dataesr/scientific-paragraphs-categorization huggingface dataset:
Colors reflected by the beak spot and the auricular patches were measured using a portable JAZ spectrophotometer (Ocean Optics Inc. , Dunedin, FL, USA) containing a pulsed-xenon light with a spectral resolution of 0. 3 nm across the spectral range of 320-700 nm, and was calibrated against a white standard (Ocean Optics Spectralon). Measures were repeated 3 times on each ornament (on both sides of the bird) using a 200 μm fiber-optic probe with a 90° angle window. Reflectance spectra of given ornaments were smoothed and averaged using an R script adapted from  Montgomerie (2008). The obtained spectra (e. g. , see Figure  2 ) were used to calculate mean brightness, hue, and chroma (see below) over the spectral range 320-700 nm, which corresponds to the full range of spectral sensitivity in birds  (Cuthill, 2006).
ACKNOWLEDGMENTS We thank  Emilie Lefol  for her help in the field and two anonymous reviewers for their comments on the manuscript. This research was funded by the  French Polar Institute  ( IPEV-Research Program 119 ) and the  French National Center for Scientific Research (CNRS-INEE ). Field logistic support was provided by  Terres Australes et Antarctiques Françaises. QS was funded by a doctoral fellowship from the  Ministère Français de l'Education Supérieur et de la Recherche.
elegans in the absence of the acceptor. In the non-FRET conditions, the mean fluorescence lifetime value of the donor was calculated from a mean of the t D by applying a mono-exponential decay model to fit the fluorescence lifetime decays. In the FRET conditions used, we applied a bi-exponential fluorescence decay model to fit the experimental decay curves fðtÞ = a DA e Àt = tDA + b D e Àt = tD. By fixing the non-interacting proteins' lifetime t D using data from control experiments (GFP-H2B C. elegans samples), the value of t DA was estimated. Then, the FRET efficiency, indicated here as E FRET, was derived by applying the following equation: E FRET = 1 À ðtDA=tDÞ at each pixel in a selected region of interest (ROI) using SPCImage software. ROIs represent pachytene nuclei in this study. The FRET distribution curves from these ROIs were displayed from the extracted associated matrix using SPCImage and then normalized and graphically represented using Excel and GraphPad Prism software.
ACKNOWLEDGMENTS We thank the members of our laboratories for helpful discussion and suggestions,  M. Girardot  for help with R statistical analysis, the  Montpellier Rio Imaging Microscopy Facility  for technical assistance, and the  Caenorhabditis Genetics Center  for strains. D. L. was supported by a  Canceropole GSO-Emergence  grant ( 2014-E17 ). R. F. acknowledges grant support from the  Fondation Recherche Medicale (FRM ; grant  DEQ20150331703 ) and the  Agence Nationale de Recherche (ANR ; grant '' IMPRINT-RNA ''). A. P. B. is supported by the  Agence Nationale de la Recherche  ( ANR; grant ''CeLeNeD'') , the  FRM  (grant  SPF20120523917 ), and  INSERM.
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) MALDI-TOF and MALDI-TOF/TOF mass spectra were recorded using an Ultraflextreme workstation controlled by FlexControl 3. 3 software (Bruker Daltonics, Bremen, Germany) equipped with a Smartbeam II laser of 355 nm and operated in positive mode. Before analysis by MALDI-TOF MS, samples were desalted with Dowex 50W-X8 (Bio-Rad Laboratories, CA, USA) and 1 μl of sample was co-crystallised with 1 μl matrix (25 mg/ml dihydroxy-benzoic acid in 50% (v/v) acetonitrile). Samples were dried under a stream of air. For TOF/TOF mass analysis, parent and fragment ions were accelerated using a LIFT device located in the flight tube using the standard LIFT method optimized for the instrument. Maltodextrin MD 20 (Avebe, Foxhol, The Netherlands) was used for calibration.
Methods: overall experimental approach In order to directly examine the off-line periods during which the motor memory trace is being consolidated, we acquired blood-oxygen-level dependent (BOLD) fMRI data during motor sequence task practice, wake resting-state and post-training sleep conditions. Brain functional images were recorded while thirteen participants performed two different finger movement tasks using a response pad one week apart. In the motor sequence learning (MSL) task, subjects practiced a selfpaced, explicitly known 5-item finger sequence task, which was compared with performance on a motor control task (CTL) in which participants were asked to produce simultaneous movements of all four fingers at the same average frequency, and for the same number of times as in the MSL task.
