dataesr/scientific-paragraphs-categorization huggingface dataset:
For spherical droplets, the phase shift introduced between the two glare points (which represent the reflected beam and the beam refracted by the droplet without any internal reflection) is then much higher than 2π. In the case of irregular particles, we will assume that the phase of each glare point is a random variable in the range [0, 2π]. These two hypothesis could be easily modified. Once the irregular particle is defined, we can further choose: -the orientation in space of the global irregular particle (using rotation matrices defined using MATLAB). -the transverse and longitudinal (along the imaging z-axis) position in space of the particle. These different parameters are then used to calculate the interferometric out-of-focus images as will be now presented.
INTRODUCTION Geomorphology is concerned with determining the processes that shape landscapes. Repeated close-range measurements offer a way to observe and quantify these processes. Two technologies have emerged recently to capture surface topography with great detail: laser scanning (terrestrial and mobile)(among many others  Rosser et al. 2005; Dewez et al. 2007 Dewez et al. , 2013;; Lague et al. 2013 ) and structure-from-motion (SFM)  (Ahmadabadian et al. 2013; James & Robson, 2011). SFM applications are expanding at growing speed because they rely on commonly available hardware: a camera. Processing can be done with widely available software such as Visual SFM at no cost if used for research purposes. To produce data of any value to geoscientists, however, SFM results need be precisely and accurately oriented in space, which is often done with Ground Control Points.
DATASET 568 photographs were shot with a set of three synchronized DSLR Nikon D7000 camera with Nikkor f/1. 4 35 mm lens on July 22 2013, around 8pm. Three cameras were combined with divergent aiming direction with ~25% overlap so as to capture a wide field of view and obtain better perspective on receding cliff faces. Photos were geotagged on-the-fly with a Solmeta Pro2 slave GPS unit (hardware v. 3). This slave GPS unit served also to trigger all three DSLR simultaneously with a single wireless remote control. Photos were shot as high quality JPEG optimized for image quality, at the highest nominal resolution of 4928 x 3264 pixels. The plane was flying at a speed of the order of 50 m/s which imposed a minimal exposure time of 1/1000s to avoid motion blur. The camera triplet was triggered manually at a frequency of about 1Hz which induces a stereo B/H ratio comprised between B/H=0.
FROM SCALE-FREE 3D POINTS TO WORLD COORDINATES To turn scale-free 3D points clouds into a georeferenced point set, we exploited the metadata stored by Visual SFM inside the cameras_v2. txt auxiliary file. Each camera point of view is recorded with scale-free parameters, and when they exist in the original image EXIF header, latitudes, longitudes and elevations written by the slave GPS. To transform scale-free coordinates into world coordinates, a 7parameters transform should be applied to determine the scale factor, model rotation and origin translations. To do so, a custom-made routine, written in R scripting language was applied. The script includes a PROJ4 library call, which enables any type of conversion between geographic and cartographic coordinates (here the Lambert-93 French official projection).
The translation in X is bi-modal, with a clearly higher peak, while the translation in Y is tri-modal, with two possible candidates. The transformation from model coordinates to cartographic coordinates was than computed as the matrix product of five square 4x4 matrices so as to apply the final transform directly inside the Cloud Compare software. Visual SFM coordinate convention is different from conventional right-hand direct axis scheme. This means that Y and Z coordinates need swapping (inside matrix M1) and inverting (inside matrix M2). Then a rotation matrix is applied (M3)which results from the multiplication of three individual rotation matrices Rkappa, Rphi and Romega, in this order, padding with the fourth line and column with zeros. Scaling (M4) is applied to the first three diagonal values.
RESULTS Using this solution scheme, SFM coordinates were transformed and compared to their respective GPS coordinates (Figure  5 ). For horizontal coordinates (Figure  5 , left panel) the range of misfits reaches 100m. The vertical misfit range is smaller (Figure  5 , middle and right panel). The distribution of errors is not uni-modal, as was already apparent during the determination of the model parameters. The cause of this could be an inhomogeneous behavior among cameras. As three simultaneous shots were acquired for each view points, we separated the misfits according to camera ID (NK1: left; NK2: middle and NK3: right camera). There does not seem to have a remarkable signature. Statistically, the Median Absolute Deviation (MAD), which is the statistically robust equivalent of standard deviation for non-Gaussian distributions  (Höhle & Höhle, 2009) , contains 66% of the data and is compiled in Table  2.
