dataesr/scientific-paragraphs-categorization huggingface dataset:
SFR [M yr -1 ] Fig. 14 : Markers show the time averaged (between 75 -250 Myr) outflow rates (top) mass loading factors (middle) and SFR (bottom) as a function of the circular keplerian velocity of G8 (41 km s -1 ) and G9 (90 km s -1 ) at the galactic scale radii. For G9, some of the markers are shifted for clarity. The data points are taken from  Heckman et al. (2015)  and  Chisholm et al. (2017). The labels with the suffix "-conv" are convergence study runs and are discussed in Section 5. The poloidal (blue diamond) run is discussed in Section 6. in Fig. 14. The SFRs are slightly higher in the higher resolution runs, and therefore the mass loading factor are slightly higher in the lower resolution run. The values of the outflow rate agree very well for the two resolutions.
Acknowledgements We thank  Corentin Cadiou  for his tremendous help with YT and other fruitful scientific discussions. We thank  Joakim Ros-dahl  for his help in setting up the initial conditions and valuable advice and comments on the paper. We thank  A. Marcowith  for enlightening discussions. This work was granted access to the HPC resources under the allocation  A0060406955. This work has made use of the Horizon Cluster hosted by the  Institut d'Astrophysique de Paris. We thank  S. Rouberol  for running smoothly this cluster for us.
Following  [43] , all parameters have been determined from experimental measurements and simulations. First, we used the singles data from the phantom acquisition, as stated in the NEMA NU 2-2018 guidelines for measurement of the scatter fraction, count losses and randoms. For the low activity range, below 3 kBq mL -1 , linear regression was performed to estimate the theoretical singles rate in ideal conditions, in the absence of deadtime and pile-up influence. The fitted line was extrapolated to the origin to estimate the background noise level. The deadtime τ was then estimated using a paralyzable model, following the equation: τ = -1 S in ln S out S in  [44] , where S in is the estimation of the singles rate without the effect of deadtime and S out is the measured singles rate from experimental data.
Image reconstruction As explained earlier, the evaluation of the simulation was performed before image reconstruction to be independent of the algorithm, but examples of reconstructions were performed to illustrate image quality. For both reconstructed and simulated data, reconstructions were made with the open-source software CASToR (Customizable and Advanced Software for Tomographic Reconstruction)  [47] , using ordered subset expectation maximization (OSEM)  [48]  with Joseph line projector  [49]  (see below for detailed parameters).
6 mm radius line source of 1. 78 GBq of 18 F, off-centered at a radial distance of 45 mm from the central axis of the cylinder. Twenty-six different measurement time points were performed over 16 h and with increasing acquisition times. The activity during the last time point was less than 10 MBq. In order to reduce the computation time, the simulations were performed with the exact activity but with a reduced acquisition time such that each acquisition contained a minimum of 2 × 10 6 prompts. The experimental listmode data sets were truncated at 2 × 10 6 prompts for comparison with simulated data.
Intrinsic spatial resolution Spatial resolution is usually determined after reconstruction, using the FWHM of the point spread function (PSF) of a point source. However, in order to remain independent of the reconstruction software for both experimental and simulation data, an original method was developed to estimate and compare spatial resolution from listmode data. Spatial resolution was assessed in the center of the axial FOV for five transverse positions, (0,1,0), (0,10,0), (0,20,0), (10,0,0), and (20,0,0) cm, using a 2. 2 GBq mL -1 18 F point source. Listmode data was acquired and simulated until a minimum of 3 million prompt events was reached for each position. Due to the cylindrical shape of PET scanner, radial sampling increases with the distance from the center of the FOV. This non-uniform sampling in the transverse direction could lead to distortion of high spatial frequency and non-uniform spatial resolution across the FOV  [52, 53].
