dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset and Synthetic images In this work, we consider a single simulation snapshot at z = 0. 0485 (snapshot 95) as done in  Rodriguez-Gomez et al. (2019). The redshift is consistent with the average redshift of the observations comparison sample (z med = 0. 06). Within this snapshot, we consider all simulated galaxies with Log(M * /M ) > 9. 5 which is also roughly consistent with the stellar masses in the observational dataset detailed in the following section. The sample selected contains 12, 468 galaxies (hereafter TNG sample). We do not require here a perfect stellar mass and redshift match between observations and simulations since all properties will be explored at fixed stellar mass. It is important though that all types of galaxies are well represented in the training set used to train the CNNs.
2008). All details of how parameters are set can be found in  Rodriguez-Gomez et al. (2019). For computational reasons, dust is taken into account only if the fraction of star forming gas is above 1% of the total baryonic mass. It is assumed for these objects that the dust content is traced by the star-forming gas. A constant dust-to-metal ratio of 0. 3 is also assumed. The final output is a 3D data cube for each galaxy, consisting of a full rest-frame SED for each pixel. We then assume that the source is located at z = 0. 0485 and generate the data cube that would be measured by a local observer, taking cosmological effects such as surface brightness dimming into account. Each SED is then multiplied by each of the SDSS filter curves (g,r,i,z) and integrated over the full wavelength range.
SDSS parent sample The observational sample used in this work comes from the 670, 722 galaxies selected by  Meert et al. (2015)  (hereafter the M15 sample) from the SDSS DR7 spectroscopic sample. We refer the reader to the aforementioned work for all the details regarding the selection. Very briefly, galaxies are 2 Trujillo, Infante private communication selected from the SDSS DR7 database according to three main criteria: (1) the extinction corrected r band Petrosian magnitude is between 14 and 17. 77. The limit at the bright end is to avoid large nearby galaxies which are typically split into multiple objects in the SDSS catalogue. The faint end limit is the lower limit for completeness of the SDSS spectroscopic survey  (Strauss et al. 2002) ; (2) the photometric pipeline classified the object as a galaxy; and (3) the spectrum was also identified as a galaxy.
Structural parameters and stellar masses A large number of derived quantities exist for the dataset described above. In particular we use for this work the Stellar Masses computed for all galaxies  (Bernardi et al. 2017b)   3. Stellar masses are derived using a  Chabrier (2003)  IMF and the M/L ratio from  Mendel et al. (2015). We do not include any variation in the IMF. We refer to  Bernardi et al. (2018)  for the implications of IMF gradients in the mass. The luminosity comes from the Sersic best fit models derived for all galaxies in the catalog (see  Meert et al. 2015  for more details). We also use the effective radii estimated through fitting Sersic models when comparing the scaling relations of observed and simulated galaxies. Domínguez  Sánchez et al. (2018)  also derived detailed visual morphologies for this sample with CNNs.
DEEP LEARNING R BAND VISUAL MORPHOLOGIES We train a CNN on the visual morphologically classified sample of  Nair & Abraham (2010)  (hereafter N10 sample). The catalog contains detailed morphologies of ∼ 14, 000 galaxies performed by two professional astronomers. The authors associate to every galaxy in the sample a numeric value (T-Type) indicating the morphological type spanning from -5 (Elliptical) to 10 (Irr) -see Table  3  in  Nair & Abraham (2010). We used this dataset instead of the Galaxy Zoo catalog even if it is smaller in size because the classification reflects well the standard Hubble Sequence which is not the case in the Galaxy Zoo classification tree. We use the same vanilla architecture as in Domínguez  Sánchez et al. (2018). However we performed a new training instead of directly using the published catalog because Domínguez  Sánchez et al.
