dataesr/scientific-paragraphs-categorization huggingface dataset:
A. WRITE LATENCY In graph databases, relationships between nodes are considered the first-class citizen  [36]. While relational databases rely on joins to answer complex queries, graph databases store physically links between nodes. Consequently, writing data on graph data warehouses is significantly longer than relational data warehouses due to the time required to create relationships between nodes. This has been checked on the experiments that we conducted. As shown in Figure  4 , the loading time of the relational data warehouse is up to fourteen times faster than the graph data warehouse. In addition, unlike relational databases where the time of creation of the star and snowflake data warehouses is the same, the creation of a graph data warehouse with a snowflake-like schema takes more time than a graph data warehouse with a starlike schema.
2) HIERARCHICAL QUERIES The run times of queries Q7 and Q27 are shown respectively in Figures  6a  and 6b. Queries Q7 and Q27 are similar. They are hierarchical queries, with a maximum depth of 2, and include the computation of aggregates and a sorting of the results. The execution times are therefore close. For the second type of queries, our results show that the snowflake schema is more time consuming than the star schema in the case of relational data warehouses. However, for graph databases, the curves of star and snowflake graph data warehouses are superposed. Surprisingly, they have the same performance. Additionally, the graph data warehouse is significantly more efficient than the relational data warehouse (up to more than twenty times faster). Also, when the data size increases, the graph data warehouse becomes more and more efficient than the relational one.
3) HIERARCHICAL AND CUMULATIVE QUERIES The run times of queries Q13 and Q48 are shown in 7a and 7b respectively. Both queries compute a few aggregates (average and sum) on sales for one year and for conditions on various sub-dimensions of customers: customer_demographics, household_demographics, customer_address. For this category, the performance of relational and graph data warehouses is similar when the data size is small. However, for larger data sizes, graph data warehouses answer queries faster than the relational ones (up to ten times faster).
Graph-oriented data warehouses have been shown to be effective for the second and third types of queries, those involving many dimensions or hierarchies. Indeed, in graphbased data warehouses, the relationships between the fact and the dimension, and between the attributes of the same dimension (hierarchies) are physically implemented. Thus, in the case of an increase in the volume to be processed, when queries go deeper in the dimensions, graph-based data warehouses are more efficient, and more robust to the increase in complexity. This is because while relational databases crawl all tables until data matching the search criteria is found, graph databases crawl only those nodes that meet the criteria. Therefore, both star and snowflake graphical data warehouses are effective depending on the use case (normalized or nonnormalized data).
VI. CONCLUSION This paper investigates the design, implementation, and evaluation of graph data warehouses. The goal of this study is to determine whether a traditional relational data warehouse or a graph data warehouse would be more effective. We have proposed a set of transformation rules called MDM2G to convert a multidimensional model of a data warehouse into a graph database. These rules transform a multidimensional model into a graph database using two schemas: star-like and snow-like schemas. Experiments are conducted using data generated from the TPC-DS benchmark. We generated respectively data sets of size 1GB, 3GB, 5GB and 7GB. The experimental setup shows the way OLAP systems can be implemented with graph databases using Neo4j. This process includes data transformation, data loading and performing complex analytical queries.
The entire process allows us to compare the different approaches with each other. We also compare the performance of graph data warehouses to similar relational data warehouses. Results show that both of our proposed graph data warehouses perform well, with denormalized schema being hardly more efficient for some queries. The results of the experiments exhibit the advantage of the use of graph NoSQL technologies for implementing OLAP systems and answer complex questions. In this work, our evaluation has been based on objective measures. However, other subjective measures could be used such as the maturity, ease of programming, security and flexibility. In our further research works we will focus on the use of graph databases to store and analyze biological networks in order to provide fast answers for complex queries and predict hidden relationships between proteins.
2017). Observations for Q3D were designed with the help of the JWST User Documentation (Jdo 2016). The first target of the Q3D program, J1652, was observed on 2022 July 15 and 2022 July 16 by JWST using the NIRSpec instrument in IFU mode  (Böker et al. 2022; Jakobsen et al. 2022). Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST) at the STScI. The specific observations analyzed can be accessed via 10. 17909/qn07-rt28. The NIRSpec field of view in IFU mode is ∼3″ × 3″ (∼26 × 26 kpc). We used the filter/grating combination F170LP/G235H, with corresponding wavelength coverage of 1. 70-3. 15 μm (corresponding to ∼0. 43-0. 79 μm at the redshift of J1652). The grating has a near-constant dispersion of Δλ = 8. 7 × 10 -4 μm, corresponding to a velocity resolution of 85-150 km s -1.
