dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION The aim of this study was to evaluate the impact of different preprocessing pipelines on the quality of corrected data. While most studies try to isolate the cofounding factors coming from acquisition settings, data or processing quality, we instead found interesting to consider the combination of eddy-current and susceptibility-induced distortion corrections into single pipelines dedicated to distinct acquisition contexts. Hence, we could highlight the resulting differences between outcome data and their consecutive and tractography measurements. As these pipelines are optimal for different acquisition contexts, our observations will help for both a careful choice of acquisition settings and a precautious interpretation of DWI analysis. In the light of our results, the acquisition of several interspersed b0 volumes plus an additional b0 volume with RPE is highly recommended as default settings, rather than the acquisition of a field-map.
A crucial outcome here is that analysis should never be conducted on datasets which underwent distinct preprocessing pipelines. Finally, further investigations should be performed to evaluate the influence of the same pipelines regarding other acquisition settings such as b-value, q-space sampling size, and noise reduction, where the correction of eddy-currents should be of major importance. The Diffuse software toolbox implemented to conduct the present study is available at this link: https://github. com/ MecaLab/Brainvisa-Diffuse. It offers an automatic selection of the optimal preprocessing pipeline given the acquired DWI data. It also provides registration with anatomy, local model reconstruction and tractography algorithms. The Diffuse toolbox is embedded in the BrainVISA platform which gives access to volume-based and surface-based anatomical data processing, as well as to an efficient database management.
In the second line (extension equation), the off-diagonal term A es results from the continuity of the extension and solid displacements at the fluid-solid interface. In the last line (fluid equation), the off-diagonal term A fs also results from the continuity of the fluid and solid velocities at the interface, while A fe represents the shape derivatives of the fluid equation. The finite-element software FREEFEM++  [47]  is used to assemble the sparse block matrices in the above eigenvalue problem. Finally, it should be noted that the eigenmodes are normalized so that q • s H B s q • s = 1, where the superscript H denotes the transconjugate operation.
The shifted eigenvalues σ s of largest magnitude correspond to the closest eigenvalues σ from the shift s. To determine the portion of the spectrum displaying the eigenvalues with the largest growth-rate, the eigenproblem is solved for several values of s close to the real axis, spanning the imaginary axis in a range of frequency where unstable eigenvalues are expected. The Implicitly Restarted Arnoldi Method (IRAM) implemented in the library ARPACK  [48]  is used to compute the eigenvalues. This library is called from the finite-element software FREEFEM++  [47] , interfaced with ARPACK library. The reverse communication interface of the latter requires, when using the shift-and-invert mode, to perform the matrix-vector product Bq i and to solve the following linear problem, for any input vector q i , (A -sB)q o = q i (47) The finite-element software FREEFEM++  [47]  is used to assemble the large scale sparse matrices B and A, as well as to solve the above linear problem.
4. the leading eigenvalue of the reduced eigenvalue problem (42) is then obtained with the shift-and-invert approach described above. A single direct LU decomposition of the shifted A -sB is performed. The shifted linear problem  (47)  is then solved at each iteration of the IRAM algorithm, with fast lower and upper triangular resolutions. The FREEFEM++ scripts allowing the computation of the non-linear steady solutions and leading eigenmodes will be made available on the website https://w3. onera. fr/erc-aeroflex/. Fig. 2. Reference configuration for the vortex-induced elastic-deformation instability. Sketch of the geometry proposed by  [29]  with the associated non-dimensional lengths. Point A is located in the elastic plate at the position (x A , y A ) = (3. 95, 0) in the reference configuration.
The non-dimensional parabolic velocity profile u = 0. 357 (2 + y) (2. 1 -y) is imposed at the inlet of the channel. The no-slip boundary conditions (u, v) = (0, 0) are imposed at the top and bottom walls, as well as on the surface of the rigid cylinder (denoted Γ cyl ). At the outlet of the channel, a zero normal-stress boundary condition is imposed. The non-linear unsteady ALE solver is also implemented in the FREEFEM++ software and a validation is provided in Appendix B. When solving these equations for the above values of the non-dimensional parameters, one systematically obtains a periodic solution, that is characterized by the unsteady shedding of vortices in the flow and an unsteady deformation of the elastic plate. Such a configuration can be referred to as a vortexinduced deformation configuration  [3] , since the vortex-shedding in the wake of the rigid cylinder induces the deformation of the elastic plate.
