dataesr/scientific-paragraphs-categorization huggingface dataset:
29 s passes to travel a distance of 10 m. The control algorithm shall be executed significantly faster to update the control more frequently with optimisation results based on new measurements in order to react to changes in the traffic situation. The computational time to solve the optimisation problem within Matlab on a desktop PC (Intel Core i7) varies between 0. 05 and 0. 45 seconds during the presented simulation. In many cases, a real-time capable computation time is already achieved, here. Solving the same problem with a C-code based quadratic programming solver, a significantly faster computation can be expected. The problem dimension is defined by the number of optimisation variables (in this case 80) and the number of inequality constraints (in this case 360). [27]  provides a study on the real-time capabilities of embedded quadratic programming using the C-code generator CVXGEN.
For a model-predictive control problem with large dimensions (336 optimisation variables, 252 inequality constraints), a computation time of 13 ms is achieved on a comparatively weak Intel Atom PC with a peak power consumption of 2 W. The comparison of the presented model-predictive control approach in the work at hand to this reference allows the conclusion, that the proposed problem formulation is real-time capable. The controller is currently being implemented experimentally on a Linux-based embedded platform in the Smart ED to finally validate the results in real driving tests.
Proteomics analysis Trypsin digestion was performed as described previously  [55]  and the digests were analyzed under standard conditions on an LTQ-Orbitrap Velos (Thermo Fisher, Bremen) equipped with Ultimate 3000 nano-HPLC (Dionex). Briefly, tryptic peptides were desalted and separated on a C-18 nano-HPLC column under a gradient of acetonitrile. Minimum signal threshold for triggering an MS/MS event was set to 5000 counts. After a survey scan, the 10 most intense precursor ions were selected for CID fragmentation (top10). Raw files were processed with Max-Quant 1. 4. 1. 2 [56]. Protein identification was done using Andromeda against a Streptococcus pneumoniae (strain ATCC BAA-255 / R6) (Taxonomy 171101-1947 proteins) or Escherichia coli (strain K12 / MC4100 / BW2952) (Taxonomy 595496-4043 proteins) database.
Single-particle EM data processing Spirosomes and transformation pili were visualized by EM as above at nominal magnifications of 49000 and 68000, respectively. The contrast transfer function parameters were assessed using CTFFIND3  [57] , and the phase flipping was done in SPIDER  [58]. Linear filament segments were boxed with e2helixboxer and single particle stacks were generated for each dataset (EMAN2  [59] ): 4309 particles for the transformation pilus (134x134 pixel box, 1. 5 Å/pixel), 728 particles for the S. pneumoniae spirosomes (180x180 pixel box, 2. 2 Å/pixel) and 555 particles for the E. coli spirosomes (180x180 pixel box, 2. 2 Å/pixel). Normalization, centering, multi-reference alignment, multi-statistical analysis, and classification (15-40 particles per class) were done in IMAGIC-4D (Image Science Software, GmbH).
Acknowledgments We are extremely grateful to  Dr. Susan J. Crennel  for providing a high-resolution structural model of G. thermoglucosidasius spirosomes,  Dr. Olivera Francetic  for the structural model of the T2SS pilus,  Dr. Ping Xu  for the kind gift of wild-type and ΔadhE S. sanguinis strains,  Dr. Bruno Dupuy  and  Nicolas Kint  for the C. difficile culture and the group of  Jean-Pierre Claverys  and  Patrice Polard  for the kind gift of purified RecA S. pneumoniae , anti-RecA antibody and several of the vectors and S. pneumoniae strains used in this study. We thank all the members of the  BSSB group  for stimulating discussions.