Computational details Electronic structure calculations have been performed with the code MOLPRO 10 using highly correlated wave functions within the UCCSD(T)-F12a method. 11, 12 Cuts through the full potential energy surfaces (PES) in linear and bent geometries have been determined for the singlet ground states and first triplet excited states of the negative ions, which have been positioned relative to the PES of the doublet ground states of the neutral molecules. The orbital basis sets used were the standard aug-cc-pVTZ basis sets (abbreviated aVTZ below) of Dunning and co-workers,  13 14  Such basis sets within the UCCSD(T)-F12a approach have been shown,  12 15  to yield as accurate results as conventional calculations with basis sets approaching aug-cc-pV5Z. Auxiliary basis sets required in the F12 procedure involved the cc-pVTZ/JKFIT sets of Weigend,  16  the aug-cc-pVTZ/MP2FIT sets of Weigend et al.
Details of parameters for the simulation were given earlier  [16]. They have been confirmed to yield an accurate adsorption energy for CO 2 on graphite surface  [16]. Classical molecular dynamics (MD) simulations were performed using the LAMMPS package  [20]. A snapshot of the system is given in Figure  1 , showing graphite at 500 K with CO 2 in a relatively dense gas and in an adsorbed state. Periodic boundary conditions were used for the reservoir in all directions. The simulation was done with time steps of 0. 001 ps. The initial configuration was constructed by randomly distributing CO 2 molecules above the graphite surface. The system was stabilized during 2000 ps by runs with constant N tot V tot T using Nosé-Hoover thermostats  [21]. In order to check the range of validity of Eq. (  3 ), the reservoir size was varied.
Conclusion We have shown that a recently developed Small System Method can be used to calculate a consistent set of thermodynamic data for surfaces from one Molecular Dynamics simulation. For carbon dioxide adsorbed to graphite, we determined the chemical potential, the activity coefficient, the entropy and enthalpy directly. Closer inspection of density profiles revealed that we can speak of two, not one layer of adsorbed gas. The thermodynamic analyses reveal a distinction between the layers. The gas has larger entropy in the top than in the bottom layer, is more mobile in this layer. The two layers are in equilibrium with one another, meaning that a lowering of the entropy can take place, if the enthalpy can compensate for the change in entropy when a top particle moves to the bottom layer.
In the present work, an inverse identification of the Johnson-Cook constitutive model is performed on the titanium alloy Ti-6Al-4 V at three strain rates until about 2•10 4 s À1 (till about 1. 1•10 4 s À1 of plastic strain rate) on a specially designed direct impact Kolsky bar device. First, the design of such a device must meet several criteria, and is shown to be the solution of an optimization problem. A systematic design procedure for such a device is then introduced. Second, an inverse analysis using the finite element code ABAQUS is carried out to identify the Johnson-Cook parameters on experimental data obtained with the designed system.
Second, experiments have been carried out with a direct impact system on the high-strength Ti-6Al-4 V titanium alloy at three strain rates until about 2 Á 10 4 s À 1 (till about 1. 1 Á 10 4 s À 1 of plastic strain rate). Quasi-static experiments have also been carried out on the same alloy. In the third section, experimental data and the post-processing associated are presented and discussed. Third, the Johnson-Cook parameters are identified in the fourth section on experimental results, using in particular for the rate-dependent part of the model an inverse analysis coupled with the finite element code ABAQUS  [27]. These data aim at feeding numerical simulations of dynamic material forming processes to make them more predictive.
Identification In this work, only the strain hardening and rate-dependence effects are studied; therefore, thermal effects are not addressed here. First of all, the parameters A, B and n are identified on the quasi-static tensile flow curve shown in Figure  5. The identification is performed by integrating the elastic-plastic constitutive model  [33]  with an isotropic hardening given by the first parenthesis of Equation  [33]  using a least square The identified values of these parameters are summarized in Table  2. Second, the identification of the parameter C requires to perform an inverse analysis involving a dynamic numerical computation carried out with the finite element code ABAQUS  [27]. This parameter is ascribed to vary within reasonable bounds (0. 005 ≤ C ≤ 0. 05) given according to the reference identified parameters of the Johnson-Cook model for Ti-6Al-4 V in  [24] [25] [26].
