dataesr/scientific-paragraphs-categorization huggingface dataset:
The screening of a set of compounds whose values for the free fraction ratio of the brain/plasma that are known in humans in vivo (Atenolol, Bupropion, Diazepam, Indomethacin, Lamotrigine, Levofloxacin, Methotrexate, Metoprolol and Verapamil) was assessed in the 96 TW system BBB model. The results obtained in vitro of the predicted unbound fraction ratio (in vitro Kp,uu,brain) were compared with the in vivo unbound fraction ratio in the cerebrospinal fluid (CSF) and plasma reported in humans  [25]  (in vivo Kp,uu,CSF) (Figure  6A ). The results were plotted in a XY correlation, presenting the nonlinear regression with a value R 2 = 0. 8808 (Figure  6B ), meaning a fair correlation between the data obtained in our miniaturized human in vitro BBB model and the data obtained in humans. Such a correlation (R 2 = 0.
Acknowledgments: The authors warmly thank the technical support collaboration in this study case from  Sophie Duban-Deweer  for the help with the LC-MS/MS. The mass spectrometer of the  Spectrométrie de Masse de l'Artois (SMART)  facilities used in this study were funded by the  European Regional Development Fund (ERDF) , the  Hauts-de-France regional council and Artois University, France. We would like to thank  Gregory Maubon  for the technical supervision of the confocal microscopy,  Véronique Doconto  for the help in the experimental design for neurotoxic compound evaluations,  Lucie Dehouck  for the Original 12 TW model cell culture training and, finally,  Emmanuel Sevin  for his analysis and training in permeability studies.
461 Wang et al. cultured primary rat cardiomyocytes on electrospun PLA/PANI sheets. Upon folding these sheets into different configurations, differences in beating frequency and maximum displacement were observed. 461 These data indicate that cardiomyocytes can be manipulated using the culture system, but these differences must be examined as experimental iterations are introduced. One study also cultured cardiomyocytes on numerous organic semiconductors. 462 This study compared viability, gauged using the MTT assay, a colorimetric assay that monitors metabolic activity, of cardiomyocytes on TIPS-pentacene, DPP(TBFu) 2 , P3HT, and PEDOT:PSS, as well as on culture plastic and glass, providing a comparative analysis of these substrates. The MTT assay showed slightly higher values for P3HT and slightly lower values for PEDOT:PSS versus cells on culture plastic, but all substrates performed well.
Numerous other MEA formats have been produced, including recessed PEDOT:PSS electrodes (Figure  30a -f),  258  PE-DOT:PSS microneedle electrodes (Figure  30g-i ),  488  and mushroom-shaped electrodes (Figure  30j -m),  489  among others. All of these electrode formats appear to show similar cellular compatibilities, at least from a visual standpoint, regardless of geometry (Figure  30d, i, m ). 258, 488, 489 Recordings of neuronal cells can even be collected through arrays of conductive hydrogel electrodes. 490 However, comparison of cellular compatibility for different geometries is difficult, given the wide range and variance among culture conditions and cell types. With proper material selection and preparation, MEAs can also be constructed to be transparent,  255, 256, 491  which is convenient for monitoring cells visually during recordings, especially given that proximity of cells to a particular recording electrode is of great relevance to the resultant data quality.
ACKNOWLEDGMENTS R. O. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (Grant Agreement No. 723951 ). This material is also based upon work supported by the  Air Force Office of Scientific Research  under award number  FA8655-20-1-7021  to RMO. A. M. P. acknowledges funding from the  Oppenheimer Junior Research Fellowship  and the  Maudslay-Butler Research Fellowship at Pembroke College, Cambridge. D. v. N. is funded by the  W. D Armstrong Trust Fund  and the  Oppenheimer Memorial Trust. Y. F. and D. I. were funded by the  European Space Agency  project  BONUS. J. S. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant ,  ICE METs  (No.
The corners and edges contribute 3. 6 and 1. 7 times more compared to the crystal faces. In the setup, the crystal is divided into equally shaped voxels, and the number of nearest neighbors (n) are identified at every time step (Figures  3a  and b ). For simplification, all voxels with n=1, 2 or 3 are considered as corner voxels. This is a preliminary study that was presented at the Goldschmidt conference in 2021. The code is available on Github (https://github. com/marthgg/dissolution). In the input file, the user defines the detachment probability for each voxel group ( c = corners,  e = edges,  f = faces) and either constructs a crystal shape (e. g. , Figure  3a ) or reads in a binary file with a predefined crystal shape (e. g. , Figure  3b ). At every iteration, the number of neighbors is calculated, and the voxels are removed based on the detachment probability.
