dataesr/scientific-paragraphs-categorization huggingface dataset:
We note that improving the balance would require more simulation results with very high v f images. However, accounting for those cases would require switching to a mesh-based simulation approach where local mesh refinement between the narrowly spaced fibers would be necessary. For practical reasons, we opted to avoid switching to a mesh-based solver and used the dataset as is. This imbalance in the dataset might have contributed to the scatter in Fig. 5a  that is more pronounced in the low permeability regime. Considering the predictions in both directions, the deviation from the Geodict results seems to be rather low for all the images except those where fiber radius is 14 pixels, as will be quantified in the following paragraph. Considering that the 14 pixel radius case is less prone to discretization (or rasterization) based representation errors, that are prominent at low pixel per radius situations, we suspect that the relatively low performance for the 14 pixel radius case could be improved by altering the padding as well as the filter sizes.
Case studies Based on the previous subsection's results, we analyze two case studies which focus on transverse permeability prediction (i. e. , K x and K z ) in the cases where the input image size differs from what the neural network takes as input, more precisely images with a resolution different from the 400 × 400 pixel dimensions. In the following subsections, we normalized with respect to the permeability of images with depth of 1000 (K i /K 1000 where i = 1, 10, 100, or 1000) for r = 10 pixel images when implicit inlet/outlet is used. c) Same results when implicit inlet/outlet is not used in the simulations. Fig. 12. A visual summary of permeability calculation of images with resolution other than 400 × 400 pixels. Pictures are not to be scaled. first investigate the prediction accuracy of the neural network with square images that can have either smaller or larger edge length than 400 pixels and then investigate the prediction accuracy on rectangular images of real microstructures where the fiber radius r as well as v f in individual slices is variable; for the latter purpose we make use of the dataset distributed within the framework of virtual permeability benchmark study, which consisted of a 3D binary image with dimensions of 1003 × 124 × 973 voxels.
On the contrary, the 4 pixel radius results with high v f display a departure from the equality line, as indicated by the orange arrow in Fig. 15 , in comparison to Fig. 13a  and this re-emphasizes the limitation of the neural network when it comes to predicting the permeability of images that contain fibers whose radii are out of the range considered during the neural network training. However, we note that there are several ways around this limitation, be it rescaling or stitching or cropping (which would also provide a route for assessing the permeability of rectangular images) and there exists an ultimate solution that involves training the neural network with a larger dataset involving a larger radius range. Similar trends are present in the case of images with 800 × 800 pixel dimensions.
Virtual permeability benchmark dataset. The ultimate test to assess the performance of our network is to subject it to real images (i) that have non-constant fiber radius r and fiber content v f along the fiber direction, (ii) non-periodic boundary conditions and (iii) that are rectangular in shape with an aspect ratio different from 1. For this purpose, we used the 3D image dataset used in the virtual permeability benchmark whose first results are reported in  [83]. The provided binary image has dimensions of 1003 × 973 × 124 voxels and was obtained using Xray computed microtomography with a carbon fiber yarn and with a spatial resolution of 0. 52 µm/voxel. Each of the 973 slices along the fiber direction contains several hundreds of fibers whose radii vary between ~ 6 and ~ 12 pixels. We followed the procedure outlined in Fig.
As the last case study, we estimated the permeability of a microtomography scan image that had rectangular shape, non-periodic boundary conditions and contained fibers with moderate varying radii. For this purpose, we used the dataset provided within the framework of the virtual permeability benchmark and we showed that our approach presents a fast and accurate alternative to the flow simulations that were performed by the benchmark participants to collect permeability information. Further extension of the CNN prediction capabilities is possible, for instance to collect tortuosity information, as well as other structural descriptors, that can be used to enhance the permeability predictions or to account for the dual scale effects that can arise for some textile reinforcements. Extending the fiber radius range in the training data is another change that could extend the capabilities of the CNNs.
Acknowledgements This research was financially supported by the  Swiss National Science Foundation  ( SNF-182669 ). We acknowledge the virtual permeability characterization benchmark organizers for making the dataset available for this work. The 3SR Lab is part of the  LabEx Tec 21 (Investissements d'Avenir -grant agreement n• ANR-11-LABX-0030 ) and the  Carnot Institute Polynat  ( ANR16-CARN0025 ).
