dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations All simulations were done in Matlab R2014b using the forward Euler method with an integration step of 0. 1 ms. We confirmed that this integration step produces stable numerical results. The bifurcation and phase-plane analyses were carried out in XPPAUT 7. 0. The model code is available on ModelDB (https://senselab. med. yale. edu/modeldb/). The parameters of the aEIF model were set to the values determined by fitting data from a representative Purkinje cell. A variation of the parameters within the range occurring in the Purkinje cell population was performed to determine their influence on the ISR range. We note that all occurring parameter combinations were restricted to a g L > t m t w , corresponding to type II excitability (Fig  4D ) Noise stimulus was modeled as I(t) = I mean + I noise (t), where I mean is constant and I noise (t) is current noise with zero mean generated using an Ornstein-Uhlenbeck process with amplitude σ and time constant τ c = 2 ms.
This work was partially funded by the grants  FRM FDT20140930942 ,  ANR-10-LABX-0087 IEC  and  ANR-10-IDEX-0001-02 PSL (BSG  and AB), and by The  Wellcome Trust and European Research Council (MH). Numerical simulations and the model analysis in this work are results of the project financially supported by the  Russian Ministry of Education  (Contract no. 14. 608. 21. 0001 , unique  ID  project  RFMEFI60815X0001 ) and were performed by AB and BSG at the  National Research University Higher School of Economics Moscow. The funders had no role in study design, data collection and input-output transformation.
Acknowledgements This work has been supported by the  Agence Nationale de la Recherche  ( ANR-HYDRIDES ), contract  ANR-12-BS05-0011-01 , by the  CNRS national program  " Physico-Chimie du Milieu Interstellaire ", and by  National Science Foundation  Grant  CHE-1152899. We acknowledge the  MCIA (Mésocentre de Calcul Intensif Aquitain) of the Universities of Bordeaux and Pau et des Pays de l' Adour  for providing computing facilities.
The intensity in the reference beam was controlled with a neutral density filter. We recorded the transmitted intensity by the coupled cavities I c , and the intensity in the reference beam I r. Next, I c and I r were combined by a second beam splitter placed between the output of the cavities and the detector. We call the combined total intensity I t. The data was analyzed with the two-beam interference equation I t = I c + I r + 2 √ I c I r cos(kz). The quantity kz corresponds to the optical path difference between the two arms of the interferometer, which is controlled by the position of the second beam splitter and the alignment of the two beams. Figures  4(d, f, h ) plot the cos(kz) term in color as a function of space. We call this quantity, bounded between -1 and 1, the normalized interferogram.
8 µeV • µm 2. Dividing by |X| 2 = 0. 16 2 we get 30 µeV • µm 2 for the pure exciton-exciton interaction constant. A similar value for the exciton-exciton interaction constant has been theoretically estimated in Ref. 24. by the Marie Curie individual fellowship PINQUAR, a public grant over-seen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (LabexNanoSaclay, reference: ANR-10-LABX-0035), the ANR project Quandyde (Grant No. ANR-11-BS10-001), the French RENATECH network, and the European Research Council grant Honeypol. I. SUPPLEMENTAL MATERIAL A. Exciton polaritons in the 2D cavity The microstructure investigated in the main text is fabricated by etching a two-dimensional (2D) cavity into the shape of two coupled cavities [Fig. 1 (a) in the main text].
PoS(ICRC2015)808 further observations were taken in May and June 2014. PG 1553+113 was observed with H. E. S. S. II between May 30, 2013 and Aug 7, 2013. To ensure the quality of the data sets and their fitness for the CT5 Mono analysis the following run quality criteria were applied. • Source position located between 0. 35 and 1. 2 • off-axis from camera center; • Radiometer temperature less than 20 • C and stable during the run within ± 3 • C; • Relative humidity < 90%; • Run duration > 5 min and live time fraction > 90%; • At least 90% of pixels in CT5 are functional; • CT5 trigger uses standard configuration, trigger rate between 1200 and 3000 Hz and stable within ±10%; • Telescope tracking functions normally.
