dataesr/scientific-paragraphs-categorization huggingface dataset:
Among the provided output, SourceXtractor++ typically obtained the best values for the global diagnostics. Interestingly, in the DS realisation the runs with free n bulge typically yielded slightly better results than those with fixed n bulge = 4, in terms of average bias (see  Fig. B. 9). As mentioned, the processing pipeline adopted for the EMC included a substantial pre-processing of the dataset, and the priors used for the measurements were calibrated on the provided samples of true values, albeit only in a statistical sense (see below).
B. 5. 1. Re-runs on raw data The NIR and LSST images were rebinned back to their original pixel scales (while the ones provided in the dataset shared with the participants had been rebinned to the I E pixel scale of 0. 1 ). Additionally, the PSFs used in the fitting process were not those provided in the dataset; instead, they were extracted from the images using PSFEx. This did not require any additional input, and therefore it was not outside the guidelines of the challenge. Still, we felt it was necessary to check the performance of the software on the non-processed images and using the official PSFs, for a fairer comparison with the other participants. To this end, a few additional runs were performed by the SourceXtractor++ team (using version 0. 16 of the code instead of 0. 12), and we compared the results with the ones officially provided for the EMC.
Acknowledgements. The Euclid Consortium acknowledges the  European Space Agency  and a number of agencies and institutes that have supported the development of Euclid, in particular the  Academy of Finland , the  Agenzia Spaziale Italiana , the  Belgian Science Policy , the  Canadian Euclid Consortium , the  French Centre National d'Etudes Spatiales , the  Deutsches Zentrum für Luft-und Raumfahrt , the  Danish Space Research Institute , the  Fundação para a Ciência e a Tecnologia , the  Ministerio de Ciencia e Innovación , the  National Aeronautics and Space Administration , the  National Astronomical Observatory of Japan , the  Netherlandse Onderzoekschool Voor Astronomie , the  Norwegian Space Agency , the  Romanian Space Agency , the  State Secretariat for Education, Research and Innovation (SERI)  at the  Swiss Space Office (SSO) , and the  United Kingdom Space Agency.
https://github. com/MegaMorph/galapagos the size and shape of the object estimated by SExtractor, Galapagos-2 takes care of neighbouring objects (deblending and fitting of bright nearby objects, masking of fainter and more distant objects), estimates the sky background level with a sophisticated and robust scheme (see Barden et al. 2012  for details), and sets up the fit using SExtractor values as initial guesses to run the fitting algorithm GalfitM for all objects, starting from the brightest objects and using the PSF provided. Once an object has been fit, it is merely subtracted from the fits of nearby objects. This significantly speeds up the process overall, as these bright, large objects take the longest to fit, but this only needs to be done once. In a fully automated pipeline, it then reads out the result and provides one final catalogue, which contains fitting information for all bands.
GalfitM itself uses a Levenberg-Marquardt minimisation to derive the bestfit parameters and uncertainties. In the multi-band realisations of the EMC, all bands are connected via physically reasonable polynomials and fitted simultaneously, to reduce the degrees of freedom of the fit and make full use of the multi-wavelength information. The software requires one additional input image compared to what was provided, namely a weight image to flag bad pixels. Since no bad pixels were in the data, this was trivially created as a uniform image of the correct size. All the requested outputs were provided; however, for DS F0 only a simultaneous fit was run, therefore including I E in the multi-band fitting process -in other words, there is no isolated fit for I E DS F0. This causes the I E fit for F0 to be substantially different from that of other fields; for this reason, it was decided not to include F0 in the analysis of I E -only results for all codes (see below).
In this paper we present an interactive visualization tool that exhibits counterfactual explanations to explain model decisions. Each individual sample is assessed to identify the set of changes needed to flip the output of the model. These explanations aim to provide end-users with personalized actionable insights with which to understand automated decisions. An interactive method is also provided so that users can explore various solutions. The functionality of the tool is demonstrated by its application to a customer retention dataset. The tool is compatible with any counterfactual explanation generator and decision model for tabular data.