The CNIa0. 02 is a complete, nearby sample of Type Ia supernova (SN Ia) multiband light curves, and it is volume-limited with host-galaxy redshift z host < 0. 02. The scientific goal of CNIa0. 02 is to infer the distributions of key properties (e. g. , the luminosity function) of local SNe Ia in a complete and unbiased fashion in order to study SN explosion physics. We spectroscopically classify any SN candidate detected (discovered or recovered) by the All-Sky Automated Survey for Supernovae (ASAS-SN) that reaches peak brightness < 16. 5 mag. Since ASAS-SN scans the full sky and does not target specific galaxies, the sample is effectively unbiased by host-galaxy properties. We obtain multiband photometric observations starting from the time of discovery. In the first data release (DR1), we present the optical light curves obtained for 240 SNe (including 182 with multiband data), and we derive parameters such as the peak fluxes and ∆m 15.
5 mag. The complete sample consists only of those with z < 0. 02 and peak V < 16. 5 mag. Note that SNe Ia on the WLR span a luminosity range of -19. 5 M V -17 (see, e. g. ,  Burns et al. 2018) , and due to the peak magnitude limits, our sample is not sensitive to all dim SNe Ia with M V -18. 2 within the volume confined by z < 0. 02. CNIa0. 02 DR1 includes V -band and g-band photometry from the 14 cm telescopes used in ASAS-SN survey. We have performed photometric follow-up observations using a number of telescopes ranging from 0. 3 m to ∼ 2 m. In this data release, most optical data are in BV ri observed by 1 m telescopes in the Las Cumbres Observatory Global Telescope network (LCOGT;  Brown et al. 2013 ) distributed in four sites covering both hemispheres, two 0. 6 m telescopes in SRO (CA, USA) and Mayhill (NM, USA) of the Post Observatory, and the 1.
DATA PROCESSING This data release contains the results of processing over 20,000 images. We developed a Python photometric pipeline PmPyeasy 1 to automatically process the images and obtain the photometry. The pipeline utilizes several external software packages that are all wrapped in a clean Python -20 0 20 40 12 14 16 18 20 Magnitude interface. The pipeline runs automatically by default, but allows manual operations at any point when necessary. The pipeline uses pyds9 2  to facilitate human inspections through XPA messaging to SAOImageDS9 3. It takes images that have already been pre-processed including bias removal and flat fielding. Here we outline the processing steps. 2015ar B + 1 V + 0 r -2 i -3 ASASSN-V.
Image Registration and Source Detection The pipeline distributes all the images to object-specific folders and adds information such as the filter, exposure time, and epoch to a database. Next, it removes cosmic rays using an implementation of L. A. Cosmic technique (van Dokkum 2001), measures the full width at half-maximum intensity (FWHM) of the stellar profiles, and estimates the background value for each image. It then employs PyRAF daofind to generate a source catalog for each image.
PSF Photometry and Image Subtraction We perform point-spread-function (PSF) photometry for SNe that have negligible host-galaxy contamination using DoPHOT  (Schechter et al. 1993; Alonso-García et al. 2012). DoPHOT generates a PSF model automatically and obtains magnitudes for all sources that meet the given criteria. A large number of targets have significant galaxy background flux and require image subtraction. To perform image subtraction, the pipeline first matches point sources detected in the science image and the template image, and then the science image is astrometrically aligned to the template image using the matched sources and resampled. The image subtraction is done with the High Order Transform of PSF ANd Template Subtraction package (HOTPANTS;  Becker 2015). The FWHMs of the template and resampled science image are used to determine the convolution direction: images with better seeing are convolved with the kernel for subtraction.
Photometric Calibration For the photometric calibration, the first step is to calibrate the instrumental magnitudes in the appropriate natural system of a certain telescope/CCD/filter set. The photometric standards are selected from the Pan-STARRS1 DR1 MeanObject database table  (Flewelling et al. 2016)  or the ATLAS All-Sky Stellar Reference Catalog (Refcat2,  Tonry et al. 2018 ) and transformed to the standard Johnson BV and SDSS ri bands as detailed in Appendix C. Then they are converted to the appropriate natural systems: B nat = B std + CT B × (B std -V std ), (1) V nat = V std + CT V × (B std -V std ), (2) r nat = r std + CT r × (r std -i std ), and (3) i nat = i std + CT i × (r std -i std ), (4) 0. 6 0. 9 1. 2 1. 5 1. 8 2. 1 ∆m 15 (B) [mag] where CT X are the color terms (X stands for B, V , r, and i).
