Relevant bibliographies by topics / Dataset nested

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dataset nested'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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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 (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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Dissertations / Theses on the topic "Dataset nested"

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DENTI, FRANCESCO. "Bayesian Mixtures for Large Scale Inference." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262923.

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I modelli mistura bayesiani sono onnipresenti in statistica per la loro semplicità e flessibilità e possono essere facilmente impiegati in un'ampia varietà di contesti. In questa tesi, miriamo a fornire alcuni contributi agli attuali metodi bayesiani di analisi dei dati, spesso motivati ​​da domande di ricerca provenienti da applicazioni biologiche. In particolare, ci concentriamo sullo sviluppo di nuovi modelli mistura bayesiani, tipicamente in un ambiente non parametrico, per migliorare ed estendere aree di ricerca che coinvolgono dati caratterizzati da grande dimensioni: la modellazione di
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Schulz, Sebastian [Verfasser], and B. [Akademischer Betreuer] Nestler. "Phase-field simulations of multi-component solidification and coarsening based on thermodynamic datasets / Sebastian Schulz. Betreuer: B. Nestler." Karlsruhe : KIT-Bibliothek, 2016. http://d-nb.info/1106330110/34.

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Schulz, Sebastian [Verfasser], and B. [Akademischer Betreuer] Nestler. "Phase-field simulations of multi-component solidification and coarsening based on thermodynamic datasets / Sebastian Schulz ; Betreuer: B. Nestler." Karlsruhe : KIT Scientific Publishing, 2017. http://d-nb.info/1185759832/34.

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Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different superv
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Calçada, David Tiago. "Predicting chelonia mydas nests survivability rates with use of machine learning techniques: applying machine learning techniques on conservation data – case study." Master's thesis, 2020. http://hdl.handle.net/10362/97228.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>It is the generalized goal of knowledge discovery techniques to help us find useful patterns in data whilst not subjecting us to the ambiguity and overcomplexity of models. In fact, it has become increasingly important to allow for a common language to exist between biologists and data scientists. In my thesis I aim to make use of Green Turtle (Chelonya mydas) nesting data obtained in surveys conducted from 2015 to 201
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Books on the topic "Dataset nested"

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Reis, Lucas Bond. Florianópolis arqueológica. Editora da UFSC, 2021. http://dx.doi.org/10.5007/978-65-5805-023-0.

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A obra apresenta uma coletânea de artigos sobre o Patrimônio Arqueológico de Florianópolis. São ao todo 12 capítulos que procuram cobrir diversos temas e períodos relacionados à história da ocupação humana do Município, com foco principal na Ilha de Santa Catarina. O livro se inicia com dois textos mais gerais: um versando sobre a História da Pesquisa Arqueológica em Florianópolis; e outro dando um panorama geral sobre a composição desse patrimônio e da sua situação hoje em termos de preservação e pesquisa. Dando prosseguimento ao livro há nove capítulos que vão tratar de temas variados relaci
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Book chapters on the topic "Dataset nested"

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Gong, Yansheng, and Wenfeng Jing. "A Fully-Nested Encoder-Decoder Framework for Anomaly Detection." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_75.

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AbstractAnomaly detection is an important branch of computer vision. At present, a variety of deep learning models are applied to anomaly detection. However, the lack of abnormal samples makes supervised learning difficult to implement. In this paper, we mainly study abnormal detection tasks based on unsupervised learning and propose a Fully-Nested Encoder-decoder Framework. The main part of the proposed generating model consists of a generator and a discriminator, which are adversarially trained based on normal data samples. In order to improve the image reconstruction capability of the gener
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More on apply family of functions - avoid loops to get more speed." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0027.

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Abstract This chapter focuses on the 'apply' set of functions. These functions are for those who need to process very large datasets, or who need to perform loop-type operations on largish datasets but perhaps in a nested fashion.
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Quicke, Donald, Buntika A. Butcher, and Rachel Kruft Welton. "More on apply family of functions - avoid loops to get more speed." In Practical R for biologists: an introduction. CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0322.

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Abstract This chapter focuses on the 'apply' set of functions. These functions are for those who need to process very large datasets, or who need to perform loop-type operations on largish datasets but perhaps in a nested fashion.
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Osakabe, Yoshihiro, and Akinori Asahara. "Proposing Novel High-Performance Compounds by Nested VAEs Trained Independently on Different Datasets." In Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08530-7_60.

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Chebotko, Artem, and Shiyong Lu. "Nested Optional Join for Efficient Evaluation of SPARQL Nested Optional Graph Patterns." In Advances in Semantic Web and Information Systems. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-992-2.ch013.

