dataesr/scientific-paragraphs-categorization huggingface dataset:
We initially calculated the standard errors on  1 , 9 https://github. com/KeplerGO/lightkurve 10 https://github. com/LucaMalavolta/PyORBIT, version 8. 1 11 https://vizier. u-strasbg. fr/viz-bin/VizieR?-source=J/ A+A/618/A20 Figure  2. In-/out-of-transit difference centroid analysis of the transit events associated with the candidates TOI-561. 01 (transit 2 and 3) and TOI-561. 03 (transit 1 and 4). The star is centred at (0,0), and the grey circles are all the other stars in the Gaia DR2 catalogue, with dimension proportional to their apparent magnitude. 2 using a Monte Carlo approach that takes into account the errors on  eff and log g as reported in Table  3 , obtaining  1 = 0. 393 ± 0. 007 and  2 = 0. 204 ± 0. 001. We however decided to conservatively increase the error on both coefficients to 0.
05. In the fit we employed the parametrization ( 1 ,  2 ) introduced by  Kipping (2013). Finally, we included a jitter term to take into account possible TESS systematics and short-term stellar activity noise. We assumed uniform, uninformative priors for all the other parameters, although the prior on the stellar density will inevitably affect the other orbital parameters. All the transit models were computed with the batman package  (Kreidberg 2015) , with an exposure time of 120 seconds and an oversampling factor of 10 (Kipping 2010). In the analysis of the radial velocities, we allowed the periods to span between 2 and 200 days (i. e. , the time span of our dataset) for the non-transiting planets, while we allowed the semi-amplitude  to vary between 0. 01 and 100 m s -1 for all the candidate planets.
We performed a series of test on a reduced dataset, and we verified that the two algorithms provided consistent results with respect to each other. For all the analyses, we assumed 1000 live points and a sampling efficiency of 0. 3, including a jitter term for each dataset considered in the model. Global optimisation of the parameters was performed using the differential evolution code PyDE 12. The output parameters were used as a starting point for the Bayesian analysis performed with the emcee package (Foreman-Mackey et al. 2013), a Markov chain Monte Carlo (MCMC) algorithm with an affine invariant ensemble sampler  (Goodman & Weare 2010). We ran the chains with 2 dim walkers, where  dim is the dimensionality of the model, for a number of steps adapted to each fit, checking the convergence with the Gelman-Rubin statistics  (Gelman & Rubin 1992) , with a threshold value of R = 1.
Another important outcome of our frequency search is the absence of a signal with a periodicity of ∼ 16 days, that is, the transiting candidate TOI-561. 03. Therefore, in order to test our ability to recover the planetary signals, we performed a series of injection/retrieval simulations, thoroughly explained in Appendix B2. The results of this injection/retrieval test are summarised in Figure  3. We found that the injected RV amplitude of. 01 is not significantly affecting the retrieved value for. 03, i. e. the cross-talk between the two signals is negligible. We verified that the same conclusion applies to the other signals as well. More importantly, any attempt to retrieve a null signal at the periodicity of the candidate planet. 03 would result in an upper limit of ≈ 0. 5 m s -1 as we actually observe with the real dataset, when exploring the  parameter in logarithmic space.
Any signal equal or higher than 1 m s -1 would have been detected (> 2), even if marginally. A signal with amplitude of 0. 5 m s -1 would not lead to the detection of the planet (intended as a 3- detection), but the retrieved posterior is expected to differ substantially from the observed one, especially on the lower tail of the distribution. We conclude that the planetary candidate TOI-561. 03 is undetected in our RV dataset, with an upper limit on the semi-amplitude of 0. 5 m s -1 ( p < 2. 0 M ⊕ ).
03 could indeed be due to the two additional planets inferred from the RV analysis. To check our hypothesis, we first analysed the RV dataset with a model encompassing four planets, of which only. 01 and. 02 have period and time of transit constrained by TESS. In other words, we performed the same RV analysis as described in Appendix B2, but without including TOI-561. 03 in the model. We repeated the analysis twice in order to disentangle the periodicity at 78 d from its alias at 180 d, and vice versa. We used the posteriors of the fit to compute the expected time of transit of the outer planets. We then performed two independent fits of transit 1 and 4 with PyORBIT, following the prescriptions as specified in Section 5. We imposed a lower boundary on the period of 22 days, in order to exclude the periods that would imply a second transit of the same planet in the TESS light curve, and an upper limit of 200 days.