RESULTS In Table  1 , we give the general information (survey names, IAU names, equatorial coordinates, host-galaxy names, and heliocentric host redshifts) for objects that have follow-up data (regardless of whether they belong to the complete sample) or have been considered for follow-up observations (regardless of whether such data are obtained). We also provide additional information (discovery date, brightest observed magnitude, and whether detected by ASAS-SN) which can be used to check whether an object meets the selection criteria of complete sample (i. e. , in the date range of 2015 June 13 and 2019 January 31, peak brightness < 16. 5 mag and detected by ASAS-SN) in addition to the redshift cut (z host < 0. 02). The host-galaxy redshifts are from either the NASA/IPAC Extragalactic Database (NED) or new measurements presented in Table  2.
Light-Curve Data In this section, we present the optical light curves of 240 SNe Ia. These data include V g-band light curves from ASAS-SN observations using image subtraction (see more descriptions in  Jayasinghe et al. 2018). We have successfully conducted BV ri follow-up observations of 216 SNe, and we present light curves for all of them except for 34 objects that require image-subtraction photometry but do not have satisfactory templates available yet. BV ri light curves from follow-up observations were conducted by LCOGT, Post Observatory, and SMARTS, as well as a small number of relatively latephase data from Hiltner, Du Pont, and NOT. The light curves of SNe Ia up to 60 days past B-band peak or discovery time (if peak time is not available) are displayed in Figure  2. The corresponding photometry results in the natural systems are provided in Table  3.
1999). Figure  3  shows the histogram of the ∆m 15 (B) distribution of the objects in this data release. As can be seen, we have objects spanning the whole range of ∆m 15 (B) of the SN Ia population from ∆m 15 (B) ≈ 0. 7 mag to ∆m 15 (B) ≈ 2. 0 mag. Further work on quantifying detection efficiency needs to be carried out to obtain the intrinsic distribution of ∆m 15 (B) for CNIa0. 02.
SUMMARY The goal of CNIa0. 02 is to produce a complete and unbiased sample of nearby SNe Ia with multiband light curves that sample the whole population of SNe Ia. We present 240 SNe with optical light curves in this data release. We expect to also publish near-UV, near-IR, and late-phase photometric data in the future data releases. When completed, the sample will provide a large and homogeneous dataset to infer the intrinsic distribution properties of SNe Ia in the local universe, which will ultimately help answer basic questions regarding SN Ia progenitor systems and explosion mechanisms. We acknowledge the Telescope Access Program (TAP) funded by the NAOC, CAS and the Special Fund for Astronomy from the Ministry of Finance. CSK, KZS and BJS are supported by NSF grants  AST-1515927, AST-1814440, and AST-1908570.
M. D. S acknowledges funding from the Villum Fonden (project numbers 13261 and 28021). A. V. F. 's supernova group is grateful for financial assistance from the Christopher R. Redlich Fund, the TABASGO Foundation, and the Miller Institute for Basic Research in Science (U. C. Berkeley). A major upgrade of the Kast spectrograph on the Shane 3 m telescope at Lick Observatory was made possible through generous gifts from William and Marina Kast as well as the Heising-Simons Foundation. Research at Lick Observatory is partially supported by a generous gift from Google. We thank the staffs of the various observatories at which data were obtained for their excellent assistance. JLP is provided in part by FONDECYT through the grant 1191038 and by the Ministry of Economy, Development, and Tourism's Millennium Science Initiative through grant IC120009, awarded to The Millennium Institute of Astrophysics, MAS.
MF acknowledges the support of a Royal Society -Science Foundation Ireland University Research Fellowship. BJS is also supported by NSF grants  AST-1920392 and AST-1911074. MG         The Las Cumbres Observatory Global Telescope network (LCOGT;  Brown et al. 2013 ) operates a fleet of robotically-controlled telescopes distributed at six sites covering both hemispheres. LCOGT currently has two 2 m, ten 1 m, and ten 0. 4 m telescopes, and their locations and site codes are shown in Table  5. Telescopes of a certain aperture class are equipped with the same set of optical imagers 8. The majority of LCOGT images for our sample were obtained with 1 m-class telescopes. Each 1 m telescope is equipped with either a "Sinistro" or an SBIG STX-16803 camera. Some images were also taken with 2 m or 0. 4 m telescopes for which the photometric results will be available in future data releases.
