dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  John Davey  and  Richard Wallbank  for assistance with the capture design and  Joe Hanly  and  Erica Westerman  for providing aristaless CRE coordinates. We are also grateful to  Krzysztof Kozak ,  Patricio Salazar ,  Gislene L. Gonc  ¸alves, and  Jake Morris  for their help with samples. We thank  Ian Warren  for help with database management. Furthermore, we thank  Christian Huber ,  Derek Setter ,  Joachim Hermisson , and  Birgit Schlick-Steiner  for helpful discussions. We acknowledge the lepbase. orgdatabase [135] for providing valuable resources for analysis and the  Welcome Trust-Medical Research Council Stem Cell Institute  for technical support. This work was performed using resources provided by the  Cambridge Service for Data Driven Discovery  ( CSD3 ) operated by the  University of Cambridge Research Computing Service  (http://www.
The project was funded by an  European Research Council  (ERC; https://erc. europa. eu/) Advanced Grant  ( FP7-IDEAS-ERC 339873 ) and a  Biotechnology and Biological Sciences Research Council  (BBSRC; https://bbsrc. ukri. org/)grant ( BB/ R007500 ) awarded to CDJ. MM was also supported by a  Swiss National Science Foundation  (SNF; http://www. snf. ch) Early Postdoc Mobility fellowship  ( P2EZP3_148773 ) and an  Austrian Science Fund  (FWF; https://www. fwf. ac. at/)Erwin Schro ¨dinger fellowship (J  3774 ). SMVB was supported by an  Isaac Newton Trust  Grant (https:// www. newtontrust. cam. ac. uk/) of  Trinity College Cambridge  awarded to CDJ. CS was supported by the  Universidad del Rosario  (https://www. urosario. edu. co/) ' Big Grant '  IV-FGD-001  and a COLCIENCIAS (https://www. colciencias.
gov. co/)grant ( FP44842-5-2017 ). NJN was supported by the  UK Natural Environment Research Council  (NERC; https://nerc. ukri. org/)through an  Independent Research Fellowship  ( NE/K008498/1 ). MJ was funded by  Agence Nationale de la Recherche  (ANR; https://anr. fr/)through grants  ANR-12-JSV7-0005  and  ANR-18-CE02-0019-02. GRPM was supported by a  Ministerio da Ciência, Tecnologia, Inovacões e Comunicacões  (CNPq; http://www. cnpq. br/)fellowship (Grant  309676/ 2011-8 ). This work was performed using resources provided by the  Cambridge Service for Data Driven Discovery  ( CSD3 ) operated by the  University of Cambridge Research Computing Service  (http://www. csd3. cam. ac. uk/), provided by  Dell EMC  and Intel using Tier-2 funding from the  Engineering and Physical Sciences Research Council  (capital grant  EP/P020259/1 ), and DiRAC funding from the  Science and Technology Facilities Council  (www.
All resequencing data generated in this study were deposited at the the European Nucleotid Archive database (Study PRJEB33224; see S1 Table for accessions). Accession numbers of already published resources are also given in S1 Table and S10 Table. Data and code are available from https://github. com/ markusmoest/SelectionHeliconius. git, https:// github. com/StevenVB12/Sample-distributions and https://github. com/simonhmartin.
The complete data set is provided as 14 netCDF files available on PANGAEA (Gentili and Gattuso, 2020a, https://doi. org/10. 1594/PANGAEA. 910898). The R package Coastal-Light, available on GitHub (https://github. com/jpgattuso/CoastalLight. git, last access: 29 July 2020), allows us (1) to download geographical and optical data from PANGAEA and (2) to calculate the surface area that receives more than a given threshold of irradiance in three regions (nonpolar, Arctic, and Antarctic). Such surface areas can also be calculated for any subregion after downloading data from a remotely and freely accessible server.
