dataesr/scientific-paragraphs-categorization huggingface dataset:
• Object-based (OB) DTW-as the segmentation reference we use the preliminary segmentation results for our method, we exploit hierarchical clustering algorithm with DTW distance matrix to regroup the obtained segments. For both datasets we use the following parameters for graph construction, see Reference  [18]  for details: α = 0. 3, τ 1 = 0. 5. We omit τ 2 as it lowers the quality of the results. The hierarchical clustering method with DTW distance matrix is applied as in the original article. • 3D convolutional AE without NDVI branch-we use the same pipeline and segmentation parameters as in our proposed method, however, the feature extraction model is different. For feature maps extraction, we have the same 4 convolutional and 2 maxpooling layers as in the original images branch that are followed by 2 FC layers with the following input and output sizes for the encoding part: S × 64 → S × 12, S × 12 → f.
Quantitative Evaluation The evaluation of the obtained results with the NMI index is presented in Table  4. For the aforementioned reasons, we present NMI for the reference number of classes  (8)  as well as the best NMI score within the selected range of classes ("NMI best" in the table). As the results obtained with the AE methods may vary between several attempts, each AE method was launched 3 times. For these methods, we present the mean NMI value with the error margins. As it was mentioned in Section 3, the SPOT-5 dataset was taken over 6 years and contains several changes. However, the GT corresponds to 2008 year (the end of the series). For this reason, we compute NMI for the whole SITS as well as for the SITS without outliers (which are mostly made of these changes and therefore do not match the 2008 end of the series GT).
We exclude the airport area when computing NMI as it is mostly represented by a grass field. Its temporal behavior is unknown to us, so we can not associate it to any of existing clusters. Table  5  presents the computation times for the most essential and time consuming steps of the presented approaches. For the DTW methods, we also present the number of segments or graphs to give an idea about the size of the distance matrix as it is the most defining parameter of the computation time. Note that we present the results for the DTW matrix computation made with tslearn library based on CPU computations with parallelisation. For a fairer comparison, we also customized parts of tslearn source code from the original library to run it on GPU with numba library. Doing so has greatly improved the computation time (maximum computation time for the longest sequence was less than 2 min).
Qualitative Evaluation. Segmentation Firstly, we analyze the results obtained for the "ideal" GT segmentation for our proposed method and for the object-based DTW. We can observe from the Table  4  that both methods gave higher scores comparatively to the user-made segmentation. At the same time, in average, our method has slightly outperformed its concurrent approach. Moreover, when using the existing Python libraries, our method has less computational cost, hence, is more adapted for big datasets. For the clustering results obtained for user-made segmentation, we observe that the results of our method are significantly better than the concurrent approaches. It can be explained by more precise segmentation results that were achieved by our proposed methodology. We remind that for our method, segmentation is realized in several steps: preliminary segmentation, segmentation of the encoded SITS image and the final corrected segmentation that combines the previous ones.
At the same time, the object-based clustering method is performed on the preliminary segmentation results. The preliminary segmentation results are always under-segmented as they can not caption all the seasonal vegetation variations that are reflected in the encoded SITS image. The obtained results highlight the necessity of the segmentation correction as one can notice the decreasing of the accuracy when none is performed. Figure  4  presents the advantage of our proposed approach with higher detalization level over the segmentation performed on the two most representative images of the dataset. Moreover, our proposed approach correctly produces segments free of border effect that perfectly correspond to true object borders (see Figure  2 ) that facilitate the interpretation of the obtained objects.
The graph-based DTW approaches gave the results similar to the object-based DTW methods in addition to the slowest computation time related to the graph construction. This poor performance can be explained by the difficulty to segment the whole SITS, especially in the urban area. However, this method might give better results for a smaller dataset with prevailing agricultural areas. 4. 2. 3. Qualitative Evaluation. Feature Extraction and Clustering Figure  5  presents clustering results for our method for both datasets for the best of 3 runs. After visual analysis and the analysis of the NMI values, it was established that 14 clusters provided the best data partitioning for both datasets. The obtained clusters were associated to the ground truth clusters and colored in the same manner. As it was mentioned in the data section, the olive plantation class was ignored during the analysis due to its small size in addition to the fact that none of the proposed algorithms has identified it in a separated cluster.