A photon index of 3. 53 ± 0. 06 stat ± 0. 10 syst was reported in  [3]  for the low flux state (2005-2007) above 200 GeV. For average and high flux states the presence of a curvature or a cut-off was favored by the spectral fit  [3]. The PKS 2155-304 data set, filtered as explained in Sect. 2, comprises 138 data taking runs. The total live time of the data set is 56. 0 hr. The observation zenith angle ranges from 7 • to 60 • , with a median value of 16 •. This data set was analyzed using Standard cuts as described in Sect. 3. The background event counts obtained for the OFF regions in each run (in the reflected background analysis) were used to perform an additional test of the uniformity of the camera acceptance. This was done by computing, using a likelihood ratio test, the p-value of the hypothesis that the event counts observed in all OFF regions come from the same Poisson distribution.
PG 1553+113 The HBL object PG 1553+113 was first established as a VHE emitter in 2005 by H. E. S. S. [5]. The H. E. S. S. I measurements  [6]  yield a photon index Γ = 4. 5 ± 0. 3 stat ± 0. 1 syst above 225 GeV. At high energies (HE, 100 MeV < E < 300 GeV) the source was detected by Fermi-LAT with a photon index of 1. 68 ± 0. 03  [17, 18]. The redshift of PG 1553+113 is constrained by UV observations to the range 0. 43 < z 0. 58  [19]. Assuming that the difference in spectral indices between the HE and VHE regimes is due to the attenuation by the extragalactic background light, the redshift was constrained to z = 0. 49 ± 0. 04  [7]. The PG 1553+113 data set, filtered as explained in Sect. 2, comprises 39 data taking runs (16. 8 hr live time), which were analyzed using Loose cuts as described in Sect.
Conclusion An analysis of two bright AGN, PKS 2155-304 and PG 1553+113, using the H. E. S. S. CT5 data has been presented. Both sources are detected with high significance, with an energy threshold of ≈ 80 and 100 GeV for PKS 2155-304 and PG 1553+113, respectively. A spectral curvature has been confidently observed for both sources. The measured spectrum of PKS 2155-304, using the 2013 and 2014 H. E. S. S. data, is approximately consistent with the quiescent spectrum reported earlier by H. E. S. S. , as well as the Fermi 3FGL and 1FHL catalogs. The obtained PG 1553+113 spectrum is in good agreement with earlier H. E. S. S. measurements and the Fermi catalogs. All results presented in this paper are preliminary.
The planned Cherenkov Telescope Array (CTA), a future ground-based Very-High-Energy (VHE) gamma-ray observatory, will be the largest project of its kind. It aims to provide an order of magnitude increase in sensitivity compared to currently operating VHE experiments and open access to guest observers. These features, together with the thirty years lifetime planned for the installation, impose severe constraints on the data model currently being developed for the project. In this contribution we analyze the challenges faced by the CTA data model development and present the requirements imposed to face them. While the full data model is still not completed we show the organization of the work, status of the design, and an overview of the prototyping efforts carried out so far. We also show examples of specific aspects of the data model currently under development.
Still their common aspects are enough to allow them to be described by a common data model. Data will be processed on the observatory sites by both a real time and a delayed analysis chains to generate science alerts and monitor the instrument. Afterwards it will be transmitted to the off site data centers for the final analysis and storage  [2]. These centers will provide data access services to the scientists and technical personnel of the observatory. Besides being able to efficiently cope with a large foreseen data rate, the CTA data management chain should provide open access to the data and implement stable formats that can last at least for the foreseen lifetime of the observatory, 30 years. In this paper we discuss how the CTA Data Model group is facing these challenges. We start in section 2 with an estimation of the data rates expected for CTA and follow by explaining the different types of data and data levels that have been identified along the data reduction chain.
Data Model products The first two levels of the Data Model working group Product Breakdown Structure (PBS) are presented in figure  2. They all proceed from the Data Model Product, numbered as 4. 1 in the CTA PBS. The PBS comprises six main products in addition to the work package documentation. The Common Components product (4. 1. 1) groups items that are related to several data levels at the same time or whose aspects can affect several data levels. Three of them have been identified: the Instrument Configuration Database (to keep the information related to array geometry, telescope geometry, etc), the Data Access Libraries, and the Metadata and Workflow Interface Description Repository which will contain the information about all the Metadata and Data exchanged among packages in the observatory.