Acknowledgements The authors thank  E. V. Antipov ,  S. Ya. Istomin  and  F. S. Napolskiy  of the  Moscow State University (Russia ) for the synthesis and characterization of perovskite materials and for valuable discussions,  P. Simonov  of the  Boreskov Institute of Catalysis (Novosibirsk, Russia ) for supplying  Sibunit Carbon , and  Tokuyama Company  for supplying alkaline ionomer. Financial support from  CNRS (France) ,  Region Alsace ,  Russian Foundation for Basic Research , and the  French Ministry  for the PhD scholarship of T. P. is gratefully appreciated.
Many of these methods are based on the modeling of brain intensities (normally using T1 weighted images due to their excellent contrast for brain tissues) combined with a set of morphological operations  [3, 5, 12]  or atlas priors  [9]. The most widely used automated methods correspond to those that are publically available. For example, the BET (brain extraction tool) software from the FSL image processing library  [2]  is one of the most used techniques probably due to its accuracy, ease of use, and low computational load. Other techniques like 3dIntracranial  [6] , hybrid watershed algorithm (HWA)  [5] , or brain surface extractor (BSE)  [13]  have been also widely used. Intracranial cavity extraction can also be obtained indirectly as part of a full modeling of brain intensities using a parametric model such as that done in Statistical Parametric Mapping (SPM)  [14]  or VBM8 (http:/dbm.
neuro. unijena. de/vbm/) software packages. Over the last decade, methods have been proposed to automatically measure the intracranial cavity volume (ICV) by using nonlinear registration atlas-based approaches  [15, 16]. More recent works of special interest for the brain extraction problem are methods like MAPS  [10]  and BEaST  [11]. Both methods rely on the application of a multiatlas label fusion strategy. MAPS uses multiple nonlinear registrations followed by a voxel-wise label fusion while BEaST uses a single linear registration in combination with nonlocal patchbased label fusion. Both techniques scored well on the LONI segmentation validation engine (SVE)  [17]  comparison for brain extraction (see http://sve. bmap. ucla. edu/archive/) although MAPS has a much larger computational load compared to  BEaST.
Template Library Construction 2. 1. 1. Library Dataset Description. A library of manually labeled templates was constructed using subjects from different publically available datasets. To include as large age range as possible, different datasets nearly covering the entire human lifespan were considered. MRI data from the following databases were used. (ii) Alzheimer's Disease (AD) Dataset. Nine patients with Alzheimer's disease (age range = 75-80 years, MMSE = 23. 7 ± 3. 5, CDR = 1. 1 ± 0. 4) scanned using a 1. 5 T General Electric Signa HDx MRI scanner (General Electric, Milwaukee, WI) were randomly selected. This dataset consisted of high resolution T1-weighted sagittal 3D MP-RAGE images (TR = 8. 6 ms, TE = 3. 8 ms, TI = 1000 ms, flip angle = 8 ∘ , slice thickness = 1. 2 mm, matrix size = 256 × 256, voxel dimensions = 0.
938 × 0. 938 × 1. 2 mm 3 ). These images were downloaded from the brain segmentation testing protocol  [18]  website (https://sites. google. com/site/brainseg/) while they belong originally to the open access OASIS dataset (http://www. oasis-brains. org/). (iii) Pediatric Dataset. Ten infant datasets were also downloaded from the brain segmentation testing protocol  [18]  website (https://sites. google. com/site/brainseg/). These data were originally collected by Gousias et al. [19]  and are also available at http://www. brain-development. org/ (this dataset is property of the Imperial College of Science Technology & Medicine and has been used after accepting the license agreement). The selected 10 cases are from the full sample of 32 two-year-old infants born prematurely (age = 24. 8 ± 2.
Denoising and Inhomogeneity Correction. All images in the database were denoised using the spatially adaptive nonlocal means (SANLM) filter  [20]  to enhance the image quality. The SANLM filter can deal with spatially varying noise levels across the image without the need of explicitly estimating the local noise level which makes it ideal to process data with either stationary or spatially varying noise (as in the case of parallel imaging) in a fully automatic manner. To further improve the image quality, an inhomogeneity correction step was applied using the N4 method  [21]. The N4 method is an incremental improvement of the N3 method  [22]  that has been implemented in the ITK toolbox  [23]  and has proven to be more efficient and robust.