3 M ⊕ ). Both planets show a single transit in the TESS light curve, previously attributed to a transiting planet with period ∼ 16 d, whose presence has however been ruled out by our analysis. This allowed us to infer a planetary radius of  d = 2. 53 ± 0. 13 R ⊕ and  e = 2. 67 ± 0. 11 R ⊕ for planet d and e respectively. We performed the stability analysis of our determined solution, computing the orbits for 100 Kyr with the whfast integrator (with fixed time-step of 0. 1 d) implemented within the rebound package  (Rein & Liu 2012; Rein & Tamayo 2015). During the integration we checked the dynamical stability of the solution with the Mean Exponential Growth factor of Nearby Orbits (MEGNO or  ) indicator developed by  Cincotta & Simó (2000)  and implemented within rebound by  Rein & Tamayo (2016).
(2015) , with no priors on the GP hyperparameters, since we could not identify any activity-related signal in the ancillary datasets (see Section 3. 3)  16. In both cases, the (hyper-)parameters of the additional signal did not reach convergence, while the results for the four transiting planets were consistent with those reported above. Considering these results, we adopt the parameters and configuration determined in this section as the representative ones for the TOI-561 system, with the only exception of the mass and semi-amplitude of TOI-561 b, that we discuss in the next section.
We did not include RV measurements with an associated error greater than 2. 5 m s -1 (see Appendix B1). We performed the analysis with PyORBIT as specified in Section 5, assuming a circular orbit for the USP planet and including a RV jitter as a free parameter to take into account possible short-term stellar variability and any underestimation of the errorbars. From our analysis, we obtained a RV semi-amplitude of  p = 1. 80 ± 0. 38 m s -1 , corresponding to a mass of  p = 1. 83 ± 0. 39 M ⊕. The resulting RV jitter is  < 0. 9 m s -1 (84. 13-th percentile of the posterior). We show the phase folded RVs of the USP planet in Figure  9. Since the greater reliability of this method over a full fit of the RV dataset is counter-balanced by the smaller number of RVs, we decided not privilege one over the other.
In fact, Table  4  suggests that in this case two additional signals need  The here reported values of planet b correspond to the weighted mean between the values inferred from the floating chunk offset method ( b = 1. 80 ± 0. 38 m s -1 ,  b = 1. 83 ± 0. 39 M ⊕ ) and from the joint photometric and RV fit ( b = 1. 39 ± 0. 32 m s -1 ,  b = 1. 42 ± 0. 33 M ⊕ ). Photometric jitter term. Uncorrelated RV jitter term. RV offset. to be added to the three transiting candidates to best reproduce the RV dataset, and therefore the five-planet model should be considered also in the joint photometric and RV modelling. According to the Bayesian evidence (Table  6 ), computed with the dynesty algorithm as specified in Section 5, the four-planet model is strongly favoured with respect to the five-planet model in both cases, with a difference in the logarithmic Bayes factor 2 Δ ln Z 10 (  Kass & Raftery 1995).
Considering the few available data (i. e. , 2 transits for planet c, 1 transit for planets d, e), additional observations are needed to unequivocally confirm our solution. Further high-precision photometric (i. e. with TESS, that will re-observe TOI-561 in sector 35 -February/March 2021, or with the CHEOPS satellite) and RVs observations will help improving the precision on the planets parameters, both allowing for the detection of eventual TTVs and increasing the time-span of the RV dataset, that could also unveil possible additional long-period companions. 5 ). The planets show a strong, anti-correlated signal. The signals of the USP planet (< 1 sec) and of planet c (< 1 min) are not reported. signals. Instead of analysing the power variation of the periodogram's peaks associated with the candidate planets while removing one point at the time, we decided to perform a full RV fit with the methodology described in Section 5, and to compare the resulting RV semiamplitudes with those derived using the full dataset.
