dataesr/scientific-paragraphs-categorization huggingface dataset:
The g/V-band light curve comparison and g-r/V-R color comparison are shown in the middle and bottom panel of Figure  3 , respectively. In order to compare photometric data in the AB and Vega systems, the V-and I-band magnitudes have been shifted to the AB magnitude system by applying a zeropoint correction. In the i/I band, SN 2016dsg is less luminous than normal SNe Ia and SN 2016jhr, brighter than some Castrong transients, and comparable to 1991bg-like transients  (SN 1991bg and SN 2016hnk) , OGLE-2013-SN-079 and SN 2018byg. The i-band decline rate of SN 2016dsg is around 0. 077(0. 003) mag day -1 , similar to those of single/double detonation candidates and slightly faster than other objects. In the g/V-band, SN 2016dsg shows a faster decline rate than other objects except for OGLE-2013-SN-079.
(2017)  using the Monte Carlo radiative transfer code TARDIS  (Kerzendorf & Sim 2014). They predict that a high-velocity He I λ10830 line can be detected As discussed in Section 3, Sim0. 58+0. 21-D at day 4. 7 can reproduce the optical spectra of SN 2016dsg well. In order to examine whether a strong high-velocity He I feature present in SN 2016dsg, we generated a new model spectrum at 16 days after the i-band peak based on the model setup described in  Boyle et al. (2017)  with their low-mass model (Sim0. 58+0. 21-D). Following  Boyle et al. (2017) , the inner velocity boundary in TARDIS is set to be 6250 km s -1. A comparison between this model spectrum and the observed spectrum is shown in the top panel of Figure  8. As a sanity check, we also overplotted the optical spectrum at day 14. 7.
The diversity of these transients in the existing sample could be explained by the variety of He-shell masses, WD-core viewing angles, and the degree of mixing in the outer layers, which, however, is to be confirmed by more observational evidence. With the help of upcoming large sky surveys, we can expect that a statistical study of large samples of these types of transients will soon be in reach, which will help us further understand the nature of these peculiar transients. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO program 1103. D-0328. This research made use of TARDIS, a community-developed software package for spectral synthesis in supernovae  (Kerzendorf & Sim 2014; Kerzendorf et al. 2022). The development of TARDIS received support from GitHub, the Google Summer of Code initiative, and from ESAʼs Summer of Code in Space program.
TARDIS is a fiscally sponsored project of NumFOCUS. TARDIS makes extensive use of Astropy and Pyne. This work made use of the Heidelberg Supernova Model Archive (HESMA, https:// hesma. h-its. org). This research has made use of the NASA/ IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Research by Y. D. , S. V. , and N. M. R is supported by NSF grants AST1813176 and AST-2008108. Time domain research by D. J. S. is also supported by NSF grants  AST-1821987, 1813466, 1908972, and 2108032 , and by the Heising-Simons Foundation under grant #2020-1864. The SALT observations presented here were taken as part of Rutgers University program 2015-1-MLT-002 (PI: Jha).
NASA Swift grant 80NSSC19K1639. L. W. and M. G. acknowledge the Polish National Science Centre (NCN) grants Harmonia No. 2018/30/M/ST9/00311 and Daina No. 2017/27/L/ST9/03221 as well as the European Unionʼs Horizon 2020 research and innovation program under grant agreement No. 101004719 (OPTICON-RadioNet Pilot, ORP) and MNiSW grant DIR/WK/2018/12. M. G. is supported by the EU Horizon 2020 research and innovation program under grant agreement No. 101004719. L. G. acknowledges financial support from the Spanish Ministerio de Ciencia e Innovación (MCIN), the Agencia Estatal de Investigación (AEI) 10. 13039/ 501100011033, and the European Social Fund (ESF) "Investing.
Acknowledgments M. C. was supported by the  Ministry of Universities of the Government of Spain , under the program " Convocatoria de Ayudas para la recualificación del sistema universitario español para 2021-2023, de la Universidad Carlos III de Madrid, de 1 de Julio de 2021 ". P. H. was supported by  JSPS KAKENHI  Grant Number  JP 21H04595.