𝐫 𝑙𝑛 𝑟 𝑙𝑚 𝑟 𝑙𝑛 -cos𝜃 𝑚𝑙𝑛 ̅̅̅̅̅̅ ) 2 𝛩(𝑟 0 -𝑟 𝑙𝑚 )𝛩(𝑟 0 -𝑟 𝑙𝑛 ) (4. 7) where  is the distance between atoms, and ,  and  are the designated atom indices. The two-body energy term (  (2) ()) contains three terms; the short-range repulsion, the Coulomb interaction due to ionicity, and the charge-induced dipole interaction caused by atomic polarizabilities. The three-body energy term (  (3) (  ,   )) includes the strength of the threebody interactions and the angles between triplets of atoms. In the potential, only the triplets O-Si-O and Si-O-Si are considered. The equations describing the two-and three-body interactions can be expressed in various ways, and there is a difference in how they are described by  Broughton et al. (1997) , and in the file that is imported by the program LAMMPS. All necessary parameters for the simulations performed in Paper III are given in the Appendix of Paper III.
The system in Paper II contains both a mineral (periclase or brucite) and water, while the system in Paper III contains only one mineral (α-quartz). The construction of a simulation setup can be quite time consuming. Therefore, along with collaborators at the Njord Centre, I have contributed to create a Python package, called 'molecular-builder' which is hosted on GitHub and can be installed from the Python Package Index: https://pypi. org/project/molecular-builder/ (Sveinsson, 2021). This package simplifies the construction of molecular systems, with focus on input files that can be directly used in simulations with LAMMPS. The package Molecular Builder is developed using Python and is heavily based on the Atomic Simulation Environment (ASE)  (Larsen et al. , 2017) , which generates crystals based on their space group description, and the Open Visualization Tool (OVITO)  (Stukowski, 2009) , which creates bonds and write the data into a LAMMPS data input file.
The simulation length often varies from a few ps to several ns. After providing all necessary parameters, the simulations are run. The input parameters contain an initial set of position and velocity for each atom, which is used to calculate the interatomic forces between atoms. These forces decides where each atom is moved. The atoms now contain a new position and velocity, and the forces acting on the atoms are calculated again. These three steps (Figure  10 ) are repeated at every time step until the end of the simulation. When the simulation is finished the time trajectories are used to do analyze and plot the results. To visualize the behavior of the system with time, the OVITO software was used. OVITO is a 3D visualization software designed for post-processing atomistic data obtained from molecular dynamics simulations, like LAMMPS.
and ten accumulations was required to obtain a spectrum with a high enough signal to noise ratio to detect bands related to the precipitates on the calcite surface. During the analysis, no change in the material was observed either in the optical microscope image or spectrum. Background removal and peak fitting were performed using the WITec Project Plus software (version 4). 2. 1. 2. 4. Water resistance. For two of the samples that had been exposed to solutions for a longer period, we tried to dissolve the newly-formed layer on top of the calcite surface to test the chemical resistance of the layer. To do so, 2 mL of deionized water was injected between every scan during the first 5-10 scans, then 5 scans were acquired without any injection, one injection and 5 scans again. The sample was in the fluid cell for ~80 min.
In the present study, we apply a mechanical load on a solid surrounded by water by letting the simulation domain change with time in x-, y-and z-directions separately to achieve a particular mechanical stress in each coordinate direction (Fig. 3a ). This lets us model a quasi-2D pore throat, representing a grain boundary, coupled to a large pore space. The mechanical stress and the temperature are applied by a Nose-Hoover barostat and thermostat  (Shinoda et al. , 2004). We run the simulations with the open source program LAMMPS  (Plimpton, 1995) , together with the ClayFF force field for the minerals  (Cygan et al. , 2004). To simulate water, the single point charge (SPC) water model is used because it is compatible with the ClayFF force field  (Berendsen et al. , 1987; Cygan et al. , 2004).
