dataesr/scientific-paragraphs-categorization huggingface dataset:
For the 13 targets, a large difference between the filtered and unfiltered data is apparent, which is not the case for the data when there were five targets. This is also confirmed by the Univariate ANOVA test presented in Table  4 , which also shows the interaction effect between filtering and the type of target. Although the overall values for the five-target stimuli are much lower, no significant difference is found. The difference between the avg and raw values is also not found to be significant. Besides the manual filtering, the application of EyeMMV's fixation detection algorithm (which is described in Section 3. 4) also includes a noise reducing component. This procedure is-next to the manual filtering-thus an alternative option to remove noise and at the same time aggregate the data.
The raw data for the 13 target points also shows a significant improvement when applying the algorithm after filtering, which is not the case for the avg data set. Overall after applying the fixation detection algorithm, the deviations are somewhat higher for the five-point targets compared to these of the 13 targets. EyeMMV's algorithm allows defining spatial parameters in order to filter out the noise in the data. The results for the parameters ranging from 0. 7 to 1. 3 are presented in Figure  12  and compared with the original data before the application of the fixation detection algorithm (filtered or not). Overall, it can be noticed that the deviations are somewhat higher for the 5 target points. The associated statistical analysis (based on repeated measures, see Table  5 ) reveal the variation in the deviations is significant for the 13 target points when applying the fixation detection algorithm on the unfiltered data.
The raw data for the 13 target points also shows a significant improvement when applying the algorithm after filtering, which is not the case for the avg data set. Overall after applying the fixation detection algorithm, the deviations are somewhat higher for the five-point targets compared to these of the 13 targets. quality of the eyes itself. Furthermore, the effect of the different viewing distances is clearly present, with a decreasing deviation in case of an increasing viewing distance. Moreover, the obtained values produced from the use of different monitor set-ups (Figure  7 ) range between 0. 1 • to 0. 7 • (with an average of 0. 25 • ) which is conform the accuracy values provided by the vendor. No clear trend in the deviations is found when considering the different sampling rates or monitors and their resolutions.
The recordings for the RP were verified according to their precision (deviation from the real target position) and accuracy (clustering of the recordings or deviation from the average location). The results show a consistently better value regarding precision than accuracy. The deviations in accuracy where especially higher in the X-direction compared to the Y-direction. This latter finding can (only partially) be explained by the fact that the monitor's dimensions are larger in X compared to Y. The manual filtering step, based on a few simple criteria, already significantly improved the precision of the data average of less than 1 • for the 13 point targets and around 1. 5 • for the 5 point targets. However, in the latter case, the effect of the manual filtering step is limited as the unfiltered data already showed smaller deviating values.
Acknowledgements The present work was gratefully supported by the  French Research National Agency (ANR)  through the  ''Villes et Batiments Durables" program  (Project  Primaterre  no. ANR-12-VBDU-0001 ). The first author was also given support by the  China Scholarship Council (CSC)  with a PhD Scholarship (File No. 201406300070 ) for his research work.
6 mm. We used VGStudio 2. 0 to visualise the datasets. We based sedimentological observations on X-ray CT scans and visual analysis of split cores. Laser diffraction using a Beckman Coulter LS 13 320 particle size analyser with aqueous liquid module provided grain size distributions for sand-rich intervals from cores JSH1/F, JSH3/F and JSH3/O and the six surface samples. We analysed 5 mm-thick samples at 5 mm or 10 mm intervals. Sample preparation involved the addition of hydrogen peroxide to remove organic matter, with sodium hexametaphosphate used as a dispersant. We analysed grain size distributions using the geometric method of moments in GRADISTAT v. 4  (Blott and Pye, 2001).
(2016). We summarise diatom assemblages into five groups based on their tolerance to salinity (cf. Lowe, 1974; Hemphill-Haley, 1993; van Dam et al. , 1994) : marine, brackish, freshwater (low salinity), freshwater (salt tolerant) and freshwater (salt intolerant). We analysed pollen and non-pollen palynomorphs from a total of 15 fossil samples from cores JSH3/F and JSH3/O and the contemporary surface of the paddy field. Samples were processed using standard techniques for pollen analysis  (Moore et al. , 1991). Identifications are based on  Beug (2004)  and  Demske et al. (2013)  for pollen and  Van Geel (1978; 2001)  for other palynomorphs. We present the data as percentages relative to the sum of all terrestrial pollen types.
