dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Table  2  shows the mean DC values using IBSR image. The parameters used by HMRF-CG are described in Table  1. The parameters used by the other methods are given in  [41]. Table  3  shows the mean DC values using BrainWeb images. The parameters used by HMRF-CG are described in Table  1. The parameters used by the LGMM method are given in LGMM  [23]. The implementation of LGMM is built upon the segmentation method  [2]  of SPM 8 (Statistical Parametric Mapping 5  ), which is a well known software for MRI analysis. As reported by  [23] , LGMM has better results than SPM 8. Figure  5  shows a sample of slices to segment obtained from IBSR image, their ground truths and their segmentation using HMRF-CG method. Figure  6  shows the slices number #95 with different noise and intensity nonuniformity from BrainWeb images and their segmentation using HMRF-CG.
Acknowledgements The study was funded by the  French Minister for Foreign Affairs (Commission for Foreign Excavations ) since 2004, the  CNRS  and the  University Paris 1 Panthéon-Sorbonne. We also wish to thank the  ANR  project  Archaedyn  for the methodological contributions and the  MSHE C. N. Ledoux  ( USR 3124 , Besançon).
Experiments and results Due to Twitter restrictions, datasets used in previous works are no longer available nor sharable. The only free way to access Twitter data is through their restful APIs that deliver amongst others information; tweets and followers or friends of a given user. However, Twitter APIs or methods have restrictions, every method allows only 15 requests per rate limit window of about 15 minutes. Hence, it will take long time to get a small dataset with some data lost between calls. For more details about the problem one could face when conducting research on Twitter we refer the reader to  [9]. To tackle this problem and to test our proposed approach, we have conducted our experiments on a collaborative dataset namely Core Dataset  [10, 11]  assimilating it to a social network.
Effectively, as an underlying graph, we have considered the co-authorship graph that we have built using the meta-data field "author list" of the repository records. Moreover, we have considered as a message the concatenation of the title and the abstract of each paper; messages are posted by the first author to all its co-authors that are assimilated to followers. Note that co-authors are not considered as followers of each other, they are only direct followers of the first author of a given paper. It should be noted that, an important motivation of our choice to conduct experiments on a collaborative network, is the fact that in this kind of networks the co-authors share more likely the same topics. Core dataset is a collection of open access research outputs from repositories of journals worldwide.
It allows free and unrestricted access to research papers. The Core Dataset offers two datasets, (1) a meta-data file of 23. 9 million items and a content dataset of 4 million items. The former contains meta-data on scientific papers in JSON format structured in 645 repositories, and the latter contains full articles. We carried out our experiments using repository 2 of the last dump of the Core dataset (i. e. , dump of October 2016). Figure  3  depicts an example of a meta-data dataset item. As a first step, we imported the repository 2 file as a collection to the NoSQL database MongoDB  [12]. Secondly, we sorted the information about the papers in ascending order by publication date ("dc:date" as shown in Fig. 3 ) to conserve the chronological order of posting. Afterwards, {"identifier":30474512, "ep:Repository":2, "dc:type":[], "bibo:shortTitle":"Learning from triads: training undergraduates in counselling skills", "bibo:AuthorList":["Smith, Kate"], "dc:date":"2015-05-06", "doi":"10.
1002/capr. 12056", "bibo:cites":[], "bibo:citedBy":[]," similarities":[]} "bibo:abstract":"Background:\\ud\nResearch has shown that counselling skills training. tutors must act proactively to ensure a safe learning environment", Fig. 3. Example of a Core dataset meta-data item we used the IBM Watson NLU APIs to generate keywords and concepts; then, we built the hash table as a python dictionary. Finally, we used the InFlowMine algorithm to extract frequent paths from the hash table built earlier.
Results and discussion As first experimental tests of our approach, we have set frequency f to 10 (ten content-tokens were diffused through all the detected paths) and obtained all paths of length 1, 2, 3 and 4 as maximal possible length extracted from the repository 2 of the Core dataset. Figure  4  depicts some of the detected length-3 paths. A line of the results below represents a path of length-3, with each author separated by a semi-colon. Williams, P. K. G. ;Stark, Craig R. ;Helling, Ch. ; George, Keith P. ;Grant, Marie Clare;Baker, Julien S. ; Ivanova, Iva;Pickering, Martin J. ;McLean, Janet F. ; Tummala, Hemanth;Khalil, Hilal S. ;Mitev, Vanio; Clifford, Brian R. ;Havard, Catriona;Memon, Amina; Havard, Catriona;Memon, Amina;Gabbert, Fiona; Clifford, Brian R. ;Memon, Amina;Gabbert, Fiona; The path "Havard, Catriona; Memon, Amina; Gabbert, Fiona;" shown in Fig.
