dataesr/scientific-paragraphs-categorization huggingface dataset:
Importance of the International Pulsar Timing Array The most recent data in the current IPTA data release were obtained in 2012. The upcoming second data release will have a further five years of data, more pulsars and improved timing precision on the most recent observations. This new data set should significantly improve the pulsarbased timescale as (1) any extra observations prior to 2005 will help constrain the variations seen in the early data, (2) new, high quality data on a large number of pulsars since 2012 will enable the extension of the timescale and an improved understanding of its long-term stability and (3) upto-date analysis methods will be applied when producing the data set, which should reduce the number of artefacts and anomalies within the data. Of course, the longer data spans and improved timing precision will also mean that low-frequency noise processes (such as pulsar timing noise) will become more important.
Of course, with sufficient numbers of equally-contributing pulsars, any individual event will be averaged-down, but unless such events can be better understood and modelled they may present a fundamental limit to the stability of the pulsar-based timescale. In  Hobbs et al. (2012)  only observations from the Parkes telescope were used. This meant that any discrepancies found between the pulsar-based timescale and a terrestrial timescale could arise from errors in the observatory time scale. In contrast the IPTA sample includes data from many observatories, often for a given pulsar. The most dominant pulsar in the IPTA sample, PSR J1713+0747, includes observations from seven telescopes each with their own independent observatory time scale. We note that the participating observatories in the IPTA do not have a well-defined common reference time.
Any inaccuracy in that time transfer would be common to all observatories and any variations that do not take the form of a quadratic-polynomial (and hence, not affected by the fitting procedures) would affect our comparisons between TT(IPTA16) and terrestrial time standards. An analysis in  Hobbs et al. (2012)  suggested that any such errors will be <10 ns and therefore have no effect on the results presented here. More detailed studies of the precision for each step in the process from measuring the ToA to the resulting timing residuals have been presented elsewhere (for instance, see the analysis of instrumental noise in  Lentati et al. 2016). We currently see no evidence for a statistically significant common signal in our results (when referred to TT(BIPM17)) and note that it is becoming easier to search for common offsets in PTA data sets as observatory instrumentation becomes more standardised and more telescopes observe the same set of pulsars with similar timing precision.
CONCLUSIONS We have used the IPTA DR1 data set with two different analysis algorithms, one frequentist and one Bayesian, to establish a pulsar-based timescale which we call TT(IPTA16). Consistent results are obtained from the two analysis methods. We show that TT(IPTA16) is consistent with the timescale TT(BIPM17). We confirm that TT(BIPM17) is currently the most stable timescale in existence and that current efforts to detect nano-Hertz gravitational waves through pulsar timing using a post-corrected BIPM timescale as a reference are not limited by instabilities in the reference timescale. The pulsar-based time standard will improve with continued observations using the existing IPTA programs. The IPTA is currently preparing a second data release that includes significantly more pulsars, uses more telescopes and upgraded instruments at the observatories.
In the longer term, even more telescopes will start to contribute to the IPTA data set. Within a few years, we expect high precision observations from the FAST telescope currently being commissioned in China, and the MeerKAT telescope in South Africa. Of course, atomic clocks will also continue to improve, as fast, or faster, than pulsar observations. However, since pulsar timescales are based on entirely different physics compared to atomic timescales, and are essentially unaffected by any terrestrial phenomena, they form a valuable independent check on atomic timescales. In addition, they provide a timescale that is, in principle, continuous over billions of years. described here was carried out within a virtual machine on a single laptop. • The arrival time and parameter files are corrected for known issues.
Such issues include correcting telescope observing codes, removing any very early or very recent observations (to ensure a well-defined time range for obtaining the clock signal). IPTA-defined parameters such as phase jumps, white noise models etc. are removed. The arrival times are subsequently sorted into time order. PSRs J0437-4715, J0711-6830, J1045-4509, J1603-7202, J1732-5049, J1824-2452A and J2129-5721 are only observed by the PPTA. For these pulsars we use the more recent  Reardon et al. (2016)  arrival time and parameter files instead of the IPTA data release. The Parkes observations for PSR J1909-3744 were also taken directly from  Reardon et al. (2016). • The parameter files were updated to ensure that the JPL DE421 solar ephemeris was used in the timing procedure and the ToAs referred to TT(BIPM17).
