dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Partial support by  ASI/INAF Agreement 2014-024-R. 1  for the Planck LFI Activity of Phase  E2  and by  ASI  through the contract  I-022-11-0  LSPE is acknowledged. C. H. -M. acknowledges financial support of the  Spanish Ministry of Economy and Competitiveness  via  I+D  project  AYA-2015-66211-C2-2-P. J. G. N. acknowledges financial support from the  Spanish MINECO  for a  Ramon y Cajal fellowship  ( RYC-2013-13256 ) and the I+D 2015 project  AYA2015-65887-P (MINECO/FEDER ). C. J. M. is supported by an  FCT   Research Professorship , contract reference  IF/00064/2012 , funded by  FCT/MCTES  (Portugal) and  POPH/FSE  (EC). M. Q. is supported by the Brazilian research agencies  CNPq  and  FAPERJ. We acknowledge the use of the  ESA Planck Legacy Archive. Some of the results in this paper have been derived using the HEALPix package.
In fact, software architects tend to favour tools that allow them to manipulate good products, i. e. , possible products that are better than every other possible product on a particular combination of objectives. Another related problem that has only been addressed in the literature recently  [13] , is feature selection in a multi-objective context when the FMs evolve. Software requirements and artefacts evolve constantly; customers and other stakeholders change their opinions about what an application should do and how it should achieve that. Such changes can be reflected in Feature Models  [44] : for instance, we have seen in our study that a large FM (such as the one behind the Linux kernel) evolves regularly and substantially (every few months a new version is released with up to 7% difference from the previous one).
In this context, it seems odd to generate random bootstrapping populations for the state-of-the-art genetic algorithms, such as SATIBEA. It is tempting on the contrary to use the fact that FMs have evolved and that the SPL configurations generated previously, while not totally applicable, are close and can be adapted. This is a strategy called seeding and our intuition is that this could prove helpful in the context of Feature Model selection -especially since SATIBEA (and the other evolutionary algorithms) is very dependent on the initial population. Seeding for search-based software engineering is not a novel idea as such (e. g. , see papers by Fraser and Arcuri  [26]  and Alshahwan and Harman  [2] ). However, our approach is novel for various reasons: -usually seeding is done by taking a few good/previous solutions that are inserted in the initial population -while in this paper, we take all the previous solutions that we adapt to create a starting population.
-the data sets we use for our experiments in this paper are large and very constrained, which is not always the case in search-based software engineering contexts for which seeding is known to work. This, of course, calls for a proper evaluation that we report here. -we also studied the performance of seeding against a large variety of data sets, of different size and demographics (varying in their numbers and ratios of features and constraints). This gives us some more assurance that our conclusions are correct. From a more general perspective, our contributions in this paper are the following: -We propose a benchmark 1  for the analysis of evolving SPL; this data set has been generated following a study of the demographics and evolution of a large SPL (Linux kernel). This data set is important to provide a good evaluation of the different algorithms under different evolution scenarios; -We propose eSATIBEA which is a modification of the state-of-the-art SATIBEA  [33]  for evolving SPL.
eSATI-BEA adapts previous solutions to new FMs to improve and speed-up the results of SATIBEA; -We evaluate SATIBEA and eSATIBEA on the evolving SPL problem and show that eSATIBEA improves both the execution time and the quality of SATIBEA. In particular, eSATIBEA converges an order of magnitude faster than SATIBEA alone. The rest of this paper is organised as follows: Section 2 defines the problem of multi-objective features selection when Feature Models evolve. Section 3 presents a large study of the evolution of a Feature Model: 20 versions of the Linux kernel (up to 13,000+ features and nearly 300,000 constraints). This study of the demographics of the Feature Model helps us to create the synthetic evolutions of 5 large and popular FMs. In particular, we are able to create evolved data sets using two parameters representing the evolution in terms of features and constraints.
