dataesr/scientific-paragraphs-categorization huggingface dataset:
2014; Lee et al. 2014 , disk radius of 90-120 au), L1448-IRS3  (Tobin et al. 2016a , disk radius of ∼400 au), Lupus 3 MMS  (Yen et al. 2017 , disk radius of 100 au), and IRAS 16253-2429  (Hsieh et al. 2019, disk radius of 8-32 au). On the other hand, no disks have been detected at scales larger than 100 au in NGC1333-IRAS2A  (Maret et al. 2014 ) and 10 au in B335  (Yen et al. 2015b ). Therefore, it is still unclear if Keplerian disks are common around Class 0 protostars. In this paper, we present high-angular millimeter line interferometric observations of a sample of 16 Class 0 protostars obtained with the IRAM Plateau de Bure interferometer (hereafter PdBI) as part of the CALYPSO (Continuum and Line from Young Protostellar Objects) survey. CALYPSO is an IRAM Large Program (P. I. Philippe André) that consists of line and continuum observations towards a large sample of nearby (d < 436 pc) Class 0 protostars that aims to understand the angular momentum problem 1.
949 442 GHz) line emission. Previous studies have shown that these lines are good tracers of disks in Class 0 protostars  (Tobin et al. 2012b; Murillo et al. 2013; Ohashi et al. 2014; Sakai et al. 2014 ). These three lines were observed simultaneously using the 1. 3 mm receivers connected to the narrow-band backend with a bandwidth of 512 channels of 39 kHz (0. 05 km s -1 ) each. The data were calibrated and reduced using the GILDAS software package (Gildas Team 2013). For sources with a continuum peak flux density higher than 80 mJy beam -1 , the line observations were self-calibrated using the continuum visibilities 4. Other sources were calibrated using strong quasars only. To improve the signal-to-noise ratio, all line observations were resampled to a spectral resolution of 0. 2 km s -1.
We assume that r c = 100 au and γ = 0. 8 (de  Gregorio-Monsalvo et al. 2013). We also assume that the disk inclination is 45 • , and that the disk major axis PA is 0 •. The disk is assumed to be in Keplerian rotation around a central mass M = 0. 3 M. Finally, we adopt a distance of 293 pc (the same distance as L1448-C) and a systemic velocity of v sys = 5 km s -1. The model does not include artificial noise. The thindisk code produces a synthetic data cube that we process with GILDAS to simulate the observations with the interferometer. First we use the UV_FMODEL task to compute synthetic visibilities, assuming the same uv coverage as our L1448-C observations. Then the visibilities are imaged using robust weighting and deconvolved. Finally, we analyze the synthetic observations using the technique outlined in Sect.
Acknowledgements. We would like to thank  Nagayoshi Ohashi ,  Nami Sakai  and  John Tobin  for fruitful discussions on the interpretation of the observations presented in this paper. We also thank the referee for his/her detailed report that helped us to improve the paper. This work is based on observations carried out under project number  U052  with the  IRAM NOEMA Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This work has benefited from the support of the  European Research Council  under the European Union's  Seventh Framework Programme  ( Advanced Grant ORIS-TARS  with grant agreement No. 291294  and  Starting Grant MagneticYSOs  with grant agreement No. 679937 ), and from the  French Agence Nationale de la Recherche (ANR) , under reference  ANR-12-JS05-0005.
The data show that at a given depth, both the flux and the spectrum vary significantly depending on local conditions. Energy-integrated fluxes of (0. 61 ± 0. 05), (1. 96 ± 0. 15), and (4. 6 ± 0. 4) × 10 -4 cm -2 s -1 , respectively, are measured for three sites within Felsenkeller tunnel IV which have similar muon flux but different shielding wall configurations. The integrated neutron flux data and the obtained spectra for the three sites are matched reasonably well by FLUKA Monte Carlo calculations that are based on the known muon flux and composition of the measurement room walls.
The present work is organized as follows. The underground site is described in Sec. II. FLUKA-based predictions for the neutron spectra and fluxes in three differently shielded rooms in this site are developed in Sec. III. A detector setup to address these expected fluxes is then developed, and its energy-dependent neutron sensitivity is calculated using FLUKA (Sec. IV). The experiment is described in V. Sec. VI shows the obtained neutron fluxes and energy spectra. The data are compared with the literature in Sec. VII, and a summary and conclusions are offered in Sec. VIII.
