dataesr/scientific-paragraphs-categorization huggingface dataset:
Though our method can be applied to the nonlinear system (2)-(  3 ), just as an example, we shall consider the Asian call option of the geometric Brownian motion of Equation 4 with µ = 1 and x 0 = 1, and g(x) = max F (0) -K + εx, 0. (8) Set the model (  2 ) and the functional (3) as follows: R> model <-setModel(drift = "x", diffusion = matrix("x * e", 1, 1)) R> T <-1 R> xinit <-150 R> K <-100 R> f <-list(expression(x/T), expression(  0  This time the setFunctional command fills the appropriate slots inside the 'yuima' object R> str(yuima@functional) Formal class 'yuima. functional' [package "yuima"] with 4 slots. @ F : num 0. @ f :List of 2. $ : expression(x/T). $ : expression(0). @ xinit: num 150. @ e : num 0. 5 Then the limit F (0) of F (ε) is easily obtained by calling the function F0 on the 'yuima' object: R> F0 <-F0(yuima) R> F0 [1] 257.
Thus our method works even under the existence of a stochastic discount factor. One can compare the result of the asymptotic expansion with other well known techniques like Edgeworth series expansion for the log-normal distribution as proposed, e. g. , in  Levy (1992). This approximation is available through the package fExoticOptions  (Wuertz 2012). R> library("fExoticOptions") R> levy <-LevyAsianApproxOption(TypeFlag = "c", S = xinit, SA = xinit, + X = K, Time = 1, time = 1, r = 0. 0, b = 1, sigma = e)@price R> levy [1] 157. 7712 and the relative difference between the two approximations is -0. 1%.
How to input data into a 'yuima' object Although most of the examples in this section are given on simulated data, the main way to fill up the data slot of a 'yuima' object is to use the function setYuima. The function setYuima sets various slots of the 'yuima' object. In particular, to estimate a 'yuima. model' called mod on the data X one can use a code like this my. yuima <-setYuima(data = setData(X), model = mod) and then pass my. yuima to the inference functions as described in the following. For example, assuming that an Internet connection is available, the following simple list of commands downloads data from the internet and constructs a 'yuima' object with the data slot containing the time series. R> data <-read. csv("http://chart. yahoo. com/table. csv?s=IBM&g=d&x=. csv") R> x <-setYuima(data = setData(data$Close)) R> str(x@data).
Then the QMLE of θ is an estimator that satisfies θ = arg max θ n (X n , θ) exactly or approximately. The yuima package implements QMLE via the qmle function. The interface and the output of the qmle function are made as similar as possible to those of the standard mle function in the stats4 package of the basic R system. The main arguments to qmle consist of a 'yuima' object and initial values (start) for the optimizer. The 'yuima' object must contain the slots model and data. The start argument must be specified as a named list, where the names of the elements of the list correspond to the names of the parameters as they appear in the 'yuima' object. Optionally, one can specify named lists of upper and lower bounds to identify the search region of the optimizer. The standard optimizer is BFGS when no bounds are specified.
The process X l is supposed to be observed over the increasing sequence of times T l,i (i ∈ Z ≥0 ) starting at 0, up to time T. Thus, the observables are (T l,i , X l,i ) with T l,i ≤ T. Each T l,j may be a stopping time, so it possibly depends on the history of (X 1 , X 2 ) as well as on the precedent stopping times. Two sequences of stopping times T 1,i and T 2,j are nonsynchronous, and irregularly spaced, in general. In particular, function cce from package yuima can be applied to estimate the quadratic variation of a single stochastic process sampled regularly/irregularly. The parameter of interest is the quadratic covariation between X 1 and X 2 : θ = X 1 , X 2 T = T 0 σ 1,t σ 2,t ρ t dt. ( 14 ) The target variable θ is random in general and it can be estimated with the nonsynchronous covariance estimator (Hayashi-Yoshida estimator) U n = i,j:T 1,i ≤T,T 2,j ≤T (X 1,T 1,i -X 1,T 1,i-1 )(X 2,T 2,j -X 2,T 2,j-1 )1 {(T 1,i-1 ,T 1,i ] (T 2,j-1 ,T 2,j ] =∅}.
