dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, it can be noted that, at a concentration of 1. 5 M, the maximum content of β-sheets is reached (32%), and at 2 M their content slightly decreases probably due to a significant increase in intermolecular β-sheets (Figure  3 ). Thus, we observe conformational transitions of α-helix→ intra-and intermolecular β-sheets. It is known that intermolecular β-sheets are frequently linked to the formation of amyloid aggregates. Indeed, our analysis of the HSA amino acid sequence by using ArchCandy program  [43]  revealed that a significant part of this protein has amyloidogenic regions, which has the potential to form intermolecular cross-β-amyloids when unfolded (Figure  A1 ). Trypsin in the same KSCN solutions undergoes intramolecular β-sheets → intermolecular β-sheets transition. As seen in Figure  4a , the maximum of the Amide I region shifts from 1638 cm -1 in the native form to 1633 cm -1 in a 2 M KSCN solution with an increase in the concentration of KSCN, suggesting a decrease of β-sheet content.
Acknowledgements The experiment was performed at RI Beam Factory operated by  RIKEN Nishina Center. We are grateful to the  RIKEN and BigRIPS  staff for their help. This work was partly supported by: the  Kakenhi Grant-in-Aid for Young Scientists  B No. 16K17719 , by the  Hungarian Academy of Sciences  under Grant No. NN114454-NKFIH  and by the  Romanian National Authority for Scientific Research  under the project  PNIII/P5/P5. 2No. 02/FAIR-RO.
Meanwhile, its main characteristics, even its existence, are yet to be precisely determined. Depending on the experiment, the 7 H mass was estimated between 0. 6 and 2 MeV above the 3 H+4n mass, with a resonance width between ∼0. 1 and 2 MeV  [6, 14, 15] ; and even excited states were reported at ∼6 MeV  [14]. As to its structure, there is no experimental data on the 7 H spin and parity. In order to contribute to these sparse results, we have explored binary, one-proton transfer reactions between a 8 He beam at 15. 4A MeV and carbon and fluorine targets to produce and characterise the 7 H ground state with the MAYA active-target  [16]  and the CATS beam-tracking detectors  [17]  at GANIL (France). The MAYA active-target detector works essentially as a Time, Charge-Projection Chamber where the filling gas, in this case He+CF 4 , also plays the role of reaction target.
07 MeV for 19 F and Q C =-20. 97 MeV for 12 C. The accumulation of events around the kinematic lines of Q F and Q C in Fig. 1  is a first indication of the production of 7 H in both channels. Since 7 H decays immediately after being formed, its characteristics are indirectly measured with the missingmass method, in which the mass of the undetected participant ( 7 H, in this case) is deduced from the energy and momenta of the remaining participants in the reaction (incident 8 He, target 19 F/ 12 C, and recoil 20 Ne/ 13 N). Figure  2  shows the missing-mass spectrum of the beam-like product from one-proton transfer reactions with respect to the mass of the 3 H+4n subsystem for data between the white dashed lines of Fig. 1. Our experimental setup does not perform element separation of the target-like recoils, thus the figure shows the missing-mass as calculated for each 19 F and 12 C targets.
Acknowledgements The authors would like to acknowledge  Dr. Soumya Ghosh ,  Dr. Zdenek Futera  and  Dr. Hui Yang  for useful discussions. S. G. , A. C. , and O. G. Z. were supported by the  European Research Council (ERC)  under the  European Union ,  Horizon 2020 research and innovation programme  (grant agreement no. 682539/SOFTCHARGE ). Via the authors' membership of the UK's  HEC Materials Chemistry Consortium , which is funded by  EP-SRC  ( EP/L000202 ,  EP/R029431 ), this work used the  ARCHER UK National Supercomputing Service  (http://www. archer. ac. uk) as well as the  UK Materials and Molecular Modelling (MMM) Hub , which is partially funded by  EPSRC  ( EP/P020194 ), for computational resources. The authors also acknowledge the use of the  UCL Grace High Performance Computing Facility.