Design and Status Despite a few choices remaining to be made the data model for CTA is almost complete. The most advanced parts of data model are those which require a close collaboration with other groups and which will be needed in the nearest future. There is a clear scheme for the Instrument Configuration Database, itself part of the Common Components product. In the definition of the DL0 several options have been proposed and are currently being tested. High level data and IRFs will be provided to users through files using FITS formats. For DL3 data, composed of lists of events, and DL4 and DL5, the development is being driven by interaction with the IVOA. For IRFs two competing formats are currently being tested. While proposals and prototypes exist for intermediate data, their development will take place jointly with the one of the pipelines which will use and produce them, since typically they will not be delivered openly.
Common Components Among the three products grouped as Common Components we single out the Instrument Configuration Database. It is conceived as a repository, which can be thought as a database, to keep information needed to define the instrument, e. g. : array coordinates, telescope types and positions, camera types, etc. It aims to reduce the dependence of the software on the time evolution of the hardware. Along the life of the observatory some components will change more often than others, therefore the database will have to be updated regularly. It will also contain Monte Carlo configurations, since the simulations will likely use simplified or averaged descriptions of the instrument. While the repository could be derived from similar products needed by other packages inside CTA, the interface to the pipeline software will need to be coded ex-novo.
Low Level Data The Low Level Data, collectively called DL0, are defined as the lowest level of Event (EVT), Calibration (CAL) and Technical (TECH) data that are permanently archived. They come directly from the DAQ and might need to, or have already been, modified on-site by some level of processing such as compression or zero suppression to meet storage requirements. Its volume is determined by the amount of data produced by the cameras, EVT data, with a contribution around 10-20% from CAL and TECH. The Data Flow for Low level data assumes that the images (EVT) from each telescope will be kept in separate files together with some calibration and technical information needed for their first processing. EVTs from different telescopes will only be merged once they are calibrated, at DL1, or in a preliminary process in the online analysis.
This scheme eases the parallel processing. Each file will contain a time ordered chain of images from the camera of the telescope, acquired at the high rates imposed by the trigger and parallel chains of CAL and TECH information acquired at lower frequencies. The detailed content of DL0 data is presently being established in Interface Control Documents (ICDs) between the Data Management and Array Control groups and the groups building the cameras. Its main component will be the camera events, composed of camera information and different levels of pixel information depending on the data reduction level applied to each pixel. For the format of the data and the files containing DL0 data three options are being considered and prototyped right now. One of them is based on google protobuffs protocol [7] and compressed FITS  [10] , another one in the PACKETLIB  [8]  format used in space missions and a third one is an extension of the format presently used for the Monte Carlo data by the H.
Intermediate Level Data Data of htis level will only be used by CTA pipelines and possibly internal CTA observatory staff. Therefore their definition is more open and will develop in conjunction with the pipeline work. A possibility which has been considered and tested is the use of the HDF5 file format. Another option proposed is the use of Regions Of Interest (ROI), keeping only sections of the camera surrounding the images. They allow to efficiently reduce the information, conserving a small fractions of pixels with no signal for calibration purposes. More information about a framework and file format based on the ROI approach, MESS, can be found in the contribution published in these proceedings  [6].
High Level Data High Level data comprises the data levels that the CTA observatory will provide to guest observers and the scientific community in general: DL3, DL4 and DL5. They must be provided in open, self documented formats. The observatory requirement is to use the FITS format. Among the high level components the most important one is the DL3, consisting of lists of selected events (eg. gamma rays or electrons) and the associated HL-IRFs needed to interpret them. DL3 data will be delivered to guest observers together with a science tools package enabling them to tailor the analysis to their needs. Details on the observer access design for CTA can be found elsewhere in these proceedings  [4]. A DL3 event will contain three kinds of information: the quantities characterizing the particle (energy, direction, gamma/hadron tagging, etc.
Metadata The Metadata group is working towards defining a full set of metadata for CTA. The work has started by sketching the global UML diagrams and then refining the description for different data levels. There is a close contact with the activities related to the IVOA. To ensure the integration of CTA data within the IVOA infrastructure the first step was to identify the building blocks from existing IVOA data models suitable for description of gamma-ray data. This type of data has never before been made publicly available in a common, open format. Current astronomical metadata standards and VHE gamma-ray data conventions are being studied for this purpose, working together with IVOA scientists.
