dataesr/scientific-paragraphs-categorization huggingface dataset:
Many initiatives have already explored solutions for integrating clinical data (e. g. caBIG  [29] , BRIDG  [30] , I2B2  [31] , STRIDE  [32] , R-oogle  [33] ), and several recent projects are of interest for data warehousing, sharing and analysing of heterogeneous clinical dataset (e. g. DebugIT to improve detection and elimination of bacteria  [34] , EU-ADR to improve detection of adverse drug events  [35]  and EHR4CR to improve clinical trials recruitment  [36] ). Recognition pattern algorithm and CBR are promising methods for the secondary use of data, for instance in an hospital information system by automatically identifying new eligible patients to the clinical trials going on at the hospital (ASTEC project  [37] ), or by automatically detecting new healthcare-associated infections  [38].
Acknowledgements We acknowledge financial support by the  German Federal Ministry of Education and Research (BMBF)  via the  Bernstein Center for Computational Neuroscience Göttingen  ( 01GQ1005B ), by the  Human Frontier Science Program  ( RGY0070/2007 ) and by the  Agence Nationale pour la Recherche  ( ANR 2011 MALZ 003 01 ). Anastassios Karagiannis was supported by a  Fondation pour la Recherche Médicale  fellowship ( FDT20100920106 ).
Data analysis An ANOVA was performed with the six age groups as intersubject factor and clinical orthoptic values as within subject factors. For ocular motor data, calibration factors for each eye were determined from the eye positions during the calibration procedure. The software MeyeAnalysis (provided with the eye tracker, e(ye)BRAIN, www. eye-brain. com, France) was used to extract saccadic eye movements from the data. It automatically determines the onset and the end of each saccade by using a builtin saccade detection algorithm. The algorithm used to detect saccades is adapted from  [18]. All saccades with an amplitude superior to 1 degree were detected. All detected saccades were checked by the researcher and corrected/discarded if necessary. The number and the amplitude of progressive saccades (prosaccades, from left to right) and regressive saccades (backward saccades, from right to left) and the duration of fixations between each saccade were analyzed.
The time to perform each task was also analyzed and was determined by the delay between the first and the last saccade. In both tasks (reading and visual search), binocular coordination was defined for each saccade and each fixation was recorded. We examined the amplitude of the disconjugate components during each saccade (left eye -right eye). The disconjugacy was measured as the change in vergence between the beginning and the end of each saccade. We also examined the disconjugate component of each post-saccadic fixation period (see  [13] ). Data were analyzed using different multiple linear regression models using the number of saccades, the amplitude of saccades (in degrees), the duration of fixations (in ms) and the duration of task (in seconds). Given that saccade disconjugacy depends on the saccade amplitude, the values of disconjugacy during and after the saccades were presented as the ratio of the disconjugacy on the saccade amplitude (in percentage).
It supports and emphasizes parallel and event-based programming, which are very popular paradigms in robotics, by providing core primitives and language constructs  [12]. UrbiScript has some limitations. First, it is an untyped language. Besides, it lacks support for synchronization among events, which is essential in some scenarios. Moreover, events in UrbiScript cannot be reconfigured at runtime to change their behavior. The KUKA Robot Programming Language is developed by KUKA, one of the world's leading manufacturers of industrial robots  [6]. KUKA is simple, Pascal-like and lacks many features. A Matlab abstraction layer has been introduced to extend its capabilities  [4]. RoboLogix is a scripting language that utilizes common commands, or instruction sets among major robot manufacturers.
RoboLogix programs consist of data objects and a program flow  [15]. The data objects reside in registers and the program flow represents the list of instructions, or instruction set, that is used to program the robot. However, RoboLogix still does not supply a well-defined mechanism for the robots to interact and react to environment. In recent years, event-driven programming has emerged as an efficient method for interacting and collaborating with the environment in ubiquitous computing. Using event-driven style requires less effort and may lead to more robust software  [8]. This style is used to write many kinds of applications: robotics, context-aware reactive applications, self-adaptive systems, interactive systems, etc. In consequence, several event-based programming languages have been developed so far  [5, 14].