B. Setup of the FLUKA simulation For the (µ, n) neutrons, a simulation was performed using FLUKA  [41, 42]  version 2011. 2x. 6. An important physical input, namely the flux and angular distribution of cosmic-ray-induced muons at the sites studied, was recently measured  [30] , and these data are used here. Using the depth obtained from the measured  [30]  muon flux, an average muon energy of 17 GeV and a parametrized muon energy spectrum are adopted from the literature  [32]. The rock composition is known from the geological literature  [34] , which was confirmed by the measured density of 2. 75 g/cm 3 of a rock sample taken. The wall compositions of the three sites MK1, MK2, and WS are known from construction records and confirmed by visual inspection. The water content of the rock is not known and may vary by season.
Then, the α-induced neutron emission rate and spectrum are calculated with the NeuCBOT  [43]  code and converted to an observed neu- E ⋅ dΦ /dE [cm -2 s -1 ] Energy E [MeV] FIG. 2 : Neutron energy spectra in the three Felsenkeller sites predicted by the FLUKA simulation: MK2 (red), WS (blue), and MK1 (green). All three are dominated by the (µ, n) component. For reference, the weak (α, n) contribution is shown separately for the case of MK1 (brown). tron flux using FLUKA.
Third, the thermal neutrons in the unmoderated detector A0 break this pattern and show a higher thermal flux in the unshielded WS than in the heavily shielded MK2. For MK2, WS, and MK1, the pattern observed for A1-A6 is again evident for B7-B9. The pairs of similar assemblies A2-B7 and A5-B8 show again a similar pattern when MK1 and MK2 are compared. The general patterns of the neutron count rates are therefore consistent across campaigns A and B. The FLUKA predictions from the FLUKA simulation (Sec. IV) were compared with the data from the 252 Cf source runs. Since the FLUKA code has been extensively validated for its description of neutron interactions  [58] , e. g. , the 252 Cf runs serve to verify the correct implementation of detector and moderator geometry in the present simulation. Here, the 252 Cf source was modeled centrally on top of the detector-moderator assembly in FLUKA.
For assemblies A2, A4-A6, and B8-B9, the detectorto-detector ratios of sensitivities are well reproduced, but the simulated rate is up to 10% below the measured rate. In those cases, due to the mismatch between the 252 Cf spectrum and the spectral sensitivity the observed count rate is influenced by features such as laboratory walls, detector stands, or other detectors, which are imperfectly modeled in the simulation. For assemblies A0-A1, thermal or epithermal neutrons dominate over the emitted 0. 75 MeV neutrons; thus, the 252 Cf data cannot be used. As a result of these tests, 5% is adopted as systematic uncertainty for the sensitivity calculated by FLUKA.
(12) For the fit to determine Φ(E j ), this linear inverse problem must be solved. To this end, subsequently two different codes called MAXED  [60]  and GRAVEL  [61] , respectively, were used. Both codes are included in the Nuclear Energy Agency's UMG 3. 3 package  [62]. Nota bene these codes do not give exact mathematical solutions to problem  (11) , because there are only ten measured count rates R exp i , one for each detector assembly. Therefore, the solution space is limited by using physically motivated a priori spectra as starting points of the fit. Here, for each of the three sites studied, the respective predicted spectrum Φ prior (E) (Sec. III) was taken as a starting point. Both codes then derive a new spectrum based on the starting point and on the measured data. The first code used here, MAXED, performs the fit by maximizing the entropy function  [60] : S = - 396 j=1 Φ(E j ) ln Φ(E j ) Φ prior (E j ) + Φ prior (E j ) -Φ(E j ).
B. Extracted neutron fluxes and their uncertainties The measured neutron fluxes as extracted with MAXED are shown in Fig. 7. For both MAXED and GRAVEL, the integral flux is listed in Table  III. The error bands in the energy spectra (shaded regions in Fig. 7 ) were obtained by using the IQU software that is contained within the UMG 3. 3 package. IQU considers variations of the measured data, quantified by their quoted uncertainty, and performs a sensitivity analysis and uncertainty propagation  [62]. For all three locations, the relative errors determined by IQU are 10%-15% for energies below 10 MeV, and 3-4 times higher above 10 MeV. This increase in uncertainty is due to the declining overall neutron sensitivity (Fig. 3 ) and a resulting decline in detected neutron events, and also due to the fact that the sensitivity of the lead-lined moderator keeps increasing toward higher energies and, thus, cannot be normalized.