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Relational technology has shown to be very useful for scalable Semantic Web data management. Numerous researchers have proposed to use RDBMSs to store and query voluminous RDF data using SQL and RDF query languages. This chapter studies how RDF queries with the so called well-designed graph patterns and nested optional patterns can be efficiently evaluated in an RDBMS. The authors propose to extend relational algebra with a novel relational operator, nested optional join (NOJ), that is more efficient than left outer join in processing nested optional patterns of well-designed graph patterns. T
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Li, Liu, and Fusong Ling. "Chinese Medical Named Entity Recognition Method Based on Word-Word Relationship." In Computer Methods in Medicine and Health Care. IOS Press, 2022. http://dx.doi.org/10.3233/atde220541.

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To solve the problem that the current deep learning method is difficult to deal with the recognition of nested entities in Chinese medical text, a deep learning model based on word-word relationship is introduced, and the relationship between words is built by multi-granularity 2D graphs to improve the recognition of nested entities. First, we use BERT (Bidirectional Encoder Representation from Transformers) for pre-training, then we use BiLSTM (directional Long Short-Term Memory) to extract the context information. Then, we merge the token representation information, the word distance informa
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Gbenga Dada Emmanuel, Hemanth D. Jude, Chiroma Haruna, Abdulhamid Shafi'i Muhammad, and Taiwo Adewale Johnson. "Non-Nested Generalisation (NNGE) Algorithm for Efficient and Early Detection of Diabetes." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2019. https://doi.org/10.3233/978-1-61499-939-3-233.

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Diabetes is a disease that is gaining popularity on daily basis in recent times globally and among different age groups. Diabetes causes damage to nerves, blood vessels, kidney, and retina. Machine learning techniques have proved to be very effective in detecting diabetes. In this study, we applied the Non-Nested Generalisation exemplar classifiers on Pima Indians diabetes dataset to effectively and efficiently classify whether patients are having diabetes or not. Our proposed algorithm proved to be highly effective and efficient with a resultant classification accuracy of 100%, very low false
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Pham, Thien, Loi Truong, Mao Nguyen, Akhil Garg, Liang Gao, and Tho Quan. "Sequence-in-Sequence Learning for SOH Estimation of Lithium-Ion Battery." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210385.

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State-of-Health (SOH) prediction of a Lithium-ion battery is essential for preventing malfunction and maintaining efficient working behaviors for the battery. In practice, this task is difficult due to the high level of noise and complexity. There are many machine learning methods, especially deep learning approaches, that have been proposed to address this problem recently. However, there is much room for improvement because the nature of the battery data is highly non-linear and exhibits higher dependence on multidisciplinary parameters such as resistance, voltage and external conditions the
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Muche Fenta, Setegn, and Haile Mekonnen Fenta. "Level and Determinant of Child Mortality Rate in Ethiopia." In Mortality Rates in Middle and Low-Income Countries. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100482.

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Background: One of the objectives of the Sustainable Development Goals (SDG) is to diminish the under-five mortality rate and improvement in maternal health. This study aims to identify factors that affect under-five mortality based on the 2016 EDHS dataset using the multilevel count regression model. Method: The EDHS data have a two-level hierarchical structure, with 14,370 women nested within 11 geographical regions. Multilevel count models were employed to predict the outcomes. Results: The data were found to have excess zeros (53.7%); the variance (1.697) is higher than its mean (0.90). Am
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Govarthan, Praveen Kumar, Sriram Kumar P, Nagarajan Ganapathy, and Jac Fredo Agastinose Ronickom. "Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230418.

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In this study, we attempted to classify categorical emotional states using Electrodermal Activity (EDA) signals and a configurable Convolutional Neural Network (cCNN). The EDA signals from the publicly available, Continuously Annotated Signals of Emotion dataset were down-sampled and decomposed into phasic components using the cvxEDA algorithm. The phasic component of EDA was subjected to Short-Time Fourier Transform-based time-frequency representation to obtain spectrograms. These spectrograms were input to the proposed cCNN to automatically learn the prominent features and discriminate varie
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Conference papers on the topic "Dataset nested"

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Chatterjee, Rishit, and Kazutaka Takahashi. "A nested cross validation approach to machine learning model performance evaluation on a small dataset for Creutzfeldt-Jakob disease diagnosis." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782803.

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Shi, Xiaoxiao, and Jie Ji. "Detection of Bird Nests on Power Transmission Lines Using an Improved YOLOv5s Model with Small Datasets." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10827879.

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Ringland, Nicky, Xiang Dai, Ben Hachey, Sarvnaz Karimi, Cecile Paris, and James R. Curran. "NNE: A Dataset for Nested Named Entity Recognition in English Newswire." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1510.

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Jonak, Martin, Stepan Jezek, and Radim Burget. "Evaluation of Nested U-Net models performance on MVTec AD dataset." In 2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2022. http://dx.doi.org/10.1109/icumt57764.2022.9943348.

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Loukachevitch, Natalia, Ekaterina Artemova, Tatiana Batura, et al. "NEREL: A Russian Dataset with Nested Named Entities, Relations and Events." In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_100.

