dataesr/scientific-paragraphs-categorization huggingface dataset:
" The statistical machine learning approach presented has four main strengths. First, data from patients' forums such as TinnitusTalk. com constitute a large set of textual data from a wide audience. This allows for a largescale statistical analysis of the patients' speeches using NLP techniques and data-mining algorithms. Second, since those methods are based on machine learning techniques, they are not hampered by any medical bias. In other words, those algorithms consider every word and every patient's opinion from a purely statistical point of view, without requiring any medical knowledge. The LDA algorithmic method has the key benefit of having absolutely no a priori on the kind of topics that are relevant for the patients. The raw dataset is taken as it is and the most interesting structures are automatically extracted from it.
We experimented only on one subsection of one forum, namely, the HA thread on TinnitusTalk. com. Therefore, we analyzed only 3,785 posts from 200 contributors. Second, since we took data from a U. K. forum, our study could be biased towards English terminology when talking about ST/HA problems. How to translate these issues into other languages and cultures is an interesting subject of research. We refer the reader to  [Hall et al. , 2018]  for a discussion on the matter. We also want to stress out that BOW techniques such as LDA do not take into consideration the order of the words in which they appear in the text, how words are used (whether it is a name or a verb), nor the context. They indeed scrutinize the corpus of text as a statistical material where the order and meaning of words is of no importance.
But groups of words, sentences and context could be important when trying to understand specific dimensions related to the problems of tinnitus and HA. An n-gram language model of the forum data or word embedding techniques  [Mikolov et al. , 2013]  could help going beyond the individual tokens analysis. The third limitation is the nature of data itself. Dealing with forum data could be problematic for at least 2 reasons. On the one hand, it can be challenged whether patients' contributions posted on an Internet forum or a blog can be considered as a suitable "ecological environment" to understanding the full range of ST/HA complaints. Indeed, such websites over-represent people who are preoccupied by their problems and therefore post biased messages towards negatives comments on symptoms or healthcare professionals.
Hence, even if forums are moderated, the dataset may not be fully representative of the overall population of ST/HA patients. On the other hand, the information shared among people inside of patients' forums is not fully validated and might not be all medically accurate. It also contains a lot of disturbances in terms of noise. The best way to overcome this problem is to boost the signal/noise ratio by increasing the volume of the data to be treated. The fourth limitation is that we present a statistical study of the actors on the forums as a "frozen cohort. " In particular, we do not take into account the interactions among actors nor the intra-subject variability during the time course of the ST/HA, which have been argued to be important.
Sources and software We collected coral-trait data from coraltraits. org (Madin et al. , 2016a)  and other resources from the peer-reviewed literature (Appendix S1). We systematically verified and corrected coral-species nomenclature using the World Register of Marine Species as a reference. We used R (version 3. 5. 0, R-Core  Team, 2017)  to manipulate datasets, for statistical analyses, and to manage model simulations. We developed the model with the open-source, Java object-oriented programming language Repast Simphony 2. 5. 0  (North et al. , 2013). We launched simulations using the R package rrepast 0. 7. 0  (García and Rodríguez-Patón, 2016)  and rJava 0. 9-10 (Urbanek, 2018). We used the R package missForest 1. 4  (Stekhoven and Bühlmann, 2012)  to impute missing trait data. We included phylogenetic information as a predictor using  Huang and Roy's (2015)  phylogenetic supertrees to improve predictions; we manipulated the supertrees using the R packages ape 5.
0  (Paradis and Schliep, 2019 ) and phytools 0. 5-38  (Revell, 2012)  (Appendix S1). We defined coral bleaching probabilities using the R packages MuMIn 1. 40. 0 (Bartón, 2017), betareg 3. 1-0 (Cribari-Neto and Zeileis, 2010) , lme4 1. 1-15  (Bates et al. , 2015) , lmtest 0. 9-35  (Zeileis and Hothorn, 2002)  and MuMIn 1. 40. 0 (Bartón, 2017) (Appendix S4). For the model sensitivity analysis, we drew a Latin hypercube sample from the parameter space using the randomLHS function from the R package lhs 0. 16  (Carnell, 2018) , and we measured the influence of parameters on different response variables by fitting boosted regression trees using the gbm. step function from the R package dismo 1. 1-4 (Hijmans et al. , 2017).
For instance, dislodgement is simulated by converting all the agents forming the dislodged colony into barren ground; a turf algae overgrowing a colony is simulated by converting the coral agents constituting the overgrown part into turf, but conserving the information about the colony (i. e. , identification number, size, species, growth form). The size of the reef and the length of a time step are changeable. We defined a 25 m 2 reef for our simulations (i. e. , 250,000 agents), which is usually the scale at which benthic communities are assessed in detail (e. g. ,  Holbrook et al. 2018 , Torda et al. 2018). We defined a 6-month time step because the empirical data we used to calibrate the model were collected biannually. Alternatively, it is possible to set three and 12-month time steps.