Introduction Many approaches are available to study the impact of climate/weather variables on crop yield. Statistical modelling, which aims to find relations between a set of explanatory variables and crop yields variability, is a widely used approach (Lobell and   Burke, 2010; Mathieu and Aires, 2016; Gornott and Wechsung, 2016; Kern et al. , 2018). This approach with various forms has many advantages, such as identifying crop production constraints (Mathieu and Aires, 2018), complementing field experiments  (Gaudio et al. , 2019) , and helping in adaptation strategies (Iizumi et al. , 2013), but it is often complex to understand and to use for several reasons. Unfortunately, the crop model is often characterised by datasets with a very limited number of samples. For instance,.
The validation dataset B V al is a sample of data held back from the training dataset, which is used to find the best model. For instance, it helps tune the model hyper-parameters: choose the more adequate inputs (i. e. , feature selection), determine the number of predictors, find the best model type (LIN, random forest, NN), determine some training choices, etc. https://doi. org/10. 5194/gmd-2021-218 Preprint. Discussion started: 6 August 2021 c Author(s) 2021. CC BY 4. 0 License. -The testing dataset B T est is held back from the training and the validation datasets to estimate the true model ability to generalise.
Acknowledgements. This work is a part of  Lan Anh's Ph. D. , which benefited from the  French  state aid managed by the  ANR  under the " Investissements d'avenir " program  with the reference  ANR-16-CONV-0003 , and the  Australian Centre for International Agricultural Research, Small Research Activity  ( SLAM/2018/209 ) conducted by the  International Center for Tropical Agriculture (CIAT).
Code availability. The Matlab code used to run an example of the leave-two-out method is available at the following Zenodo link for the revision process of GMD: https://zenodo. org/record/5159363 (Anh and Filipe, 2021). Data availability. The coffee data were provided by the General Statistics Office (GSO) of Vietnam for the 2000-2018 period. These data are available from GSO on reasonable request. For any inquiries, please send an email to banbientap@gso. gov. vn. The data on French grain maize (and other French crops) are available at http://agreste. agriculture. gouv. fr from 1989 on. In addition, the weather data, i. e. , ERA5-Land data, can be downloaded from https://cds. climate. copernicus. eu (last access: 22 Apr 2021).
SIMPLE and PIMPLE algorithms are used for pressure-velocity coupling for steady-state and unsteady cases, respectively. The finite volume open source code OpenFOAM© is adopted. On average, a CPU time of about 1. 5 h is required for each steady-state simulation and a CPU time from 72 to 96 h is required for each unsteady simulation on Intel(R) Core(TM) dual-processor Sandy Bridge server @ 2. 6 GHz with 16 cores employed.
segment located near Al Ain along the Ethiad Rail line in the United Arab Emirates. The chosen site is threatened by the sand of Rub' al-Khali desert. The examined railway segment develops along the NE-SW direction. The local sand granulometry is composed of fine grained, moderately well-sorted sand with mean diameter d ¼ 0. 16 mm (Edgell 2006). The probability density function of u Ãt is derived from Raffaele et al. (2016) as a function of d. Conversely, the probability density functions of u Ã is derived from an anemometric station in proximity of Al Ain. The employed dataset of the 10 min average wind velocity U 10 at 10 m from the ground refers to 10 years, from January 2008 to December 2017.
Acknowledgments The study has been developed in the framework of the MSCA-IF-2019 research project  Hybrid Performance Assessment of Sand Mitigation Measures (HyPer SMM , hypersmm. vki. ac. be). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie grant  Agreement No. 885985. In particular, we acknowledge  J. van Beeck ,  S. Buckingham  from the von  Karman Institute for Fluid Dynamics , and  S. Khris ,  A. Lo Giudice ,  R. Nuca  from  Optiflow Company  and  Politecnico di Torino  for the helpful discussions about the topics of the study. The study has been jointly developed in the framework of the Windblown Sand Modeling and Mitigation (WSMM, www. polito. it/wsmm)research, development, and consulting group established between  Politecnico di Torino and Optiflow Company.
C. The frequency of letters, bigrams, and quadrigrams in English (y axis) and in French (x axis) for all 14 categories of stimuli (2520 stimuli in total, one dot indicates one stimulus; unit = log10 count per million). The English and French real words (WE and WF) were matched with the corresponding high-frequency quadrigram stimuli (QE and QF) in all 3 parameters. D. Organization of an fMRI run, consisting in an alternation of fixation periods and homogeneous blocks of fast stimuli presentation. We first examined whole-brain word-specific activation at the group level. To facilitate inter-subject averaging, data were smoothed using a Gaussian filter with full-width at half-maximum of 6 mm. We examined the contrast of English & French words > faces, bodies, houses, tools, with a cluster size threshold corrected for multiple comparisons using Monte-Carlo simulations (p<0.