Computational details The mineral structures of periclase and brucite, with space groups Fm3m and P3 À m1 are constructed from published unit cells  (Zigan and Rothbauer, 1967; Sasaki et al. , 1979)  using the Atomic Simulation Environment (ASE) software  (Larsen et al. , 2017). The water is simulated with the single point charge (SPC) model  (Berendsen et al. , 1987). In the simulation domain, the water has an initial density of 1000 kg/m 3. To pack the water, randomly and without overlap, we use the software package packmol  (Martinez and Martinez, 2003) , and set the tolerance distance to 2. 0 A ˚, which is the minimum distance between a mineral structure and water molecules. All simulations are run using periodic boundary conditions in the three directions of space. The ClayFF potential has a 12-6 Lennard-Jones potential.
Acknowledgements iii Acknowledgment We thank  A. Røyne  for fruitful discussions and feedback on the manuscript. V. Rapelius is thanked for helping with ICP-OES analyses at Münster University and  Sarah Bureau  and  Sylvain Campillo  for help with ICP-AES and ICP-MS analyses at the  University Grenoble Alpes. C. V. P. acknowledges funding through the  Marie Curie ITN CO2React. Funding from Labex OSUG@ 2020 (Investissement d'avenir- ANR10-LABX56 ) and the  University of Oslo  are also acknowledged. The data (AFM images and ICP-AES data) is available upon request to the corresponding author.
Our experiments were based on the multitasking simulator NASA MATB-II software  [63] , because of its recognized similarities with pilot activities in the cockpit. See Fig. 12  for an illustration of the software MATB-II interface. Given the literature on mental workload  [64] [65] [66] , we decided to expose subjects to short-duration scenarios leading to three levels of mental workload ("Low" , "Medium" and "High"). The latter differ by the frequency and difficulty of the tasks asked to the subjects. Each experiment operates with the following steps: 1. The initial training phase: During this phase lasting thirty minutes, the experiment is first presented. Then, the subject uses the simulator, but only one of the tasks is active at a time. Then, a four-minute multitask scenario is launched.
Conclusion In this paper we described the zNLP system for the BUCC 2017 shared task. We proposed a three-step approach to parallel sentence identification in Chinese-English Comparable Corpora by 'translating' the monolingual ZH corpus into English, filtering out candidate parallel sentence pairs with Solr and then selecting the final parallel source-target sentence pairs by using an SVM classifier. Our system identifies parallel sentences with an F1-score of 45. 13% in the test data. The proposed method is fast and does not rely on external resources except a Chinese-English dictionary. The code is publicly available at https://github. com/zzcoolj/ Parallel-Sentences-Identifier. Potential pathways for future work include adding more filter conditions to Step 2 (e. g sentence length ratio, word overlap threshold) for candidate source-target parallel sentence pairs.
The first three categories have Boolean domains, whereas SPOT5 has maximum domain size of 4. For each category, we report in Tab. 1 the mean value of the size of the problem, the number of cliques found, the number of selected cliques among them and their arity, and the CPU time to find and select the cliques. We limit the maximum number of cliques found to 10,000 in order to control the computation time. The largest CPU time was 11. 61 seconds for MaxClique/c-fat200-5 (n = 200 variables, e = 11, 627 cost functions, and 10,000 selected cliques of arity 3). The arity of selected cliques varies from 3 to 67 (MaxClique/san1000). We compare solving time to find and prove optimality for toulbar2 exploiting cliques (denoted as toulbar2 clq ) against the original code without cliques (both using default options, including hybrid best-first search  [1] ), and against the CP solver gecode 3 , the MaxSAT solvers maxhs 2.
51  [11, 10]   2. Number of solved instances and mean solving computation time in seconds, for toulbar2 solver without using cliques compared to toulbar2 clq exploiting cliques, cplex, maxhs, eva and gecode. toulbar2 clq solving time does not take into account clique generation time (see Tab. 1). A CPU time limit of 1 hour was used for unsolved instances for reporting mean solving times. [30] , and IBM-ILOG cplex 12. 6. 0. 0 (using a direct encoding  [19]  and parameters EPAGAP, EPGAP, and EPINT set to zero to avoid premature stop). All computations were performed on a single core of AMD Opteron 6176 at 2. 3 GHz and 8 GB of RAM with a 1-hour CPU time limit 4. In Tab. 2, we give the number of solved instances within a 1-hour CPU time limit. Among 252 instances, cplex solved 224 instances, maxhs 216, toulbar2 clq 213, eva 208, toulbar2 205, and gecode 137.
