dataesr/scientific-paragraphs-categorization huggingface dataset:
Mass of the clusters The spectroscopic redshifts obtained from the GAMA database allow us to obtain a new mass estimate of the galaxy cluster that hosts the Exemplar, and to search for a cluster/group of galaxies around the Double Irony. The velocities v i in the rest frame of a cluster at redshift z cluster are simply v i = c z i -z cluster 1 + z cluster , (6) where z i are the redshifts of the galaxies. The velocity dispersion within the virial radius can be used to obtain an estimate of the virial mass. However, this mass estimate relies on a single number and does not take into account the full distribution of galaxies on the sky and in redshift space. A more sophisticated method is the caustic method developed by  Diaferio & Geller (1997)  and  Diaferio (1999). The idea is to estimate the group's escape velocity from the characteristic trumpet shape of the distribution of galaxies in the plane defined by the group/cluster-centric distance and the line-of-sight velocities with respect to the median recession velocity of the group.
We used the modified caustic mass estimation algorithm written by  Alpaslan et al. (2012)  to calculate the total masses of galaxy groups in the GAMA group catalog (G 3 Cv1;  Robotham et al. 2011). A&A proofs: manuscript no. Horellou_final_20180723 Fig. 9. Multiband false-color image of the Double Irony: i-band image from the CFHT in blue, GMRT 610 MHz image in red. The galaxies at a redshift close to that of the host of the Double Irony are shown as yellow circles labeled by their spectroscopic redshift from the GAMA database.
2018 ) that is 95. 5% redshift complete to a magnitude r < 19. 8 mag. In this section, we begin with a qualitative description of the distribution of galaxies located within a radius of 1 Mpc of the centers of the two radio galaxies and with a spectroscopic redshift from the GAMA database close to that of the radio galaxy (see Fig. 11 ). Then we carry out a more quantitative analysis in terms of Fourier components of the distribution of the galaxies surrounding the radio sources. In Fig. 11  we show the distribution of the galaxies with a spectroscopic redshift z RG -∆z < z < z RG + ∆z, where z RG = 0. 138 is the redshift of the radio galaxy and ∆z = 0. 003. The galaxies are color-coded in redshift bins of 0. 001, increasing from blue to red. The overlaid contour corresponds to the 5σ level (0.
The distribution of galaxies with spectroscopic redshifts from the GAMA database close to that of the two radio galaxies is displayed in Fig. 11. The Fourier analysis was applied to this data set and the resulting A k components are listed in Table  9. The main results can be summarized as follows: -N (= A 1 ) and N/ Nenv : There are significantly more galaxies within a radius of 1 Mpc of the host of the Double Irony than around the Exemplar. The overdensity compared to the local environment is almost twice as large for the Double Irony as for the Exemplar. Given such a large number of galaxies in the Double Irony, the non-detection of X-ray emitting gas may seem surprising. Could it be that the galaxies around the Double Irony are less massive than in and around the Exemplar's cluster? To answer this question we retrieved the ugriz magnitudes (model magnitudes from SDSS) of the galaxies with spectroscopic redshifts from GAMA.
6. Large-scale structure at z = 0. 14 In Fig. 14  we show the distribution of the galaxies with a spectroscopic redshift from the GAMA database between 0. 135 and 0. 141 (top panel) and the GMRT 610 MHz image (bottom panel) of the region of XXL-North that encompasses the supercluster XLSSsC N03 identified in XXL Paper XX. The supercluster contains eight cluster members, five of which are securely detected clusters in X-rays (C1 class). The cluster hosting the Exemplar, XLSSC 112, is a secure X-ray detection and its position is circled in red. There is an interesting alignment on the sky of four clusters, with a decrease in size and mass from the more massive Abell cluster (XLSSC 60) to the other clusters on the right. This large structure might lead to the formation of a massive cluster if infall and merger occur.
