dataesr/scientific-paragraphs-categorization huggingface dataset:
This work was enabled by observations made from the Gemini North telescope and UKIRT telescopes, located within the Maunakea Science Reserve and adjacent to the summit of Maunakea. We are grateful for the privilege of observing the Universe from a place that is unique in both its astronomical quality and its cultural significance. UKIRT is owned by the University of Hawaii (UH) and operated by the UH Institute for Astronomy. When the data reported here were obtained, the operations were enabled through the cooperation of the East Asian Observatory (EAO). The international Gemini Observatory is a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation on behalf of the Gemini Observatory partnership: the National Science Foundation (United States), National Research Council (Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência, Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space Science Institute (Republic of Korea).
MGB coordinated the working group that interfaces with external facilities and contributed to operations. KB served in the ENGRAVE/HST team. TDB, CCL, EAM and RW contributed PS1 data and processing. MB contributed to governance as a member of the ENGRAVE Governing Council and provided comments on the manuscript. SJB performed photometry on optical and IR images with autophot. EB served in the Governing Council of ENGRAVE contributing in the governance activities. EC performed data reduction and presentation. KCC and MEH led the PS1, Gemini and UKIRT observing, proposals and data management. SC contributed to the revision of the manuscript and to scientific discussions. TWC coordinated the reduction and analysis of GROND data, compared bolometric light curves and joined the weekly discussion meetings during the preparation of the draft.
LW was part of the on call operation team. SY served on the on-call operations team. DRY developed and maintains many of the software tools essential to the work of the consortium. The Pan-STARRS1 system (PS1) comprises a 1. 8 m telescope with a 1. 4 Gigapixel camera (GPC1) with 0. 26 pixels and a field-of-view area of 7. 06 sq deg  (Chambers et al. 2016). It is equipped with a filter system, denoted as grizy P1 as described in  Tonry et al. (2012) , and the PS1 Science Consortium conducted the 3π Survey of the whole sky north of δ = -30 • in these filters. With these images as reference frames, all new images can be immediately reduced with the Image Processing Pipeline  (Magnier et al. 2020; Waters et al. 2020) , including difference imaging. Individual detections from survey operations are ingested into the PS1 Transient Server database at Queens University Belfast and assimilated into distinct objects with a time variable history, cross-matched with all catalogued galaxies, AGN, CVs and historical transients  (Smartt et al.
58 d) in H, J+K s and H+K s bands, respectively. Data reduction was done using theli version 3  (Erben et al. 2005; Schirmer 2013 ), which is a tool for automated reduction of astronomical data, which includes (bright and dim) flat-field corrections, background and collapse corrections (to correct for gradients due to residual reset anomaly), astrometry (to construct the dithering pattern for coaddition), sky-subtraction and co-addition. We manually removed any images where effects from the reset anomaly are still visible before applying the astrometry. Likewise, the GTC/EMIR photometric observations were reduced using the theli package, albeit with version 2 and not version 3 as the WHT/LIRIS images  (Erben et al. 2005; Schirmer 2013 ). Point-spread function (PSF) fitting photometry was performed on the NOT+ALFOSC, GROND and ACAM images using the AutoPhOT code (Brennan & Fraser 2022).
65 µm. We obtained six sets of ABBA sequences with 300s exposure times for each frame, resulting in a total integration time of 7200 s. The slit was positioned along the parallactic angle. The GNIRS data were reduced using the python-based PypeIt data reduction pipeline  (Prochaska et al. 2020a,b). Raw images have been treated with the cosmic-ray algorithm lacosmic (van Dokkum 2001) before being processed by the pipeline. The wavelength calibration is performed using the OH night sky lines visible in the stacked science exposures. Flux calibration was accomplished using observations of the A0V telluric standard HIP 14719 with the same setup as SN 2019wxt. The EMIR instrument mounted at the Nasmyth "A" focus of the GTC was also used to obtain spectroscopic data of SN 2019wxt on two occasions: 19 Dec 2019 and 19 Jan 2020.
The GTC/EMIR spectroscopic data were reduced using a GTC pipeline written in python, RedEmIR, with the aim of eliminating the contribution of the sky background in NIR using A201, page 27 of 34 consecutive A-B pairs of spectra. They were subsequently flatfielded, calibrated in wavelength, and the different A-B pairs were then co-added to obtain the final spectrum in the K band. Telluric correction is needed in NIR, and a version of Xtellcor  (Vacca et al. 2003)  was used for this. The software was improved and tailored to the atmospheric conditions of the La Palma observatory  (Ramos Almeida et al. 2009 ). The spectrum was then divided by the HIP 10559 A0 star spectrum to remove telluric contamination.