In the following, Early type galaxies include Ellipticals and S0/a's (also called lenticulars) and Late-type galaxies include Sabs and Scds. We will also refer to Sab galaxies as early-type spirals, and to Scd objects as latetype spirals. Figures 2 to 5 show some example stamps of galaxies classified in the 4 types ordered by increasing stellar mass both in the TNG and in the M15 datasets. As can be appreciated, elliptical galaxies are mostly pure dominated bulge systems. S0 or lenticular galaxies present a dominant bulge component but tend to have a disk with no marked features. Sab galaxies (early-type spirals) have smaller but still noticeable bulges and large disks with spiral arms and/or visible structure in the disk. Finally, Scd galaxies (late-type spirials) have a very small bulge or no bulge at all and a clumpy disk component or with irregular morphology.
HOW REALISTIC ARE THE TNG MORPHOLOGIES FROM THE MACHINE LEARNING PERSPECTIVE? The previous section has shown that the CNNs trained on the visual morphologies from the N10 samples find objects in all four classes also in the TNG simulation. One first interesting question is how confident the networks are about the classification in the simulation. Machine learning algorithms will always try to associate objects with the classes they were trained with because there is an implicit assumption that there is a perfect match between the training and test datasets. This is not necessarily the case in this work since we are training in the observational domain and inferring in the simulated one. In the following we try to quantify the similarity between the simulated and observed morphologies from the neural network perspective.
We then use the standard deviation of the distribution for each galaxy as an estimator of the model uncertainty. We compute the uncertainties in three different samples. First on the fraction of the N10 set which was not used for training (test set). This should be considered as the best case since it has the same properties as the training set. Uncertainties are also computed on the complete M15 dataset (see section 3) and in the TNG one. The cumulative distributions of uncertainties for the three datasets are shown in figure  6. As expected, the test dataset presents on average lower uncertainties. We use this dataset as a reference to define objects with large uncertainties in the SDSS and TNG datasets. To that purpose we compute the median error value and the standard deviation of the distribution of uncertainties in the test dataset and define as outliers objects with a measured uncertainty larger than 3 times the standard deviation.
We find that the number of outliers defined that way is ∼ 4% for TNG and ∼ 12% for the M15 dataset. The first point we can conclude is that the fraction of simulated galaxies with more uncertain classification as compared to the reference observational sample is only∼ 4%. This confirms the realism of galaxy morphologies in the TNG run or at least that the features learned by the CNNs to identify the different morphologies are found in both the simulations and the observations. One interesting question that arises is what are these features. We try to address this in the following section by exploring the attribution maps. In the left panel of figure  7  we show the images of the 16 galaxies in TNG with largest uncertainties. A distinguishing feature for many of them is a ring of clumpy star formation around the galaxies.
This feature is apparently not seen in the observations at least around early-type galaxies which can explain the high uncertainty values. It confirms that the MonteCarlo Dropout technique is able to identify objects with peculiar features. A surprising result is that the fraction of outliers is larger in the M15 sample than in the simulations. Since the CNN was trained with a sample from SDSS, one would have naturally expected that the M15 dataset and the N10 sample used for training should be closer. In the right panel of figure  7  we explore the objects in the M15 sample with larger error values. In most of the cases they are small galaxies with very bright companions. These objects are less abundant in the training set since a clean sample of very nearby galaxies was selected by  Nair & Abraham (2010).
We do see that the attribution maps generally trace the pix-els belonging to the galaxy, confirming that the network is ignoring the noise when classifying galaxies. This might appear like a trivial statement, but it is not given that there might be some S/N trends in the training set. For example, Scd galaxies tend to be fainter than Ellipticals so the networks might have learned that a lower S/N is correlated with the morphological type. The attribution maps show it is not the case. We do also observe that for elliptical galaxies the important pixels seem to be more concentrated towards the central regions than for later types which indicates that the network is focusing on the bulge component for these systems as one would expect. IllustrisTNG Sabs SDSS Sabs The comparison between the maps in TNG and SDSS qualitatively reveals that similar features are found in both datasets at fixed morphology.