By manipulating letters, bigrams and quadrigrams in this manner, we obtained 12 categories of pseudoword stimuli increasingly similar to real words, crossing English/French × high/low frequency × letters/bigrams/quadrigrams, to which we further added 2 sets of real English and French words (see Figure  1B -D, Materials and Methods). Each letter string was presented only once in the experiment. We defined 773 bilingual word-specific ROIs in individual participants using the localizer data (p<0. 001 uncorrected, cluster size > 4). We checked that almost all of these ROIs also showed abovebaseline activity for words (English + French) in the main fMRI runs (749/773 ROIs, including 294/299 VOTC ROIs). We then extracted the activity values (beta, equivalent to % signal change) for the 14 conditions of the main fMRI runs and averaged them across voxels in each ROI.
Stimuli Functional localizers The stimuli for the English-French functional localizer (here-on abbreviated as "localizer", Figure  1A ) were gray-scale images from 8 different object categories (20 exemplars per category). They were adapted from stimulus databases and previous experiments, including faces (neutral, 10 males)  (Lundqvist et al. , 1998) , bodies without heads (neutral standing still, 10 males) (de Gelder & Van den Stock, 2011), English words  (Keuleers et al. , 2012) , French words (http://www. lexique. org/)  (New et al. , 2004) , numbers (famous mathematical constants, 6-7 digits, including the decimal point and +,signs), false fonts (6 letters, no letter was repeated on the same position)  (Vinckier et al. , 2007) , houses, tools (half in a position graspable by the left hand).
English-French stimuli In order to build the English-French materials, we first computed the (log) frequencies of letters, bigrams and quadrigrams in English and French, using lexical frequency information from the SUBTLEX field from the British Lexicon Project database  (Keuleers et al. , 2012) , http://crr. ugent. be/programsdata/lexicon-projects), and the FREQFILMS field from the French Lexique database  (New et al. , 2004) , version 3. 82, https://chrplr. github. io/openlexicon/datasets-info/Lexique382/README-Lexique. html), Secondly, we generated all possible 6-letter strings using the 26 letters of the alphabet (ignoring French accented letters). We then excluded all strings which did not include a single quadrigram existing in French or in English, thus reducing the corpus by a factor of ~10.
g. two connecting characters from two adjacent words). Two other conditions contain Chinese radicals in orthographically possible (condition RP) and impossible (condition RI) positions, respectively. Condition RP was generated by decomposing all 360 real characters from condition CP into radicals, and reassembling them in orthographically possible positions, with a slight change in visual form when appropriate (in Chinese calligraphy and typography, the form of a given radical can subtly change when used in different sizes and positions; e. g. the characters 石磊蠹 share the same radical 石 in different positions). For Chinese readers, the resulting RP stimuli were perceived as extremely low-frequency real Chinese characters that did not remind them of common real words. We checked that RP stimuli were absent from GB2312, GBK, or GB18030 Chinese encoding standards (~21,887 ideographs); the Source Han mono font's CJK glyph database (48,966 ideographs); and the character decomposition series in zh.
Rendering The English and Chinese stimuli were both rendered in the monospaced font Source Han Mono (https://github. com/adobe-fonts/source-han-mono), whose strokes are visually similar to the alphabetical font Consolas. English fonts were rendered at 56 points and regular weight (corresponding to the size of 66 points in Consolas), Chinese fonts were rendered at 71. 27 points with normal weight, so that the stroke thickness and spatial areas covered by the two characters were roughly matched to the 6-letter English strings. The Chinese stimuli were decomposed and recombined in vector forms in Adobe Illustrator CS 6. 0. All stimuli were converted into images to make sure the font rendering remained exactly the same for all participants. We controlled the mean pixel values within the English and Chinese conditions.