2). Performance is positive, with smaller values indicating higher performance (lower difference between simulated and empirical values). The algorithm then selects the ten runs providing the best performance and generates for each of them, the five closest (using the Gower's distance metric; Gower 1971) and untested parameter combinations. The algorithm then launches these new simulations and repeats the procedure once more. To compare the performance of model runs to a null expectation, we generated a null distribution of performance values for each empirical dataset by randomizing cover values within each row and calculating the distance from the original datasets.
Hierarchically structured model validation Models are often validated by comparing outputs of a single level of organization (i. e. , individual, population, community) to equivalent empirical datasets (individual species covers in our case), but this approach only examines lower dimensionality for more complex models. Following the recommendation of  Kubicek et al. (2015) , and aligned with the approach of pattern-oriented modelling  (Grimm et al. , 2005) , we assessed whether the different processes implemented in our model produce ecologically realistic patterns by comparing them to expectations formulated a priori. We assessed the following processes of our model: (i) we expected colony lateral growth to equal the species growth rate in absence of spatial interaction and to decrease as space becomes saturated by colonies; (ii) recruitment rate should increase as a population grows from low initial cover, and then decreases as space saturates; for competition under different (iii) disturbance-regime intensities -we expected the competitive species to dominate the community under low-disturbance regimes, and ruderal or stress-tolerant species otherwise; (iv) larval connectivity -we expected species with higher colony fecundity or brooding mode of larval development to dominate the community under low connectivity, and the competitive ones otherwise; (v) grazing -under low grazing pressure, the benthic community should be dominated by algae, and by corals otherwise.
Below we discuss the primary sources of uncertainty in our model calibration and suggest realistic ways for improvement. Grazing: We estimated average grazing pressure over six months (% of reef grazed) based on a biannual assessment of sea urchin and herbivorous fish (Scaridae spp. and Acanthuridae spp. ) populations. Acanthuridae spp. are mobile herbivores  (Thibaut et al. , 2012) , so frequent assessments are necessary to obtain accurate estimates of mean population size. More data collection could improve the accuracy of the modelled processes, has others have done for several fish species (e. g. ,  Bozec et al. , 2016; Mumby, 2006).
Reducing this uncertainty requires better estimates of recruitment rates, which can be achieved with tile experiments (e. g. ,  Ritson-Williams et al. 2016)  or visual assessment of new recruits along transects (e. g. ,  Gilmour et al. , 2013; Holbrook et al. , 2018). The proportion of locally versus regionally recruited larvae can be estimated with population genetics (e. g. ,  Almany et al. , 2017; Johnson et al. , 2018)  or by modelling larvae plumes (e. g. ,  Golbuu et al. , 2012; Wolanski and Kingsford, 2014). Trait data: The hierarchically structured validation showed that between-species trait differences influenced community dynamics. Considerable gaps in the coral-trait database  (Madin et al. , 2016a)  limited our capacity to estimate traits accurately for many coral species. Collecting reliable trait data is critical to predict coral-community dynamics and ecosystem functioning  (Madin et al.
g. , grazing, bioerosion) are empirically established for different taxonomic or functional groups (e. g. , Appendix S3: §2. 3). Algal diversity is potentially as important as coral and herbivore diversity for reef functioning and recovery (e. g. ,  Roff et al. 2015 ). Yet, most coral-reef models describe the algal community with no more than three functional groups (macroalgae, crustose coralline algae, turf). Our model is the first to implement six functional groups, which accommodated additional ecological details such as grazing preferences and coral-algae interactions (Appendix S2: §2. 1 and §6. 3, respectively). To date, trait-based research on tropical reef algae is modest compared to fishes and corals  (Brandl et al. , 2019)  and an algal-traits database has not yet been created. We implemented the effects of hydrodynamic variation, thermal disturbances, and changes in grazing pressure, but reefs are also affected by other disturbances, and some of these have been implemented in previous models -including ocean acidification (e.
Objective 2. To develop a global coronal MagnetoHydroDynamics (MHD) model for EUHFORIA, capable of quantifying the source regions of CMEs and the global coronal magnetic field. Objective 3. To integrate current state-of-the-art SEP transport models in EUHFORIA for simulation of SEP emission from coronal shocks and to develop methodology and tools for predicting the SEP emission from CMEs. Objective 4. To develop an operational prediction tool for GICs in power grids. Objective 5. To develop more reliable operational prediction tools for harsh radiation in geospace. Objective 6. To exploit EUHFORIA by creating completely novel space weather forecasting service facilities tailored carefully to the needs of selected target groups. pg. 6 Referring to the modelling chain in Figure  2 , we will replace the coronal model in EUHFORIA with a more advanced one, improve the heliospheric part of EUHFORIA (using data assimilation techniques), and couple our SEP transport and acceleration models to EUHFORIA so that we put the SEP source much closer to the Sun and capture the highenergy events too.