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Dinh, Tuan Le, Suk-Hwan Lee, Seong-Geun Kwon, and Ki-Ryong Kwon. "Cell Nuclei Segmentation in Cryonuseg dataset using Nested Unet with EfficientNet Encoder." In 2022 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2022. http://dx.doi.org/10.1109/iceic54506.2022.9748537.

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Wu, Shuhui, Yongliang Shen, Zeqi Tan, and Weiming Lu. "Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/613.

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Nested named entity recognition (nested NER) is a fundamental task in natural language processing. Various span-based methods have been proposed to detect nested entities with span representations. However, span-based methods do not consider the relationship between a span and other entities or phrases, which is helpful in the NER task. Besides, span-based methods have trouble predicting long entities due to limited span enumeration length. To mitigate these issues, we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction network for nested NER. In the propose stage, we u
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Zeng, Yu, Yan Gao, Jiaqi Guo, et al. "RECPARSER: A Recursive Semantic Parsing Framework for Text-to-SQL Task." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/504.

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Neural semantic parsers usually fail to parse long and complicated utterances into nested SQL queries, due to the large search space. In this paper, we propose a novel recursive semantic parsing framework called RECPARSER to generate the nested SQL query layer-by-layer. It decomposes the complicated nested SQL query generation problem into several progressive non-nested SQL query generation problems. Furthermore, we propose a novel Question Decomposer module to explicitly encourage RECPARSER to focus on different components of an utterance when predicting SQL queries of different layers. Exper
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Couto, João M. M., Breno Pimenta, Igor M. de Araújo, et al. "Central de Fatos: Um Repositório de Checagens de Fatos." In Dataset Showcase Workshop. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/dsw.2021.17421.

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Recentemente, o interesse por frentes de pesquisa analisando os mecanismos, bem como maneiras de evitar a disseminação de desinformação aumentou significativamente. Neste cenário, um recorrente obstáculo a indisponibilidade de checagens de fatos. Neste trabalho, compilamos uma extensa coleção de checagens oriundas de importantes agências de checagem de fatos brasileiras. Oferecemos à comunidade cientifica uma coleção inédita contendo checagens de diversas fontes confiáveis que abrangem um largo espectro de tópicos. Ao todo, a coleção resultante engloba 11647 instâncias de checagem de fatos col
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Silva, Mariana O., Amanda F. Paula, Gabriel P. Oliveira, et al. "LiPSet: Um conjunto de Dados com Documentos Rotulados de Licitações Públicas." In Dataset Showcase Workshop. Sociedade Brasileira de Computação, 2022. http://dx.doi.org/10.5753/dsw.2022.224925.

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Neste trabalho, é apresentado o LiPSet, um conjunto de dados com documentos rotulados de licitações públicas de Minas Gerais. Após uma visão geral do processo de coleta e rotulação manual, uma breve análise exploratória de dados é apresentada para resumir as principais características e contribuições do conjunto de dados proposto. Além disso, são discutidas potenciais aplicações e principais desafios que envolvem o uso do LiPSet.
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Reports on the topic "Dataset nested"

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Maldonado, Leonardo, and Osmel Manzano. Measuring Regional Inequality in the Andean Countries: A Multiple-Stage Nested Theil Decomposition Using Night Light Emissions. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0012952.

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This paper examines inequality in the Andean countries using satellite-recorded nighttime lights and gridded population datasets from 2012 to 2021. We follow a multiple-stage nested Theil index decomposition method accounting for each country's lowest administrative divisions to enhance our understanding of how spatial dimensions contribute as primary sources of inequality and how these contributions vary across each country. The main findings reveal a decrease in overall inequality for the Andean region throughout the period (primarily driven by a decline in between-country inequality) and an
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Bravo, Adriana, Nora Bynum, Michelle Cawthorn, et al. Practice Your Data Analysis Skills! American Museum of Natural History, 2012. http://dx.doi.org/10.5531/cbc.ncep.0049.

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In this exercise, we present you with a realistic dataset for nest parasitism and a hypothetical analysis performed by a hypothetical student. Using a “rubric” or assessment guide, students will evaluate the analysis, and, where necessary, correct it or improve it. In the process, students will review and reflect on what is required for good data analysis, and they can use that knowledge to improve their own skills.
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR
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Alviarez, Vanessa, Michele Fioretti, Ken Kikkawa, and Monica Morlacco. Two-Sided Market Power in Firm-to-Firm Trade. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003493.

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Abstract:
Firms in global value chains (GVCs) are granular and exert bargaining power over the terms of trade. We show that these features are crucial to understanding the well-established variation in prices and pass-through across importers and exporters. We develop a novel theory of prices in GVCs, which tractably nests a wide range of bilateral concentration and bargaining power configurations. We test and evaluate the models predictions using a novel dataset merging transaction-level U.S. import data with balance sheet data for both U.S. importers and foreign exporters. Our pricing framework enhanc
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