Clusters corresponding to water surfaces and urban areas were correctly identified for both datasets. Three vegetation classes corresponding to annual crops, vineyards and prairies were mostly correctly detected as well due to the specificities of their temporal behavior. We do not dispose of finer classification level for annual crops, however, the hierarchical algorithm has divided it in several clusters which we can easily distinguish by analyzing crops temporal behavior. At the other hand, the orchard class is mixed with other vegetation classes for both datasets and the natural areas with small vegetation are mixed with wooded areas. It can be explained by the fact that both classes have similar growing cycles and spatial textures. After comparing the obtained clusters with change maps from Reference  [6] , we associate one of the clusters obtained for SPOT-5 dataset with changes corresponding to the construction of a new area.
However, for a larger number of clusters, the readers should understand that no ideal unsupervised clustering algorithm exists. When the number of ground truth classes is much higher, we usually have to deal with look-alike classes. For example, two classes have the same growing cycle, but slightly different textures. These classes will be difficult to distinguish when no supervised constraint is applied. Furthermore, besides the weakness that we mentioned for the clustering of linear objects, and the cases of a large number of lookalike land cover classes, we did not detect any limitations specific to our algorithm. Although, we insists that our algorithm still possesses all the limitations common to unsupervised neural networks and clustering. For example, for a dataset with unbalanced classes, it may be difficult to extract the features for small size classes; the final model varies from one experiment to another; setting the right parameters for the network may sometimes be complex and depends on the data.
Conclusions In this article, we have presented a fully unsupervised approach for SITS clustering based on a two branch multi-view 3D convolutional AE that does not demand any labeled data. The proposed approach exploits the AE model to compress a time series into an encoded image by extracting its spatio-temporal features. Then it performs the segmentation of the encoded image with the eventual correction of shifted segment borders related to the specificity of the encoding. The proposed approach was tested on two real-life datasets and showed its efficiency comparatively to the concurrent approaches. The main advantages of the proposed algorithm include the improvement of traditional segmentation methods that are not initially adapted for the SITS that leads to higher NMI score. In addition, we have shown that we can improve clustering results by simply introducing a temporal NDVI branch in the AE model.
The presented approach is a good alternative to traditional DTW-based methods as deep learning techniques are able to extract more robust and complex features compared with traditional Machine Learning methods. In our future plans, we want to adapt our algorithm to all spectral bands of Sentinel-2 datasets as well as improve its accuracy for the clustering of linear objects. Other spectral indices can be also integrated in the proposed model, however, a closer study is needed to estimate the influence of each index on the clustering results. Moreover, the contextual constraint may be introduced to distinguish more classes (e. g. , artificial areas such as beaches can be discriminated from urban areas as they are close to the water).
2002). Trinitario is a hybrid of Forastero and Criollo cacao  (Motamayor et al. 2003). Colombia has limited access to readily available and digitised datasets of these species occurrences, limiting our ability to extrapolate and understand climate conditions in which the cultivated types can grow in. Furthermore, very little is known about species richness and endemism among cacao CWR in Colombia. Most published case studies report the presence of the species in certain places but a comparison between observed and predicted diversity at various geographic scales has not yet been documented  (Baker et al. 1954; Cuatrecasas 1964; Richardson et al. 2015). Colombia is not an isolated case, documentation of other plant groups of wild relatives around the world is limited  (Hijmans and Spooner 2001; Jarvis et al.
Species distribution datasets Two types of data were used in our analysis: fieldwork and historical (Fig. 1 ). During the expeditions, a fieldwork dataset was compiled consisting of herbarium collections of cacao CWR. Samples of branches were taken from different wild cacao species, and where possible, fruits and flowers were collected. The fieldwork dataset collected on the expeditions was made up of 211 herbarium collections: 174 samples of Theobroma and 37 of Herrania. Within the samples of Theobroma, 63 belongs to wild species of nondomesticated T. cacao that are found only on the riverbanks. All samples were geo-referenced, processed in the field and stored as dry specimens at the herbarium of the Universidad de los Andes (https://bit. ly/39A1qtw). Using QGIS Desktop V. 3. 10. 2. (2019), we mapped each of the occurrences sampled during the expeditions.