B. Comparison of data and simulation The energy-integrated neutron fluxes predicted by FLUKA follow the trend of the data (Table  III ), but they show relative differences of +(26±10)%, -(6±10)%, and -(16±10)%, for MK2, WS, and MK1, respectively. When comparing predicted and experimental energy spectra (Fig. 9 ), it is seen that in all cases, the simulation overpredicts the intermediate to fast energy range (E = 10 -5 -10 MeV). In contrast, the thermal neutron flux (E ∼ 2. 5×10 -8 MeV) is always underpredicted. These two effects may be due to existing low-density materials such as plexiglass housings for detectors, wooden tables and shelves, and the liquid nitrogen in the dewars of the HPGe detectors that were all neglected in the FLUKA simulation. These materials may moderate higher energy neutrons to lower energies, explaining the disequilibrium between the two above mentioned energy ranges.
VIII. SUMMARY AND CONCLUSIONS Using two sets of altogether nine moderated 3 He counters and one unmoderated 3 He counter, the neutron flux and spectrum were investigated in three sites in tunnel IV of the Felsenkeller underground facility, Dresden, Germany. The resulting energy-integrated fluxes were (0. 61±0. 05), (1. 96±0. 15), and (4. 6±0. 4)×10 -  cm 2 s -1 , for sites MK1, WS, and MK2, respectively. The data are matched reasonably well by a detailed FLUKA simulation taking into account the known muon flux and angular distribution and the known specific radioactivity of the rock. In view of the crucial importance of a proper understanding of the underground neutron background, it seems advisable to reinvestigate the underground neutron flux and energy spectrum at several other sites, including deep-underground laboratories.
ACKNOWLEDGMENTS The authors are indebted to  Alfredo Ferrari  ( CERN ) for helpful discussions regarding the FLUKA code. -Financial support by  DFG  ( BE 4100/4-1 ), the  Helmholtz Association  ( NAVI VH-VI-417  and  ERC-RA-0016 ), the  Spanish Ministerio de Economía y Competitividad  (Grants No. FPA2014-52823-C2 , No. FPA2017-83946-C2 , No. RTI2018-098868-B-I00 , and No. SEV-2014-0398  / program Severo Ochoa), and the  COST Association (ChETEC CA16117 ) is gratefully acknowledged.
Note that the corresponding Belle data sets analyzed in Refs. [90, 92]  cannot be readily used to set bounds on axion couplings, because the analyses either cut out two-body decays with a massless axion  [92]  or used multi-variate methods  [90]  that are difficult to re-interpret for different purposes  [93]  (see also Ref. [94] ). K + → π + a [85], B + → K + a The available experimental information is summarized in Table  I , where we give the 90% CL upper limits on the branching ratios for decays involving neutrinos or invisible massless axions. We include the limits on the decays involving axions that we obtained by recasting the experimental searches for decays involving neutrinos ("recast"). Table  I  shows bounds on the branching ratios for decays to pseudoscalar mesons, P 1 → P 2 a, for K + → π + a (s → d transition, experimental analysis in Ref.
For instance, the experimental error on the D + → τ + ν branching ratio is projected to be reduced by a factor of 3 with 20 fb -1 integrated luminosity at BESIII  [99]  compared to the present value  [100] , indicating a potential significant improvement in sensitivity to the c → ua transition. The reach on the branching ratios for the axionic decay modes of B mesons will be improved with the amount of data expected to be collected at Belle II, which is roughly a factor of 50 larger compared to the BaBar and Belle samples. Several potentially interesting channels are lacking any experimental analyses so far. For example, there is no experimental analysis of c → ua transitions that are sensitive to the axial-vector coupling, i. e. , there are no D → ππX inv or D → ρX inv , We show the 90% CL upper bounds on the branching ratios of a pseudo-scalar meson P1 to another pseudo-scalar (P2) or vector (V2) meson (for sd transitions V2 = ππ instead).
Until then one can use as a rough guide the bounds that were obtained for the related two body decays, i. e. , B + → π + a (on F V bd ) and B + → ρ + a (on F A bd ), as a ball-park figure for what an interesting experimental reach for B + → ρ 0 π + a (bounding a combination of F V bd and F A bd ) might be. Finally one can also use LHCb di-muon data collected for the B q → µµ analysis to constrain the axial couplings F A bd and F A bs. As long as no vetos on extra particles in the event are applied in the LHCb analysis, their datasets can be used to constrain decays with additional particles in the final state such as axions. In Ref. [70]  the present data were used to derive constraints on F A bq of the order of 10 5 GeV, cf. Table  III. With 300 fb -1 the bounds can be strengthened by about an order of magnitude, and could be further improved by using also the ATLAS and CMS data on B q → µµ.
