dataesr/scientific-paragraphs-categorization huggingface dataset:
Unsupervised analysis An unsupervised analysis was also done on down-sampled data using self-organizing map (FlowSOM) and projected onto uniform manifold approximation and projection (UMAP). For visualization, highdimensional single cell data were reduced to 2 dimensions using UMAP1 and UMAP2 with each FlowSOM metacluster represented by a different color on the map. Based on expression of markers of peripheral blood cell populations, similar clusters were merged using an adapted script from Catalyst R pipeline (available at https://github. com/github/catalyst). A similar analysis pipeline was used to analyze both the samples directly from patients, and the ex vivo cell stimulation assays. Code for these analyses is available to download at: https://github. com/michaeljamescarter.
ACKNOWLEDGEMENTS Dr Carter  was supported by a  NIHR   Academic Clinical Lectureship. This work was supported by  Evelina London Children's Hospital PICU Research Fund , a  King's Together grant  from  King's College London  and by funding from the  Huo Family Foundation. Prof Shankar-Hari  is funded by a clinician scientist fellowship  2016-16-011  from the  National Institute for Health Research  and reports receiving grant from the  Chief Scientists O ce, Scotland , for time-critical precision medicine in adult critically ill patients and highlights industry interactions for TRAITS research programme (https://www. ed. ac. uk/in ammationresearch/clinical-trials/traits-ci-trial). This research was supported by  Research and Development, Guy's and St Thomas' NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS.
This work was supported by the  European Union 's  Horizon 2020 Program  under grants ( 848196 DIAMONDS ,  668303 PERFORM ,  279185 EUCLIDS ,  Prof Levin ), by the  Imperial Biomedical Research Centre (BRC)  of the  National Institute for Health Research (NIHR) , grants ( 206508/Z/17/Z  and  MRF-160-0008-ELP-KAFO-C0801  to  Dr Kaforou ) from the  Wellcome Trust  and the  Medical Research Foundation , a grant ( 215214/Z/19/Z  to  Dr Jackson ) from the  Wellcome Trust  and.
individual source, using the Taxonomic Name Resolution Service (TNRS) and considered all Amazonian tree species with diameter at breast height (DBH) ≥10 cm. We also obtained estimates of the current population size for most species from a published approach based on data from 1900 tree inventory plots (1-ha each) distributed across the Amazon region and part from the Amazon Tree Diversity Network (ATDN). We then identified the hyperdominant timber species. In addition, we overlapped our timber species list with data for species that are used for commercial purposes, according to the International Tropical Timber Organization (ITTO), the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the International Union for Conservation of Nature (IUCN) taxa assessment and Red List categories.
Finally, we also included IUCN Red List categories based on combined deforestation, and climate change scenarios for these species. Our final Amazonian timber species dataset contains 1112 unique species records, which belong to 337 genera and 72 families from the lowland Amazonian rainforest, with associated information related to population, conservation, and trade status of each species. The authors of this research expect that the information provided will be useful to strengthen the public forestry policies of the Amazon countries, inform ecological studies, as well for forest management purposes. The data are released under the Creative Commons Attribution 4. 0 International license.
Simple descriptors of the strain distribution, such as the maximum strain and the strained depth, are predicted with accuracies of 94% and 91%, respectively. The exact shape of the strain distribution is predicted with a 82% accuracy, and 76% for strain levels <2% where the amount of meaningful information in the XRD data is significantly decreased. The robustness of the CNN against the number of predicted parameters and the size of the training dataset, as well as the uniqueness of the solution in some challenging cases, are critically discussed. Finally, the potential of the CNN has been tested on real, experimental, data. Interestingly, while the CNN has not been trained to operate on experimental data, it still shows promising performances with predictions achieved in a few seconds and corresponding root-mean-square errors in the 0.
Machine leaning algorithms, and in particular artificial neural networks (NNs), can be used to solve that type of inverse problems, thereby considerably decreasing computing times and eliminating the need of a priori knowledge. The general idea is to map reciprocal space features (obtained by diffraction) to real space features (here, the strain profile), by feeding XRD data into a NN and adjusting its parameters so that its output matches the expected (strain profile) result; a procedure referred to as 'training' the NN. In the recent years, NNs have been increasingly used in the field of the characterization of materials using the scattering of x-rays or neutrons. One can cite symmetry identification  [16] [17] [18] , space group identification  [19] [20] [21] [22] [23] , phase identification  [24] [25] [26] [27] , phase and crystallite size quantification  [28] , lattice parameter prediction  [29] , classification of 2D diffraction data in single particle imaging  [30, 31] , or in small and wide angle scattering data  [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] , data correction  [42] [43] [44]  or automated analysis of 2D diffraction images  [45] [46] [47] [48] [49] , and phase retrieval with synchrotron coherent x-rays  [50] [51] [52] [53] [54] [55].