5%. Systematic uncertainties introduced by the usage of the two-dimensional signal distribution function of Eq. (  4 ) are negligible. Detailed comparisons of the shape of the radio signal distribution measured with LOFAR with the predictions from CoREAS show no indication of any systematic discrepancy  [43]. We determined the influence of the 2D-LDF model on the radiation energy in a representative CoREAS Monte Carlo data set for the AERA detector and found a systematic effect of less than 5%. Combining all uncertainties in quadrature, the systematic uncertainty of the electric-field amplitude is 14%. The radioenergy fluence and the energy estimator scale with the amplitude squared. Therefore, the systematic uncertainty of the absolute scale of the radiation energy is 28%. We note that, as the cosmic-ray energy is proportional to the square root of the radiation energy (see next section), the systematic uncertainty of a radio cosmic-ray energy scale would remain at 14%.
We found that the effect is negligible for our data set. The uncertainty on the absolute scale of the energy estimator as discussed in Sec. IV C is calibrated out by correlating S radio with E CR. The method, however, inherits the uncertainties of the SD energy scale. This scale uncertainty is dominated by the FD scale uncertainty, which is used to calibrate the SD. It is 14% at energies ≥ 10 18 eV  [48]  and increases to 16% at 10 17. 5 eV. A power law is fitted to the data using a likelihood approach which takes all uncertainties and detection efficiencies into account. Green filled circles denote air showers where the core position has been determined in the radio LDF fit, i. e. , all air showers with at least five stations with signal. Open circles denote events with less than five stations with signal and use the SD core position.
VI. CONCLUSIONS The Auger Engineering Radio Array is the radio detector of the Pierre Auger Observatory. It is located within the lowenergy extension of the Observatory where additional surface detector stations with a smaller spacing are present, which enables access to cosmic-ray energies down to 0. 1 EeV. For the analysis presented here we only use the thoroughly calibrated 24 LPDA radio stations of the first stage of AERA deployment, with data collected between April 2011 and March 2013. At several observer positions, the energy deposit per area of the radio pulse of an extensive air shower is measured. Using recent progress in understanding the lateral signal distribution of the radio signals, this distribution is described by an empirical function. The spatial integral of the lateral distribution function gives the amount of energy that is transferred from the primary cosmic ray into radio emission in the 30 to 80 MHz frequency band of AERA during the air-shower development.
ACKNOWLEDGMENTS The successful installation, commissioning, and operation of the  Pierre Auger Observatory  would not have been possible without the strong commitment and effort from the technical and administrative staff in Malargüe. We are very grateful to the following agencies and organizations for financial support: Comisión  Nacional de Energía Atómica, Agencia Nacional de Promoción Científica y Tecnológica (ANPCyT) ,  Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) ,  Gobierno de la Provincia de Mendoza ,  Municipalidad de Malargüe ,  NDM Holdings  and  Valle Las Leñas , in gratitude for their continuing cooperation over land access, Argentina; the  Australian Research Council  ( DP150101622 );  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundacão de Amparo à Pesquisa do Estado de Rio de Janeiro (FAPERJ) ,  São Paulo Research Foundation (FAPESP)  Grants No.