Implementation The implementation is based on Flask, which is a micro-framework for web development in Python to present data and display web pages. Visualizations and interactions are created using JavaScript and d3js. Flask applications can easily be embedded in website or even in Jupyter Notebooks. In this demo, we use HTML and CSS to create the web pages. We can interface with any prediction model, and any counterfactual explanation generator. The data needed for the visualization is provided via a JSON file which must include the variables names and 2 variable/instance matrices, one with the instances to explain and another with the counterfactuals. The file must also contain some classification results: the prediction probabilities of the model and the predicted classes both for the instances to explain and their counterfactuals.
6 3 Use case study We illustrate the tool on the Telco Customer Churn dataset [1] which contains 7,043 instances described by 20 input variables. The goal is to predict the churn of a telecom operator's customers (with 2 classes: churn vs no churn). For our experiments we used VCNet, an architecture that is able to generate at the same time the decision and a counterfactual explanation and is well adapted for processing mixed tabular data  [6]. We discuss the analysis of the example shown in Figure  1. It corresponds to an individual (Id 1682) labelled by the decision model as a churner with a probability of 69%. The displayed counterfactual changes the class of the example from churn to no churn with a 79% probability of no churn (21% probability of churn). The counterfactual was obtained by the modification of 7 variables from the initial example (37% of the input variables).
Antlia B (Ant B), a slightly brighter dwarf galaxy (M V = -9. 7, M ∼ 8 × 10 5 M ), promises more data, while at this stellar mass it is still expected that any star-formation-induced core would be very small (e. g. Bullock & Boylan-Kolchin 2017; Orkney et al. 2021). This makes Ant B well suited for testing models of dark matter. We present here an in-depth analysis of the kinematics of Ant B A38, page 2 of 17 Notes. (RA, Dec) are the coordinates of the centre of the galaxy; D is the distance; M V is the absolute magnitude in the V band; and r h is the half-light radius. (Zoutendijk et al. 2021b ) from MUSE-Faint  (Zoutendijk et al. 2020) , a survey of UFDs with the Multi Unit Spectroscopic Explorer (MUSE;  Bacon et al. 2010). With these data, which cover a wide range of radii, and adopting the Jeans analysis code GravSphere  (Read & Steger 2017) , we are able to derive the dark matter density profile of Ant B and, ultimately, to place constraints on the properties of SFDM.
In Sect. 2 we describe the observations (Sect. 2. 1), summarise the data reduction (Sect. 2. 2), and describe the selection of the members of our dwarf galaxy, showing the determination of line-of-sight velocities from the spectra as well as its velocity distribution (Sect. 2. 3). This is followed in Sect. 3 by the models of dark matter used in this paper (Sect. 3. 1), by a brief description of GravSphere (Sect. 3. 2), the analysis tool used, and by the priors used for each model (Sect. 3. 3). We continue in Sect. 4 with our results for the constraints on the properties of the studied dark matter models (Sect. 4. 1) and for the recovered dark matter density profiles (Sect. 4. 2). We end with a discussion in Sect. 5 and our conclusions in Sect. 6. In Appendix A we show the numerical implementation of the SFDM model, presented in Sect.
Data reduction The data reduction procedure used in this work follows the one described in  Zoutendijk et al. (2021a). In brief, we adopted the standard method for reducing MUSE data with the MUSE Data Reduction Software  (Weilbacher et al. 2020) , complemented with a bad-pixel table from  Bacon et al. (2017). Next, the produced data cube was post-processed with the Zurich Atmosphere Purge (version 2. 0;  Soto et al. 2016)  to remove residual sky signatures. We used the public images of Ant B from the HST (HST-GO-14078; PI: J. Hargis) in the V band (F606W filter) and another in the I band (F814W filter) in order to create a photometric catalogue using SExtractor  (Bertin & Arnouts 1996). Spectra. We extracted spectra of the stellar sources in the MUSE cube using the PampelMuse code  (Kamann et al.
In total, 2514 spectra were extracted with PampelMuse. Line-of-sight velocities. The line-of-sight velocities were determined from the spectra collected from PampelMuse using spexxy  (Husser 2012). This software allows the determination of stellar properties by comparing spectra with synthetic spectra. We adopted the same grid of synthetic Phoenix spectra (the Göttingen Spectral Library;  Husser et al. 2013 ) used by  Husser et al. (2016)  for the globular cluster NGC 6397, by  Roth et al. (2018)  for the nearby galaxy NGC 300, and  Zoutendijk et al. (2020)  for the UFD Eridanus II. The models are calculated on a grid of effective temperature, T eff , logarithm of surface gravity, log g, iron abundance, [Fe/H], and alpha-element abundance, [α/Fe], and for each model the line broadening, σ, and line-of-sight velocity, v LOS , can be fit.