Conclusion In this paper we have presented an approach to extend previous works on information flow frequent paths detection. The main objective is to capture in addition semantics of propagated information in a given social stream, considering in that both underlying graph structure and the content of interactions. Our experiments were conducted on a collaborative dataset that we have assimilated to a social network; the results showed an improvement that might reveal useful information missed when considering only cascades of exact content. As findings, we have noted that the vocabulary (keywords) of actors tend to be reused in their neighborhood, but vanishes in the case of long cascades and considering concepts remain the best alternative.
The use of digital soil mapping, with the help of spectroscopic data, provides a non-destructive and cost-efficient alternative to soil property laboratory measurements. Visible, near-infrared and short wave infrared (VNIR/SWIR, 400-2500 nm) hyperspectral imaging is one of the most promising tools for topsoil property mapping. The aim of this study was to test the sensitivity of soil property prediction results to coarsening image spectral resolution. This may offer an analysis of the potential of forthcoming hyperspectral satellite sensors, e. g. , HYPerspectral X IMagery (HYPXIM) or Environmental Mapping and Analysis Program (EnMAP), and existing multispectral sensors, e. g. , SENTINEL-2 Multispectral Sensor Instrument (MSI) or LANDSAT-8 Operational Land Imager (OLI), for soil properties mapping.
This study used VNIR/SWIR hyperspectral airborne data acquired by the AISA-DUAL sensor (initial spectral and spatial resolutions of approximately 5 nm and 5 m, respectively) over a 300 km 2 Mediterranean rural region. Ten spectral configurations were built and divided in the following two groups: i) six spectral configurations corresponding to simulated sensors with regular spectral resolution from 5 nm to 200 nm (i. e. , the Full Width at Half Maximum (FWHM) remains constant throughout the considered spectral domain; this includes the simulation of the forthcoming HYPXIM and EnMAP hyperspectral satellites) and ii) four spectral configurations corresponding to existing multispectral sensors with irregular spectral re-solution (i. e. , the FWHM differs from spectral sampling interval; Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SENTINEL-2 MSI, LANDSAT-7 Enhanced Thematic Mapper (ETM+) and LANDSAT-8 OLI).
In addition, spatial resolutions of 60 m or coarser were not suitable for clay content mapping over areas characterized by small short-scale clay content variability and small field sizes. The effect of coarsening spectral resolution (from 1 nm to 200 nm) on the accuracy of soil properties prediction models was studied only from laboratory spectral databases. Castaldi et al. (2016)  conducted a study using several laboratory spectral databases to compare the performances of soil texture and soil organic content estimation from present (EO-1 ALI and Hyperion, LANDSAT-8 OLI, SENTINEL-2 MSI) and forthcoming (EnMAP, PRISMA and HyspIRI) multi and hyperspectral sensors. Adeline et al. (2017)  used a laboratory spectral database to compare estimation performances of four soil properties (with different spectral absorption features due to their various physico-chemical interactions with soil substrates), clay content, free iron oxides, calcium carbonate and pH, from seven spectral configurations (number of spectral bands decreasing from 328 to 10 and coarsening spectral resolution from 3 nm to 200 nm).
We simulated both artificial sensors (characterized by regular spectral resolutions) and existing multispectral sensors (characterized by irregular spectral resolutions). All these sensor simulations were based on real airborne hyperspectral VNIR/SWIR data acquired over landscapes at a 5-m spatial resolution (AISA-DUAL hyperspectral sensor). The simulation of sensors allowed us to assess the influence of the spectral resolution on estimated soil property, independently to others specifications (e. g. spatial resolutions, acquisition dates, signal to noise ratio).