Geospatial data To investigate the occurrence and timing of recent terrestrial mass movements, we analysed aerial photographs taken in August 1947 (US Air Force sortie M415-1, scale 1:40,000) and May 1959 (Geographical Survey Institute, sortie P28, scale 1:28,000), accessed through the online data service of the Geographical Survey Institute (http://maps. gsi. go. jp). The photographs were orthorectified and analysed using Imagine OrthoBASE Pro 8. 6 and Stereo Analyst 1. 3  (Leica Geosystems, 2002a, b).
Acknowledgements We thank landowners in the study area for allowing us access to their land, Martin Seeliger, Stephen Obrochta, Gen Nagano, Laura Lamair and Eisuke Ono for their help in the field, Thomas Govaerts for preparing samples, Maarten Van Daele for providing his CT scanning expertise and Yoshiki Sato, Yuki Sawai and Tomonori Naya for helpful discussions over diatom taxonomy. This paper is a contribution to IGCP project 639 and the INQUA focus group 'Late Quaternary records of coastal inundation due to earth surface deformation, tsunamis, and storms'. Funding This research was undertaken under the auspices of the QuakeRecNankai project, funded by the Belgian Science Policy Office (BELSPO BRAIN-be BR/121/A2).
The system: L-tyrosine ethyl ester The subject of this study, the aforementioned L-tyrosine ethyl ester is a prodrug for the delivery of the amino acid L-tyrosine in the human body. The esterification increases the lipophilicity of the amino acid improving absorption in the intestines. 25, 26   ] [28]  The molecule consists of a hydroxyl phenyl moiety linked through an additional carbon atom to the amino acid moiety and an ethyl ester group (see Figure  1 ). The ethyl ester tail exhibits two main conformations leading to crystalline trimorphism (Table  1 ). 29 The first structure, that of polymorph I, was first described in 1970 for the stereoisomer D-tyrosine ethyl ester (Cambridge Structural Database (CSD): TYREST) *. 30 ts crystal system is orthorhombic with space group P212121, as is the case for the other two polymorphs.
Synchrotron X-ray diffraction Diffraction data were collected at the high-pressure diffraction beamline PSICHE at the synchrotron SOLEIL (Gif sur Yvette, France). The diffraction data were obtained in two separate experiment runs with different wavelengths: 0. 4499 and 0. 4859 Å. The temperature was controlled with a liquidnitrogen cryostat and an in-house constructed heater. Diffraction images were treated with the program fit2D. 34 easurements have been carried out by varying the pressure at a set temperature (200K, 250 K, 294 K, 323K, and 337 K, and a few measurements at 303 K). The sample was allowed to equilibrate before each measurement for about 15 minutes, which was extended to 1 hour at 200 and 250 K. Pawley refinement has been carried out using TOPAS-Academic  35, 36  on all diffraction profiles of forms I, II, and III and the unit-cell volumes have been extracted and presented below as a function of pressure and temperature.
Specific volumes of forms I, II, and III as a function of pressure and temperature From these data and those published previously, the unit cell volumes of forms I, II, and III have been determined as a function of pressure and temperature. The unit cell volume of form I is presented in Figure  3a. It can be seen that the dependence for form I in the pressure interval up to 400 MPa Above 850 MPa, the unit cell data fall roughly on a single line for all different temperatures: VII (>850 MPa) = 1053(3) -0. 0349(13) P ( 7 ) The data is not necessarily exactly the same for the different temperatures, but within the present experimental setting the variation is indistinguishable. The unit-cell volumes of form III as a function of pressure and temperature are provided in Figures  4b  and 5. At higher pressures, above 1000 MPa (Fig.
Birch-Murnaghan fits The equations relating the unit-cell volume (or specific volume) directly with the pressure are convenient to interpret and construct the phase diagram discussed below, however these simple polynomials do not provide any information on the bulk modulus. The latter can be obtained with the Birch-Murnaghan expression of which the 4 th order equation can be found in the supplementary information (SI. 21). Fits on the data have been carried out with the program EosFit7 by Angel et al. 37, 38  The resulting values are compiled in Table  2 : the unitcell volume at zero pressure for a given temperature (V0T), the bulk modulus at zero pressure for a given temperature (K0T), its first derivative with respect to the pressure(K'0T), and its second derivative with respect to the pressure (K''0T).