Benchmark for Feature-Model Evolution Evolution of large software artefacts and applications, typical of what software product lines engineering usually addresses, is a known fact  [44]. In this paper we want to study the performance of a classical algorithm for software product line configuration (SATIBEA  [33] ) in the context of evolution -and in particular we want to evaluate whether seeding previous configurations improves the performance of this algorithm. A simple option for our study would be to use Feature Models for which we can find various versions. For instance, we present below a large Feature Model and its evolutions. However, our objective is to study the behaviour of two algorithms -and we would like to have a good control of the various evolution parameters: number of features removed/added, number of constraints added/removed etc.
Study of the Linux Kernel We studied the largest open source Feature Model we could find  [1] : the Linux kernel  [51]  containing a maximum of 13,322 features and 277,521 constraints in its version 2. 6. 32 (see Table  1 ). We evaluated the demographics (features, constraints) and evolution of 21 versions of the kernel from version 2. 6. 12 to version 2. 6. 32 that are publicly available in the Linux Variability Analysis Tools (LVAT) repository  [52]. Feature Models in the LVAT repository are not directly ready to use for our approach (i. e. , they are not in the form of an instance of the SAT problem) as they are based on Kconfig model extracts (. exconfig). Therefore, they have to be translated into SAT instances first  [10]. To achieve that, we use a tool called VM2BOOL 2 which converts every Feature Model (originally in a Kconfig file) into propositional formulas (stored on a.
We also evaluated the size of the clauses/constraints in the problem, as we need to know how the constraints we add in the problem should look like. We found that a large proportion of the FMs' constraints have 6 features (39%), 5 features (16%), 18 features (14%) or 19 features (14%). See Figure  3  for a more detailed report of the constraints sizes. Now we will be able to create randomly new constraints (or delete existing constraints  if need be) according to the proportions we find on this big data set. From this study, we generated a synthetic benchmark of FM evolution based on the real evolution of the Linux kernelhence a realistic benchmark but with more variability than in a real one, allowing us also to get several synthetic data sets corresponding to these characteristics. Our FM generator uses two parameters representing the percentage of feature modifications (added/removed) and the percentage of constraint modifications (added/removed).
Evaluation We evaluate in this section two algorithms: SATIBEA, known in the literature as the best algorithm for multi-objective configuration of SPLs, and our contribution: eSATIBEA. First, we evaluate their performance on the Linux kernel benchmark described earlier in this paper, both in terms of quality and diversity of solutions while varying the evolution targets. We show the average results of 30 runs for each evolution target and each algorithm. We perform first this thorough and detailed study of the Linux kernel in order to give the reader a good sense of what is happening with the two algorithms. We also evaluate the significance of the results obtained by eSAT-IBEA over those of SATIBEA at four key optimisation snapshots (i. e. , the initial, first, middle and last generations of the genetic algorithms).
Quality Performance of SATIBEA and eSATIBEA on the Linux Kernel We compare the quality of the solutions in every generation of eSATIBEA and SATIBEA when run on the different Linux kernel instances with various evolution targets. Figure  4  shows the evolution of SATIBEA's and eSATIBEA's performance on the different evolutions of the Linux kernel in terms of HV, and IGD. First, we see that eSATIBEA starts with a high HV from its start (389. 15% better than SATIBEA's HV on average) and maintains this HV quality in the following generations; whereas SATIBEA starts with a low HV that it keeps improving over time to eventually reach eSATIBEA's HV on some targets. SATIBEA does not always reach eSATIBEA's HV on all targets though (eSATIBEA outperforms SATIBEA on HV on almost all evolutions except 10 1 and has an average improvement over SATIBEA of 8.
Diversity Performance of SATIBEA and eSATIBEA on the Linux Kernel We compare the evolution in terms of diversity for both eSATIBEA and SATIBEA against our Linux kernel data set and various evolution targets. Figures 5 show the evolution of SATIBEA and eSATIBEA's performance on the different evolutions of the Linux kernel in terms of PFS and Spread. We see that eSATIBEA achieves a good PFS of ∼ 290 from the start (28% better than SATIBEA's PFS on average) and oscillates within an interval of +/-10 around this value. SATI-BEA starts with a lesser PFS at ∼ 230 but it improves quickly (within the first few generations of the genetic improvement) to reach eSATIBEA's results and even slightly outperforming them sometimes. At the end of the execution time, eSATIBEA outperforms SATIBEA on PFS on almost all evolutions (except 5 1) with an average improvement of 1.