Methodology The methodology of the proposed project is directly linked to the six specific objectives mentioned in Section 1. 2, namely as follows. Objective 1: Implementing advanced flux-rope models for the internal structure of CMEs. We will improve the current wind model in EUHFORIA using data-assimilation techniques exploiting currently available satellite data and exploring the usefulness of L5 data. We will also apply Machine Learning techniques to quantify the sensibility of the predictions on the CME input parameters in order to optimize the ensemble modelling for the forecasts. We will also explore Lagrangian methods to increase the cost-effectiveness, starting from the SLURM code developed at KU Leuven  (Bacchini et al. 2017) , and coupling it to EUHFORIA to demonstrate the ability to run a rapid simulation of CMEs.
Different options to reduce the I/O bound are being investigated, including the use of stretched grids in the radial direction, reducing the latitudinal extent of the grid, and increasing the snapshot cadence. The EU H2020-SPACE-2019 project EUHFORIA 2. 0 started in December 2019 and involves eleven research teams, supported by an 'International Expert Advisory Panel'. The project will develop an advanced space weather forecasting tool, combining an MHD solar corona and wind model with one or more SEP models. The tool will be applied to study the geoeffectiveness of the impacts of CMEs, CIRs and SEPs and will help to mitigate (part of) the damage these cause. Extreme events will also be considered, though the emphasis will be on improving the prediction of daily space weather and its effects.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  870405. Additional support from the projects  C14/19/089  ( C1  project  Internal Funds KU Leuven ), G. 0D07. 19N  ( FWO-Vlaanderen ),  C 90347  ( ESA Prodex ),  Belspo BRAIN  project  BR/165/A2/CCSOM  is greatly acknowledged. For the computations at  KU Leuven  we used the infrastructure of the  VSC -Flemish Supercomputer Center , funded by the  Hercules foundation  and the  Flemish Government -department EWI.
Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration of these datasets fails to fully profit from the potential of both sensors. An optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when data are collected at different times, from different platforms, under different acquisition configurations. This paper presents a coarse-to-fine registration method of optical imagery with airborne LiDAR data acquired in such context. Firstly, a coarse registration involves processes of extraction and matching of building candidates from the two datasets. Then, a Mutual Information-based fine registration is carried out.
It involves a super-resolution approach applied to LiDAR data to generate images with the same resolution as the optical image, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with this difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48. 15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the superresolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a checkpoint assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further researches for a complete fusion methodology between these datasets in this challenging context.
A. Motivation Over the years, existing works in the domain of data fusion between optical imagery and airborne LiDAR data have addressed dedicated acquisition contexts, in which the respective image and the LiDAR point cloud are already registered and/or they are acquired from the same platform at identical or very close dates. For instance, solutions submitted to the 2013 Data Fusion Contest of the IEEE Geoscience and Remote Sensing Society (GRSS)  [11]  focused on the fusion between LiDAR data and hyperspectral imagery with the same spatial resolution, acquired on two consecutive days. The same contest in 2015  [12] ,  [13]  involved extremely high resolution LiDAR data and RGB imagery collected from the same aircraft with the sensors being rigidly fixed to the same platform. In other words, the solutions submitted to these contests, as well as many others  [14] -  [16] , have not intended to cope with the inherent obstacles of the context where datasets are collected from different platforms with different acquisition configuration (i.
B. Challenges The development of a relevant registration approach in this unresolved context faces many challenges. 1) Spatial shift between datasets: The first challenge relates to the differences between the dataset point of view and field of view, which lead to a significant spatial shift between them. For instance, a spatial shift exists approximately 1-2 meters between the orthorectified airborne image (2016) and the LiDAR data (2011), or up to 40 meters between the Pliades image and the LiDAR data  (2011). According to our literature review, a coarse registration, which is necessary to reposition the two datasets, has not been rigorously studied by existing works. This step is often inadvertently bypassed using the dataset geospatial coordinates provided by a GPS/IMU system  [7] ,  [16] ,  [19].