At present, the pipeline still uses preflight flux calibration files; however, we do not require absolute flux calibration for the analysis presented in this paper. For NIRSpec, additional steps are taken to flag pixels affected by open MSA shutters. At this point, we skipped the imprint subtraction step due to increased spatial variation in the background across many spectral channels. Due to known issues with the outlier detection step in the Spec3Pipeline (JWST Help Desk, private communication), we opted to use the Montage package 20  to combine the different dither positions into a single data cube using their drizzle algorithm. The dither positions are combined onto a common grid with a spatial pixel size of 0 05.
PSF Subtraction To remove the bright, spatially unresolved quasar emission, we employ the method outlined in  Vayner et al. (2016 Vayner et al. ( , 2021b)). We are developing a dedicated software package for PSF subtraction, q3dfit (D. S. N. Rupke et al. 2022, in preparation) , which we will apply to these data in future work. For this initial analysis, we construct a PSF model with a spatial distribution that is wavelength-independent using a small wavelength range near [O III]. We use the blueshifted wing of the Hβ line, selecting velocity channels offset by < -3000 km s -1 from the quasar redshift to avoid extended Hβ emission from the quasar host galaxy. In particular, we use the [1. 898, 1. 900 μm] range, median combining five spectral channels. Next, we apply a 5σ signal-to-noise ratio cut on the resulting image, leaving behind only flux from the PSF structure that has an FWHM of ∼200 mas.
Finally, we normalize the PSF image by the peak flux. To remove the PSF from the data in each spectral channel in the range [1. 7002, 2. 42945 μm], we scale the flux of the PSF image by the peak flux in that channel. Finally, we perform additional processing to mitigate issues caused by spatial undersampling of the JWST PSF by the NIRSpec IFU at these wavelengths. We will discuss these additional steps in a forthcoming paper (A. Vayner et al. , in preparation).
2018; Vayner et al. 2021a ). This suggests that, despite the complexity of the galactic and circumgalactic environment, the illuminated part of the outflow is bipolar  (Wylezalek et al. 2016) , and therefore the obscuration of the nucleus is well organized in an axisymmetric structure  (Antonucci 1993). A full multiline, in-depth analysis of the NIRSpec data for this quasar will be presented by A. Vayner et al. in preparation and complemented by upcoming MIRI MRS observations. The picture that emerges in this first look at the NIRSpec data is one that we did not anticipate. The HST imaging had already suggested that J1652 was undergoing a major merger due to a large but faint tidal feature. Obscured quasars undergoing major mergers in the nearby universe sometimes host large-scale, complex [O III] nebulae at 10-20 kpc scales  (Rodríguez Zaurín et al.
On the basis of the properties of the core and the larger-scale environment (where kinematic data hint at a bimodal distribution of velocities), as well as using comparisons with simulations  (Springel et al. 2006; De Lucia & Blaizot 2007) ,  Kuiper et al. (2011)  concluded that the Spiderweb protocluster is likely an ongoing merger of two galaxy groups with dark matter halo masses of several × 10 13 M e. Another example is a z = 4. 3 protocluster in the field of the submillimeter source SPT 2349-56  (Miller et al. 2018). Using deep ALMA spectral imaging, multiple cluster members within 15 kpc are confirmed with velocity offsets of up to 700 km s -1. This cluster is different from the Spiderweb cluster in that the observed kinematics and spatial configuration of the galaxies suggest that the structure more likely represents a single gravitationally bound halo rather than multiple groups.