Default tempo2 parameters (such as models for the dispersion measure variations caused by the solar wind) were used. • The NANOGrav data sets are provided in multiple subbands. We have used the averageData plugin in tempo2 to produce a single ToA and corresponding uncertainty for each independent observation. The averageData plugin (1) identifies observations close in time (for us we select all simultaneous observations over different bands observed by NANOGrav), (2) for each block of data the non-weighted mean of the observation times are determined and all the time-dependent parameters in the timing model updates to this epoch, (3) a weighted fit is carried out for a phase offset and its uncertainty using only the specific block of data, (4) a pseudo-observation is added with the arrival time being the new epoch and the frequency and observatory site-code being determined from the closest, in time, actual observation.
The residuals for a specific observing instrument and telescope site are "whitened" using an interpolation scheme (known as "IFUNCS"). The plugin then trials a well-defined number of EFAC and EQUAD parameters. For each parameter pair a Kolmogorov-Smirnov (KS) test is carried out to compare the normalised, whitened residuals with Gaussian, white noise. The EFAC/EQUAD parameter pair that yields residuals closest to white, Gaussian noise is subsequently recorded and used for later processing. • A new set of arrival time flags are introduced including specific observing bands with bandwidths of 500 MHz (i. e. , 0-500 MHz, 500-1000 MHz etc. ). • We add the data from each observing system into a final arrival-time file in order of data span. We start with the longest data span and then identify an overlapping data set in the same observing band.
ACKNOWLEDGEMENTS The  National Radio Astronomy Observatory  and  Green Bank Observatories  are facilities of the  NSF  operated under cooperative agreement by  Associated Universities, Inc. The Arecibo Observatory is operated by  SRI International  under a cooperative agreement with the  NSF  ( AST-1100968 ), and in alliance with  Ana G. Méndez-Universidad Metropolitana , and the  Universities Space Research Association. The Parkes telescope is part of the  Australia Telescope  which is funded by the  Commonwealth Government  for operation as a National Facility managed by  CSIRO. The Westerbork Synthesis Radio Telescope is operated by the  Netherlands Institute for Radio Astronomy (ASTRON)  with support from The  Netherlands Foundation for Scientific Research NWO. The 100-m Effelsberg Radio Telescope is operated by the  Max-Planck-Institut für Radioastronomie at Effelsberg.
Some of the work reported in this paper was supported by the  ERC   Advanced Grant 'LEAP' , Grant Agreement Number  227947  (PI Kramer). Pulsar research at the  Jodrell Bank Centre for Astrophysics  is supported by a consolidated grant from  STFC. The Nancay radio telescope is operated by the  Paris Observatory , associated with the  Centre National de la Recherche Scientifique (CNRS)  and acknowledges financial support from the  'Programme National de Cosmologie et Galaxies (PNCG) ' and ' Gravitation, Références, Astronomie, Métrologie (GRAM) ' programmes of  CNRS/INSU, France. LG is supported by  National Natural Science Foundation of China  No. 11873076. The Flatiron Institute is Supported by the  Simons Foundation. The NANOGrav work in this paper was supported by  National Science Foundation (NSF)   PIRE program  award number  0968296  and  NSF Physics Frontiers Center  award number  1430824.
KJL is supported by  XDB23010200 ,  National Basic Research Program of China, 973 Program ,  2015CB857101  and  NSFC U15311243 ,  11690024 , and the  MPG  funding for the Max-Planck Partner Group. LG was supported by  National Natural Science Foundation of China (NSFC)  (nos. U1431117 ) and # 11010250. EG acknowledges the support from  IMPRS Bonn/Cologne  and the  Bonn-Cologne Graduate School (BCGS). YW acknowledges the support from  NSFC  under grants  11503007 ,  91636111  and  11690021. SBS acknowledges the support of  NSF  EPSCoR award number  1458952. KL and GD acknowledge the financial support by the  European Research Council  for the  ERC Synergy Grant BlackHoleCam  under contract no. 610058. Part of this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration.
