dataesr/scientific-paragraphs-categorization huggingface dataset:
The palm leaf manuscript datasets for isolated character/glyph recognition task are presented in Table  3. For the Balinese character dataset, Balinese philologists manually annotated the segment of connected components that represented a correct character in Balinese script from the word-level binarized images that were manually annotated  [11, 17, 20]  using Aletheia (http://www. primaresearch. org/tools/Aletheia) [62, 63]  (Figure  14 ). The Sundanese character dataset was annotated manually  [22]  (Figure  15 ). For the Khmer character dataset, a tool has been developed to annotate characters/glyphs on the document page. The polygon boundary of each character is traced manually by dotting out its vertex one by one. A label is given to each annotated character after its boundary has been constructed  [21]  (Figure  16 ).
Datasets The palm leaf manuscript datasets for word recognition and transliteration task are presented in Table  4. For the Khmer dataset, all characters on the page have been annotated and grouped together into words (Figure  17 ). More than one label may be given to the created word. The order of how each character in the word is selected is also kept  [21]. Balinese (Figure  18 ) and the Sundanese (Figure  19 ) word dataset was manually annotated using Aletheia  [63]. how each character in the word is selected is also kept  [21]. Balinese (Figure  18 ) and the Sundanese (Figure  19 ) word dataset was manually annotated using Aletheia  [63]. how each character in the word is selected is also kept  [21]. Balinese (Figure  18 ) and the Sundanese (Figure  19 ) word dataset was manually annotated using Aletheia  [63].
Binarization The experimental results for the binarization task are presented in Table  5. These results show that the performance of all methods on each dataset is still quite low. Most of the methods achieve less than a 50% FM score. This means that palm leaf manuscripts are still an open challenge for the binarization task. The different parameter values for the local adaptive binarization methods show significant improvement in performance, but still give unsatisfactory results. In these experiments, the ICFHR G1 method was evaluated for the Khmer and Sundanese datasets using the pre-trained Balinese training set weighted model. Based on these experiments, Niblack's method gives the highest FM score for Sundanese manuscripts (Figure  20 ), ICFHR G1 method gives the highest FM score for Khmer manuscripts (Figure  21 ), and ICFHR G2 gives the highest FM score for Balinese manuscripts (Figure  22 ).
Text Line Segmentation The experimental results for text line segmentation task are presented in Table  6. According to these results, both methods perform sufficiently well for most datasets, except Khmer 1 (Figures  23 24 25 ). This is because all images in this set are of low quality due to the fact that they are digitized from microfilms. Nevertheless, the adaptive path finding method achieves better results than the seam carving method on all datasets of palm leaf manuscripts in our experiment. The main difference between these two approaches is that instead of finding an optimal separating path within an area constrained by medial seam locations of two adjacent lines (in the seam carving method), the adaptive path finding approach tries to find a path close to an estimated straight seam line section.
The unbalanced number of image samples for each character class means the CNN method did not perform optimally. For the Sundanese dataset, the handcrafted feature with NN slightly outperformed the CNN method. The UFL method slightly increased the recognition rate of the pure NN method for the Khmer and Balinese datasets. [25]  87. 44 --ICFHR G1: VMQDF  [25]  88. 39 --ICFHR G3  [25]  77. 83 --ICFHR G5  [25]  77. 70 --.
Word Recognition and Transliteration The experimental results for word recognition and transliteration task are presented in Table  8. The error rates for word recognition and transliteration tests set on each training model iteration are shown in Figures  26 27 28. The LSTM-based architecture of OCRopy seems very promising in terms of recognizing and directly transliterating Balinese words. For the Khmer and Sundanese datasets, the LSTM architecture seems to struggle to learn the training data. More synthetic data training with a more frequent word should be generated in order to support the training process. For the Balinese dataset, a sequence depth of 100 pixels with a neuron size of 200 gives a better result for both LSTM and BLTSM architecture. Most of the Southeast Asian scripts are syllabic scripts.
The problem of inadequate training data also influences glyph recognition and word transliteration. The unbalanced number of image samples for each character class means the CNN methods did not perform optimally in glyph recognition. The differences in the recognition rates of the CNN methods are not too significant with the handcrafted feature combinations. For future work, more synthetic data training for palm leaf manuscript images should be generated in order to support the training process. Especially for the word transliteration task, more synthetic data training with a more frequent word should be generated in order to improve the training process. Many examples of glyph-to-syllable association should be synthetically generated to transliterate syllabic scripts from Southeast Asia. The special characteristics and challenges posed by the palm leaf manuscript collections will require a thorough adaptation of the DIA system.