(15) That is, the product of any pair of increments (X 1,T 1,i -X 1,T 1,i-1 ) and (X 2,T 2,j -X 2,T 2,j-1 ) will make a contribution to the sum only when the respective observation intervals (T 1,i-1 , T 1,i ] and (T 2,j-1 , T 2,j ] are overlapping. It is known that U n is consistent and has asymptotically mixed normal distribution as n → ∞ if the maximum length between two consecutive observing times tends to 0. See  Hayashi and Yoshida (2005 , 2006 , 2008a,b)  for details. Example: Data generation and estimation by yuima package We will demonstrate how to apply function cce to nonsynchronous high frequency data by simulation. As an example, consider a two dimensional stochastic process (X 1,t , X 2,t ) satisfying the SDE dX 1,t = σ 1,t dB 1,t , dX 2,t = σ 2,t dB 2,t. (16) Here B 1,t and B 2,t denote two standard Wiener processes, however they are correlated as B 1,t = W 1,t , (17) B 2,t = t 0 ρ s dW 1,s + t 0 1 -ρ 2 s dW 2,s , (18) where W 1,t and W 2,t are independent Wiener processes, and ρ t is the correlation function between B 1,t and B 2,t.
We consider σ l,t , l = 1, 2, and ρ t of the following form in this example: σ 1,t = √ 1 + t, σ 2,t = 1 + t 2 , ρ t = 1 √ 2. To simulate the stochastic process (X 1,t , X 2,t ), we first build the model by setModel as before. It should be noted that the method of generating nonsynchronous data can be replaced by a simpler one but we will take a general approach here to demonstrate a usage of yuima which shows that it is a comprehensive package for simulation and estimation of stochastic processes. R> diff. coef. )"), 2, 2) R> cor. mod <-setModel(drift = c("", ""), diffusion = diff. coef. matrix, + solve. variable = c("x1", "x2")) The parameter we want to estimate is the quadratic covariation between X 1 and X 2 : θ = X 1 , X 2 T = T 0 σ 1,t σ 2,t ρ t dt. ( 19 ) Later, we will compare estimated values with the true value of θ given by R> CC.
An example of model selection for interest rates data The lasso method is implemented in the yuima package. Let us consider the full CKLS model  (Chan, Karolyi, Longstaff, and Sanders 1992)  dX t = (α + βX t )dt + σX γ t dW t and let us try to estimate the parameter on the U. S. Interest Rates monthly data from 06/1964 to 12/1989 (see Figure  16 ). We prepare the data from package Ecdat (Croissant 2014), the model and the constraints for optimization R> data("Irates", package = "Ecdat") R> rates <-Irates[, "r1"] R> plot(rates) R> X  <-window(rates, start = 1964. 471, end = 1989. 333 ) R> mod <-setModel(drift = "alpha + beta * x", + diffusion = matrix("sigma * x^gamma", 1, 1)) R> yuima <-setYuima(data = setData(X), model = mod) R> lambda10 <-list(alpha = 10, beta= 10, sigma = 10, gamma = 10) R> start <-list(alpha = 1, beta = -.
Miscellanea and roadmap of the YUIMA project Other statistical techniques have already been implemented in the development version of the yuima package although not yet released into the current distribution. Among these we mention: the QMLE approach for SDE's with Lévy noise  (Shimizu and Yoshida 2006; Ogihara and Yoshida 2011) ; the parametric estimation for the fractional Ornstein-Uhlembeck model  (Brouste and Iacus 2013) ; different simulation schemes as in  Iacus (2008)  for multidimensional diffusion processes; lead-lag estimation  (Hoffmann, Rosenbaum, and Yoshida 2013) ; hypotheses testing via φ-divergence  (Gregorio and Iacus 2013). For example, a nice (yet incomplete) utility is the method toLatex for objects of class 'yuima' and 'yuima. model'. A simple writing like toLatex(my-yuima-obj) produces the related L A T E X code which can be copied and pasted in a mathematical paper.
Acknowledgments This work was in part supported by  Japan Society for the Promotion of Science Grantsin-Aid for Scientific Research No. 24340015  ( Scientific Research ), No. 24650148  ( Challenging Exploratory Research ); the  Global COE Program "The Research and Training Center for New Development in Mathematics" of the Graduate School of Mathematical Sciences, University of Tokyo ; by a  Cooperative Research Program  of the  Institute of Statistical Mathematics  and by the project  PRIN 2009JW2STY ,  Ministero dell'Istruzione dell'Università e della Ricerca. Project  YUIMA I  was in part supported by  JST Basic Research Programs PRESTO. Project  YUIMA II  is in part supported by  NS Solutions Corporation. NS Solutions Corporation and The Graduate School of Mathematical Sciences, University of Tokyo  are conducting a joint study ( Azzurro  project) on " The Application of Advanced Mathematical Statistics Theories in the Financial Industry ".