In auditory research, however, similarly efficient data-driven strategies have not yet been common practice, by lack of tools able to manipulate the high-level stimulus dimensions that are relevant for similar judgement tasks when they apply on, e. g. , voice or music. Here, we present an open-source audio-transformation toolbox, called CLEESE (Combinatorial Expressive Speech Engine, named after British actor John Cleese, with reference to the "Ministry of Silly Talks"), able to systematically randomize the prosody/melody of existing speech and music recordings. CLEESE works by cutting recordings in small successive time segments (e. g. every successive 100 milliseconds in a spoken utterance), and applying a random parametric transformation of each segment's pitch, duration, amplitude or frequency content, using a new Python-language implementation of the phase-vocoder digital audio technique.
Functionality CLEESE is an open-source Python toolbox used to create random or fixed pitch, time and amplitude transformations on any input sound. CLEESE can also be used to create spectral transformations, a technique not described here-see  [32]. The transformations can be both static or time-varying. Besides its purpose of generating many stimuli for reverse-correlation experiments, the toolbox can also be used for producing individual, user-determined transformations. CLEESE is available for download upon free registration at http://forumnet. ircam. fr/ product/cleese. CLEESE operates by generating a set of random breakpoint functions (BPFs) for each transformation, which are then passed to a spectral processing engine (based on the phase vocoder algorithm, see below) for the transformation to occur.
Usage CLEESE is implemented as module for the Python (versions 2 and 3) programming language, and distributed under an open-source MIT License. It provides its own implementation of the phase vocoder algorithm, with its only dependencies being the numpy and scipy libraries. In its default 'batch' mode, CLEESE generates many random modifications from a single input sound file, called the base sound. It can be launched as follows: import cleese inputFile = 'path_to_input_sound. wav' configFile = 'path_to_config_file. py' cleese. process(soundData = inputFile, configFile = configFile) where inputFile is the path to the base sound (a mono sound in WAV format) and configFile is the path to a user-generated configuration script containing generation parameters for all transformations. For each run in batch mode, the toolbox generates an arbitrary number of random BPFs for each transformation, applies them to the base sound, and saves the resulting files and their parameters.
2 SD), linearly interpolated between time-points, using the procedure shown in Fig 3A  and 3B. The choice of SD in this experiment was set empirically, based on the plausible pitch changes in natural speech (<200 cents between consecutive time points). See S3 Sound for examples of stimuli. Procedure. 700 pairs of randomly-manipulated voices were presented to each of N = 5 observers (male: 3, M = 22. 5yo), all native French speakers with self-reported normal hearing. Participants listened to a pair of two randomly-modulated voices and were asked which of the two versions was most interrogative. Inter-stimulus interval in each trial was 500 ms, and inter-trial interval was 1s. Apparatus. The stimuli were mono sound files generated at sampling rate 44. 1 kHz in 16-bit resolution by Matlab. They were presented diotically through headphones (Beyerdynamic DT 770 PRO; 80 ohms) at a comfortable sound level.
Discussion By providing the ability to manipulate speech and musical dimensions such as pitch, duration and amplitude in a parametric, independent manner in the common environment of the Python programming language, the open-source toolbox CLEESE brings the power of datadriven/reverse-correlation methods to the vast domain of speech and music cognition. In two illustrative case-studies, we have used CLEESE to infer listeners' mental representations of interrogative intonation (rising pitch at the end of the utterance) and of the rhythmic structure of a well-known musical melody, and shown that the methodology had potential to uncover potential individual differences linked, e. g. , to participant's training or perceptual abilities. As such, the toolbox and the associated methodology open avenues of research in communicative behavior and social cognition.
