dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmark In order to overview the performances of Bulldozer, a benchmark has been set up. This benchmark is based on a comparison between Bulldozer and a highly resolved ground truth provided by IGN 2 over French territory. In order to evaluate the ability of the method to process heterogeneous landscapes, the areas were selected for their topographic diversity: urban area (high frequency reliefs), rural area (few reliefs) and mountainous area (high altimetric variation). In this benchmark, the DSMs were generated with the CARS software from Pleiades stereo and tristereo images (50 cm resolution). The results of the comparison between the DTM computed with Bulldozer results and ground truth are available in Table  2. The selected metrics are: mean, standard deviation (STD), Root Mean Square Error (RMSE), median and Median Absolute Deviation (MAD).
Then, a DTM can be initialized using this filled DTM by a local inverse distance weighting interpolation using local height minima. The first results are really promising. A second axis of research would be the analyse the benefits of using an above-ground mask as input of the cloth simulation algorithm by straigthly assigning some pixels of the cloth to the DSM pixels marked as ground. Finally, a third axis of research would  be to take into account the confidence map provided by the recent CARS software via Pandora to improve the detection of the disturbed areas.
This includes data from the public surv e y with a 3-d cadence and the ZTF partnership and Caltech surv e ys with a faster cadence ( ≤2 d) o v er smaller areas  (Bellm et al. 2019a ). Additional photometry was obtained using SEDM and the optical imager (IO:O) on the LT. The ZTF data reduction and pipelines are managed by the Infrared Processing and Analysis Center (IPAC) at Caltech, as described by  Masci et al. ( 2019 ). We have also obtained photometry, including upper limits, from the IPAC-forced photometry service. 2  The IPAC photometry makes use of the ZOGY algorithm  (Zackay, Ofek & Gal-Yam 2016 )  for subtraction of reference images. The SEDM imaging data were processed using point-spread function photometry, and the magnitudes were calibrated against either SDSS or Pan-STARRS1 reference images, using FPIPE  (Fremling et al.
2016 ). LT data were processed by a similar custom-built software  (Taggart 2020 ). ZTF and LT griz magnitudes are calibrated to the Pan-STARRS1 photometric system and u -band data to the SDSS system. We include a table of all photometry used in this study as supplementary material; a sample of this table is presented in Table  A2. Reductions of P200/DBSP spectra were carried out using two pieces of software, PYRAF-DBSP  (Bellm & Sesar 2016 )  as well as a newer PYTHON pipeline, DBSP DRP  (Roberson, Fremling & Kasliwal 2022 ) , based on PYPEIT  (Prochaska et al. 2020 ). Keck/LRIS data reduction used the custom-written, publicly available IDL-based software, LPIPE  (Perley 2019 ). ALFOSC and ISIS spectra were reduced using standard procedures in IRAF  (Tody 1986 ). SPRAT spectra were reduced by the automated LT pipeline  (Barnsley, Smith & Steele 2012 ).
SEDM spectra were processed by the PYTHONbased PYSEDM pipeline  (Rigault et al. 2019 ) ; we note that the extracted spectra contain host galaxy light. All spectra presented in this paper will be available on WISEREP 3   (Yaron & Gal-Yam 2012 ). Swift /UV O T data (for 8 of the 14 SNe) were retrieved from the NASA Swift Data Archive 4  and processed using the UV O T data analysis software HEASOFT 6. 19. 5  The count rates were obtained from the images using the Swift tool uvotsource , using a circular 3 arcsecradius region. The background was estimated using a significantly larger region close to the SN position. Counts were converted to magnitudes using the UV O T photometric zero-points  (Breeveld et al. 2011a  ) and calibration files from 2020 September. Any host galaxies significantly contributing to the measured photometry 6  (based on visual inspection of Swift and GALEX images of the SN and its location) were subtracted from the Swift data using template images taken in late 2021.
AC K N OW L E D G E M E N T S We thank the anonymous referee for suggestions that helped to impro v e the paper. This study is based on observations obtained with the Samuel Oschin Telescope 48-inch and the 60-inch Telescope at the Palomar Observatory as part of the ZTF project. ZTF is supported by the National Science Foundation under grants no. AST-1440341 and AST-2034437 and a collaboration including current partners Caltech, IPAC, the Weizmann Institute for Science, the Oskar Klein Center at Stockholm University, the University of Maryland, Deutsches Elektronen-Synchrotron and Humboldt University, the TANGO Consortium of Taiwan, the University of Wisconsin.