To reduce computational time, we decided to remove from the dataset 5 consecutive observations at once (i. e. , performing 17 iterations rather than 82), and then performed the leave-one-out cross-validation on those subsets showing deviating RV semi-amplitudes in order to identify the anomalous RV measurement. With this approach, we found out that a total of 5 RV measurements, with associated errors greater than 2. 5 m s -1 and S/N < 35 were systematically producing a decrease in the semi-amplitude of candidates. 01 and. 02 by ≈ 0. 1 -0. 2 m s -1 , and we therefore removed these points from our dataset in order to improve the accuracy of our results, even if the total variation in RV semi-amplitude was within the error bars. We note that these observations are clearly outliers at more than 2 in both the S/N of the spectra and the RV error distributions (see Section 2.
03 (i. e. , the candidate with period of ∼ 16. 3 d) remains undetected with an upper limit of  0. 5 m s -1 , corresponding to a rather nonphysical mass of 2 M ⊕ (at 1) for a planet with  p 2. 7 R ⊕. We thus performed a series of injection/retrieval simulations in order to assess the influence of the observational sampling and of the precision in the mass measurements of the other planets. In a first run, the synthetic datasets were simulated by assuming the orbital parameters as previously determined in the RV fits for the candidate planets. 01,. 02, and the non-transiting candidates, while the RV semi-amplitude of the candidate planet at 16 d was varied between 0. 0 m s -1 and 1. 5 m s -1 in steps of 0. 5 m s -1. For computational reasons, we performed this analysis only with the 78-d solution for the outer planet.
ACKNOWLEDGEMENTS We thank the anonymous referee for the constructive comments and recommendations which helped improving the paper. This paper includes data collected by the TESS mission, which are publicly available from the  Mikulski Archive for Space Telescopes (MAST). Funding for the TESS mission is provided by the  NASA   Explorer Program. Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Advanced Supercomputing (NAS) Division at Ames Research Center  for the production of the SPOC data products. Based on observations made with the  Italian Telescopio Nazionale Galileo (TNG)  operated on the island of  La Palma  by the  Fundación Galileo Galilei of the INAF at the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias (GTO program , and  A40TAC_23 program  from  INAF-TAC ).
The  HARPS -N project was funded by the  Prodex Program of the Swiss Space Office (SSO) , the  Harvard-University Origin of Life Initiative (HUOLI) , the  Scottish Universities Physics Alliance (SUPA) , the  University of Geneva , the  Smithsonian Astrophysical Observatory (SAO) , and the  Italian National Astrophysical Institute (INAF) ,  University of St. Andrews, Queen's University Belfast and University of Edinburgh. Parts of this work have been supported by the  National Aeronautics and Space Administration  under grant No. NNX17AB59G  issued through the  Exoplanets Research Program. This research has made use of the  NASA Exoplanet Archive , which is operated by the  California Institute of Technology , under contract with the  National Aeronautics and Space Administration  under the  Exoplanet Exploration Program.
DATA AVAILABILITY HARPS-N observations and data products are available through the Data & Analysis Center for Exoplanets (DACE) at https: //dace. unige. ch/. TESS data products can be accessed through the official NASA website https://heasarc. gsfc. nasa. gov/ docs/tess/data-access. html. All underlying data are available either in the appendix/online supporting material or will be available via VizieR at CDS.
Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. We apply GMJMCMC to reanalyze QTL mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and from a backcross population in Drosophila where we identify several interesting epistatic effects. The method is implemented in an R package which is available on github.
The first study is concerned with the hypocotyledonous stem length in Arabidopsis thaliana using Recombinant Inbred Line (RIL) data  (Balasubramanian et al. , 2009) , the second one considering various traits from backcross data of Drosophila Simulans and Drosophila Mauritana is presented in the web supplement. The method is implemented as an R package which is freely available on GitHub at http://aliaksah. github. io/EMJMCMC2016/, where one can also find examples of further logic regression applications.