Therefore, in order to show the benefit of using eSATIBEA, we run both tests with eSATIBEA over SATIBEA for metrics to maximise, and SATIBEA over eSATIBEA for metrics to minimise. We see in Table  6  that the initial generation of eSATIBEA is of a better quality than SATIBEA's initial generation on all targets of the Linux kernel. The initial generation of eSATI-BEA is 346. 85%, 95. 44% and 93. 36% better on average than SATIBEA's initial generation respectively on HV, and IGD. The initial generation of eSATIBEA is also of a better diversity than SATIBEA's initial generation as it is 30. 31% better on PFS and 126. 96% better on Spread on average. We also notice that all comparisons at this snapshot are statistically significant with at most an MWU p-value in the order of 1E-11. In addition, we also see that all the Â12 measures are at 1 regardless of the metric, which clearly indicates that the initial generation of eSATIBEA is better than the one from SATIBEA in all runs.
We also see in Table  6  that the first generation created by eSATIBEA is of a better quality than SATIBEA's first generation on all targets of the Linux kernel despite a reduction in performance in comparison to what has been seen in the initial generation. The first generation of eSATIBEA is 89. 92%, 79. 60% and 63. 84% better on average than SATIBEA's first generation respectively on HV, and IGD. The first generation of eSATI-BEA is also of a better diversity than SATIBEA's first generation as it is 10. 16% better on PFS and 13. 41% better on Spread on average. We also notice that all comparisons at this snapshot are statistically significant with at most an MWU p-value in the order of 1E-8. In addition, we also see that all the Â12 measures are at 1 for quality metrics, which clearly indicates that the first generation of eSATIBEA is still qualitatively better than the one from SATIBEA in all runs.
Gain of eSATIBEA over SATIBEA The previous subsections described in depth the results obtained by SATIBEA and eSATIBEA on the evolutions of the Linux kernel. The current subsection is showing aggregate results for the 5 data sets (Linux kernel, eCos, Fiasco, FreeBSD, and µLinux). This will give us a sense of the trends observed on each of those data sets and whether the conclusions made on the Linux kernel can be generalised. Figures  6  show the evolution of the gain achieved by eSAT-IBEA over SATIBEA on each metric and for every evolution target when run on the five different data sets. Notice that the gain is obtained from the subtraction of SATIBEA's results from eSATIBEA's at any given time for metrics that are to be maximised (i. e. , HV, PFS and Spread), and by subtracting eSATIBEA's results from SATIBEA's for the metrics that are to be minimised (i.
However, this gain stays positive (good for eSATI-BEA) for a larger number of evolution targets. When it comes to the gain on IGD, we notice two different trends: (i) on Linux kernel and FreeBSD data sets where we have the same behaviour as with the gain on HV, and (ii) on eCos, Fiasco and µClinux where we start with a positive gain which drops sharply to even turning negative (meaning eSATI-BEA is better for these values) for a short duration, before get-ting back to the positive side and stabilising around zero for the rest of the execution. Notice that despite these two trends, the difference is not significant enough given the small size of the results. The gains on the variety metrics look more erratic as they oscillate a lot between positive and negative values. The gain on FPS is mostly positive on the Linux kernel as it starts with large values which decrease over the execution time.