Programmers may use built-in events (listed in Table  1 ), or write user-defined events (in Java or in C/C++), and then integrate them to their INI programs through bindings. By developing custom events, one can process data which are captured by sensors. To illustrate events in INI, let's consider a program which uses sensors to capture and collect weather and climate data like humidity, temperature, wind speed, rainfall, etc. In our program, we can define separate events to handle these tasks as shown in Figure  1. For instance, we can define an event @humidityMonitor to observe the humidity level periodically. This event has one input parameter named humPeriod that sets the periodicity of the checks (time unit is in hours). Besides, it has one output parameter named humidity to indicate the current humidity.
]) in order to modify the values of input parameters of the event referred to by eventId. For instance, in the example of Figure  1 , we can call reconfigure event(h, [humPeriod=0. 5]) to set the humidity data collection period to 30 minutes. Now our event will gather data every 30 minutes instead of one hour as before. Besides, we also allow programmers to stop and restart events with two built-in functions: stop event([eventId1, eventId2,. ]) and restart event([eventId1, eventId2,. ]). For example, we can stop all data collection processes when the energy level of the system is too low, and restart them later when the energy is restored. Last but not least, events in INI may be used in combination with a boolean expression to express the requirement that need to be satisfied before they can be executed.
Introduction to Nao and Its Moving Mechanism Nao is the humanoid robot that is built by the French company Aldebaran-Robotics  [13, 25]. It is equipped with many sensor devices to obtain robot's close environment information (see Figure  2 ). Nao has for instance become a standard platform for RoboCup, an international initiative that fosters research in robotics and artificial intelligence  [11]. NAOqi is the middleware running on Nao that helps prepare modules to be run either on Nao or on a remote PC. Code can be developed on Windows, Mac or Linux, and be called from many languages including C++, Java, Python and. Net. The company Aldebaran Robotics developed many modules built on top of this framework that offer rich APIs for interacting with Nao, including functionalities related to audio, vision, motion, communication or several lowlevel accesses.
INTRODUCTION The near neighbor reporting problem (a. k. a. range query) is defined as follows: given a collection P of n points, build a data structure which, given any query point, reports all data points that are within a given distance r to the query. The problem is of major importance in several areas, such as databases and data mining, information retrieval, image and video databases, pattern recognition, statistics and data analysis. In those application, the features of each object of interest (document, image, etc) are typically represented as a point in a d-dimensional space and the distance metric is used to measure similarity of objects. The basic problem then is to perform indexing or similarity searching for query objects. The number of features (i. e. , the dimensionality) ranges anywhere from tens to thousands.
Numerical analysis In order to assess the numerical simulations of both MFH and APHR methods, we have carried out FE simulations of a continuous VE VP matrix reinforced by spherical inclusions with linear elastic behavior. For this, the numerical algorithm introduced for the VE VP model was integrated into the ZeBuLon FE software  [1]  as a User defined MATerial (UMAT) routine. The volume fraction of particles is 15%. Assuming a periodic microstructure and uniaxial stress tests, a 2D axisymmetric unit cell is defined and depicted in Fig. 1a. The prescribed boundary conditions are the following. Zero displacements are imposed in the radial direction on the left vertical side and in the vertical direction on the bottom horizontal side. The right vertical side is   Fig. 2. Finite-element, incrementally affine homogenization, and a priori hyper reduction results of the stress-strain curves in tension test at the strain rate of 1 s 1 for a volume fraction of inclusions equal to 15%.
In addition, when extending the wet and dry lines of the Tα space, both lines cross at α ≈ 0. 4 although the wet and dry edges of the Tf vg space never cross for 0 ≤ f vg < 1. In this paper, a new ET (EF) model (SEB-1S) is derived by revisiting the classical physical interpretation of the Tα space to make its wet edge consistent with that of the Tf vg space. SEB-1S is tested over a 16 km by 10 km irrigated area in northwestern Mexico during the 2007-2008 agricultural season. The classical Tα space-based model is implemented as benchmark to evaluate the performance of SEB-1S. Input data are composed of ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) thermal infrared, Formosat-2 shortwave, and station-based meteorological data. The fluxes simulated by SEB-1S and the classical Tα space-based model are compared on seven ASTER overpass dates with the in situ measurements collected at six locations within the study domain.
