dataesr/scientific-paragraphs-categorization huggingface dataset:
This potentially hampers application to high-dimensional quantum systems. So far, three approaches have been pursued to cope with this issue. (i) Optimization of the state space representation: For the high-temperature regime of ensemble NMR spectroscopy, the SPINACH package  [169]  uses state propagation with interactions limited to pertinent short-range ones (thus leading to efficient truncation of the underlying spin Lie algebra) for simulating unprecedentedly large spins systems such as entire biomolecules  [355]  with striking precision. (ii) Gradient-free optimization: For many-body quantum systems, the chopped random basis (CRAB) method uses a parametrization of the control and interfaces the tensor compression of the time-dependent density matrix renormalization group with parameter optimization  [69, 107].
In magnetic resonance, an efficient and user-friendly software landscape is starting to emerge -there are a few software packages that support sophisticated quantum mechanical magnetic resonance simulations as well as optimal control. Spinach  [169]  supports all forms of magnetic resonance spectroscopy under the same roof, implements sophisticated spin relaxation theories as well as most mainstream optimal control algorithms that are presently used in magnetic resonance spectroscopy: GRAPE  [210] , GRAPE-BFGS  [98, 264] , Krotov  [226]  and Krotov-BFGS  [118]. Another advantage of Spinach is the availability of polynomially scaling spin dynamics simulation algorithms that make previously intractable NMR and EPR simulation (and therefore control) problems accessible  [111, 355]. In the solid-state NMR community, the SIMPSON software package (SIMulation Program for SOlid state Nuclear magnetic resonance) has been the most extensively used general-purpose software and also includes an optimal control toolbox to facilitate robust experiment design  [415].
MATPULSE  [137, 255] , DYNAMO  [264]  and QuTiP  [184]  are versatile Matlab and Python based simulation and optimization programs. Other software packages for the simulation and/or optimization of spin dynamics include SIMONE  [141] , OCTOPUS(SI)  [115, 215] , Gamma  [351]  and SPINEVOLUTION  [429]. Both analytical and numerical methods have been proven very effective for two-and three-level systems  [6, 35, 35, 38, 41, 42, 43, 135, 138, 139, 233, 238, 239, 286, 381, 384, 385, 386, 400, 403, 465] , two uncoupled spins  [15, 16] , and two coupled spins  [29, 116, 163, 204, 208, 338, 432, 457]. Moreover, significant progress has been made in understanding how to optimally control coupled spin systems with more than two spins  [36, 37, 45, 64, 71, 72, 73, 74, 202, 203, 209, 235, 296, 339, 363, 369, 395, 396, 424, 440, 441, 456, 458, 459, 460, 462].
Acknowledgements This strategic report has grown out of the Pan-European Coordination and support action on  Optimal Control of Quantum Systems (QUAINT) , funded by the  European Commission  under grant number  297861. We would like to thank all people who have participated in the community consultation for their comments and suggestions. In particular we acknowledge contributions from  Andreas Buchleitner ,  Tobias Brixner ,  Daniel Burgarth ,  Enrico Prati ,  David Tannor , and  Zdeněk Tošner. We are indebted to  Per J. Liebermann  for assisting the writing process as well as  Frank Langbein ,  Peter K. Schuhmacher  and  Andrea Dumont  for managing the community consultation.
One such technique is Adaptive Multilevel Splitting (AMS)  [2, 3] , which has been successfully tested on Monte Carlo simulations. Thus, one interest of the current work is to test for the first time the validity of AMS applied to phase space Langevin dynamics. The AMS algorithm is less sensitive to the choice of the reaction coordinate than other methods. In particular, it can be shown that the estimator of the (very small) probability to observe a reactive trajectory is unbiased whatever the choice of the reaction coordinate. Due to its versatility, AMS has promising prospects in biological MD simulations, where prior information about trajectories is often lacking due to experimental difficulties or sheer complexity of the system. We applied the AMS algorithm to a simple MD test case involving an ion in a harmonic well, using the software NAMD  [1] , developed by the Theoretical and Computational Biophysics Group in the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign.