Summary and conclusion We have presented an analysis of two radio galaxies at z ∼ 0. 14 identified in the GMRT-XXL-N 610 MHz survey of XXL-North. We have made use of the extensive multiwavelength coverage of XXL to gain a better understanding of those sources. We were able to identify their host galaxies, and in the case of the Exemplar the host cluster that had been detected in the X-ray and cataloged as an XXL cluster. The second and more spectacular source, the Double Irony, is a giant radio galaxy with a linear size of about 1100 kpc on the sky. We were able to show that it is hosted by the BCG of a lower mass cluster with member galaxies detected in the optical and listed in the GAMA database. Using a friends-of-friends algorithm, we were able to show that both clusters are part of the same supercluster.
Acknowledgements. XXL is an international project based on an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼ 5 × 10 -15 erg cm -2 s -1 in the [0. 5-2] keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multiband information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programs, summarized at http://xxlmultiwave. pbworks. com/. We thank the staff of the GMRT who made these observations possible. The GMRT is run by the  National Centre for Radio Astrophysics of the Tata Institute of Fundamental Research. This work is based on observations obtained with  XMM-Newton , an  ESA  science mission with instruments and contributions directly funded by  ESA Member States  and  NASA. It is also based on observations obtained with  MegaPrime/MegaCam , a joint project of  CFHT  and  CEA/IRFU , at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
This work is based in part on data products produced at Terapix available at the  Canadian Astronomy Data Centre  as part of the  Canada-France-Hawaii Telescope Legacy Survey , a collaborative project of  NRC  and  CNRS. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This research has made use of the VizieR catalog access tool,  CDS, Strasbourg, France. The original description of the VizieR service was published by Ochsenbein et al. (2000). We have also made use of the table analysis software topcat (Taylor 2005) and of the caustic mass estimation algo-rithm written by Alpaslan et al. (2012). This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al.
VS acknowledges support from the  European Union 's  Seventh Frame-work program  under grant agreement  337595  ( ERC Starting Grant , "CoSMass"). MERC acknowledges support from the  German Aerospace Agency (DLR)  with funds from the  Ministry of Economy and Technology (BMWi)  through grant  50 OR 1514. The Saclay group acknowledges long-term support from the  Centre National d'Etudes Spatiales (CNES). SF acknowledges financial support from the  Swiss National Science Foundation. CH thanks  Michael Olberg  for his help with the R software and  John H. Black  for useful comments and moral support. We thank the referee for pointing out relevant references and for other constructive comments.
3 e illustrate our formal argument with case studies from the recent history of Africa. Most importantly, however, we provide robust cross-country empirical evidence which is consistent with our main theoretical predictions. In particular, using a dataset on conflict incidence as well as novel data on presence of oil and diamonds fields over the period  1960-2007, we  show that the likelihood of autocratic and ethnically-polarized countries experiencing civil war is higher when weak rulers govern states endowed with natural resources. This finding is robust to controlling for the variables identified by the recent literature on civil war as the most robust correlates of conflict  (Collier and Hoeffler 2004; Fearon and Laitin 2003) , as well as to the inclusion of regional dummies. In addition the results survive the inclusion of country and time fixed effects.
Data and sample The dependent variable is a dummy that describes whether a civil conflict took place in country i at year t. The source is the Uppsala/PRIO conflict dataset, available from the Uppsala Conflict Data Program  (Gleditsch et al. 2001 ). 13   We use the Polity IV dataset  (Marshall and Jaggers 2002) , which assigns to each country (each year) a score in the autocracy-democracy spectrum. Because our story is one of the incentives of autocratic rulers, we keep only the subsample of countries closer to pure autocracy using as threshold the median of the distribution of country-years in the regime type spectrum. Our results are robust to variations in the arbitrary cutoff. Similarly, and in line with our model which highlights that the perverse incentives of the autocratic ruler occur in ethnically polarized societies, our sample of country-years gets further reduced when we take the countries above the median of the ethnic polarization index of  Montalvo and Reynal-Querol (2005).