Hence, listmode data was first resampled at a uniform sample rate of 2 LOR mm -1 in the radial direction by the arc correction method  [52, 53]. Then, for each event in the listmode, the orthogonal projection (A) of the point source (S) onto the LOR defined by the event was computed. The vector -→ SA, which represents the smallest vector between the LOR and the source, was projected along each axis X, Y, and Z. Each projected distance distribution along X, Y, and Z was then binned into histograms and the intrinsic spatial resolution for a given direction was determined by the FWHM. FWHM values were determined as preconized in the NEMA standard for the spatial resolution test  [6]. This method requires a precise knowledge of the source position, which is the case for simulation but not for experimental data.
Image quality Illustrative reconstructed images were compared with the following protocol. A three minute PET acquisition of the International Electrotechnical Commission (IEC) torso phantom was performed. The phantom was filled with 18 F of 5. 3 kBq mL -1 for the background activity and four times higher in four spheres of 10-, 13-, 17-, and 22-mm diameters. The 28-and 37-mm diameter spheres were left cold  [54]. Images were reconstructed with OSEM using 10 subsets and 1 to 10 iterations. For experimental data, all corrections required for quantitative reconstruction (attenuation, scatter  [55] , random  [56] , normalization  [57] ) were precomputed outside CASToR using a software provided by Philips. For simulated data, only random and attenuation corrections were computed. Other corrections were not available.
Funding This work was supported in part by  Philips Healthcare. This work was performed within the framework of the  SIRIC LYriCAN Grant INCa-INSERM-DGOS-12563 , and the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  ANR.
This work has been supported by the  Orsay Nuclear Physics Institute , the  P2IO Laboratory of Excellence , the  French National Institute for Nuclear and Particle Physics , the  Paris-Sud University , and the  European Union 's  HORI-ZON 2020 Program  under grant agreement no. 654002. We thank the  ALTO technical group  for their professional assistance. Author contributions D. T. Y. initiated the project and contributed in securing financial resources along with S. F. , G. G. , E. M. R. , L. V. R. , and other collaborators. Implementation took place under the technical coordination of A. S. M. L. B, A. K. , S. L. , D. N. , L. V. R. , and D. T. Y. prepared the instrumentation. D. A. developed the software for data acquisition. All authors contributed to the measurements and the preparation of the manuscript.
This will allow to operate coherent superconducting qubits in magnetic fields beyond 1 T. Furthermore, by adapting the design to decrease the grAl volume and cirtical current density, the value of the anharmonicity can be increased by one order of magnitude in transmon type qubits, while much larger values of anharmonicity could be achieved in fluxonium qubits  [2, 41]  We acknowledge qKit  [42]  for providing a convenient measurement software framework. The waveguide sample holder can be equipped with a 2D vector magnet, which consists of a pair of Helmholtz coils (HH) and a solenoid for field alignment, denoted compensation coil in the following (cf. Fig. 5a ). The field direction of the HH coils is aligned within machining precision with the in-plane direction of the chip, y. The compensation field is oriented perpendicular to it, in the z direction, out-of-plane with respect to the sample.
We are grateful to H. Rotzinger, U. Vooland W. Wulfhekel for fruitful discussions, and we acknowledge technical support from S. Diewald, A. Lukashenko and L. Radtke. Funding was provided by the Alexander von Humboldt foundation in the framework of a Sofja Kovalevskaja award endowed by the German Federal Ministry of Education and Research, and by the Initiative and Networking Fund of the Helmholtz Association, within the Helmholtz Future Project Scalable solid state quantum computing. PW and WW acknowledge support from the European Research Council advanced grant MoQuOS (N. 741276). AVU acknowledges partial support from the Ministry of Education and Science of the Russian Federation in the framework of the Increase Competitiveness Program of the National University of Science and Technology MISIS (Contract No.
Introduction Electrons and photons are present in many Standard Model processes as well as in searches for phenomena beyond the Standard Model. The ATLAS physics programme relies on an efficient trigger system to record a highly signal-rich subset of all collision events produced by the Large Hadron Collider (LHC) at CERN. The ATLAS Collaboration has published several electron and photon trigger performance results since the start of data-taking: the early 2010 data are covered in Ref. [1] , the 2011 data in Ref. [2]  and the 2015 data in Ref. [3]. This paper addresses the evolution of performance of the electron and photon triggers from 2015 to 2018 (Run 2). The major challenge for the trigger in this period was the need to maintain excellent performance for the ATLAS physics programme while adapting to a nearly fourfold increase in the LHC peak luminosity and in the number of interactions per beam-crossing.
