dataesr/scientific-paragraphs-categorization huggingface dataset:
A real dataset The dataset available as CanadianWeather in the R package fda contains daily temperature and precipitation at 35 different locations in Canada, averaged over annual reports starting in 1960 and ending in 1994 (see e. g. [20] ). We intend to look at the direct links between the minimal and maximal rainfall (on the log 10 scale) and the temperature pattern in the 35 weather stations, so as to identify the times of the year that have a strong effect on rainfall (positive as well as negative). In this context, n = 35, q = 2 and p = 365. Figure  6  shows temperature and log-precipitation measured over a year in Montreal, chosen as an example, together with the empirical distribution of the minimal and maximal log-precipitation for the 35 weather stations. We can note that, since the data are averaged over numerous years, outliers are unlikely even for the extremes (min and max).
Some authors (see e. g. [24] ) have already highlighted the pertinence of using the matrix L defined in (3. 2) in this dataset, because the predictors are ordered temporally so that the selection of isolated days instead of relevant sequences of days seems an unreliable procedure for statistical interpretation. To assess the models, we repeat N = 100 times the following experiment: n t = 25 observations are randomly selected for calibration (via 2-fold cross-validation) and estimation, the remaining n v = 10 observations are used to compute the MSPE (3. 1) related to the prediction of the minimum (min p ) and maximum (max p ) precipitation. We can see in Figure  7  that all structured PGGM perform almost identically, with the phenomenon described in the previous section still visible but to a lesser extent.
We can even notice that structuring is hardly beneficial for this dataset, from a purely numerical point of view. This conclusion can also be found in  [24] , where the author compares the structured Elastic-Net with unstructured alternatives to predict the 0. 25-, 0. 50-and 0. 75-quantiles of the logprecipitation, through independent regressions. But we will see that, in terms of variable selection and statistical interpretation, L and β still have a substancial role to play. The point is that we have observed that the best prediction error does not usually coincide with a sparse solution (see Rem. 3. 3) when the coefficients or the covariates are not very contrasting. In particular, this was the case of our simulation study with ± 1 2 coefficients and N (0, 1) covariates. So, just as they look at the Lasso's regularization paths, practitioners may choose the desired degree of sparsity, depending on p/n, by adjusting the hyperparameters.
Based on these studies and observations, we might conclude that β is insignificant when we are interested in the best prediction error on a validation set (even counterproductive with respect to computational times, e. g. compared to Spr), whereas it seems to have a substancial role  to play when focusing on selection, by accelerating the discrimination of variables. In the first case, η has to be carefully adjusted while in the second case, β will quickly help to reach the desired sparsity. Remark 3. 4 (Structure matrix). For the simulations and the real dataset, we have used the popular first finite difference operator given in  (3. 2). Other examples can be found in the literature, like the promotion of a genetic distance for genomic selection in Brassica napus  [7]  or the bidimensional discretization of the Laplacian to work on handwritten digit recognition  [24].
Acknowledgements and Fundings. The authors warmly thank the two anonymous reviewers for the careful reading and for making numerous useful corrections to improve the paper. We thank  ALM (Angers Loire Métropole ) and the  ICO (Institut de Cancérologie de l'Ouest)  for the financial support. This work is partially financed through the  ALM grant  and the " Programme opérationnel régional FEDER-FSE Pays de la Loire 2014-2020 " noPL0015129 (EPICURE). The authors also thank  Mario Campone  (project leader and director of the ICO),  Mathilde Colombié  (scientific coordinator of EPICURE clinical trial) and  Fadwa Ben Azzouz , biomathematician in Bioinfomics, for the initiation, the coordination and the smooth running of the project.
II. O3: THE THIRD ADVANCED-DETECTOR OBSERVING RUN A. Data set The O3 data set extends from April 1, 2019 to March 27, 2020. A commissioning break between October 1, 2019 and November 1, 2019 separates the first 6-month epoch (O3a) of the observing run from the second epoch (O3b). Figure  1  shows typical spectral sensitivities of the detectors. The Hanford-Livingston (HL) network is analyzed during times where these two detectors operated in coincidence. In addition, results for the Hanford-Virgo network (HV) and the Livingston-Virgo network (LV) are presented for times when data from either of the LIGO detectors is not available. See Section III for an explanation of why the two detector network is preferred over the three detector Hanford-Livingston-Virgo (HLV) network for this search. During the six months of O3a, 130.