We find that in all bands the metal-poor SMC Cepheids are intrinsically fainter than their more metal-rich counterparts in the LMC and MW. In the K band the metallicity effect is -0. 23 ± 0. 06 mag/dex while in the V, (V -I) Wesenheit index it is slightly stronger, -0. 34±0. 06 mag/dex. We find some evidence that the PL relation zero point-metallicity relation might be nonlinear, becoming steeper for lower metallicities. Conclusions. Using sizeable Cepheid samples in the MW, LMC and SMC with very accurate photometric and radial velocity data we establish the metallicity sensitivity of the Cepheid PL relations in the optical and near-infrared regimes. We find a significant effect in all bands in the sense that the more metal-poor SMC Cepheids are intrinsically fainter than their LMC and Galactic counterparts.
The K-band photometric data The K-band data presented in this paper were collected with the ESO NTT telescope at the La Silla observatory in Chile, equipped with the SOFI near-IR camera  (Moorwood et al. 1998). With the "LARGE_FIELD_IMAGING" setup that we used,   Udalski et al. (2008)  the field of view was 4. 9x4. 9 arcmin, with a scale of 0. 288 arcsec/pixel. A total of 754 data points were collected including the data for three additional stars (OGLE-SMC-CEP-1413, -1696, and -1812) for which we do not have radial velocity data. All the data is given in Tab. 2. The observations were performed between  Oct. 10, 2012 and Sep. 23, 2017. 26  selected Cepheids were grouped in 16 fields, containing from one to four Cepheids in each field. During this period each field was observed between 17 and 33 times.
The VMC data is on the VISTA system so we convert it first to the 2MASS system as described in  Ripepi et al. (2016)  and then to the SAAO system as described in  Carpenter (2001). We then applied small zero point shifts to the VMC magnitudes for each star to ensure the best agreement between the two data sets. One star (OGLE-SMC-CEP-1365) showed a significant offset of +0. 18 mag. For the remaining stars the mean offset is only -0. 01 mag with a standard deviation of 0. 01 mag which gives us confidence that the photometric zero point is very well established. Our K-band photometric data is tabulated in Tab. 2 and light curves for stars can be found in the figures A. 1-A. 26 in appendix A. Where appropriate the VMC data with the proper zero point offsets applied have been overplotted in those figures.
The Harvard variables from earlier studies For the five stars which have been studied earlier in  Storm et al. (2004b)  we have used the data from  Storm et al. (2004a)  and  Welch et al. (1987) , and for HV837 we have used the data from  Udalski et al. (2008)  (V-band) and  Laney & Stobie (1986)   (K-band). The cross-identification with the OGLE catalogue is given in Tab. 3. For all these stars the data have been supplemented with the VMC  (Ripepi et al. 2016 ) and OGLE  (Udalski et al. 2015; Soszyński et al. 2015)  data. The star HV1345 showed a phase shift corresponding to 0. 3 days between the old data and the newer data, we have shifted the the old data accordingly. For the V-band data we have kept the old photometric zero points as they were in good agreement for three of the stars, but for the two stars HV1333 and HV1345 we shift the OGLE-IV data by -0.
04 and -0. 05mag respectively. Where necessary we have shifted the old K-band data to the VMC system appropriately shifted to the SAAO system as described in the previous section. In the case of HV 1345 the shift was +0. 11 mag while for the remaining stars the shifts were on average -0. 01 mag with a standard deviation of 0. 03 mag. Finally we have shifted these K-band magnitudes by the mean VMC offset of -0. 01 mag to be consistent with the adopted SOFI photometric zero point.
We appeal to continuity arguments and try to fix the offset for a given epoch by requiring data points at the same phase but for different epochs give approximately the same radial velocity. We thus assume that the variation of the orbital motion is small for a certain period of time, typically hundreds of days, and thus that the orbital period is much longer than the pulsational period. In appendix B we present the detailed analysis for the individual stars. The stars A&A proofs: manuscript no. ms  in question are OGLE-SMC-CEP-1680, -1686, -1693, -1729, -1977, -2905 and HV837. In the case of OGLE-SMC-CEP-1686 the radial velocity amplitude is quite low and it was difficult to find consistent velocity offsets so we prefer to leave the data unchanged for this star. In the case of OGLE-SMC-CEP-1693 the offsets seems to be large but we did not manage to find sensible offsets and this star had to be disregarded in the further analysis.