Temporal dynamics of the motor memory trace during NREM sleep Given that we first observed an elevation of connectivity within the consolidated pattern during NREM sleep following MSL, we further examined the pattern of dynamic changes in CI over the course of the post-training night as compared to the intermittent periods of wakefulness. To do so, we employed a sliding-window approach over the temporally-concatenated fMRI data of each state and condition. Specifically, we separated NREM stage two and SWS periods, and calculated CI values in NREM stage 2, as well as, the intermittent periods (epochs) of wake, for which we had sufficient data in our group of participants. On average 22 min of data (600 volumes) for each subject and condition were selected (see Materials and methods for more details, and Table  1  for sleep architecture information in the scanner).
Participants were given a two-hour opportunity to sleep. The mock scanner noise (recorded from the scanner and presented at same approximate sound level) and lighting conditions (i. e. , lights off) were similar to those of the experimental nights in the actual MRI scanner. EEG signal was recorded using the same MR-compatible electrode cap as that during the experimental nights. Following this two-hour sleep opportunity in the mock scanner, EEG electrodes were removed and subjects were permitted to sleep in the nearby sleep laboratory until 7:30AM. In order to be included in the study, a minimum of five minutes of consolidated NREM sleep (i. e. , the minimum amount of data necessary for data analysis purposes) during the two-hour screening period was required. Finally, 13 subjects met these inclusion criteria and completed the study, and were thus included in the data analyses (see power analysis at the end of Materials and methods).
Image preprocessing Preprocessing of the imaging data was carried out using the FSL software package  (Beckmann et al. , 2003)  and in-house programs developed in MATLAB. This included (1) removal of the first two volumes in each scan series, (2) slice time correction, (3) non-brain tissue removal (4) motion correction, (5) global intensity normalization, (6) spatial smoothing (Gaussian kernel of FWHM 6 mm) and (  7 ) temporal high-pass filtering (s = 100 s). To achieve the transformation between the low-resolution functional data and the standard stereotaxic space (MNI152: average T1 brain image constructed from 152 normal subjects), we performed two transformations. The first was from the functional image to the T1-weighted structural image (using a 6 degree of freedom (DOF) transformation), and the second was from T1-weighted structural image to the average standard space (using a 12 DOF linear affine transformation, voxel size = 2 Â 2 Â 2 mm).
The normalization by the regression residuals makes this index specific to the co-activation of the desired pattern (X v ð Þ), and not to any general activation in all or parts of brain areas. This connectivity measure was selected as opposed to other methods such as ICA and seedbased analysis due to the following reasons. First, unlike the application of data driven approaches such as ICA, we sought to examine specific hypotheses regarding the reactivation and reorganization of pre-defined activation patterns recruited during the learning and retest task performances. Second, we sought to investigate the reactivation of the entire learning/consolidated pattern using a multivariate approach, as opposed to univariate methods such as seed-based analysis, which are suited to examine distinct connections at each time.
In this analysis, the preprocessed fMRI data registered to the MNI standard space during the MSL condition in both S1 and S2 sessions were temporally-concatenated across all subjects. This time-concatenated matrix was then fed to the fast-ICA algorithm (Hyva ¨rinen, 1999) to extract 30 group-level spatial components. These Z-score spatial maps were correlated with the templates of four highly-reported resting-state networks including the default mode, visual, and left and right fronto-parietal networks  (Damoiseaux et al. , 2006) , and the corresponding group-level spatial maps with the highest correlation with templates were selected (Figure  2 -figure supplement 1). Similar to the learning and consolidated patterns, these group-level spatial maps were then used as the pattern of interest within which CI was calculated during each of the resting-state and NREM sleep conditions in both MSL and CTL nights.
Temporal dynamics connectivity analysis To evaluate changes in the strength of functional connectivity in a given brain pattern over time, we calculated CI in a sliding window over the sleep fMRI session. In this analysis, for each subject, we selected NREM stage two sleep segments (epochs) of more than 50 volumes and concatenated all epochs in each run (note that we did not have enough data during SWS to perform this analysis). Table  1  reports sleep architecture information inside the MRI scanner, including the average time spent awake and asleep, and in specific sleep stages, and sleep onset time relative to the start of simultaneous EEG-fMRI recording. We also applied the same concatenation procedure to the intermittent wakefulness periods (epochs) during the sleep run. This resulted in 954 ± 156 (mean ± s.