A historical dataset composed of records was used for the analyses  (Yockteng et al. 2017 ). The historical dataset was generated using national and international museum and herbarium samples and was made up of 499 records of geo-referenced wild species: 376 Theobroma (10 species) and 123 Herrania (6 species). We used QGIS Desktop V. 3. 10. 2. (2019) to check each record for spatial accuracy. As part of the cleaning process, missing or incorrect spatial coordinates were either excluded or corrected where adequate information was available. During the preanalysis of this data, we found low values of redundancy estimation for Theobroma and Herrania in Colombia (Fig. S3c ) in the historical collections, where a ratio of species occurrence and number of samples per grid cell show low representation (0.
Predictor variables Eight climatic predictor variables were used in the SDM: precipitation, maximum temperature, minimum temperature, mean temperature, diurnal temperature range, relative humidity, wind speed and solar radiation. We chose these variables because they provide high spatial resolution and accurately represent the environments where cacao CWR can be found. These variables were at * 1 km 2 spatial resolution and extracted from the global Wordlclim version 2  (Fick and Hijmans 2017). Slope and elevation at a spatial resolution of * 1 km 2 were also included in the SDM (data from DAPA project, CIAT). The hydrography of the expedition site in the Amazon region was mapped using the WaterWorld tool which uses the hydro-SHEDS model applied at a spatial resolution of approximately 1 km 2  (Lehner et al.
Species distribution modelling We used the historical and the fieldwork datasets to conduct our SDM analyses. We divided the SDM analyses into three components: (a) a national distribution based on the historical dataset; (b) a subnational distribution based on the fieldwork data and historical dataset covering the Amazon and Pacific region; and (c) a national distribution analysis based on the historical dataset only and then combined with the fieldwork expedition data as a separate sub-group. To conduct our predicted distribution analyses, ensemble modelling at a scale of * 1 km 2 was carried out using the BiodiversityR software  (Kindt and Coe 2005; Kindt 2018; Gonzalez-Orozco et al. 2020). Each ensemble map consisted of fitted probability raster layers, based on individual suitability models of wild species, which were combined using a consensus of several models that predict presence.
Species diversity and endemism Biodiverse v2. 0 software was used to calculate the observed diversity metrics of the historical dataset at a spatial resolution of 100 9 100 km  (Laffan et al. 2010). Species richness represents the total number of taxa in each grid cell. Species endemism as a corrected weighted endemism (CWE) derivation of weighted endemism (WE) was calculated. CWE partitions the region of interest into equivalent cells, counts the species present in the cells and divides each by its distribution range  (Crisp et al. 2001; Rosauer et al. 2009). Species richness calculated from herbarium datasets are often a poor estimate of real species richness  (Schmidt-Lebuhn et al. 2012). Hence, an abundance based Chao1 estimator of species richness was calculated for all cells of Theobroma and Herrania found in the historical dataset.
Species richness and endemism of historical and collected taxa The historical database found higher species richness of Theobroma (six species) in the Amazon region than the Pacific. However, we found higher species richness of Theobroma in the Pacific (nine species) compared to the Amazon. In the case of Herrania both the historical dataset and our collections reported higher species richness in the Pacific than in the Amazon (Fig. 3a-c ). Both datasets found endemism to be higher in the Pacific region than in the Amazon for Theobroma. The historical dataset showed undefined patterns of endemism for Herrania in both regions while our collections found endemism to be high in the Pacific only (Fig. 3b-d ).
