dataesr/scientific-paragraphs-categorization huggingface dataset:
The next step was to calculate the intra-slice covariance matrix Γ: n N x x x x , 5 h H h h h T 1 spectra å G = - - = ( ¯¯) • ( ¯¯) () where H is the total number of slices and x ¯is the average spectrum defined in Equation (2). The final step was to calculate the matrix CΓ -1 and evaluate its eigenvector corresponding to the largest eigenvalue. This eigenvector was used for the inversion procedure and for the derivation of parameters (Equation (9) of  Kassounian et al. 2019). The role of the PCA in this technique was to reduce the dimension of the initial training database by selecting the first few ∼1000 spectra that are the closest, in terms of d (Equation (3)), to the observed one. This new matrix was then used to calculate the covariance matrix used, in turn, by SIR.
Method We ran iSpec through a pipeline adapted from that of  Casamiquela et al. (2020). We used the synthetic spectral synthesis fitting technique, running the radiative transfer code SPECTRUM  (Gray & Corbally 1994)  and the ATLAS9 atmospheric models  (Castelli & Kurucz 2003) , which are implemented in iSpec. We used both the Gaia-ESO survey linelist  (Heiter et al. 2021 ) and the selection of lines used in  Gebran et al. (2008) ,  Gebran & Monier (2008) ,  Gebran et al. (2010), and Monier et al. (2018) , which are adapted for A and F-type stars and were used in the derivation of the stellar parameters in Section 4. We did an additional cleaning of the lines based on the characteristics of our particular spectra and the performance of our method, rejecting lines which systematically gave nonsatisfactory fits.
From the observational point of view one has to consider that the fundamental parameters of the Am stars in Pleiades, Coma Ber, and the Hyades were derived using the UVBYBETA calibration of Strömgren photomery  (Napiwotzki et al. 1993) , and that the abundances were found using the procedure of  Takeda (1995). In contrast, the analysis of Stock 2 we performed relied on SIR for the atmospheric parameters and SPECTRUM (through iSpec) for the abundances. These differences might be enough to bias the comparison presented in Figure  9 , and could hide a small time dependence (regardless of measurement uncertainties). Additionally, the age range for which we have data remains limited, and a larger sample of clusters is necessary to truly examine the effects of the diffusion mechanism on the studied elements between 100 Myr and 1 Gyr.
Conclusion The identification of chemically peculiar stars in open clusters presents an opportunity to study the effect of diffusion mechanisms in the atmospheres of intermediate-mass MS stars. Stock 2 is the perfect target to search for chemically peculiar stars, since it is a relatively rich (>1000 members) and nearby (≈400 pc) cluster with an age between that of benchmark clusters such as the Pleiades and Hyades. We performed a spectroscopic study of 71 upper MS members of the cluster. Sixty-four of these stars have spectra from our own observations with the ESPaDOnS and SOPHIE spectrographs, and 13 from archival data taken with HARPS. We derived the fundamental atmospheric parameters T eff , g log , [M/H], and v i sin e for these stars using a combination of PCA and SIR. We then obtained 1D-LTE chemical abundances of 12 chemical species (O, Mg, Si, Ca, Sc, Ti, Cr, Fe, Sr, Y, Zr, and Ba) using spectral synthesis.
Characterization of optimal NiFe 2 O 4 thin film The NEMROD Software program  [34]  was used to determine the optimal condition of the NiFe 2 O 4 thin film. The structure of the NiFe 2 O 4 thin film has been confirmed by X-ray diffraction using a D8 diffractometer (λ(Cu-Kα) = 1. 5406 Å) with a scanning range of 2θ = 20 -90 • at room temperature. Fourier transform infrared (FTIR) and Raman were applied to identify chemical bonds of NiFe 2 O 4 thin film in the range of 200-4000 cm -1. The morphology of the film has been studied by Merlin scanning electron microscopy (SEM) equipped with Silicon Drift Detector (SDD)-X-Max 50 from Oxford Instrument. Vibrating Sample Magnetometer (VSM) has measured the magnetic behavior of the prepared thin film at room temperature with maximum applied field of ± 15 kA/m.