5 do not in any way rely on an ensemble distance to either the LMC nor the SMC but relies on the individual distance estimates for each star. We are thus entirely free of influence from any depth effects in the clouds. The main remaining limitation is that we still rely on mean metallicity values for each sample. The new data presented here allows us for the first time to determine the PL relations directly for SMC Cepheids. We now have SMC and LMC samples of comparable size and spanning the full range of periods for classical fundamental mode Cepheids. The slopes are in excellent agreement with the slopes for the LMC and MW samples and the large sample allows us to constrain much better the metal poor magnitude zero point as was the case in  Storm et al. (2011b). In fact the estimated uncertainties on the metallicity effect is now pushed down to 0.
Our data suggests that the change of the PL relation zero points with metallicity might not be entirely linear in the different studied bands, but might become steeper for lower metallicities. We stress that our IRSB analyses of the Cepheids in MW, LMC and SMC samples have been carried out following identical procedures leading to a strictly differential analysis between the absolute magnitudes of the Cepheids in the three galaxies, making our results and conclusions independent of eventual systematic errors on the distances due to imperfections in the technique. We have also shown that there is no systematic offset between the reddening scales adopted for the SMC W. Gieren et al. :  The effect of metallicity on the Cepheid PL relations Cepheids in the present work, and for the LMC Cepheids in our previous work, which combined with the reddening insensitivity of the method itself means that reddening effects on the final relations for the K-band and Wesenheit indices are negligble.
Acknowledgements. W. G. and G. P. gratefully acknowledge financial support for this work from the  BASAL Centro de Astrofisica y Tecnologias Afines (CATA)   AFB-170002. W. G. , D. G. and M. G. also gratefully acknowledge financial support from the  Millenium Institute of Astrophysics (MAS) of the Iniciativa Cientifica Milenio del Ministerio de Economia ,  Fomento y Turismo de Chile , Project  IC120009. This research has also been supported by funding from the  European Union 's  2020 research and innovation program  (grant agreement No. 695099 ). We are greatly indebted to the staffs at the  European Southern Observatory La Silla  and Paranal sites, as well as the staff at  Las Campanas Observatory , for their excellent support during the many visitor mode runs which allowed us to collect the data for this study.
2 (we caution, however, that the small number of  complete particles observed means that these values may not be representative). The 3D nature of a fine region of the cloudy zone was explored through the use of APT (Fig. 4  and SI Appendix, Movies S3-S5). Fig. 4  A and B show APT data from the fine cloudy zone (tetrataenite particles with long diameter of 30 nm or less). These are two datasets collected from the same needle (additional APT studies of other parts of the cloudy zone are provided in SI Appendix). Isosurfaces defined by a Ni content of 32. 5% identify 260 tetrataenite islands within the APT needle; of these, only 8 of these volumes are fully contained in the tip shown in Fig. 4A. For these particles, we estimate the major axis of the best fitting ellipse at 20 nm, based on the extent of the isosurface.
4  A and B and SI Appendix). The transition from tetrataenite to matrix corresponds to a sharp compositional gradient that is just 2 nm wide, as seen in the proxigrams in Fig. 5A. The average composition of tetrataenite particles is found to be 49. 61 ± 0. 16% Fe, 50. 00 ± 0. 16% Ni, and 0. 39 ± 0. 02% Co. The matrix phase is found to be 81. 95 ± 0. 15% Fe, 17. 69 ± 0. 16% Ni, and 0. 36 ± 0. 02% Co. The error presented is equal to 2 SD based on counting statistics. The chemical composition information contained in the 3D-EDS datasets was also examined (see Materials and Methods) and agrees very well with APT measurements (albeit with significantly greater uncertainties). Our quantification of the 3D-EDS measurements produces an average spectrum for each phase, as seen in Fig. 5B. Cliff-Lorimer quantification of the average spectra gives the composition of coarse cloudy zone phases as tetrataenite at 52 ± 2% Fe, 48 ± 2% Ni, and the matrix at 84 ± 2% Fe, 16 ± 2% Ni (errors reflect uncertainties in X-ray counting statistics).