D. W. and C. B. acknowledge support through an  Emmy Noether Grant  of the  German Research Foundation , a stipend by the  Daimler and Benz Foundation , and a Verbundforschung grant by the  German Space Agency. A. V. , D. S. N. R. , N. L. Z. , and S. V. are supported by  NASA  through  STScI  grant  JWST-ERS-01335. N. L. Z. further acknowledges support by the  Institute for Advanced Study  through a  J. Robert Oppenheimer Visiting Professorship  and the  Bershadsky Fund. J. B. -B. acknowledges support from grant  IA-101522  ( DGAPA-PAPIIT, UNAM ) and funding from  CONACYT  grant  CF19-39578. This research made use of Montage. It is funded by the  National Science Foundation  under grant No. ACI-1440620  and was previously funded by the  National Aeronautics and Space Administration's Earth Science Technology Office, Computation Technologies Project , under cooperative agreement No.
g. Borucki et al. 2010; Buchhave et al. 2014; Ricker et al. 2015) , but also yield precise data for the studies of stellar interiors with asteroseismology techniques (e. g. Christensen-Dalsgaard 2002; Huber et al. 2013; Serenelli et al. 2017). Future facilities, such as PLATO  (Rauer et al. 2014) , are opening entirely new perspectives for studies of exoplanets in different environments and for studies of stars at the level of details that have so far been only available for our Sun. In this work, we introduce the SAPP pipeline 1 that will be used for the determination of atmospheric parameters of stars observed within the core program of the PLATO space mission  (Montalto et al. 2021). The code will provide stellar effective temperature (T eff ), metallicity ([Fe/H]), surface gravity (log g), radial velocities, and detailed chemical composition, among other parameters.
Our ambitious goal is to reach uncertainties as low as 1% on T eff and [Fe/H], for instance, in order to match the high spectral data quality provided by upcoming ground-based facilities, such as WEAVE and 4MOST. The SAPP is written in Python and the source code is public and available for use 2. Our approach to the analysis of our targets is somewhat different from other available pipelines and codes (e. g. , SME:  Valenti & Piskunov 1996; Piskunov & Valenti 2017 , The Cannon:  Ness et al. 2015 , The Payne: Ting et al. 2016 , Rix et al. 2016 , Ting et al. 2019 , MATISSE: Recio-Blanco et al. 2006 , CNN StarNet: Bialek et al. 2020) , although our analysis shares many of the elements with these software units. It is beyond the scope of this paper to provide a detailed comparison of our code with the other programs, as ultimately the design of a computer program follows the needs and objectives of a given research project, and this sets the conceptual and numerical basis of a code.
It has been already shown (e. g. 1 We stress that the detailed approach to the analysis of PLATO targets will evolve. 2 https://github. com/mg477/SAPP Pont & Eyer 2004; Jørgensen & Lindegren 2005; Gruberbauer et al. 2012; Bazot et al. 2012 ) that the analysis of fundamental parameters of stars, masses and metallicities, especially benefits from the Bayesian approach. Therefore, in order to take the full advantage of the Bayesian formalism, we also include stellar evolution models that give us the ability to determine masses, luminosities, radii, and ages of stars in a single consistent framework taking into account the correlations between all relevant parameters. The paper is organised as follows. Section 2 gives an overview of the observed sample of stars that are used to validate the approach developed in this work.
For the majority of stars, spectra obtained with the high-resolution UVES spectrograph mounted on VLT are available. For the Kepler stars, reduced combined spectra taken with HARPS-N facility were kindly provided by P. E. Nissen (priv. comm. ). The UVES spectra (4800 to 6800 Å) have a resolving power of λ/δλ ≈ 47 000  (Dekker et al. 2000)  and the HARPS data of 115 000  (Mayor et al. 2003). The signal-to-noise ratio (S/N) of the UVES and HARPS spectra is in the range from 200 to over 1 000. The spectra are all pre-processed (described in Appendix B. 1) by the SAPP using different procedures developed, this includes continuum normalisation, radial velocity correction and contaminant treatment. Then we degrade the resolving power of the spectra to R = 20 000 and reduce the wavelength coverage to 5300 -5600 Å in the optical spectral window.
(2011) : R k 1 =                      4. 23, k 1 = B T 3. 24, k 1 = V T 0. 86, k 1 = J 0. 5, k 1 = H 0. 3, k 1 = Ks (11) For the Gaia EDR3 dataset, the A G value can be given, however it is not always available. In this case, (G Bp -G Rp ) colordependent extinction coefficients presented in  Casagrande et al. (2021)  are used for G, G Bp and G Rp. For the magnitudes, we separate the PDFs entirely (G, G Bp , G Rp ) and (H, J, Ks, B, V).
