dataesr/scientific-paragraphs-categorization huggingface dataset:
However, a closer look at this statement shows two sides of the coin. On the one hand, although the tool included a wide variety of connectors for the most popular data sources in the software industry, companies needed additional ones, which was not trivial in terms of knowledge required. One company reported that choosing the appropriate factors, defining the metrics, and finding the best data sources for this purpose turned out to be extremely challenging due to several factors (e. g. , access to relevant databases). This raised the issue of which competencies are needed in the process, such us knowledge on data protection regulations (e. g. , EU GDPR). On the other hand, adding new strategic indicators appears to be much easier. To sum up, software analytics tools should offer easy deployment options such as dockers, and facilitate as much as possible the setting up of the tool (e.
g. , user interface for the configuration of the different elements to decrease the technological knowledge required). Challenge #7. Need for an efficient tool configuration process. Some situations make the adoption of the Q-Rapids tool (and other software analytics tools) challenging from a technological perspective. The first of these is the configuration of the connectors. One constraint is that connector configuration parameters (such as user name, password and end-point) need to be set up per project, even though the data sources are the same across projects (e. g. , data sources may have multiple deployments). In other words, for each data source in each case, the connectors for mining the data as well as for the feedback loop back to the backlog must be customized. To increase the feasibility of any software analytics tool, it is necessary to provide configurable connectors via a file or user interface, as this decreases the need for specific coding skills and decreases the overall adoption time.
B. LESSONS LEARNED FROM THE USE OF THE Q-RAPIDS TOOL Lesson learned #1. Embrace an incremental adoption approach. It usually takes some time for any new solution to be fully embedded into a company's software development style, and any new process is usually based on some play&try in practice sessions. We recommend adopting the Q-Rapids tool in a company for a smallerscale product or solution being developed in order to enable extension of the solution to a wider number of products later on. This will allow adjusting the QM as well as potentially tailoring the data connectors used. Lesson learned #2. Boost transparency as a business value. Incremental adoption also helps to get an overview and enable fruitful discussions among developers and related stakeholders of the product regarding valuable indicators.
Have a single access point to software-quality-related data. With the Q-Rapids tool, the project manager no longer needs to know which tools provide which metrics and to connect to them in order to understand the various measurement processes and to link and synchronize results from different tools. The various indicators give the project manager a very easy and fast way to analyze and understand a potentially complex situation. This aspect is completely built into the Q-Rapids QM and its associated dashboards where the various aspects collected by the different software development lifecycle metrics are analyzed. Lesson learned #5. Use QMs integrated into software analytics tools as a communication space. The Q-Rapids tool provides effective means for different product stakeholders to discuss and interact on the basis of a common dashboard.
Lesson learned #7. Involving experts. Putting a product like the Q-Rapids tool into action requires knowledge from software engineers, especially technology experts and data scientists. The companies pointed out some particular situations. First, to set up the connectors, a lot of expertise is needed: end-users of the tool who can identify the relevant data content from the UI; software engineers who can assess the quality of raw data with respect to the functionality of the connector; and data scientists for verifying the relation between UI fields and the actual structure of the database. Second, some of the techniques used by the analytics tool, such the option of Bayesian networks for creating strategic indicators  [58] , require specific competence. Last, implementation also requires knowledge of particular technologies that a company might not have, e.
Thus, the results can only be interpreted as an indication of appropriateness. Furthermore, we were aware that we would only get a small sample size (i. e. , 22 participants) and looked for appropriate statistical tests. Internal validity refers to whether observed relationships are due to cause-effect relationships, and whether it is possible to explain unequivocally the changes in the dependent variable due to the influence of the independent variables. We evaluated the Q-Rapids tool by drawing a convenient sample of product owners, managers, and developers working in several application domains and software development environments. One limitation of our work is that we were not able to get a random sample of participants in the pilot projects of the companies. In addition, we defined an evaluation protocol in advance, which included a specific description of our planned procedure and the order of using the materials, i.