Likewise for the medium cloudy zone, we observe the tetrataenite composition to be 52 ± 2% Fe and 48 ± 2% Ni, with the matrix phase measured at 85 ± 2% Fe and 15 ± 2% Ni. The elemental compositions of islands and matrix do not change significantly between the coarse, medium, and fine regions (Fig. 5C ). Instead, the variations in both APT and 3D-EDS datasets correspond to the uncertainties in the quantification methods. Ultimately, APT provides us with the most accurate chemical picture of the cloudy zone, whereas 3D-EDS provides us with the most accurate geometric information. Crystallographic Analysis of the Cloudy Zone. Scanning precession electron diffraction (SPED) allows a 2D array of electron diffraction patterns to be recorded with a step size of 5 nm, while minimizing dynamical scattering effects and increasing the recorded area of the Ewald sphere.
SPED experiments produce a series of diffraction patterns, used to map out the distribution of tetrataenite c-axis orientations as well as to identify an ordered superstructure in the matrix phase. Recording a diffraction pattern at every pixel results in a statistically dense dataset, to which we applied machine learning strategies to deconvolve signals arising from overlapping features and noise. Our approach relies on first denoising the data using PCA. The processed maps are then analyzed using a cluster analysis scheme, to isolate and identify regions that are most self-similar (see Materials and Methods). A 110 lamella was tilted parallel to 100 for the collection of SPED data. The effective lamella thickness at this tilt angle inevitably leads to the overlap of multiple islands in the recorded data, requiring statistical unmixing postacquisition.
The improvement in chemical analysis is explained by the more sophisticated methods of 3D-EDS quantification used here, combined with the enhanced capability to isolate signals from each phase through the use of high-resolution APT. This brings our composition data into agreement with the work of Miller et al. (27) , who examined meteorites with a similar cooling history. Our results contrast with the APT results of Rout et al. (26) , who examined a more rapidly cooled IVA meteorite. This suggests that the composition of the matrix phase is a function of the cooling rate, with slower cooling rates leading to a more Fe-rich matrix, consistent with expectations based on the low-temperature phase diagram  (2). Morphologically, we observe distinct characteristics in the coarse, intermediate, and fine cloudy zones.
The EDS tilt series collected produces a statistically dense spectral dataset. Integrated Fe and Ni spectral peak intensities were extracted from the EDS tilt series using spectral and machine-learning tools in the open source Python library, Hyperspy  (37). The spectra were denoised using PCA and background-subtracted before performing the intensity integration on the two respective elemental peaks. Intensity integration produces a grayscale tilt series for each element, where the peak intensity is proportional to the quantity of iron or nickel present in each pixel. The tilt series are aligned using TomoJ  (38)  and then reconstructed using an in-house CS algorithm  (14, 15). The two resulting volumes were individually segmented using a random walker algorithm  (39) , and the resulting volumes were then compared with the structural morphologies for tetrataenite and matrix phases seen in Figs.
2  and 3. Morphological analysis was performed on the segmented Ni maps, as these provided a stronger contrast between tetrataenite particles and the Fe 3 Ni matrix. CS reconstruction preserves the boundaries and physical morphologies of features found in the tilt series. However, the resulting grayscale values in the reconstruction are not linearly preserved. This results in uncertainty for the use of the reconstructed grayscale information for EDS quantification. We addressed this uncertainty by developing an alternative method for the quantification of 3D chemical data. First, the CS reconstruction volume was segmented to label voxels as either Ni-rich or Fe-rich. These chemical phase-specific subvolumes were then reprojected, using a discrete Radon transform in Sci-Kit Image (Python), giving a phase-specific thickness map at each tilt angle  (40, 41).
These thickness maps were registered to the raw EDS spectra using image-processing routines in Matlab. The reconstruction problem was then recast as a system of linear equations, assuming the observed spectra were a linear combination of signals arising from the island and matrix phases. At each energy channel, the 348,000 simultaneous equations provided for an overdetermined problem to recover the coefficients corresponding to the intensity at each energy channel for each of the two phases. Across the entire spectrum, this analysis determined the characteristic EDS spectra of the island and matrix phases based on the physically segmented tomography volume (Fig. 3A ). Cliff-Lorimer methods were used to quantify the Fe-Ni ratio from the resulting EDS spectra. We estimate the relative uncertainty in the ratio to be at 2% based on counting statistics.