However, we see a totally different trend on other data sets, as gains start with negative values before narrowing the interval of their oscillation (usually within  [-30, 30] ). The gains on the Spread are also varying a lot between positive and negative values. The gain starts with positive values on all targets but quickly drops below zero. This is then followed by an increase to reach null values and oscillates around it. Linux kernel eCos Fiasco FreeBSD µClinux Figure  6 : Gain of eSATIBEA on SATIBEA for all data sets on all metrics. Note that while the gain is computed as result(eSATIBEA) -result(SATIBEA) for metrics that are to be maximised, we inverse that for those that are to be minimised. Overall, Figure  6  confirms what has been seen in Figure  4 : eSATIBEA does not only find solutions of better quality than SATIBEA only on the Linux kernel, but also on the other data sets.
Threats to Validity We have identified some threats to the validity of our evaluation. The first threat to validity in our work is in the choice of data sets. Our work is based on five data sets (i. e. , Linux kernel, eCos, Fiasco, FreeBSD and µClinux). Therefore, results might not generalise to other data sets. In order to lower this risk, we have selected data sets of different sizes, which are, moreover, the same as those used by Henard et al. [33]. The second threat to validity is in the creation of the benchmark and the choice of target distances. We generate in our work 10 different targets with a difference of features between 1 and 20% and a difference of number of constraints between 1 and 10% from the initial version. Next, given that we could only find the different versions of the Linux kernel data set to study their evolution patterns, we use the same patterns on all the data sets which might not be true in reality.
Last, we generate the target instances with some random parameters which might lead to a lack of coverage of specific cases. In our work, we generate 10 target instances for each target in order to lower this risk. The last threat to valid is in the experiments themselves with errors in the code, disturbances from the machine used for the experiments, and the effect of randomness in both within SAT-IBEA and during the generation of seeds. To lower this risk, we use the code of SATIBEA made available by Henard et al. [33]  that we extend using the same language and libraries. We also run the experiments on the same machine in total isolation (no parallel processes with the exception of those proper to the OS itself). Furthermore, we run our experiments for 30 iterations (each run of eSATIBEA given a different set of seeds as initial population, that is found by SATIBEA on the original instance) and check the significance of the results using two different tests.
Conclusion This paper has presented a new problem: the configuration of Software Product Lines when the Feature Models they are based on evolve. To study this problem, we have proposed a benchmark based on a study of the evolution of a large Feature Model (up to 13,000+ features and nearly 300,000 constraints, 20 different versions over 4+ years). Our empirical study gave us the characteristics and demographics behind the evolution of that Feature Model -and we have replicated it to generate synthetic versions of 5 different data sets (Feature Models). We have compared SATIBEA, the leading algorithm in the literature, and our contribution eSATIBEA (which takes an adaptation of the previous solutions as initial population) in this evolving context. Our experiments show that eSATIBEA improves both the execution time and the quality of SATIBEA by feeding it with previous configurations.
g. ,  Goldman 1999, M17 etc. ) , used entirely or some photographic plate data in order to gain sufficient maximum epoch difference, so the faint magnitude limit is roughly at the sky brightness, R ≈ 19. 5 mag. This has significantly restricted the survey volume: in H06, even though the photometry is given by the SDSS, the pairing criterion limits the depth of their catalogue to the magnitude limits of the USNO-B1. 0 survey; in RH11, the SuperCOS-MOS Sky Survey was compiled by digitising several generations of photographic plate surveys which has roughly the same photometric limits in H06. Using the state-of-the-art Panoramic Survey Telescope and Rapid Response System 1 (Pan-STARRS 1 or PS1,  Kaiser et al. 2010 ), with multi epoch data which has on average 60 epochs, proper motion objects were not limited to the ones that were also detected in the past by photographic plates.
Galaxies Fainter than ∼19 mag, galaxies become unresolved (i. e. , point-like) and have photometric parameters that overlap with stars. Therefore, it is necessary to include galaxies in the synthetic number counts. Galaxy counts to faint magnitudes have been determined in many independent studies. The Durham Cosmology Group has combined their own results (see e. g. ,  Jones et al. 1991 ; Metcalfe et al. 1991) with many other authors. These are available online along with transformations to different photometric bands 2. They are provided in terms of log-number counts per square degree per half-magnitude unless specified otherwise. A cubic spline was fitted over all available observations to obtain the galaxy counts as functions of magnitude in each band. PS1 has a very complex variation in the data quality as a function of position.