In order to implement the algorithm, alterations to the current released version (2. 9) of the software were necessary, as described herein. Direct long-time simulations of the test case, as well as an analytic derivation of key physical quantities, were performed along with AMS simulations for comparison. We found that AMS faithfully yielded results in agreement with measurements from the direct simulations and values obtained from the analytic calculation. In one of the simulations, AMS was able to sample events that were too rare for direct simulation to reproduce, and at a measured rate that agreed with the analytic calculation using a simple model. This study is an indication that AMS is a feasible sampling method in MD simulations, and is a precursor to simulations of more complicated biological systems and efforts to streamline the software for better efficiency.
Software Modifications To establish the feasibility of applying the AMS methodology to complex MD simulations, the initial implementation described herein is largely focused on resolving several technical challenges, namely, rapid prototype development, simulation scalability, data management, and method parallelization. A general outline of the AMS implementation and organization is shown schematically in Fig. 1. Under this scheme, the AMS control code works in conjunction with NAMD to set up, run, and analyze each simulation step. A pool of NAMD instances utilize a shared file system to allow cross-process communication, enabling dynamic initialization and termination of simulations as guided by the AMS control logic. As the centerpiece of the described implementation, the NAMD software package  [1]  was chosen for its highly efficient, scalable, and feature-rich MD engine that can run on a variety of platforms and is maintained at most supercomputing centers around the world.
Two NAMD features of primary importance to AMS are the native "Colvars" implementation  [8]  and the embedded Tcl interpreter. The colvars module is leveraged to define and monitor the AMS reaction coordinate using the built-in collective variables, which are easily defined and can be combined to describe complex reaction coordinates. Access to the Tcl scripting interface, a feature unique to NAMD, allows a great deal of flexibility to rapidly develop and debug the AMS control code without requiring detailed knowledge of the internal workings of NAMD. Separating the AMS control from the NAMD "black box" requires only minor modifications to the NAMD source code-adding mechanisms for reading/ writing of restart data and sequential trajectory files. Although these features were added specifically to enable the AMS method as described herein, they are of general utility to NAMD users and will be included in the NAMD 2.
10 software release. From the outset, coupling the AMS methodology to MD simulations raised significant concerns regarding data management; MD is data-intensive (generates large positional trajectories and restart files), while AMS is highly duplicative (reactive trajectories are "copied" up to the branch point). Accordingly, the storage requirements of an AMS run are mitigated using two concurrent approaches. The first approach is centered on minimizing the number of simulation restart files that are stored. As described in the Branching Step of the algorithm, simulations are restarted at the first crossing of a particular isosurface of the reaction coordinate. In practical terms, this specification restricts potential restarts to frames in which the z-value is a new maximum observed value, as shown in Fig.
Streamlining of the code, together with enhancement in parallelization through "pseudo-parallelization" or a parallel variant of AMS (refer to Remark 1), will significantly increase gains in efficiency. These improvements will eventually be required as AMS is used for practical applications in biology, which are more challenging than the simple test cases in this study. The complexity of biological systems may require more than a straightforward increase in software efficiency. For example, one application of AMS in MD would be the measurement of dissociation rates, such as that of a drug molecule from its protein target. However, protein mechanics can be complex, sometimes involving coordinated movements along several degrees of freedom. It is not yet clear what the limitations of AMS are when applied to such systems.
Starting from a state with the ion near r 0 , the system was run at equilibrium for 10 ns with data taken every 10-fs interval. Eq. (  5 ) was then used to calculate the value of D at the respective points. The runs were repeated for k r = 0. 1, 0. 3, 0. 6 kcal/mol Å 2. The results are as follows:  Schematic of AMS implementation. The AMS control logic running within each NAMD instance monitors global progress using a shared file system to communicate information between processes. Upon selecting a system replica for restart, the associated positional, velocity, and periodic cell data is loaded into an available NAMD instance and the simulation is launched. During the course of each simulation, NAMD continually saves the simulation trajectory to disk and updates multiple elements of shared data used to drive the AMS control logic.
is gratefully acknowledged. We acknowledge the financial support of  Sanofi R&D  and beneficial discussions with  Dr. Marc Bianciotto ,  Dr. Claire Minoletti  and  Dr. Hervé Minoux , from  Sanofi-Aventis. This work has also received support from the  EU/EFPIA Innovative Medicines Initiative Joint Undertaking ,  K4DD  grant n ○  115366. The work of T. Lelièvre is supported by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013) /  ERC  Grant Agreement number  614492. The work of  C. Mayne  and  I. Teo  was supported by the  National Institutes of Health (NIH)  Grant  9P41GM104601. Last but not least, we are grateful to  Dr. Mahmoud Moradi  for the invaluable discussions about technical aspects of the NAMD colvars module, and to  Dr. James Phillips  for his assistance in modifying the NAMD code for implementation of the AMS algorithm.
