dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions This paper proposes and implements an effective method for quickly recognizing and correcting poor writing postures using a digital dot matrix pen to conduct in-depth experiments. First, fifteen volunteers used the digital dot matrix pen to write on dot paper, generating over 16,000 writing strokes. All this data was then exported, the noise was removed, and the data could be processed and used. After obtaining the valid feature data, the pen holding postures (PHPs) are first recognized using traditional machine learning classification models. Additional feature filtering is required before continuing the experiments. We find that tree-based models, such as random forest and XGBoost, perform better than other traditional machine learning classification models. This result is due to the fact that tree models have decision flow shapes close to the hyperplane and have an efficient representation capability for tabular data.
In addition, the TabNet model achieved a higher correction rate than the traditional tree model due to its ability to not only mimic tree models but also to learn from one end to the other. Among the different deep learning models, the SimAM integrated model was found to improve the classification effect. Finally, we composed the samples in temporal order using multiple occurrence feature data. These samples are fed into LSTM in order, and their models are better than those of single samples. To boost the accuracy further, an existing parameter-free attention module (SimAM) dedicated to convolutional neural networks (CNNs) is integrated with long short-term memory (LSTM) and applied to a CNN to extract the basic features. We also use Mogrifier long short-term memory (MLSTM) to have the input x interact with the output h prev of the previous unit.
Finally, it is worth noting that the accuracy of this model reached 72. 1%, the F1-score reached 74. 2%, and the AUC value reached 0. 85, indicating excellent classification performance. All the experiments conducted show that SimAM-CNN-MLSTM performs well and thus allows us to build the corresponding classification models to correct PHPs. In the experiments, the erroneous PHPs are produced by imitation of the volunteers. The handwriting data were collected based on the description of the posture and the imitation of the image and may deviate slightly from reality. In the future, we will collect more data from volunteers accustomed to erroneous PHPs to build a relevant public dataset. We also note that the current study does not specifically distinguish between types of erroneous PHPs. Therefore, recognizing PHP types will be a new direction for our research.
SimAM-CNN-MLSTM network data size for multi-stroke datasets. ×46 × 1] "8" is the number of channels. n× [16× 22 × 1] "16" is the number of channels. n × [32 × 20 × 1] "32" is the number of channels. n × [64 × 9 × 1]"64" is the number of channels.
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the number, size and surface structure of the polyp are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicenter purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from 6 unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3446 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists.
To develop such a system large segmented image databases are required. While current deep learning approaches has been instrumental in the development of computer-aided diagnosis (CAD) systems for polyp identification and segmentation, most of these trained networks suffer from huge performance gap when out-of-sample data have large domain shifts. On one hand, training models on large multi-center datasets all together can lead to improved generalisation, but at an increased risk of false detection alarms  11. On the other hand, training and validation on center-based splits can improve model generalisation. Most reported works are not focused on multi-center data at all. This is mostly because of the lack of comprehensive multi-center and multi-population datasets. In this paper, we present the PolypGen dataset that incorporates colonoscopy data from 6 different centers for multiple patient and varied populations.
Attentive splits are provided to test the generalisation capability of methods for improved clinical applicability. The dataset also is suitable for exploring federated learning and training of other time-series models. PolypGen can be pivotal in algorithm development and in providing more clinically applicable CAD detection and segmentation systems. Although there are some publicly available datasets for colonoscopic single frames and videos (Table  1 ), lack of pixel-level annotations and preconditions applied for access of them pose challenges in its wide usability for method development. Many of these datasets are by request which requires approval from the data provider that usually takes prolonged time for approval and the approval is not guaranteed. Similarly, some datasets do not include pixel-level ground truth for the abnormality location which will cause difficulty in development or validation of CAD systems (e.
g. , El salvador atlas  12  and Atlas of GI Endoscope  13  ). Moreover, most of the publicly available datasets include limited number of images frames from one or a few centers only (e. g. , datasets provided in  14, 15, 15, 16  ). To this end, the presented PolypGen dataset is composed of a total of 6282 frames including both single and sequence frames. The provided comprehensive dataset consists of 3762 positive sample frames and 2520 negative sample frames collected from six different hospitals comprising of varied population data, endoscopic system and surveillance expert, and treatment procedures for polyp resections. A t-SNE plot for positive samples provided in the Supplementary Figure  1  demonstrates the diversity of the compiled dataset.