the water column to derive open-ocean primary production (e. g. ,  Antoine et al. , 1996). However, in the coastal ocean, primary production also occurs at the bottom when enough light reaches the seafloor. For example, on coral reefs, benthic primary production can represent 90 % of the total primary production  (Delesalle et al. , 1993). Primary production in coastal vegetated habitats such as mangroves, seagrass beds, and tidal marshes, the so-called blue carbon ecosystems, has received considerable interest in the past 10 years because of their disproportionately large contribution to global carbon sequestration  (Macreadie, 2019). It has been recently suggested that benthic macroalgae also contribute to global carbon burial  (Krause-Jensen et al. , 2018). Gattuso et al. (2006)  used SeaWiFS data collected between 1998 and 2003 to estimate, for the first time on a nearly global scale, the irradiance reaching the bottom of the coastal ocean.
Remote sensing data Monthly level-3 data of PAR (mol photons m -2 d -1 ), K PAR (m -1 ), concentration of chlorophyll a (C sat ; mg m -3 ), and remote sensing reflectance at 555 nm (R rs (555); sr -1 ) from the satellite-borne sensors SeaWiFS, Moderate Resolution Imaging Spectroradiometer (MODIS), MEdium Resolution Imaging Spectrometer (MERIS), and Visible Infrared Imaging Radiometer Suite (VIIRS) were obtained from the GlobColour project (http://www. globcolour. info, last access: 2 May 2019). The GlobColour project generates global ocean color products by merging data from current and past ocean color instruments (SeaWiFS, MERIS, MODIS, VIIRS, and the two OLCIs), but data retrieved from GlobColour in January 2019 did not comprise data from the Ocean and Land Colour Imager (OLCI). Merged products are generated through a weighted average of the level-2 geophysical products (e.
g. , chlorophyll) from individual missions. The weights are assigned to each mission under the form of a global uncertainty value derived through validation with respect to global databases of field observations. Alternative products are also generated through the Garver-Siegel-Maritorena (GSM) model  (Garver and Siegel, 1997; Maritorena et al. , 2002 Maritorena et al. , , 2010)). The resolution is 1/24 •. Together, the 252 monthly images downloaded (a level-3 image contains values of a product on a regular longitude-latitude grid) cover the period 1998 to 2018.
Bathymetry and coastline Depths were estimated from the 2019 General Bathymetric Chart of the Oceans (GEBCO; https://www. gebco. net, last access: 2 May 2019) gridded bathymetry data (1/240 • resolution). The coastal zone (0 to 200 m) was determined using a land mask and coastline (Global Self-consistent, Hierarchical, High-resolution Geography, GSHHG) as implemented in the Generic Mapping Tools (GMT;  Wessel et al. , 2013). The full resolution was used. The Arctic, Antarctic, and nonpolar regions represent, respectively, 24. 1 %, 0. 6 %, and 75. 3 % of the surface area of the coastal zone.
P functions for a subregion A subregion may be defined within one of the three main regions. In this case, data sets are clipped according to the subregion's boundaries, and the months used are those of the main region. Calculation is identical to that described above for the climatological global P function (Sect. 2. 5). The R package CoastalLight (see Sect. 4) can be used to calculate a P function for a subregion with the help of a remote server.
These functions only provide rough estimates and are not used in the present study. The R package CoastalLight has been developed in the present study to provide more accurate estimates (Sect. 4) calculated from the underlying data, which is to say the number of pixels and their size. These changes in approach, together with the different data sets used for the optical and bathymetric data, have led to significant changes in three factors that affect bottom PAR (PAR B ; Fig. 4 ; Table  4 ). Two of them contribute to a decline of PAR B : (1) a change in the depth distribution leading to an increase in the median depth (39 versus 31 m) and (2) a distribution of K PAR that moved towards higher values in the present study. Also, (3) surface PAR tends to be higher in the present study than in the previous one.