Acknowledgments: The authors would like to thank  Museum Gedong Kertya ,  Museum Bali ,  Mr. Undang  Acknowledgments: The authors would like to thank  Museum Gedong Kertya ,  Museum Bali ,  Undang Ahmad Darsa , the philologists from  Sundanese Centre Studies of Universitas Padjadjaran , the  Situs Kabuyutan Ciburuy Garut, all families in Bali, Indonesia , the  EFEO team , the  Buddhist Institute , and the  National Library in Cambodia  for providing us with samples of palm leaf manuscripts. We also thank the students from the  Department of Informatics Education  and the  Department of Balinese Literature, University of Pendidikan Ganesha , the  Institute of Technology of Cambodia , and the  National Institute of Post, Telecommunication and ICT  for helping us with the ground truthing process for this research project.
The SSA filtering of the Hall probe data is so successful this task that the resulting magnetic field map does not require an overdetermined QR inversion, allowing Fourier inversion of the Biot-Savart problem. Our implementation of SSA filtering of the Hall scan measurements, followed by Biot-Savart inversion using the Fast Fourier Transform (FFT) is applied to both simulations and real samples of HTS tape stacks. The algorithm works in cases where ill conditioning ruled out the application of Fourier inversion, achieves a finer resolution, and for a fraction of the cost, of the QR inversion used up to date. The computation passes physical and statistical validity tests in all cases, and in 3-d samples it is shown to yield the average, with a depth-dependent weight, of the current density circulating in the different layers of the sample.
We have found that the filtering of the magnetic field measurements, on the basis of the novel technique of Singular Spectrum Analysis (SSA), produces greatly improved reduction of error, without introducing artifacts, in the Hall probe measurements when compared to filtration with previous techniques. Even when the inversion problem has a high resolution and is not well conditioned, this technique allows the Biot-Savart inversion by means of a FFT procedure with a significatively reduced computational cost, yielding maps of electrical currents with finer resolution and greater accuracy compared to previous inversion procedures. The authors have implemented for Matlab and Octave this SSA filtering technique, complete with Fourier inversion of the Biot-Savart problem for 2-dimensional or planarly chrystallized HTS materials, further SSA improvement of the achieved solution, and a double physical and statistical test of the margin of error of this computation.
Computation of current density by Fourier inversion The algorithm based on the SSA filtering of B z described in 2. 3, and Fourier inversion as explained in 2. 2 has been implemented by the authors as a Matlab program, with 100% compatibility with its GNU licensed clone Octave. It has been run on a typical desktop PC computer with a 2GHz CPU and 8GB RAM memory. The Biot-Savart inversion itself is performed in under 1 second, and the available memory supports computation on grids of size 400 × 400 and larger. Our verification of the margin of error of each computation follows a double procedure explained in 3. 2 which requires no additional memory but takes a longer time, typically about 30 min for a 200 × 200 inversion. The authors have implemented our SSA filtering from scratch as a Matlab/Octave program to ease its 2-dimensional application.
For the SVD decomposition of the trajectory matrix, and for the 2-dimensional (inverse) FFT the routines available in Matlab or Octave are used. The scheme that our program follows for a Biot-Savart inversion is: (i) SSA filtering of measured 2-dimensional table of B z , as described in 2. 3. (ii) Computation of the discretized M from the filtered B z by Fourier inversion, applying the FFT, deconvolution, and IFFT steps of the algorithm introduced in  [5]. (iii) Cropping of the edges of the discretized M , which are away from the sample and thus any nonzero value on them is spurious, and SSA filtering of the resulting M to denoise it, with the same procedure and parameters of 2. 3. (iv) Computation of the current density as the rotational J = ∇ × M, following the 4-point difference scheme of  [9]  (i.
(ii) A noise term, which is a normally distributed random variable with average 0 and standard deviation Bz,max 500. (iii) After adding the drift and noise terms to B z , we perform a rounding of the resulting values to the nearest multiple of 0. 035 Gauss to mimic the resolution of our Hall system on a typical measurement. The information fed to the authors' program consists simply of the grid of degraded values of B z , shown in Figure  2b , their x, y coordinates, their height above the sample and the total thickness of the sample. This data is first SSA filtered, and of B z and by all values of its perturbation), while the values of B z obtained by SSAfiltering the perturbed values have a relative error of 4. 80% with respect to the original values. This means that the main improvements introduced by SSA filtering in this case, and typical in Biot-Savart inversions, are: to smooth the values of B z in a way that approaches their original values, rather than imposing some a priori model, and to shift the error in B z from the central area where the current is located to the boundary where it is simple to filter out after inversion.