2 days of data were collected at Hanford, 138. 5 days of data were collected at Livingston, and 139. 5 days of data were collected at Virgo. The amount of data actually analyzed is reduced by requiring coincidence between two detectors, removing poor periods of data quality as described in Section II B, and requiring at least 200 seconds of continuous observation-quality data. This results in the following total amounts of analyzed data: 104. 9 days for the HL network, 14. 8 days for the HV, and 25. 6 days for the LV network. During O3b, data were collected for 115. 7 days at Hanford, for 115. 5 days at Livingston, and for 113. 2 days at Virgo. The actual analyzed data amounts are 93. 4 days for HL, 17. 8 days for HV, and 14. 8 days for the LV network. The calibration uncertainties for the LIGO detectors in the 20-2000 Hz frequency range are <7% in amplitude, <4 • in phase, <1 µs in timing for O3a  [16] , and <12% in amplitude, <10 • in phase, <1 µs in timing for O3b  [17].
The effectiveness of each data quality veto is determined based on the ratio of the percentage of glitches removed to amount of observation time vetoed. The most significant data quality issues successfully discarded by these vetoes are high signal-tonoise ratio (SNR) glitches associated with light intensity dips in both LIGO interferometers, radio frequency beatnotes (also known as Whistles), and a single half-hour period of extremely rung-up violin mode resonances of the LIGO Hanford suspension system. An additional stage of automated statistical vetoes using the hveto  [29]  algorithm is subsequently applied using the same procedure as in O2  [13]. Hveto uses a hierarchical method to produce a ranked list of statistically significant vetoes generated by applying a specific list of SNR thresholds and time windows to a subset of LIGO's auxiliary channels.
In O3 the LIGO interferometers have better sensitivity than Virgo (see Figure  1 ), and for many source directions the difference in detector orientation enhances this disparity. In addition, there is a negligible loss in detection efficiency when narrowing the analysis of HL-only data to search for the GW polarization projection that best matches the network from each sky direction. This allows implementing stricter requirements on the signal coherence between the Hanford and Livingston detectors and results in a more effective rejection of noise transients. This advantage is not shared by analyses of networks involving Virgo due to its misalignment with the LIGO detectors. To make full use of Virgo data, the analysis has to either open the search to both GW polarization components over the sky, or relax the requirements on the signal coherence between participating detectors.
A. Search algorithms In order to make the results of the search more robust, two independently developed search algorithms are deployed: coherent WaveBurst (cWB) and BayesWave (BW). The cWB algorithm is used to analyze the entire dataset. BW is computationally more intensive, thus it is only used to follow up a subset of the dataset identified by cWB in order to provide a partly independent measurement of the candidates' significance. Both of these algorithms and their results are described below.
The low-frequency analysis covers the frequency range between 16-1024 Hz. Triggers with mean reconstructed frequency below 24 Hz and 32 Hz are rejected for O3a and O3b, respectively, to avoid contamination from loud and frequent low-frequency glitches. The HV and LV networks are analyzed only when one of the LIGO interferometers is unavailable, i. e. there is no overlap in time of data set for any of the networks considered and the livetimes for each network are mutually exclusive. The requirement on the signal coherence across detectors is implemented as a threshold on the network correlation coefficient (referred to as c c in  [34] ), which is the fraction of coherent energy in the network of detectors. After inspection of the overall performance over the set of signal models listed in Table  I , triggers are required to pass c c thresholds of 0.