Data availability The geographical and optical data generated and used in this paper are openly available at the World Data Center PANGAEA (Gentili and Gattuso, 2020a; https://doi. org/10. 1594/PANGAEA. 910898). They consist of 14 netCDF files with a unique dimension (the coastal pixel number) which is identical for all files: a netCDF file with geographical information (latitude, longitude, depth, area of the pixels) (Coastal-Light_geo. nc; about 1. 2 Gb); a netCDF file with the climatology over the whole 21 year period calculated as the mean values of the monthly data of PAR, K PAR , and PAR B (Coastal-Light_00. nc; about 1. 1 Gb); -12 netCDF files with monthly climatologies (mean of the values of PAR, K PAR and PAR B ). The 12 netCDF files include the following: -Monthly climatology, January: CoastalLight_01.
nc (6. 2 Gb); The surface area of three regions (Arctic, Antarctic, and nonpolar) receiving an irradiance above a certain threshold is available using the R package CoastalLight (https://github. com/jpgattuso/CoastalLight,  Gentili and Gattuso, 2020b). The package can be installed and used as follows: - install. packages("devtools") library(devtools) -install_github("jpgattuso/CoastalLight") use function cl_surface of the CoastalLight package. The surface area of a subregion of one of the regions above receiving an irradiance above a certain threshold can be derived as follows (complete information can be found in the documentation of the CoastalLight package): connecting to the web server http://obs-vlfr. fr/ Pfunction/ (last access: 30 July 2020) to calculate and download its P function; then using this P function with function cl_surface of the CoastalLight package.
Acknowledgements. This paper is a product of The Ocean Solutions Initiative (http://bit. ly/2xJ3EV6,last access:  30 July 2020 ) and a contribution to a EuroMarine project (http://euromarinenetwork. eu/activities/role-macroalgae-global-ocean-carbon-budget, last access:  30 July 2020 ) led by  Dorte Krause-Jensen. Participants of the foresight workshop held in Granada in 2019, especially  Jorge Assis ,  Carlos Duarte , and  Dorte Krause-Jensen , are gratefully thanked for stimulating discussions. Financial support. This research has been supported by the  Prince Albert II of Monaco Foundation, Veolia Foundation ,  IAEA Ocean Acidification International Coordination Centre , the  French Facility for Global Environment , as well as the  EU-H2020  project  INTAROS  (grant no. 727890 ). Review statement.
We conclude section II with a problem statement and short details of our contribution. In section III we propose a framework to robustly train Deep Graph Neural Network for the document analysis problem. In section IV we give information about the experimental setup and the dataset used in our experiments. We provide an extensive analysis of the benefits of the proposed framework on the letters dataset in section V. We also discuss the results and provide quantitative insights. We conclude our work with a conclusion where we suggest future directions for our work.
a: Dataset: We used the Letters Dataset  [8]  from IAm Graph repository. This dataset is proposed by Riesen et. al. [8]  in 2008, This becomes a defacto standard for almost every Graph Based technique since then, particularly the graph based methods for hand written character recognition. The dataset consists of three categories as LOW, MED, and HIGH based on the distortion. The LOW category has less distortion in the graphs of letters as compared to the MED and HIGH. Similarly, the HIGH category has more distorted graphs such that some characters are even not readable by humans, as  shown in Figure  4. The dataset is further divided into train, test, and validation sets. Each set is consists of 750 graphs. So, 2250 graphs are in each category and in total 6750 graphs. As shown in Table  1  it consists of 15 classes A, E, F, H, I, K, L, M, N, T, V, W, X, Y, Z in each subset of LOW, MED, and HIGH.
V. RESULTS AND DISCUSSION The main focus of our experiment is to train a Deep Graph Neural Network Classifier Based on Additive Angular Margin Loss. We train our classifier with the four margin loss Our approach contributes towards the document analysis problem; we used a deep neural network on the letters dataset, which is categorized into three categories (LOW, MED, and HIGH) and evaluated the performance of the model on the bases of four different loss functions. SphereFace Loss Function used the idea of angular margin in loss function. CosFace loss uses cosine margin in the Loss Function. ArcFace loss by Deng et al. [18]  used Additive Angular Margin in Loss function to improve the performance in terms of discrimination between the classes. We provide comparisons of all these Loss Functions with SoftMax Loss in our proposed approach using DGNN.