For Auction, toulbar2 clq is more than two orders of magnitude faster than toulbar2, gecode (which cannot solve 57 Auction/path instances in 1 hour) and eva on Auction/path (cat paths 60 170 0005 instance unsolved in 1 hour). Still toulbar2 clq is one order of magnitude slower than cplex and maxhs. On MaxClique (resp. SPOT5), toulbar2 clq solved 6 (resp. 2) more instances than without using cliques. For example, toulbar2 clq solved MaxClique/MANN a45 in 57. 9 seconds (taking 0. 24 supplementary seconds to generate the 330 selected cliques) whereas without using cliques it could not finish in 1 hour (maxhs took 28 seconds, cplex 93 sec. , and eva and gecode could not solve in 1 hour). SPOT5/404 was solved in 6 seconds using 32 cliques and 88. 7 seconds without using cliques (cplex took 0. 02 seconds, eva 0.
07 sec. , maxhs 8 sec. , and gecode could not solve in 1 hour). Finally, we summarize the evolution of lower and upper bounds for each solver over all instances in Figure  1. Specifically, for each instance I we normalize all costs as follows: the initial lower bound produced by toulbar2 is 0; the bestbut potentially suboptimal -solution found by any solver is 1; the worst solution is 2. This normalization is invariant to translation and scaling. Additionally, we simply normalize time from 0 to 1, corresponding to 1 hour. A point x, y on the lower bound line for solver S in Figure  1  means that after normalized runtime x, solver S has proved on average over all instances a normalized lower bound of y and similarly for the upper bound. We show both the upper and lower bound curves for all solvers evaluated here, except gecode which produces no meaningful lower bound before it proves optimality.
III. PROPOSED ONTOLOGY In this section, we first describe the autonomous robot architecture ontology (ROA), which defines the main concepts and relations regarding robot architecture for autonomous systems, and which inherits from SUMO/CORA ontologies  [8]. Then, we present the implementation of the ROA ontology for AuR in Web Ontology Language (OWL). The goal of our ROA ontology and its implementation is to serve as a conceptual framework so that people and robots can share information about robot architectures. ROA users can instantiate its concepts to represent information about specific as well as generic architectures. For example, the common architecture diagrams found in robot-architecture papers can be thought as instances of ROA. In essence, the conceptualization described below is akin to the metamodels of concept representation languages such as Unified Modelling Language (UML) (see Fig.
Image parameters for the GRE sequence are: T R/T E = 400 ms/8. 9 ms, f lip angle = 80 • , axial images with a F OV = 160 × 160 mm 2 , slice thickness = 2 mm, spacing between slices = 0. 5 mm, matrix = 256 × 256, receiver bandwidth = 11. 90 kHz or ±5. 95 kHz (pixel receiver bandwidth = 46. 48 Hz/Px). The SNR calculation and the registration of the MR images were performed using MATLAB language (Mathworks, Milwaukee, USA). The SNR was determined by the mean of the signal intensity in the entire MR image divided by the mean standard deviation of a region of interest chosen outside the image of the phantom (in the air) which is the noise. For the registration, a self-made MATLAB program was developed to align MR images to a common coordinate system. For the purpose of quantifying the performance of the endoluminal coils in terms of SNR uniformity distribution, the same GRE and FSE sequences were applied, this time, using the endoluminal coil as a receiver.
Acknowledgment This work was supported by the  Rhône-Alpes region  (ADR and CIBLE projects) and the  DGA (French Military Programs Management and Procurement Agency -RAPID  and  SNIFER projects). This work was performed within the framework of  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Lyon University , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR).
This comparison step is computed inside the SE using an embedded On-Card-Comparison (OCC) module (Cf. Figure  2 ). Another important feature about fingerprint concerns its type. Even if a fingerprint is considered as unique, it necessarily corresponds to one of the five classes (know as Henry classes) as defined by Henry: Arch, Left Loop, Right Loop, Tented arch and Whorl, as illustrated in Figure  1. This compliant standard representation can be usefull for Europay Mastercard Visa (EMV) transactions with biometric authentication. Indeed, since 2015, the EMVco  (EMVCo, 2008)  allows to use biometric data in the Cardholder Verification part (Figure  3 ). This is the context of this study that justifies the use of the ISO Compact Card II representation instead of any other existing representation.