Ethical and privacy aspects of the data Our multi-center polyp detection and segmentation dataset consists of colonoscopy video frames that represent varied patient population imaged at six different centers including Egypt, France, Italy, Norway and the United Kingdom (UK). Each center was responsible for handling the ethical, legal and privacy of the relevant data. The data collection from each center included either two or all essential steps described below: 1. Patient consenting procedure at each individual institution (required) 2. Review of the data collection plan by a local medical ethics committee or an institutional review board 3. Anonymization of the video or image frames (including demographic information) prior to sending to the organizers (required) Table  2  illustrates the ethical and legal processes fulfilled by each center along with the endoscopy equipment and recorders used for the data collected.
Study design PolypGen data was collected from 6 different centers. More than 300 unique patient videos/frames were used for this study. The general purpose of this diverse dataset is to allow robust design of deep learning models and their validation to assess their generalizability capability. In this context, we have proposed different dataset configurations for training and out-of-sample validation and proposed unique generalization assessment metrics to reveal the strength of deep learning methods. Below we provide a comprehensive description of dataset collection, annotation strategies and its quality, ethical guidelines and metric evaluation strategies.
Video acquisition, collection and dataset construction A consortium of six different medical data centers (hospitals) were built where each data center provided videos and image frames from at least 50 unique patients. The videos and image samples were collected and sent by the senior gastroenterologists involved in this project. The collected dataset consisted of both polyp and normal mucosa colonoscopy acquisitions. To incorporate the nature of polyp occurrences and maintain heterogeneity in the data distribution, the following protocol was adhered for establishing the dataset: 1. Single frame sampling from each patient video incorporated different view points 2. Sequence frame sampling consisted of both visible and invisible polyp frames (at most cases) with a minimal gap 3. While single frame data consisted of all polyp instances in that patient, sequence frame data consisted of only a localised targeted polyp 4.
Positive samples Positive samples consist of video frames from the patient with a diagnosed polyp case. The selected frames may or may not have the polyp in it but may be located near to the chosen frame. Nevertheless, a majority of these frames consists of at least one polyp in the frame. For the sequence positive samples, the continuity of the appearance and disappearance of the polyp similar to real scenario has been taken into account and thus these frames can have a mixture of polyp instances and frames with normal mucosa. Table  3  is provided to detail 13/23 sequence data included in our dataset. It can be observed from Figure  2  that varied sized polyps are included in the dataset with variable view points, occlusions and instruments. Exemplary pixel-level annotations of positive polyp samples for each center and their corresponding bounding boxes are presented in Supplementary Figure  2.
Annotation strategies and quality assurance A team of 6 senior gastroenterologists (all over 20 years of experience in endoscopy), two experienced post-doctoral researchers, and one PhD student were involved in the data collection, data sorting, annotation and the review process of the quality of annotations. For details on data collection and data sorting please refer to Section Video acquisition, collection and dataset construction. All annotations were performed by a team of three experienced researchers using an online annotation tool called Labelbox 1. Each annotation was later cross validated for accurate segmentation margins by the team and by the center expert. Further, an independent binary review process was then assigned to a senior gastroenterologists, in most cases experts from different centers were assigned.
e. , sub-folder level 0 only as these sequence samples consists of patients with no polyps during the surveillance. To further assist the users, we have also provided the folder structure inside the main folder "PolypGen". The size of images provided in the dataset can range from 384 × 288 pixels to 1920 × 1080 pixels. The size of masks correspond to the size of the original images, however, the polyp sizes in the provided dataset is variable (as indicated in Figure  1 ).
Technical Validation For the technical validation, we have included single frames data (1449 frames) from five centers (C1 to C5) in our training set and tested on out-of-sample C6 data on both single (88 frames) and sequence frames (432 frames). Such out-of-sample testing on a completely different population and endoscopy device data allows to comprehensively provide evidence of generalisability of current deep learning methods. The training set was split into 80% training only and 20% validation data. Here, we take most commonly used methods for segmentation in the biomedical imaging community  [28] [29] [30] [31] [32]  , including that for polyps. For reproducibility, we have included the train-validation split as. txt files as well in the PolypGen dataset folder. However, users can choose any set of different combined training or split training schemes for generalisation tests as they prefer, e.
Benchmarking of state-of-the-art methods To provide generalisation capability of some state-of-the-art (SOTA) methods, we have used a set of popular and well-established semantic segmentation CNN models on our PolypGen dataset. Each model was run for nearly 500 epochs with batch size of 16  for image size of 512 × 512. All models were optimised using Adam with weight decay of 0. 00001 learning rate of 0. 01 and allowing the best model to be saved after 100 epochs. Classical augmentation strategies were used that included scaling (0. 5, 2. 0), random cropping, random horizontal flip and image normalisation. All models were run on Quadro RTX6000. Evaluation metrics for segmentation. We compute standard metrics used for assessing segmentation performances that includes Jaccard Index (JI = F β = (1 + β 2 ) • p • r (β 2 • p) + r , (1) where F β -score is computed as weighted harmonic means of precision and recall.