VIII. CONCLUSIONS Agile software development companies need tools to continuously assess and improve software quality. For this study, we conducted a case study across four European ASD companies to explore practitioners' perceptions on the integration of QMs into software analytics tools. We aimed at exploring key aspects needed for the widespread adoption of these tools in industry, namely understandability, reliability, usefulness, right level of detail, relevance, perceived usefulness, and behavioral intention. Twenty-two practitioners involved in the study agreed that they understood the integration of QMs into software analytics tools and found the QM elements reliable and useful for assessing software quality. In addition, the practitioners found the QMs to be at the right level of detail and the actions taken from the QMs to be relevant and perceived useful, and they intend to use QMs within software analytics tools.
Funding Information V. A. has been supported by the  European Union 's  programme Horizon 2020  under the  Marie Sk lodowska-Curie  grant No. 702612 -OEMBS. B. B. has been supported by the  European Research Council  under the Advanced Grant No. 694544 -OMNES , and by the  Slovenian Research Agency (ARRS)  under the grant  P1-0402. M. F. has been supported by a grant LabEx  PALM  ( ANR-10-LABX-0039-PALM ) and by the  European Research Council  under the Starting Grant No. 805252 -LoCoMacro. Part of the work has been done at the  Erwin Schrödinger Institute (ESI) in Vienna , during the workshop " Quantum Paths ", and at the  International Institute of Physics (IIP) in Natal , during the workshop "Transport in strongly correlated quantum systems".
Mode identification In this section we explain our methodology to isolate chaotic modes. To achieve this goal we proceed by elimination. The basic idea is to identify all regular modes and remove them from the dataset until only chaotic modes remain. To begin with, we identify 2-period island modes using the fact that their frequency spectrum is regular and of the form ω n,l,m = nδ n (m) + δ (m) + α, where n (resp. ) is the number of nodes along (resp. transverse to) the central orbit γ of the island, shown in Fig. 4 , and α is a constant  (Pasek et al. 2012 ). Thus the frequency spectrum is completely determined by the regular spacings δ n and δ , with the half large separation δ n (m) = 2π/ γ (ds/c s ) 1. However, the regularity of the 2-period island modes may be altered by two aspects : first the deviations from asymptotic theory, expected at finite frequency, and secondly the occurrence of avoided crossings between an island mode and a chaotic mode.
6, to be dominated by the high degree components. For a given m, we sum over j to get the averaged coefficients āl ≡ 1 N j N j j=0 |a l j |. Thus the dominant degree l max corresponds to the value of l where āl is the greatest. In the bottom panel of Fig. 5 , the distribution of whispering gallery modes with respect to l max is shown for a given rotation. There is indeed no whispering gallery mode below l max = 10. The top panel displays the same distribution for chaotic modes, showing that they are instead dominated by their low degree components. In fact, there is a critical value l c of l max such that l max ≤ l c for every chaotic mode and l max > l c for any whispering gallery mode. Therefore, it is sufficient to find l c to remove all whispering gallery modes from the dataset. At finite frequency, here ω ≤ 47ω p , the Berry-Robnik regime is not perfectly satisfied  (Vidmar et al.
Any time we encountered an ambivalent mode, we applied the following rules to categorize it. If the amplitude is high near an internal caustic, then the mode is a whispering gallery-like interface mode. The difference between chaotic, island-like and island modes is illustrated in Fig 6. We consider that a mode is not chaotic if its amplitude is concentrated around the central orbit of the main island. In parallel, we examine the placement of the mode in the spectrum, and look for regularities. Even at the highest frequencies considered, = 4 island modes cannot be distinguished from interface modes without ambiguity from the amplitudes. Our dataset of chaotic frequencies is described in table 1. All axisymmetric modes were identified using the methodology described above. For non axisymmetric modes the last step, i.
Moreover, by choosing trajectories with initial conditions around the main island and evolving the system forward in time, we see clearly the contours of the partial barrier reveal themselves (see Fig. 18 ). For the frequencies considered in our dataset, the partial barriers may act as complete barriers and isolate some modes, as observed in other systems  (Shim Fig. 18. Snapshot of the evolution of a bundle of trajectories as they intersect the PSS, represented by black dots, at Ω/Ω k = 0. 706. The phase space zones where black dots are dense correspond to regions enclosed by partial barriers. The trajectories are initially in the neighborhood of the two main islands. One of the central periodic orbits of these zones is indicated by colored dots (large gray dots). et al. 2011). Quantitative estimates of the area and outgoing flux have been given in  Evano et al.