These bounds will certainly be improved by dedicated searches in the future. Belle II expects to gather 50 ab -1 of data in the next five years, roughly a factor 100 larger than the final integrated luminosity at BaBar. The gain in the bounds on the branching ratios depends on the scaling behaviors of the backgrounds. In the absence of dedicated experimental projections, we simply assume an "optimistic" scaling inversely proportional to the increase in luminosity with respect to the integrated luminosities on which the respective BaBar analyses were performed. The "conservative" scaling inversely proportional to the square-root of the number of total events would result in slightly weaker bounds. Assuming similar reconstruction efficiencies at Belle II as those achieved at BaBar one can expect an improvement in the sensitivity to F V,A bs and F V bd by at least an order of magnitude, see Table III for the optimistic projections.
ACKNOWLEDGMENTS We thank  Daniel Craik ,  Marko Gersabeck ,  Pablo Goldenzweig ,  Aida El-Khadra ,  Martin Heeck ,  Phil Ilten ,  Alex Kagan ,  Hajime Muramatsu ,  Uli Nierste ,  Steven Robertson ,  Luca Silvestrini ,  Abner Soffer ,  Sheldon Stone ,  Olcyr Sumensari ,  Shiro Suzuki ,  Giuseppe Ruggiero  and  Emmanuel Stamou  for useful discussions. JMC acknowledges support from the  Spanish MINECO  through the  "Ramón y Cajal" program   RYC-2016-20672  and the grant  PGC2018-102016-A-I00. JZ acknowledges support in part by the  DOE  grant de-sc0011784. PNHV acknowledges support from the  Non-Member State CERN Summer Student Programme.
Acknowledgments. The authors are grateful to  K. Behnia ,  A. Cortijo ,  Y. Ferreiros ,  I. Garate ,  J. Heremans , and  D. Kharzeev  for lively discussions. This work was conceived during the Workshop on Weyl Metals held at the  Instituto de Física Teórica de Madrid , February 2019. This paper was partially supported by  Spanish MECD  grants  FIS2014-57432-P ,  PGC2018-099199-B-I00  from  MCIU/AEI/FEDER, UE, Severo Ochoa Center of Excellence  grant  SEV-2016-0597 , the  Comunidad de Madrid MAD2D-CM Program  ( S2013/MIT-3007 ), Grant No. 0657-2020-0015  of the  Ministry of Science and Higher Education of Russia , and  Spanish-French  mobility project  PIC2016FR6/PICS07480.
ACKNOWLEDGMENTS This work has been conducted within the framework of the  OPTIMAL  project, funded by the  European Union by means of the Fond Européen de développement regional (FEDER). The authors acknowledge financial support from  Agence Nationale de la Recherche (ANR)  through the  Conneqt  ( ANR-2011-EMMA-0002 ),  Hylight  ( ANR-17-CE30-0006-01 ) and  SPOCQ  ( ANR-14-CE32-0019 ) projects,  Conseil Régional PACA  through the  Distance  project (Apex 2011:  2001-06938 ),  CNRS  through  Conneqt  ( PEPS INSIS 2011 ), the  Majlis Amanah Rakyat (MARA) ,  Université Côte d'Azur (SPEED ,  CSI 2015 ) and the  French government through its Investments for the Future program  under the  Université Côte d'Azur UCA-JEDI  project ( Quantum@UCA) managed by the ANR  ( ANR-15-IDEX-01 ). The authors acknowledge technical support from  DRAKA (Prysmian group ) and IDQuantique.
The generalization of the algorithm to multiclass and multiple hyperparameters cases is straightforward. The main concept here is that the training, validation and test datasets not only are always disjoint as it happens in traditional cross-validation, but they were also acquired in different and chronologically separated measurement sessions. Even though it should be considered good practice, this methodology is not always implemented and in this work we show some possible misleading consequences. (2) A 1 , B 1 , A 2 , B 2 ,. , A N s , B N s (3) for i = 1, 2,. , N s : A i train ← n� =i A n , B i train ← n� =i B n for j = 1, 2,. , N s -1 : A ij train ← n� =i,j A n , B ij train ← n� =i,j B n for k = 1, 2,. , N h : θ ijk ← train classifier(A ij train , B ij train | h k ) , p ijk ← test classifier(A j , B j | θ ijk , h k ) hi ← select best hyperparameter(p ijk ) θ i ← train classifier(A i train , B i train | hi ) , p i ← test classifier(A i , B i | θ i , hi ) p final ← average(p i ) Applying the proposed intertwined measurements and validation algorithm, we obtained better classification performance (Fig.