Secondary objectives are (1) to take advantage of the potential synergy between the Tα and Tf vg spaces in the determination of temperature endmembers and (2) to assess the usefulness of constraining the unstressed (minimum) vegetation temperature with available air temperature data. The modeling approach is tested over a 16 km by 10 km irrigated area in northwestern Mexico using ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer) and Formosat-2 data collected on seven dates during the 2007-2008 agricultural season. Experimental data are described in Sect. 2. In Sect. 3, SEB-1S is described and the classical Tα space-based model is reminded. In Sect. 4, the surface fluxes simulated by both ET models are compared with in situ measurements at six locations.
Flux stations Seven micro-meteorological stations equipped with an eddy covariance flux measurement system were installed in different fields. For each of the seven sites, the net radiation was acquired with CNR1 or Q7. 1 (REBS) radiometers depending on the stations. The soil heat flux was estimated with HUKSEFLUX HFP-01 plates buried at 0. 05 m at the top and bottom of the furrow (when applicable). Those data were acquired at a frequency of 10 s and then averaged and recorded each 30 min. Latent and sensible heat fluxes were measured with KH20 fast response hygrometers (Campbell) and Campbell CSAT3 or RM Young 81000 3-D Sonic anemometers at a frequency of 10 Hz and converted to a 30 min average, respectively. Meteorological data including air temperature, solar radiation, relative humidity and wind speed were monitored throughout the agricultural season at a semihourly time step from 27 December 2007 until 17 May 2008.
ASTER thermal infrared data ASTER was launched in 1999 on a sun-synchronous platform (NASA's Terra satellite) with 10:30 ECT (Equator crossing time) and a 16 day revisit cycle. The ASTER thermal sensor provides scenes of approximately 60 km by 60 km. Data are collected on request over specified areas. There are five thermal bands centered at 8. 30, 8. 65, 9. 05, 10. 60 and 11. 63 µm with a 90 m resolution. ASTER official products were downloaded from the Earth Observing System Data Gateway and extracted over the 16 km by 10 km study area. The 90 m resolution surface skin temperature (T ) and channel emissivity retrieved by the "temperature and emissivity separation" algorithm  (Gillespie et al. , 1998; Schmugge et al. , 1998)  were used. The absolute registration of temperature/emissivity data was performed using a background 8 m resolution Formosat-2 image  (Merlin et al.
Formosat-2 red and near-infrared data Formosat-2 is an Earth observation satellite launched in 2004 by the National Space Organization of Taiwan. It provides high (8 m) resolution images of a particular area every day (09:30 ECT) for four bands (blue, green, red and nearinfrared) and with the same view angle  (Chern et al. , 2008). In this paper, the Formosat-2 data collected on the nearest date from each of the seven ASTER overpass dates were used to estimate f vg and α from the red and near-infrared reflectances aggregated at ASTER thermal sensor resolution. The reason why Formosat-derived instead of ASTERderived α was used is mainly because the ASTER shortwave infrared (SWIR) data were unusable on four out of the seven ASTER overpass dates  (Chirouze et al. , 2013) : ASTER SWIR detectors are no longer functioning due to anomalously high SWIR detector temperatures (http://asterweb.
Fractional green vegetation cover Fractional green (photosynthetically active) vegetation cover is estimated using the expression of  Gutman and Ignatov (1998) : f vg = NDVI -NDVI s NDVI vg -NDVI s , (1) with NDVI vg corresponding to fully covering green vegetation and NDVI s to bare soil or to bare soil partially covered by senescent (non-photosynthetically active) vegetation. In the study, NDVI vg and NDVI s are set to the maximum (0. 93) and minimum (0. 18) value of the NDVI (Normalized Difference Vegetation Index) observed during the agricultural season within the study domain. NDVI is computed as the ratio of the difference between resampled Formosat-2 nearinfrared and red reflectances to their sum. As an illustration, Fig. 1a  presents the Tf vg space obtained for data on 27 April 2008.