Influence of seismic priors on stellar parameters In the subsequent analysis, we investigate whether the use of asteroseismic data improves the constraints on stellar parameters, compared to the approach when only stellar spectra are used. So far, most studies employing asteroseismic constraints on log g have reported an improvement in the accuracy of results  (Bruntt et al. 2012; Buchhave & Latham 2015; Nissen et al. 2017). We use two asteroseismic quantities: ν max -the frequency of the maximum oscillation power -and ∆ν -the large frequency separation (e. g. Serenelli et al. 2017). These seismic data are used as a prior in two methods. The first approach is to estimate a log g value from the asteroseismic PDF, which utilizes both quantities and then fix it in the spectroscopic analysis. We note that instead of fixing the surface gravity, a prior on log g can be imposed, however, for the benchmark stars this alternative approach yields the same result.
In BO-SAPP, all modules combine together into SAPP's Bayesian framework to produce best-fit parameters T eff , log g, [Fe/H], chemical abundances, V mic , Luminosity, etc. Currently, the code makes use of photometry, spectra, and parallaxes, whereas IRFM and SBCR techniques are used to constrain T eff independently and to validate the spectro-photometric results. We furthermore plan to include the interferometry data. Stellar evolution models will not be used, to minimise overlap with other modules of the PLATO Stellar Analysis System (SAS) system. Furthermore, we anticipate that once the Gaia RVS spectra are available, these will be used to complement spectra from instrument archives and surveys. The details of combining different observed spectra, such as, e. g. Gaia RVS, SDSS-V, and 4MOST data, will be presented in a separate study, however, the main advantage of the SAPP is that same physical models and the same statistical approach will be used for the analysis of all different kinds of spectroscopic observations to maximise the consistency of the results.
Photometric data will be sourced from 2MASS, SPHEREx, and other surveys. SAPP's parameters will be used by different PLATO workgroups to determine radii, mass, and ages of stars, as well as astrophysical parameters of their orbiting planets. Therefore, very stringent requirements are posed on the accuracy of the atmospheric characterization. For example, a 2% error in T eff translates into a 3 percent error in radius and a 5 % error in mass for a typical FGK-type un-evolved star  (Serenelli et al. 2017). Owing to the complexity of PLATO algorithms and the details of work organisation within different PLATO packages, the exact scope of both branches of the SAPP pipeline may change slightly in future.
2017). High-and medium-resolution spectra (R > 40, 000 and R = 20, 000 respectively) with high-S/N (S/N > 300) spectra for these stars are available from the ESO archives. The combined sample includes 27 FGK-type stars that cover a broad range in T eff , log g, and metallicity. The majority of our benchmarks are main-sequence stars, but there are several sub-giants. The reference parameters of these targets were adopted from recent literature studies that employed asteroseismology, interferometric angular diameters, and spectro-photometry. We find that spectroscopic data provides most the reliable information about atmospheric parameters of stars. From spectra alone, we can determine T eff , log g, metallicity, and individual abundances of αand iron-group elements with a precision of a 50 to 100 K (T eff ) and 0.
Kovalev et al. (2019)  presented comprehensive tests and validation of the NLTE spectroscopic module on highand medium-resolution spectra of evolved and un-evolved stars across a broad range of metallicities -2. 5 [Fe/H] +0. 3, effective temperatures 4000 T eff 7000 K, and surface gravities 1. 2 log g 4. 6 dex. The advantages of combining spectra, photometry, astrometry, and stellar models to derive ages and masses of evolved stars were carefully explored by  Serenelli et al. (2013)  and  Schönrich & Bergemann (2014). We therefore conclude that the SAPP code delivers robust estimates of stellar astrophysical parameters, which makes the code useful for the analysis of low-mass stars observed with different large-scale spectroscopic surveys, such as 4MOST and WEAVE (e. g. Dalton et al. 2014; Bensby et al.