Identification of GINIP, a G αi -Interacting Protein In an attempt to increase the repertoire of markers defining discrete subsets of primary nociceptive neurons, we used Affimetrix microarrays to compare global gene expression profiles of DRG of wild-type and trkA trkC/trkC knockin mice at birth  (Moqrich et al. ,2004; Legha et al. , 2010). Among the several hundred potential markers identified, mkiaa1045 named GINIP (for Gα inhibitory interacting protein) encodes a highly conserved vertebrate 400 amino acid protein of unknown function (Figure  1A ). SMART program analysis showed that GINIP is endowed with two functional domains: a plant homeodomain (PHD) zinc finger and two putative EF hand domains. In order to assign a potential function to GINIP, we launched a yeast two-hybrid assay using the full-length GINIP as bait to screen an adult mouse brain cDNA library.
ACKNOWLEDGMENTS We would like to thank  Robert Kelly  for his comments on the manuscript. We also thank members of the laboratory for scientific discussions, the IBDM imaging, and animal facilities for assistance. Thanks to the  ARPEGE Pharmacology core facility at IGF  and to  Sebastien Leulier  for his assistance with the BRET experiments. We are grateful to  L. F. Reichardt  for providing anti-TrkA antibody. G. S. is supported by  NIH  grant  DA031777 , and S. A. L. is supported by an  HHMI Medical Research  fellowship. This work has been funded by the  ERC -starting grant, paineurons  260435. The authors declare that they have no competing financial interests.
Overall, this figure will have to be multiplied by ∼1. 5 to take into account the power dissipation of linear power supplies. 7. 5 kW is thus the likely power requirement at room temperature. For heat loads from cabling we consider a composite of Kapton / Apical with glue layers and stainless steel conductors. The signal tracks are in the middle of the cable and there is a screening layer on either side. Overall the composite is ∼0. 85 mm thick and the track pitch is 0. 5 mm. The thickness of the stainless steel layers is 18 μm. The three scenarios discussed below are EDELWEISS detectors ( Table  13  ), CRESST detectors (based on 300 g per module, Table  14  ). The EURECA scenario is based on scintillation detectors also achieving a mass per module of 800 g and enough cabling to allow total flexibility in detector installation ( Table  15  ).
Data analysis and simulation This section deals with data storage and processing, the hardware and infrastructure needed to access all data banks, as well as the software developments to analyse data offline. It presents the procedures of defining and implementing analysis cuts as well as the large variety of simulation packages needed to model different background components, calibration sources and potential WIMP signals. The topics, described briefly below, are standard approaches in astroparticle physics experiments and the analysis of data from dark matter searches. It is obvious that analysis and simulation tools will be developed according to the experimental progress. Thus, for the sake of completeness of this report, the following describes a generic procedure for data analysis and modelling of signals and background.
An easy access database will be established, with all relevant links to experimental raw data and slow control parameters and with appropriate secured access to all collaboration members. It is expected that the access to raw data will take place through this database. An example of implementation is already used in EDELWEISS-III, based on the CouchDB database  [ 40 ]. The computing resources used for offline processing will be provided by computing centres and member institutions. Most of the CPU usage will be dedicated to pulse fitting operations. Based on the experience from EDELWEISS and CRESST, the total anticipated CPU for data processing will be very easily provided by computing centres such as the CC-IN2P3.
Data reduction Data reduction will comprise mostly the offline processing of raw bolometer traces and their precise calibration. There are already data reduction tools developed for pre-EURECA experiments, such as the Oxrop set of tools, the KData framework  [ 40 ]  and other EDELWEISS and CRESST data processing software. These existing analysis tools will be further developed, integrated and adapted for a specific implementation for EURECA. It is foreseen that a significant effort to organise and direct the adaptation of these tools will be needed. The data reduction processes will require multiple databases containing input data needed for the data reduction analysis (such as pulse shape templates), output calibration coefficients, and the meta data associated with the raw and calibrated data files.
Analysis, simulations and WIMP sensitivity The analysis will be done by groups from different institutions within EURECA, providing multiple, independent analysis chains for cross-checks on the analysis of physics runs. Coordination between analysis groups will define the analysis principles and standard cuts. For WIMP search data, blindness procedures will be imposed in order not to bias the different cuts, which will be set using only calibration data or background data incompatible with WIMPs (for example multiple scattering events). A substantial effort in simulations will be dedicated to the modelling of signals generated by WIMPs in both types of detectors, and to background processes such as gamma, neutron, alpha and beta radioactivity from materials in the detector vicinity, or muon-induced backgrounds.
