dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Gérard Labonne  for aphid rearing and  Sophie Le Blaye  for plant care,  Yves Prin  for help with histology, and  Takii Seed Company  for providing turnip seeds. We are also deeply grateful to  Takashi Hashimoto  for sending Arabidopsis GFP-TUA6 seeds. Macroscope acquisitions were performed at the MRI imaging platform (Montpellier, France). Thanks to  Anouk Zancarini  for designing the aphids in Figures 1 and11. The language of the manuscript was proofread and corrected by  Brandon Loveall  (http://www. improvence. net). AB acknowledges a PhD fellowship financed by the  SPE department of INRA  and the  Région Languedoc-Roussillon ; AMa thanks the  French government  for a PhD fellowship. MD and AMa thank  Carl Zeiss ,  Soichiro Honda  and  Robert Pirsig  for ZEN and the art of motorcycle maintenance.
Computation of the EP based on geographic vectors In order to compute the Encounter Probability between a vehicle and an event, two movement vectors are defined for a vehicle (computed by sampling the vehicle's locations periodically): the direction vector and the mobility vector. The direction vector allows estimating future positions of the vehicle on a short term, whereas the mobility vector captures an overall impression of the vehicle's direction and allows to estimate future positions on the long term. Each vehicle can compute its direction vector and its mobility vector easily. Similarly, each vehicle can compute the mobility and direction vectors of the events it receives. For that purpose, it uses the data associated to the events, and more precisely the CurrentPosition attribute and either the DirectionRefPosition or the MobilityRefPosition attribute, respectively.
We performed several experiments using real road maps corresponding to the city of Lille in France (see Fig. 6 ). In these experiments, we compared a solution based on sharing data about available parking spaces using VESPA with a solution that also uses the reservation protocol proposed in this paper. Specifically, we measured the improvement in the average search time and the maximum search time when the reservation protocol is also used. Several situations were considered by varying the amount of competition with respect to the existing number of parking spaces in a scenario (to evaluate a challenging scenario, we assumed that 75% of the vehicles in the scenario search for a parking space). The results are shown in Fig. 7 , where we vary the number of vehicles in excess of the number of parking spaces available (averages of 100 tests for each competition scenario are reported).
The network of parking meters (pm-nodes) is considered a distributed database that can be queried by vehicles through a nearby (within communication range) pm-node. They also consider the use of unicast (position-based) routing for drivers to reserve a spot controlled by a certain parking meter. Finally, they consider that the competition between drivers can be managed by enhancing the information provided about available parking spaces with information about potential competitors; this extra information includes data such as the location of the competitors, which may be considered privacysensitive information. In this approach, a vehicle must be within range of some parking meter to receive this information. Moreover, no experimental evaluation is presented. Parking payment terminals (parking automats) are also used in  [10]  to disseminate information about available parking spaces.
Acknowledgments We thank the support of the  French ANR  agency in the scope of the  MURPHY  project and the  CICYT  project  TIN2010-21387-C02-02. We also gladly acknowledge the funding provided by the  Ministry of Education (Ministerio de Educación) in Spain  through the  program  " Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I-D+i 2008-2011" ("José Castillejo ", ref. JC2011-0196 ). Finally, we thank  Dorsaf Zekri  for her help with some aspects related to the simulations.
Root bundle reinforcement The tensile force (root reinforcement) of a bundle of roots is obtained by summing the force contributions from each root multiplied by the survival function S F tot ( x) = N i=1 F (φ i , x) S( x * i ), ( 9 ) where N is the number of roots. This new extension of the RBM is called RBMw. Equation (  9 ) can be rewritten considering the number of roots (n) in a given root diameter classes ( ) of a bundle in the form F tot ( x) = max =1 n F (φ , x) S( x * ), ( 10 ) where φ is the mean root diameter of each root diameter class, max is the maximum root diameter class considered, and x * is the normalized displacement of each root diameter class. The RBMw was implemented in a R code and can be downloaded at the following link: www. ecorisq. org/ openFTP/Schwarz. zip.