Smart library design requires the identification of key residues, often through functional predictions using bioinformatics followed by high-throughput experimental analyses. Many such recently-developed bioinformatic approaches can be used to identify promising amino acid candidates, following the semi-rational method, which is a combination of directed evolution and rational design methodologies  [1, 3, 6]. Specifically, the Molecular Class-Specific Information Systems (MCSIS)-based bioinformatics tools, which include the components of (structure-based) large-scale multiple sequence alignments, co-evolution, structure, and automated literature mining, have been shown to be immensely beneficial for a semi-rational method of protein engineering  [8] [9] [10]. An example of such an MCSIS-based bioinformatics approach is our 3DM software suite that contains the standalone 'Comulator' tool, which uses a correlated mutation analysis (CMA) algorithm to identify co-evolved residues in large structure-based multiple sequence alignments (MSAs)  [11].
PfPGI Mutant Library The cloning of the gene pgiA has been described previously  [22]. A site-saturation library was designed and created based on CMA using the Comulator software. The constructed library consisted of pgiA variants that had alterations in two strongly correlated amino acids: Pro132 and Tyr133. The corresponding numbering in the 3DM alignment was Pro27 and Tyr28. The created pgiA mutants were cloned in expression vector pET24d and used to transform E. coli.
5, and 20% PEG4000. A total of 50 mM F6P was added to the AD and VY crystals before mounting. For each different mutant, a single crystal was briefly washed in a cryoprotectant consisting of 25% ethylene glycol in the crystallization buffer, flash cooled to 100 K, and stored in liquid nitrogen before data collection on the Diamond synchrotron light source. Data were processed using Xia2 software  [33] , and structures determined by molecular replacement using the wild-type PfPGI coordinates as a search model (PDB code: 1X82)  [23]  and the program Phaser  [34]. Rounds of model building using Coot  [35]  and refinement in Refmac  [36]  gave acceptable models, verified using MolProbity  [37]. For each structure, electron density was present for all the polypeptide chain, and the models had no missing residues.
However, the density was weak for the side chains of Lys21, Lys188, and Lys189 (AG); Arg25, Glu114, Asp116, Lys118, Lys188, and Lys189 (RG chain A); Glu114, Lys188, and Lys189 (RG chain B) and Lys188 and Lys189 (AD and VY, chains A and B). Data collection and refinement statistics are given in Supplementary Tables  S1  and S2. The four mutant structures were compared to the wild-type Mn/5PAA structure (PDB code: 1X7N) by superposition of all the protein atoms of the residues that coordinate the Mn (His88, His90, Glu97 and His136).
AQUA-DUCT Analysis AQUA-DUCT software version 0. 7. 12  [41]  was used to trace the paths of all water molecules that were identified during each simulation in each of the analyzed cavities. The analysis of the main cavity was carried out with the object defined as a sphere of 6 Å radius from the center of masses of Val130, Ala135 and the manganese ion, whereas the rear-side cavity analysis was carried out with the object defined as a sphere of 5. 5 Å radius from the center of masses of the C-alpha atoms of His90, Leu93, Pro131 and His136. The different radii reflect the size of the cavities detected in the crystal structures. The scope was defined as the interior of the convex hull of C-alpha atoms of each monomer. Each water molecule path was cut to fit to the protein surface (auto_barber set to protein).
All inlets were then clustered using the Barber method with cutting sphere correction to the van der Waals radius of the closest atom (auto_barber_tovdw set to True). For the results' visualization, PyMOL  [42]  software was used. AQUA-DUCT was also used for the analysis of the local water molecules' distribution in the area corresponding to all the paths of all traced molecules. The high-density points or hot spots were located. These are regions where water molecules are either attracted by favorable interactions with nearby amino acids or where they are trapped in hydrophobic cages. In both cases, hot spots can mark regions of particular importance for the enzyme's functions  [43].