14 The proxy is the inverse of the number of oil fields in each country-year (source:  Lujala et al. 2007 ). We take the countries below the median number of fields as indicating the places where resource distribution is likely to be worse. Our results are robust to changes in this cutoff. Because this proxy is only a rough approximation of the actual distribution of natural resources among groups in society, we treat this analysis as exploratory and leave it in the appendix (section A. 4). (13. 5%) when looking at the sample of ethnically polarized autocracies. 15 ur proxy for the presence of natural resources is a time-varying dummy that equals one if a country produces either oil or diamonds. We compute this using two recent and comprehensive datasets that record longitudinal world-wide production of the two minerals (oil:  Lujala et al.
8 percentage points when the autocrat is weak and the country has natural resources. Columns 3 to 6 illustrate the robustness of our result by additively including various controls. We start with the variables identified in the cross country empirical literature as the most robust correlates of civil war (column 3). These are population size, per capita GDP (both of which we measure in logs) and the rate of growth of the economy. 17 The size of the coefficient is virtually unchanged compared to the regressions without controls. Column 4 adds two additional controls, also frequently significant in the empirical literature, namely the roughness of the terrain (as measured by the proportion of mountainous terrain from Fearon and Laitin's dataset) and openness. The coefficient does not lose significance but doubles in magnitude.
That is, we estimate the following variant of the model described in (26): Y i,t = α i + λ t + b 2 N atRes i,t + g(P ostColdW ar × N atRes) i,t + δX i,t + i,t where, in addition to the terms already defined, α i and λ t are respectively country and year fixed effects. 19 he coefficient on our interaction of interest (in this case g) remains positive and significant. Our main proxy for the weakness of the autocrat is a dummy for the post Cold War period. One limitation of this variable is that it presents no time variation across units or in terms of intensity. As shown respectively in columns 3 and 4 of Table  3 , our results are robust to using two other proxies of weakness. Borrowed from the dataset constructed by  Humphreys (2005) , we look at the interaction of the presence of natural resources with a measure of regime instability as well as a measure of regime strength, both country and time-varying.
3,064 2. 249 1. 415 0 4. 557 F&L (2003) Log Openness 3,225 4. 221 0. 748 0. 697 6. 434 PWT 6. 3 Dummy Prod. Nat. Res 4,312 0. 414 0. 493 0 1 CSCW/PRIO Sample of ethnically polarized autocracies with unequal resource dist. Incidence of civil war 3,479 0. 101 0. 301 0 1 UCDP Log Population 3,479 7. 295 1. 937 2. 249 11. 353 PWT 6. 3 Log Real GDP pc 2,638 8. 406 1. 173 5. 743 11. 624 PWT 6. 3 GDP growth 2,585 2. 277 8. 839 -46. 877 131. 243 PWT 6. 3 Log Mountainous terr. 2,231 1. 969 1. 466 0 4. 557 F&L (2003) Log Openness 2,638 4. 296 0. 719 0. 697 6. 434 PWT 6. 3 Dummy Prod. Nat. Res 3,479 0. 323 0. 468 0 1 CSCW/PRIO Notes: UCDP is the Uppsala Centre Data Program that maintains the Uppsala/PRIO Armed Conflict Dataset.
To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.
Experimental Results In this section, we demonstrate the effectiveness of the proposed approach by conducting numerical recognition results on two radar images databases. The first one is composed of ISAR images and the second contains SAR images. To the best of our knowledge, until now there has not been a generic approach proposed in the literature that has the ability to recognize with the same treatment the ISAR and SAR images, excepting our previous work  [18]. That is why aside from our MSKD-MSRC, we also implement three ATR methods which are practically close to the proposed method for a fair comparison. The first one uses the SIFT with matching (SIFT + matching). The second one consists of using the MSKD method in combination with the matching (MSKD + matching). The last one uses the SIFT descriptors as input of the MSRC (SIFT + MSRC).