We have not included other intracranial tissues in our mask definition such as dura, exterior blood vessels, or veins because they are normally of no interest for brain analysis. This mask definition has been traditionally used to estimate the total intracranial volume (TIV) in many methods such RBM  [28] , SPM8, or VBM8 methods to normalize brain tissue volumes  [29, 30]  as it is expected to be nearly constant in each subject during the adult lifespan. To generate the template masks we followed a similar approach as described in BEaST paper since full manual labeling was too time consuming and error prone as discussed in Eskildsen et al. [11]. All template images in the library were automatically segmented using BEaST software to have an initial mask. Conditional mask dilation (only over CSF voxels) was applied to include external CSF not already included in the BEaST mask.
Finally, all the images were manually corrected by an expert on brain anatomy using the ITK-SNAP software  [31]  to remove segmentation errors. In Figure  2  we show an example of our mask definition compared to BEaST definition for a patient with Alzheimer's disease. To further increase the number of available priors on the library, all the cases were flipped along the midsagittal plane using the symmetric properties of the human brain yielding a total number of 98 labeled templates (original and flipped) as done in BEaST paper  [11]. Compared to BEaST template library creation, the main differences are the use of a denoising method to improve data quality, the use of a different registration method (ANTS instead of ANIMAL), and the application of different intensity normalization method.
Accuracy Datasets LOO Dataset. To measure the accuracy of the proposed method, we used the template library dataset by using a leaveone-out (LOO) cross validation. The characteristics of this dataset have already been described in Section 2. 1. Each of the 49 (nonflipped) library images was processed with the remaining images as priors (after removing the current case and its flipped version). The resulting segmentation was compared to the corresponding manual labels in the library. Independent Validation Dataset. To avoid any factor associated to our IC mask definition that could bias the comparison of the compared methods, we decided to use an independent dataset with its corresponding manual segmentations. Therefore, we performed a validation using an independent dataset available in the online Segmentation Validation Engine (SVE)  [17].
The SVE IC segmentation followed rules similar to those used here. This dataset consists of 40 T1w MRI scans and its associated manual labels (20 males and 20 females; age range. This high-resolution 3D Spoiled Gradient Echo (SPGR) MRI volume was acquired on a GE 1. 5 T system as 124 contiguous 1. 5 mm coronal brain slices (TR range 10. 0 ms-12. 5 ms; TE range 4. 22 ms-4. 5 ms; FOV 220 mm or 200 mm; flip angle 20 ∘ ) with in-plane voxel resolution of 0. 86 mm (38 subjects) or 0. 78 mm (2 subjects).
Reproducibility Dataset. Although the accuracy of a method is very important, another important feature is its reproducibility. Indeed, the capability to detect changes induced by the pathology in a consistent manner is a key aspect. To measure the reproducibility of the different compared methods, we used the reproducibility dataset of the brain segmentation testing protocol website (https://sites. google. com/site/brainseg/). This dataset consists of a test-retest set of 20 subjects scanned twice in the same scanner and sequence (SSS) and another set of 36 subjects scanned twice on different scanner and different magnetic field strength (DSDF) (1. 5 and 3 Tesla). SSS Dataset. To measure the reproducibility of the different methods compared on the same subjects and using the same MRI scanner, we used a subset of the OASIS (www.
oasisbrains. org) dataset consisting in 20 subjects (age = 23. 4 ± 4. 0 years, 8 females) who were scanned using the same pulse sequence two times (1. 5 T Siemens Vision scanner, TR = 9. 7 ms, TE = 4 ms, TI = 20 ms, flip angle = 10 ∘ , slice thickness = 1. 25 mm, matrix size = 256 × 256, voxel dimensions = 1 × 1 × 1. 25 mm 3 resliced to 1 × 1 × 1 mm 3 , averages = 1)  [37]. DSDF Dataset. To determine the consistency of the segmentations when different MRI scanners and different magnetic field strength were used, 36 adult subjects were scanned using two MRI scanners (1. 5 T and 3. 0 T General Electric Signa HDx scanner), mean interscan interval between 1. 5 T and 3 T scanner = 6. 7 ± 4. 2 days)  [18].