Predicted distribution of historical and collected taxa Wild cacao is known to be of lowland restricted lineage  (Richardson et al. 2015). However, we do not know to what extent new lowland environments of similar characteristics outside its current distribution could be potentially suitable for cacao CWR in Colombia. We found that 95% of the grid cells with the highest predicted suitability scores for Theobroma and Herrania in the Choco and Amazon regions were not predicted by the historical dataset. Our SDM results estimate an increase of 50 to 70% of the suitability areas after new field surveyed geo-locations were included in the SDM model (Fig. 4 ). Both the Amazon and Pacific regions showed high levels of predicted suitability (Fig. 4c, d, f, g ) that promotes unique niche conditions and favors biodiversity  (Myers et al.
Conservation strategies There are many conservation strategies that could be used to improve conservation of cacao CWR, including identification of priority taxa, establishment of sites for active in situ conservation and development of ex situ collection plans  (Maxted et al. 2008; Maxted and Kell 2009; Maxted et al. 2010; Hunter and Heywood 2010; Maxted et al. 2013; Rubio Teso et al. 2013; Magos Brehm et al. 2017; Dulloo and Thorman 2017; Zair et al. 2018). We suggest a conservation strategy (Fig. 5 ) involving further expeditions to gather herbarium collections and germplasm samples and the establishment of new protected areas. These samples could be used to establish a more complete and representative dataset that accurately map the distributional patterns of cacao CWR and plant breeding programs to improve the cultivation of cacao.
Acknowledgements The authors acknowledge the following funding sources:  Expedicion Cacao Colombia BIO, Colombia BIO program , funded by  Departamento Administrativo de Ciencia, Tecnologı ´a e Innovacion de Colombia (Colciencias)  under the special cooperative Agreement No. FP44842-142-2018 , between  Agrosavia  and  Universidad de Los Andes. The authors wish to thank  Mario Porcel ,  International Centre of Tropical Agriculture (CIAT) , the  Colombian Amazon Research Institute (SINCHI) ,  Universidad de la Amazonia -Florencia , the  Cacao BIO  expeditions participants, the communities in Caqueta, Putumayo and  La Victoria Choco  ´for their support of this work. The manuscript was proofread and edited by  Julia Alice Veronica de Raadt.
Transfer collision low energy was set to 5 V and trap collision low energy was set to 4 V. The high energy ramp was applied from 4 V to 5 V for the trap collision and from 19 V to 45 V for the transfer collision enabling fragmentation of the ions after the ion mobility cell and before the time-of-flight (TOF) MS. Each sample was injected in triplicate. The acquired files were imported into Progenesis QI software Version 2. 0 (Nonlinear Dynamics, Newcastle, UK) for label-free quantification analysis. The data were automatically aligned against W. magna proteins (https://www. mediterranee-infection. com/acces-ressources/donnees-pour-articles/ willaertia-magna-c2c-maky/) and normalized. Processing parameters were 150 counts for the low energy threshold and 30 counts for the elevated energy threshold.
The database used was the same as the one described above. Search tolerance parameters were peptide and fragment tolerance, 15 ppm, FDR < 1%; Minimum Ion matching requirements were three fragments per peptide, seven fragments per protein and two peptides per protein. The enzyme specificity was trypsin allowing 1 missed cleavage. The accepted modifications were carbamidomethyl of cysteine (fixed), oxidation of methionine (variable), carbamylation of lysine and N-terminal (variable), and deamidation (variable) of asparagine and glutamine. Protein normalization was performed according to the relative quantitation using non-conflicting peptides. To determine the significance of changes between samples, a significant ANOVA (p-value < 0. 001) and a fold change greater than 2 were used as thresholds to define differently regulated proteins (DRPs).
Analysis on Biological Function of Differentially Regulated Proteins To assign the biological functions of DRPs, we proceeded to homology searches for the proteins in public protein databases. First, the protein sequences were searched by BLASTp against the NCBI non redundant protein sequence database with an E-value cut-off of 1 × 10 4  [46]. We mapped the protein sequences against the Cluster of Orthologous Group of proteins (COG) and Gene Ontology (GO) databases using EggNOG  [47, 48]  with diamond as mapping mode. The visualization of GO annotation was performed using WEGO online software  [49]. In a second time, we compared the protein sequences against the Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG; http://www. genome. jp/kegg) using BLASTKoala online  [50]. Conserved domain database (CDD) and InterPro were used for the identification of conserved domains in the sequence of DRPs  [51] [52] [53].