GRAVSPHERE To measure the dark matter density profiles of Ant B, we used the updated version of GravSphere Jeans modelling code 2. A detailed explanation of its implementation can be found in  Read & Steger (2017)  and  Read et al. (2018) , which we briefly mention here for the reader's convenience. GravSphere solves the Jeans equation  (Jeans 1922)  for our member stars while assuming that the stellar system is spherical, non-rotating, and in a steady state, given by 1 ν ∂ ∂r ν σ 2 r + 2β(r)σ 2 r r = - GM(<r) r 2 , (7) where σ 2 r = v 2 r -v r 2 with v n r = v n r f d 3 u, (8) and M(<r) is the total cumulative mass as a function of the radius, r. The tracer number density ν characterises the radial density profile of a population of massless tracers (in our case, stars moving in a galaxy) that move in the gravitational potential of its mass distribution M(r), modelled with three  Plummer (1911)  profiles ν (r) = 3 j=1 3M j 4πa 3 j        1 + r 2 a 2 j        5/3 , (9) with masses M j and scale length a j for each individual component.
To bin the data in bins of the projected radius, R, we used an algorithm called Binulator  (Collins et al. 2021). After performing the Binulator routine, we applied our adaptation of GravSphere 3  to the Ant B data in order to get our dark matter density profile and, consequently, the dark matter constraints we were looking for. GravSphere solved the Jeans equation for the projected velocity dispersion. It also fitted the two VSPs, with the initial guesses being the profiles estimated by Binulator. GravSphere uses the ensemble sampler emcee  (Foreman-Mackey et al. 2013 ) to fit the model to the data. Each individual Markov chain (walker) communicates with the other walkers at each step, contrary to the classic Metropolis-Hastings algorithm, and hence it allows the chains to sample the posterior distribution more efficiently  (Genina et al.
Results Using GravSphere, discussed in Sect. 3. 2, we sampled the parameter spaces of our implemented dark-matter density models, examined in Sect. 3. 1. To do this, we used the kinematical measurements of Ant B, discussed in Sect. 2. Here, in Sect. 4. 1, we present the obtained dark matter constraints and, in Sect. 4. 2, the recovered density profiles as well as their comparison. To achieve this goal, we need the kinematic and photometric data of Ant B. Binulator starts by fitting the photometric data in order to get the light profile. In our case, we made a mock photometric catalogue by drawing 10 000 sample photometric positions from the exponential distribution from  Sand et al. (2015)  and used them as photometric observations in Binulator. Next, the 127 member stars of Ant B were binned radially from the centre of Ant B (listed in Table  1 ) into 5 bins, each of them having ∼25 sources.
The binned data that GravSphere uses is described in Table  3. Figure  4  shows the projected radii for all the stars, their position after the binning routine, and the velocity dispersion associated with each bin in dark blue. Our best-fitting CDM and SFDM models are shown in Fig. 4 , compared with the stellar velocity dispersion data. Both models fit the data reasonably well but do not reproduce all features. In particular, both models have difficulties in fitting the low central velocity dispersion when they are anchored at the half-light radii. This figure also shows that, as expected, we lack constraining power where there is no data, both at small (∼10 pc) and large radii ( 1 kpc). The recovered fits from GravSphere for the VSPs and the surface brightness profile can be seen in.
Testing the robustness of the results The centre measured by  Sand et al. (2015)  might be miscalculated since there is a bright star close to it, partially overlapping the galaxy. To test the robustness of the results, we started by changing the centre of Ant B by (α ∼ 1 , δ ∼ 3 ) to see how much our results would be affected. We reran Binulator and GravSphere on both models for Ant B with the new centre. The velocity dispersion for each bin, the recovered velocity dispersion profile, and the anisotropy profile associated with the new centre can be seen in. 66  -4. 60 for the SFDM model. We find R TF 0. 11 kpc at the 68% confidence level and R TF 0. 68 kpc at the 95% confidence level. Let us notice that this time we ran the code with half the steps to make the process faster since we wanted solely to check whether our results were not dramatically affected by changing the centre.
