dx.doi.org/10.3390/ijms25073661.

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Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease’s complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylat

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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 (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

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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 (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

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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 mor

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Singh, Kuvar Satya, Sridhara Nayak, Suman Maity, Hara Prasad Nayak, and Soma Dutta. "Prediction of Extremely Severe Cyclonic Storm “Fani” Using Moving Nested Domain." Atmosphere 14, no. 4 (2023): 637. http://dx.doi.org/10.3390/atmos14040637.

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The prediction of extremely severe cyclonic storms has been a long-standing and challenging issue due to their short life period and large variation in intensities over a short time. In this study, we predict the track, intensity, and structure of an extremely severe cyclonic storm (ESCS) named ‘Fani,’ which developed over the Bay of Bengal region from 27 April to 4 May 2019, using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. Two numerical experiments were conducted using the moving nested domain method with a 3 km horizontal resolution, one with the F

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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 (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 resear

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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 (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 in

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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 (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

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Zhang, Han, Xinghua Lu, Binfeng Lu, and Lujia Chen. "scGEM: Unveiling the Nested Tree-Structured Gene Co-Expressing Modules in Single Cell Transcriptome Data." Cancers 15, no. 17 (2023): 4277. http://dx.doi.org/10.3390/cancers15174277.

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Background: Single-cell transcriptome analysis has fundamentally changed biological research by allowing higher-resolution computational analysis of individual cells and subsets of cell types. However, few methods have met the need to recognize and quantify the underlying cellular programs that determine the specialization and differentiation of the cell types. Methods: In this study, we present scGEM, a nested tree-structured nonparametric Bayesian model, to reveal the gene co-expression modules (GEMs) reflecting transcriptome processes in single cells. Results: We show that scGEM can discove

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O’Neill, Andrea C., Kees Nederhoff, Li H. Erikson, Jennifer A. Thomas, and Patrick L. Barnard. "A Dataset of Two-Dimensional XBeach Model Set-Up Files for Northern California." Data 9, no. 10 (2024): 118. http://dx.doi.org/10.3390/data9100118.

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Here, we describe a dataset of two-dimensional (2D) XBeach model files that were developed for the Coastal Storm Modeling System (CoSMoS) in northern California as an update to an earlier CoSMoS implementation that relied on one-dimensional (1D) modeling methods. We provide details on the data and their application, such that they might be useful to end-users for other coastal studies. Modeling methods and outputs are presented for Humboldt Bay, California, in which we compare output from a nested 1D modeling approach to 2D model results, demonstrating that the 2D method, while more computatio

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Park, Yesol, Gyujin Son, and Mina Rho. "Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances." Applied Sciences 14, no. 20 (2024): 9302. http://dx.doi.org/10.3390/app14209302.

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Biomedical named entity recognition (BioNER) aims to identify and classify biomedical entities (i.e., diseases, chemicals, and genes) from text into predefined classes. This process serves as an important initial step in extracting biomedical information from textual sources. Considering the structure of the entities it addresses, BioNER tasks are divided into two categories: flat NER, where entities are non-overlapping, and nested NER, which identifies entities embedded within another. While early studies primarily addressed flat NER, recent advances in neural models have enabled more sophist

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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 (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 (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 Bre

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Nacinben, João Pedro Coli de Souza Monteneri, and Márcio Laurini. "Multivariate Stochastic Volatility Modeling via Integrated Nested Laplace Approximations: A Multifactor Extension." Econometrics 12, no. 1 (2024): 5. http://dx.doi.org/10.3390/econometrics12010005.

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This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related to chain convergence can also arise. In light of these challenges, this research aims to establish a computationally efficient approach for estimating multivariate stochastic volatility models. We p

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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 (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;

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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 (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 alte

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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 (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 infor

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Albahli, Saleh. "An Advanced Natural Language Processing Framework for Arabic Named Entity Recognition: A Novel Approach to Handling Morphological Richness and Nested Entities." Applied Sciences 15, no. 6 (2025): 3073. https://doi.org/10.3390/app15063073.

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Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP) that supports applications such as information retrieval, sentiment analysis, and text summarization. While substantial progress has been made in NER for widely studied languages like English, Arabic presents unique challenges due to its morphological richness, orthographic ambiguity, and the frequent occurrence of nested and overlapping entities. This paper introduces a novel Arabic NER framework that addresses these complexities through architectural innovations. The proposed model incorporates a Hybrid

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Pavelchek, Cole, Andrew P. Michelson, Amit Walia, et al. "Imputation of missing values for cochlear implant candidate audiometric data and potential applications." PLOS ONE 18, no. 2 (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 i

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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

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Kong, Lei, Xiao Tang, Jiang Zhu, 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 (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 detai

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