dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, Rhino 3D software is able to generate accurate and free-formed models of complex and irregular geometries, especially using NURBS to represent surfaces. The current tight combination of graphics geometry modelling and BIM semantic modelling, such as the Rhino 3D-Grasshopper-ArchiCAD connection developed by ArchiCAD and Rhino 3D, empowers architects to start and modify their designs in any of the three design environments (Rhino 3D, Grasshopper or ArchiCAD). Also, Rhino 3D builds BIM semantic models and has been expanded to Rhino BIM. The design of Rhino 3DBIM is 3D-centric, where the design is modelled and captured in a 3D database compared to the conventional BIM software.
Automate as-built HBIM based on point cloud processing approaches Built heritage geometric modelling has often been conducted using reality-based data  [109] [110] [111]. BIM goes further than the traditional geometric model with parametric and semantic elements. As a 3D information modelling environment, BIM software is increasingly supportive of 3D point cloud representing the entity surface, which is called as a scan-to-BIM process. By importing the point cloud into the BIM software, the solid building components can be created using the existing BIM IFC classes and self-defined structures. Unfortunately, the scan-to-BIM process remains mostly a manual process  [81, 106, 112] , and a key challenge today is thus to automate the process leading to as-built BIM from a point cloud. To reduce the manual work in such scan-to-BIM process, many commercial tools and algorithms exist.
Although existing BIM platforms are supportive of the point cloud, the parametric modelling tools are still limited because most of BIM software is not 3D-centric modelling environment and cannot directly parametrically design the shape on the reference of the point cloud in 3D space. It is usually a manually time-consuming concept in BIM software to parametrically model the historical elements, and they are limited in their ability to create complicated irregular geometry. That is, the time complexity and geometric accuracy for parameterizing the complex and irregular historic structure elements are the main challenges. Besides, there have been lots of point cloud processing libraries and algorithms available in the field of reality-based modelling. These approaches pay attention on the semantic segmentation of the holistic geometric models, and they need additional platforms to manage the semantic and relationship information.
BuildingS-MART IFC is also extending itself for GIS project application, such as the new entity for spatial zones, geographic elements and external spaces. A united system integrating GIS and BIM concepts without information loss is not foreseen. It benefits information consistency among the different BIM software, GIS software and other modelling software if the representation of the complexity of the irregular shapes of the elements tends to be simplified. Unfortunately, when the simplification causes the loss of the details of the architectural surface, it goes against the aim of heritage conservation. The complexity of many elements and the lack of unified criteria make it difficult to simultaneously minimise information loss and optimise geometric architectural conservation during the exchange process between the two types of information systems.
Heterogeneous knowledge management by the connection with ontology HBIM provides a modelling environment where geometrical information and some intangible data are structured in a coherent database, and GIS provides a management environment where geometrical, attribute and relationship can be queried and analysed. But they still leave out of the model a large amount of knowledge, not directly relatable to heritage's components but still needed to fully represent the object  [51]. Thus, ontology plays increasingly important roles in connection with HBIM to comprehensively represent the historical buildings  [123] [124] [125] : (i) Knowledge modelling: Ontology-based knowledge model is combined with HBIM to enrich the semantic information. Ontology serves as a data collector to represent a large amount of semantics possibly independent from the built heritage (for example historical context, social information, environmental resources, other heritage information, etc.
)  [126]. (ii) Information extraction and querying: The combination of ontology can extend the HBIM model with the capabilities of information extraction and querying. The typical workflow is to use Autodesk Revit as HBIM platform and Protégé for editing ontologies and then to export the project database and query the information by Revit DBLink  [127]. Besides, based on ontology-based feature modelling, ifcXML is utilised to conduct query processing to extract information relevant to construction practitioners from a given HBIM  [128]. (iii) Connect and transfer the information between HBIM and GIS: Ontological framework can be built for dealing with the interoperability issue between BIM hierarchy structure and GIS relational database. HBIM model and GIS model are first mapped to ontology respectively and then connected and queried using ontology rules  [129, 130].