In fact it is even difficult to specify q, the total number of feasible trees. To solve this problem we present an adaptive algorithm called Genetically Modified MJMCMC (GMJMCMC), where MJMCMC is embedded in the iterative setting of a genetic algorithm. In each iteration only a given set S of trees (of fixed size d) is considered. Each S then induces a separate search space for MJMCMC. In the language of genetic algorithms S is the population, which dynamically evolves to allow MJMCMC exploring different reasonable parts of the unfeasibly large total search space. To be more specific, we consider different populations S 1 , S 2 ,. where each S t is a set of d trees. For each given population a fixed number of MJMCMC steps is performed. Since the MJMCMC algorithm is specified in full detail in  Hubin and Storvik (2018) , we will concentrate here on describing the evolutionary dynamics yielding subsequent populations S t.
For all three operators it holds that if the newly generated tree is already present in S t then it is not considered for S t+1 but rather a new replacement tree is proposed instead. The pseudo-code Algorithm 1 describes the full GMJMCMC algorithm. For each iteration t the initial model for the next MJMCMC run is constructed by randomly selecting trees from S t with probability P init. For the final population S Tmax , MJMCMC is run until M f in unique models are visited (within S Tmax ). M f in should be sufficiently large to obtain good MJMCMC based approximations of the posterior parameters of interest based on the final search space S Tmax. Algorithm 1 GMJMCMC 1: Run the MJMCMC algorithm for N init iterations on X 1 ,. , Xm and define S 0 as the set of d 1 variables among them with the largest estimated marginal inclusion probabilities.
Simulation study The GMJMCMC algorithm was evaluated in a simulation study divided into two parts. The first part considered three scenarios (numbered 1-3) with binary responses and the second part three scenarios (4-6) with quantitative responses. For each scenario we generated N = 100 datasets according to a regression model described by Equations (2. 1) and (2. 2) with n = 1000 observations and p = 50 binary covariates. The covariates were assumed to be independent and were simulated for each simulation run as X j ∼ Bernoulli(0. 3) for j ∈ {1,. , 50} in the first two scenarios and as X j ∼ Bernoulli(0. 5) for j ∈ {1,. , 50} in the last four scenarios. All computations were performed on the Abel cluster 2. For Scenarios 3, 5 and 6 the effect sizes (β j 's) for higher order interactions might seem unrealistically large compared to real applications.
5. This corresponds to the median probability model of  Barbieri et al. (2004). To evaluate the performance of the different algorithms we estimated the following metrics: Individual power -the power to detect a particular tree from the data generating model; Overall power -the average power over all true trees; FP -the expected number of false positive trees; FDR -the false discovery rate of trees; WL -the total number of wrongly detected leaves. (FP)  and FDR are compared between FBLR, MCLR and GMJMCMC using either Jeffreys prior (Jef. ) or the robust g-prior (R. g. ). For GMJMCMC the default Cmax = 5 is used. For the first two scenarios we also present results for Cmax = 2 (inside parentheses) corresponding to the parameters used by MCLR and FBLR. All algorithms were tuned to use approximately the same computational resources.
We consider here only the hypocytol length in mm under different light conditions 3. Genotype data is available for 220 markers distributed over the 5 chromosomes of Arabidopsis thaliana with 61, 39, 43, 31 and 46 markers, respectively. Balasubramanian et al. (2009)  had genotyped 224 markers but we dismissed 4 markers which had identical genotypes with other markers. The amount of missing genotype data is relatively small with a genotype rate of 93. 9% and most importantly the data contains only homozygotes (AA:49. 6% vs. BB:50. 4%). This means that the RIL population contains no heterozygote markers and logic regression can be directly applied using the genotype data as Boolean variables. Missing data were imputed using the R-QTL package (http://www. rqtl. org/). The imputed data was then analyzed with our algorithm GMJMCMC to detect potential epistatic effects and the results are summarized in Table  3.
36. Additionally there is some rather small posterior probability for an epistatic effect between this region and a marker on chromosome 1 which is rather close to x44607889. Finally both for Far Red Light and for White Light our analysis essentially yielded the same results as the interval mapping analysis, when observing that under the first condition the posterior probability was again almost equally distributed between the neighboring markers MSAT4. 37 and NGA1107. In summary the sample size in this data set might be slightly too small to detect epistatic effects, although under the first two light conditions there was at least some indication for a two-way interaction. We have analyzed a second data set concerned with QTL mapping for Drosophila where we compare logic regression with a more traditional approach to modeling epistasis.