Two 24-inch CDK24 telescopes operated by the Post Observatory were used mainly for northern objects. One is located at the Sierra Remote Observatories (SRO 9 ; California, USA) and the other at Post Observatory Mayhill (New Mexico, USA). Two different cameras were used during our followup campaign, an Apogee Alta U230 camera and an Apogee Alta U47 camera. Both cameras are back-illuminated, with similar quantum efficiency > 90% over a broad region. The U230 camera was used by default at both sites. The telescope at SRO utilized the U230 for almost all images, while 4000 5000 6000 7000 8000 9000 Wavelength   2003)  was used mainly for southern objects. The optical CCD for ANDICAM is a Fairchild 447 2048 × 2048 pixel CCD. We will publish the near-IR data taken by ANDICAM in a future data release.
B. 2. Filters The LCOGT 1 m telescopes have a standard Johnson-Cousins set (U BV RI) and an SDSS/PanSTARRS set (ugrizy) of filters. SMARTS/ANDICAM is equipped with standard KPNO-recipe Johnson-Kron-Cousins BV RI filters. Commercial filters are used on the Post Observatory telescopes. Astrodon Photometrics BV 10 and Sloan ri 11 filters were used for the images in our sample. The filter set used for observations in this data release are compared to Landolt BV  (Landolt 1992 ) and the SDSS ri  (Fukugita et al. 1996 ) standard bandpasses in Figure  5.
C. PHOTOMETRIC CALIBRATION STANDARDS For the purpose of homogeneous photometric calibration, photometry from an all-sky reference catalog is preferred to be used for photometric standards. Pan-STARRS1 has carried out multiband (grizy P 1 ) observations of the sky north of declination -30 • known as the 3π Steradian Survey  (Chambers et al. 2016). Pan-STARRS1 is performed with a well-characterized photometric system, and transformations to other photometric systems are also available  (Tonry et al. 2012). The primary calibration standards are selected from the Pan-STARRS1 DR1 MeanObject database table  (Flewelling et al. 2016). For the remaining 1/4 of the sky, we use the ATLAS All-Sky Stellar Ref-10 https://astrodon. com/products/astrodon-photometrics-uvbri-filters/ 11 https://astrodon. com/products/astrodon-photometrics-sloan-filters erence Catalog (Refcat2), which has been assembled from a variety of sources and brought onto the the same photometric system as Pan-STARRS1  (Tonry et al.
is supported by the Polish NCN MAESTRO grant 2014/14/A/ST9/00121. Polish participation in SALT is funded by grant no. MNiSW DIR/WK/2016/07. Astropy (Astropy Collaboration et al. 2018), PyRAF (Science Software Branch at STScI 2012), FITSH (Pál 2012), ccdproc (Craig et al. 2017) General Properties of SNe Ia in the Sample.
The adoption of edge and fog systems, along with the introduction of privacypreserving regulations, compel the usage of tools for expressing complex data queries in an ephemeral way. That is, queried data should not persist. Database engines partially address this need, as they provide domain-specific languages for querying data. Unfortunately, using a database in an ephemeral setting has inessential issues related to throughput bottlenecks, scalability, dependency management, and security (e. g. , query injection). Moreover, databases can impose specific data structures and data formats, which can hinder the development of microservice architectures that integrate heterogeneous systems and handle semi-structured data. In this article, we present Jolie/Tquery, the first query framework designed for ephemeral data handling in microservices.
Jolie/Tquery joins the benefits of a technology-agnostic, microservice-oriented programming language, Jolie, and of one of the most widely-used query languages for semi-structured data in microservices, the MongoDB aggregation framework. To make Jolie/Tquery reliable for the users, we follow a cleanroom software engineering process. First, we define Tquery, a theory for querying semistructured data compatible with Jolie and inspired by a consistent variant of the key operators of the MongoDB aggregation framework. Then, we describe how we implemented Jolie/Tquery following Tquery and how the Jolie type system naturally captures the syntax of Tquery and helps to preserve its invariants. To both illustrate Tquery and Jolie/Tquery, we present the use case of a medical algorithm and build our way to a microservice that implements it using Jolie/Tquery.