It should be stressed that the optimal R value is application-specific. In particular, R should be chosen so that the classification accuracy is maximized, while trying to achieve the target cytometer throughput. Moreover, the number of available training samples and the classifier complexity (e. g. given by the image resolution) play two major roles in the choice of R, because of the need to avoid overfitting. We furthermore double-checked whether the classifier would still be biased by the measurement conditions, in spite of our intertwined class measurements. This was done by training it on the same dataset but with half of the measurement sessions mislabeled, i. e. in list (Eq. 2): A 2 → B 2 , B 2 → A 2 , A 4 → B 4 , B 4 → A 4 , and so on. In this way, the characteristic features given by the different sizes of the beads (corresponding to the true classes) were equally present in both the nominal classes (those presented to the training algorithm).
e. background subtraction + application of acceptance threshold + machine learning inference , see Fig. 2 ), was evaluated for different image resolutions running a Python script on a normal laptop (Intel Core i5-8250U, 1. 60GHz ×8 ). Ultrafast image classification was achieved with computational times per particle in the order of 100 µs to 10 µs depending on the resolution (Table  1 ). It should be stressed that these values could be easily further decreased by, e. g. , employing multi-core computing, a graphics processing unit (GPU) or a dedicated hardware. Classification performance when mixing with diffractive layers. From a machine learning perspective, one might intuitively assume that applying a simple linear classifier on the raw pixel values of an image would generally provide a much weaker classification power w.
The observed colour-magnitude diagram is shown in Fig. 2. The statistical uncertainties on B -V are typically around 0. 03 mag. Systematic errors may be larger, but should be less than 0. 1 mag, although there could be some colour dependence in this. The systematics may, however, be different for bright (V < 11) and faint (V > 11) phases, due to the use of different comparison stars. In quiescence, B -V is approximately 0. 7, and SS Cyg becomes bluer in outburst, with a B -V reaching 0. 35; this is not unexpected, but what is more remarkable is that the colours during the rise and the decay are significantly different, the rise being redder than the decay. We then consider the case of VW Hyi, which is also a source well-observed by amateur astronomers, and we have selected two consecutive outbursts from the AAVSO database that occurred on 2018, Sep.
Acknowledgements. We acknowledge with thanks the variable star observations from the AAVSO International Database contributed by observers worldwide and used in this research. This work was supported by a  National Science Centre, Poland  grant  2015/19/B/ST9/01099. JPL was supported by a grant from the  French Space Agency CNES. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
The method of deconvolution of the second derivative of the IR spectrum was used to track the changes in the secondary structure of HSA and trypsin depending on various factors. The protein spectra used for analyses were obtained by subtracting the spectra of an aqueous solution using Opus 7. 0 software. The secondary structure was determined by deconvolution of the second derivative of the Amide I band in the OriginPro 2015 software (Figure  1 , Figures  S1-S6 ). All second derivatives have been multiplied by -1 to correspond to the curves that were published in the previous works. The procedure for calculating the secondary structure of proteins using the deconvolution of the second derivative of the spectrum is described in detail in work  [32].
Wavenumber, cm -1 Range, cm  Albumin peaks at 1650 and 1657 cm -1 refer to 60. 3% of the total area of the second derivative (Figure  1b ), and peak 1655 cm -1 of trypsin refer to 11. 2%, which agrees with the data obtained from the DSSP, STRIDE, and the secondary structure assigned by authors of the X-ray structures PDB codes 1AO6 (HSA) and 1FMG porcine beta trypsin)  [34] [35] [36] [37] [38] [39]  (Table  3 ). HSA proteins do not have a Albumin peaks at 1650 and 1657 cm -1 refer to 60. 3% of the total area of the second derivative (Figure  1b ), and peak 1655 cm -1 of trypsin refer to 11. 2%, which agrees with the data obtained from the DSSP, STRIDE, and the secondary structure assigned by authors of the X-ray structures PDB codes 1AO6 (HSA) and 1FMG porcine beta trypsin)  [34] [35] [36] [37] [38] [39]  (Table  3 ).