Conclusions In this work, we present the first spectroscopic observations of Ant B, a distant dwarf found by  Sand et al. (2015). With them, we were able to determine kinematic data, previously presented by  Zoutendijk et al. (2021b) , and ensure the membership of 127 stars. These observations allowed us to determine the intrinsic mean line-of-sight velocity, v LOS = 375. 39 +0. 98 -0. 97 km s -1 , and the velocity dispersion, σ v LOS = 7. 87 +1. 02 -0. 98 km s -1 , of this galaxy. The estimated v LOS is in agreement with the velocity of H i gas present in this galaxy (v H i ∼ 375 km s -1 ). The kinematic data of Ant B also allowed us to derive the dark matter density profiles for this dwarf. From them we were able to determine the virial mass, M 200 ≈ 10 9. 2 M , and the concentration parameter, c 200 ≈ 17, of Ant B.
The dark matter density profiles of UFDs have only started being estimated very recently. The measurements of the velocities of stars in these galaxies with the spectroscopic data from MUSE-Faint allow us to improve the constraints on their inner dark matter density profiles. With a larger number of UFDs analysed, we will be able to place stronger constraints on the physical properties of dark matter. Furthermore, we would also like to explore alternative dark matter models, which will take us one step closer to understanding this unknown form of matter.
Acknowledgements. We thank the anonymous referee for their helpful comments, which improved the manuscript. M. P. J. thanks  Marcel S. Pawlowski ,  Pengfei Li ,  Salvatore Taibi  and  Jamie K. Kanehisa  for the valuable discussions and advice. M. P. J. and J. B. acknowledge support by  Fundação para a Ciência e a Tecnologia (FCT)  through the research grants  UID/FIS/04434/2019 ,  UIDB/04434/2020 ,  UIDP/04434/2020  and  PTDC/FIS-AST/4862/2020. J. B. acknowledges support from  FCT  work contract  2020. 03379. CEECIND. S. L. Z. acknowledges support by The  Netherlands Organisation for Scientific Research (NWO)  through a  TOP Grant Module 1  under project number  614. 001. 652. S. K. A. acknowledges funding from  UKRI  in the form of a  Future Leaders Fellowship  (grant number  MR/T022868/1 ).
To do so, we used a hybrid approach that combined a processbased model with machine learning to accurately simulate maize and wheat phenology and yield across Europe. The detailed cropping schemes are de ned and illustrated by Supplementary Fig. 1. The process-based model, ORCHIDEE-CROP  20  , is an updated version of ORCHIDEE land surface model  21  which incorporates basic modules from the STICS crop model  22. We rst calibrated ORCHIDEE-CROP for Germany based on the crop phenology datasets  23, 24  and a reference crop yield dataset  25. Yet, this calibration did not reproduce the spatial patterns of phenology and yield across Europe well, because ORCHIDEE-CROP cannot capture the diversity of cultivars and practices. To improve the performance of ORCHIDEE-CROP across distinct European countries where crop cultivars and practices differ  26  , we collected phenology data from other European countries  24  in order to train machine learning models using the maize and wheat phenological stages and yield simulated by ORCHIDEE-CROP as inputs.
It is reported that when plants are subjected to nitrogen stress, the grain-lling phase duration is shortened while the presilking phase is extended, as a result, the combined impact of nitrogen stress on the duration of the entire growth cycle from sowing to maturity is relatively small 38. However, nitrogen stress could lead to a lower grain yield 38. Furthermore, as the crop phenology dataset  23, 24  and yield dataset  25  used to build our model do not always include the cultivar information of maize and wheat, our study was limited to an indirect empirical analysis of trends associated with different cultivars. In this study, we attributed the geographic distribution of yield and length of crop growing season to differences in cultivar selection, farming practices and climate conditions, which can be captured by machine learning models.