Results described in Table  2  show that if marginal loss function is applied then it will provide better results as compared to the standard Softmax loss function. Figure  5  shows the results of LOW subset from Letter dataset  [8]. The proposed approach using the Additive Angular Margin loss function in Deep Graph Neural Network outperforms many recent states of the art methods that show the importance of symbolic and numeric information of edges along with the significance of the Additive angular margin loss function. Using the cosine margin loss function also performs well as compared to the multiplicative loss function and the standard Softmax loss function. and 7  show the results comparisons of MED and a HIGH subset of the letter dataset respectively. This shows that how the discriminating power of a model can be increased by applying the additive angular marginal loss function.
It helps to increase the decision boundary between the classes. Although there is numerical similarity. We test the proposed model and compare the results of the four-loss functions on the Letters dataset using different (GED) Graph edit methods. From the results of Table  2 , the Hausdorff Edit Distance (HED) method of computing graph distance performs better when the ArcFace is used in the DGNN. The loss function is applied to all three categories (LOW, MED, HIGH) of the Letters dataset, and we can say that Standard SoftMax Loss Function performs least effectively on three categories of data using HED distance metrics. Using the HEOMD distance metric ArcFace Loss Function outperforms all three categories of data compared to all other loss Function. While comparing the results of these recent state of the art methods, we proposed that the HEOMD distance metric provides significant results using the ArcFace Loss  Function.
122. so total execution/training time is 1000*0. 122 = 122. The test time of our model for calculating distance average execution time during the test using k-NN is 0. 327. In this work, We compare our results with six different states of the art methods, and the fairness of our results is ensured by applying four different loss functions to the proposed methods and compare the results with three subsets of the Letters dataset: LOW, MED, HIGH. Experiments were performed in the same conditions for all methods which guarantee the fairness of the results. None of the methods were optimized to have an explicit advantage over other methods.
2020. 3035886 ,IEEE Access Nadeem et al. : Preparation of Papers for IEEE TRANSACTIONS and JOURNALS image. ACKNOWLEDGMENT This research has been partially funded by the  LabCom IDEAS  under the grand number  ANR-18-LCV3-0008. The research was also supported by  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( NRF-2019R1A2C1006159 ),  MSIT(Ministry of Science and ICT) ,  Korea , under the  ITRC ( Information Technology Research Center) support program  ( IITP-2019-2016-0-00313 ) supervised by the  IITP(Institute for Information communications Technology Promotion) , and the  Brain Korea 21 Plus Program (No. 22A20130012814 ) funded by the  National Research Foundation of Korea (NRF).
Methods: The methods in this study comprise (1) collecting and coding of transcripts of older adults' conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts.
We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. Results: Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0. 91,  AP=0. 56, precision=0. 5, recall=0. 45, F1=0. 48, specificity=0. 98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0. 89, AP=0. 54, precision=0. 35, recall=0. 59, F1=0. 44, specificity=0. 94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs.
Motivated by the availability of reminiscence data from the naturalistic observation study  [6]  and the results of Yordanova et al  [24] , this study aims to develop pipelines with NLP and machine learning strategies to automatically detect reminiscence in older adults' everyday conversations in German using their written transcripts. To do so, we introduce various NLP features (bag-of-words models, part-of-speech [POS] tagging, and pretrained word embeddings) to preprocess written transcripts, which are fed into four families of machine learning algorithms (RF, adaptive boosting [ADA], extreme gradient boosting [XGB], and SVM); multiple learning strategies are setup to cope with class imbalance in data. The methods of this study support the understanding of a key activity of the healthy aging model, that is, reminiscence, in a real-world setting by leveraging transcriptions of everyday life conversations.
Preparing Data Of the 13,275 audio files generated during the study by Demiray et al  [6] , 2214 contained conversations of older adults. The data set used for this study comprises these 2214 transcripts, of which 109 (4. 9%) were coded as reminiscence. Before applying NLP to the transcripts, we preprocessed the data with regular expressions to remove coding artifacts like "xxx," "YYY," "xxxx," etc, denoting utterances from the audio recording that were not possible to transcribe as well as leading and trailing whitespaces.