15. We conclude that, with 95% confidence, the relative error in the resulting map of M is 289 times the relative error in B z , and the 6σ upper bound for error propagation is 297. 71. The computed J is the average, with weight 1 h-z , of the current density along the thickness of the stack. The vertical magnetic field that it generates coincides with the originally measured one with an error around 2. 5% above the sample. The time used by a desktop PC running Matlab for this computation is 1s for the SSA filtering and Fourier inversion, and 87 min for the verification of the margin of error of the computation. From the B z field map, we can deduce that magnetic induction decays in the central region of the sample as corresponds to the valley in the field profile, thus showing the presence of a defect along the affected zone.
Acknowledgements The authors thank  Marta Pérez Casany  for her statistical advice. We acknowledge financial support from  Spanish Ministry of Economy and Competitiveness  through the  Severo Ochoa Programme  ( SEV-2015-0496 ),  CONSOLIDER Excellence Network  ( MAT2015-68994-REDC ),  COACHSUPENERGY  project ( MAT2014-51778-C2-1-R , co-financed by  FEDER) ,  GAP  project ( MTM2015-69135-P ) and  SU-PERINKS project  ( RTC-2015-3640-3 , co-financed by FEDER); the  European Union  for  FASTGRID  project ( H2020-NMBP-18-2016-IA-72109 ) and WPMAG 5 ( H2020-EUROfusion ) and the  Catalan Government  with  2017-SGR-932 ,  2014-SGR-753  and Xarmae.
e. a complete field course on insects that are easy to observe and collect in their natural habitat. We report the feasibility of the novel "Des Insectes et des Hommes" ("Insects and Humans") education programme in Ethiopia (Eastern Africa) as a case in point. To our knowledge, no biological education programme has been yet proposed using a scientific framework in the study of insect communities in the French education system. The programme addresses every step of entomological research projects and is comparable to a typical undergraduate entomology programme. These activities include: (1) acquisition of general entomological knowledge and understanding the role of insects in terrestrial ecosystem functioning and services; (2) proposal for research questions including hypothesis development and evaluation using scientific literature; (3) fieldwork using light traps as the main collecting technique; (4) sorting and the identification of the 10 main orders of insects using illustrated plates and entomological supports; (5) database preparation, data collection and analysis and interpretation of the results; (6) communicating their findings to the public audience via oral presentation and written reports.
Sequence #2: Workshop for building research questions (day 1, lab activities ~2h). We allocated student groups to two classrooms to keep the number of students manageable during the workshop. Instructors, GPAL and YJ, led the workshop discussion. In each class, in concert with the students that reached some specific interests   (Evans, 1977). in particular insect group, we proposed a series of simple research questions on insect community composition and structure (community ecology). These questions were related to the previous lectures and were feasible to investigate within the duration of the fieldwork (i. e. generating a sufficient dataset) and the timeline of the project (Table  1 ). Instructors needed to prepare a list of questions prior to the workshop to facilitate discussion and, if necessary, modify questions in the context of the local area, forest structure, compositions and weather conditions.
We discussed the origin and differences in attraction of insect groups to lights (phototactic responses). Sampling efficiency was compared in terms of capturing the dominant, representative species of the local habitats and in terms of capturing species of the target group (e. g. moths). We discussed with the students how to structure a dataset (order × sample × trap) we will generate from the light trapping (Table  1 ). For some groups, we devel- oped a posteriori research question during the fieldwork. For instance, a couple of hours of rainfall during the second night of trapping created the opportunity to study the effect of weather variations on insect activities and, ultimately, the assemblage of captured insects. Students also studied the differences in abundance of Coleoptera (beetles) and Lepidoptera (moths), the two most dominant taxa, between clear and rainy nights.
At least 500 m from the first light-trap, we set up a portable light trapping device. This second light trap illuminated an 80 cm cylindrical white sheet using a 30 cm actinic tube of 12 W black light (Figure  2 ). This type of actinic lamp powered by a small-sized battery could also be used alone for the programme, as mercury vapour lamps, ballasts and generators are not always accessible. During the day, we also introduced the use of aerial fruit traps and pitfall traps for collecting the butterflies, wasps, ants, beetles and spiders commonly found in the understorey and in the canopy (Suppl. material 1: Figure  S1 ). The students, however, ultimately only collected data from the light-traps, as this technique yielded the highest abundance and diversity of insects in the limited sampling period available.
Separating the different orders in the field (after observation and collection) and the lab (under microscope) received positive feedback by the students and was generally perceived as less challenging than the identification of specimens at higher taxonomic level (family or genus). However, confirmation of students' identifications by instructors was needed. This often generated valuable discussions within a small group of students on the process of discriminating key morphological features amongst insect orders and on functional attributes related to ecosystem services (e. g. pollen-carrying apparatus in bees). Sequence #5, #6: How to present your data and report your findings? (day 5, lab activities ~5h). We introduced Microsoft Excel software and gave instructions on correct data formatting procedures (Excel was projected on a screen from the teacher's computer).