Database Description The ISAR images used in our work were acquired in the anechoic chamber of ENSTA Bretagne (Brest, France). The experimental setup of this chamber is depicted in Figure  3. The radar targets are illuminated with a frequency-stepped signal with a band varying between 11. 65 GHz and 18 GHz. A sequence of pulses is emitted using a frequency increment ∆ f = 50 MHz. By applying the inverse fast Fourier transform (IFFT), we obtain 162 grayscale images per class with a size of 256 × 256 pixels. To construct the ISAR database images, we have used 12 reduced aircraft models with 1/48 reduced scale. For each target class, 162 ISAR images are generated. Consequently, the total number of ISAR images in this database is 1944. For rigorous details about the experiments conducted on the anechoic chamber, the reader is refereed to  [1, 43].
Target Recognition Results We study in Figure  5  the influence of the number of atoms in the dictionary (the size of training set) on the recognition rate. We select randomly 195, 390, 585 and 780 atoms that correspond to 10%, 20%, 30% and 40% of all ISAR images in the database respectively. The remaining ISAR images are used for the test. Consequently, we adopt configurations where the number of ISAR images in the training set are less than those of the test set. It is observed that all methods are sensitive to the number of dictionary atoms. When this number increases, the recognition rate rises as well. The proposed method outperforms the remaining ones under the different considered number of dictionary atoms. Additionally, comparing the matching and the MSRC to recognize the MSKD, it is observed that with the decreasing number of atoms, the recognition rate of MSKD + matching descends faster than that of the proposed method.
Runtime Measurement We study in this part the comparison of the runtime between the proposed method and three other methods. Table  2  records the mean runtime for pre-processing, feature extraction and recognition for each radar image in the database. We note that the mean runtime is computed by dividing the runtime of all ISAR images by the cardinal of the database. The sum of the runtime of these three steps gives the global mean runtime of each method. It is observed that the MSKD method is faster than the SIFT one for feature extraction. That is due to the small number of keypoints located using the MSKD. However, the pre-processing for the MSKD needs a remarkable runtime. For recognition, the matching or MSRC combined with the SIFT needs a heavy runtime compared with the MSKD. Moreover, the MSRC needs more runtime than matching.
Databases Description Regarding the SAR images, the moving and stationary target acquisition and recognition (MSTAR) public dataset (https://www. sdms. afrl. af. mil/index. php?collection=mstar) is used. This database is developed by Air Force Research Laboratory (AFRL) and the Defense Advanced Research Projects Agency (DARPA). The SAR images in this dataset are gathered by the X-band SAR sensor in spotlight mode. The MSTAR dataset includes multiple ground targets. Samples of each military ground target class of this dataset are displayed in Figure  7. Two major versions are available for this dataset: • SAR images under standard operating conditions (SOC, see Table  3 ). In this version, the training SAR images are obtained at the 17 • depression angle and the test ones at 15 • depression angle.
- The depression variations (EOC-2, see Table  5 ). The SAR images acquired at 17 • depression angle are exploited for training, while the ones taken at 15 • , 30 • and 45 • depressions angles are used for testing. The main difference between the SOC and EOC versions is that in the SOC, the condition of training and test sets are very near contrary to the EOC. We note that in the opposite case of the ISAR images database, the MSTAR is already partitioned to training and test datasets.
Target Recognition Results We provide in Table  6  the quantitative comparison between the different methods on several versions of MSTAR dataset. As can be seen from this table, the MSKD performs much better than the use of the whole SIFT keypoints. This is because not all SIFT keypoints are useful to characterize the SAR images, and it can be remedied by the adopted filtration method as illustrated in Figure  2. This conclusion is an important motivation for coupling the saliency attention and the SIFT. Additionally, the use of the multitask SRC leads to an overwhelming superiority compared to the matching approach thanks to the sparse vectors extracted from each task in the SIFT and MSKD. Considering the 10-class ground target (SOC), the recognition rate of the proposed method is 80. 35% which is 35.