Bag-of-Words Models Bag-of-words models  [38, 39]  represent transcripts as real-valued vectors by tokenizing all transcripts in the provided data set, collecting unique tokens, and counting their occurrences before applying normalization (eg, term frequency-inverse document frequency [tf-idf]  [38, 39] ). Bag-of-words models do not consider the order of words in transcripts and generate high-dimensional representation of textual data. In fact, bag-of-words models represent each transcript by a real-valued vector whose dimension is equal to the size of the vocabulary of the whole data set of transcripts. They are widely used for text classification tasks, including studies in digital health  [29, [40] [41] [42]. We computed bag-of-words models using the TfIdfVectorizer() function in the Python sklearn library  [43].
Bag-of-POS Models POS tagging is the process of assigning a POS tag to each word in a given corpus  [38, 39] ; the algorithm that performs the tagging is called a POS tagger; a set of all tags is called a tagset. POS tagging enables including information from a word's context (ie, its relationships with close and related words in a document) in text classification tasks  [29]. In this study, we used the POS tagger provided in the core model for the German language "de_core_news_sm," which is available in the Python library SpaCy  [44] , to generate the POS tags for all tokens retrieved from the corpus of 2214 transcripts. The SpaCy POS tagger has a tagset comprising 17 distinct tags; similar to bag-of-words models, a bag-of-POS model extracts all POS tags (instead of words) from a transcript and counts their occurrences before applying normalization, such as tf-idf.
Word Embeddings Word embeddings are real-valued representations of textual data encoding the "distributional hypothesis"  [45]  about language and words: words that occur in similar contexts tend to be closer to each other as real-valued vectors. Moreover, word embeddings are generally real-vector representations of textual data of much lower dimension than, for example, those in bag-of-words models. They have emerged as a common technique to compute representations of textual data, including studies in digital health  [29, 40]. In this study, given the limited number of available transcripts, we opted for pretrained German word embeddings using the SpaCy core model for the German language "de_core_news_sm. " The model is "German multi-task CNN trained on the TIGER and WikiNER corpus"  [46]  and each word embedding has 300 dimensions.
Machine Learning Methods In this study, to cope with class imbalance during the training of machine learning models to detect reminiscence, we implemented the following learning strategies: (1) class-weighted learning (CWL), (2) meta-classifier (MC) learning, and (3) learning in the presence of data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm  [56]. For all the learning strategies, we trained machine learning classifiers  [57]  using the RF, ADA, XGB  [58] , and SVM algorithms fed on all NLP features: bag-of-words, bag-of-POS NLP models, and pretrained word embeddings. In the cases of RF or boosting algorithms trained on either bag-of-words or bag-of-POS features, we computed feature importance using the sklearn property. feature_importances_. Table  1  summarizes all the NLP features, classifiers, and learning strategies used in this study.
On the other hand, considering the data augmentation strategy and POS features, the XGB algorithm presented in Multimedia Appendix 1 performs on 10 boosting iterations, in the presence of shallow trees, 2-grams, and K=5 neighbors used by SMOTE. In the case of the class weighted learning strategy and POS features, it performs on 50 boosting iterations, shallow trees, and 2-grams. The POS 2-grams with the highest feature importance comprised punctuation signs followed by a conjunction (eg, ". Und," ". Aber," or ", oder"), adverbs and auxiliary verbs (eg, "dann habe/n" and "da hat/ben"), prepositions and determinative articles (eg, "mit einer/m" and "mit der/m," [ie, "with a(n)" and "with the"]) and adverbs followed by verbs (eg, "aber weisst," "mehr gegessen," and "selber gefahren"). Finally, considering word embeddings, SVM with radial basis kernel (and scaling) on augmented data outperformed all other classifiers, with K=5 neighbors used by SMOTE (F1=0.