5×11. 3. The MEGARA data were reduced using the megaradrp v0. 11 pipeline  (Pascual et al. 2019 (Pascual et al. , 2021)). The pipeline uses several python-based recipes to perform bias subtraction, fibre-tracing, flat-field correction, wavelength calibration, spectra extraction and sky subtraction. Flux calibration was also performed using observations of the spectrophotometric standard star HR 3454 obtained with the same instrument settings. The final product is a row-stacked spectrum that is converted into a datacube of 0. 2 square spaxel on spatial dimensions using the megararss2cube task of the megara-tools suite v0. 1. 1  (Gil De Paz et al. 2020).
B. 8. 1. ALMA ALMA observations of SN 2019wxt were taken on 5 Mar 2020, in Band 3 (113. 4 GHz). The observations were centred at 01 h 55 m 41 s. 941, Dec +31 • 25 04. 550 and taken in Time Division Mode (4 spectral windows with 128 channels 15. 6 MHz wide), we tuned the band to cover the CO(1-0) transition (rest frame frequency 115. 271 GHz) at a z = 0. 036. The data were calibrated and imaged using manual scripts to quickly access the possible detection of the transient source, using the software CASA (NRAO, ESO, & NAOJ 2021) version 5. 6. 1. A continuum image was produced using the Multi Frequency Synthesis technique. The rms noise achieved is 13µJy/beam, and a reso-lution of 1. 66 × 0. 9. At the position of the transient no continuum emission is detected: the 3σ upper limit on the source detection is 39µJy/beam.
Calibration was carried out with the e-MERLIN CASA pipeline and checked interactively. The data from the two observations were combined in a single dataset and imaged in CASA. The restoring beam is 44 mas × 35 mas with a position angle of 25 • and the image r. m. s. noise is 14 µJy beam -1. No pixel brighter than 3σ is found within 1 from the position of the target: this implies, assuming a point source, a 3σ flux density upper limit F ν (5 GHz) < 42 µJy for SN 2019wxt. A highly significant (> 7σ) source is detected at ∼7. 4 west of the target and pointing position. A circular Gaussian fit to the image plane returns the following parameters for the component (nominal uncertainties returned by CASA's ImageFitter): (RA, Dec)=(01:55:41. 36006±0. 00012, +31:25:05. 0938±0. 0018); (b ma j , b min , b p.
5 GHz) < 63 µJy can be set for SN 2019wxt. A highly significant (> 7σ) source is detected at the same position as in the C-band observations. A circular Gaussian fit to the image plane returns the following parameters for the component: Chandra+ACIS upper limits to the x-ray luminosity of SN 2019wxt, compared to the X-ray lightcurve of the Type Ibn SN 2006jc. The Swift+XRT limit < 6. 6 × 10 41 erg s -1 at 0 d is more than two orders of magnitude shallower than the data plotted here, and hence not plotted. The comparison of the C-and L-band data (average frequency of 5. 07 and 1. 5 GHz, respectively) indicate a flat, slightly decreasing spectrum, which can be described by S ν ∝ ν -α with α ∼ 0. 38. B. 9. X-ray observations B. 9. 1. Swift XRT The site of SN 2019wxt was observed for 2. 6 ks on 18 Dec 2019 (i.
e. at maximum light) using the X-Ray Telescope (XRT) on board the Neil Gehrels Swift Observatory. Using the online Swift-XRT data products generator to analyse these data 22 , no source was detected at the position of SN 2019wxt, and so we follow Evans et al. (  2020 ) and calculate a 3σ upper limit to the XRT count rate of 5. 61×10 -3 ct sec -1. Using the online WebPIMMS tool 23 , and taking the Galactic column density of HI from Dickey & Lockman (1990) to be 5. 06 × 10 20 cm -2 and a photon index of 2, we calculate an upper limit to the luminosity of SN 2019wxt in an energy range 0. 3-10 keV to be < 6. 6 × 10 41 erg s -1. As expected given the distance to SN 2019wxt, this limit is not particularly constraining, and only the x-ray brightest SNe with strong circumstellar interaction would be detected (Dwarkadas & Gruszko 2012).