(iv) Help to create the parametric model: This kind of combination could be the basis for automated HBIM parametric design, although no work has thus far paid attention to the ontologybased BIM modelling. Lin  [131]  mentioned the possibility to applied ontology to computer graphics parametric design which can automate the modelling work to some degree. To fully represent and comprehend a historical/archaeological artefact, an integration with BIM and ontology can provide both object-oriented information (typically found in BIM software) and semantic information (typically found in ontological modelling systems). Currently, ontology semantics can be connected to BIM by direct ad-hoc development in BIM platform  [51] , migrating IFC files to RDF/OWL  [132]  and developing a unified platform  [57].
HBIM provided a unified platform for semantic/parametric modelling, 2D/3D visualization, and spatial/attribute database and management of the built heritage. The integration of existing techniques can extend the capability of HBIM on heritage documentation and management, such as computer graphics for parametric modelling of irregular structures, reality-based modelling for automated semantic segmentation, GIS for spatial information analysis and ontology for heterogeneous semantics management. However, the integration still faces challenges to enhance interoperability and reduce information loss among different platforms. A unified HBIM platform for better heritage documentation can be generated when addressing these challenges.
Total column of ozone (TCO) time series analysis and accurate forecasting is of great significance in monitoring the status of the Chapman Mechanism in the stratosphere, which prevents harmful UV radiation from reaching the Earth's surface. In this study, we performed a detailed time series analysis of the TCO data measured in Buenos Aires, Argentina. Moreover, hybrid data-driven forecasting models, based on long short-term memory networks (LSTM) recurrent neural networks (RNNs), are developed. We extracted the updated trend of the TCO time series by utilizing the singular spectrum analysis (SSA), empirical wavelet transform (EWT), empirical mode decomposition (EMD), and Mann-Kendall. In general, the TCO has been stable since the mid-1990s. The trend analysis shows that there is a recovery of ozone during the period from 2010 to 2017, apart from the decline of ozone observed during 2015, which is presumably associated with the Calbuco volcanic event.
The study by Ball et al. [5]  indicated that, while the stratospheric ozone layer has stopped declining across the globe, there is no clear increase observed at latitudes between 60° S and 60° N outside the polar region (60°-90°). Therefore, it is for this reason that TCO time series studies and the consequent design of models that can predict and forecast the dynamics of the ozone concentration in the atmosphere are imperative. Studies of the time series analysis of satellite and ground-based TCO data revealed a significant decline of -3% to -6% per decade (depending on latitude) throughout the 1980s and 1990s. These were linked to CFCs increases in the atmosphere  [6, 7]. Chehade at al. [8]  investigated the long-term evolution of zonal mean annual mean total ozone measurements (between 65° S and 65° N) from merged data sets of various satellites over a period of 1979-2012.
Long short-term memory networks (LSTM) recurrent neural networks (RNNs), which were proposed by Hochreiter and Schmidhuber  [23] , have been proven as an enhanced variant of RNNs that can learn the information contained in time series data more robustly. LSTM, a deep learning method, can more effectively capture the variability of time series data compared with other models such as ARIMA and ANN. However, the model accuracy is not high, using the LSTM network in its simplest form, to predict the time series data. Therefore, applying it in its simplest form, in a nonstationary time series data like the TCO time series used here, consideration should be given to first decomposing the time series to several more predictable components, each having less nonstationarity in order to improve overall accuracy.
LSTM has been previously used in digital currency forecasting  [18] , daily land surface temperature forecasting  [11] , wind speed forecasting  [20] , and wind power short-term prediction  [19] , among others. In this paper, the analysis of the growth rate and trend of the long-term data series of the TCO measured at Buenos Aires for the period from 1966 to 2017 is carried out. This data is one of the longest recorded time series of TCO in the Southern Hemisphere. A number of studies have indicated that the EMD technique can be an important method for trend identification  [10] [11] [12]. This research proposes the use of the EWT technique for the first time to identify the trend of the TCO time series. In addition, the authors aim to develop a robust data-driven hybrid model that uses the EWT technique in the decomposition stage of the LSTM, namely EWT-LSTM, and use this model in forecasting the TCO time series.