Introduction Thanks to the observations of the space photometers CoRoT  (Baglin et al. 2009) , Kepler  (Borucki et al. 2011) , and now TESS  (Ricker et al. 2015) , red giant (RG) stars are known to commonly display solar-like (SL) oscillations  (De Ridder et al. 2009 ). As for main-sequence (MS) SL stars, these global oscillations are stochastically excited by turbulent convection in their outer convective envelopes. From a revised classification of the Kepler targets  (Berger et al. 2018 ) based on the second data release of the ESA GAIA mission  (Gaia Collaboration et al. 2018 ) and using the revised temperatures from the Kepler Stellar Properties Catalog (KSPC DR25,  Mathur et al. 2017) ,  Hon et al. (2019)  detected oscillations in about 92 % of the 21, 000 RGs observed by the original Kepler mission.
2011 ), but no dedicated observations have been led to test it. Only archived data from large surveys were considered in recent works  (Tayar et al. 2015; Ceillier et al. 2017) , leading to rather inconclusive results on that matter. The goal of the present paper is firstly to determine what fraction of active RGs are in close binary systems, and secondly to understand the origin of magnetic activity in the RGs that do not belong to close binaries. We base our work on a selfconsistent approach where we select a sample of about 4500 RGs observed by Kepler that are not known to belong to any binary system, and that are expected to exhibit SL oscillations (Sect. 2). Among them, we show that about 370 (≈ 8 %) display significant rotational modulation, which appears to be correlated with weak oscillations (Sect.
The Kepler light curves We primarily worked with the Kepler public light curves that are available on the Mikulski Archive for Space Telescopes (MAST) 1. Two types of time series are available: the Simple Aperture Photometry (SAP) and the Pre-search Data Conditioning Simple Aperture Photometry (PDC-SAP) light curves. The latter consist of time series that were corrected for discontinuities, systematic errors and excess flux due to aperture crowding  (Twicken et al. 2010). They do not meet our requirements for monitoring the rotational modulation, which is often removed during the process (e. g. ,  García et al. 2014b; Gaulme et al. 2014). We thus made use of the SAP data to preserve any possible longterm signal. This choice entails our own detrending and stitching operation on the light curves while ensuring that the rotational modulation is preserved after each interruption of the time series.
Autocorrelation is used to check the estimate obtained from the power spectrum, by verifying that the highest peak lies in the same period range within a factor 1/2 or 2, as misestimates can happen. Visual inspection was then used to decide the actual rotation period in case of disagreement between power spectrum and autocorrelation periods. Beyond measuring rotation periods, we need to be careful with possible photometric contamination from nearby stars. Indeed, it can happen that a considered RG does not have any spot but that a neighboring MS stars display spots and that the light curves leak into each other. Ceillier et al. (2017)  tracked this possible source of errors by making use of the crowding parameter available on the Kepler database at MAST. The crowding parameter is defined as the fraction of the flux coming from the considered target in the light curve.
A second estimate of ∆ν is performed again with EACF from the whitened power spectral (power spectrum divided by background function). Oscillations are considered to be detected when both the maximum value of the EACF is larger than 8  (Mosser & Appourchaux 2009)  and peaks are visible in the spectrum. We then compute the stellar masses and radii thanks to the asteroseismic scaling relations that were originally proposed by  Kjeldsen & Bedding (1995)  for SL MS oscillators, and then successfully applied to RGs (e. g. ,  Mosser et al. 2013). The stellar effective temperatures, also needed in the scaling relations, are those from the Kepler GAIA data release 2 with their corresponding errors. We employ the asteroseismic scaling relations as proposed by  Mosser et al. (2013)  for RGs, i. e. , where ν max, = 3104 µHz, ∆ν = 138.
A dedicated pipeline to analyze the sample We developed an automatic pipeline -the Data Inspection Tool, hereafter DIT -to analyze the whole sample, which cleans the light curves and extracts the properties of both rotational modulation and solar-like oscillations. The code is similar to that described in  Gaulme & Guzik (2019)  to look for stellar pulsators in eclipsing binaries. Beyond the measurements of rotational modulation, background and oscillations, the DIT produces a vetting sheet for each target that includes a series of figures allowing the user to appreciate the quality of the measurements. An example of vetting sheet is displayed in Fig. 5. All vetting sheets were visually inspected. The objective of the visual inspection was to double check the detection of rotational modulation, assess the value of the rotation period, and look for oscillations that had been missed by the DIT.