Theoretical models of spin-dependent and spin-independent interactions will be used, as well as specific models of WIMP halos and their systematics. Finally, a combination of EURECA data and results from the LHC experiments at CERN will provide complementary constraints on various models of new TeV-scale physics such as generic supersymmetric scenarios or extra dimensions. The EURECA detectors will also be able to address other physics topics. Alternate dark matter models will be explored such as inelastic-scattering dark matter, isospin-violating WIMPs and axions. In fact, EURECA data will provide a relevant insight into all new physics models which predict low-energy, low-rate interactions on a solid target.
Background from radioactivity Existing data on material radio-purity were first used to calculate the expected background rate in EURECA from the most massive components. Full details are given in Appendix G. Some important conclusions can be made based on the results of these simulations. The most radio-pure copper known at present -NOSV from Norddeutsche Affinerie (Germany)  [ 41 ]  with contamination levels of < 1. 6 ppt for U, < 5. 6 ppt for Th, < 2. 8 ppb for K and < 10 μBq / kg of 60 Co will produce an electron recoil event rate which can be rejected by a detector with 10 -5 discrimination factor. The expected nuclear recoil event rate is much less than one in a tonne of target per year. Due to higher contamination levels, no more than a few kg of stainless steel or any other materials with concentrations of about 1 ppb of U / Th can be placed near the detectors.
We acknowledge the achievements of the BOREXINO  [ 42 ] , CUORE  [ 43 ]  and EXO  [ 44 ]  collaborations and plan to use a similar approach. These methods are complementary depending on the nature and size of the sample. NAA and ICP-MS have already proved to be well-suited for very small samples. But these methods do not check the total activity of a sample and have to be combined with a high sensitivity gamma-counting facility. More details are given in Appendix G. The European-funded ILIAS project started compiling an online database  [ 45 ]  containing the results of radio-purity measurements for different materials and components. The measurements of radiopurity of materials using HPGe detectors and mass-spectrometry are currently ongoing for the upgrade of the EDELWEISS experiment. A large number of materials and components tested for EDELWEISS-III, which pass the criteria for EURECA, will be used later in the construction of EURECA.
-11 pb corresponding to a WIMP mass of 60 GeV / c 2. The EURECA project is funded in part by the German ministry of science and education (BMBF) within the "Verbundforschung Astroteilchenphysik" grant 05A11VK2, by the Helmholtz Alliance for Astroparticle Physics (HAP), by the Initiative and Networking Fund of the Helmholtz Association, by the Spanish ministry of science and innovation (MICINN) under grants EUI2009-03957 and MULTIDARK CSD2009-00064, by the Science and Technology Facilities Council (UK), by the first ASPERA Common Call funding scheme and by the French Agence Nationale de la Recherche under contracts ANR-06-BLAN-0376-01 and ANR-10-BLAN-0422-03. This research was further supported by the DFG cluster of excellence: Origin and Structure of the Universe ( www. universecluster. de ), the Maier-Leibnitz-Laboratorium (Garching) and by the BMBF: Project 05A11WOC EU-RECAXENON.