B. 9. 2. Chandra A series of deep observations totaling 157. 2 ks were taken of the site of SN 2019wxt using the Chandra x-ray telescope + ACIS-S between 12 Mar and 16 Aug 2020. We downloaded these data 22 https://www. swift. ac. uk/user_objects/index. php 23 https://heasarc. gsfc. nasa. gov/cgi-bin//Tools/ /w3pimms//w3pimms. pl from the Chandra archive, however examination of the ACIS Level 2 imaging event files revealed no source present at the position of SN 2019wxt (this was also found to be the case by  Shivkumar et al. 2022  in their analysis of these Chandra data). We used the CIAO srcflux tool to calculate 1σ upper limits to the flux in each observation. We calculate these limits in a broad energy range from 0. 5 to 7. 0 keV, assuming a power law SED for with a photon index of 2, and taking the Galactic column density of HI from  Dickey & Lockman (1990).
Acknowledgements. We thank the referee for their careful reading of the manuscript and helpful suggestions. We also wish to acknowledge the valuable Contribution of  Alex Kann , who sadly passed away during the preparation of this paper. His contributions to the ENGRAVE Collaboration will be missed. The full acknowledgments are available in Appendix A. MIUR 2020 -2020KB33TP METE. FEB acknowledges support from  CONICYT Basal   AFB-170002  and the  Ministry of Economy  through grant  IC120009  to The  Millennium Institute of Astrophysics  (MAS). MGB acknowledges support from  ASI  grant  I/004/11/5. MB acknowledges support from  MIUR PRIN   2017 , grant  20179ZF5KS. SJB thanks the  Science Foundation Ireland  and the  Royal Society  ( RS-EA/3471 ). EB acknowledges support from the  GRAWITA  grant funded by  INAF.
MDC-G and YDH acknowledge support from the  Ramón y Cajal Fellowship   RYC2019-026465-I  (funded by the  MCIN/AEI/ 10. 13039/501100011033  and the  European Social Funding). EC acknowledges support from  MIUR PRIN   2017. TWC  acknowledges  Marie Sklodowska-Curie  grant  H2020-MSCA-IF-2018-842471. AJCT acknowledges support from the  Spanish Ministry  project  PID2020-118491GB-I00  and  Junta de Andalucia  grant  P20_010168. PDA acknowledges support from  ASI  grant  I/004/11/5  and from  MIUR PRIN   2017 , grant  20179ZF5KS. AF acknowledges the support of the  ERC  under the  EU Horizon 2020 research and innovation program  ( ERC Advanced Grant   KILONOVA  No. 885281 ). MFr is supported by a  Royal Society -Science Foundation Ireland University Research  Fellowship. LG acknowledges  RYC2019-027683-I ,  PID2020-115253GA-I00  &  PIE20215AT016  grants.
CG is supported by a  VILLUM FONDEN Young Investor Grant  (project number  25501 ). JG-R acknowledges support from  Spanish AEI  under  Severo Ochoa Centres of Excellence Programme   2020-2023  ( CEX2019-000920-S ), and from  ACI-ISI  and  ERDF  under grant  ProID2021010074. GG acknowledges the  PRIN MIUR 'Figaro ' for financial support. MG is supported by  EU Horizon 2020 programme  under grant No  101004719. KEH acknowledges support by a  Project Grant  ( 217690-051 ) from The  Icelandic Research Fund. JH was supported by a  VILLUM FONDEN Investigator grant  (project number  16599 ). AI acknowledges the research programme Athena with project number  184. 034. 002 , which is financed by the  Dutch Research Council (NWO). LI was supported by research grants from  VILLUM FONDEN  (proj. 16599 ,  25501 ).
ZPJ has been supported by  NSFC  under grant No. 11933010. DAK acknowledges support from  Spanish National Research  Project  RTI2018-098104-J-I00  (GRBPhot). ECK acknowledges support from the G. R. E. A. T. research environment funded by the  Vetenskapsrådet , and from The  Wenner-Gren Foundations. GL was supported by a research grant ( 19054 ) from  VIL-LUM FONDEN. AJL has received funding from the  European Research Council search Council (ERC)  via grant number  725246. JDL acknowledges support from a  UK Research and   Innovation Future Leaders Fellowship  ( MR/T020784/1 ). KM EU H2020 ERC  grant no. 758638. IM is partially supported by  OzGrav  (ARC project  CE17010000 ). BM acknowledges support from the  Spanish MCINN  under grant  PID2019-105510GB-C31  and through the  María de Maeztu  award  CEX2019-000918-M.