Lipids were ionized in the positive and negative ionization modes using a Z-spray source (2. 8 kV, 35V for ESI+ and 1. 9 kV 40 V for ESI-). Ions were then monitored using an HD-MS method including ion mobility and single stage MS scans (ranged from 50 to 2000 m/z, 0. 1 s per scan, lockmass calibration using Leucine Enkephaline). Multivariate statistics were performed for each ionization mode on all detected ion components using the UNIFI and EZ info software (with normalization of data)  [58]. A PLS-DA model was calculated from the principal component analysis (PCA) in order to select form S-Plot (>97%) and VIP (>1) plots, the most discriminating markers for both culture conditions. Lipid classes were attributed according to the retention times of an injected standard (Splash Lipidomix, Avanti Polar Lipids, Alabaster, AL, USA).
Fatty Acid Methyl Ester (FAME) Analysis by Gas Chromatography/Mass Spectrometry Cellular fatty acid methyl ester (FAME) analysis was performed by GC/MS, as previously described  [59, 60]. Fatty acid methyl esters were prepared as described by M. Sasser  [61]. Briefly, fatty acid methyl esters were analyzed by gas chromatography/mass spectrometry (GC/MS). Compounds were separated using an Elite 5-MS column and monitored by mass spectrometry (Clarus 500-SQ 8 S, Perkin Elmer, Courtaboeuf, France). Spectral database search was performed using MS Search 2. 0 operated with the Standard Reference Database 1A (NIST, Gaithersburg, MD, USA) and the FAMEs mass spectral database (Wiley, Chichester, UK).
Identification of Differentially Regulated Proteins A total of 804 non-redundant proteins were identified using UPLC. The significance of differences in protein abundance was established using p ≤ 0. 001 and | FC | ≥ 2 as thresholds (Table  S5 ). Based on these criteria, 61 differentially regulated proteins (DRPs) were detected between W. magna cultivated in suspension and adhesion, of which 21 (34. 4%) were down-regulated and 40 (65. 6%) were up-regulated in adhesion compared with suspension cell growth (Table  S5 ). Among the 61 DRPs, 32 had a function assigned in the NCBI nr database, whilst 28 proteins had an uncharacterized function, including 5 hypothetical proteins, 21 predicted proteins and 2 ORFans. Most of the proteins (n = 52) had best hits with Naegleria gruberi, a free-living amoeba phylogenetically close to W.
Identification of Differentially Regulated Proteins A total of 804 non-redundant proteins were identified using UPLC. The significance of differences in protein abundance was established using p ≤ 0. 001 and |FC| ≥ 2 as thresholds (Table  S5 ). Based on these criteria, 61 differentially regulated proteins (DRPs) were detected between W. magna cultivated in suspension and adhesion, of which 21 (34. 4%) were down-regulated and 40 (65. 6%) were up-regulated in adhesion compared with suspension cell growth (Table  S5 ). Among the 61 DRPs, 32 had a function assigned in the NCBI nr database, whilst 28 proteins had an uncharacterized function, including 5 hypothetical proteins, 21 predicted proteins and 2 ORFans. Most of the proteins (n = 52) had best hits with Naegleria gruberi, a free-living amoeba phylogenetically close to W.
magna (Table  S5 ). The proteins exhibiting the greatest differential abundance between the two conditions were proteins with unknown functions for amoebas in both suspension and adhesion culture (Table  S5 ). A total of 48 DRPs (78. 7%) identified by proteomics analysis, were classified into 14 COG categories, among which "post-translational modification, protein turnover, chaperones" represented the largest group (group O, 12 DRPs), followed by "energy production and conversion" (group C, 8 DRPs) and "translation, ribosomal structure and biogenesis" (group J, 5 DRPs) (Figure  2 , Table  S5 ). GO analysis was carried out to obtain fundamental function of genes and information on molecular functions, cellular components and biological processes. After mapping the DRPs against the GO database, we observed that 28 genes (45%) were assigned to GO terms.