For Sab galaxies for example (figure  4 ) we appreciate how the activation pixels similarly trace the disk region. For elliptical galaxies it appears that the attribution maps in the observations look more scattered than in the simulations. This is likely due to the fact that ellipticals in SDSS tend to be in over dense regions with satellites which is not the case in the TNG images since only the central galaxy was rendered. In summary the maps provide limited information (or at least not easily interpretable in terms of physical quantities measured in images) but allow one to check that no major differences are found between the simulated and observed samples. In order to better understand if the CNN appreciates noticeable differences between the TNG and SDSS datasets we perform an exploration of the features learned by the network.
We extract the features after the last convolutional layer before the dense part of the network and compare them. To that purpose we create a feature vector for a subsample of 500 galaxies from the N10 sample used for training, 500 additional galaxies from the M15 dataset and 500 galaxies from TNG. Since the feature space is highly dimensional (∼ 100, 000) we project it into a 2-dimensional space for visualization purposes using the dimensionality reduction algorithm tSNE (t-Distributed Stochastic Neighbor Embedding, van der Maaten& Hinton 2008). We use a learning rate of 900 and a perplexity value of 30. However changes in these parameters do not change the main trends. The result of this exercise is shown in figure  8. Recall that the axes have arbitrary units and do not encapsulate any physical meaning.
The TNG (blue squares) and the N10 (red stars) samples form a unique cluster in the space defined by the 2 features extracted with tSNE. It confirms that similar features are found by the CNN in both datasets, which trigger comparable responses of the neurons. Data points from the M15 (green circles) sample are also in the same cluster but seem to be slightly off centered with respect to the 2 other distributions. The reason seems to be that the M10 sample contains fainter galaxies. This is in some sort a limitation of the approach followed in this work since we are using a sample of bright galaxies to train while we are inferring on significantly fainter objects. We will show in the following sections that this does not seem to significantly alter our main conclusions. It confirms however that the visualization of the feature space is sensitive to differences in the galaxy properties.
SUMMARY AND CONCLUSIONS We have quantitatively analyzed the visual morphologies of galaxies at z ∼ 0 in the IllustrisTNG simulation. We have trained a CNN on detailed visual morphologies estimated on 14, 000 galaxies in the SDSS and applied the same network to classify a complete sample of 12, 000 galaxies in TNG with stellar mass larger than 10 9. 5 solar masses. In order to produce images with similar properties than in the observations, the output of the simulations was post-processed with a radiative transfer code to create mock observations with realistic instrumental effects. Our morphological classes include early-type galaxies, in turn divided in ellipticals E and lenticulars S0/a, and late-type galaxies, in turn classified as early-type spirals (Sab) and late-type spirals (Scd), the latter including irregulars.
VPR estimation The VPR estimation is applied separately for each 1 × 1 km 2 pixel pi of the radar domain. The method developed in this study to retrieve VPRs is based on the Bayesian approach used by  Kummerow et al. (1996 Kummerow et al. ( , 2001) )  in the Goddard profiling algorithm (GPROF). The latter was also used by  Caumont et al. (2010) ,  Augros et al. (2018)  and  Borderies et al. (2018a, b)  for the validation and assimilation of radar reflectivity and dual-polarisation observations in the French high-resolution model AROME. In the same way, we use here a large database made of simulated profiles V P R mod in the vicinity of the considered radar pixel p i to find the most probable VPR (P OV P R(p i )) given the observed apparent V P R rad. Thus, P OV P R(p i ) is defined as a linear combination of the V P R mod weighted by a factor P depending on the distance d in terms of reflectivity between the apparent simulated VPRs (V P R mod,app ), i.
Additionally, simulated VPRs where the ground level is higher by more than 300 m above the ground level of p i are also eliminated from the data set. Indeed, VPRs from higher terrain are, by nature, not defined at the ground altitude of p i and consequently cannot provide relevant information about the vertical structure of the precipitation at this altitude. On the contrary, keeping VPRs from lower terrain allows us to potentially extend the simulated VPR data set for mountainous pixels. Finally, in order to give more importance to the nearby VPRs, only the 100 closest remaining VPRs are kept. In the hypothesis in which no VPR would be excluded from the initial data set  (1, 200 VPRs)  during the previous filtering steps described above, this last one step would keep VPRs distant of less than 30 km from p i.