Polyp segmentation The U-Net architecture developed by Ronnerberger et al. 29  has shown tremendous success in medical image segmentation  34  including endoscopy  16, 35. PSPNet 30 is designed to incorporate global context information for the task of scene parsing. Similarly, dilated convolutions enabled the construction of semantic segmentation networks to effectively control the size of the receptive field that was incorporated in an a family of very effective semantic segmentation architectures, collectively named DeepLab  31. All of these networks has been explored for polyp segmentation in literature  [36] [37] [38]. Here, we benchmark our dataset on these popular deep learning model architectures. Out-of-sample generalisation results for both single frame (Table  5 ) and sequence data (Table  6 ) has been included in our technical validation of the presented data.
Usage Notes All released dataset has been published under Creative Commons Attribution-NonCommercial 4. 0 (CC-BY-NC 4. 0) license. Dataset can only be used for educational and research purposed and must cite this paper. The released dataset has been divided into positive and negative samples. Additionally, positive samples are divided into single frames and sequence frames. Users are free to use the samples according to their method demand. For example, for fully convolutional neural networks we adhere to the use of positive samples as done in our technical validation, while for the recurrent techniques that exploit temporal information users may use both positive and negative sequence data.
Acknowledgements The research was supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the authors and not necessarily those of the  NHS , the  NIHR  or the  Department of Health. S. Ali and JE. East is supported by  NIHR Oxford BRC. J. Rittscher  by  Ludwig Institute for Cancer Research  and  EPSRC   Seebibyte Programme Grant.
Code availability To help users with the evaluate the generalizability of detection and segmentation method a code is available at: https: //github. com/sharibox/EndoCV2021-polyp_det_seg_gen. The code also consists of inference codes that to assist in center-based split analysis. Benchmark codes of the polypGen dataset with provided training and validation split in this paper for segmentation is also available at: https://github. com/sharibox/PolypGen-Benchmark. git.
Ocean biogeochemistry component The biogeochemical module (BIOGEM) represents the transformation and spatial redistribution of biogeochemical tracers  (Ridgwell et al. , 2007). The state variables are inorganic nutrients and organic matter. Organic matter is partitioned into dissolved and particulate organic matter (DOM and POM). The model includes iron (Fe) and phosphate (PO 4 ) as limiting nutrients but similar to  Asselot et al. (2021) , we do not consider nitrate (NO - 3 ) here. Furthermore, BIOGEM calculates the air-sea CO 2 and O 2 exchange. These fluxes depend on the gas transfer velocity, the water density, the concentration of dissolved gas in the ocean surface, the solubility coefficient calculated from  Wanninkhof (1992) , the concentration of gas in the atmosphere, and the fraction of the ocean covered by sea ice  (Ridgwell et al.
Code availability. The code for the model is hosted on GitHub and can be obtained by cloning or downloading: https://zenodo. org/record/5676165. The configuration file is named "RA. ECO. ra32lv. FeTDTL. 36x36x32" and can be found in the directory "EcoGENIE_LA/genie-main/configs". The user-configuration files to run the experiments can be found in the directory "EcoGENIE_LA/genie-userconfigs/RA/Asselotetal_ESD". Details of the code installation and basic model configuration can be found on a PDF file (https://www. seao2. info/cgenie/docs/muffin. pdf). Finally, section 9 of the manual provides tutorials on the ECOGEM ecosystem model.
However, there is no knowledge on how changes in forest restoration policy affected the spatial and temporal patterns of disturbance regimes in China's forests. Recent global-scale disturbance data is often limited by the temporal extent (e. g. , post-2000  3, 4  ), to a particular disturbance type (e. g. , wildfire  31  ), or at spatial resolutions that are often too coarse to capture landscape-scale disturbance dynamics  32. Therefore, the main objective of this study is to provide a high quality, spatially explicit disturbance dataset to characterize the disturbance regimes in China's forests. Here, we used a spectral-temporal segmentation method to map and characterize the disturbance regimes of China's forest at 30-m spatial resolution for the period 1986-2020 utilizing all available Landsat time series images (i.
3 ), suggestive of increasing intensity of forest operations in Southern China. Our spatially explicit forest disturbance dataset provides a baseline to assess the impacts of changing disturbance on forest dynamics, biodiversity, and climate forcing. Forest management and wildfire are major disturbance types in temperate and boreal forests, but with large regional variations  4. Our study confirmed that natural disturbance was a minor disturbance type in China's forests; Instead, forest management activities were the dominant drivers of disturbance regimes and had stronger effects on the extent, composition, and stand structure across China's forests. Over the past few decades, afforestation/reforestation projects have led to a continuous increase of forest cover which was also observed in the recent national forest inventory statistics  45, 46.