T. Kangas et al. MNRAS 516,  1193-1218 (2022 ) at Mil w auk ee,  Trinity College Dublin, Lawrence Livermore National Laboratories, IN2P3, University of Warwick, Ruhr University Bochum, Northwestern University  and former partners the  University of Washington ,  Los Alamos National Laboratories , and  Lawrence Berkeley National Laboratories. Operations are conducted by COO, IPAC, and  UW. SED Machine  is based upon work supported by the  National Science Foundation  under grant no. 1106171. The ZTF forced-photometry service was funded under the  Heising-Simons Foundation  grant # 12540303  (PI: Graham). This work uses the GROWTH Followup Marshal (Kasliwal et al. 2019 ) and was supported by the  GROWTH  project funded by the  National Science Foundation  under grant no. 1545949. This study is based partially on observations made with the NOT, owned in collaboration by the  University of Turku and Aarhus University , and operated jointly by  Aarhus University , the  University of Turku , and the  University of Oslo , representing  Denmark, Finland , and  Norway , the  University of Iceland and Stockholm University at the Observatorio del Roque de los Muchachos, La Palma, Spain , of the  Instituto de Astrof ísica de Canarias.
The Observatory was made possible by the generous financial support of the  W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. TK acknowledges support from the  Swedish National Space Agency  and the  Swedish Research Council. SS acknowledges support from the  Gravitational Radiation and Electromagnetic Astrophysical Transients (GREAT)  research environment, funded by Vetenskapsr ådet, the  Swedish Research Council , project number  2016-06012. RL acknowledges support from a  Marie Skłodowska-Curie Individual Fellowship  within the  Horizon 2020 European Union (EU) Framework Programme for Research and Innovation  ( H2020-MSCA-IF-2017-794467 ).
TWC acknowledges  EU funding under Marie Skłodowska-Curie  grant  H2020-MSCA-IF-2018-842471. LT acknowledges support from  Ministero dell'Istruzione (MIUR) ;  Progetti di Ricerca di Interesse Nazionale (PRIN)   2017  grant  20179ZF5KS. AGY's research is supported by the  EU via European Research Council (ERC)  grant no. 725161 , the  Israel Science Foundation  (ISF) gravitational wav e (GW) e xcellence center, an  Isreal Ministry of Science (IMOS)  space infrastructure grant and  Binational Science F oundation (BSF)/Transformativ e and Global Innovation Fund (GIF ) grants, as well as the  Andr é Deloro Institute for Advanced Research in Space and Optics , the  Schwartz/Reisman Collaborative Science Program  and the  Norman E Alexander Family M Foundation ULTRASAT Data Center Fund ,  The Kimmel center for Planetary Sciences, Minerva  and  Yeda-Sela ; AGY is the incumbent of the The Arlyn Imberman Professorial Chair.
In particular, we demonstrate how landmark correspondences can be promoted throughout this iterative refinement. Furthermore, we exploit the landmark-awareness of our basis to provide a simple initial guess of the correspondence. We test our approach on various benchmark datasets, both isometric and non-isometric. We compare our results to both stateof-the-art functional maps approaches, as well as recent methods that exactly preserve landmark correspondences. We report stateof-the-art accuracy on non-isometric datasets and near state of the art on isometric ones. Meanwhile, the computation time of our approach is significantly lower than that of the competing landmarkpreserving methods.
Evaluation We evaluate our method † on standard shape matching datasets, which we describe in Sec. 7. 1. We first analyze the parameters involved in our computations (Sec. 7. 2). Second, we conduct an in-depth evaluation to compare our method to state-of-the-art approaches on shape matching benchmarks (Sec. 7. 3). For our quantitative evaluation in Fig. 12  (right), Fig. 14 , Fig. 16  and Fig. 18 , we follow the commonly-used protocol, introduced in [KLF11] by plotting the percentage of correspondences below a certain geodesic distance threshold from the ground truth.
Datasets We perform all our experiments on the following datasets. FAUST  [BRLB14]. This dataset contains models of ten different humans in ten poses each. Despite the variability in the body types of said humans, this dataset is typically considered as nearisometric. We remesh the shapes of the dataset to shapes with approximately 5K vertices and use 300 shape pairs following the procedure of the authors of  [RPWO18]. Note that the shapes in question are remeshed independently and do not share the same connectivity. TOSCA  [BBK08]. This dataset consists of meshes of humans and animals. Following  [RPWO18] , we split this dataset into isometric and non-isometric shape pairs. We call the resulting datasets TOSCA isometric (284 shape pairs) and TOSCA non-isometric (95 shape pairs) respectively.