Voronoi Tessellation A Voronoi tessellation is made by partitioning a plane with n points into n convex polygons such that each polygon contains one point. Any position in a given polygon (cell) is closer to its generating point than to any other points. For use in astronomy, such a tessellation has to be done on a spherical surface (two-sphere). In this work, the tessellation is constructed with the SciPy package spatial. SphericalVoronoi, where each polygon is given a unique ID that is combined with the vertices to form a dictionary. The areas are calculated by first decomposing the polygons into spherical triangles with the generating points and their vertices  (Reddy 2015)  and then by using L'Huilier's Theorem to find the spherical excess. For a unit-sphere, the spherical excess is equal to the solid angle of the triangle.
0, the cooling ages for DA drop to 3. 44, 4. 24, 6. 33, 9. 02 and 11. 62 Gyr. However, this is inconsistent with the assumption of a fixed surface gravity log(g) = 8. 0 in our analysis. In the 25 pc volume limited sample from  Holberg et al. (2016) , at the 17. 5 mag bin, there are a massive DB, with log(g) = 9. 0, belonging to a widely separated double degenerate system and a DA presumed to have log(g) = 8. 0. SED fitting with 5-band broadband photometry cannot reliably fit the surface gravity or surface hydrogen/helium ratio as free parameters. It will only be possible for a large sky area survey to expand the fitting parameter space with, for example, the future Gaia data release where parallax and low resolution spectra would be available for most of the nearby sources. The smaller the range of tangential velocities, the fewer contaminants from the disc main sequence stars.
ACKNOWLEDGMENTS The Pan-STARRS 1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  under Grant No.
Description of the closed-loop simulator The main results of this study are based on the output of a closed-loop simulator designed in MATLAB, which enables to synthesise realistically degraded observables, namely the estimated orbit, the satellite angular velocity and attitude and the gravity gradients from which the gravitational model is finally derived. The same approach has been used for both the nadir and quasi-inertial mode, with different processing in what concerns the attitude. Starting from a global gravity field model and an orbit sampled at 1 Hz, the GGT is computed at each measurement point in the LNOF (Local North Oriented Frame). Then the GGT is rotated into the gradiometer Reference Frame which axes define the measurement axes of the gradiometer. This requires to know for each time the true attitude of the GRF with respect to a reference frame, say the IRF.
As the magnitude of the high-degree harmonics of the gravitational gradients decreases rapidly with the altitude, it is crucial to have an altitude as low as possible. For the sake of rigorous comparisons to GOCE, we have chosen 71 days of the GOCE orbit starting from the 2 nd of March 2013. During this period the circular orbit had an average altitude of 239 km and a repeat cycle of 65 days. It must be pointed out that this altitude is lower than the altitude that GOCE had most of its lifetime which was typically of 255 km. The time series of the orbit coordinates have been interpolated at 1 Hz from the GOCE L2 dynamical orbit data sampled at 0. 1 Hz using spline functions. The static GGT expressed in the LNOF is computed using the EIGEN-6c4 model  Förste et al. (2014)  up to degree/order 360.
Discussion We analyse now to what extent the CAI gradiometer concept can outperform GOCE when assuming a nominal gradiometer noise of 5 mE/ √ Hz. To this end, we extrapolate the error on these models from 71 days to 8 months assuming the error is purely stochastic and reduces as √ t i where t i is the time of integration. To gain an idea of the GOCE solution error, we first  compute a gravity model based on the three diagonal gravitational gradients of the whole GOCE mission period (November 2009 -October 2013, about 47 months). The least-squares adjustment is performed for GOCE data in the same way as for our simulations. Then, we compare it to 2 different reference gravity models: on the one hand, the EIGEN-6C4 model used for the synthesis of the gravitational gradients in the simulation and, on the other hand, the GOCO05S (Mayer-Guerr, 2015).