Recent studies have found that increases in forest cover contributed greatly to increasing leaf area index or land greening  32, [47] [48] [49]  , and the enhancement of China's terrestrial carbon sink over the past three decades  29, [50] [51] [52]. On the other hand, forest conservation projects, such as the NFPP, have effectively decreased the disturbance rate in China's forests and to what extent such decreasing disturbances contributed to leaf area index and carbon sequestration remains to be quantified. Our spatially explicit forest disturbance dataset provides a baseline to disentangle the contribution of increasing forest cover and changing disturbance regime to forest dynamics, carbon cycle, and climate feedback  [53] [54] [55]. However, it is also worthwhile to note that although intensified afforest/reforestation has significantly increased forest covers in China, there is an increasing need to enhance forest structure and quality via effective management  26.
Disturbance classification Reference data. High-quality consistent reference data is key to train and classify disturbance types. To do so, we generated a total of 31225 reference points using a hierarchical approach. We first generated a large number of potential disturbance points using forest loss data from 2001 to 2020  3. Then we separated fire disturbances from non-fire disturbances by overlaying MODIS burned area (BA) with potential disturbance points following the procedure used by  63. Specifically, fire disturbances were determined if the MODIS BA data coincided with the Landsatderived forest loss for the fire year and 2 years postfire (i. e. , t + 0, t + 1, t + 2) to account for delayed post-fire tree mortality. Following this step, we derived points as potential disturbances that consisted of fires and non-fire disturbances (including forest conversion to other land use types and silvicultural practices at various intensities).
Random forest classification. We used machine learning modeling to classify each pixel into fire disturbance, non-fire disturbance, or no disturbance. The reference data points were used to sample the LandTrendr-derived disturbance-related parameter layers described above, which resulted into a dataset consisting of disturbance types. We divided the dataset into 70% of training data, and 30% as validation data. Using the training data, a Random Forest (RF) model was trained to classify each reference point into fire, non-fire disturbance, or no disturbance. Our RF approach showed that short-wave infrared (SWIR)-based moisture indices (e. g. , B7, TCW) were strong predictors for detecting forest disturbances (Supplementary Fig. 11 ) likely because of their sensitivity to vegetation water content and canopy structure  64.
Finally, we applied the trained RF model to the full classification stack to consistently map the disturbance types from 1986 to 2020 across China's forests, assuming that the spectral trajectories derived from reference data period 2001-2020 can be extrapolated to the whole mapping period 1986-2020. However, note that our approach was meant to detect relatively acuate and discrete disturbances that caused canopy opening, rather than subtle changes of forest structure or composition resulted from low intensive silvicultural practices and chronic disturbances. Year of disturbance. We used the LandTrendr to determine the year of disturbance as the onset of magnitude of spectral change. Since we ran LandTrendr on five spectral indices, there were five possible years of disturbance for each pixel.
We derived a confusion matrix to report user's and producer's accuracy (Supplementary Table  1 ) as the main accuracy assessment metrics. At the national level, we compared forest disturbance detected in this study to available existing dataset. Specifically, we compared the area of forest fire disturbance between our study and the national fire records during 2003-2009 (Supplementary Fig. 5 ). We compared the disturbance rates between our study and Landsat-derived global forest cover changes from 2001 to 2019 3 (Supplementary Fig. 4 ). Post-processing. We applied a series of spatial filters to minimize the unrealistic outliers from two potential sources of uncertainty, including speckle in time-series spectral trajectories or misregistration among images. This may lead to individual pixel or small patches including only a few pixels, which were (a) detected as disturbances, thus increasing the commission errors, or (b) not detected as disturbances, while their surrounding pixels were mostly disturbed, thereby increasing the omission errors.
g. , management, pullulation density, road density, and distance to the nearest road), and variability in Landsat observations on annually disturbed forest area at each 0. 5degree grid cell. Climatic factors were used to investigate if background climates had an influence on disturbance patterns. We summarized annual mean air temperature (MAT), mean annual precipitation (MAP), and vapor pressure deficit (VPD) from 1985 to 2020 using 4-kilometer, monthly gridded TerraClimate data  65. VPD was then only included in the analysis due to its collinearity with MAT and MAT. Forest management variables included percent of forest management area and the status of ecological projects. The percent of forest management area was calculated as percent of pixels with forest management activities divided by all forest pixels at 0.