dataesr/scientific-paragraphs-categorization huggingface dataset:
Intensity calibration spectra Using the IRIS radiometric calibration, we convert the data number (DN) to intensity units. We use the IDL routine iris_calib. pro and get the intensity value (I c ) in erg cm -2 s -1 sr -1 Å -1. The exposure time per slit position was ≈ 2 sec for this conversion. The radiometric intensity calibration is done with a code developed by H. Tian, with the following formula: I c = hc λ × n × I o A e f f × d × ω (1) The photon energy hc/λ is calculated with Planck constant h = 6. 63 × 10 -27 erg-s, speed of light c = 3 × 10 10 cm s -1. n is the number of photons per DN, it has a value 4 for FUV and 18 for NUV spectra. I o is the observed intensity in DN s -1 and A e f f is the effective area in cm -2 and obtained through iris_get_response routine available in SolarSoftWare (SSW).
Discussion and conclusion We extend the analysis of the IRIS data of GOES B6. 7 micro flare (or mini flare) observed on March 22, 2019 focusing on the IRIS SJIs in the 2832 Å filter and the NUV continuum spectral variation in the two extreme wavelength domains of the IRIS 2896 Å spectra bandpass (between 2784-2791. 5 Å and 2827-2834. 5 Å). The mini flare and its site of reconnection identified by bidirectional outflows concern only one or two pixels along slit position 1 of the IRIS rasters  (Joshi et al. 2021 ). Therefore we focus our analysis on the spectra along this slit. Through the paper we demonstrate that the enhancement of the NUV continuum was due to electron beams and not to the flare heating by inter-playing the analysis of IRIS images and spectra. A brightening in the 2832 filter is visible in the area of the flare.
Acknowledgements. We thank the referee for his/her numerous insightful comments which have greatly helped to improve the manuscript. IRIS is a NASA small explorer mission developed and operated by LMSAL with mission operations executed at  NASA Ames Research center  and major contributions to downlink communications funded by  ESA  and the  Norwegian Space Centre. We thank  Jana Kasparova  for the discussion and suggestions. We are thankful to  Hui Tian  and  Krzysztof Barczynski  for the discussion regarding the IRIS data calibration. RJ thanks to CEFIPRA for a  Raman Charpak Fellowship  ( RCF-IN-00136 ) under which this work is initiated at the  Observatoire de Paris, Meudon. RJ also acknowledges the support from  Department of Science and Technology (DST), New Delhi, India  as an INSPIRE fellow.
Context. Many radio continuum catalogs with different sensitivity limits and spatial resolutions are published via the VizieR database at the Centre de Données astronomiques de Strasbourg (CDS). The diversity of spatial resolutions of different catalogs makes the cross-identification of different flux density measurements of individual sources complex. The SPECFIND tool is able to handle radio surveys at different frequencies from different instruments with different resolutions. Aims. Since the former version of the SPECFIND catalog was released ten years ago, hundreds of new radio continuum catalogs have been published. We upgraded the SPECFIND tool to reach a wider frequency range, especially the lower-frequency radio regime, as well as to have better spatial sky coverage. Methods. We adapted selection criteria and applied them to all of the radio catalogs listed in the VizieR database to define a final sample of new catalogs.
6 million. The final SPECFIND V3. 0 catalog is publicly available via VizieR. By applying SPECFIND to two subsamples of the catalogs with frequency cuts at 325 MHz and 1. 4 GHz, spectral break and PS source candidates could be identified. We encourage follow-up observations of these candidates to confirm their nature because the population we identify has a relatively low reliability. Conclusions. The SPECFIND V3. 0 catalog is a very useful resource and a powerful open access tool, reachable via VizieR. By tripling the resulting spectra and including many radio continuum surveys from the last 50 years, we provide a significantly extended catalog of cross-identified radio continuum sources. Furthermore, the SIMBAD database will be updated using the SPECFIND V3. 0 catalog and will contain more radio continuum data, serving the needs of future projects.