Soil sample database 129 ground soil samples were collected over the Lebna study site (Fig. 1b ), in fields that were bare during the hyperspectral data acquisition in November 2010. Among the 129 ground soil samples, 58 were collected in October 2008, 30 were collected in October 2009, and 41 were collected in November 2010. Each soil sample was composed of five soil sub-samples. And these five sub-samples were collected to a depth of 5 cm and within a 10 × 10 m square centered on the geographical position of the sampling plot as recorded by a Garmin GPS instrument. Each soil sample was homogenized in the laboratory, and approximately 20 g was devoted to the soil property analysis. Each soil sample was air-dried and sieved with a 2 mm sieve prior to being transported to the laboratory for analysis.
PLSR model The partial least squares regression (PLSR) method was used to establish relationships between the soil clay content and the VNIR/SWIR imaging data (AISA-DUAL and simulated data). The usefulness of the PLSR method comes from its ability to analyze data with many, noisy, and collinear variables in both predictor variables (X-variables, the spectra) and response variables (Y-variables, the clay content in our case)  (Wold et al. , 1984). The general concept of PLSR is to extract the orthogonal predictor variables (also called latent variables, LV) accounting for the maximum amount of variation of the Y-variables. A detailed description of the PLSR model can be found in  Geladi and Kowalski (1986). Before building PLSR models, the reflectance spectra (X-variables) were first converted into "pseudo absorbance" (log [1/reflectance]) and noise reduction was achieved using a standard normal variate correction to remove additive and multiplicative effects  (Barnes et al.
, 1993). Second, the dataset was divided into two groups. One group corresponding to 3/4 of the dataset is used for PLSR calibration and one group corresponding to 1/4 of the database is used for PLSR validation. The response variables (Y, clay content in our case) are sorted in ascending order. First, the lower clay content is placing in a validation set and the next three samples are placed in the calibration set. Then the procedure is pursued by alternately putting the following sample in the validation set and the next three samples in the calibration set. This subdivision ensures a similar distribution of clay contents in both calibration and validation set. A leave-one-out cross-validation procedure was adopted to verify the prediction capability of each PLSR model for the calibration dataset  (Wold, 1978).
Each time, N -1 samples from all N samples within the calibration dataset were used to build the regression model. Based on this model, the value for the soil property of the sample that was not used to develop the model was predicted. This procedure was repeated for all N samples. The optimal number of Latent Variables corresponded to the first local minimum of the root mean square errors of cross-validation (RMSECV) to avoid under-fitting, and do not exceed 10 to avoid over-fitting. The optimal PLSR model was then applied to the validation dataset. An outlier is defined as a variable (predictor or response variable) that is not consistent with the majority of the data  (Chiang et al. , 2003; Pearson, 2002). The presence of spectral outliers in the calibration dataset, which are samples spectrally different from other samples, has been studied using the Mahalanobis distance  (Mark and Tunnell, 1985).
Configuration • Its b-coefficient was larger than the standard deviation of the b- coefficients for all the spectral bands  (Viscarra Rossel et al. , 2008)  • Its variable importance in the projection (VIP) was higher than 1  (Chong and Jun, 2005; Wold et al. , 2001) , following the equation: ∑ =⋅ ⋅ = VIP N w R n p p pn p 1 , 2 2 opt (2) where w p, n 2 is the loading weight for the p th latent variable at wavelength n, p opt is the optimal number of latent variables, R p 2 is the coefficient of determination obtained for the p th latent variable and N is the number of wavelengths. Once each PLSR model was built using the calibration dataset corresponding to each spectral configuration, each PLSR model was applied to all of the spectra over bare soil from the corresponding spectral configuration to produce predicted clay content map.
Preliminary results A reference PLSR model was built from the AISA-DUAL spectra and observed clay content values associated to the 129 available soil samples. Four spectral outliers were removed from the calibration database of this reference model, and 4 latent variables were selected following the rule of the first local minimum of the RMSECV (Table  4 , Fig. 3a ). The performance of the model was accurate, with R 2 cal and R 2 val values of 0. 77 and an RMSEP value of 82 g/kg (Table  4 ). This reference PLSR model was applied to all AISA-DUAL spectra over bare soil to produce a predicted clay content map (Fig. 4a ). As previously observed in  Gomez et al. (2012b) , the Kamech catchment is characterized by strong variations in soil patterns on a small scale, with a close succession of clay-rich areas and clay-poor areas oriented northwest/southeast, corresponding to marl and sandstone outcrops, respectively (Fig.