Data acquisition The brain images were acquired using a 7-Tesla Magnetom scanner (Siemens, Erlangen, Germany) with a 1Tx/32Rx head coil (Nova medical, Wilmington, USA) at the NeuroSpin center of the French Alternative Energies and Atomic Energy Commission (CEA). Dielectric pads were used for 30/31 participants (not used for SE01 due to insufficient space within the head coil). To minimize head movements, a tape (padded with tissue papers for comfort) was fixed on the forehead of each participant and the head coil, to give tactile feedback to the participants whenever they attempted to move their heads  (Krause et al. , 2019). To minimize light reflections inside the head coil, a piece of black paper was inserted to cover the inner surface of the transmitter coil element. Stimuli were presented via a BOLDscreen 32 LCD screen (Cambridge Research Systems, Rochester, UK, 69.
84 x 39. 29 cm, resolution=1920 x 1080 pixels, refresh rate=120 Hz, viewing distance ~200 cm), at the head-end of the scanner bore. Participants viewed the screen through a mirror attached to the head coil. The whole scanning session lasted about 90 minutes. Within each experiment, one localizer run (9 min 12 s, 276 volumes) and 3 main fMRI runs (13 min 18 s per run, 399 volumes) were acquired (due to scanner technical issues, only two main fMRI runs were acquired for participant SB01). Functional data were acquired with a 2D gradient-echo EPI sequence (TR = 2000 ms, TE = 21 ms, voxel size = 1. 2 mm isotropic, multiband acceleration factor=2; encoding direction: anterior to posterior, iPAT=3, flip angle = 75, partial Fourier=6/8, bandwidth=1488 Hz/Px, echo spacing=0. 78 ms, number of slices=70, no gap, reference scan mode: GRE, MB LeakBlock kernel: off, fat suppression enabled).
To correct for EPI distortion, a 5-volume functional run with exactly the same parameters apart from an opposite phase encoding direction (posterior to anterior) was acquired immediately before each task run. Participants were instructed not to move between these two runs. Manual interactive shimming of the B0 field was performed for all participants. The system voltage was set to 250 V for all sessions, and the fat suppression was decreased per run to ensure the specific absorption rate (SAR) for all functional runs did not surpass 62%. To minimize artifacts and increase signal-to-noise (SNR) ratio around the VOTC, for 28/31 participants, the functional data acquisition slab was placed in a way that excluded the eyes and the ear canal signal dropout region, so that the VOTC, especially the anterior occipital-temporal sulcus (OTS) above the ear canal, was covered as much as possible (see Figure  S1 ).
The ear canal signal dropout only affected the anterior VOTC data of the first 3 participants (SB01, SB02, SE01), but did not affect the more posterior location of the classical VWFA. High-resolution MP2RAGE anatomical images were obtained after 2 or 3 functional runs (resolution=0. 65 mm isotropic, TR=5000 ms, TE=2. 51 ms, TI1/TI2=900/2750 ms, flip angles=5/3, iPAT=2, bandwidth=250 Hz/Px, echo spacing=7 ms).
Whole-brain contrasts were initially thresholded at p<0. 001, then underwent Monte-Carlo simulation for multiple-comparisons correction  (Forman et al. , 1995)  using the Clusterlevel statistical threshold estimator plugin in BrainVoyager software: 5000 samples of null data with the same map smoothness as real data were generated, and the contiguous cluster size that occurred in null data with an overall frequency lower than 0. 05 (alpha<0. 05) was set as the cluster size threshold. For descriptive and visualization purposes (Figure  2B ), the event-related average time course per condition for the group-level cluster (and individual clusters/ROIs) were computed, subtracted and divided by the baseline (activity of -2 to 0 TRs before each block). Functional data smoothing was only applied to the group-level whole-brain analysis; no data smoothing was applied to subsequent individual-level analyses, either at the whole-brain level or at the individual ROI level.
Non-parametric ICA analysis decomposing sub-voxel components in word-specific ROIs We used the code for non-parametric ICA analysis from (S. Norman-Haignere et al. , 2015)  (https://github. com/snormanhaignere/nonparametric-ica ), which maximizes the non Gaussianity and finds independent components in the data. The English-French and English-Chinese data were analyzed separately. The input data were voxel beta values for the 14 conditions in the main fMRI runs from all bilingual word-specific ROIs (the ones shown in Figure  3  and Figure  7 , defined by the localizer). For English-Chinese participants, removing the overlapping voxels showing Chinese or English language specificity did not change the results. We searched for 3 components, to allow us to separate the effects of the baseline, the word similarity effect, and putative language differences.