ANG acknowledges support to TLS. FO acknowledges support from the GRAWITA/PRIN project 'The new frontiers of the Multi-Messenger Astrophysics' and from the H2020 grant 871158. MAPT was supported by grants RYC-2015-17854 and AYA2017-83216-P. GP is supported by ANID -Millennium Science Initiative -ICN12_009. JQV acknowledges support from ANID folio 21180886. AR acknowledges support from Premiale LBT 2013. OSS acknowledges the Italian MUR grant 1. 05. 06. 13 and INAF-Prin 1. 05. 06. 13. RS-R acknowledges support under the CSIC-MURALES project with reference 20215AT009. SS acknowledges support from the G. R. E. A. T. research environment, funded by Vetenskapsrådet project number 2016-06012. SJS sTFC Grant ST/P000312/1 and ST/N002520/1. RLCS acknowledges funding from STFC. HFS acknowledge the support of the Marsden Fund Council managed through Royal Society Te Aparangi.
SDV aknowledges fundings from PNHE of INSU/AA. DV acknowledges the financial support of the German-Israeli Foundation (GIF No. I-1500-303. 7/2019). DW is supported by Independent Research Fund Denmark grant DFF-7014-00017. The Cosmic Dawn Center is funded by the Danish National Research Foundation. LW acknowledges support from the Polish NCN DAINA No. 2017/27/L/ST9/03221, EC H2020 OPTICON No. 730890 and ORP No. 101004719. SY has been supported by the Knut and Alice Wallenberg Foundation, and the G. R. E. A. T. research environment funded by the Swedish Research Council. Based on observations collected by the ENGRAVE collaboration at the  European Southern Observatory  under  ESO programmes  1102. D-0353 ,  0102. D-0348 ,  0102. D-0350 ; also on observations collected at the  European Southern Observatory  under  ESO programmes  1103.
On 18 Dec 2019, Pan-STARRS 1 (PS1) reported the discovery of a potential optical counterpart during these normal survey operations, PS19hgw (McBrien et al. 2019a), located at RA(J2000) = 28. 92473 • (01 h 55 m 41 s. 94), Dec(J2000) = +31. 41791 • (+31 • 25 04. 4). It is clearly associated to the host galaxy KUG 0152+311 at z = 0. 035785, corresponding to d L = 154 ± 11 Mpc (NASA Extragalactic Database, NED) assuming Planck Collaboration XIII (2016) cosmological parameter (flat ΛCDM, H 0 = 67. 8 km s -1 Mpc -1 , Ω M = 0. 31) A201, page 3 of 34.
The growing number of edge devices in everyday life generates a considerable amount of data that current AI algorithms, like artificial neural networks, cannot handle inside edge devices with limited bandwidth, memory, and energy available. Neuromorphic computing, with low-power oscillatory neural networks (ONNs), is an alternative and attractive solution to solve complex problems at the edge. However, ONN is currently limited with its fully-connected recurrent architecture to solve auto-associative memory problems. In this work, we use an alternative two-layer bidirectional ONN architecture. We introduce a two-layer feedforward ONN architecture to perform image edge detection, using the ONN to replace convolutional filters to scan the image. Using an HNN Matlab emulator and digital ONN design simulations, we report efficient image edge detection from both architectures using various size filters (3 × 3, 5 × 5, and 7 × 7) on black and white images.
HNN-based Matlab emulator In order to easily validate our architectures, and their configuration for image edge detection, we first test our methods using an HNN emulator developed on Matlab for each architecture. HNN emulators consists of modeling ONN architectures using bipolar spins as neurons, like in HNN. For the fully-connected architecture, the Matlab emulator reproduces identically an HNN, but it is not used for the image edge detection application. For the two-layer bidirectional architecture, we consider a fully-connected HNN with zero weights between input neurons, such that we emulate the parallel behavior of the bidirectional architecture. Finally, for the two-layer feedforward architecture, we create a Matlab code which emulates the two-layer feedforward network with bipolar spins as neurons.
In this case, output neurons are initialized with -1 for HNN or 0 • phase for ONN that we associate to a no-edge information. Then, we expect the feedforward ONN to change the output information to +1 for HNN, or 180 • phase for ONN if an edge is detected. However, figure  6  highlights that not all edges are correctly retrieved by our HNN Matlab emulator. It can be explained by the difference between the Sobel gradient calculation and the HNN or ONN activation functions. In Sobel, a large negative intensity and a large positive intensity of gradient will correspond to an edge. However, with HNN or ONN, if the weighted sum is negative, the states will evolve to -1 for HNN, and 0 • phase for ONN, corresponding to a no-edge information. A solution to counter this effect could be to use two output neurons per kernel, initializing both with opposite values, and checking if at least one changed during the computation.