The Problem Ideally, a tool for ephemeral data handling in microservices would give us the best of the MongoDB aggregation framework and of the Jolie programming language: a query framework designed for semi-structured data and a language for working in heterogeneous environments. structures. MQuery formalises the key operators of the MongoDB aggregation framework, dispensing from some unnecessary idiosyncrasies that can lead to counterintuitive behaviour. Tquery inherits this good feature-the reader interested in the technical differences w. r. t. the MongoDB aggregation framework can consult  (Botoeva et al. , 2016, Appendix C). The key difference between this work and  Botoeva et al. (2018)  is that Tquery comes with an implementation, whereas  Botoeva et al. (2018)  investigated the theoretical expressivity of the MQuery operators w.
demonstrated for MongoDB). There exist works on the integration of relational query frameworks with general-purpose programming languages, including: object-relation mapping frameworks (ORMs), which map objects to database entities  (Fussel, 1997) ; Opaleye, a Haskell DSL for generating PostgreSQL commands  (Ellis, 2014) ; and LINQ  (Meijer, Beckman & Bierman, 2006) , which provides query operators targeting SQL tables and XML structures for. NET languages. Tquery could be a reference to implement similar frameworks for semi-structured data in these languages. A convenient feature of Jolie/Tquery is that all its queries can work with any data format that Jolie can handle: Jolie automatically converts data in different formats (including JSON, XML, and some binary formats) to its abstract data model  (Montesi, Guidi & Zavattaro, 2014; Montesi, 2016).
As we are going to exemplify in the next section, a typical use case for semi-structured data handling and Jolie/Tquery is the reactive processing of events. Stream-processing languages have been explored for similar tasks, but they feature different kinds of primitives and are usually not based on semi-structured data. The landscape of stream-processing languages is quite wide, e. g. , data-centric  (Chen et al. , 2000; Barbieri et al. , 2009) , time-or hardware-constrained execution-centric  (Caspi et al. , 1987; Hirzel, Schneider & Gedik, 2017; Tommasini et al. , 2019) , focussed on the relationalor document-oriented  (Chen et al. , 2000; Diao et al. , 2002; Mendell et al. , 2012)  approach. In particular, SQL-based stream-processing languages  (Esteves et al. , 2017; Babu & Widom, 2001)  recently gained popularity in industry (thanks to the familiarity of programmers with the SQL language), with commercial tools such as Apache Flink (Apache, 2022a), Apache Kafka (KSQL)  (Narkhede, 2017) , Apache Samza (Apache, 2022b), Apache Storm (Apache, 2022c), WSO2 Stream Processor (WSO2, 2022), Siddhi (Siddhi Streaming SQL)  (Siddhi, 2022).
We deem StreamQL  (Kong & Mamouras, 2020)  the work closest to Jolie/Tquery. This is a query language for efficiently processing IoT data streams. The StreamQL Engine is implemented as a lightweight Java library and does not depend on the external engine. However, StreamQL is a functional language that is based on formal semantics residing on the class of monotone functions over streams. It works with the typical functional primitives on list-based data, supporting a variety of operators that simplify stream-processing at the level of data aggregation (filtering, windowing, etc. ) and data-flow control (e. g. , parallel composition). StreamQL does not handle explicitly semi-structured document-oriented data and requires additional processing for data translation, while Jolie/Tquery handles it natively(tree-shaped data simplifies integration with Jolie).
Unlike Jolie/Tquery, StreamQL has built-in primitives for temporal control typical for data streaming languages. In Jolie/Tquery time contracts can be implemented by adding information to the data structures and need to be managed explicitly by the programmer. Widening our scope, we deem two works, CQL  (Arasu, Babu & Widom, 2006)  and EQL  (Elasticsearch, 2022) , close to Jolie/Tquery. CQL is a declarative streaming SQL-based query language, implemented in the STREAM DSMS  (Arasu et al. , 2016)  with data captured with sliding windows  (Babcock et al. , 2002)  based on time-(e. g. , update the data every 30 s) and data-related conditions (e. g. , capture the data as soon as it arrives). EQL  (Elasticsearch, 2022)  is an event-based data manipulation library developed in Python. Similarly to CQL, EQL expects data to follow an event-oriented schema.