This paper is organised as follows. The ATLAS detector is described in Section 2. The trigger system is introduced in Section 3. Section 4 introduces the data sets used in this publication. The following two sections detail electron and photon reconstruction and identification at the analysis level (offline) and the trigger level (online). Section 7 presents the techniques used to measure the trigger performance. The performance of the photon and electron triggers from 2015 to 2018 is described in Sections 8-11. The data quality monitoring is described in Section 12, and the conclusions are presented in Section 13.
Menu composition and trigger thresholds are optimised for the LHC running conditions (beam type, luminosity, etc. ) to fit within the event acceptance rate and the bandwidth constraints of the data acquisition system of the ATLAS detector as well as the offline storage constraints. In addition to the triggers described above, there are 'rerun' triggers which never accept an event on their own, but are configured to run only on the events accepted by other triggers, and their decision is recorded for offline use. This information is used for studies of the trigger efficiency, which is calculated separately for each object (leg) of the multi-object triggers. Trigger thresholds and identification criteria have to be modified sometimes to maintain a stable output rate. To ensure an optimal trigger menu within the rate constraints of a given LHC luminosity, prescale factors can be applied to both the L1 and HLT triggers independently and configured during data-taking.
Performance measurement techniques 7. 1 Rate measurements The ATLAS data-taking conditions are archived with a time interval of the order of a minute, which defines a luminosity block. In order to obtain the rate of a given trigger as a function of the instantaneous luminosity  [9, 45] , individual rate measurements on different luminosity blocks from all data collected in a given year are used. If, for a given rate measurement, the ratio of trigger rate to instantaneous luminosity varies by more than 20% from the average of other measurements, that measurement is not taken into account as an estimator of the rate for that trigger. This avoids averaging rate measurements that fluctuate because of unpredictable and temporary changes of LHC collisions.
Monitoring and data quality During data-taking, the performance of the electron and photon triggers is monitored online while data are being collected, and offline, right after data are recorded. The online monitoring check is performed during the data-taking by shift personnel based in the ATLAS control room at CERN, located at ground level, 100 m above the ATLAS detector, in the Point 1 of the LHC ring. Observables related to electron and photon candidates at different stages of the HLT reconstruction are checked. These observables are defined in the reconstruction and hypothesis-testing algorithms executed online. Only a set of representative electron and photon triggers are monitored online. Monitoring of the full set of triggers would require a large fraction of the HLT farm's computing power and is not necessary, as many of the electron and photon triggers share the same algorithms.
In the fast reconstruction step, only the coverage in η-φ space and the distributions of E T are monitored for both the electrons and photons. Ringer algorithm variables, track positions, and distances between tracks and calorimeter clusters are monitored for electron triggers. In the precision step, in addition to those observables, the value of µ at the HLT and some calorimeter shower shapes are monitored. For electrons, the distribution of the value of the likelihood discriminant is also monitored. An 'express stream' is defined, containing a fraction of the collected data reconstructed with high priority for offline monitoring and data-quality purposes. It takes around one day for the ATLAS computing centre at CERN to calibrate and reconstruct data from the express stream. Once these data are available, offline monitoring of the performance and data quality for several electron and photon triggers is performed.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC  and  Wallenberg Foundation ,.