The plotting of the forward and backward sequential statistic in the same figure allows for the detection of the approximate beginning of a developing trend. It is noteworthy to also mention that, in the SQ-MK trend analysis, sometimes the forward and backward trends cancel each other and finally do not give a significant trend. This method has been successfully employed by many researchers to detect trend turning points and its significance  [28, 30]. The Mann-Kendall trend test and the SQ-MK trend analysis were performed using package 'trend'  [31]  and 'pheno'  [32] , respectively, run in R version 3. 5. 0.
Novel Hybrid Model Design The use of the EWT to decompose data before use in the model is expected to improve the prediction accuracy and to solve the issue of long-term dependencies forecasting problems of the TCO monthly time series, such as the one used here. The EWT-LSTM developed here is also compared to the hybrid method based on the EMD and LSTM neural networks, namely EMD-LSTM. This hybrid model (EMD-LSTM) was applied to geophysical data for the first time by Zhang et al. [11]. The two above models are also compared to the LSTM model that uses wavelet denoised (WD) data (WD-LSTM), wavelet denoising EWT-LSTM (WD-EWT-LSTM), and wavelet denoising EMD-LSTM (WD-EMD-LSTM). For denoising, this study uses a Daubechies (db8) wavelet family in the PyWavelets Wavelet Transform software for Python.
This family of wavelets is selected because it performed better than others in terms of applications in the forecasting of the TCO data. In addition, the LSTM system used here is a Python TensorFlow LSTM system that is run in miniconda  [36]. Figure  3  illustrates the detailed procedures of the architecture of the hybrid model used in this study. Apart from the models that used the Wavelet denoising step, implementation of the EWT-LSTM and EMD-LSTM models developed here occurs in three important steps. The first step utilizes the EMD or EWT technique to decompose the TCO time series data into relatively stable IMFs and one residue item. In the second step, the LSTM neural network is used to forecast each normalized IMF, including the residue. In the third step, the forecasting results of the LSTM neural network are reverse-normalized before being accumulated through summation as the final predicted results.
In this study, we utilized several time series analysis techniques such as wavelet analysis, Mann-Kendall test, and sequential Mann-Kendall test model, and applied them to the Buenos Aires TCO data. The wavelet analysis technique was efficient in analyzing the localized variation of the spectral power within the OCBA TCO data. Overall, wavelet analysis is a common tool for this purpose and this signal processing method makes it possible to determine the dominant modes of variability, in addition to their variation within the time series. The annual Mann-Kendall trend test method was also performed by calculating annual z-score values throughout the time series. We found this non-parametric trend test method very suitable for the TCO trend analysis for two reasons: (1) it does not require the data to be normally distributed, and (2) it does not depend on the magnitude of the data.
Furthermore, this method was supported by employing the sequential version of the Mann-Kendall test, in order to determine the change detection points in the OCBA TCO time series trend. Further details about the theory of the above mentioned time series analysis methods and their application can be found in a study by Mbatha et al. [28]. Figure  8  shows OCBA TCO monthly mean time series for the period from 1966 to 2017 with its smooth trend (a), normalized wavelet power spectra of the TCO data (b), annual mean Mann-Kendall calculated for TCO time series (c), and sequential Mann-Kendall statistics of progressive (Prog) ( ) and retrograde (Retr) ( ) (d). In the normalized wavelet power spectra in Figure  8b , the white thick lines represent the 95% confidence level, and areas of the wavelet power that are considered are those that are within the cone-of-influence (indicated by solid upside-down "u" shaped line).