1. As indicated in Sect. 2. 6, we could monitor 51 targets with ARCES on the 3. 5-m ARC telescope 4 , and make use of 34 archived RV values from the APOGEE/SDSS IV survey 5. For ARCES, we selected the brightest targets among the active ones to maximize the number of observable stars, and we made sure that a significant fraction of the targets did not show any oscillations. Overall, our spectroscopic sample is composed of 85 RGs with clear rotational modulation, including 30 with no oscillations and 55 with oscillations. The result appears to be very different depending whether oscillations are detectable (Fig. 8 ). Among the 30 non-oscillating stars, 26 are clear SBs, i. e. ∼ 85 %. In contrast, among the 55 oscillating ones, only 8 or 9 are SBs, i. e. , ∼ 15 %. To check the significance of the SB fraction, we looked for RV data of a null sample, where no rotational modulation is observed.
Two groups of single active RGs We retrieve the masses, radii and evolutionary status of the giants thanks to an asteroseismic analysis described in Sect. 2. 4. By evolutionary status, we mean the RGB where stars burn H 4 Actually, we monitored 53 targets with ARCES but one (KIC 11551404) is not part of the sample of 4465 RGs because its GAIA radius is too small (2. 7 ± 0. 2R ). We picked it before  Berger et al. (2018)  was published because it displayed strong rotational modulation and was part of earlier Kepler RG catalogs. The RV dispersion clearly indicates it is a close binary system. We also observed KIC 3459199 which actually has an EB signal in its light curve. It is also an SB. We decided to keep these two in Table  B. 1 for the records. 5 347 RGs displaying rotational modulation were actually available from the APOGEE database, but only 36 were observed more than three times over a time span longer than 10 days, and 4 were already part of our ARCES sample.
1984; Wright et al. 2011) , we consider the generation of magnetic field in the frame of the turbulent dynamo scenario (e. g. ,  Charbonneau 2014 ). In such a scenario, the efficiency of the magnetic field generation is characterized by the Rossby number Ro = P rot /τ c. The efficient dynamo regime, and hence surface magnetic fields and spots, is associated with Ro ≤ 1. We thus need an estimate of the convective turnover timescale τ c for the stars in our sample. In Fig. 13 , isolevels of τ c are overplotted with the data; evolutionary tracks are also shown for reference. We use the τ c extracted from the stellar evolution tracks from the YaPSI database  (Spada et al. 2017 ). It should be noted that the values of τ c shown in the Figure were calculated using the mixing-length treatment of convection (see  Spada et al.
Acknowledgments P. Gaulme ,  F. Spada , and  C. Damiani  acknowledge funding from the  German Aerospace Center (Deutsches Zentrum für Luftund Raumfahrt)  under PLATO Data Center grant  50OO1501. M. Vrard was supported by  FCT -Fundação para a Ciência e a Tecnologia  through national funds and by  FEDER  through  COMPETE2020 -Programa Operacional Competitividade e Internacionalização  in the context of the grants:  PTDC/FIS-AST/30389/2017 & POCI-01-0145-FEDER-030389  and  UID/FIS/04434/2013 & POCI-01-0145-FEDER-007672. M. B. has been funded by the  PLATO CNES  grant. Much of this paper is based on observations obtained with the  Apache Point Observatory 3. 5-meter telescope, which is owned and operated by the  Astrophysical Research Consortium. The authors also used archived data from the data release DR16 of the APOGEE/SDSS IV survey.
A wide range of ancillary data is available for each field to produce a sample for which completeness and purity are well controlled, and to infer physical parameters, most importantly stellar masses and SFRs through SED fitting performed with the knowledge of the accurate redshift. In the VVDS02h field we use photometry from the 7th data release of CFHT Legacy Survey (CFHTLS,  Cuillandre et al. 2012) , which covers the u * , g , r , i , z optical bands. It is complemented by infrared data in J, H, K bands from WIRCam Deep Survey (WIRDS,  Bielby et al. 2012)  and in two IRAC bands (3. 6 and 4. 5 µm) from the Spitzer Extragalactic Representative Volume Survey (SERVS,  Mauduit et al. 2012). The full range of photometric observations in the COSMOS field is presented on the COSMOS web site 1. In this work, we use the optical data from CFHT for u * band and Subaru broad bands B, V, g+, r+, i+, z+, the near-infrared bands J and K from UltraVista  (McCracken et al.