This was due to the lack of a suitable parallelised tool for distributed analysis of scientific Big Data. In this paper, we discuss the VELaSSCo software platform aiming to address this problem  19. This platform is developed within an EC FP7-funded project, VELaSSCo: "Visualisation for Extremely Large-Scale Scientific Computing". VELaSSCo is a three-year project which ran from January 2014 to December 2016. The goal of VELaSSCo was to create the VELaSSCo platform for distributed post-processing and visualisation of very large engineering data sets  20. These data sets include DEM, the finite element method (FEM)  [21] [22] [23]  and computational fluid dynamics (CFD)  [24] [25] [26]   The development of the VELaSSCo platform has been guided by a user panel. The panel was consulted at the start of the project to learn of their requirements for the platform, they were kept apprised of developments and they evaluated both an early prototype of the platform and the final platform.
User feedback on the prototype has been invaluable to inform its subsequent development. The user panel had around 60 members, with approximately a 70:30 balance between academia and industry. Usability testing in VELaSSCo followed a 'goal, question, metric' or GQM approach to software metrics  27   The aim of this paper is to show, by means of selected use cases, how the VELaSSCo platform facilitates distributed post-processing and visualisation of large engineering data sets. A comprehensive overview of the platform is provided along with a description of the D2C transformation for DEM data. Some practical guidance is provided for using the platform, while two DEM use cases demonstrate the capabilities of the VELaSSCo platform.
Overview of the VELaSSCo Platform The VELaSSCo platform was developed to work both with open-source and closed-source software. The open-source version is built on top of the open-source Hadoop software stack: a Java-based framework for distributed storage and processing of big data. The software components within the Hadoop framework include the Hadoop Distributed File System (HDFS) which stores files in a distributed, split and redundant way for parallel access; HBase, a distributed database built on top of HDFS providing a transparent means for storing and accessing data on the cluster; YARN which manages the computing resources and schedules jobs across the cluster; and MapReduce which refers to the programming model for large-scale data processing. Figure  1  shows the three main layers in the architecture: the visualisation clients, the Core Analytics Module and the Data Layer.
Visualisation Clients The visualisation client triggers queries through the access library which is the interface between the client and the platform. The complex D2C query is explained in a later section. The visualisation clients (currently two: GiD 28 and iFX 29 ) implement a plugin to access the platform. Integration between components is done through a Thrift API, so the plugin implements a public API provided by the platform. The results output by the queries are returned to the clients through an internal VELaSSCo format which is designed to minimize latencies through high-speed transfer. The following requirements were considered when designing the format: • Minimal data size. Although the headers, separators, and metadata make parsing and conversion easier, they increase file size.
To reduce the file size, they are minimized. • Binary data. Since data are being transferred over a network, the payload data are in binary form to avoid conversion from an ASCII representation. • Processing friendly layout. The stored data are directly accessible by processing units without any conversion. The payload can be uploaded to OpenGL/D3D buffers directly. While the GPU is the typical target, this also applies to CPUs. In this format, a batch of data is represented as a file. A file is a self-contained binary 'blob' of data that includes all the information to display a scene. Such a file consists of two sections, a header section and a data section. The header contains a description of how the payload is handled. The data section is simply a collection of buffers. Within a C/C++ program a file could be accessed as the struct shown in Listing 1.
Later, files could either contain new data that replace old data (e. g. , a new set of triangle strips), but they could also provide incremental data. For example, in case of progressive meshes, new files could add new vertices and edges that are used to expand available geometries to a new detail level. How new data are to be interpreted could be defined in the description buffer. The rendering algorithms are optimized to provide highest efficiency during rendering of the received data. In DEM cases in which the particles need to be drawn, they are received in a compact format. As mentioned before, the client can redirect it to the rendering module without any conversion. The iFX visualisation client supports two rendering modes: rasterization-based and ray-tracing-based. In both cases, particle data are sent to the rendering device without any conversion.