Indeed, let us suppose the observation is made of a unique precipitating column C o which is perfectly represented by the simulated column C s (C o = C s ) but further from the radar. Due to the different beam altitudes and widths at the column locations, the projection of C s on the radar beam geometry will be most probably different from the projection of C o even though the unprojected columns are identical. This statement can be generalised to larger data sets. Thus, the preliminary quantile mapping correction on distances ensures that the proportions of precipitating columns at a particular distance from the radar are the same in D rad and D mod data set, which mitigates the effects of the model positioning errors. Furthermore, to have comparable samples, simulated reflectivities below theoretical noise level are eliminated and each elevation of the model data set is repeated as often as it is present in the radar data set.
(9) An example of the application of the quantile mapping correction is provided in Figure  2. For purpose of this study, the 2h lead time forecasts from AROME-NWC are used. On the one hand, this short forecast range offers the advantages to be beyond the spin-up period of the model. On the other hand, it is close enough to the analysis time that the initial D mod data set can be brought as close as possible to the D rad data set by quantile mapping, and most representative VPR profiles can be found in the VPR estimation process. However, in situations where the simulations are too different from the observations, in terms of timing (convection initiation time not well predicted for example) or intensity (embedded convection within stratiform precipitation observed but not simulated by the NWP model for instance), the use of an adaptive time window and/or multiple model runs, could be considered.
Evaluation process To evaluate the performances of the POVPR and the operational methods, hourly and 6-h rain gauge accumulations (resolution of 0. 2 mm) are compared with the co-located retrieved radar accumulations. For clarity, accumulations are removed from the data set if (i) the rain gauge has a class equal to 5 according to the WMO classification, or (ii) the radar was not able to detect any signal (signal weaker than noise level or radar beam above the precipitation), or (iii) the simulated freezing level height above the ground level is lower than 300 m (enhanced risk of snow which cannot be correctly measured by the non-heated rain gauges from the operational network). Once the hourly and 6-h accumulations data set are built, the root mean square error (RMSE), the mean bias and the Pearson correlation coefficient (r) are computed for both radar data sets (Panthere and POVPR) by considering the rain-gauge data set as a reference.
MAS Approach In our approach, we used cognitive and communicative agents. Thus, to communicate, agents need a developed language to be able to exchange messages. However, a simple model of message is not enough to tolerate conversations between agents and to allow language acts to fulfill their meaning. For this, we introduced the notion of protocol to support such conversations. The negotiation strategy is proposed based on the Contract Net protocol implemented using the JADE platform that aims to simplify the development of MAS while providing a complete set of services and agents complying with foundation for Intelligent Physical Agents specifications  [7]. In this paper, the OT is divided into several departments according to their specialty, (e. g. orthopedics, gynecology, etc. ), while each one contains all necessary facilities to be autonomous (e.
Experiments and Results In this section, we present the experimentations with the proposed MA algorithm based on MILP formulation using a set of previously generated data. We exploit the small instances used for the exact formulation in Refs. [3, 4]  for the SOTLP and AOTLP to constitute the needed departments, each of no more than 17 facilities to make possible the use of the developed MILP by the MAS. For running the MILP, we used (IBM, Armonk, NY, USA) ILOG CPLEX Optimization Studio 12. 5 software to solve the model using Windows 7 platform, Intel 5 Core i5-3380M CPU@ 2. 90 GHz and 8 Gb of RAM, while the MAS platform has been implemented using JAVA language and modeled using UML. An example of used data can be found in Appendix A. First, the program structure has been modeled using the package diagram provided by UML (Figure  5 ).