The shapes of these datasets are remeshed independently to count around 5K vertices per shape. Once again, the remeshed shapes have distinct connectivity. † Our code is available at https://github. com/mpanine/ DirichletSteklovLandmarkMatching 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 4 4. 2 4. 4 •10 -2 r f.
Geodesic Error Figure  9 : Impact of the r f parameter on the shape matching quality. The mean geodesic error is averaged on the 95 shape pairs of the TOSCA non-isometric dataset (remeshed to 5K vertices). Notice how stable our method remains, even for extreme values of r f. SHREC'19  [MMR*19b]. This challenging dataset is composed of human shapes with high variability in pose, vertex count (ranging from 5K to 200K vertices) and topology (some shapes are watertight manifold meshes whereas other have holes and other surface noise sources). FAUST "Wild"  [SACO20]. This dataset is a variant of FAUST in which challenging differences in connectivity are introduced via remeshing. We use the following types of remeshing of the dataset: a uniform isotropic remeshing (iso), a remeshing where randomly sampled regions are refined (dense), and the remeshing proposed in  [GH97]  (qes).
Radius r f The construction of the landmark boundaries Γ i explained in App. C relies on the user-defined scalar parameter r f ∈ (0, 1). In Fig. 9 , we study the influence of r f on the geodesic matching error averaged on the TOSCA non-isometric dataset, with 7 landmark correspondences at their standard locations (see App. I). It demonstrates empirically that this parameter has no significant impact on the matching performance. We therefore set r f = 0. 5 in all our other experiments.
Landmark placement In order to study the influence of landmark placement on our method, we conduct the following experiment on 10 shapes of the TOSCA Isometric dataset (cat category). We consider an increasing number of landmark correspondences, ranging from 3 to 100,  placed according to four standard surface sampling strategies: (i) random, (ii) euclidean farthest point (iii) geodesic distance farthest point (iv) Poisson disk (as implemented in  [Jac*18] ). The outcome of these experiments is illustrated in Fig. 10. The farthest point sampling strategies result in the fastest decrease of the error, Poisson disk is slightly slower and random placement is predictably the slowest. This indicates that our method performs best when the extremities of the shapes are prioritized for landmark placement.
Remeshing invariance In order to show that our method remains applicable on shapes with different triangulations, we remesh independently the target pair of each FAUST shape pair and compute the mean geodesic error in Fig. 12 (left). We additionally experiment with the FAUST "Wild" dataset created in  [SACO20]  to assess invariance to the remeshing proposed by the authors. Fig. 12  (right) and Tab. 1 present the output of this experiment. We observe marginal difference when considering the various remeshing approaches tested, which highlights the insensitivity of the proposed approach to the shape connectivity. Fig. 13  illustrates qualitatively the median transfer obtained on this dataset. Figure  12 : Left: remeshing stability when varying the triangle reduction factor r of the target shape.
The geodesic error, averaged over 300 test pairs of the FAUST data set, slightly increases when the target mesh becomes coarse (low value of r). Right: stability of our method when performing resmeshings on the FAUST dataset (Remeshed to 5K vertices and FAUST "Wild" (see Sec. 7. 1) ). The geodesic error is measured in mean geodesic distance ×100 after normalizing by the geodesic diameter. The mean values, mean execution times and vertex counts for each remeshing is presented in Tab. 1. Figure  13 : Qualitative illustration of the median map quality obtained with our method on three types of remeshing in the FAUST "Wild" dataset (see Sec. 7. 1). Despite the great disparity of the underlying meshes, our method provides smooth transfers.
Results In this section, we present our main results on shape matching. Isometric shape matching. The evaluation on FAUST and TOSCA Isometric are illustrated in Fig. 14 , with averaged errors and runtimes displayed in Tab. 2. On the FAUST data set, our approach remains competitive with a mean geodesic error of 1. 40 × 10 -2 and a mean computation time of 8. 83 s. On the TOSCA isometric data set, we obtain a slightly better average geodesic error score than competitors. Qualitatively, our method produces smooth texture transfers on both data sets, as highlighted in Fig. 15. Non-isometric shape matching. We run an evaluation of our method on the TOSCA non-isometric and the SHREC'20 datasets (Fig. 16 ). The mean error values and timings are showed in Tab. 3. In this challenging setup, our method has the best results in terms of mean geodesic error, while being the second best in terms of computation time.