2010/07359-6  and No. 1999/05404-3 ,  Ministério de Ciência e Tecnologia (MCT), Brazil ; Grant No. MSMT-CR LG13007 , No. 7AMB14AR005 , and the  Czech Science Foundation  Grant No. 14-17501S ,  Czech Republic ;  Centre de Calcul IN2P3/CNRS ,  Centre National de la Recherche Scientifique (CNRS) ,  Conseil Régional Ile-de-France ,  Département Physique Nucléaire et Corpusculaire  ( PNC-IN2P3/CNRS ),  Département Sciences de l'Univers (SDU-INSU/CNRS) ,  Institut Lagrange de Paris (ILP)  Grant No. LABEX ANR-10-LABX-63 , within the  Investissements d'Avenir Programme  Grant No. ANR-11-IDEX-0004-02 , France;  Bundesministerium für Bildung und Forschung (BMBF) ,  Deutsche Forschungsgemeinschaft (DFG) ,  Finanzministerium Baden-Württemberg ,  Helmholtz Alliance for Astroparticle Physics (HAP) ,  Helmholtz-Gemeinschaft Deutscher Forschungszentren (HGF) ,  Ministerium für Wissenschaft und Forschung ,  Nordrhein Westfalen, Ministerium für Wissenschaft, Forschung und Kunst, Baden-Württemberg, Germany ;  Istituto Nazionale di Fisica Nucleare (INFN) ,  Istituto Nazionale di Astrofisica (INAF), Ministero dell'Istruzione, dell'Universitá e della Ricerca (MIUR) ,  Gran Sasso Center for Astroparticle Physics (CFA) ,  CETEMPS Center of Excellence, Ministero degli Affari Esteri (MAE), Italy ;  Consejo Nacional de Ciencia y Tecnología (CONACYT) ,  Mexico ;  Ministerie van Onderwijs, Cultuur en Wetenschap ,  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Stichting voor Fundamenteel Onderzoek der Materie (FOM) ,  Netherlands ;  National Centre for Research and Development , Grants No.
Then, results of simulations are filtered by the multi-detector replica. Simulated spectra are normalized to the area of experimental spectra. For sequential emission the GEMINI++ code  [12]  was used. Before discussing N α sources, as said before, we must consider the possible evaporation from Ca projectiles. Excitation energy for projectiles is deduced from E * =E * (N α)+E rel +Q. E rel is the relative energy between the N α source and the associated fragment (evaporation residue). Fig. 2  displays results of simulations with reconstructed excitation energy distribution for 40 Ca (< E * >=88. 2 MeV) and gaussian distributions centred at 15 and 25h for spins (RMS=1. 5h) as inputs; note that no more 20 N e residues are produced for spin distributions centred at values larger than 25h. Comparison with experimental data shows a rather poor agreement indicating that such an hypothesis seems not correct.
Consequently, a classical particle would never be confined by such a potential, independent of its energy. Both the effective potential W and the fundamental quantum state ψ 0 are obtained using 320,000 triangular Lagrange finite elements of degree 3, with a code based on the FEniCS finite element software environment  [16]. In order to examine in detail the long-range decay of this quantum state, the quantity |∇ log(|ψ|)| which can be interpreted as the inverse of the local length of decay of the wave function, is plotted in Figure  2(d). Superimposing the level sets of this quantity with the crest lines of 1/u (which are also the valleys of the landscape u) reveals a very strong correlation: the local length of decay increases substantially in the vicinity of the barriers of W. The long-range exponential decay in Anderson localization does not occur uniformly but rather appears as the consequence of successive and cumulative decays across the dense network of barriers generated by W.
Finally, our approach brings a new perspective to such open questions as the nature of the transition from localized to delocalized states in three dimensions (the mobility edge) and the onset of localization in many body systems. D. N. A. is partially supported by the U. S. NSF Grant No. DMS-1418805. G. D. is member of the Institut Universitaire de France, and is partially supported by an ANR Grant, programme blanc GEOMETRYA, Grant No. ANR-12-BS01-0014. D. J. is partially supported NSF Grants No. DMS-1069225 and No. DMS-1500771. S. M. is partially supported by the Alfred P. Sloan Fellowship, NSF CAREER Grant No. DMS-1056004, the NSF MRSEC Seed Grant, and the NSF INSPIRE Grant No. DMS-1344235. M. F. is partially supported by a PEPS-PTI grant from CNRS.
2012) , it probes at high angular resolution the rest-frame optical morphologies of galaxies from z ∼ 3 with a similar depth to the deepest NIR ground-based surveys. In this sense, it is currently the best available dataset for establishing robust constraints on the abundance of different morphologies in the early universe. This is the main purpose of the present work. In  Huertas-Company et al. (2015) , we used new deeplearning techniques to estimate the morphologies of all galaxies with H < 24. 5 in the five CANDELS fields with unprecedented accuracy 1. We now use these morphologies, together with robust stellar mass estimates from extensive multi-band imaging, to study the evolution of the SMFs of quiescent and star-forming galaxies of different morphologies from z ∼ 3, for the first time.