The studies which are more closely related to the present work are those dealing with the inversion of neutron or x-ray reflectivity data recorded from thin films or multilayers, so as to retrieve physical parameters such as the film thickness and surface/interface roughness  [56] [57] [58] [59] [60]. These parameters then allow the authors to reconstruct the, so-called, scattering length density profile. In these studies the number of layers constituting the multilayer structure were limited to 2  [56] [57] [58] [59] , and up to 3  [60] , with impressive performances on actual samples. Arbitrary profiles have been considered in  [61]  but without any application to actual samples. Although there is a clear methodological similarity between the inversion of neutron/x-ray reflectivity curves and XRD curves, the latter differs from reflectivity in many aspects: • Ion-irradiated or ion-implanted materials in general display both strain and disorder, where the former is related to long range variations of interatomic distances, and the latter refers to random atomic displacements.
Network architecture and training 2. 2. 1. Network architecture The CNNs have been developed and trained with the Python TensorFlow (www. tensorflow. org/) library  [71]  using the Keras (https://keras. io/) front-end  [72]. The CNN we developed in this work is displayed in figure  2  and contains ∼1. 104 × 10 6 trainable parameters. There are no definite rules for designing the architecture of a NN, and those essentially derive from empirical knowledge associated to a particular problem. Despite this, we underline below some characteristics that turned out to play an important role in the performances of the CNN. All the aspects discussed below are natively included in Keras and do not require additional coding. Firstly, the dilatation between the flatten layer (which transforms the 4 × 128 tensor into a linear 512 vector) and the first dense layer (1000 pixels), figure  2 , was essential to reach convergence.
g. 3 × 3 pixels, used in image recognition. This kernel size turned out to be more well suited to XRD data where there are no sharp and well defined edges, but rather continuous intensity variations. N of such kernels were used, which produces N different output vectors (also referred to as feature maps) of size M/s where M is the size of the vector of the preceding layer, and s is the stride (i. e. the step used in the convolution operation)  6. The jth component in the mth feature map of the lth layers writes  [73] : a l j,m = σ ( ∑ n ∑ k w l k,n a l-1 s×j+k,n + b l n ) (6) where w l k,n is the kth pixel value of the kernel in the nth feature map of the lth layer of the CNN, and b l n is the bias in the nth feature map of the lth layer. It can be observed that the weight and bias do not depend on the position (j) of the output neuron, i.
Predictions, errors and accuracy A typical learning curve is given in the supplementary material (figure  S1 ). The mse remains constant after 65-70 epochs with a value of 0. 001. The mse evaluated on the validation set reaches 0. 0016, and no sign of over-fitting (i. e. a degradation of the validation mse) could be detected, which is likely a consequence of the large amount of data used for training. To evaluate the performances of the CNN, the test data set (with 150 000 XRD curves) has been used to predict the different parameters considered in this work which, we recall, are the thickness of the strained region, t, the maximum strain, e max , the minimum DW factor, DW min , the width of the Bragg peak, H, and the dynamic range, DR. The B-splines weights, which are the parameters describing the shape of the strain profile (equation (  3 )), will be discussed separately.
Role of physical parameters on the CNN performances In order to get a deeper insight into the influence of each parameter (strain, thickness, DW, etc) on the accuracy of the CNN, it can be useful to plot the relative strain rmse as a function of the ground truth value of each parameter. Such plots are given in the supplementary materials (figure  S2 ). Although some tendencies can be detected in these plots, it is not straightforward to deduce any quantitative information. We therefore suggest the following procedure: for each parameter we select the 10 3 datasets with the lowest σ e /e max values and compute the histogram of the values taken by each parameter. We perform the same operation on the 10 3 datasets exhibiting the highest σ e /e max values. The corresponding histograms are given in figure  5.
The first observation that can be made is that the distribution of parameters in the data with the lowest errors (figures 5(a)-(e)) is in general more uniform than in the case of the data with the highest errors, where narrow peaks appear in certain ranges of parameters (figures 5(f)-(h)). This demonstrates that, while it is possible to get good performances over a rather broad range of parameters, some narrow ranges of parameters catastrophically deteriorate the CNN performances. A careful examination of figures 5(f)-(h)) reveals that these parameters are thickness t < 100 nm, e max < 0. 5% and DW < 0. 05, which confirms the qualitative observations made in the previous section: these ranges of parameters do not contain enough information in the XRD data to allow the CNN to learn features from those data.
Robustness against the training data set size Another legitimate question that can be asked, is how much the size of the training dataset can be reduced while still giving good predictions. This is particularly important since it is well known that NN are easily subject to overfitting when the dataset size is too small: the NN memorizes the values of the training dataset and the validation mse increases after reaching a minimum; its performance as a predictor decreases  [72]. Although no overfitting has been observed in the training (see figure  S1 ), the question of the maximum accuracy that can be reached on the test data (which, we recall, is not used in the training) for a given CNN architecture, is essential. For this purpose we generated a training dataset containing 3 × 10 6 input vectors, and iteratively reduced its size by 25%.