Acknowledgments The authors thank  Etienne Thoret ,  Leo Varnet ,  Vincent Isnard  for useful discussions on the audio signal processing aspects of this work, as well as  Pascal Belin ,  Frederic Gosselin ,  Philippe Schyns  and  Rachael Jack  for discussions on data analysis. We thank  Louise Vasa  for help with data collection. All data collected at the  Sorbonne-UniversiteINSEAD Center for Behavioural Sciences. This study was supported by  ERC  Grant  335536 CREAM  to JJA.
The funder ( H2020   European Research Council)  provided funding for the project to  J-J. A  (Award Number  CREAM -335536 ), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author J. J. B. , affiliated as Independent Researcher, is registered as a sole-proprietorship company in France dedicated to research and development consulting. Its participation to this study was fully funded by the above-mentioned grant.
Soil organic carbon (SOC) is a crucial component of the terrestrial carbon cycle and its turnover time in models is a key source of uncertainty. Studies have highlighted the utility of δ 13 C measurements for benchmarking SOC turnover in global models. We used 13 C as a tracer within a vertically discretized soil module of a land-surface model, Organising Carbon and Hydrology In Dynamic Ecosystems-Soil Organic Matter (ORCHIDEE-SOM). Our new module represents some of the processes that have been hypothesized to lead to a 13 C enrichment with soil depth as follows: 1) the Suess effect and CO 2 fertilization, 2) the relative 13 C enrichment of roots compared to leaves, and 3) 13 C discrimination associated with microbial activity. We tested if the upgraded soil module was able to reproduce the vertical profile of δ 13 C within the soil column at two temperate sites and the short-term change in the isotopic signal of soil after a shift in C3/C4 vegetation.
, 2010; Elliott et al. , 1996). In the long-term, we may use innovative approaches that radically change the soil carbon modules of ESMs  (Abramoff et al. , 2017; Huang et al. , 2018; Luo et al. , 2016)  but such approaches would first require a deep rethinking of the ESMs themselves. In the short-term, we may use C-tracers to better constrain the model parameters, which is something already being done in site-scale models  (Braakhekke et al. , 2014). Using observational isotopic data to constrain soil turnover times is not yet possible for ESMs, because global models usually do not represent either stable carbon or radiocarbon isotopes even though some attempts have been carried out with ORCHIDEE for radiocarbon  (Tifafi et al. , 2018). The Community Land Model (CLM) has fully implemented photosynthetic discrimination, and tracks δ 13 C within the biomass and soil carbon but without post-photosynthetic discrimination  (Koven et al.
, 2005; Keeling et al. , 2017; Tans et al. , 1979; Wynn et al. , 2006). ORCHIDEE-SOM is a recently-developed soil module within the land-surface model ORCHIDEE that represents a substantial advance toward accurate future predictions of global soil carbon fluxes by modeling deep, soil layer-discretized SOC to 2 m  (Camino-Serrano et al. , 2018). However, the model in still in early stages of its development, with attendant uncertainty in both its output and their interpretation, for which data-based stable isotope parameter calibration will help to constrain the parameters involved in carbon transfers and SOC turnover times  (Luo et al. , 2016). Our aim was therefore to incorporate 13 C into ORCHIDEE-SOM and to test if the upgraded model could reproduce the stock of 13 C in the following two cases: 1) observed stock of 13 C at two temperate sites, a forest and a grassland and 2) the short-term change in the isotopic signal of soil after a C3/C4 shift in vegetation at two temperate experimental sites where C4 crops were planted in C3 soils a few decades ago.
The ORCHIDEE-SOM Model: The Starting Point ORCHIDEE-SOM is an extension of the soil module in ORCHIDEE, the global land-surface component of the Institut Pierre-Simon Laplace ESM. ORCHIDEE-SOM starts from the default structure of the ORCHIDEE soil model, based on the CENTURY model  (Parton et al. , 1987)  that represents four litter (metabolic and structural below-and above-ground litter) and three SOC (passive, slow, and active) pools distinguished by their turnover times. Two pools were added to represent dissolved organic carbon (DOC) and labeled stable and labile DOC with low and high turnover times, respectively. The values of the decomposition parameters are based on Table  1  from  Camino-Serrano et al. (2018). In this study, we did not perform any formal data integration due to the computational resources required to run the model.