Introduction Within the last few decades, a variety of radio continuum observations of major parts of the sky have been conducted, leading to many different radio continuum source catalogs. The observations are usually undertaken with different interferometric arrays or single dish telescopes, leading to a huge range of resulting resolutions and sensitivities as well as pointing precisions. The SPECFIND tool  (Vollmer et al. 2005a) , with applications and later versions  (Vollmer et al. 2005b (Vollmer et al. , 2008 (Vollmer et al. , 2010)) , was introduced to handle these diverse radio catalogs. It uses catalogs from the VizieR database 1  (Ochsenbein et al. 2000)  of the Centre de Données astronomiques de Strasbourg (CDS) to cross-identify radio sources of the different catalogs and produces spectral energy distributions (SEDs) over a large frequency range in the radio continuum regime.
3 above the turnover frequency as concave source candidates. The paper is organized as follows. In Section 2 we provide basic explanations of how the SPECFIND tool works. Section 3 describes the upgrade in terms of software and the selection criteria for the newly added radio continuum catalog tables. In Section 4 the results are presented and compared to the previous version of the SPECFIND catalog. We describe an application of the SPECFIND tool to find PS sources and provide example sources that have a clear spectral break either around 325 MHz (MPS source candidates) or around 1. 4 GHz (GPS sources candidates) in Section 5. In Section 6 we explain how to access the public SPECFIND V3. 0 catalog via VizieR as well as the structure of the published tables. The summary and conclusions are provided in Section 7.
The SPECFIND tool Generally, the SPECFIND tool cross-identifies flux density measurements of sources from radio continuum catalog tables at different frequencies from the VizieR database and fits a single power law (linear spectral slope in log-log space) to the crossidentified flux density measurements. In principle, SPECFIND allows for one break in the spectrum, which means it can fit two different slopes to the radio continuum spectrum. However, since the SPECFIND algorithm is optimized for the robust fitting of a single spectral slope, spectral breaks are rarely fit to the data (see Fig. 10  in  Vollmer et al. 2010). Similarly, any curvature in a spectrum due to flattening toward lower frequencies (synchrotron self-absorption or free-free absorption) or steepening toward higher frequencies (CRE aging) is only very rarely fit by the SPECFIND algorithm.
Adding new catalogs In order to add relevant catalogs, we searched in the VizieR database for catalogs containing radio data with source posi-Article number, page 3 of 30 tions available in the table. This search can be done directly in VizieR via the Unified Content Descriptor (UCD) search capability. The UCDs are an International Virtual Observatory Alliance (IVOA 2  ) standardized  (Derriere et al. 2004; Martinez et al. 2018)  description of astronomical quantities. The basic UCD search can be accessed on the VizieR web page. The other way to obtain catalogs with certain UCD criteria is via the TABFIND services, a Structured Query Language (SQL) search by the Tool for OPerations on Catalogs And Tables (TOPCAT 3  , Taylor (2011)). In order to deal with the resulting tables and to apply additional selection criteria we used TOPCAT.
We obtained all radio data catalogs with positional and flux information included (UCD:"(pos. *)&(phot. flux*;em. radio*"). The VizieR database holds more than 1200 catalogs containing radio data 4. Some of these catalogs consist of several tables, which leads to approximately 2400 tables containing radio data or being connected to radio data. To find useful catalogs for the SPECFIND tool, we first chose the number of records that is the number of rows within each table to be larger or equal to 150. We ended up with approximately 1000 tables to investigate manually. We discarded all tables not related to radio continuum observations of point sources such as, all tables containing neutral hydrogen (HI) surveys or description tables containing no radio continuum data at all. Applying further criteria (radio continuum data between a few MHz and up to 31 GHz and no time series) led to roughly 110 tables.
From these two figures, it becomes clear that the catalogs TGSSADR as well as GLEAM and LOTSSDR1 have a major impact on the result-Article number, page 5 of 30 A&A proofs: manuscript no. output ing increased number of spectra. While all three contribute in the low-frequency radio continuum regime, the GLEAM catalog additionally covers the southern hemisphere, where fewer catalogs are available. For GLEAM, we use the mean spectral point to not give an overweight to this survey. As a sanity check, we compared the two updated catalogs, VLSSr and FIRST14, with their former versions within SPECFIND V3. 0. By replacing the VLSS  (Cohen et al. 2007)  with the VLSSr catalog, the number of VLSS sources was increased from ∼68 000 to ∼92 000. By replacing the FIRST  (White et al. 1997)  with the FIRST14 catalog, the number of FIRST sources was increased from ∼810 000 to ∼946 000.