2) Uncertainty, imprecision and incompleteness: Distortions in the information extracted from optical images can be caused by radiometric errors like sensor sensibility, illumination changes, atmospheric effects, and geometric errors such as relief displacement, occlusions or shadows  [20]. On the other hand, points may be missing in the LiDAR data due to occlusion or presence of water  [21]. These errors, distortions and missing data from each of the two datasets induce incompleteness, imprecisions and uncertainties within the registration and fusion processes of these data  [22]. 3) Spatial resolution and level of detail: There are significant differences in spatial resolution and level of detail between the airborne or satellite imagery and LiDAR data. For example, as highlighted in Table  I , the considered LiDAR datasets in 2011 and 2017, respectively, have a point spacing 70 cm and 35.
4 cm. On the other hand, the aerial image (2016) has a ground sampling distance (GSD) of 15 cm, whereas that of the Pliades panchromatic (PAN) and multispectral (MS) images are 50 cm and 2 meters, respectively. They provoke the scene elements and objects into appearing differently on different datasets. Such differences affect the appearance of the same scene elements to be different on the two datasets, making it difficult to determine and extract the corresponding features between them  [23]. This issue has not always been addressed by existing multi-source registration works. For instance, the authors of  [24]  and  [25]  proposed a registration framework between LiDAR image and optical image in a context where the datasets always have the same spatial resolution. Among the proposed solutions to overcome the spatial resolution and level of detail differences between the datasets, some involve a multi-resolution approach  [9] ,  [26]  or a resampling step  [20] ,  [27].
4) Relevance of registration features: The nature of a scene, either in urban or natural environment, conditions strongly the entities within the datasets that would be relevant to perform the registration  [28]. 5) Accuracy of dataset registration: When performing the fusion of airborne LiDAR data and optical imagery, even a small misalignment between them can lead to an unfavorable impact on the quality of the integrated product, or a significant reduction of data information content  [29]. Thus, an accuracy level of 1-pixel is recommended for the data set registration  [15]. As a matter of fact, a sub-pixel level of accuracy, assessed by measuring the distances between control points, is usually preferred for a good registration. However, such a qualitative criterion is difficult to achieve because the image pixel resolution can vary from several dozens of centimeters to several meters depending on the platform (i.
C. Contribution This paper addresses the need for a versatile and relevant registration approach able to overcome the aforementioned challenges. The versatility of our proposed method is reflected through its capability of registering the datasets that are not acquired simultaneously, nor from the same platform and same acquisition configuration, nor having same spatial resolution. These assumptions are crucial to the existing works  [14] -  [16] ,  [24] ,  [25]. It should be noted that the proposed method does not aim to address every scene possible, as we focus on a registration on urban scenes. In this regard, we propose a coarse-to-fine registration approach. • Firstly, a coarse registration is performed to reposition the datasets closer to each other. It addresses the challenge of spatial shifts between datasets which is problematic but usually overlooked  [7] ,  [16] ,  [19] ,  [30].
In this paper, we present a coarse registration relying on the primitives that are buildings. • Secondly, a fine registration is carried out based on a local transformation model estimation. It is enabled by a super-resolution approach applied to LiDAR data in order to generate images with the same resolution as the optical image. This approach is devoted to overcome the hindering caused by the spatial resolution difference between datasets. Such a coarse-to-fine approach is necessary in order to register an airborne LiDAR dataset with an optical image. The mentioned coarse registration aims to reposition the two datasets in a fast but reliable manner. As a result, a global transformation model, composed of a set of coarsely estimated camera pose parameters, is determined. Even though the global transformation does not permit the dataset to be precisely registered, it narrows down the search space for optimal camera pose parameters from an initial set of values during the fine registration.
However, the main drawback of this feature-based coarse registration is that the building primitives are not distributed evenly throughout the datasets. Hence, the global transformation has the tendency to prioritize a region exhibiting more primitives than others. Therefore, we propose a subsequent fine registration that focuses on determining the optimal parameters for each local region of the considered urban area. Such a local approach brings two benefits, namely a higher registration accuracy and a reduced computational cost of this fine registration. Then, we also propose a refinement of locally optimized transformation models, in order to avoid conflicts between them. Lastly, the proposed method relies on tailored series of well-known processes and algorithms while avoiding complicated and labor-intensive processes.
II. LITERATURE REVIEW Accurate registration of LiDAR data and optical imagery is the crucial prerequisite to any data fusion applications using them  [14]. The majority of automatic methods for registering such datasets can be classified into two categories, namely area-based and feature-based methods. On the one hand, areabased methods determine the optimal pose of the camera by maximizing a statistical similarity, e. g. Mutual Information (MI), between the values of optical image pixels and LiDARderived image pixels  [7] ,  [15] ,  [26]. The LiDAR-derived image is either a Digital Surface Model (DSM), an intensity image, or an image of pdet (probability of detection) attributes derived from the LiDAR point cloud  [7]. Their main drawbacks, in addition to the high computational cost, are the necessities for the datasets to be spatially close to each other, as well as to have the same resolution and display similar intensity characteristics.