The small-angle electromagnetic calorimeter ECAL2 detects photons up to scattering angles of about 40 mrad. The data presented in this Letter were recorded in the year 2009 using alternatively either hadron or muon beams. The trigger logic selects events with an energy deposit of more than 70 GeV in the central part of ECAL2 in coincidence with an incoming beam particle. In the data analysis, exactly one scattered, negatively charged particle, which is assumed to be a pion, is required to form with the incoming pion a vertex that is consistent with an interaction in the target volume. Exactly one cluster in ECAL2 with an energy above 2 GeV, which is not attributed to a produced charged particle, is required and taken as the produced photon. In order to avoid the kinematic region that is dominated by multiple scattering of the outgoing pion in the target material, only events with p T > 40 MeV/c are accepted, as shown in  As the calorimetric energy resolution is approximately constant over the range of interest and about 3 GeV, the energy balance is required to be |∆E| <15 GeV.
This work is supported by  MEYS  ( Czech Republic ); " HadronPhysics2" Integrating Activity in FP7  ( European Union );  CEA ,  P2I  and  ANR (France) ;  BMBF, DFG cluster of excellence "Origin and Structure of the Universe" , the computing facilities of the  Computational Center for Particle and Astrophysics  ( C2PAP ),  IAS-TUM and Humboldt foundation (Germany) ;  SAIL (CSR) (India) ;  ISF (Israel) ;  INFN (Italy) ;  MEXT ,  JSPS, Daiko and Yamada Foundations (Japan) ;  NRF (Rep. of Korea) ;  NCN (Poland) ;  FCT (Portugal)  and  CERN-RFBR (Russia).
We analyze the implications for inflation of the recently released Planck Cosmic Microwave Background data and explain why the single-field slow-roll scenarios with minimal kinetic terms are favored. Within this class of models, we show how Bayesian model comparison can be used to further exclude about one third of the inflationary scenarios. We also study the end of inflation and show that Planck can already constrain the reheating phase. Finally, we conclude by discussing how future missions will be able to improve our knowledge of the inflationary mechanism. Nous discutons les implications pour l'inflation des données récemment obtenues par le satellite Planck. Nous expliquons pourquoi les scenarios à un champs scalaire et terme cinétique standard, dans le régime de roulement lent, sont favorisés.
Results In the previous section, we have explained why the performances of a model can be estimated by calculating its Bayesian evidence. We now apply this ST Classification 1 1-2 2 2-3 3   technique to vanilla single-field inflationary models, the data set considered being the recently released Planck data. In Fig. 2 , following the work of Ref. [22] , we display the Bayesian evidence of nearly 200 inflationary models taken from Encyclopaedia Inflationaris  [11]. Note that contrary to the numbers quoted in section 1, derived from the Planck 2015 results, Fig. 2  is obtained from the Planck 2013 data. However, the Planck 2015 data being completely consistent with those of 2013, the main conclusions will remain unchanged. The color code corresponds to the Schwarz-Terrero-Escalante classification  [23] , see Ref.
[22]  for further details. This analysis reveals that about one third of the models can be considered as being ruled out ("strongly disfavored" in the Jeffreys scale). This gives an idea of the constraining power of the Planck data. Some of them are excluded because they provide a very poor fit to the data, while some of them are re-jected because they require an extreme level of fine tuning. Among the most favored models ("inconclusive" in the Jeffreys scale), a large majority has "plateau" shaped potential. These include Khäler moduli inflation, Higgs inflation and the Starobinsky model, exponential SUSY inflation, brane inflation, KKLT inflation, loop inflation, etc. This confirms that the Planck data have the power to constrain the shape of the inflationary potential and, therefore, can provide information with regards to the physical conditions that reigned in the very early Universe.