LF2 contains the remaining triggers that are characterized by Q ≤ 3, also resembling Blip glitches. LF3 contains the higher Q low-frequency triggers and shows the cleanest background distribution. Unlike O1 and O2, non-stationary spectral lines do not contribute significantly to the background in O3. The low-frequency cWB analysis is performed separately for O3a and O3b. The background distribution of triggers is calculated by time-shifting the data of one detector with respect to the other detector by an amount that breaks any correlation between detectors for a real signal. The HL network is time-shifted to obtain total background livetime of around 2000 years. For the HV and LV networks, around 1000 years of background are generated using all coincident data. The use of full coincident time for the HV and LV networks is necessary because the exclusive livetime is not sufficient to produce such large background statistics.
BayesWave BW  [38] [39] [40]  is a Bayesian algorithm modeling both GW signals and non-Gaussian noise transients as sums of sine-Gaussian wavelets. The number of wavelets used is marginalized over using a trans-dimensional Reversible Jump Markov Chain Monte Carlo algorithm. The detection statistic used is the natural logarithm of the signalto-glitch Bayes factor (ln B S,G ), i. e. the Bayes factor between the signal model consisting of Gaussian noise and an astrophysical signal coherent across detectors; and the glitch model, which describes the data as Gaussian noise and glitches modeled independently in each detector. Thus a positive ln B S,G indicates that the presence of a GW signal is favored, while a negative ln B S,G shows support for the event being a glitch. Due to the trans-dimensional sampling it requires, analyzing the entire O3 dataset with BW is computationally prohibitive.
The two most-explored mechanisms in the literature for these neutron star excitations are starquakes and superfluid crust interactions  [67]. In the superfluid mechanism there is an interaction of internal superfluid with the solid crust of a neutron star  [68, 69]. Because of superfluid vortex avalanches during the spin-up phase of pulsar glitches, the excitation of one or more families of global oscillations in the neutron star leads to a GW signal on a time scale around 40 s before the observed jump in frequency. A search for short transient GW emission associated with oscillations of the fundamental quadrupole mode excited by a pulsar timing glitch was performed with the data from LIGO's fifth science run (S5). No GW detection candidate was found associated with a timing glitch in the Vela Pulsar in August 2006 and a Bayesian 90% credible upper limit of 6.
ACKNOWLEDGMENTS This article has been assigned the LIGO document number  P2100045. This material is based upon work supported by  NSF 's  LIGO Laboratory  which is a major facility fully funded by the  National Science Foundation. The authors also gratefully acknowledge the support of  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Netherlands Organization for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
The authors gratefully acknowledge the support of the  NSF ,  STFC ,  INFN  and  CNRS  for provision of computational resources. This work was supported by  MEXT ,  JSPS Leading-edge Research Infrastructure Program ,  JSPS   Grant-in-Aid for Specially Promoted Research 26000005 ( Kajita 2014-2018 ),  JSPS   Grant-in-Aid for Scientific Research on Innovative Areas 2905 :  JP17H06358 ,  JP17H06361  and  JP17H06364 ,  JSPS Coreto-Core Program A. Advanced Research Networks, JSPS   Grant-in-Aid for Scientific Research  (S)  17H06133  and  20H05639  ,  JSPS   Grant-in-Aid for Transformative Research Areas (A)   20A203 :  JP20H05854 , the joint research program of the  Institute for Cosmic Ray Research, University of Tokyo, National Research Foundation (NRF)  and  Computing Infrastructure Project  of  KISTI-GSDC in Korea, Academia Sinica (AS), AS Grid Center (ASGC)  and the  Ministry of Science and Technology (MoST) in Taiwan  under grants including  AS-CDA-105-M06 ,  Advanced Technology Center (ATC) of NAOJ, Mechanical Engineering Center of KEK.
The overarching ORA-CLES science goals, which encompass the specific science themes and questions in the abstract and Table  1  below, are as follows. 1. Determine the impact of African BB aerosol on cloud properties and the radiation balance over the South Atlantic, using state-of-the-art in situ and remote-sensing instruments to generate datasets that can also be used to verify and refine current and future observation meth-ods, including instrument concepts with potential for deployment to space. 2. Acquire a process-level understanding of aerosolcloud-radiation interactions and resulting cloud adjustments that can be applied in global models. In this paper, we provide an overview of all three OR-ACLES deployments, highlighting aerosol absorptive and cloud-nucleating properties, their vertical distribution relative to clouds, the locations and degree of aerosol mixing into clouds, and cloud changes in response to such mixing.