Empirical Models Results and Its Performance To improve the prediction accuracy of complex geophysical data from a simple time series model, one needs to consider taking advantage of signal preprocessing methods such as denoising, decomposition, and ensembling the predicted results. Therefore, to understand the performance of the hybrid EWT-LSTM model, the predicted results of the EWT-LSTM model are compared with the LSTM, EMD-LSTM, WD-LSTM, WD-EMD-LSTM, and WD-EWT-LSTM. In this study, the OCBA TCO time series is divided into 70% training part of the time series and the 30% testing part of the time series. Thus, the model was trained using data from January 1966 to May 2002, and the model forecast testing part of the time series started from 2002 to 2017. Figure  9  shows the predicted results of the six models plotted together with the original time series (testing time series).
4, 7. 5, and 2. 6%, respectively. The LSTM model, a model that does not consider any of the data preprocessing methods used in this study, is the worst performing model. The aforementioned is presumably owing to the non-stationary and non-linear nature of the original monthly mean TCO data series, which is captured by the preprocessing methods used here. On the other hand, preprocessing of data with methods such as denoising and decomposition is known to improve the performance of the LSTM model and other artificial neural network models  [11, 14, 17]. The use of the wavelet transform denoising technique, a model design reported for the first time in this study, seems to improve the LSTM model accuracy. Better model accuracy is obtained when the model's original data are decomposed using EMD  [11]  or EWT.
Conclusions The present study utilized different time series analysis methods to investigate the trend and variability of the TCO monthly mean time series, in addition to developing and testing six LSTM recurrent neural networks based data-driven time series forecasting models. The TCO data, longterm ozone data measured at the Buenos Aires site, Argentina, from 1966 to 2017, were used in this study. Overall, the trends of TCO time series data extracted using the SSA, EWT, EMD, and Mann-Kendall confirm that the TCO has been stable since the mid-1990s. The SSA growth rate and trend, EMD trend, and EWT trend capture the dynamical variability of the TCO well, and their results are somewhat consistent. All trend analysis methods seem to report a significant recovery of ozone during the period from 2010 to 2017, apart from the decline of ozone observed during 2015, which is presumably associated with the Calbuco volcanic event (a Chilean volcano that has injected volcanic plume up to the stratosphere, as reported by Bègue et al.
Noise reduction techniques via wavelet transform improved the LSTM model accuracy, but it appears unnecessary to denoise data to the data when utilizing the decomposition process. Overall, the results presented in this study show that the EWT-LSTM model can be used as a successful tool for monthly mean TCO forecasting. For future work, it is recommended to use a method that has good mathematical theory such as the EWT technique to perform trend analysis of ozone data, or any other geophysical time series. The continuation of this study will include applying the techniques developed to the stratospheric column ozone, lower stratosphere ozone, and upper stratosphere ozone measured in the high latitude Southern Hemisphere, in order to forecast the process of ozone hole recovery. Additionally, more TCO measuring sites at different latitudes will also be considered in future studies.
Acknowledgments: Authors acknowledge the  French South-African PROTEA  programme and the  CNRS-NRF LIA ARSAIO (Atmospheric Research in Southern Africa and Indian Ocean) , for supporting research activities, and the  National Research Foundation (NRF) of South Africa. We are thankful to the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC)  for data archiving, the PI and operators of Dobson instrument at  Buenos Aires , and the  National Meteorological Service of Argentina (SMNA).
In  [33] , Xu et al. proposed MutGAN, a multitask GAN, to segment and quantify myocardial infarction. MutGAN consists of a generator and a discriminator module, and is implemented by 3 seamless connected networks, with each network learning different bits of information for use in specific tasks. However, apart from  [28] , these methods are all fully-supervised. In  [28]  the model assessed the quality of the segmentation using a scoring method and provides a segmentation for unannotated images through an iterative adversarial training process. The above methods have yet to be applied for cardiac MRI segmentation, though. Furthermore, discriminators are designed to determine whether or not a segmentation is real, and do not consider the spatial resolution of the image, which is very important for medical image segmentation.