Data and calibration of root parameters and survival function To estimate parameters of the tensile force power-law function (Eq. 2), we use three sources of data obtained from the roots of Spruce (Picea abies L. ). Data from field pullout tests on bundle of roots done by  Schwarz et al. (2011)  at Üetliberg were used to compare the behavior of single roots and bundle of roots. In these tests, 5 or more roots with diameters ranging from 0. 9 to 3. 7 mm were pulled in parallel with a testing machine. In order to compare the results of the maximum pullout force with data obtained in laboratory, we performed 43 tensile tests with root segments excavated at the same location where the pullout tests were performed (a forested site dominated by Spruce at Üetliberg near Zürich (Switzerland) -47. 349 Their root diameter ranged from 3.
We recognize two important factors influencing the fitting of the power law curves. The first one is the natural variability of the maximum root tensile force. As the model sensitivity ultimately depends on the relation between tensile force and root diameter, variability of normalized failure displacement depends also on the width of the cloud points. Variability of maximum root tensile force must be checked and residuals analysis becomes important to exclude outliers and diameter classes that are not represented by a minimum number of data points. The second factor arises from the type of algorithm used for fitting the diameter-force relationship (Eq. 2). For instance, algorithms implemented in R software or in Microsoft Excel use different fitting methods that yield M. Schwarz et al. : Root reinforcement using Weibull distributions !"""""""""#"""""""""$" !"""""""""#"""""""""$" !"""""""""#"""""""""$" !"""""""""#"""""""""$" !"""""""""#"""""""""$" !"""""""""#"""""""""$" !"""""""""#"""""""""$" ! %"#"&&" $"&&" '"&&" ("&&" )"&&" *"&&" +"&&" surprisingly different results (see Appendix A).
Sensitivity of the RBMw to power law fitting of the root diameter -force data In order to verify the influence of the force-diameter method to fit the power law curves on the calculation of the normalized displacement, we fitted the diameter-force curves for different number of class diameter, excluding classes with few samples, and using two softwares (Excel and R). Hesse (2006)  argues that the exponential functions in  Excel (2008)  are done incorrectly because they use logarithmic transformations, whereas R 2. 15. 3 performs a gradient search for the optimal coefficients. The results shown in Fig. A1  indicate that the different methods lead to quite different values of the equation parameters. Summarizing, small changes in the fitting of the root diameter-force curve lead to considerable changes in the normalized scaling factor (λ * ), and thus in the value of normalized failure displacement ( x * ) and exponent (ω) of the Weibull survival function.
SPIRE data All 12 sources were also observed at 250, 350, and 500 µm with the Spectral and Photometric Imaging Receiver (SPIRE;  Griffin et al. 2010). The scan maps of each source were obtained simultaneously at all three bands at the nominal scan speed of 30 /sec and over a (16 -20 ) 2 area, resulting in a total integration time of 10 -16 mins per map. These data were processed up to level 1 with HIPE v. 5. 0. 1892 and calibration tree 5. 1, which was the most up-to-date version at the time all data became available. In Sect. 6. 1, we show that using the newest HIPE v. 9. 1. 0 only leads to nonsignificant minor changes in the SPIRE maps that are well below all other uncertainties. For this reason, we considered it unnecessary to re-reduce the data. Up to level 1 (i. e. , the level where the pointed photometer timelines are derived), the steps of the official pipeline (POF5 pipeline.
py, dated 2. 3. 2010) provided by the SPIRE Instrument Control Center (ICC) have been performed. The level 1 frames were then processed with the Scanamorphos software version 9  (Roussel 2012). All maps were reduced using the 'galactic' option. The final map pixel sizes are 6, 10, and 14 at 250, 350, and 500 µm, respectively. Photometric color corrections for both PACS and SPIRE data, that account for the difference in spectral slopes between flux calibration (flat spectrum) and actually observed source spectrum, are applied only in the data analysis and are described in Sect. 4. 1.
For one source (CB 27), we could obtain only one coverage. Effective map sizes (central pixel coverage) were adapted to the individual source sizes and were in the range 5 -10 , resulting in total mapping times per coverage of 40 min to 1. 5 hrs. The raw data were reduced using the standard pipeline "mapCSF" provided with the MOPSIC software 6. Basic reduction steps include de-spiking from cosmic ray artifacts, correlated signal filtering to reduce the skynoise, baseline fitting and subtraction, restoring single-beam maps from the dual-beam scan data using a modified EKH algorithm  (Emerson et al. 1979) , and transformation and averaging of the individual horizontal maps into the equatorial system. Correlated skynoise correction was performed iteratively, starting with no source model in the first run.