Namibia -2016 During the field campaign held in Namibia in 2016, the gathering of science data not only benefited the scientists directly involved in the project; through an outreach program the science was extended to the Namibian population (and to some extent the broader southern African region). In addition to these broader public engagement outreach activities, a targeted science development program was initiated with support from the US Embassy in Namibia and NUST. This 3-week full immersion outreach program was developed to provide promising local and regional young scientists with an opportunity to experience different components of a large complex airborne research field campaign. In total, seven post-graduate students (master's and PhD level), from Namibia (five students) and South Africa (two students), participated in the student guest program (Fig.
Meteorological context The key feature of the circulation that transports fire emissions from the African continent over the southeast Atlantic is the easterly flow above about 2 km. Figure  6  shows the 4 km flow and relative humidity (RH) from the ERA-Interim reanalysis, along with the southerly limit of significant rainfall from the monthly 0. 25 • satellite-based 3B43 dataset  (Huffman et al. , 2007)  for the  19-year September mean (2000 19-year September mean ( -2018)). The easterly flow maximizes around 8 • S, reaching minima near 4 • N and 18 • S. This flow is maintained by the thermally direct circulation over the continent, which is driven by heating of the elevated African plateau south of 10 • S  (Adebiyi and Zuidema, 2016). The moister regions to the north are cooler, consistent with the thermal wind relation and the easterly shear below the jet.
Heavy solid and dashed red contours indicate elevated wind speeds (7 m s -1 -solid; 8 and 9 m s -1 -dashed). The white rectangle encompasses 8-16 • E and 14-3 • S. The cyan lines refer to 9. 75 • S and 9. 75 • E, respectively (see Fig. 7 ). RH and winds are from the ERA-Interim analyses, and the rainfall is from the monthly 0. 25 • satellite-based 3B43 dataset. ber of features are apparent. Rainfall in September 2016 was greater in the SAEJ region than typical, and RH values were higher. The strength of the SAEJ in 2016 was about average. The August SAEJ strength was about average during the 2017 deployment year, though RH values were lower than climatology. The October 2018 SAEJ strength was also about average, rainfall was about typical, and RH values were lower than the climatology. Similar to the SAEJ feature, the southeasterly low-level jet and boundary layer flow intensity during deployment years are within the range of climatological mean, even though some months deviated substantially from the average.
In this section, we describe the interannual variability of MODIS-detected fire counts, ACAOD, and Sc cloud fractions in August, September, and October for the three ORACLES flights years, i. e. , 2016-2018. We compare them to the climatologies of the same quantities for the period of 2003 to 2018 for context (Fig. 9 ). MODIS data are a composite of Terra and Aqua, where available. Within each panel, Fig. 9  shows monthly averages of fire counts as blue-to-red shading over land, ACAOD as yellowto-red shading over ocean  (Meyer et al. , 2015) , and low cloud fractions as black contours over ocean, for August, September, and October. The top row of panels shows the 2003 to 2018 climatological means, while the second, third, and fourth row of plots provide the August, September, and October means for the 3 deployment years, i.
, 2001). N c is computed from COD and CER products that have accounted for the above-cloud AOD  (Meyer et al. , 2015). These products are used instead of those from the MODIS standard product (i. e. , MXD06) where CODs are typically underestimated in the retrievals due to the top of atmosphere (TOA) shortwave reflectance absorption by overlying smoke aerosols  (Haywood et al. , 2004; Coddington et al. , 2010; Meyer et al. , 2013). Within each panel, Fig. 10  shows monthly averages of N c calculated from MODIS Aqua and Terra cloud optical depth (COD) and effective radius (CER) retrievals as yellow-to-red shaded contours, along with 925 mb wind data from the National Centers for Environmental Prediction (NCEP) reanalysis. As in Fig. 9   N c. Detailed explanations for these interannual variations are currently the subject of at least one ORACLES-related investigation.