Depending on the effective operators one needs to construct, a subset of operators in the UOLEA will need to be selected and reorganized into the form of commutators. The non-denegerate results are available upon request. • We use the same notation in the Mathematica notebook as in the Eq. (2. 6) where P,W0, W1 stand for the covariant derivative, scalar, and pseudo-scalar structures, respectively. To avoid conflict with other Mathematica packages, we denote vb , āb for vector and axial-vector structures. We follow the conventions of Ref. [20]  for γ 5 and the total antisymmetric tensor µνρσ , 0123 = +1. The trace of Dirac matrices is evaluated using the FeynCalc package  [21] [22] [23]  and thus the output operator structures are also written in the language of this package. • Regarding the hermiticity of the operator structures, the operators which are not selfhermitian need to be accompanied with their hermitian conjugates.
Acknowledgments We thank the authors of Ref. [32] for making us aware of their related work on fermionic results for the UOLEA being finalised at the same time as our manuscript. J. Q. thank the authors of Ref. [11] for useful discussions. T. Y. is supported by a  Branco Weiss Society in Science fellowship  and partially supported by  STFC  consolidated grant  ST/P000681/1. S. A. R. E. would like to thank the " Institut de Physique Théorique de La Côte"  for hospitality while this work was completed. S. A. R. E. is supported by the  U. S. Department of Energy  under Contract No. DE-AC02-76SF00515  and by the  Swiss National Science Foundation ,  SNF  project number  P400P2 186678. Z. Z. 's work is supported by the  U. S. Department of Energy, Office of Science, Office of High Energy Physics , under Award Number  DE-AC02-05CH11231.
3(a ) is near to its theoretical maximum for this level of stripe disorder. The simulations performed with the experimental parameters exhibit a lower degree of asymmetry in accordance with its greater in-plane surface component compared with that of the ficticious sample. The aim of the XRMS measurements and simulations was to estimate the out of plane/in plane ratio present, to which effect we additionally simulated the asymmetry ratio as a function of out of plane component using a faster simulation script replacing the micromagnetic simulations with an analytical form of the Nèel type domain walls given by M z = tanh(x/w) where w is the domain wall width and x is the perpendicular distance from the domain wall. M x was chosen in order to maintain a constant magnitude of the magnetization vector M.
In Fig. 5 , we display the calculated volume fraction for the three kinds of domains at remanence. By taking into account the simulation, the volume ratio of the out of plane domain to the closure one, A s /A c , is ∼3. 7 which is very far from the volume ratio required for obtaining the AMR inversion (less than ∼0. 60). Also, we have performed supplementary calculations by slightly changing the magnetic parameters (M s and K PMA less than 10%), in order to study the changes in the volume ratios. We have observed that the volume fraction is almost unaffected when such parameters are modified. This would mean that our simple model of resistors is insuficient for explaining the CIW-MR. In order to obtain a more realistic picture that allows a better quantitative description of the magnetotransport, from the magnetic structure calculated via OOMMF, we proceed to calculate, via the finite element code COM-SOL Multiphysics 40 , the resistivity in the perpendicular and parallel configurations for several values of applied field with the aim of calculating the MR ratio.
ACKNOWLEDGMENTS The authors gratefully acknowledge  S. Bustingorry  for fruitful discussions about this work and also  R. Benavides ,  C. Pérez  and  M. Guillén  for technical support. B. P. , M. B. and J. M. acknowledge partial financial support by  PIP 11220120100250CO ,  PICT 2013-0401  and  SIIP 06/C510.
The distribution of MRC-5 cells in co-culture spheroids was visualized using fluorescence imaging (MRC-5 cells were pre-stained with green fluorescent PKH67 membrane marker). For T10F10 and T10F30 co-culture spheroids, the MRC-5 cells were mainly concentrated in the central part of the spheroid with some individual cells on the periphery. For spheroids generated from 2500 MCF-7 cells (T25F25 and T25F75), the PKH67 labeling seems to be more diffuse, perhaps due to the low cell density at the necrotic center of the spheroid. The data obtained from cryosections confirmed the results with fluorescence imaging of entire spheroids (Fig. 2 ). To complete the characterization of co-culture spheroids, we assessed the expression of stromal markers such as fibronectin and α-SMA in spheroid cryosections.