Sensitivity to the spectral resolution with irregular spectral range Among the four spectral configurations of the ISR-Sensors, the spectral configurations of ASTER and SENTINEL-2 MSI seemed to offer accurate PLSR models for clay prediction, corresponding to R 2 cal > 0. 69, RMSEP of 95 g/kg and R 2 val > 0. 71 (Table  4 ). Between these two sensors, the PLSR model built from the spectral configuration of ASTER provided a more accurate clay prediction than the PLSR model built from the spectral configurations of SENTINEL-2 MSI, indicated by a lower bias and a higher RPIQ and RPD (Table  4 ). The PLSR models built from spectral configurations of LANDSAT-7 ETM+ and  LANDSAT-8 OLI performed the worst, with an R 2 cal ≤ 0. 4, RMSEP > 120 g/kg and R 2 val < 0. 45 (Table  4 ). In addition, 2 to 3 spectral outliers were removed from the calibration database for all tested ISR-Sensors (Table  4 ).
Many of the multispectral data studies focused on the classification of pedological groups (e. g. ,  Escadafal and Pouget, 1987). Other multispectral data studies tested mapping of quantitative physico-chemical soil properties with modest success. Shabou et al. (2015)  achieved a R 2 of 0. 73 for clay content estimation using LANDSAT-TM5 data and a spectral index called MID-Infrared index. These results do not match with ours. However, their model was calibrated using only 30 samples, which is low and may produce unrobust model or overfitted model (as is the case of our model obtained from the SENTINEL-2 MSI configuration). VNIR/SWIR spectroscopy, the sensitivity of soil properties to instrumental spectral configurations depends on the chemical absorption features and the correlations with other soil properties.
, 2007). The NDVI index can be calculated by all sensors simulated in this paper. However, the CAI index needs spectral information measured at 2000, 2100 and 2200 nm, which are not measured by any of the ISR sensors studied in this work or any of the RSR artificial sensors from the 100/100 nm spectral resolution. Dry vegetation is usually masked on LANDSAT data using the Modified Soil Adjusted Crop Residue Index (MSACRI) index  (Bannari et al. , 2000)  and on ASTER data using the Lignin-Cellulose Absorption (LCA) index  (Daughtry et al. , 2005). Therefore, the masking step of pixels covered by some dry vegetation may be an additional drawback for the potential of sensors with low spectral resolution. It may be interesting, in futures works, to estimate the impact of this step (and the impact of these indexes on dry vegetation identification) on the prediction performances.
Conclusion The launch of the forthcoming sensors, in addition to existing ones, will produce an increasing amount of VNIR/SWIR data over the world, and soil surface quality could be mapped over larger areas than it is currently. This work investigated the sensitivity of spectral configurations on clay content estimation and mapping to identify adequate sensor(s) for soil mapping. Performances of the PLSR models built from six simulated sensors (with regular spectral ranges from 5 nm to 200 nm) and four existing multispectral sensors (with irregular spectral ranges relating the characteristics of ASTER, SENTINEL-2 MSI, LANDSAT-7 ETM+ and LANDSAT-8 OLI) were analyzed. Moreover, spatial structures of maps obtained using these PLSR models were also analyzed. Analysis of the PLSR model performances highlighted that the RSR sensors with spectral configurations up to 100/100 nm and the ASTER spectral configuration may allow for the estimation of clay content.
Acknowledgments This research was granted by the  TOSCA-CNES  project " MiHySpecSol -Mission HYPXIM: Impact de la résolution Spectrale pour la cartographie des propriétés pérennes des Sols en milieu Méditerranéen " (2014)(2015). This research was also supported by the  French National Research Agency (ANR)  through the  ALMIRA  project ( ANR-12-TMED-0003 ). The authors are indebted to UMR LISAH (IRD, France) and to CNCT ( Centre National de Cartographie et de Télédétection, Tunisia ), for providing the AISA-DUAL hyperspectral images for this study. This hyperspectral data acquisition was granted by  IRD, INRA  and the  French National Research Agency (ANR)  ( ANR-O8-BLAN-C284-01 ).