f vg,TSMIN is a threshold value (set to 0. 5 by default) that stabilizes the determination of the slope. The use of f vg,TSMIN is needed to avoid defining a line (the wet edge in this case) from two data points very close together  (Merlin et al. , 2012b). Similarly, the dry surface edge is defined as the line passing through the point (α s , T max ), with T max being the maximum T , and the point with α > α vg such as the slope of the line is maximum (meaning that all the other data points are located below the dry surface edge). -in the Tf vg polygon, alternative estimates of the minimum soil temperature (T s,min,2 at f vg = 0) and minimum vegetation temperature (T v,min,2 at f vg = 1) are obtained by drawing a line passing through the two points belonging to the wet surface edge, and alternative estimates of maximum soil temperature (T s,max,2 at f vg = 0) and maximum vegetation temperature (T v,max,2 at f vg = 1) are obtained by drawing a line passing through the two points belonging to the dry surface edge.
All the SFG/DFG data are normalized to the SFG/DFG signal of a ZnSe reference crystal in order to compensate for eventual laser fluctuations or atmospheric absorption. The geometrical configuration depicted in Fig. 1  is the same for each sample. The only difference resides in the detection scheme for SFG and DFG. In fact, SFG and DFG signals have a different emission direction. In these conditions, to switch from SFG to DFG configuration, only one mirror is tilted in the lateral direction to send either SFG or DFG photons in the same direction for their detection after spatial and spectral filtering through a monochromator. To compensate for eventual misalignment of the SFG detection direction, the baseline of the SFG spectra is recovered by a linear fit of the experimental data. In order to compare quantitatively the SFG data with respect to the DFG data, an experimental scaling factor set to 1.
We want to know how much SFG/DFG could be quantitative and how it is possible to assess the quantity of adsorbed thiophenol on the AuNps. To that end, the SFG/DFG intensity in reflection mode (coherent emission) is modelled by the following equation  [8] : I FG ω IR ð Þ ¼ χ 2 ð Þ eff 2 ¼ C FG e iϕ FG þ a 0 ω IR -ω 0 AE iΓ 0 2 : We then apply the SFG/DFG data fitting developed in a procedure detailed elsewhere  [8]. Moreover, to allow a direct comparison of a 0 as a function of the dipping time, the bandwidth of the vibration mode (Г 0 damping constant) is set to 7. 5 cm -1 in the equation of the intensity I FG where the subscript FG stands either for SFG (+iГ 0 ) or DFG (-iГ 0 ). χ eff  (2)  is the nonlinear second order effective susceptibility of the interface, which includes the surface reflectivity (Fresnel factors).
1. In the MG model, the composite layer is defined as a host matrix of air (ε h =1) with a low concentration of gold spherical inclusions. In the BM model, because of the high concentration of gold inclusions, the matrix host is considered as the composite layer (ε h =ε*), i. e. each of its component is considered as an inclusion. The interested reader will find in the Electronic Supplementary Material the details of the procedure used to deduce a zzz from the SFG and DFG data in the framework of both effective medium models as well as the Fresnel factors calculations. We finally find, as discussed further on Fig. 6 , that the vibrational amplitude is essentially proportional to the actual molecular coverage N s for both SFG/DFG measurements when considering the appropriate effective medium model.
Conclusions In summary, we have studied and compared the submonolayer sensitivity threshold of thiophenol adsorbed on AuNps as small as 17 nm and grafted on silicon for different filling factors f checked by AFM imaging. We showed that, on silicon, SFG/DFG spectroscopy is intrinsically more sensitive than conventional FTIR spectroscopy especially for f below 9 %. Moreover, we have shown that SFG/DFG spectroscopy could take profit of the LSPR amplification by the incident visible laser beam to enhance the sensitivity without the need of hot spots as mandatory required for instance in SERS spectroscopy of small nanoparticles with similar diameters. We have quantitatively related the nonlinear SFG/DFG molecular amplitudes to the samples reflectance measured by UV-vis measurements by developing the three-layer effective medium models of MG and BM and showing that the BM approach was more appropriate to explain the SFG and DFG data when AuNps aggregation occurs.