Seed-based functional connectivity To ensure that the method employed here was reproducible using more conventional seed-based approaches  (Vahdat et al. , 2011 (Vahdat et al. , , 2014)) , we defined 12 ROIs based on peaks of activity during either the learning (6 ROIs) or retest (6 ROIs) practice sessions (Figure  3 -source data 1). We defined a spherical mask (radius = 6 mm) around each seed in standard space. We re-sampled this mask first to the T1 weighted structural image of each subject and from there to the low-resolution functional space of that subject. For each subject, the average time course of the BOLD signal within the transformed mask during the resting-state (RS1, RS2, and RS3) and NREM sleep (stage two and SWS) was calculated. The mean BOLD time-course of each ROI was used as a predictor in a subject-level GLM to assess the functional connectivity of that ROI with every other voxel in the brain.
Data were analog filtered by a low pass filter at 250 Hz and a high pass filter at 0. 0159 Hz. Data were transferred via fiber optic cables to a personal computer where Vision Recorder Software, Version 1. x (Brain Products, Gilching, Germany) was synchronized to the scanner clock. Sleep EEG was monitored online with Brain Products RecView software using online artifact correction. EEG data were preprocessed by a low-pass filter (60 Hz), down-sampled to 250 samples/sec and re-referenced to averaged mastoids. Scanner artifacts were removed using the 'fMRI Artifact rejection and Sleep Scoring Toolbox (FASST)' for MATLAB (Mathworks, Natick, Massachusetts, USA  [Leclercq et al. , 2011] ), using an adaptive average subtraction method. Ballistocardiographic artifacts were then removed using an algorithm based on a combination of artifact template subtraction and event-related independent component analysis  (Leclercq et al.
Considering the numerical resolution of nonlinear differential equations to get a digital implementation, our proposed approach has been designed and verified using a VHDL (VHSIC Hardware Description Language) description with a fixed-point representation of all data on 48 bits leading to a successful hardware implementation targeted to FPGA technology (Field-Programmable Gate Array). The obtained implementation results provide good performance in terms of hardware area and throughput. Finally, the sturdiness of our proposed architecture has been tested using security analyses and statistical tests. Consequently, the experimental results presented in this paper prove that the added HC generator enhances the randomness and robustness of SNOW-3G algorithm against different analyses and statistical attacks.
The rest of this paper is organized as follows. Section 2 briefly reminds the reader with the architecture of the regular SNOW-3G stream cipher. Section 3 details the processing steps and architecture of the enhanced SNOW-3G stream cipher including a Lorenz's HC generator as PRNG to perturb  the digital output of the regular SNOW-3G stream cipher. The digital implementation results on Virtex-5 Xilinx FPGA technology and evaluations are presented in Section 4. In this section, the performance and architectural simulations prove the feasibility and the efficiency of our proposed HC SNOW-3G stream cipher. In Section 5, the robustness of our new scheme is evaluated through three security analyses: exhaustive key search, key sensitivity, and statistical tests to prove that the proposed HC SNOW-3G stream cipher resists against cryptanalysis attacks and exhibits truly random sequences suitable as cipher keys for the data encryption compared to the regular SNOW-3G cipher.
(13) Mentioning that HC signals are real, we used the fixedpoint format of 48 bits as 1632. The real part is encoded on 16 bits and the decimal part is encoded on 32 bits. The used arithmetic fixed-point allows achieving a good compromise between performance and cost. Equation set  (8)  is simulated in MATLAB platform using the numerical tool RK-4, and the results are illustrated in Figures  3  and 4.
Digital Implementation of the Proposed HC-PRNG. To realize the HC-PRNG component, we opt for a hardware behaviour description based on Moore's Finite State Machine  [35]. This FSM system describes the numerical resolution based on Runge-Kutta method performing  (7). Thereby, the RK-4 method is implemented in digital way suitable for FPGA using the FSM system coded in VHDL language and detailed in Figure  5. This figure describes the FSM system corresponding to the internal states of the HC-PRNG layer. A detailed description at each state of the FSM system is presented as follows. State 1. It is the initialization state. All outputs are set to zero and the state variables are initialized with the initial conditions ( 0 =  0 =  0 =  0 = -10  State End. It is the last state of the FSM, the first chaotic key values , , , and  are generated to perturb SNOW-3G, and they are also used to update the variables , , , and , respectively, for the next chaotic key calculation.