Fig. 17  presents a qualitative evaluation using a texture transfer on a pair of shapes for each data set. SHREC'19 benchmark. The quantitative evaluation is reported in Fig. 18 , with the associated averaged geodesic errors on the right of the figure. Our method obtains the best mean geodesic error score for this difficult benchmark. In addition, a qualitative evaluation via texture transfer is depicted in Fig. 19     ) is displayed for our method and competing approaches. performance on this dataset is indicative of its stability and applicability across diverse changes in shape topology, such as the introduction of small holes. This is a general feature of the functional maps methods, which our approach inherits.
Conclusion, Limitations and Outlook We have proposed an efficient functional maps-based shape matching approach that promotes conformal maps and exactly preserves landmark correspondences. This was achieved via the introduction of a novel functional basis and an energy promoting bijective conformal maps. The efficiency of our solution comes from an adaptation of the ZoomOut procedure [MRR*19; RMWO21] using our energy and novel basis. The resulting method exhibits state-ofthe-art performance on non-isometric benchmark datasets and near state of the art performance on isometric ones. Recall, however, that our usage of the ZoomOut procedure was not fully principled. Indeed, we needed to make some approximations in order to use Lemma 5, which converts certain optimization problems into nearest-neighbor searches.
A Word of Warning As a final note on the discretization of the considered eigenproblems, we would like to warn the reader of a small issue one may encounter when numerically solving them. Recall that we want the Dirichlet-Steklov eigenfunctions to be normalized with respect to the boundary mass matrix S M. Solvers for generalized eigenvalue problems, such as Matlab's eigs routine, which we use in our implementation, will typically do so automatically. However, according to our observations, sometimes this automated process will not happen. This seems to be related to the fact that S M is a positive semi-definite matrix rather than a positive definite one. Thus, one needs to explicitly normalize the solutions with respect to S M. In fact, we suggest explicitly normalizing even the Laplacian eigenfunctions, despite the fact that there the mass matrix A M is positive definite on (good quality) triangle meshes.
Additional Qualitative Evaluations We provide additional qualitative evaluations on isometric and nonisometric shape pairs in order to show best-and worst-case shape matching scenarios for our method. For isometric shapes, the best pairs are depicted in Fig. 22  and the worst pairs in Fig 23. For non-isometric shapes, the best pairs are illustrated in Fig. 24  and the worst pairs in Fig. 25. Figure  22 : Qualitative evaluation of our method and competitors on isometric shapes from the FAUST dataset (top row) and the TOSCA isometric dataset (bottom row). The shape pair is selected such that the geodesic error of our method is the best over the dataset.
Acknowledgements The authors would like to acknowledge the anonymous reviewers for their helpful feedback and suggestions. Parts of this work were supported by the  ERC Starting  Grants No. 758800  ( EXPRO-TEA) , the  ANR AI Chair AIGRETTE , the  Swiss National Science Foundation (SNSF)  under project number  188577  and the  Association Nationale de la Recherche et de la Technologie (ANRT)  via the  Convention industrielle de formation par la recherche (CIFRE)  grant No. 2019/0433. Finally, the authors wish to thank  Prof. Alexandre Girouard  for his assistance in navigating the literature on the Steklov eigenproblem,  Jing Ren  and  Simone Melzi  for providing tools for shape analysis via functional maps and the  TOSCA/FAUST/SHREC '19 datasets,  Nicholas Sharp  for releasing the " FAUST Wild" dataset and Patrick Schmidt  for computing a baseline to his work [SCBK20].
Data and Analysis This study is based on data from the Cosmic Ultraviolet Baryon Survey (CUBS;  Chen et al. 2020 ) and the MeerKAT Absorption Line Survey (MALS;  Gupta et al. 2016). Below, we describe the data reduction and analysis of the UV absorption-line spectroscopy of J2339-5523 in Section 2. 1, H I 21 cm observations from MeerKAT in Section 2. 2, and optical integral field unit (IFU) observations from the Multi Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT) in Section 2. 3.