Characterization of spheroids. Fluorescence microscopy & immunohistological staining. Spheroids were collected and embedded into Tissue-Tek (Shandon Cryomatrix, Thermo Fisher Scientific, USA), before being frozen at -80 ∘C. Cryosections of 10 µm thickness were made using a Cryostat. The distribution of fibroblasts, pre-stained with PKH67, within heterospheroids was observed using 460-490 nm excitation bandpass filter associated with a 505 nm dichroic mirror and 510-550 nm emission bandpass filter by means of an epifluorescence microscope (AX-70 Provis, Olympus, France). The analysis of images was performed using ImageJ (NIH, USA) software. Custom macros for ImageJ was used to calculate the profile of dye penetration into the spheroids  43, 44. Briefly, the spheroid area was divided into 50 concentric rims with a linearly decreasing diameter.
In the images obtained by bright field microscopy, collagen fibers were colored in red, while the cytoplasm appeared as yellow. Images were analyzed with Image J (NIH, USA) software. To quantify the cumulative collagen expression, the mean intensity of Picro Sirius Red staining across the cryosection area was calculated. In brief, the images were inverted, and then a green channel of RGB image was analyzed. The automatic subtraction of background was performed for all images. Statistics. Data were analyzed using Origin software (OriginLab, Northampton, MA, USA) and presented as the means ± standard deviation (SD). At least three independent experiments were performed. The normality of the data was checked out using the Shapiro-Wilks W test. For comparison of two groups, an unpaired, two-tailed Student's t-test with Welch correction was used with a significance level of p < 0.
Acknowledgements. Numerical simulations were performed at the TGCC high-performance computer center located in Bruyèresle-Châtel (near Paris), and we thank the staff for their support ( DARI  project; grant no. A0050400295 ). ISSI (Bern, Switzerland)  is thanked for supporting the collaboration network for " Resolving the Microphysics of Collisionless Shock Waves ". The authors wish to thank  Yann Pfau-Kempf  and the anonymous referee for their helpful comments. Financial support. This research has been supported by  Labex PLAS@PAR  (grant no. ANR-11-IDEX-0004-02 ) and by the  Centre National d'Études Spatiales (CNES ; grant nos. APR-W-EEXP/10-01-01-05  and  APR-Z-ETP-E-0010/01-01-05 ). Review statement. This paper was edited by  Minna Palmroth  and reviewed by  Yann Pfau-Kempf  and one anonymous referee.
Data availability. Test particle data have been deposited in the archive of the LPP laboratory at https://ao. lpp. polytechnique. fr/ index. php/s/YtN3Bk4LttjpdD7 (Savoini, 2020). A "Readme" file has been added, which includes the creators, title, and date of last access as well as the description of the HDF5 data structure produced and a Python program that can be used to read the data directly and display the particle trajectories.
Further investigation of the dissipative effects indicated by peaks in the imaginary component reveals unexpected low temperature behaviour in both samples. The results of the SANS measurements are considered in Sec. VI, with reference to the associated features of the AC susceptibility data in Sec. V. The volume fractions of all magnetic structures are assessed, revealing that the helical-conical phase transition exhibits two distinct, thermally-separated behavioural regimes. In Sec. VII, we examine temperature dependent SANS measurements, and with comparison to previous studies of other chemically substituted skyrmion materials, discuss the observed behaviour in the context of the large, temperature-dependent cubic anisotropy exhibited by Cu 2 OSeO 3 , which may explain the unique behaviour observed in this study.
Secondly, the peak can be reduced when phase coexistence in the first order transition is suppressed, as there can be no excitation between the two magnetic states, and therefore no energy losses  [27]. Finally, when considering the decay of a metastable state, transitions to the ground state are energetically preferred. Thus, the oscillating field may only drive the transition in one direction, and no dissipative signal is expected  [27] , despite the coexistence of the magnetic states. AC susceptibility data sets measured at a range of temperatures for both the pristine and substituted samples, and following the ZFC, HFC and FC measurements procedures, are shown in Fig. 3. Critical field points at each phase boundary are indicated by the coloured vertical lines, and were determined by features in the data.