The assumption of a flat-spectrum source in the definition of the nominal wavelength of the MAMBO detectors in combination with the broad bandpass made it necessary to apply a "color correction". In contrast to the Herschel FIR observations, (sub)mm wavelengths are sufficiently close to the Rayleigh-Jeans regime and the emission is optically thin for all our sources, such that the slope of the SED follows S ν ∝ λ -4 (for the dust opacity submm spectral index β = 2). Therefore, we follow  Schnee et al. (2010)  and correct the reference wavelength for all MAMBO data to 1100 µm. Color corrections for the SCUBA data are negligibly small (cf. Schnee et al. 2010)  and were not applied. We also verified the pointing in the final maps by comparing the peak positions with interferometric positions where possible  (CB 26, CB 68, CB 230, CB 244, B 335, BHR 12)  and found that deviations are in all cases smaller than 2 , hence confirming the pointing stability and making additional pointing corrections unnecessary.
The final maps of those sources for which we had both old and new 1. 2 mm data  (CB 6, CB 17, CB 26, and CB 230)  were generated by weighted averaging, after adapting beam sizes and correcting small pointing offsets. Additional complementary NIR extinction maps as well as different molecular spectral line maps were also obtained with the aim of studying the relation between density, temperature, and dust properties on the one side, and gas phase abundances and chemistry on the other side. These latter data will be described and analyzed in forthcoming papers and are not presented here.
iram. fr/IRAMFR/GILDAS 9 The Digitized Sky Surveys were produced at the Space Telescope Science Institute under U. S. Government grant NAG W-2166. The images of these surveys are based on photographic data obtained using the Oschin Schmidt Telescope on Palomar Mountain and the UK Schmidt Telescope. The plates were processed into the present compressed digital form with the permission of these institutions. tail-like extensions like, e. g. , in CB 17 (Fig. 14 ), the ∼18 SPIRE maps usually cover the entire extent of the detectable FIR dust emission down to the more or less flat background levels. The slightly smaller PACS maps (∼10 ) also usually cover the entire extent of the clouds, but in some cases cut off a bit more of some filamentary or tail-like extensions (e. g. , Fig. 14 ). The PACS 100 µm maps often show significantly fainter or less extended emission than the maps at longer wavelengths, owing to the fact that 100 µm samples the steep short-wavelength side of the SED at the low temperature of the dust in these clouds (Fig.
10), as are the core masses and luminosities listed in Table  6. Solid lines between 3 mm and 100 µm mark the modified blackbody fits to the data points, while dashed lines only represent a logarithmic interpolation of the data points shortward of 100 µm (see Sect. 5. 3). available and appropriate, we also included ground-based optical and NIR data, Spitzer IRAC and MIPS maps, as well as ISO and IRAS fluxes (see  Launhardt et al. 2010). The resulting SEDs where fitted between λ 100 µm and 1. 2 mm in the same way as the individual image pixel SEDs (Sect. 4. 2). To facilitate integration of the SEDs at wavelengths shortward of the SED peak, fluxes at λ ≤ 100 µm were logarithmically interpolated as described in  Launhardt et al. (2010). Bolometric luminosities (L bol ) were derived by integrating the SEDs over the complete wavelength range.
Submillimeter luminosities (L smm ) were derived by integrating the SEDs at wavelengths longward of 350 µm. The resulting total cloud luminosities range from 1. 3 L (CB 17, B 68, B 335) to 29 L (BHR 12), with a mean of L cloud ∼ 6. 7L. See Table  6  and Fig. 7  for the distribution of masses and luminosities. In four out of the twelve globules, the Herschel and submm maps reveal multiple cores (CB 26, BHR 12, CB 130, CB 244all known before to be multiple), while the other eight globules appear single-cored at the Herschel resolution. In the tempera-Fig. 3. Radial profiles of column density and LoS-averaged dust temperature of B 68 (cf. Fig. 9  of  Nielbock et al. 2012  for the ray-tracing results of the same data). Small black dots show the data pixel values over radial distance from the column density peak (see Fig.