We encourage follow-up observations of these candidates to confirm their nature. SPECFIND is based on the radio catalogs in VizieR, and as described, the results of SPECFIND are integrated back into VizieR as a value-added compilation with accompanying services for visualization and access to the data. Moreover, the cross-identified radio continuum sources will be ingested into the SIMBAD astronomical database. This is part of the global effort to make astronomy data interoperable, with the objective of enabling new science with combined data sets. SPECFIND is an example of interoperability based on standardization of spectral properties, and using this to unify data extracted from hundreds of individually published and heterogeneous catalogs. IVOA and CDS standards play an important role for combining data in SPECFIND, and we anticipate that interoperability of data over many spectral regimes and also for time domain data will open many new areas of research based on combined data.
Acknowledgements. This research has made use of the VizieR catalog access tool, CDS, Strasbourg, France (DOI: 10. 26093/cds/vizier). The original description of the VizieR service was published in A&AS 143, 23 This research has made use of "Aladin sky atlas" developed at  CDS, Strasbourg Observatory , France published in 2000A&AS. 143. 33B and  2014ASPC. 485. 277B. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France.
(2020) , and based on the workflow outlined in  Kessler et al. (2019). We simulate SNe using the SuperNova ANAlysis software (SNANA;  Kessler et al. 2009 ) integrated into the pippin framework  (Hinton & Brout 2020). SALT2 parameters  1 (stretch) and  (colour) are drawn randomly from the intrinsic asymmetric Gaussian distributions described in  Scolnic & Kessler (2016)  and "blurred" following the intrinsic scatter model of  Guy et al. (2010) , while redshifts are drawn following the volumetric rate evolution of  Frohmaier et al. (2019). Synthetic CCSNe light curves are generated from the templates of  Vincenzi et al. (2019) , with a rate following the CSFH of  Madau & Dickinson (2014)  and normalised by the local Universe rate of  Frohmaier et al. (2020). Given the training set of synthetic light curves, SNN returns a classification (SN Ia, CCSN, or peculiar) for observed light curves.
SOFTWARE All software used in this publication is publicly available. In particular, we made extensive use of numpy  (Harris et al. 2020 ), Astropy (Astropy Collaboration et al. 2013 , 2018) , matplotlib  (Hunter 2007) , SciPy  (Virtanen et al. 2020) , pandas (McKinney 2010), Stan  (Carpenter et al. 2017) , seaborn  (Waskom et al. 2020), and ChainConsumer (Hinton 2016).
ACKNOWLEDGEMENTS We thank  J. Michael Burgess  for discussions regarding model fitting in Stan. We thank the reviewer,  Mickael Rigault , for constructive comments on the manuscript. P. W. acknowledges support from the  Science and Technology Facilities Council (STFC)  grant  ST/R000506/1. M. Su. and  M. Sm. acknowledge support from  EU/FP7-ERC  grant  615929. MS is funded by the  European Reearch Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation program  (grant agreement no  759194 -USNAC ). L. G. acknowledges financial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  under the  2019 Ramón y Cajal program   RYC2019-027683  and from the  Spanish   MICIU  project  PID2020-115253GA-I00. Funding for the DES Projects has been provided by the  U.
DATA AVAILABILITY The DES-SN photometric SN Ia catalogue and associated host galaxy data will be made available as part of the DES5YR SN cosmology analysis at https://des. ncsa. illinois. edu/releases/sn. Field galaxy data including photometric redshifts will appear alongside other data products released as part of the DES Y3 weak lensing cosmology analysis at https://des. ncsa. illinois. edu/releases.