(1999). For Closeaux and Boigneville, we used the δ 13 C of wheat leaves measured in 2005 as a reference. δ 13 C had not been measured for leaves or litter at Feucherolles or Mons, so we used δ 13 C measured in the uppermost soil layer as a reference for calculating the input δ 13 C for 1901 (Table  2 ). Site-specific measurements were used for pH, clay content, and bulk density. For the European scale simulation we used pH, clay content, and bulk density from the harmonized world soil database (HWSD;  Nachtergaele et al. , 2012)  product.
The overestimation of the modeled SOC in the upper soil layers for the Closeaux site was expected, because the current soil module in ORCHIDEE-SOM does not yet account for the effect of plowing, so the concomitant reduction in SOC stocks cannot be reproduced. Nevertheless, Boigneville has been unplowed for some years, and model overestimation was due to other model errors such as root profile or net primary production.
Journal of Advances in Modeling Earth Systems and CO 2 fertilization effect, 2) 13 C enrichment of root material compared to leaves, and 3) 13 C discrimination associated with heterotrophic respiration. Furthermore, it is important to note that the 13 C entering the soil is not directly calculated by the plant module but instead is imposed directly on the litter pool. To our knowledge, only one global model, the CLM4. 5, represents the stable carbon isotopic tracer in the soil, but unlike the ORCHIDEE-SOM soil module presented in this study, CLM4. 5 does not include any representation of post-photosynthetic 13 C discrimination  (Duarte et al. , 2017; Raczka et al. , 2016). The version presented here is therefore the first soil module that can model the transport of soil 13 C through the soil column and the post-photosynthetic 13 C discrimination at large scales.
Litter δ 13 C was unfortunately not measured at all sites. In Closeaux and in Boigneville litter δ 13 C was measured in 2005 and in 1987, respectively, with values of leaves and straw for the C3 plots (resp. C4) of -27. 9‰ (resp. -12. 1‰) for Closeaux and -26. 3‰ (resp. -12. 5‰) for Boigneville. Our estimation gave values of -28. 4‰ (resp. -13. 2‰) and -27. 1‰ (resp. -13. 3‰) for Closeaux and Boigneville for the corresponding years. We thus recommend that a fully climate coupled photosynthetic discrimination model be included in future experiments and have confidence that the modeled δ 13 C profile in ORCHIDEE-SOM will improve when our soil module is merged with the representation of 13 C in vegetation, since this will give us more realistic litter δ 13 C values. Transitions of vegetation from C3 to C4 photosynthesis (or vice versa) or species shifts that change the input δ 13 C to litter are recorded in the SOC δ 13 C.
We can add more processes and thus model complexity as we continue to formulate and parameterize new soil modules to reduce uncertainty, increasing the accuracy of the newly developed module. Finally, ORCHIDEE-SOM is a model intended for global simulations, although we applied it in this study at site and European-scale. The extrapolation of our results is, however, limited because the model has only been tested for a temperate climate and for loamy soils with slightly acidic-neutral pH. More model validation exercises should be carried out in different ecosystems and climates to fully evaluate model capability before global scale application. Moreover, more effort should be dedicated towards improved model parameterization and optimization procedures which often need large and detailed data sets  (MacBean et al.
, 2016). For SOC-related parameters, we would need full profiles of total SOC and their δ 13 C associated with all the necessary boundary conditions, which are often not available (e. g. ,  Desjardins et al. , 2006; Navarrete et al. , 2016). We therefore encourage the creation of large datasets covering SOC and their associated isotopes. An optimization process may also benefit from the implementations of other tracers like 14 C  (Menichetti et al. , 2016)  or  Cesium-137 (Evrard et al. , 2010). Another version of ORCHIDEE-SOM has already implemented a 14 C tracer  (Tifafi et al. , 2018) , and using both, 14 C and 13 C may be useful for evaluating the model's reproduction of observed emerging properties, such as the age of primed organic carbon at different depths.