Data selection To ensure a high level of accuracy, we selected galaxies with highly secure redshift (3. 0 ≤ z flag ≤ 4. 5, i. e. >90% confidence level;  Scodeggio et al. 2018)  reducing the sample to 33 785 galaxies; of these, 14 989 have available measurements of all emission lines we need to estimate the metallicity. This effectively limits the redshift sample to z max ∼ 0. 9. The crossmatching process further reduces the sample to 10 126 galaxies. The spectroscopic redshift, the emission line fluxes, and their reliability flags have been estimated with the software Easy Redshift  (Garilli et al. 2010 , 2014, EZ). This software is based on a decision tree, namely, the sequence of the operations to obtain the measurement, which can be customized according to the data studied. Additionally, all spectra and final spectroscopic redshift values were human-verified.
09 dex and a maximum ∆ log (O/H) = 0. 11 dex -∆ log (O/H) is defined as the difference in metallicity between the SDSS and VIPERS samples in each bin. This means that metal-poor galaxies at a given M es have intrinsically higher S/N of emission lines, an effect that is in agreement with the one described by  Curti et al. (2020). This type of galaxies also has a higher SFR, with a younger stellar population that can emphasize the emission lines increasing the photo-ionization state of the gas-phase metals. It is important to note that the sample with S/N in the same range of the VIPERS data has higher metallicity in the range 9. 0 ≤ log M [M ] ≤ 10. 25 than the full sample. In the metallicity versus the SFR plane, the cut is mainly at low SFR for sources with higher metallicities and flatten the curve.
Another effect is to remove the metal-rich galaxies. This cut leads to flattening the MZR and the relation in the metallicity-SFR plane; while the relation in the metallicity-sSFR is not sensitive to these selections (it is only sensitive to the reduction of data at high M and low SFR). The main sequence (bottom right panel of Fig. 7 ) shows instead a shift towards the top of the diagram. The same shift is found in the case of the S/N selection (Fig. 6 ). Although flattening of the relations is negligible compared to the error on metallicity (∼0. 1 dex), the selection of the quality flag remains a possible source of a bias. Figure  8  shows the projections of the FMR for the SDSS sample. The selection flattens the relations as already seen for the VIPERS sample. Thanks to the larger statistics of the SDSS sample and a wider interval in the SFR, it is possible to see that the selection on oxygen line flag leads to an inversion of the relation with the metallicity increasing at high SFR.
In this way, we take into account the evolution of the function itself (B * = -20. 95 for VIPERS and B * = -19. 11 for SDSS, these values are used for the whole redshift range,  Ilbert et al. 2005 Ilbert et al. , 2006)). Then, we can observe the same luminosity interval of the distribution after taking into account its shift due to the redshift. Figure  10  shows the distributions of both samples in B-B * with the cutoffs at -1. 5 ≤ B-B * ≤ 2 mag. B * is defined as the magnitude at which the luminosity function changes dependence (from power law to exponential) and it is redshift-dependent. In this way, we can analyze the same interval of the luminosity function for both samples. Figure  11  shows the comparison of projections of the FMR between the full sample of SDSS data and after the cut.
Biases analysis We analyzed the effects of four different biases introduced by observations (range in B-B * and fraction of blue galaxies) and data selection (S/N ratio and quality flags) on the SDSS sample to understand how they could affect the comparison between different samples. We find that the biggest bias is the data selection on quality flags of the spectra. The main result of this analysis is that the metallicity versus the sSFR relation is the least sensitive to the biases analyzed here (completely independent on the selection on the range in B-B * and fraction of blue galaxies; dependent on the selection criteria on oxygen lines). This adds value to the non-parametric framework described by which requires galaxies with high S/N line detection, can lead to a non-physical MZR. It can result in a non-monotonic relation with a fall at high M or a complete cancellation of the anti-correlation between metallicity and SFR.
Comparison with the literature As we have shown above, a direct comparison between samples is not straightforward, but, nonetheless, this kind of comparison is often done in the literature. Therefore, it is interesting to perform a comparison taking into account all the limitations on the conclusions. For example, the VIPERS sample shows a flattening at log M [M ] < 10 in the MZR, which is not reproducible by any of the biases studied and a comparison with the literature can give some ideas on the reason for this behavior. Savaglio et al. (2005)  studied the MZR for galaxies at 0. 4 < z < 1. 0 with data from the Gemini Deep Deep Survey (GDDS) and Canada-France Redshift Survey (CFRS) with a total of ∼60 galaxies, finding a shift for this relation. They found a linear MZR described by 12 + log (O/H) = 0.