Conclusions The analysis presented here explains how the CMB data can constrain the physics of the early universe in an efficient way. In very brief, Planck favors vanilla inflationary scenario and, within this class of models, is able to exclude one third of the scenarios and leads to a prior to posterior ratio reduction of about 40%. The next generation of CMB missions, such as COrE+ or LiteBIRD, is currently being prepared and it is also interesting to discuss how the situation may evolve in the years to come. In Fig. 3 , we have presented forecasted results for these two missions for the case where mock data are generated assuming Higgs inflation as the fiducial model. These experiments would typically allow us to exclude three quarters of the models  [24]. This quantifies how much the constraining power of the data would increase in terms of model selection.
Acknowledgments We acknowledge the support from the  EU community Marie Curie International Incoming Fellowship (IIF) ,  FP7-PEOPLE-2011-IIF , Contract No. 302103 ,  TauKitforNewPhysics  ( Tau Toolkit  for opening the  New Physics Window at LHC  and possible spin off effects). And we thank our colleagues from the  CMS  collaboration for providing the overall CMS simulation framework.
Using a dataset corresponding to an integrated luminosity of 3. 0 fb -1 collected in pp collisions at centre-of-mass energies of 7 and 8 TeV, the B 0 s → φφ branching fraction is measured to be B(B 0 s → φφ) = (1. 84 ± 0. 05 (stat) ± 0. 07 (syst) ± 0. 11 (f s /f d ) ± 0. 12 (norm) )×10 -5 , where f s /f d represents the ratio of the B 0 s to B 0 production cross-sections, and the B 0 → φK * (892) 0 decay mode is used for normalization. This is the most precise measurement of this branching fraction to date, representing a factor five reduction in the statistical uncertainty compared with the previous best measurement. A search for the decay B 0 → φφ is also made. No signal is observed, and an upper limit on the branching fraction is set as at 90% confidence level. This is a factor of seven improvement compared to the previous best limit.
Using the current value of the B 0 s → J/ψφ branching fraction  [7] , the CDF result gives B(B 0 s → φφ) = (1. 91 ± 0. 26 ± 0. 16) × 10 -5 , where the first uncertainty is from the measured ratio to B 0 s → J/ψφ, and the second is due to the knowledge of the B 0 s → J/ψφ branching fraction. Various predictions from theories based on QCD factorization exist for the B 0 s → φφ branching fraction  [8] [9] [10]. These suffer from uncertainties related to weak annihilation diagrams. These uncertainties are controlled using experimental information from decays such as B 0 → φK * (892) 0. Several recent predictions are summarized in Table  1. The central values are in the range (1. 5 -2. 0) × 10 -5. In this paper the B 0 s → φφ branching fraction (the use of charge-conjugate modes is implied throughout) is measured using the full LHCb Run 1 dataset, comprising data corresponding to an integrated luminosity of 1.
The relative yield of each decay mode is weighted according to the expectation from the simulation. The yield of this component is left to float in the fit. Backgrounds from B 0 s → φφ and open charm decay modes are negligible after the vetoes described in Section 3 have been applied. Figure  2  shows the result of the fit of this model to the B 0 → φK * (892) 0 dataset after all selection criteria are applied. The yield of B 0 candidates determined by the fit is 6680 ± 86. The branching fraction of B 0 s → φφ relative to that of the B 0 → φK * (892) 0 decay mode is determined using B(B 0 s → φφ) B(B 0 → φK * (892) 0 ) = N φφ N φK * (892) 0 ε sel φK * (892) 0 ε sel φφ B(K * (892) 0 → K + π -) B(φ → K + K -) • 1 f s /f d , where the terms B are the branching fractions of the stated decay modes, N are the signal yields, ε sel are the selection efficiencies, and the fragmentation fraction ratio, f s /f d , is the ratio of the B 0 s to B 0 production cross-sections.
Acknowledgments The work of M. F. , A. M. , and R. W. is sponsored by the  Natural Environment Research Council (NERC)  under grants  NE/L012669/1  and  NE/H024921/1. S. G. is supported by  CNRS. The data used for this publication are available upon request from eesmf@bath. ac. uk. M. F. wishes to thank  David Ll. Jones ,  Elisabeth Blanc , and  Gerhard Müller  for inspiration and encouragement to pursue this project and acknowledges helpful discussions with  Frank Tschepke ,  Antony Fraser-Smith ,  Michael Stock , and  Daniel Kotovsky.