Conclusions This study has presented modifications made to ORCHIDEE-SOM for representing the vertical profile of soil δ 13 C, and evaluated the resultant module against δ 13 C observations from four temperate sites: a deciduous forest, a grassland and two experimental sites with C3/C4 crop transitions. The new soil module has shown itself capable of accounting for the Suess effect and CO 2 fertilization, root versus shoot 13 C enrichment, and 13 C discrimination associated with heterotrophic respiration. δ 13 C model-data comparison indicate that the model performed better when simulating the shift in the isotopic signal due to short-term vegetation cover changes (C3 to C4) as compared with simulating the observed stock of 13 C (arising from a combination of long-term processes). We tested the processes individually to assess their relative importance; however, the model underestimates the vertical variation in of δ 13 C across the soil column.
Acknowledgments This study is partially funded by  Agence Nationale de la Recherche (ANR)  Grants  ANR-14-CE01-0004   DeDyCAS  project and  ANR-10 Blanc 605-AGRIPED. Marta Camino-Serrano  acknowledges funding from the  European Research Council Synergy  Grant  ERC-2013-SyG-610028 IMBALANCE-P. B. Guenet acknowledges funding from the  European Union   Horizon 2020  Project  CRESCENDO  under contract  641816. The authors are grateful to  INRA of Mons en Chaussée, Grignon and Arvalis  for providing the access to their long-term experimental site and the associated data, which are available in the articles cited in the main text. The code is freely available here https:// forge. ipsl. jussieu. fr/orchidee/wiki/GroupActivities/ CodeAvalaibilityPublication/ ORCHIDEE_SOM_13C_r4859. We thank  Matthew McGrath  and  Simon Bowring  for their valuable comments on the manuscript.
The cost in human resources to build such corpora is such that unsupervized summarization methods cannot be excluded from the research field. In the last decades, efforts have been made in different fields of computer science to release open source systems that encode several methods. GATE 1   (Dowman et al. , 2005)  platform is an example of such an open source system for NLP. Other research fields such as machine learning benefit from several open source modular systems, e. g. , Weka  (Hall et al. , 2009) , SPMF  (Fournier-Viger et al. , 2014) , or Orange  (Demšar et al. , 2013). To our knowledge, no such tool exists for AS. A modular and open source tool for automatic summarization could allow to easily test different automatic summarization methods on different tasks / corpora. If such a tool is proven to be reliable, it can also be used as an acknowledged baseline for new systems -abstractive or extractive -to compare to.
In fact, we found that some recent papers in neural abstractive summarization compare their results with naive extractive baselines or even no extractive baseline at all (e. g. (See et al. , 2017)  with a lead-based extractive baseline,  (Chopra et al. , 2016)  with no extractive baseline at all). These works could have sorely benefited from a straightforward and easy-to-use summarization platform to establish fair comparisons to older extractive systems. In this paper, we present an open source modular tool dedicated to automatic summarization. Written in Java, it is designed to first answer the lack of such a tool and so provide the community with an easy-to-use summarization tool, to allow a straightforward maintenance of existing modules and development of new modules, and to allow methods comparison in a unified framework.
SUMMA  (Saggion, 2014 ) is a summarization toolkit implemented with GATE. It includes several sentence scorers, such as LexRank, Centroid and shallow features based. It benefits from GATE NLP methods. Sumy 2 is a more complete toolkit that implements eight different extraction methods, including baseline systems (random, first sentences only). As for PKUSum-Sum 3 ,  (Zhang et al. , 2016)  implements ten different methods and handles three different summarization tasks: mono-document, multi-document and topic-based multi-document summarization. Modularity for these two system is however limited to tokenization/stemming and the choice of extraction method which is not decomposed itself in modules. This is the main asset of our tool: it is modular on a fine-grained level so automatic summarization methods are not defined globally but as a combination of small interchangeable modules.