Cresci et al. (2012)  analyzed the FMR and the MZR for galaxies in the zCOSMOS sample in the range 0. 2 < z < 0. 8 divided in 169 galaxies at z < 0. 47 and 165 at z > 0. 49. They found an agreement with the relations in the local Universe with the SDSS data, but they used a different method to estimate the metallicity using also other lines as compared to  Mannucci et al. (2010). Both studies used the calibration described by  Nagao et al. (2006) : for the local galaxies where more lines are available, the metallicity was estimated from the average between values obtained using [N ii] λ6584/Hα and R 23 ; the metallicity of zCOSMOS galaxies was instead estimated using the ratio [N ii] λ6584/Hα for z < 0. 47 and the ratio R 23 for z > 0. 49. As shown in Figs. 15  and 16 , the VIPERS and zCOSMOS data are in great agreement with each other.
2 is a measure of how fast the relation reach the asymptotic value. Bellstedt et al. (2021)  studied the metallicity histories of ∼4500 galaxies from the Galaxy And Mass Assembly (GAMA) survey with the SED fitting code ProSpect considering an evolving metallicity. With these metallicity histories, they can infer the MZR at different epochs. These authors described the evolution of the metallicity in function of M and look-back time (t lb ) as: log Z gas (M , t lb ) = 3 i=0 f i (t lb ) m i , (18) where Z gas = Z × 10 [12+log(O/H)]-[12+log(O/H)] , (19) Z = 0. 0142 5 , (20) 12 + log (O/H) = 8. 69, (21) m = log (M [M ]) -10, ( 22 ) f i (t lb ) = 5 j=0 a i, j t j lb. (23) In this way, they can reproduce the MZR from z ∼ 3. 5 to z ∼ 0. 5 This value differs from the commonly used value of 0. 02. Figure  15  shows the MZRs of the VIPERS sample and other relations from the literature described above.
At high M , VIPERS data are in agreement with both  Lee et al. (2006) ,  Huang et al. (2019), and Bellstedt et al. (2021)  relations. The VIPERS sample follows the relation described by  Curti et al. (2020)  quite well. From this comparison with the literature, we can conclude that an overestimation of the metallicity or an overselection of metal-rich galaxies at lower M es seems to be present. In this plot are reported also the zCOSMOS data by  Cresci et al. (2012). These data are divided into four bins (in M or log M -0. 32 log SFR) with the same number of galaxies per bin (to take into account the much lower statistics, ∼100 galaxies per redshift bin, compared with SDSS or VIPERS sample) and the errorbars are estimated via the 16th and 84th percentile in each bin. The zCOSMOS data are in good agreement with the VIPERS sample.
Conclusions Since many studies of FMR are based on comparisons of differently selected samples, we have analyzed the effects of different biases that can be introduced during the sample selection on the relations between metallicity, M , and SFR to assess how comparable in reality so constructed samples are. We studied biases introduced by physical constraints (evolution of the luminosity function and differences in the fraction of blue galaxies) or data selection (S/N and quality of the spectra). The study of FMR projections is not the same comparison as for the full FMR. For example, the evolution of the MZR does not affect the FMR  (Mannucci et al. 2010). For this reason, the study of a more direct comparison from non-parametric analysis (being, indeed, a more reliable study of FMR) will be the subject of a subsequent separate paper.
The main conclusions reached in this paper can be summarised as follows. -The VIPERS sample is in good agreement with the SDSS sample with "standard" data selection, with an average metallicity difference of ∼0. 6 s VIPERS. The biases taken into account can reduce the metallicity difference between these samples to ∼0. for instance, to the oxygen lines -especially if applied to [O iii] λ4959. -The plane metallicity versus log M -0. 32 log SFR reduces the metallicity difference between the two samples. In this plane and in the MZR, the VIPERS sample is in good agreement with the zCOSMOS data  (Cresci et al. 2012 ). -VIPERS sample is in agreement with the relation found by  Savaglio et al. (2005)   Savaglio et al. 2005 ; purple dash-dotted line by  Lee et al. 2006 ; and brown dotted line by  Huang et al.
2019 ; pink solid line by  Bellstedt et al. 2021 ). In the same plot, we report the zCOSMOS data (red dotted lines) for both redshift bin from  Cresci et al. (2012). -S/N cutoffs affect the MZR and metallicity versus the SFR selectively cut the high metallicity at higher M and lower SFR flattening the curves. In the plane metallicity versus the sSFR, this cut has negligible effects within uncertainties. -All the projections of the FMR are insensitive to the fraction of blue galaxies selection. -The plane metallicity versus log M -0. 32 log SFR and metallicity versus sSFR are the least sensitive to observational biases among the 2D relations. -When analyzing metallicity versus M or SFR, we have to be careful when carrying out the sample selection as this may introduce biases. As demonstrated, a sample-selection-based comparison can be complicated to do even if often used in the literature (e.