Constraints on the interstellar radiation field We also use the observed outer dust temperatures and halo column densities (N out ) of our sources to derive constraints on the mean ISRF. For this purpose, we compare the observed temperature profiles of the cores in this paper to a grid of 1D radiative transfer models  (Shirley et al. , in prep. ). Dust temperatures are self-consistently calculated using the CSDUST3 code  (Egan et al. 1988 ) for an input density profile, input dust opacities, and input heating conditions. The density profiles are isothermal BES with the BES temperature derived iteratively at the half-central density point of the profile from the radiative transfer calculation. The central densities of the BESs vary from 9 × 10 3 cm -3 to 6 × 10 6 cm -3 with an outer radius set to 30,000 AU.
Acknowledgements. The authors gratefully acknowledge the valuable help from  Hélène Roussel  in the production of Scanamorphos PACS and SPIRE maps as well as  Gonzalo Aniano 's assistance and careful work with the Herschel convolution kernels used in this work. We greatly benefitted from discussions with  Yaroslav Pavlyuchenkov  (Moscow) on the data analysis. We also thank the anonymous referee for comments and suggestions that helped to improve the clarity and completeness of the paper. Part of this work was supported by the  German Deutsche Forschungsgemeinschaft ,  DFG  project number  Ts 17/2-1. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
This article describes the development and application of an integrated, generalized, and efficient Monte Carlo simulation system for diffusion magnetic resonance imaging (dMRI), named Diffusion Microscopist Simulator (DMS). DMS comprises a random walk Monte Carlo simulator and an MR image synthesizer. The former has the capacity to perform large-scale simulations of Brownian dynamics in the virtual environments of neural tissues at various levels of complexity, and the latter is flexible enough to synthesize dMRI datasets from a variety of simulated MRI pulse sequences. The aims of DMS are to give insights into the link between the fundamental diffusion process in biological tissues and the features observed in dMRI, as well as to provide appropriate ground-truth information for the development, optimization, and validation of dMRI acquisition schemes for different applications.
On the other hand, it has the potential to build a ground-truth database to support the development and application of dMRI studies (see below). One of the most outstanding contributions and applications of dMRI is its ability to visualize anatomical connections between different brain areas, non-invasively and on an individual basis, which has emerged as a major breakthrough for neurosciences  [31, 32, 33, 34]. There are two essential procedures to create a reliable map of brain connectivity using so-called fiber tractography: The first step is to accurately estimate fiber orientations using an adequate diffusion reconstruction method, and the second step is to implement a robust fiber-tracking algorithm. Obviously, it is necessary to have an appropriate model to serve as a ''gold standard'' for assessment and validation of these processes.
g. fiber curvature). Furthermore, the WM structures of animal models cannot capture all of the fiber configurations that exist in human brains, and thus it may not be sufficient to evaluate the inherent limitations of diffusion models and fiber-tracking algorithms. (ii) A physical phantom is able to provide experimental datasets acquired using practical dMRI setting and is more flexible than an animal model in terms of the geometric designs. The physical phantoms can be broadly classified into two types according to material: the hollow capillary  [37, 38, 39, 40, 41]  and the synthetic fiber  [42, 43, 44, 45]. The advantage of the former is that it has a diameter closed to the scale of axonal fibers (,10 mm) and is able to capture the nature of intra-and extra-axonal diffusion. However, it is not feasible to build complex configurations (e.
g. bending fibers). On the contrary, the latter is highly flexible to construct curving structures similar to WM fibers, whereas it is limited to simulate the extra-axonal compartment. Note that both of the above materials lose the properties of biological tissues, such as membrane permeability and local viscosity. (iii) Numerical simulations have been widely chosen to generate synthetic dMRI datasets  [46, 47, 48, 49] , however, most of the numerical simulations usually rely on a number of assumptions on tissue models and pulse sequences. The Gaussian mixture model, for example, is commonly used to generate diffusion-weighted (DW) signal. Although the tensor model provides a good approximation, the Gaussian assumption is not sufficient to model diffusion anisotropy observed in living tissues, which is generally acknowledged to the results of restrictions and hindrances to the movements of water molecules  [24].