We try to fit two functional forms of the luminosity functiona standard Schechter function form and a DPL. We fit the parameters of the luminosity function in these two representations with a MCMC method implemented within the python package pymc. Because our sample is mostly built from bright star forming galaxies, our measurements of the faint end are not well constrained, while we set strong constraints on the bright end. In order to fit the luminosity function, we set the faint end slope to values from the literature ( α = -1. 76 from  Bouwens et al. (2015b)  and α = -2. 0 from  Bowler et al. (2015) ). We also set φ * , when fitting with a DPL, due to our small sample. Our results are shown in Fig. 13  and listed in Table  3. Both a Schechter or a DPL fit represent well our data at all magnitudes.
Acknowledgements. This work is supported by funding from the  European Research Council Advanced  Grant  ERC-2010-AdG-268107-EARLY. We acknowledge the support from the grants  PRIN-MIUR 2015  and  ASI n. I/023/12/0  and ASI n. 2018-23-HH. 0. This work is based on data products made available at the  CESAM data center, Laboratoire d'Astrophysique de Marseille, France.
This is because the updates can simply be absorbed into the original gates, to give a circuit which has , trained on the data, equation (44). 500 data points are used for training, with 400 used as a training set, and 100 used as a test set. Plots show mean, maximum and minimum values achieved over 5 independent training runs on the same dataset. (a) TV difference between training methods, with regularisation parameter = 0. 1 for SHD, and 3 eigenvectors for Spectral Stein method. Both Sinkhorn divergence and Stein discrepancy are able to achieve a lower TV than the MMD. Inset shows region of outperformance on the order of ∼ 0. 01 in TV. We observe that the Spectral score method was not able to minimise TV as well as the exact Stein discrepancy, potentially indicating the need for better approximation methods.
We showed numerically that these methods have the ability to outperform previous methods in the random dataset we used as a test case. Finally, we demonstrated an application of the model as a heuristic compiler to compile one quantum circuit into another via classical optimisation techniques, which has the advantage of requiring minimal quantum overhead. These techniques could potentially be adapted into methods to benchmark and verify near term quantum devices. The major question that this work raises is whether or not a provable notion of quantum learning could be achievable for a particular dataset, thereby solidifying a use case for quantum computers in the near term with provable advantage. The best prospect for this is the quantum supremacy distributions we know of (for example IQP), but they are not efficiently testable  63.
1. 2. Notice that even with the hurdle (difficulty in compute the score), the SD is still more suitable than the KL divergence to train these models, since the latter requires computing the circuit probabilities, p θ (x), which is in general intractable, and so could not be done for any dataset. In contrast, the Stein Discrepancy does not require the circuit probabilities, only the data probabilities, which may make it amenable for generative modelling using some datasets.
III. 1 Computing the Stein Score Function Here we address the computability of the score function of equation (75). For every sample, x ∼ p θ , that we receive from the Born machine we require the score function of that outcome being outputted from the data distribution, x ∼ π. This involves computing π(x), and also ∆ x π(x), i. e. π(¬ i x), ∀i ∈ {1,. , n}. Of course, the Stein discrepancy can be immediately used for any classical problem (classical datasets) for which the probability density is already known, or can be computed efficiently. However, if one is interested in implicit models: models which admit a means of sample generation, but not explicit access to the probability density, then this not immediate. In particular, for those distributions which admit some complexity theory result indicating that they cannot be simulated on a classical device efficiently, it will not be possible to efficiently compute the probabilities required in order to compute the score.
Plots show mean, maximum and minimum values achieved over 5 independent training runs on the same dataset. (a) L 0. 08 SHD using 500 samples and a batch size of 250. (b) L MMD using 500 samples and a batch size of 250, with three different initial learning rates. (c) L SD using 500 samples and a batch size of 250 for Exact score, and 40 samples and batch size of 20 for the Spectral score. Training on test set observed as thin lines without markers for all methods, excluding the Exact score method, since this uses exact probabilities. Corresponds to Fig. 3in main text.
e. Rigetti 4q-qvm,. Plots show mean, maximum and minimum values over 5 independent training runs on the same dataset. (a) TV Difference between training methods. Both Sinkhorn divergence and Stein discrepancy can achieve lower TV values than the MMD. (b) Final learned probabilities of target data, equation (44) [ ]. (c) L 1 SHD using 500 samples and a batch size of 250. 400 samples used as a training set, 100 for the test set. Trained using Hamming optimal transport cost function (d) L MMD using 500 samples and a batch size of 250, with three different initial learning rates. (e) L SD with the Exact score and a learning rate of η init = 0. 05 and Spectral score, using 50 Samples and batch size of 25 using a learning rate η init = 0. 01. 40 samples used as training data, 10 samples used for test set.