Acknowledgements. This research was supported by the  Polish National Science Centre  grant  UMO-2018/30/M/ST9/00757  and by  Polish Ministry of Science and Higher Education  grant  DIR/WK/2018/12. K. M. is grateful for support from the  Polish National Science Centre  via grant  UMO-2018/30/E/ST9/00082. M. F. acknowledges support from the  First TEAM  grant of the  Foundation for Polish Science No. POIR. 04. 04. 00-00-5D21/18-00  (PI:  A. Karska ). This paper uses data from the  VIMOS Public Extragalactic Redshift Survey (VIPERS ). VIPERS has been performed using the  ESO Very Large Telescope , under the " Large Programme "  182. A-0886. The participating institutions and funding agencies are listed at http://vipers. inaf. it. We thank  B. Garilli  for the line measurements for the VIPERS sample.
Finally, the event-based sensor does not use a fixed frame rate and thus does not return a stack of frames. This implies that the temporal resolution of the acquisition is not limited by userdefined parameters. Since the temporal bandwidth is not infinite (each pixel has a certain dead time in the range of a few µs to a few hundreds of µs  [33] ), the time resolution is determined in the pro-cessing stage by the time base used for the data treatment. This feature is very interesting for SMLM imaging as it allows to process the same dataset with different time bases to investigate biological processes at different time scales. We used such an event-based sensor in an optical setup designed for SMLM experiments presented in Fig. 1d. The detection path is built to offer some modularity by distributing the fluorescence light in either one or both of the sensors, the EMCCD and the event-based sensor (see Methods).
For these reasons, we recorded the dSTORM pumping phase with both the event-based sensor and the EMCCD camera, using the the same AF647-labelled fixed sample as in Fig. 3. We chose a region where the microtubules are closely interwoven to ensure a high PSF density, and the acquisition was started at the same time as the laser excitation. Using a 50:50 beamsplitter cube, we compared EMCCD and eventbased performances over the course of a short 250 seconds acquisition (during which the density of PSFs remains high). Processing that short event-based dataset produced a strikingly well-sampled 2D SMLM image (Fig. 4f ). The image exhibits a very uniform resolution, with little loss of sampling density or resolution either in dense areas or at the edges of the field of view. A more detailed view (Fig.
A low power continuous 405 nm excitation was also added during the second half of the acquisition to increase the density of detections. EMCCD frames were processed using a wavelet algorithm (similar to  [60] ) to detect the PSFs and each PSF was localized on a ±4 pixels area centered around the maximum. Positions were estimated using either Gaussian fitting or center of mass calculation. Drift was corrected using a direct crosscorrelation algorithm using the sample itself as a reference (no fiducial markers were used). Event-based data were processed as follows. Positive events were binned in ∆t=20 ms frames, on which PSFs were detected using a wavelet algorithm. This yielded the rough reference space and time positions x 0 , y 0 and T 0 for each PSF. Molecules were then localized from a subset of all the events (both positive and negative) corresponding to an area of ±4 pixels around (x 0 ,y 0 ) and to times in the interval [T 0 -60 ms, T 0 + 120 ms].
The position of the center was estimated using a center of mass calculation. Other data were extracted-the total number of events N in the subset, the time of the rising edge t + (taken as the mean time of the positive events) and the time of the falling edge t - (taken as the mean time of the negative events). These were used to calculate the ON time of each molecule t ON = t -t +. Positions were drift corrected using the same direct cross-correlation as for the frame-based data. High density biological experiments (Fig. 4b-h ). High density DNA-PAINT acquisitions (Fig. 4be ) were done with an sCMOS camera set to 50 ms exposure time, and the event-based sensor with a sensitivity corresponding to the level called 'high' in the characterization. All the photons were sent sequentially to the sCMOS camera first, then to the event-based sensor, taking advantage of the resistance of DNA-PAINT to photobleaching.
We used an imager 1 concentration of 8 nM and a 638 nm continuous excitation with an irradiance of 0. 5 kW/cm 2 in TIRF configuration. A pumping phase was allowed to stabilize the density of PSFs before starting the acquisition, and data was recorded during 10 minutes for each sensor. High density dSTORM acquisitions (Fig. 4f-h ) were done with an EMCCD camera set to 30 ms exposure time and a gain of 100, and with the event-based sensitivity corresponding to 'high' in the characterization. The detection path used a 50:50 non-polarizing beamsplitter cube for simultaneous EMCCD/event-based acquisition. Both sensors were not synchronized but dual-view acquisitions were started and stopped almost simultaneously. We used a dSTORM buffer composed of 100 mg/ml glucose, 3. 86 mg/ml MEA, 0. 5 mg/ml glucose oxidase and 1.