Biomolecules 2018, 8 ,Figure 4. 84 Figure 4. Comparison of the structure of the loops adjacent to the mutation position in the four mutant structures. In each panel, the structure of the wild-type enzyme (PDB code: 1X7N) is shown in white, with the relevant residues highlighted in stick representation and labelled. Y3′ and K4′ refer to the Nterminal strand from the adjacent subunit in the dimer. (a) Mutant AD (slate), (PDB code: 4LUL); (b) mutant RG (wheat), (PDB code: 4LTA); (c) mutant AG (yellow), (PDB code: 4LUK); and (d) mutant VY (pink), (PDB code: 4LUM).
g. the Spherical Harmonics Discrete Ordinate Method  Evans (1998) ) and those using statistical approaches: Monte Carlo (MC) methods  (Marchuk, Mikhailov, Nazaraliev, et al. , 1980). Our proposition builds upon one of the major strengths of MC models: the computing time being only weakly sensitive to the size of the geometrical and spectral dataset. The theoretical reasons of this weak sensitivity have been identified since the origin of the method (e. g. in  Marshak et al. (1995) ), but it is only quite recently that Monte Carlo codes could practically handle highly refined surface descriptions and large cloud fields such as those produced by today's high-resolution atmopsheric models (with typically hundreds of million to a few billion grid points). This practicability has paved the way to numerous applications, even outside atmospheric sciences.
Along the line of the continuous development of Monte Carlo codes since the 1960s  (Collins & Wells, 1965; Marchuk, Mikhailov, Nazareliev, et al. , 1980; Marshak et al. , 1995; Iwabuchi & Kobayashi, 2006; Mayer, 2009; Pincus & Evans, 2009; Cornet et al. , 2010) , we here try to contribute with: 1. connections with the literature and practice of the computer graphics community, 2. a freely-available C library of general use in Monte Carlo problems involving large cloud scenes above complex surfaces. Although we also present in this paper a code built with the library, we do not wish to focus on this particular code example, but on the library itself, that is designed to help the coding of a wide diversity of Monte Carlo algorithms while taking advantage of the recent developments in computer graphics.
, 2017; Novák et al. , 2018; Novák et al. , 2014). Section 3 describes a new library inspired of such recent experiences. It allows the construction of acceleration grids for both surfaces and volumes. It makes use of the Embree library for surfaces  (Wald et al. , 2014)  and preserves its essence: an ensemble of low-level functions that help the design of Monte Carlo codes involving large geometric models and large volumetric datasets. The library elements remain independent, as much as possible, of the specificity of the (null-collision) Monte Carlo algorithm. In this sense, the present contribution is conceived in the spirit of the I3RC Community Monte Carlo model  (Cahalan et al. , 2005; Pincus & Evans, 2009; Jones & Di Girolamo, 2018) , designed as a platform to facilitate the development of atmospheric radiative transfer codes by radiation physicists in a wide range of applicative contexts.
Construction and Use of Hierarchical Grids A development environment constituted by a set of independent libraries is freely available online (Meso-Star, 2016): the functions that are dedicated to the construction and crossing of hierarchical grids are implemented in Star-VoXel, which is one of these distributed libraries. These tools are addressed to radiative transfer specialists who are either developing new Monte Carlo codes or upgrading the ray-casting routines in existing ones. Independent modules offering functionalities such as random sampling of probability density functions, parallel integration of a realization function, sampling and evaluation of scattering and reflection functions, and ray casting in surfaces and volumes are described in Table  C2  of Appendix Appendix C. The module that handles ray casting in surfaces is based on the Embree library  (Wald et al.
Integral Formulations and Filtering Functions As mentioned before, a strong attention was devoted to the separation of concepts while designing the library. As much as possible, we tried to preserve a coherence with the computer graphics libraries from which we started  (Pharr & Humphreys, 2018; Wald et al. , 2014) , but above all we systematically favored all possible connections with the integral formulation concepts of the radiative transfer community. The specificities of these formulations were illustrated when null-collision methods were introduced in Section 2. 3. One of these specificities is the recursivity associated to the rejection of null collision events. In order to separate the physical part of the code (e. g. where the treatment of scattering or reflection events is implemented) from the treatment associated to the recursivity of the ray casting, filtering functions are used.