To shift the spectra to rest frame with a RV value known, the following equation is required, λ 0 = λ obs /(∆RV/c + 1) (B. 1) Our procedure took direct inspiration from PyAstronomy RV correction module, PyAstronomy. pyasl. crosscorrRV, which is a collection of astronomy packages  (Czesla et al. 2019). The procedure we follow involves testing a range of RV values, doppler shift the observed spectrum and compare it to a model which is in the rest frame. As all the stars in our benchmark sample are on the main-sequence and so sub-giant/dwarfs, we only require one model for RV correcting our spectra, a Main-Sequence star such as our Sun. A spectral model of R ≈ 500,000 was created using Turbospectrum  (Plez 2012)  with the classical parameters of the Sun (see table. 1). This model works as a template because despite some changes in the HR Diagram, all the stars' line profiles should approximately match the location of the Solar line profiles.
Acknowledgements. This work presents results from the  European Space Agency (ESA)  space mission PLATO. The PLATO payload, the  PLATO Ground Segment  and PLATO data processing are joint developments of  ESA  and the  PLATO Mission Consortium (PMC). Funding for the PMC is provided at national levels, in particular by countries participating in the  PLATO Multilateral Agreement (Austria, Belgium ,  Czech Republic, Denmark, France, Germany, Italy, Netherlands, Portugal, Spain, Sweden, Switzerland, Norway, and United Kingdom ) and institutions from  Brazil. Members  of the PLATO Consortium can be found at https://platomission. com/. The  ESA PLATO  mission website is https://www. cosmos. esa. int/plato. We thank the teams working for PLATO for all their work. We thank  P. E. Nissen  for providing the reduced spectra of the Kepler Legacy stars.
AS acknowledges support from  MICINN  grant  PID2019-108709GB-I00. TM acknowledges financial support from  Belspo  for contract PRODEX PLATO mission development. UH acknowledges support from the  Swedish National Space Agency (SNSA/Rymdstyrelsen ). SM has been supported by the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , and by the  ÚNKP-20-5 New National Excellence Program  of the  Ministry for Innovation and Technology. NN acknowledges  Anthony Salsi  for useful discussions. V. A. is supported by  FCT -Fundação para a Ciência e Tecnologia (FCT)  through national grants:  UID/FIS/04434/2019 ;  UIDB/04434/2020 ;  UIDP/04434/2020. V. A. also acknowledges the support from FCT through Investigador FCT contract nr. IF/00650/2015/CP1273/CT0001. M. T. acknowledges the funding from  MIUR  Premiale 2016: MITIC.
Contrary to antibody detection, which can indicate either past or present contact with trypanosomes, detection of trypanosome antigens identifies active infection, and can inform treatment decisions. The principle of the detection of parasite products in the host fluids remains a valid proposition, even though the antigen detection ELISAs for AT developed in the past had unsatisfactory diagnostic performances  (Eisler et al. , 1998). COMBAT will develop two innovations in the detection of trypanosome antigens: i) the capture of a T. congolense target enzyme, followed by detection through its enzymatic activity; and ii) the coupling of nanobodies with a radio frequency identification (RFID) sensor format. Both are well suited to adaptation into a rapid diagnostic format for point-of-care diagnosis.
Disease mapping, surveillance systems and risk of spread. Despite the vast amount of data collected in the field over the years, Africa-wide AT maps are absent and continental tsetse maps are long out-of-date  (Ford & Katondo, 1977). Comprehensive national-level datasets are also lacking in most countries. To address these gaps, the continental atlas of tsetse and AT is being developed  (Cecchi et al. , 2015; Cecchi et al. , 2014; de Gier et al. , 2020) , and the adaptation and uptake of the methodology at the national level is being promoted in several countries  (Ahmed et al. , 2016; Diarra et al. , 2019; Ngari et al. , 2020; Percoma et al. , 2022; Shereni et al. , 2021). COMBAT will complete the development of the continental atlas, extend it to surra (T. evansi), and develop, enhance or update the national atlases in project countries.
Socio-economic burden of AT. Estimates of the AT burden in Africa are outdated and based on sparse data  (Kristjanson et al. , 1999). Knowledge at the national and sub-national level is also poor. This constrains cost-effective targeting of control activities and weakens the commitment of decision makers. COMBAT will assess the burden of the disease at the continental level and, in selected countries, at the national and local level. These assessments will benefit from comprehensive disease datasets developed by the project (i. e. the atlases). Estimates of the burden of AT will inform strategic decisions on where disease control is most needed and provide a baseline to measure progress in disease control. Furthermore, COMBAT will investigate whether vector control activities against gambiense HAT in two countries, Chad  (Rayaisse et al.