Discussion and conclusion In this paper, we have computed high frequency p-modes in stellar polytropic models for rotation rates between Ω/Ω k = 0. 48 and Ω/Ω k = 0. 81. Following the methodology of section 2. 3, we have then identified chaotic modes and built a dataset of chaotic frequencies. As expected, the nearest-neighbor statistics of the chaotic spectra for most rotations follow the Wigner-Dyson distribution, a well-known generic property of wave chaos systems. The frequency autocorrelations of the chaotic spectra have been computed. All of them exhibit peaks above the noise level. The presence of peaks in the frequency autocorrelation is clearly not generic in wave chaos systems. Our analysis shows that chaotic modes are organized in series. The frequency difference between consecutive modes being approximately constant and of similar value across all series.
19). As explained in section 4. 5, the small variance of acoustic travel times (σ 0 /T 0 1) implies the observed quasidegeneracy of ∆ i and ∆ c. Thus, we expect rapidly rotating stars to be characterized by a unique large separation ∆ ∼ ∆ i ∼ ∆ c. Autocorrelation peaks at the large separation detected in δ scuti stars  (García Hernández et al. 2015 ) could be produced not only by island modes, as previously thought, but also in part by chaotic modes. This would be important for stars that rotate rapidly enough to harbor a significant number of chaotic modes. A way to distinguish the contribution of island modes is to look for a peak at half the large separation as our analysis indicates that it is due to island modes only. To go further in the comparison with observed spectra, one should construct a database of low-frequency synthetic spectra as in  Reese et al.
Acknowledgements. We thank CALMIP (" CALcul en MIdi-Pyrénées ") for the use of their supercomputer. We used the code Top developed by  D. Reese  and made user-friendly by B. Putigny. We thank  ISSI  (" International Space Science Institute")  through the  SoFAR ("Seismology of Fast Rotating Stars") program  for their support.
These groups are chosen because either show large MCC or had been previously discussed in the context of the kinks in radii. We do not discuss volume parameters, which have been found to have a rather small impact on the kinks. We recall that both parametrizations SV-min and FyðΔr; HFBÞ are fitted to basically the same dataset; hence, differences between their results are primarily related to the form of underlying functionals. Details on the two energy functionals are provided in Supplemental Material  [61]. Figure  3  displays the results of our statistical analysis. The first four panels show the MCCs of the radius kinks in 132 Sn and 208 Pb with the groups of model parameters. For both SV-min and FyðΔr; HFBÞ, pairing and surface groups are the primary drivers. The common MCC (P þ S) is smaller than the sum of the separate MCCs, which indicates the expected strong intercorrelations between pairing and surface parameters.
We thank the  ISOLDE technical group  for their professional assistance. This work has been supported by the  Max-Planck Society , the  German Federal Ministry for Education and Research  under Contract No. 05P15RDCIA ; the  Helmholtz International Center for FAIR (HIC for FAIR)  within the  LOEWE program  by the  State of Hesse ; the  Belgian IAP  Project No. P7/12 , the  FWO-Vlaanderen ;  GOA 15/010  from  KU Leuven ; the  European Union  seventh framework through ENSAR under Contract No. 262010 ; the  Science and Technology Facilities Council  ( ST/L005670/ 1  and  ST/L005794/1 ), and by the  Office of Science, U. S. Department of Energy  under Grants No. DE-SC0013365  and No. DE-SC0018083  ( NUCLEI SciDAC-4 Collaboration ). M. K. acknowledges funding from  ERC Starting  Grant No. 640465  and  D.