Acknowledgments This work was supported by  The Agency for French Education Abroad  through the  Actions Pédagogiques Pilotes  ( APP-AEFE) ` 2015-2016 ' under Grant  275H01. The  European Research Council  Grant (No. 669609 ) supported GPAL during the writing of the paper. We are thankful to education services and direction of the  French High school Guebre-Mariam  that helped during the week. Discover Abyssinia Tours provides the logistic support, safety and vehicles (http://www. dabyssinia. com. et/). We are thankful to the rangers and employees of the Menagesha-Suba Forest associate to the enthusiasm of all the students that participated in the project. We are grateful to  Stéphane Brûlé  and  Vincent Vedel  ( SEAG ) for sharing pictures. We thank  Yves Basset ,  Simon Segar  and  Christopher Jeffs  for valuable comments and discussions.
Four project development teams worked in parallel in four Global Challenges 2018, 2, 1700081  domains (clinical practice, coverage, tests, and health system and public health). They included health researchers from nine DECIDE project partner institutions and seven countries,  [7]  with expertise in GRADE methodology, evidence synthesis, guideline production, information design, and software development. A fifth team exploring patients' and public perspectives on guideline output informed our work. In Phase 1, the teams focused on developing EtD framework content for their respective topic areas. In Phase 2, teams continued to refine content and test frameworks in real-life guideline panels,  [16]  while a smaller group of researchers, designers, and software developers carried out the development of the iEtD tool based on findings (content, user tasks, and needs) from Phase 1.
Analysis, Idea Generation, Consensus, New Prototypes We analyzed data as it were collected. The aim of our analysis was to draw out findings that could inform content and improve design from the perspective of users and stakeholders. Drawing from the user experience framework, we sought in particular to identify issues regarding usefulness, usability (ease of use), and understandability. [17] Rather than performing formal qualitative analyses, we adhered to a quicker method of identifying problems as they emerged and rapidly trying out solutions in new prototypes. [23, 24] he teams shared a common approach to processing data from piloting and feedback collection, based on the detailed method description in the project protocol  [6]  and modifying it as needed. This shared approach consisted of a set of steps: (1) based on notes, interview transcripts, or oral description (e.
This included both templates for draft reports and for final publications aimed at end users. For the latter, we achieved a layered approach by reordering the framework sections so that the conclusion came first. We also created templates for "Recommendations to Decisions" for people making decisions based on recommendations (e. g. , policy makers making a national decision based on an international recommendation). We implemented several features that enable organizations to tailor the iEtD. Templates for creating or exporting EtD frameworks are editable: criteria can be added or removed, response options (the range of answers provided for making judgments and detailed judgments) can be changed, text in the guidance section can be edited (so that organizations can create tailored instructions), and all template text can be rewritten in another language.
Barriers to Using an Unfamiliar Digital Tool Most workshop or training attendees found the iEtD intuitive and easy to learn. However, others felt more comfortable using the paper-based framework prototypes from Phase 1. Some technical teams felt that learning to use unfamiliar technology placed an unnecessary extra stress on their already overloaded agendas. "Everything that is digital is a barrier -learning to use it takes time. " (Guideline program manager) One guideline program manager noted that they constantly had newcomers joining their teams who needed to learn many other things at the same time, such as GRADE methodology. This lowered their ability or desire to engage with new technology at the same time. Some feedback also indicated that people had mistaken expectations that the digital tool would produce automatically calculated conclusions.
Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4. 2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model.
These choices contribute to the structure of the model. For example, how one chooses to discretize the upper soil layers will affect the soil moisture dynamics  [12] , similarly the inclusion of a soil resistance term for water transfer will greatly affect evapotranspiration. It is important to separate the different sources of errors to understand whether the deficiencies in the model process are due to the forcing used or the model structure and configuration. To improve the representation of a variable such as SM in LSMs, the model can be confronted with the observations. These observations can be measured in situ, i. e. , locally, or remotely, for example, from space instruments. Since soil moisture is difficult to observe at large scales due to its high spatial and temporal variability, satellite data can provide much needed consistent and comparable global datasets.
Extra care needs to be taken with SM data since it is not directly observed but rather estimated using backscattering signals and brightness temperatures through different inversion algorithms. Over the last decade, the quality and quantity of the Earth Observations has vastly improved and has been used to evaluate the performance of LSMs over different climatology and hydrological environments. For example, GRACE (Gravity Recovery and Climate Experiment) data has been used to evaluate land water storage in several LSMs (e. g. , ORCHIDEE; [13]). GRACE has been used to evaluate the CLM (Community Land Model) over Africa, specifically to assess a soil resistance parameterization in the model  [14]. Ref. [15]  evaluate the CLM and GLDAS models over Africa using GRACE and other remote sensing data to highlight discrepancies between the models' evapotranspiration outputs.