For each dataset we trained the CNN and evaluated all accuracies. The results are displayed in figure  7. Let us first discuss the training metrics, figure  7(a). Upon decreasing the training set size, both the training and the validation mse increase. The mse increase slowly for dataset sizes down to ∼8 × 10 5 ; below this value, the mse increase at a much faster rate. This indicates that the CNN becomes harder to train: when the learning curve saturates at a constant value (see figure  S1 ), the values inferred by the CNN exhibit higher errors. Interestingly, the validation mse increases faster than the training mse (see the green difference curve). This observation evidences that, when the dataset size decreases, the predictions performed by the CNN are degraded at higher rate than the degradation of the learning, i.
e. the value of the CNN as a predictor decreases. Conversely, increasing the data size narrows the gap between the training and validation mse, indicating an improved prediction reliability. Let us now discuss the accuracies, figure  7 (b). Similarly to what is observed for the training metrics, the accuracies are slowly degrading down to a dataset size threshold value, below which the rate of degradation increases. It also appears that this threshold value depends on the initial accuracy of a given parameter. For instance, e max , whose initial accuracy is 94%, remains roughly constant down to ∼1 × 10 6 , whereas the accuracy of the strain profile, with initial value of 80%, exhibits a higher degradation rate below ∼1. 3 × 10 6. For this reason, in the present study, we selected a training dataset size around this threshold value.
Increasing the dataset size would only yield minor improvements in the accuracies of all parameters, except the strain profile which would benefit more from the increased dataset size (80% instead of 76%). Obviously, this increased dataset size result would result in considerably increased computation times: a factor of ∼2. 5 in the present case (i. e. more than 6 h to generate the dataset, instead of 2. 5 h). Considering the relative weak improvement in the accuracy we decided not to increase the dataset size.
Test on real data Experimental XRD data corresponding to the 400 reflection of disordered Y 2 O 3 -doped ZrO 2 single crystals irradiated with 300 keV Cs ions are shown in figure  10. The colours blue, orange and green are associated to increasing ion fluences, 7. 5 × 10 13 , 3 × 10 14 and 6 × 10 14 cm -2 , respectively, which correspond to increasing levels of strain and disorder within the crystals. In the case of experimental data, no ground truth is available. Therefore, one possible way to assess the quality of the predictions of the CNN is to compute the XRD curves, I (p) , using the predictions of the CNN as an input, and comparing them with experimental XRD data, I exp. The result is shown in figure  10 (a) as dash-dotted lines, together with the predicted strain profiles given in figure  10(b) , also as dash-dotted lines.
The first observation that can be made is that, while they are not perfect, the predictions of CNN are remarkably close to the experimental XRD data. The prediction time varies from 100 ms/prediction down 22 µs/prediction to for a data set size increasing from 1 to 150 000  7. The quality of the prediction can be provided by the intensity rmse, σ I = √ ⟨ [ log ( I (p) ) -log (I exp ) ] 2 ⟩ , which ranges between 0. 35 and 0. 37. Using the predictions of the CNN (figure 10(b)) as an input, it is possible to further optimize the strain profile using a conventional least-square fitting of the XRD intensity with adjustable strain profile parameters. This has been implemented in an automated data prediction and least-square fitting pipeline (CNN + leastsq). The result is shown as full lines in figures 10(a) and (b).
• The profiles obtained by CNN + leastsq exhibit bump in the strain at larger depth which do not exist in the manual + leastsq solution. This observation can be traced back to the discrepancy that exist between the experimental and predicted intensity curve, in the very close vicinity of the intense peak at ∆Q/Q B = 0, which arises from a slight underestimation of the width of the peak by the CNN. To conclude this section, it is worth reminding that the training data has not been created with the objective of operating on experimental data, but rather to provide a proof of concept that a CNN can be used to extract strain profiles from XRD data. As such, several features that characterize actual XRD data have been voluntarily neglected: • First and foremost, the DW profile was assumed to be controlled by the strain profile, according to equation (  4 ).
Conclusions and future work We have provided a proof of concept that CNNs can be used to determine strain profiles from XRD data in ion irradiated or ion implanted materials. The CNN is built on usual convolutional, Max Pooling and batch normalization layers, together with dense neuron layers, implemented in TensorFlow/Keras. The CNN has been trained with 1. 215 × 10 6 numerically generated data (plus 1. 35 × 10 5 used a validation dataset), corresponding to an equivalent number of strain profiles, described by a reduced set of 11 parameters (10 B-spline weights for the overall shape and magnitude of the strain profile plus the disordered thickness) and three additional parameters (the minimum DW factor, the dynamic range and the width of the Bragg peak). The performances of the CNN have been tested using 1.