Dataset The proposed method was evaluated on short-axis cardiac MR image sequences provided by the Department of Diagnostic Imaging of the Hospital for Sick Children in Toronto, Canada (  [35] ). The MRI dataset is comprised of 33 subjects and each patient's scan consisted of 20 frames, each with 8 to 15 slices, for a total of 10,022 images. Patient ages ranged from 2 to 18 years old. Two patients were normal. The remaining subjects displayed a variety of heart abnormalities, such as cardiomyopathy, aortic regurgitation, enlarged ventricles and ischemia. The images were scanned with a GE Genesis Signa MR scanner, using the FIESTA scan protocol. All slices were 256×256 in size, but the pixel resolution and spacing between slices differed for each patient. This dataset is composed of 3D images, but for this study, we focused on the segmentation of 2D images.
Image pre-processing Each MR image was scanned using the same MR scanner, and using the same protocol. However, the pixel spacing differed from one patient to the next, and all volumes had to be normalized. For each image, we applied the following preprocessing steps: As explained in 3. 1 the segmentation of the endocardium and the epicardium were provided by the dataset, in the form of 2D coordinates in the image. We used these coordinates to create a binary mask as the ground truth, as shown in Figure  1.
Experimental setup The model was implemented using Python and the deep learning framework, PyTorch. We trained and evaluated our model on the dataset described in 3. 1. To avoid overfitting, we applied cross-validation during the training. Because the dataset was composed of 33 patients, we broke it down into three different sets: training (25 patients), testing (4 patients) and validation (4 patients). Then, all images of patients in each set were shuffled to avoid considering two subsequent images in a batch and avoid biasing the training. We used the Adam optimizer for both networks with a weight decay of 10 -4 and a learning rate of 2. 5 × 10 -4 for the segmentation network and of 10 -4 for the discriminator network. After several experiments, we set λ adv and λ semi to 0. 1 and 0. 001, respectively, similarly to what is present in  [21] , because we observed no noticeable change in the training.
We trained our model over 5000 iterations and started semisupervised learning after 700 iterations to prevent the model from suffering from initial wrong predictions; we then stopped it early to avoid overfitting. Due to the random selection of patients, the shuffling of images, a bad resolution and poor segmentation on some images, we averaged the results of 10 ex-periments to evaluate the model accuracy and robustness. Finally, we implemented the U-Net segmentation model for the LV segmentation proposed in  [10]  to compare the performance of our model against another segmentation network. U-Net model is composed of 23 layers. We also test our model with the adult cardiac MRI dataset ACDC (  [39] ).
Results Tables 1, 2 and 3 show the results we obtained for the prediction of the endocardium, the epicardium and the myocardium for different models. "DT-GAN" represents the results of our model based on  [21]  with the loss we defined in 3. 3. We evaluated the segmentation performance with different amounts of data used for the supervised learning phase and two different datasets. The first dataset, the York dataset is a pediatric dataset. The second dataset, the ACDC dataset (  [39] ), is an adult dataset we used to validate our method. Finally, we compared the introduction of the distance loss penalty map with the initial model of Hung et al. [21] , using a masked cross-entropy loss, and with a U-Net model for LV segmentation for both datasets.
We can note in Figures  3b, 3h , 3e and 3k that our model outputs a prediction which is closer to the ground truth (in red) than does the original GAN model. The distance map loss penalty term helps our model predict more realistic forms (see Figures  3h to 3k ). Furthermore, compared to U-Net predictions in Figures  3f  and 3l  our model predicts shapes at least as good as those predicted by U-Net, irrespective of the amount of data used. Figures  3b, 3c , 3d, 3h, 3i and 3j as well as table  1  shows that our approach produces a very good segmentation of the endocardium, even with a small amount of data submitted for training. Finally, regarding ACDC dataset we draw the same conclusions as the York dataset. We obtain similar Hausdorff distances using our method as using U-Net although the Dice scores of DT-GAN are lower than U-Net.