Low Correlation Coefficient. The correlation is an important test to prove the independence of encrypted data from the original ones. Indeed, the encrypted data are increasingly different from the original ones when the correlation coefficient becomes as low as possible. The correlation coefficient is computed according to  [39]   , = cov (, )  where √𝐷 (𝑥) × 𝐷 (𝑦) , (22) cov (𝑥, 𝑦) = 𝐸 [{𝑥 -𝐸 (𝑥)} {𝑦 -𝐸 (𝑦)}] , 𝐸 (𝑥) = 1 𝑛 × 𝑛 ∑ 𝑖=1 𝑥 𝑖 , 𝐷 (𝑥) = 1 𝑛 × 𝑛 ∑ 𝑖=1 {𝑥 𝑖 -𝐸 [𝑥]}. (23) For performing this test, we have analysed 500 encrypted packets with different keys, and in each case we have calculated the correlation coefficient between original packet and its corresponding encrypted packet. The results are shown in Figure  9. The illustrated results indicate clearly that no detectable correlation exists between the original and corresponding cipher packets considering that the correlation coefficient is always close to ideal value zero.
Then, it evaluates the result vectors by the 15 NIST tests. (2) Short keystream data set test: it begins by generating 2 10 bits for each of the 307200 random CK (set IV to zero in all the cases). The concatenation of all the keystreams of 2 10 bits allows obtaining 300 vectors of 1048576 bits. Then, it evaluates the result vectors by the 15 NIST tests. (3) IV data set: it begins by generating 256 sequences of 4096 bits each for 300 random CK. Note that the first sequence of each key is generated with IV set to zero. Then, it is incremented for the following 255 sequences. As a result, we obtain 300 vectors of 1048576 bits for evaluation by the 15 NIST tests. To perform the analysis, we compare the results of our proposed HC SNOW-3G stream cipher with results of the regular SNOW-3G done in  [14].
The aim of this analysis is to prove the amelioration of the initialization process and randomness of short keystream data set. As a result, we find that both algorithms (regular SNOW-3G and our proposed design) pass in success all the fifteen NIST tests for test set 1 (long keystream data set) and test set 3 (IV data set). However, only our proposed design passes in success all the fifteen NIST tests for test set 2 (short keystream data set). Indeed, the regular SNOW-3G fails in the eight NIST tests as it is presented in Table  2. More precisely, this table gives a comparison of short keystream generated by the regular SNOW-3G cipher with short keystream generated by our proposed HC SNOW-3G design. We can clearly observe that all tests are passed compared to the regular SNOW-3G cipher where NIST tests based on the short keystream data set have failed  [14].
Conclusion In this paper, an enhanced SNOW-3G stream cipher is proposed. The objective is the improvement of the randomness and the complexity of the regular SNOW-3G stream cipher which presents a weakness in its initialization mode leading to failure of the NIST test based on the short keystream data set. The adopted original approach is to include a HC-PRNG (Hyperchaotic Pseudo-Random Number Generator) based on 4D Lorenz system in the regular SNOW-3G cipher as an additional layer while keeping standardization and in order to generate a more robustness keystream. The proposed architecture performs the perturbation concept of digital LFSR (Linear Feedback Shift Register) sequences and the digital output of the regular SNOW-3G cipher part through one hyperchaotic generator which is used as PRNG (Pseudo-Random Number Generator).
Combined Mapping Method. We combined geologic maps  [52, 75] , interpretation of SPOT (Système Pour l'Observation de la Terre: French remote sensing satellite) images (5 m resolution), aerial pictures (5 m resolution), and digital elevation model (USGS, 30 m resolution) with the QGIS 2. 12 software for precise interpretation of the structures in the study area. The main Neogene brittle faults are highlighted by characteristic geomorphological objects (reliefs, triangular facets, and basins)  [76]. Mapping of these faults has been mainly derived from topography ruptures observed in DEM (see Section 5. 1) allowing the author to make the distinction between brittle-Neogene normal faults from ductile Hercynian reverse faults (shear zones) and from brittle to ductile-Pyrenean reverse faults. Smaller faults patterns are identified using lineament mapping on satellite and aerial images and confirmed or completed by fieldwork.