The first planetary candidate discovered by  Latham et al. (1989)  with radial velocities around a solar-like star other than the Sun, HD 114762 b, was detected with a minimum mass of 11 M J. The small v sin i∼0 km s -1 that is otherwise measured by spectral analysis indicated that this companion of a late-F subgiant star better corresponds to a massive brown dwarf (BD) or even a low-mass M-dwarf seen nearly face-on. To our knowledge, the nature of HD 114762 b is still undetermined. The astrometric noise measured for this system in the first data release, DR1, of the Gaia mission allows us to derive new constraints on the astrometric motion of HD 114762 and on the mass of its companion. We use the method GASTON, introduced in a preceding paper, which can simulate Gaia data and determine the distribution of inclinations that are compatible with the astrometric excess noise.
The final release of the Gaia mission (Gaia Collaboration et al. 2016a), based on an astrometry of an extreme precision, is forseen to allow determining the exact mass of many systems that are detected with radial velocities (RV)  (Perryman et al. 2014). Kiefer et al. (2019) , hereafter K19, showed that even at the level of the astrometric noise recorded in the first data release (DR1) of the Gaia mission, the powerful precision of Gaia has been able to provide strong constraints on the mass of companions that are detected with RV. In particular, the masses of a few brown dwarf (BD) candidates were constrained to rather be in the M-dwarf mass regime, while some were confirmed to have a BD mass. Applying the analysis and methods developed in K19, called GASTON, to the case of HD 114762, we here add new constraints on the inclination of the orbit and mass of its companion.
Properties of the HD 114762 A system Table  1  summarizes the main stellar properties of HD 114762 A. This sub-giant F9 star is located at 40 pc from the Sun, with a parallax of π=26 mas and a 7. 3 apparent magnitude in the Vband. Radial velocity data have been collected along the years since the discovery of the low-mass companion of this source. The best radial velocity solution is given in  Kane et al. (2011). This is based on Lick observatory spectra collected on a time line of 19 years. An 84-day orbit leads to a robust Keplerian fit of the star reflex motion. The fitted Keplerian as reported in  Kane et al. (2011)  and corrected with the last stellar mass derivation by  Stassun et al. (2017)  is given in Table  2. A possible linear trend of 3. 5 m s - 1  /yr, indicative of an exterior companion, was reported by  Kane et al.
Proper motions of the HD 114762 system We suggested in Section 3 that with an orbital period longer than 500 days, HD 114762 B might be compatible with the small ∆Q factor given in Table  3. For a given system, this quantity is calculated by measuring the significance of the proper motion difference between Hipparcos and Gaia DR1. Table  5  shows, however, that the proper motions of the HD 114762 system found by Hipparcos-2 (van Leeuwen 2007) differ from the Gaia DR1 proper motions by a factor that is only compatible with zero at 3σ. A ∆µ∼2 mas would better correspond to the orbital reflex motion of HD 114762 A as a result of HD 114762 Ab, as is typically found for the simulated Gaia DR1 data in Table  4. The proper motions found by  Halbwachs (2000)  using Hipparcos measurements account for the orbital reflex motion caused by HD 114762 Ab.
Conclusions Based on the Gaia astrometry that was published in the first data release of the mission (DR1; Gaia Collaboration 2016), we addressed the puzzling question of the true nature of HD 114762 b. With the GASTON method  (Kiefer et al. 2019 ) and the large astrometric excess noise of 1. 09 mas measured by Gaia for this system, we derived an inclination of 4. 87 +1. 11  -0. 91 degree for the orbit of HD 114762 b. This leads to a mass of the companion of M b =141 +35 -28 M J and greater than 68 M J at the 3σ level. This confirms the initial doubts on the planetary nature of the companion, which were based on the v sin i of the primary star. This had been found to be significantly lower than the expected rotational velocity for a G-type star like this. We investigated the effect of the presence of the wide-orbit companion HD 114762 B.