Neurofilaments are increasingly recognized as a prognostic biomarker for ALS  [22, 23]. For blood levels of neurofilament light chains (NfLs), a moderate correlation with disease progression rate has repeatedly been reported  [24] [25] [26] [27] [28]. The good accessibility, objectivity, and prognostic value have made NfL a promising candidate biomarker to improve prediction models. In this study, we set out to study if NfL levels could be used to improve prognostic models, and evaluate the transferability of the prediction models to new datasets to test their practical applications and to quantify the potential impact on trial power.
In summary, these results show that the Nfl-based predictive models meet the basic requirements for application in clinical studies. Importantly, we demonstrated that the NfL-based prediction model is transferable to new datasets and that the predictions do not systematically depend on the progression rate or disease duration. Using mixed-effects models to simulate randomization and treatment effects of an actual trial, we observed that the prediction model could significantly increase the statistical power by up to 61%. As far as we know, only two previous studies had quantified the impact of prediction models for ALS disease progression on the statistical power. Küffner et al. [18]  reported a 20% increase of trial power using the abovementioned combination of complex models without neurochemical biomarkers.
Acknowledgements The authors wish to acknowledge  Valerie Cuvier  for her important assistance in collecting patient samples from the Trophos clinical trial and the  ARSLA charity  ( Association pour la Recherche sur la Sclérose Latérale Amyotrophique et Autres maladies du motoneurones ) for its support. ClinicalTrials. gov:NCT: 00868166. The authors wish to acknowledge the  LIPCAL-ALS Study Group  for the important assistance in collecting patient samples from the  LIPCAL-ALS Trial  and the  DFG (Deutsche Forschungsgemeinschaft)  for its support. Clinical-Trials. gov: NCT02306590. The authors wish to acknowledge  Kornelia Günther  and  Prof. Dr. Dorothée Lulé  for their important assistance in collecting and processing data and patient samples at the  Department of Neurology in Ulm.
The coronavirus pandemic highlights the urgent need for increased support related to mental health concerns. This study aimed to synthesize the findings of empirical studies reporting the post-traumatic stress symptoms in the general population during the coronavirus pandemic. Whittemore and Knafl's (Journal of Advanced Nursing, 52, 546, 2005)  integrative review methodology was used to analyse and synthesize the peer-reviewed studies. Five electronic databases, PubMed, CINAHL, PsychINFO, Cochrane and Google Scholar were searched using terms related to the coronavirus pandemic and post-traumatic stress symptoms. The quality of the studies was screened and evaluated using the Mixed Methods Appraisal Tool. The sample size of the 16 studies included in this review ranged from 41 to 3480 participants, with a total of 18 039 participants.
Search methods A literature review was conducted with the assistance of medical research librarians (specializing in literature search) and two researchers. The initial screening was conducted using EndNote library (version X9). An electronic keyword search was conducted using PubMed, CINAHL, PsychINFO and Cochrane databases from journal inception to October 2020. A manual search on Google Scholar was conducted based on a retrieved reference list. The search included a combination of MeSH terms, CINAHL Headings and Index Terms. We used the following search terms: (COVID OR COVID-19 OR Coronavirus* OR Coronavirus Infections) AND (posttraumatic* OR post-traumatic stress* OR posttraumatic stress disorder OR posttraumatic stress disorder symptoms OR Stress Disorders, Post-Traumatic OR PTSD OR PTSS OR trauma).
All electronic databases used syntax search as detailed in Table  1. We modified the search terms for each database as needed and selected eligible studies based on the inclusion and exclusion criteria (Table  2 ). The final references from the selected articles were manually examined, and the manual search of Google Scholar was conducted. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework  (Moher et al. 2015)  as shown in Figure  1.
Data abstraction and synthesis This study extracted relevant data from the included articles, for the analysis using  Whittemore and Knafl's (2005)  methodology. Researchers extracted and coded data from each article. Key concepts were collected by comparing and contrasting codes. The extracted data were discussed by the researchers until consensus was reached. The results of this study integrated the key categories and subcategories of the data. The data were included only if there was a consensus. The total number of articles finally included for the review was 16 (Appendix S1).