Most of the springs, including their temperatures, were collected from the BRGM database, Annales des Mines database, and the Ph. D. thesis of Krimissa  [50]. Unfortunately, these studies are unclear on certain names or locations from the whole springs catalogue (rare maps and no GPS waypoints). Fieldwork precisely locates and references the essential features of springs (altitude, temperature, pH level, and electric conductivity). The numerical values are presented in Table  1.
Numerical Modeling. A 2D numerical model of the study area has been performed in order to integrate our observations with the petrophysical properties of the crust. Using the Comsol Multiphysics6 software, the heat transfer equation and Darcy's law have been coupled through a similar procedure as that used by Guillou-Frottier et al. [77]. We first reproduce one of the McKenna and Blackwell's numerical results  [11]  as a benchmark experiment (Figure  2 ). Indeed, the Têt Valley presents similar features to the Dixie Valley hydrothermal system. However, the Têt Valley reliefs are higher, basins are smaller or even inexistent, and geometry is asymmetrical. Figures  2(a ) and 2(b) show the temperature and velocity fields, respectively, obtained in McKenna and Blackwell's models, for a fault permeability of 10 -14 m 2 and a host rock permeability of 10 -16 m 2.
Acknowledgments Thanks are due to  Clare Bond  whose constructive comments helped to improve the paper. This work corresponds to one part of a Ph. D. thesis included in the  RGF (Référentiel Géologique de la France ) project led by the  BRGM , which the authors acknowledge for its financial and technical support. The authors wish also to thank  K. Valera ,  J. Villard , and  P. Y. Bres  for their participation in the field campaign,  B. Celerier  for the explanations about the FSA software,  P. Labaume  and  A. Chauvet  for thin sections interpretations, and  B. Ladouche  and  G. Vasseur  for the interesting discussions about geochemistry and hydrothermal systems.
Data acquisition For the experiments on spectral characterization, electrical signals were recorded through a Multiclamp 700B patch-clamp amplifier and digitized using a Digidata 1322A interface and pClamp software (Molecular Devices). All other recordings were performed using a Multiclamp 700B patch-clamp amplifier and digitized using a National Instruments data acquisition board (#USB-6259). NeuroMatic software environment in conjunction with IgorPro software (Wavemetrics) were used for data acquisition and analysis. Results are quoted as mean Ϯ SD unless otherwise specified in the text. Voltage-and current-clamp recordings were filtered at 10 kHz and sampled at 50 kHz.
This work was supported by the  Agence Nationale de la Recherche  (Grants  ANR-10-INBS-0401  and  ANR-14-CE13-0016 ), the  National Institute of Health  (Grant  1-U01-NS090501-01 ), the  Getty Lab , the  Medical Research Foundation  (Grant  DVS20131228920 ), the  Federation pour la Recherche sur le Cervau (FRC) , the  Rotary Club  through the  program Espoir en Tete 2012 , the  Human Frontier Science Program  (Grant  RGP0015/2016 ), and a  National Science Foundation International Postdoctoral Research Fellowship  (A. J. F. ). We thank  Marta Gajowa  and  Fabio Simony-Conesa  for help in mice viral injections;  Christophe Tourain  for technical support in building the holographic system;  Vincent de Sars  for software development;  Cecile Jouffret  for help in cell culture maintenance. The authors declare no competing financial interests.
For PTU, four images were taken: preoperative, intraoperative 1 after S2, intraoperative 2 after F1, and postoperative after F2. For PTN three images were taken, preoperative, intraoperative after X1 and postoperative after X2. All images were stored in a computer, and composite images were created using software (Image-Pro Plus, Media Cybernetics, Silver Springs, MD, USA).
Measurements Image-Pro Plus software was used to measure the amount of resin removed 1 mm from the preparation end-point toward the outer side of the apical curvature (AR-1mm) (Fig. 1 ). Measurements were taken of the maximum amount of resin removed on the inner side of the apical curvature (Max-AC) and of the entire canal width at this level. Finally, measurements were taken of the minimum canal width between the two curvatures (Min-W).