Acknowledgments We thank  Alexandre Vignaud  for the help in setting up the 7T scanning sequences; the nurses and MR-technicians at NeuroSpin for the help in participant recruitment and scanning;  Aakash Agrawal  for running simulations on bilingual neural networks. MZ is supported by  Fondation Bettencourt Schueller , and  Programme Investissements d'Avenir IHU FOReSIGHT  ( ANR-18-IAHU-01 ). LC is funded by the  Programme Investissements d'avenir  ( ANR-10-IAHU-06 ) to the  Paris Brain Institute. LC and SD are supported by the " TOPLEX" ANR program  ( ANR-20-CE37-0002 ).
Bacterial swimming is strongly influenced by the micro-topography and bacteria deploy strategies to sense and adapt their motion to their environment  [25] , with specific implications for biofilm formation and dynamics  [9]. Model-based studies were conducted to characterize bacterial active motion in interaction with an heterogeneous environment. An image and model-based analysis showed non-linear self-similar trajectories during chemotactic motion with obstacles  [24]. Theoretical studies explored Brownian dynamics of self-propelled particles in interaction with filamentous structures such as EPS  [20]  or with random obstacles, exhibiting continuous limits and different motion regimes depending on obstacle densities  [8, 7]. Image analysis characterized different swimming patterns in polymeric fluids  [33] , completed by detailed comparisons between a micro-Table  1 : Dataset characteristics.
We detailed, for each batch, the number of trajectories, the average number of time points by trajectory (and standard deviation), the total number of time points in the dataset, the total movie duration and the time interval between two snapshots. scale model of flagellated bacteria in polymeric fluids and high-throughput images  [29]. Models of bacterial swimmers in visco-elastic fluids were also developed to study the force fields encountered during their run  [26]. However, to our knowledge, no study tried to characterize swimming patterns in the highly heterogeneous environment presented by an exogenous biofilm matrix. In this study, we aim at providing a quantitative characterization of the different swimming behaviours in adaptation to the host biofilm matrix observed by microscopy.
The method is validated on synthetic data and applied to a microscopy dataset to decipher the swimming behaviour of the 3 Bacillus. Bacillus cereus (B. cereus) -swimming in a Staphylococcus aureus (S. aureus) host biofilm were acquired (see Fig. 1d ). Swimmers and host biofilms were imaged with different fluorescence dies, allowing their acquisition in different color channels, and to recover in the same spatio-temporal referential the swimmer trajectories and the host biofilm density (see Materials and Methods and Fig. 1 ). Namely, for each species s and individual swimmer i, we recover the initial (T s 0,i ) and final (T s end,i ) observation times (when the swimmer goes in and out the focal plane, see sect. 4. 2), and the number T s i of time points in the trajectory. We then extract from the 2D+T images the observed position, instantaneous speed and acceleration time-series t → X s i (t), t → V s i (t), t → A s i (t), for t ∈ (T s 0,i , T s end,i ).
( 7 γ s ≥ 0, v s 0 ≥ 0, v s 1 ≥ 0, s ≥ 0 We note that Equation (  5 ) can be seen as a likelihood equation of the parameter θ s knowing A s i (t), b(t), V s i (t) and X s i (t). The parameter s can now be seen as a corrector of both modelling errors in the deterministic drift and observation errors between the observed and the true instantaneous acceleration. Alternative settings where these uncertainties sources are separated and a true state for position and acceleration is inferred can be defined (see Annex A. 8). The inference problem is implemented in the Bayesian HMC solver Stan  [38]  using its python interface pystan  [35].
Assessment of the inference with synthetic data To assess the inference method, synthetic data are built. We arbitrarily fix a parameter vector and solve system (1) from random initial positions, in a host biofilm arbitrarily chosen in the image dataset. We then extract the swimmer positions at given timesteps and recover accelerations and speeds with the same post-processing pipeline as for microscopy images and solve the inverse problem (  5 )-  (7). The ground truth parameters are correctly recovered by the inference procedure (Table  2 ), indicating that the parameters are correctly identifiable and that the inverse problem is well-posed. An error of respectively 1. 28, 1. 34, 2. 98 and 0. 68% on the parameters γ, v 0 , v 1 and is observed in this controlled situation, β being inferred with lower accuracy (9.