Method Participants. 139 participants were recruited online via prolific. ac for Experiment 1. The study itself was hosted on millisecond. com. We aimed for at least 40 participants per condition. With no directly comparable prior studies, this number was determined subjectively, but fixed a priori. There is some randomness in study completions after participant number assignment on millisecond. com, but participants were at least roughly distributed across the three conditions (52 combined cue, 44 words-only, and 43 shapes-only). An additional 12 submissions were deleted due to excessive errors (see Data Analysis). Another three incomplete submissions (the participant pressed Ctrl+Q to exit early) were also deleted. Participants were paid £1. 5 for the experiment, which lasted approximately 15 minutes.
J-key: blue K-key: red L-key: green Guess if you are unsure. This was followed by three randomly-ordered trials, each with one of the initially trained stimuli in black. Data analysis. Both mean correct response times and error rates were assessed. Trials on which participants failed to respond were eliminated from analyses. Participants with more than 20% errors were also removed from analysis (see Participants). We supplement each key Null Hypothesis Significance Test (NHST) with Bayesian analyses in this and the following experiment, particularly relevant for non-significant effects. In all cases, Bayesian tests were computed with a half normal distribution with a mean of zero and the effect in the control condition as the prior standard deviation (with an online Bayes calculator from  Dienes, 2014).
BF 10 indicates evidence in favour of an effect and BF 01 indicates evidence in favour of a null. We report the Bayes factor for the direction that the data favours (e. g. , BF 01 if the is more evidence for the null than for an effect). In line with convention, we refer to a BF > 3 as "moderate" 1 and a BF > 10 as "strong. " Subjective and objective awareness questions were coded as percentage correct. We report correlations with nonparametric Spearman's ρ (which the lead author uses as standard practice), but the parametric Pearson's r produced similar results. The data and analysis scripts for this and the following experiment are available on the Open Science Framework (https://osf. io/ev7nh/).
Participants were recruited from the same pool as Experiment 1 and none participated in more than one experiment. An additional 6, 12, 17, and 14 participants, respectively, were excluded based on the same error cut-off as Experiment 1, and another 1, 4, 1, and 1 submissions, respectively, were excluded for not completing the study correctly (either by exiting with Ctrl+Q or skipping blocks with Ctrl+B). Apparatus, design, procedure, and data analysis. The apparatus, design, procedure, and data analysis of Experiment 2 were identical in all respects to Experiment 1 with the following exceptions. First, there were six between-subject groups, made of the orthogonal combination of study condition (words-first, shapes-first, and overshadowing) and instruction condition (instructed vs. non-instructed).
Two loops (A1 and A2) connect the two layers and define the heme-binding site, via two axial heme iron ligands, His32 on loop A1 and Tyr75 on loop A2 and a nearby His83 residue on loop A2. In the absence of heme, loop A1 is backfolded on the core of the protein. Upon heme binding, it undergoes a 30 Å displacement toward the edge of the core. The crystal structure of the wild type HasA~HasR~heme complex [PDB entry 3CSL  (Krieg et al. 2009) ], termed WT complex in the following, formed by incubating holoHasA with apoHasR, shows a very tight complex between the two proteins (see Fig. 1 ). HasA is oriented with its heme-binding site facing the receptor and heme is found translocated by 9 Å from HasA into its HasR-binding site, where two histidine residues form the axial ligands of the heme iron.
Model generation The crystal structures of the apoHasA~WT-HasR~heme complex (PDB code 3CSL, termed 'WT complex' or 'WT complex structure' in the following) and of the holoHasA~DM-HasR complex as described below (PDB code 5C58, termed 'DM complex' or 'DM complex structure' in the following) were the basis for all simulations. 3CSL lacks the HasA-loop A1 that could not be modeled during structure determination due to weak electron density. To generate a complete reference structure of HasR alone, the loop was modeled using the program Prime from the Schrodinger Package 8 (2015;  Jacobson et al. 2004 ). This and the other missing loops in the DM structure were added using the Modeller 9. 10 software using 3CLS as template for the loops and 5C58 for the rest of the proteins and the heme molecule.