Acknowledgements B. C. thanks  Andru Gheorghiu  for useful discussions and title suggestion. We also thank  Jean Feydy ,  Patric Fulop ,  Vojtech Havlicek  and  Jiasen Yang  for clarifying pointers. We thank  Atul Mantri  for comments on the manuscript. This work was supported by the  Engineering and Physical Sciences Research Council  (grants  EP/L01503X/1 ,  EP/N003829/1 ),  EPSRC   Centre for Doctoral Training in Pervasive Parallelism  at the  University of Edinburgh, School of Informatics  and  Entrapping Machines , (grant  FA9550-17-1-0055 ). We also thank  Rigetti Computing  for the use of their quantum compute resources, and views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of  Rigetti Computing.
35 MMDFigure 3 : 353 Figure 3: MMD [ , , ] vs. Sinkhorn [ ] and Stein training with Exact Score function [ ] and Spectral Score method [ ] for 3 qubits with fully connected topology, Rigetti 3q-qvm,, trained on the data, equation(44). 500 data points are used for training, with 400 used as a training set, and 100 used as a test set. Plots show mean, maximum and minimum values achieved over 5 independent training runs on the same dataset. (a) TV difference between training methods, with regularisation parameter = 0. 1 for SHD, and 3 eigenvectors for Spectral Stein method. Both Sinkhorn divergence and Stein discrepancy are able to achieve a lower TV than the MMD. Inset shows region of outperformance on the order of ∼ 0. 01 in TV. We observe that the Spectral score method was not able to minimise TV as well as the exact Stein discrepancy, potentially indicating the need for better approximation methods.
We use qubit topology in fully connected graph for four qubits, i. e. Rigetti 4q-qvm,. Plots show mean, maximum and minimum values over 5 independent training runs on the same dataset. (a) TV Difference between training methods. Both Sinkhorn divergence and Stein discrepancy can achieve lower TV values than the MMD. (b) Final learned probabilities of target data, equation (44) [ ]. (c) L 1 SHD using 500 samples and a batch size of 250. 400 samples used as a training set, 100 for the test set. Trained using Hamming optimal transport cost function (d) L MMD using 500 samples and a batch size of 250, with three different initial learning rates. (e) L SD with the Exact score and a learning rate of η init = 0. 05 and Spectral score, using 50 Samples and batch size of 25 using a learning rate η init = 0.
As MASCOT rotates within this field, it observes a sinusoidal signal around the zero level in the spin plane. We used again the variance analysis, this time on the data cleaned from the main sources of disturbance as discussed in the previous step. The purpose is to find the rotation axis, which should be roughly aligned with the minimum variance direction. As the main part of the MASCOT's disturbance is removed, the variance in the signal is caused by the rotating (from MASCOT's perspective) external magnetic field. Therefore, the maximum and intermediate variance direction gives us the spin plane. The converted data into the variance system are used to find the rotation period, amplitude, and offset using a least squares fit. For the model parameters, we assume a constant background field, linearly changing DC offsets, rotation axis, rotation period, and its initial phase.
The assumption about constant magnetic field is used solely for the fit purposes and does not influence the data postprocessing. On the contrary, the offset gained from the fit is removed from the data before despinning. The assumption of linearly changing offset is reasonable, as the temperature of MASCOT changed during the descent and offset is in general temperature dependent. The offset change is not caused by the magnetometer itself, as its temperature stability is in the order of 20 pT/K and the temperature change of the sensor itself during the descent was about 1 • C. However, some MASCOT units saw temperature change of about 10 • C, which could cause change in magnetic properties. Unfortunately, there were no preflight magnetic temperature calibrations of the whole MASCOT, so this is only assumption.