SFinge databases FVC databases do not provide any information on the fingerprint class. Nevertheless, it will be shown in different works  (Maio et al. , 2004; Fiérrez-Aguilar et al. , 2005)  than SFinge has the same behavior and similar performance than real databases. Hence, we may expect a similar performance of class recognition than real databases. We have generated five databases with the SFinge software, one for each class of fingerprint described in Table  1.
Experimental results Here, we want to determine the number of bins needed to have the minimal impact on the recognition rate performance. We tested different values of bins (8, 16, 32 and 64) on the feature structure. We only consider the recognition results when 80% of the databases is used for learning for the four value of bins. Results are presented on Table  2  We can observe than the best results are obtained with 16 bins for the structure based feature. We can explain this result by the fact that the redundancy with 64 bins is high for this application. With only a features with 64 values (50 = 3 × 16 + 2), we obtain 80. 37% of fingerprint class recognition with the standard svm parameters (without any optimization). In the following, we keep this size of the feature vector. The ISO template only contain four information, and we want to know which information is important for the fingerprint class recognition.
Feature structure for each template For each template, we have computed the Delaunay triangulation-based on minutiae (Figure  6  shows an example of Triangulation). For each obtained triangle, we extract different parameters: • the three angles, • the three edges lengths, • the area. To resume, the feature vector TriInf jk is generated for the template j of the subject k and it consists of three main characteristics: TriInf j,k = {{AngleA jkl , AngleB jkl , AngleC jkl }, {LengthAB jkl , LengthAC jkl , LengthBC jkl }, {Area jkl }}, ∀l ∈ [1; M j ], (2) where {AngleA jkl , AngleB jkl , Angle C jkl } is the vector of data related to angle values of the M j triangles of the template j, {LengthAB jkl , LengthAC jkl , LengthBC jkl } represents the vector of data related to computed lengths for the M j triangles of the template j, {Area jkl } corresponds to the vector of data related to the area of the M j triangles of the template j.
Each SFinge database has 800 templates. Label Fingerprint classes 1 Arch 2 Left Loop 3 Right Loop 4 Tented 5 Whorl Table 1: Fingerprint classes databases label 3. 2. 2 SVM setup We need to create a database for the training and one for the test. Multiple train-test sequences were run. In each, the fingerprint database was subdivided into dis- tinct training and test sets. In each train-test sequence, 80% of the 5 SFinge database content was chosen for training, and the 20% for testing. Specifically, each training set contained 640 fingerprints, while each test set contained the 260 remaining fingerprints. 10 ran- (Chang and Lin, 2011) and test sets were obtained and the class recognition rate was run over the 10 iterations. We have used the well known libsvm(Chang and Lin, 2011)with the default parameters.
In the coastal ocean zones, satellite altimetry data processing and interpretation poses specific difficulties, due to the interaction of the radar signal with land topography, inaccuracies in some of the geophysical corrections and to the fast changes in the sea level. In order to optimize the completeness and the accuracy of the sea surface height information derived from satellite altimetry in coastal ocean areas, a dedicated post-processing software, called X-TRACK, has been developed by the Center of Topography of the Ocean and Hydrosphere in Toulouse. It is tailored for extending the use of altimetry data to coastal ocean applications and provides freely available along-track Sea Level Anomaly time series that cover today all the coastal oceans. Here, we present the improvements made in version 2016 of X-TRACK and show the gain in near-coastal data accuracy using in situ tide gauge observations.
e. development of coastal data processing strategies, understanding of the near-shore sea level variations, analysis of the usefulness and limitations of altimetry data in coastal ocean applications), the experience gained from the existing altimetry data records is absolutely crucial. A number of projects have been funded by space agencies to improve conventional satellite radar altimetry products over coastal areas and continental waters (PIS-TACH and PEACHI for the French Centre National d'Etudes Spatiales, COASTALT and eSurge for the European Space Agency). In parallel, a post-processing algorithm specifically tailored to recover altimetry data over marginal seas was developed at LEGOS (Laboratoire d'Etudes en Geophysique et Oceanographie Spatiale) more than ten years ago. This software, called X-TRACK, is now routinely operated at the Center of Topography of the Ocean and Hydrosphere (CTOH, French Observation Service dedicated to satellite altimetry studies) and a large part of the archive of altimetry data has been reanalyzed.