Moreover, in order for a CNN to potentially completely replace simulations (and the need for a human supervision), several improvements should be brought to the network and, more specifically, to the training data: • Decouple the disorder (DW) profile from the strain profile. While this is easy to envision for the training data, it might require adjustments in the CNN architecture, since the amount of parameters would significantly increase (up to 23 in the present case, instead of 14). • Include non-Poisson noise and a non-constant background. • We here only considered one lattice plane family of one single material. While it is possible train specific networks for different materials and different lattice planes, a smarter approach would be to develop a suitable scaling scheme in order to bring all possible datasets on a common angular or Q range.
The correction of the atmospheric effects on optical satellite images is essential for quantitative and multitemporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass.
g. , leaf chlorophyll, mineral concentration in soil, water quality parameters, etc. Consequently, researchers have focused on developing innovative AC approaches utilizing various radiative transfer models (RTMs), single or multitemporal images, constant or various aerosol models, new sources of ancillary data, etc. In addition, data users request the provision of corrected data, i. e. , geometrically, radiometrically and atmospherically, which are consistent and can be used for multitemporal analysis. Towards this end, several entities have started to investigate ways to generate Analysis Ready Data (ARD)  (Dwyer et al. , 2018; Frantz, 2019; Potapov et al. , 2020) , described by CEOS as "satellite data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets" (http://ceos.
org/ard). The validation of the available AC methodologies is usually performed independently by developers and/or users either based on reference data over a certain, typically small, number of sites or by comparing with other processors' outcomes  (Claverie et al. , 2015; Vermote et al. , 2016; Rouquié et al. , 2017; Li et al. , 2018). With the intention to investigate all the AC aspects and issues in a consolidated way, a benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in order to compare the state-of-the-art AC processors. ACIX is a voluntary and open-access initiative to which every AC processor's developer is invited to participate. ACIX-I was an initial attempt to study the variability of AC performances over diverse atmospheric and land cover conditions using Landsat 8 and Sentinel-2A input data.
The main objective of this paper is to give an overview of the performance of the most commonly used AC processors over a globally distributed dataset of Sentinel-2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) acquisitions. A detailed description of all the guidelines, i. e. , datasets, sites, metrics and reference sources, is given in Section 2 of this paper. In Section 3, the main results are highlighted in two sub-sections dedicated to the processors applied on Sentinel-2 and Landsat 8 data. A discussion on the overall analysis is presented in Section 4, while conclusions are given in the final section.
Methods In this section the validation strategy is presented thoroughly per AC product, with the aim to define a set of guidelines for future similar activities. The processors involved in the analysis are described in Section 2. 1, while datasets and sites of interest in Section 2. 2. The validation of AOD and WV with the help of AERONET measurements  (Holben et al. , 1998; Giles et al. , 2019)  is given in Section 2. 3. 1. As an equivalent network for surface reflectance validation is not available yet, several alternative approaches had to be engaged as outlined in the rest of Section 2. 3. In particular, simulated reference SRs were produced similarly to the methodology introduced by Vermote and  Kotchenova (2008)  and  Roger et al. (2022)  and ground measurements were employed by Centre National d' Études Spatiales (CNES) RObotic Station for Atmosphere and Surface characterization (ROSAS) stations  (Meygret et al.
Input and validation datasets For the first ACIX experiment the AC performances were analysed over a small set of 19 sites, which nonetheless included a variety of land cover and aerosol types  (Doxani et al. , 2018). The number of sites was considered adequate for the first implementation of such a type of exercise, considering the processing effort for the participants, who were responsible for running the processors in their own premises, and suitable to achieve reliable performance conclusions. The sites were selected as part of AERONET, which is regarded as a reliable, ground-based measurements network for atmospheric variables, such as the Aerosol Optical Depth (AOD)  (Holben et al. , 1998; Giles et al. , 2019). In this second exercise, organisers and participants agreed that a wider selection of sites was necessary to acquire more global and robust outcomes.
The set of Sentinel-2 and Landsat L-1C image scenes were in their original format and structure as provided by ESA (Sentinel-2 L-1C User guide) and USGS (Landsat Level-1 Processing Details) respectively. The original scene size remained the same, i. e. , 10,980 × 10,980 pixels for Sentinel-2 10 m bands, 7791 pixels x 7671pixels for Landsat 8. However, due to the big data volume and the computational demands implied, all the pixels outside a 30 km × 30 km area centered on AER-ONET station location are assigned to 'No data' values. The size of the 'valid' pixel area (30 × 30 km) was selected as the minimum adequate for the processors that require the detection of Dark Dense Vegetation (DDV) pixels in the scene in order to estimate AOD. Eventually, the intercomparison analysis was performed on 9 km × 9 km image subsets, always centered on the AERONET station.