Composition framework, static optimizations, modeling and performance analysis The skeletons and multi-variant components will be implemented by an integrated composition framework which will perform the static parts of application analysis, optimization, code generation and deployment. It will be organized around a generic, wholeprogram compiler-like core framework that browses and processes the annotations of multi-variant application components, translates skeleton calls and data container uses, builds a whole-program highlevel intermediate representation (IR), and coordinates program analysis, code optimizations and composition code generation for the application to be deployed. Part of this core functionality is inspired from technical contributions to EC projects PEPPHER  [20]  and EXCESS  [26].
Fault-tolerance mechanisms Current large-scale HPC systems experience failures on the order of every few days. However, models indicate that an exascale-sized system will likely experience system failures several times an hour  [28]. EXA2RPO will provide a set of fault-tolerance mechanisms based mainly on data replication. The StarPU runtime system will be extended accordingly to support these mechanisms. Application profiling at design-time will assist the identification of mechanisms which are the most suitable for each application under deployment. Apart from cluster-level fault-tolerant support, EXA2PRO will provide mechanisms for fault-tolerance at intra-node level, as well. The runtime system will extended to provide support for virtual data  [8]  to keeping replicates of critical data in multiple memory storages.
In case of faults, dynamic scheduling will be able to rebalance the load according to the remaining resources and the availability of valid replicates. In addition to the above, EXA2PRO will develop and expose to the Maxeler's Data Flow Engines code designer basic primitives to easily create highly customized fault tolerant dataflow implementations at arbitrary granularity levels. DataFlow Engines (DFEs) are FPGA based PCIe devices specially designed for streaming dataflow acceleration. By integrating fault tolerance features, the area of the resulting kernel will increase and sometimes the operating frequency might also be penalized. We will evaluate the applicability of classic techniques, e. g. , triple modular redundancy, data replay, rollback and rollforward, just to name few. All techniques found appropriate will be exposed through the MaxJ programming language (the first implementation of OpenSPL) and the generation of the corresponding structures will be automated.
Run-time system The ground of EXA2PRO will be a runtime system based on StarPU  [9]. It will provide support for the combination of various programming models: it currently supports CUDA, OpenMP and OpenCL front-ends, and in the context of EXA2PRO it will use MaxJ for the support of the DFEs architecture. This will allow the composition framework to seamlessly retarget application tasks across diverse architectures. The runtime system will also carry out efficient resource management. State-of-the-art dynamic task scheduling  [7]  will be extended to selecting among the task variants proposed by the composition framework, as well as combining multiple scheduling criteria, including task performance, data transfers  [8] , and energy consumption. This will allow to abstract the question of performance out of the writing of application software.
g. CPU, GPU, multithreaded, and/or multi-node) from third parties to allow for a direct comparison with the base-line programming models, also in programmability and code complexity aspects. Such benchmark codes will be taken from well-established benchmark suites in parallel and heterogeneous computing, e. g. from Rodinia and PARSEC. Dense Linear Algebra: The Chameleon solver is a dense linear algebra library, which notably includes the classical Cholesky, LU, QR factorizations. It is part of the MORSE (Matrices Over Runtime Systems @ ExaScale) project , which aims at leveraging runtime systems for dense and sparse linear algebra. Chameleon is thus a collection of state-of-the-art dense linear algebra algorithms, which are expressed as task graphs  [6] , allowing for more parallelism than the previous vectorized approaches (such as LINPACK) or panelled approaches (such as LAPACK), thanks to more task pipelining.
g. IntelâĂŹs AVX-512 instructions introduced with the most recent generation of Xeon Phi processors. Finally, a subset of applications uses GPUs as accelerators, for which programming languages like CUDA or OpenCL (to a lesser extend) as well as directive-based programming models like OpenACC are used. In future it can be expected that application developers will also have to address the topic of resilience as in future jobs will depend on the absence of major failures for a larger number of components. As of today the main strategy for coping with such errors is checkpoint-restart. The integration of such techniques as of today largely remains with application developers. Only for optimization of the required I/O operations support is provided by supercomputing centers. EXA2PRO addresses the above challenges by proposing a programming environment that enables the efficient exploitation of accelerators in heterogeneous exascale computing systems.