Method Parameter Settings. To study the impact of the method parameters, an exhaustive search of the optimum values was performed using the LOO dataset using the library segmentations as gold standard references. Each one of the 49 subjects in the library was processed using the remaining cases of the library as priors and the resulting segmentation was compared to the manual labeling. To measure segmentation accuracy, the Dice coefficient  [38]  was used. Method parameters such as patch size and search area were set as in BEaST method while an exhaustive search for the optimal number of templates  used for the segmentation process was carried out (see Figure  4 ). This search demonstrated that the segmentation accuracy stabilizes around  =  [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]  range which is in good agreement with previous results from  BEaST.
Accuracy Results. In Table  1 , the average DICE coefficient, sensitivity, and specificity for all 49 cases of LOO dataset using the different methods compared are provided. Results for all the cases together and separated by dataset subtype are provided (Alzheimer's disease (AD), normal infants (infant), and normal adult subjects (adult)). As can be noticed, NICE method obtained the best results in all the situations. Table  2   shows the statistical significance of these differences (paired -test). Intracranial cavity volume is normally used to normalize brain tissue volumes to provide a tissue measure independent of head size. Therefore, the ability of the compared methods to provide an accurate ICV estimation has to be assessed. To this end, volume estimations using the different compared methods were obtained and compared to gold standard manual volumes.
Validation of NICE using the SVE test dataset resulted in a mean DICE of 0. 9819 ± 0. 0024 (see http://sve. bmap. ucla. edu/archivel/). At the time of writing, this result was the best ( < 0. 01) of all the methods published on the website. BEaST had a DSC of 0. 9781 ± 0. 0047 and VBM8 obtained a DSC of 0. 9760 ± 0. 0025. Sensitivity and specificity results are also included in Table  3. A visual representation of false positive and false negative as supplied by the website is presented at Figure  7.
Reproducibility Results. In Table  4 , the average ICV differences for the different methods and datasets is provided. As can be noticed, NICE method obtained the most reproducible results in all situations. For the SSS dataset experiment (testretest), NICE significantly improved BEaST method while these differences were not significant for VBM8 method. For the DSDF dataset experiment, volume differences were higher than in the previous experiment. In this case, NICE was found to yield significantly improved estimates ( < 0. 05) compared to the two other methods. Finally, execution times of the different methods were compared. NICE method took around 4 minutes (NICE was implemented as a multithreaded MEX C file), BEaST method  However, it is worth noting that if we reduce the number of selected templates to 10 cases, we can reduce the processing time to less than 1 minute with only a small reduction of the segmentation accuracy (0.
Also the fact that it operates at 1. 5 mm 3 resolution introduces a blurring effect which increases the method reproducibility at the expense of the accuracy. The increased reproducibility/ accuracy of our proposed method may have a significant impact on the brain image analysis methods by increasing their sensitivity to detect subtle changes produced by the disease. While the advantage of NICE in segmentation accuracy of 0. 9911 versus 0. 9762 for VBM8 may appear small when compared over the three datasets evaluated, it is statistically significant and corresponds to more than a 2-fold reduction in error, from 2. 38% to 0. 89%. In a large volume such as the intracranial cavity (1500 cc), this reduction in error can represent a volume of approximately 20 cc, a nonnegligible amount. The improvement over BEaST is smaller (35%) but still statistically significant.
(i) Normal Adults Dataset. Thirty normal subjects (age range: 24-75 years) were randomly selected from the open access IXI dataset (http://www. brain-development. org/). This dataset contains images from nearly 600 healthy subjects from several hospitals in London (UK). Both 1. 5 T (7 cases) and 3 T (23 cases) images were included in this dataset. 3 T images were acquired on a Philips Intera 3 T scanner (TR = 9. 6 ms, TE = 4. 6 ms, flip angle = 8 ∘ , slice thickness = 1. 2 mm, volume size = 256 × 256 × 150, voxel dimensions = 0. 94 × 0. 94 × 1. 2 mm 3 ). 1. 5 T images were acquired on a Philips Gyroscan 1. 5 T scanner (TR = 9. 8 ms, TE = 4. 6 ms, flip angle = 8 ∘ , slice thickness = 1. 2 mm, volume size = 256 × 256 × 150, voxel dimensions = 0. 94 × 0. 94 × 1. 2 mm 3 ).