JSC = Overlappingedges Unionedges  (5)  As already mentioned, to the best of our knowledge, there is no GT for edge detection algorithms using our dataset. Thus, to evaluate performances of our solutions, we propose to use results from state-of-the-art edge detection algorithms as GT. Canny is known as the most precise solution for image edge detection, while Sobel is a cheaper and faster solution with less precision and more sensitivity to noise. Thus, we take Canny as GT and evaluate its similarity with Sobel and our solutions using the JSC metric. We use images generated with built-in Sobel and Canny Matlab functions.
Results After exploring and validating methods to perform image edge detection using our two-layer ONNs with the HNN Matlab emulator, we validate and assess performances of the methods with the digital ONN design. We test both architectures on black and white 28 × 28 images from modified national institute of standards and technology database (MNIST), gray-scale 28 × 28 MNIST images, as well as on large scale black and white images. For each architecture, we perform image edge detection using 3 × 3 input filters, 5 × 5 input filters, and 7 × 7 input filters, and we evaluate precision of each solution. Finally, we extract resource utilization and latency performances of each ONN architecture and size to assess real-time performances of our solutions.
Black and white 28 × 28 MNIST images We first evaluate our two-layer ONNs for image edge detection using black and white images of handwritten digits from MNIST database  [39]. The MNIST database contains 28 × 28 gray scale images, that we binarize to obtain black and white images. Figure  9  displays an example of output images generated by the digital ONN design simulation for multi-scale bidirectional and feedforward architectures. Figure  9  also highlights outputs of previous work  [21] , and outputs of state-of-the-art Sobel and Canny configured with 3 × 3 kernels. Additionally, figure  9  illustrates the similarity between our ONN bidirectional filter and Sobel filter with GT Canny. It highlights that our customized weight solution for the 3 × 3 bidirectional architecture improves the number of detected edges from previous work  [21].
Gray scale 28 × 28 MNIST images After investigating ONN for image edge detection on black and white MNIST images, we also analyze its efficiency on gray scale images. Figure  11  shows ONN output on a 28 × 28 gray scale MNIST image for the various architecture configurations. It also shows the similarity between our ONN feedforward filter and Sobel filter with GT Canny. First, we highlight that the bidirectional architecture, even with our customized weights does not allow to perform image edge detection on realistic gray scale images. The HNN Matlab emulator was able to detect edges on gray scale octagon map, however, the ONN digital design is not efficient on a realistic MNIST image. For various part of the image, the digital ONN system is unstable and never stabilizes to one of the patterns.
Discussion Assessing precision of edge detection algorithms is challenging. A common GT is needed for comparison. However, up to our knowledge, to date there is no edge detection database providing input and GT output. A first solution to counter the lack of GT is to compare to state-of-the-art algorithms. Another solution is to integrate the image edge detection solution in a more complex algorithm to check the global outputs of the algorithm. For example, some use edge detection as part of image segmentation applications  [24] , or in feature extraction applications  [15]. It could be interesting to set up a more complex demonstrator to better assess the quality of our edge detection solutions. In this work, we evaluate our ONN edge detection algorithms by comparing the output of our solutions with other state-of-the-art edge detection algorithms, Sobel and Canny.
Metatranscriptomics in turn records genes which are actively transcribed into RNA and thus drive ongoing biological processes  17  , informing about the metabolic activity of soil community members  18  , and functional changes in these communities  17. Comprehensive genome collections are the backbone for metagenomics and metatranscriptomics. If genome databases are available, shotgun metagenomics and metatranscriptomics have shown to provide unprecedented insights  17, 19  , e. g. , into vegetation change over glacial cycles  15  , historic population genomic processes  20, 21  , and kingdom-spanning processes of ecosystem functioning  22. Large genome sequencing initiatives like the Earth Biogenome Project  23  will provide this data ultimately, but progress so far mainly focused on large, prominent organisms, such as mammals  24  , birds  25  , insects  26  and plants  15.
Differences in vegetation are known to influence soil invertebrate community composition, although our analysis may lack power to equivocally detect these. Differences in community composition between the study years may reflect year-specific environmental differences. However, we caution not to over interpret these results. The power of an analysis of drivers of community composition and richness on this gradient should be increased with more extensive sampling. The analyses nonetheless demonstrate the value of a dedicated soil invertebrate genome database for the identification of shotgun-sequenced environmental nucleotide samples from soils. For each taxonomic group we also list the number of species with publicly available genome assemblies (as of June 2022). Fig. 2  Maximum likelihood phylogenetic tree of soil invertebrate genomes.