Acknowledgments Research leading to these results has been supported by the  Région Île-de-France  in the framework of the funding program  C'Nano IdF  under grant agreement CREMOSOFT. The authors acknowledge the BQR financial support of the  Université Paris-Sud. The authors also acknowledge  C. Six  and  A. Gayral  for technical assistance on the SFG/DFG experimental setup and  L. Dalstein  for fruitful discussion on theoretical aspects of SFG and SFS spectroscopy of nanoparticles. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
In both channels additional same-side flavour tagging information is used. The data were obtained from pp collisions collected by the LHCb experiment at a centre-of-mass energy of 7 TeV during 2011, corresponding to an integrated luminosity of 1. 0 fb -1. This paper is organised as follows. Section 2 presents the phenomenological aspects related to the measurement. Section 3 presents the LHCb detector. In Sect. 4  the selection of B 0 s → J/ψK + K -candidates is described. Section 5 deals with decay time resolution, Sect. 6 with the decay time and angular acceptance effects and Sect. 7 with flavour Table  1 : Results for φ s and ∆Γ s from different experiments. The first uncertainty is statistical and the second is systematic (apart from the D0 result, for which the uncertainties are combined).
4% at 5 GeV/c to 0. 6% at 100 GeV/c, and impact parameter resolution of 20 µm for tracks with high transverse momentum. Charged hadrons are identified using two ring-imaging Cherenkov detectors  [23]. Photon, electron and hadron candidates are identified by a calorimeter system consisting of scintillating-pad and pre-shower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. The trigger consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage which applies a full event reconstruction  [24]. Simulated pp collisions are generated using Pythia 6. 4  [25]  with a specific LHCb configuration  [26]. Decays of hadronic particles are described by EvtGen  [27]  in which final state radiation is generated using Photos  [28].
Opposite side tagging The OS tagging algorithms and the procedure used to optimise and calibrate them are described in Ref. [36]. In this paper the same approach is used, updated to use the full 2011 data set. Calibration of the estimated wrong-tag probability, η, is performed using approximately 250 000 B + → J/ψ K + events selected from data. The values of q and η measured by the OS taggers are compared to the known flavour, which is determined by the charge of the final state kaon. Figure  10  shows the average wrong tag probability in the B ± → J/ψ K ± control channel in bins of η. For calibration purposes a linear relation is assumed ω(η) = p 0 + ∆p 0 2 + p 1 (η -η ) , ω(η) = p 0 - ∆p 0 2 + p 1 (η -η ) , (10) where ω(η) and ω(η) are the calibrated probabilities for wrong-tag assignment for B and B mesons, respectively.
5 in the log-likelihood, are reported. The variation of δ Sδ ⊥ with m(K + K -) is shown in Fig. 14. The decreasing trend confirms that expected for the physical solution with φ s close to zero, as found in Ref. [40]. All results have been checked by splitting the dataset into sub-samples to compare different data taking periods, magnet polarities, B 0 s -tags and trigger categories. In all cases the results are consistent between the independent sub-samples. The measurements of φ s , ∆Γ s and Γ s are the most precise to date. Both ∆Γ s and φ s agree well with the SM expectation  [2, 41]. These data also allow an independent measurement of ∆m s without constraining it to the value reported in Ref. [38]. This is possible because there are several terms in the differential decay rate of Eq. 1, principally h 4 and h 6 , which contain sinusoidal terms in ∆m s t that are not multiplied by sin φ s.
00 -0. 39 0. 37 -0. s → J/ψ K + K -decays (data points) with the one-dimensional projections of the PDF at the maximal likelihood point. The solid blue line shows the total signal contribution, which is composed of CP -even (long-dashed red), CP -odd (short-dashed green) and S-wave (dotted-dashed purple) contributions. → J/ψ K + K - dataset. Only the statistical uncertainty is included. The SM expectation of ∆Γ s = 0. 087 ± 0. 021 ps -1 and φ s = -0. 036 ± 0. 002 rad is shown as the black point with error bar  [2, 41]. Table  8 : Results of the maximum likelihood fit for the S-wave parameters, with asymmetric statistical and symmetric systematic uncertainties. The evaluation of the systematic uncertainties is described in Sect. 10. 10 Systematic uncertainties for B 0 s → J/ψK + K -decays.