Funding Statement: This research was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Education  ( 2020R1A6A3A13066486 ). This research was also supported by grants from the  Brain Korea 21   FOUR  Project funded by the  National Research Foundation (NRF) ,  Yonsei University College of Nursing ,  Korea. Conflict of interest statement: All authors declare they have no conflicts of interest.
The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cases is catastrophic forgetting, i. e. , the tendency of neural networks to underfit past data when new ones are ingested. A first group of approaches tackles forgetting by increasing deep model capacity to accommodate new knowledge. A second type of approaches fix the deep model size and introduce a mechanism whose objective is to ensure a good compromise between stability and plasticity of the model. While the first type of algorithms were compared thoroughly, this is not the case for methods which exploit a fixed size model. Here, we focus on the latter, place them in a common conceptual and experimental framework and propose the following contributions: (1) define six desirable properties of incremental learning algorithms and analyze them according to these properties, (2) introduce a unified formalization of the class-incremental learning problem, (3) propose a common evaluation framework which is more thorough than existing ones in terms of number of datasets, size of datasets, size of bounded memory and number of incremental states, (4) investigate the usefulness of herding for past exemplars selection, (5) provide experimental evidence that it is possible to obtain competitive performance without the use of knowledge distillation to tackle catastrophic forgetting and (6) facilitate reproducibility by integrating all tested methods in a common open-source repository.
Moreover, we introduce promising combinations of components from different algorithms and assess their merits experimentally. Third, we propose a thorough evaluation framework. Four public datasets designed for different visual tasks are used to test performance variability. Three splits in terms of the number of incremental states and three sizes for past memory are tested to assess performance robustness for these IL parameters, which were previously identified as being the most important  [14, 56]. We also propose an evaluation of the case when no past memory is allowed because this setting has a strong influence on algorithm performance. Fourth, we examine the role of herding-based exemplar selection for past classes. Introduced in  [74]  and first used in an IL context by  [56] , its usefulness was questioned in  [14, 29, 37]  where it was reported to provide only marginal improvement compared to random selection.
We run extensive experiments with the two selection algorithms and conclude that herding is useful for all methods tested. Fifth, we show that it is possible to obtain interesting performance without the widely used knowledge distillation component  [14, 28, 29, 37, 56, 75]. Instead, we use vanilla fine-tuning as a backbone for class IL with memory and model the problem as a case of imbalanced learning. The well-known thresholding method  [12]  is used to reduce the classification bias between past and new classes. Last but not least, we integrate the tested methods into a common open-source repository. We notably modify the implementations to make inputs and outputs uniform. These changes will facilitate future experiments in the existing setting and will allow an easy extension to other datasets.
This method is basically designed to work with unlabeled datasets, but was later adapted for usage with unlabeled datasets  [4]. This use case is very interesting but, for now, limited to specific tasks such as face recognition. Self-Organizing Maps (SOMs) are online unsupervised learning algorithms that rely on approximate stochastic gradient technique, and can be adapted to Incremental Learning. Neural Gas (NG) networks  [43]  and its growing NG variant  [18]  are related to SOMs which are often exploited for incremental learning. PROjection-PREdiction ( )  [21]  is an incremental learner based on NG and SOMs, which implements an extra supervised read-out layer implemented as a linear regression, as well as a concept drift detection mechanism in order to make the SOM usuable in IL context.
Accuracy Performance is correlated to the amount of model growth allowed. If growth is limited, MG-based methods have lower performance compared to that of FTbased ones  [35]. If significant growth is allowed significantly  [59] , performance comes close to that of classical learning, but this is somewhat contradictory to the requirement to keep models complexity close to constant. Accuracy is lower compared to FTbased methods because the model is not updated incrementally. High performance can be obtained with fixedrepresentations if the initial model is learned with a large dataset  [6] , but the existence of such a dataset is a strong assumption in an incremental setting. Recent approaches report strong performance gain compared to previous work such as  [14, 29, 56]  either through more sophisticated definitions of knowledge distillation  [28]  or through the casting of IL as an imbalanced learning problem  [7, 8]  or a combination of both  [75].