2017). Finally, we showed here that the GASTON method and Gaia DR1 astrometric data can be useful for characterizing the mass of RV-detected companions. This will be the subject of a future article  (Kiefer et al. , in prep). λ θ = ψ(θ) = tan(2θ -π/2) λ P = ψ(P) = ln P λ e b = ψ(e b ) = tan(π(e b -1/2)) λ ω = ψ(ω) = tan( ω -π 2 ) λ T p = ψ(T p ) = T p λ M b sin i = ψ(M b sin i) = ln M b sin i λ M = ψ(M ) = ln M λ π = ψ(π) = ln π λ f, = ψ( f ) = ln f λ f,K = ψ( f K ) = f K. In addition to the inclination, the prior distributions mostly include Gaussian constraints on all orbital parameters (P, e b , ω, T p , M b sin i, π, and M ). The uncertainty on 2 is estimated based on theoretical calculations developed in Appendix A. 3. It might be over-or underestimated. A hyperparameter f allows rescaling the uncertainty of 2 to f σ 2.
HD114762_arxiv 2 tot,simu = N DR1 i=1 R 2 i /(N DR1 -5) 2 tot,mes = 2 DR1 + σ 2 AL, with DR1 the excess noise published in Gaia DR1. Ideally, parameters should be found that lead to residuals R i that are compatible with N DR1 i=1 R 2 i /(N DR1 -5)= 2 tot,mes following the definition of the excess noise measured by Gaia in  Lindegren et al. (2012) , for instance. Using 2 tot rather than DR1 as a data point in the MCMC appeared the most natural choice. According to the above formula, 2 tot is calculated like a χ 2 with N dof =N DR1 -5 degrees of freedom (dof); where N DR1 is the number of data points and ν=5 is the number of astrometric parameters that are fit by Gaia  (Lindegren et al. 2016 ). To some extent, the purely stochastic part of 2 tot follows a χ 2 distribution with large N dof (typically 100) and thus an approximate Gaussian distribution.
Acknowledgements. I am thankful to the anonymous referee for his help in improving the manuscript and the analysis. I warmly thank  Michel Mayor  for the fruitful discussions on the present subject. I am also thankful to  A. Lecavelier  and  G. Hébrard  for their advices and careful readings of the paper. This work was supported by a fellowship grant from the  Centre National d'Etude Spatiale (CNES). This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC;https://www. cosmos. esa. int/web/gaia/dpac/consortium).
A search for highly collimated photon-jets using 36. 7 fb -1 of LHC proton-proton collision data collected by the ATLAS detector in 2015 and 2016 at a center-of-mass energy of 13 TeV is presented. Candidate photon-jet pair production events are selected from those containing two reconstructed photons (denoted "γ R "), using a set of identification criteria much less stringent than that typically used for the selection of photons, and with additional criteria applied to provide improved sensitivity to photon-jets. Narrow excesses are searched for in the spectra of the reconstructed diphoton mass m γ R γ R. The results of the search are interpreted in the context of a benchmark BSM scenario involving a highmass, narrow-width scalar particle, X, with mass m X > 200 GeV, originating from the gluon-gluon fusion process and that can decay into a pair of intermediate particles with spin 0, a, as shown in Figure  1.
Several reference selections are defined, including those referred to as "loose" and "tight". The loose selection is based only on shower shapes in the second layer of the EM calorimeter and on the leakage in the hadronic calorimeter, and is used by the photon triggers, including the diphoton trigger used for the collection of the data sample for this search. The tight selection is based on all nine variables and is used for the standard photon identification in ATLAS, but is not used in this search. The criteria for the tight selection change as a function of the η values of the reconstructed photons, to account for the calorimeter geometry and effects from the material upstream of the calorimeter, and are separately optimized for reconstructed photons with and without an associated conversion vertex to increase the photon identification efficiency.
For instance, for m X = 600 GeV and m a = 5 GeV, the width is σ CB = 8 GeV for the process X → aa → 4γ, and σ CB = 9 GeV for the process X → aa → 6π 0. For a given m X and m a , the same signal mass shape modeling results are used for the analysis of the two orthogonal event categories (the low-∆E category and the high-∆E category), since only a small dependence of the signal mass distributions on ∆E is observed. To validate the mass shape modeling results, injection tests are performed, where a fixed number of signal events are inserted into a pseudo-dataset reproducing a background-only m γ R γ R spectrum of one of the two event categories, and the number of events inserted is then compared with the number determined by fitting the DSCB. The pseudo-datasets are generated from background probability density functions [represented by Eq.