Model description In this study, we used ORCHIDEE-CROP  20, 21  , a branch of the IPSL land surface model ORCHIDEE  55  which incorporates basic routines of the STICS crop model  22, 56  to simulate the growth of maize and wheat in Europe. We calibrated ORCHIDEE-CROP with the data on phenological stages of maize and wheat based on the Phenology data (including location, planting year, planting date, owering date and harvest date) observed across Germany 23,24 (over 949 sites for maize, and 1941 sites for wheat), by adjusting the parameters that control the required growing degree days for maize and wheat to reach different crop phenological stages, to nd the minimum RMSE when simulating the crop harvest dates. We calibrated the yield of maize and wheat based on a global crop yield dataset covering several European regions  25  , by adjusting the parameters that control the harvest index, to nd the minimum RMSE when simulating crop yields.
For the double cropping scheme, we can only have the double cropping after the initial year (2009 or 2089), so we have a maximum of 10 years of double cropping. The climate change scenarios are based on IPSL-CM5A-LR model and RCP8. 5 scenario  72  , but we also perform some sensitivity analysis based on RCP2. 6 and RCP6. 0 scenarios (Fig. 1 , Supplementary Fig. 6 7 8 ). All the climate have been bias-corrected by delta change method  73  based on the CRU JRA historical reanalysis dataset  [74] [75] [76]. In detail, for each grid cell, we calculated the differences of average annual max/mean/min temperature, and mean daily precipitation based on the climate data in the period of 2009-2020 from CRU JRA dataset versus RCP 2. 6, RCP 6. 0, and RCP 8. 5, respectively. The differences are then added back to the RCP scenarios for both current and future climate data.
And we call the predictions from ORCHIDEE-CROP. Y orchidee (Xs) ORCHIDEE-CROP correction: Build a dataset (D1) covering Europe that contains the observed harvest dates/yield or Y, outputs from ORCHIDEE-CROP or Y orchidee , and Xs. Train a generalized linear model ( Y ∼ Y orchidee + Xs ) based on D1, to capture the linear effects. We call the predictions from the linear model. Y GLM (Y orchidee , Xs) Calculate the residue: ϵ = Y -Y GLM. Build a new dataset D2 that contains , ϵ Y orchidee , and Xs. Train a random forest model ( ϵ ∼ Y orchidee + Xs ) based on D2, to correct the prediction from the linear model and capture the non-linear effects. We call the predictions from random forest model. Y RF (Y orchidee , Xs) Predict Y with the hybrid model as:. And Y = Y GLM + ϵ = Y GLM (Y orchidee , Xs) + Y RF (Y orchidee , Xs) we call the predictions from the hybrid model Y hybrid.
Hence, the new method that we propose integrates both visual and contextual information in a computer-vision pipeline that we designed to detect taxi street-hailing cases from video streams collected by capturing devices mounted on moving taxis. We tested our pipeline using a dataset that we collected with a taxi on the roads of Tunis. Considering both explicit and implicit hailing scenarios, our method yields satisfactory results in relatively realistic settings, with an accuracy of 80%, a precision of 84% and a recall of 84%.
We distinguished between visual information and contextual information. The visual information captures the visual elements that taxi drivers rely on to identify explicit hailing behaviors, such as passenger gestures, relative position to the road and head orientation. Contextual information represents the non-visual information that taxi drivers rely on to recognize implicit hailing situations, such as time, space, etc. Elements of both visual and contextual information are included in a computer-vision pipeline that we designed to recognize taxi street hailers from video streams that were collected by capturing devices mounted on moving taxis. We tested our pipeline on a dataset collected by a taxi on the roads of Tunis, and our results are promising. Consequently, in this paper, we contribute with a new computer-vision-based method that uses visual and contextual information for the detection of both explicit and implicit hailing scenes from video streams.
The paper is structured as follows. In Section 2, we present a discussion of the related state of the art. In Section 3, we summarize the main findings from the taxi drivers' survey regarding street-hailing detection and the role of visual and contextual information. In Section 4, we present our computer-vision-based approach for the detection of explicit and implicit street-hailing scenes. In Section 5, we present the training and test datasets that we used to experiment with our approach, along with the experimental settings. In Section 6, we present our quantitative and qualitative results and we discuss the strengths and weaknesses of our approach. We conclude in Section 7 by giving a brief overview of our future work.