3. Treating intersected voxels by filtering them and optionally evaluating quantities at each intersection can be associated to other types of algorithmic operations due to transformations made at the integral formulation level. Sensitivity evaluation is only one example of such. A second example is the possibility of handling negative null-collision coefficients, i. e. configurations for which k is not a true overestimate of k  (Galtier et al. , 2013). A third example is the sampling of absorption lines when the gaseous part of k cannot be precomputed in line-by-line Monte Carlo algorithms dealing with large spectroscopic databases  (Galtier et al. , 2016). More generally speaking, as soon as the introduction of null collisions is perceived as a formal way to handle the nonlinearity of Beer's extinction in heterogeneous fields, the door is open to the design of Monte Carlo algorithms departing widely from the intuitive addition of virtual collisionners, and the use of filtering functions is a practical way to simplify such developments: the iteration over intersected voxels is handled by the ray-casting procedure, and the treatment specific to the recursive algorithm in its integral form can be directly implemented in the filtering function.
Implementation and Performance Tests In this section, a rendering algorithm is implemented using the library described above, to show that null-collision algorithms that track paths in hierarchical structures allow to compute radiance fields of clouds described by large datasets (up to 1000×1000×1000 cells), and that the rendering time is almost insensitive to the resolution of the cloud field (i. e. to the size of the dataset). This is the main achievement reported in this paper, and this whole section is dedicated to the analysis of performances in terms of rendering time, as a function of the amount of volumetric data, but also of the type of clouds, and of the merging strategy used when constructing the hierarchical structures.
g. BOMEX) benefit more from the hierarchical grids than globally cloudier fields. Computational times for smaller values of τ show, as in Figure  5 , that fields described by larger datasets benefit more from the hierarchical grids. Looking at the partitionning into i/ crossing and accessing acceleration structure voxels (SVX) vs ii/ accessing raw data and testing collision nature (NCA), Figure  7-b ) shows that as expected, the optimum strategy for building hierarchical grid is between the limits of systematically crossing each cell of the original data, and using a fully homogeneized collision field.
Parametric Sensitivities The idea of evaluating sensitivities has been introduced in Section 1 and used in Section 2 to illustrate the practical meaning of filtering functions. A first example of such sensitivity simulations is implemented, starting from a backward Monte Carlo algorithm estimating the monochromatic ground flux density at a given location. Clear-sky optical depth is set to zero, only clouds interact with radiation. The sun, directed along a given direction, illuminates the TOA uniformly. Using integral developments such as those of Equation  3 , only a few extra code lines were required to implement the simultaneous computation of the sensitivity to the absorptivity (single scattering albedo) of cloud droplets, α (the ratio of absorption over extinction coefficients). This parameter is an output of Mie computations, that rely on hypothesis such as droplet size distribution and purity, and is typical of the radiative transfer uncertainties associated to cloud microphysics modeling.
State of the Library and Current Limitations Through the developments presented in this paper, our contribution to the field of atmospheric radiative transfer is as follows: We freely distribute our library online, together with atmospheric data and a rendering code that produces synthetic images of cloud fields. It is coded in C, for CPU technology. Part of the library is based on Embree. The low level modules (detailed in Appendix Appendix C) are elementary bricks that can be used for a large range of applications. They implement well separated concepts and are easily maintained. The modules that are related to the atmosphere, e. g. sky that builds the hierarchical grids from a combination of 1D clear profiles, 3D cloud fields and spectral properties in the form of k-distributions, will evolve as needs for improvement arise.
To be able to handle large datasets that do not fit into main memory, as for instance full 3D atmospheres with multiple cloud layers, and energetic systems such as solar plants on the ground, one has to adopt the out-of-core paradigm for the whole library, i. e. all the data are stored on disk and (un)loaded on demand. As described in Section 3. 1, both the raw data and the acceleration grids were thought with this perspective: cloud fields can already be dynamically loaded from disk, and acceleration grids are carefully structured with this objective in mind. However, this will only be efficient if the algorithms that require data access are designed according to the out-of-core nature of the data. For instance, the strategy implemented in Hyperion, Disney's out-of-core renderer  (Burley et al.