MeerKAT H I 21 cm Analysis We observed the field centered at J2339-5523 using the MeerKAT-64 array on 2020 June 14 and 22. Of the 64 antennas, 59 and 60 antennas participated in the first and second observing run, respectively. We used the 32K mode of the SKA Reconfigurable Application Board correlator to split the total bandwidth of 856 MHz centered at 1283. 9869 MHz into 32,768 frequency channels. The resultant frequency resolution is 26. 123 kHz, or 5. 7 km s -1 , at the redshifted H I 21 cm line frequency of the DLA. Objects PKS 1939-638 and PKS 0408-658 were observed for flux density, delay, and bandpass calibrations, and the compact radio source J2329-4730 was observed for complex gain calibration. The total on-source time on J2339-5523 was 112 minutes. The MeerKAT data were processed using the Automated Radio Telescope Imaging Pipeline; we refer the reader to  Gupta et al.
VLT-MUSE Analysis We observed the field of J2339-5523 on 2020 November 22, December 5, and December 9 using VLT-MUSE. The total exposure time on source was 9390 s. We reduced the data using the standard ESO MUSE pipeline  (Weilbacher et al. 2014)  and the custom software CubExtractor from S. Cantalupo  (Cantalupo et al. 2019). We subtracted the quasar light using a high-resolution spectral differential imaging method (e. g. ,  Haffert et al. 2019). The wavelength coverage is λ = 4700-9350 Å, and the spectral resolution at the blue (red) end is R ≈ 1610 (R ≈ 3570). This corresponds to an FWHM of ≈185 km s -1 (85 km s -1 ), with approximately two wavelength bins sampling the spectral resolution element. Multiple pointings mapped a region of ¢ ´¢ 1. 3 1. 4 around the QSO. We spatially smoothed the MUSE data cube with a boxcar kernel of 3 × 3 pixels, where the kernel size was chosen to approximate the seeing disk at the time of the observations (≈0 6, or 0.
To allow for a possible velocity offset between the stars and the gas, we adopt a broad window of ±500 km s -1 (±180 km s -1 ) when constructing the narrowband images of the Balmer lines for spaxels with (without) this correction applied. We derive an extinction correction using the extinction law of  Cardelli et al. (1989)  from the reddening, E(B -V ), calculated from the Balmer decrement following  Calzetti et al. (1994)  for every spaxel for which Hβ and Hα are detected at the 3σ level. Finally, we fit the six strongest emission lines (Hα, Hβ, [O III] λλ4960, 5008, and [S II] λλ6718, 6732) with a single Gaussian profile using the IDL software KUBEVIZ. 24 This yields a velocity centroid and velocity dispersion, corrected for the instrumental resolution, for the ionized gas in each spaxel with statistically significant emission.
g. ,  Besla et al. 2012). Thus, models of the evolution of dwarf galaxy pairs-both within and outside of the halos of massive galaxies-must account for the role of dwarf-dwarf interactions in producing tidal features that enhance the cross section for neutral absorbers. Our comprehensive characterization of the galactic origin of the z = 0. 026 DLA was only made possible by the joint analysis of HST UV absorption-line and MeerKAT H I 21 cm emission-line data alongside detailed galaxy spectroscopy. As we move further into the era of the Square Kilometre Array and the next-generation Very Large Array with much higher sensitivity and spatial resolution, leveraging the time-limited availability of space-based UV resources in conjunction with spatially resolved 21 cm mapping will be critical to enhance our understanding of the diverse galactic environments that host the majority of the neutral gas in the universe.
001-A;and the  Research Corporation for Science Advancement  through a  Cottrell Scholar Award. J. K. K. acknowledges support by the  Swiss National Science Foundation  under grant  185692. S. L. was funded by  FONDECYT  grant No. 1191232. F. S. Z. is grateful for the support of a  Carnegie Fellowship  from the  Observatories of the Carnegie Institution for Science. This material is based upon work supported by  NASA  under award No. 80GSFC21M0002. This work is based on observations made with ESO telescopes at the  Paranal Observatory  under program ID  0104. A-0147 (A), observations made with the 6. 5 m  Magellan Telescopes  located at  Las Campanas Observatory , and spectroscopic data gathered under the  HST-GO-15163. 01Aprogram using the  NASA/ESA Hubble Space Telescope , operated by the  Space Telescope Science Institute  and the  Association of Universities for Research in Astronomy, Inc.