Motion Simulation and Dose Computation Motion simulation and dose computation were performed in in-house software named Motion Simulator for Proton Therapy (MSPT) (http://code. google. com/p/mspt/). MSPT is an opensource treatment planning software that enables 4D dose computation. MSPT can import patient CT, structure, and spot scanning treatment plan in DICOM RT format. It can simulate user-defined motion pattern on the patient dataset on a voxel-by-voxel basis, and compute delivered dose in each temporal moment considering the temporal model for the beam delivery system, using analytical dose computation algorithm. It then sums the dose for each patient voxel along the time axis and produces the total dose distribution for the patient. A detailed description about MSPT and its validation has been published elsewhere (Morel et al.
Three sets of parameters are then estimated during the analysis: zenith tropospheric delays (ZTDs), gradients, and post-fit residuals, which are the difference between the modeled atmosphere and the measurements. The GPS data were processed using GAMIT software package v10. 32  (King and Bock, 2007) , which solves the tropospheric and other parameters using a constrained least squares algorithm. The GPS network used in our typical differential simulation includes 21 other local stations mainly located around the Réunion volcano massif and about 15 stations overseas to ensure a sufficiently high numbers of baselines. The cutoff elevation angle was fixed to 10 •. The ZTD, estimated by the software, is then split into its hydrostatic (usually called dry) and wet components at zenith: ZTD = ZHD + ZWD, where ZHD refers to zenith hydrostatic delay and ZWD to zenith wet delay.
, 2015). For the MALICCA-1 campaign, we used a mobile Vaisala model-SPS 220 S/N: Y49101 mobile station, owned by CNRS/INSU and METEO FRANCE. The software used was VAISALA DigiCORA V3. 64. The ground check station of radiosonde initialization was a VAISALA GC Set 25 S/N: Z35204. Totex 1200 g balloons were used for all flight. 15 RS92 GP radiosondes were launched within 2 weeks. The Vaisala RS92 radiosonde is based on thin-film technology  (Salasmaa and Kostamo, 1975)  that uses dual H-Humicap sensors, which consist of a hydrophilic polymer film acting as dielectric of a capacitor applied on a glass substrate. A reconditioning procedure that alternately heats the two sensors eliminates the problem of sensor icing in clouds. The RS92 response time strongly depends on temperature and on the polymer's ability to adsorb and desorb water vapor.
Given these considerations, further tests will be performed to determine and reduce the factors increasing the variability with the aim of optimizing the IWV GPS procedure so that it could be used as the standard calibration methodology for RMR-H 2 O lidar. The estimated calibration coefficient will be then compared daily and validated through radiosonde data derived by the meteorological station located 20 km from the station as well as to the other sensors in case of intensive measurement campaigns.
Acknowledgements. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ), the  University of Versailles and Saint-Quentin in Yvelines (UVSQ)  under grant agreement  RBUCE-UP N • 246556 , the  CNRS-INSU  through the annual support of the  NDACC Observing Service ; the  Alliance AllEnvi  through the  ROSEA  project ( French initiative  from the ALLENVI structure dedicated to observation and investigation of atmospheric water). RS92 were launched thanks to the cooperative Vaisala receiver provided by  GMEI/4M  group of the  CNRM (CNRS-Météo-France). The authors acknowledge the European Communities, the  Région Réunion, CNRS , and  Université de La Réunion  for their support and contribution in the construction phase of the research infrastructure  OPAR  ( Observatoire de Physique de l'Atmosphère à La Réunion ).
Acknowledgement I am grateful to Jean-Yves Fortin for his early collaboration on this project and daily discussions. I would like to thank Jean-Marie Stéphan for crucial help and remarks on the height mapping and Christophe Chatelain, Jérôme Dubail, Malte Henkel, Jesper Jacobsen, Shahin Rouhani and Loïc Turban for insightful discussions and useful comments at different stages of the elaboration of this article. This work was partly supported by the Collège Doctoral Leipzig-Nancy-Coventry-Lviv (Statistical Physics of Complex Systems) of UFA-DFH.