Monte Carlo Simulator Scene modeler. This module contains the essential parameter settings for MC simulations, including the spatial dimensions, the spatial subvolumes (see below), the temporal resolution (i. e. the simulation time step, t s ), the number of iterations (N I ), and the global diffusion model. The scene modeler acts as an interface between the users and the DMS system. It enables users to construct a virtual tissue environment by adding cell membranes and diffusing particles (i. e. water molecules), and to control the start-up of MC simulations. It also contains the functions to save the results of particle diffusion data (e. g. the trajectory and probabilistic density function of water molecules) as well as to animate and visualize the dynamic events of MC simulations. Cell membrane.
The purpose was to demonstrate that DMS could reproduce the simulation results shown in the literature  [39]. Accordingly, we created a simulation space (460640061,000 mm 3 ) where a bundle of parallel impermeable fibers with a diameter and length of 19 and 1,000 mm respectively were placed on a 24624 hexagonal lattice using a center spacing of 19. 1 mm. Each of the fibers was modeled using a cylindrical mesh built by 40 triangles. We performed random walk MC simulations using N p = 5610 5 , N I = 10 5 , t s = 5 ms, and N x 6N y 6N z = 1606160620. D ( = 2610 23 mm 2 /s) was assumed to be equal in the intra and extracellular spaces. Synthetic dMRI datasets were then collected using both single and double PGSE sequences with d/D/T M = 2/ 200/0 ms. A range of G (S R = 5,000 T/m/s) from 0 to 1,200 mT/ m with a 10 mT/m increment were applied for the single PGSE, and from 0 to 600 mT/m with a 5 mT/m increment were used for the double PGSE.
The RMS distances were 0. 19 and 0. 11 mm for D fast and D slow , respectively. For each of the MC simulations, two noise-free synthetic dMRI datasets were collected using single PGSE sequences: Firstly, the DT datasets were synthesized using a single shell q-space sampling scheme of 80 gradient orientations at a b-value of 1,000 s/mm 2 , where d/D = 21/27 ms, G = 40 mT/m, and S R = 200 T/m/s. We performed DT reconstruction to estimate the ADC  [4]. Secondly, the DW signal along x-, y-, and z-axis were synthesized at 51 bvalues linearly increased from 0 to 5,000 s/mm 2 , which were achieved by fixing d/D = 2/70. 5 ms and varying G (S R = 5,000 T/ m/s). The normalized diffusion signal attenuation along each axis was then fitted using a biexponential function given as follows: S(b)=S 0 ~Ff exp ({bD f )zF s exp ({bD s ) ð6Þ In Eq.
Application on HARDI and Fiber Tractography In this section, we demonstrated that DMS could produce ground-truth dMRI datasets of different designs of fiber distribution. Numerous conditions can be simulated for evaluation and comparison of diffusion reconstruction models and fiber-tracking algorithms (e. g. modifying the signal-to-noise ratio or the fiber density). Our results shown in Figs. 8-9 revealed that the fiber tractography based on SDT and probabilistic fiber-tracking method could distinguish complex fiber pathways. However, using the deterministic approach produced the false-positive streamlines that did not match with the ground-truth fiber configuration, as illustrated in Fig. 9(b ). In addition, a part of fibers passing through the area ''i'' (see Fig. 9 (a)) were missing, which implied that the results of the subsequent tract-based processing such as fiber clustering or network analysis could be affected while using the deterministic fiber-tracking algorithm.
Even in mild AD, these neuropathological changes cause neuronal loss in the entorhinal cortex and hippocampus  [6] , and these changes result in decreased volume  [7]. Many magnetic resonance imaging (MRI) studies have indeed reported smaller volumes in AD patients than in controls  [8] [9] [10] , which indirectly reflects faster atrophy in AD. However, a more direct way of measuring the atrophy is the use of repeated MRI scans within the same individual. This can then be used to monitor the disease progression in patients. More and more volumetric studies used longitudinal dataset and reported a higher rate of hippocampal volume loss in patients with AD than in elderly controls  [11] [12] [13] [14] [15] [16] [17]  However, global hippocampal volumetry is not always sensitive enough to follow changes within a single population  [18] , which may reflect conversion from healthy state or disease progression.