18 µl/ml catalase in PBS and a 638 nm continuous excitation with an irradiance of 5 kW/cm 2. No pumping phase was allowed, so the acquisitions were started immediately and stopped after 250 seconds. A low power continuous 405 nm excitation was also used during the entire acquisition to increase the detection density. In each case, camera frames were processed using a wavelet algorithm to detect the PSFs and each PSF was localized on a ±3 pixels area centered around the maximum. Positions were estimated using either Gaussian fitting or center of mass calculation. Drift was corrected using a direct crosscorrelation algorithm using the sample itself as a reference (no fiducial markers were used). Note that, while all the results presented were obtained with our usual custom-written Python processing algorithm, we also tried the widely used ThunderSTORM ImageJ plugin  [61] , which yielded very similar results (not displayed here).
Event-based data was processed as follows. The dataset was split in two subsets containing the positive and negative events respectively. Positive events were binned in ∆t=10 ms frames, on which PSFs were detected using a wavelet algorithm. This yielded the rough reference space and time positions x 0 , y 0 and T 0 for each PSF. Molecules were then localized from a subset of all the events (both positive and negative) corresponding to an area of ±3 pixels around (x 0 ,y 0 ) and to times in the interval [T 0 -30 ms, T 0 + 30 ms]. The position of the center was estimated using a center of mass calculation or a Gaussian fitting. The total number of events N in the subset corresponding to the molecule was also extracted. The same processing was run on the negative events subset with the same parameters.
FRC resolution calculation All FRC resolution measurements were done using the Fiji plugin NanoJ-SQUIRREL  [36]. Localization lists were randomly split in two statistically independent sets and super-resolution images were generated with a pixel size of 5 nm. FRC maps were calculated with a number of blocks per axis of 30 (for full images) or 10 (for zooms on small sub-regions). We noticed little variation of the results with the calculation parameters. Cramér-Rao Lower Bound calculation (Fig. 2b ). CRLB values were estimated for the EMCCD camera using equation 4 from  [62]. The values of the parameters were measured experimentally. Localization and data treatment software. The localization and data treatment software is described in Supplementary note 2. It should be noted that no filtering was used in the event-based processing.
Acknowledgements We acknowledge  Christian Hubert  as well as  Tual Monfort  and  Olivier Thouvenin  for their invaluable help with conceiving the project, as well as for hardware loan. We also thank  R. Margoth Córdova-Castro ,  Vittore Scolari ,  Antoine Coulon ,  Sandrine Lévêque-Fort  and  Emmanuel Fort  for fruitful discussions and feedback.
DATA The Dark Energy Survey (DES) conducted its six-year survey finalising in January 2019 using a 570-megapixel camera mounted on the 4-meter Blanco Telescope at the Cerro Tololo Inter-American Observatory (CTIO). The photometric survey used five filters and collected information of more than 300 million galaxies in an area of roughly 5000 deg 2 , allowing for the measurement of shapes in addition to positions of galaxies. The analysis of the first year of data, denoted by DES-Y1, used two independent pipelines  (Zuntz et al. 2018)  to produce shape catalogues for its shear analysis: metacalibration and im3shape. Here we will focus on the metacalibration catalogue that was used in the real-space fiducial analysis, with a final contiguous area of 1321 deg 2 containing 26 million galaxies with a density of 5.
ACKNOWLEDGEMENTS This research was partially supported by the  Laboratório Interinstitucional de e-Astronomia (LIneA) , the Brazilian funding agencies  CNPq  and  CAPES , the  Instituto Nacional de Ciência e Tecnologia (INCT) e-Universe  ( CNPq grant   465376/2014-2 ) and the  Sao Paulo State Research Agency (FAPESP)  through grants  2019/04881-8 (HC)  and  2017/05549-1  (AT). The authors acknowledge the use of computational resources from LIneA, the  Center for Scientific Computing (NCC/GridUNESP)  of the  Sao Paulo State University (UNESP) , and from the  National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) , where the SDumont supercomputer (sdumont. lncc. br)was used. This research used resources of the National Energy Re-.