Above-plume leg -APL A constant-altitude leg above the BB aerosol layer, usually intended for full-column lidar assessments with HSRL-2. flights provided an excellent dataset for the evaluation of remote-sensing concepts (see Sect. 5. 3. 7), often on the basis of successful coordination of the P-3 with the ER-2 in 2016 or with satellite assets in 2017 and 2018. Table  3  provides a list of "golden days" for each observational focus, to be interpreted as the likely best start of exploration of the ORACLES dataset for an uninitiated science user. The ordering of flights within each category is based on a preliminary assessment of the utility and quality of the data to address the observational objective. Flights with coordinated P-3 and ER-2 sampling in 2016 are postulated to provide superior data and hence appear before any single-aircraft flights.
2. 1, raises the question of how accurate the CALIOP-indicated frequent separation between the BB plume and Sc cloud tops (shown in Fig. 2 ) really is. To help address this question, we investigate here how frequently the HSRL-2 aerosol extinction profiles in the three ORACLES deployments indicate separation or lack thereof between the BB plume base and the Sc cloud tops. The left panel of Fig. 13  shows mean aerosol extinction profiles at 532 nm for each ORACLES deployment year, along with mean cloud top heights as horizontal solid lines. Because ORACLES-2016 had a somewhat different geographic focus from ORACLES-2017 and ORACLES-2018, Fig. 13  only shows data for the geographic box bounded by 5-15 • S and 2. 5-7. 5 • E, where there are data present in all 3 flight years. The mean extinction profiles show that the BB plume in ORACLES-2017 (August) has a local maximum of aerosol extinction at altitudes between 2-3 km, while the ORACLES-2016 (September) and ORACLES-2018 (October) mean profiles have maximum aerosol loading between 3.
Overall, we found significant spatial and temporal variability in the degree of contact between BB aerosol and Sc clouds in the SEA region and that the frequency of occurrence of aerosol-free gaps is likely much lower than previously assumed  (Costantino and Bréon, 2013). These findings need to be considered when interpreting previous results on aerosol-cloud interactions in the region. ORACLES data provide a useful test bed for algorithm development in support of future satellite missions, for example NASA's ACCP (Aerosols, Clouds, Convection and Precipitation) mission. For instance, ORACLES observations are currently being used to develop joint polarimeter-lidar retrievals of aerosol and cloud properties. Whether such observations will successfully detect features such as the clear-air layers in the SEA will depend on specific instrument characteristics, but the ORACLES measurements should provide useful benchmarks for the testing of candidate observing concepts.
Chemical aging Ongoing analysis of the ORACLES dataset is relating the observed vertical structure in aerosol composition and optical properties to aerosol aging. Rather than using an aircraft to track the evolution of a smoke plume as it advects, or using an aerosol chemical component as a "chemical clock" to determine aerosol age, an alternative approach links a modelderived mean aerosol age to in situ aerosol characteristics. Particles, including at fire emission sources, are marked at the time of the model initialization and tracked in time thereafter  (Saide et al. , 2016) , with the particle ages subsequently extracted along the flight legs and these modeled ages combined with the in situ datasets. An example is shown in Fig. 15  for 24 September 2016 (a profile from this flight is also shown in Fig.
Boundary layer clouds -CCN, cloud, and rain statistics ORACLES scientific objective 3 (Table  1 ) seeks to understand the extent to which smoke aerosol from southern Africa mixes into the MBL and impacts marine low cloud microphysical processes. Smoke may enhance the population of cloud condensation nuclei (CCN), which can lead to increases in cloud droplet concentration and smaller droplet sizes. It can also potentially induce changes in cloud condensate through suppressing precipitation rates, enhancing cloud top entrainment, and otherwise influencing cloud macrophysical properties. These indirect effects lead to significant increases in albedo in global models, but little in situ data are available over the SE Atlantic to test these models. According to HSRL-2 observations (Fig. 13 ), a denser, more frequent smoke layer in the FT occurred in 2017 than in 2016 or 2018.