Glossary ADC: analogue-to-digital converter ANR:  Agence Nationale de la Recherche ASPERA :  Astroparticle European Research Area BGO: bismuth germanium oxide BMBF: Bundesministerium f ür Bildung und Forschung CC: computing centre CDM: cold dark matter CDMS: Cold Dark Matter Search CERN: Organisation Europ éenne pour la Recherche Nucl éaire CL: confidence level CPU: central processing unit CRESST: Cryogenic Rare Event Search  with Superconducting Thermometers CUORE:  Cryogenic Underground Observatory  for Rare Events DAQ: data acquisition DB: database DOMUS:  Deep Observatory for Multidisciplinary Underground Science DR : dilution refrigerator EDELWEISS: Exp érience pour D étecter les WIMPs en Site Souterrain EDI: electrodeionisation EMC: electromagnetic compatibility EURECA: European Underground Rare Event Calorimeter Array EXO: Enriched Xenon Observatory FEA: finite element analysis FID: fully interdigitised FLT: first level trigger FR4: flame retardant fibreglass FWHM: full width at half maximum GEANT4: Geometry and  Tracking GEMPI: Max Planck Institute Ge-spectrometer GERDA: Germanium Detector Array GPS: Global Positioning System HAP :  Helmholtz Alliance for Astroparticle Physics  HEMT: high-electron-mobility transistor HEPA: high-efficiency particulate air filter HPGe: high-purity germanium HV: high voltage IC: integrated circuit ICP-MS: inductively coupled plasma mass spectrometry ID: interdigitised  IEC: International Electrotechnical Commission ILIAS: Integrated Large Infrastructures for Astroparticle Science IN2P3: Institut national de physique nucl éaire et de physique des particules IR: infrared ISO: International Organisation for Standardisation JFET : junction gate field-effect transistor KATRIN: Karlsruhe Tritium Neutrino experiment KData: Karlsruhe Data Analysis LAN: local area network LED: light emitting diode LHC:  Large Hadron  Collider LHe: liquid helium LKP: lightest Kaluza-Klein particle LNGS:  Laboratori Nazionali del Gran Sasso   LN2 : liquid nitrogen LSM:  Laboratoire Souterrain de Modane  LSP: lightest supersymmetric particle LTP: lightest T-odd particle LUX:  Large Underground Xenon experiment MICINN: Ministerio de Ciencia e Innovaci ón MPOD: Myriapoda, voltage supply system MSSM: minimal supersymmetric standard model MULTIDARK: Multimessenger Approach for Dark Matter Detection MUSIC: Muon Simulation Code MUSUN: Muon Simulation Code for Underground Physics  m.
The main part of the Cluster Spatio-Temporal Analysis of Field Fluctuations (STAFF) experiment consists of triaxial search coils allowing the measurements of the three magnetic components of the waves from 0. 1 Hz up to 4 kHz. Two sets of data are produced, one by a module to filter and transmit the corresponding waveform up to either 10 or 180 Hz (STAFF-SC), and the second by the onboard Spectrum Analyser (STAFF-SA) to compute the elements of the spectral matrix for five components of the waves, 3 × B and 2 × E (from the EFW experiment), in the frequency range 8 Hz to 4 kHz. In order to understand the way the output signals of the search coils are calibrated, the transfer functions of the different parts of the instrument are described as well as the way to transform telemetry data into physical units across various coordinate systems from the spinning sensors to a fixed and known frame.
Introduction Data calibration of spectra and waveforms issued from a search coil magnetometer is not a new problem. Among previous space physics missions using search coil magnetometers, let us mention GEOS-1 and GEOS-2 as the first ESA spacecraft dedicated to the study of waves and particles in the magnetosphere  (Knott, 1975; Jones, 1977). The GEOS wave consortium (S300 experiment) comprised a tri-axis search coil magnetometer built by the predecessors of the spatial team of the Laboratoire de Physique des Plasmas (LPP). The technology used in CLUSTER-STAFF experiments has been substantially upgraded since this epoch, but the principle remains the same: how to calibrate magnetic waveforms issued from a search coil rotating across a high ambient DC field, knowing that the transfer function varies with the frequency? This kind of problem has been solved in this epoch for timefrequency studies  (Robert et al.
This was done in the GEOS epoch, where the agreement found was ∼ 4 % in magnitude and ∼ 4 • in direction  (Robert, 1979b). As the CLUSTER data are archived and will be used a long time after the lifetimes of the instrument and the corresponding PI and engineers, it was about time to do this work now to get the best possible calibration and to do necessary checks of cross-calibration. Special care has therefore been devoted to the calibration and cross-calibration of the magnetic wave measurements before launch on the ground, then onboard during the commissioning phase, and throughout the mission. A special effort to compare STAFF-SC data with the FGM onboard flux gate magnetometer data  (Balogh et al. , 1997 (Balogh et al. , , 2001) )  has been undertaken from the beginning of the mission until now.
Corrections applied to the initial transfer functions While measurements during the commissioning phase showed that the sensitivity and transfer functions were as expected from ground measurements (see Fig. 2  of   Cornilleau-Wehrlin et al. , 2003) , it appeared during scientific operation that we observed a systematic underestimation of SC1 measurements of about 10 % at low frequencies, in particular at the spacecraft spin frequency. Moreover, comparisons of STAFF waveform data with FGM data evidenced another 10 % underestimation. The reasons for these differences have been studied and explained, leading to a correction of the transfer functions. It is those corrected transfer functions that are given in Figs. 3  and 4. Let us explain the different issues. First we look at the 10 % difference between SC1 and the others.