17%, 32. 52% and 7. 3% better than the SIFT + matching, the MSKD + matching and the SIFT + MSRC. The SOC version of the MSTAR database is a big challenge due to its high number of existed SAR images. The confusion matrix of all methods are displayed in Figure  8. The proposed method provides a confusion between the BMP2, BRDM2 and BTR70 targets because they have the same vehicle type which is the armored personnel carriers. It is not able to correctly classify the BMP2 target. However, it has the high recognition rates for all classes compared to the remaining methods. Regarding the configuration variations (EOC-1), the high recognition rate of 84. 54% is given by the proposed method compared to the remaining ones. It is an improvement of 40. 14%, 17. 22% and 13. 7% compared to the competitors.
Similarly, the 30 • depression angle enjoys the trend with a more moderate to 45 • depression angle. We record in the Table  9  a comparison between the execution time on different versions of the MSTAR database. Generally, the remarks presented in the Section 3. 1. 3 for ISAR images are also suitable for SAR images for MSTAR database. Additionally, the SIFT + MSRC requires more runtime than the proposed method. It is observed also that the SOC version needs a high runtime for MSRC. This is due to the large size of the dictionary (2747 atoms) that increases the time of the resolution of the optimization problem.
Discussion The experiment results are obtained on two types of radar images which are ISAR (Section 3. 1. 1) and SAR images (Section 3. 2. 1). Lastly, different versions of MSTAR database (SOC, EOC1 and EOC2) are tested. We compared our method with three others. Several comparison criteria are used: the overall recognition rate, the recognition rate by class and the runtime. The objective of this contribution is to demonstrate that such an approach can be applied efficiently to SAR and ISAR images in the automatic radar target recognition problem. Regarding the radar image characterization, the experiment results demonstrate that the proposed strategy enhances significantly the target recognition rates and speeds up the task of recognition. This is due to the pre-processing step that efficiently locates the ROI region through the saliency attention model.
Consequently, the computation of the SIFT keypoints is faster than the use of the whole radar image. Additionally, the keypoints are concentrated in the salient region which increases the descriptor discrimination. For the recognition stage, it is clear that the MSRC is slower than the matching but it contributes to increasing the recognition rate. On the other hand, the number of task used in the MSRC equals exactly to the number of keypoints. From that, the MSKD reduces the number of tasks and therefore accelerates the recognition step. We can conclude that the proposed method provides a trade-off between recognition rate and runtime. Comparing both databases, the ISAR images provide the higher recognition rate. This is because the strong noise existed in the SAR images. In addition to this, the proposed method performs poorly for the high angles for the EOC-2 version of the MSTAR database.
Conclusions and Future Work This paper proposed a new generic algorithm called MSKD-MSRC for radar target recognition in ISAR/SAR images. Our approach represents each radar image with a set of salient keypoint descriptors (MKSD) located in the target area. For each test descriptor in the MSKD, a sparse reconstruction error (residual) according to each class is computed. After that, we sum all obtained residuals for the whole MSKD descriptors for all classes. The class with the minimum residual is affected to the test image. Extensive experiments are conducted on the ISAR images database and in the different versions of MSTAR dataset. Despite that, the SIFT and matching method performs worst for 10 classes (SOC) and 11 classes (ISAR images); it is a competitive method for three classes (EOC1 and EOC2).
For all of the above performance comparisons in the experimental results, it can be concluded that despite that our approach does not provide the high recognition rate for all classes, it achieves in most cases the high overall recognition rates with a balanced performance for all ISAR and SAR classes in a reasonable runtime. Additionally, it effectively deals with the challenge of target recognition under EOC with a slight degradation for EOC-2 (45 • ). Considering the minor flaws of the proposed method, the future work will focus on using other local descriptors as well as testing the proposed system in other radar images databases such as those acquired in the maritime environment.
5. 35, [24] ) and filtered to remove unwanted adaptors, lowquality reads and reads with ambiguous nucleotides. Reads with ≥15 nucleotides were mapped to the mm10/ GRCm38 Mus musculus assembly using Bowtie2 (version 2. 2. 1,  [25] ) allowing 1 mismatch. Stranded miRNA read counts were produced by FeatureCounts from the Subread package (version 1. 4. 4, [26] ) using the miRBase gff3 annotation file (version 20,  [27] ).