Data description In total, 115 CBCT scans from 10 prostate cancer patients were analyzed. Each one of these patients underwent a single planning CT scan and multiple daily CBCT scans over the course of treatment. All the patients were instructed to follow a dietary protocol in order to have a full bladder and an empty rectum free of air at the time of the planning CT acquisition and during the treatment. The planning CT data were acquired using a General Electrics Light Speed scanner. The treatment system was an ELEKTA Synergy linear accelerator (LINAC) equipped with CBCT imaging (named x-ray volume imaging (XVI)). During the CT (CBCT) acquisition, the peak-voltage, the X-ray tube current and the exposure time were 120 kVp (120 kVp), 300 mA (40 mA or 64 mA) and 1000 ms (40 ms), respectively.
Software For this study, all the data processing and visualization were performed on a Linux computer with distribution openSUSE 11. 4 ×86 64, with an Intel Dual Core i5-560 M 2. 66 GHz processor, 3 MB L2 cache, 4 threads, and 8GB RAM. For the implementation of our registration algorithms, the following open-source software, based on C++, was used: the Insight Toolkit ITK [33] [a]  the ITK-based Command Line Image Toolkit clitk [b]. The software versions used were ITK 4. 3. 2, CMake 2. 8. 3 and gcc 4. 5. 1.
Impact of rectal distension on local RR quality We investigated in which cases the 8-mm local RR failed. Figure  5  illustrates the impact of the variation of rectal filling between the registered images on the 8-mm local RR quality. With our database, the F values obtained ranged from 0. 1 to 410. 7. We observed that if the F factor as defined in Equation  2 was lower than or equal to F 1 = 61. 2, registrations were all successful (93 cases out of 115). All failed registrations (6 in total, representing almost one-third of the 22 remaining cases) appeared to have F values higher than F 2 = 147. 6. We evaluated the performance of the 8-mm local RR with an F cut-off of (F 1 + F 2)/2 = 104. 4 (median between F 1 and F 2). We found a sensitivity of 1 and a specificity of 0. 85 with our dataset.
For clinical routine, semi-automated algorithms that are proposed by commercial image post-processing software are largely used. For retrospective studies, research studies, or large database studies, automated segmentation algorithms are preferentially used in order to avoid the labor intensive and time consuming manual segmentation task and reduce the intraand inter-operator variabilities  [2]. To assess the performance of these automated segmentation algorithms, the common approach consists in comparing the contours resulting from the automated segmentation with the ones obtained by one or several experts who are known to often outperform automated methods  [3]. When visually comparing segmentation results obtained by different automated methods as in  [3] , the respective performance of two methods depends on the data: when a first segmentation method provides more accurate contours than a second method on a specific database, the second algorithm might actually be more relevant for a sub-database or, at least, for some particular MR examinations.
Therefore, it is reasonable to hypothesize that there might be an advantage in combining several automated segmentation methods to overcome the specific limitations of each one. To combine segmentation approaches, different algorithms have been proposed  [4] [5] [6] [7] [8]. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm  [5]  is very popular and highly cited. Furthermore, the associated software is freely available for academic purposes upon written request. For these reasons, we evaluated the performance of STAPLE. To objectively assess the segmentation accuracy, criteria based on estimated contours and associated image classification are often used. These include various metrics allowing to compare boundaries at a local level such as distances between contours, overlap criteria like the Dice coefficient  [9] , or the sensitivity, the specificity, the predictive negative value and the predictive positive value criteria computed by the STAPLE algorithm.
All these criteria assume that there is a "gold standard" segmentation, at least implicitly. Furthermore, these criteria are partly correlated and are also directly related to the optimization process involved in STAPLE. To avoid these limitations, we rather focused our evaluation on the clinical task and evaluated the accuracy of clinical parameters of interest, and particularly the LVEF parameter. To evaluate the interest of the STAPLE algorithm for combining segmentation results, we applied it to a cardiac cine MRI database including LV segmentation obtained from eight independent segmentation approaches: five resulted from five different automated image processing approaches, and three volume contours were drawn by three different experts. Sixteen combinations of the five automated methods (all five methods, four among the five methods, and three among the five methods) were tested against results provided by the three experts, using the LVEF values as the clinical parameter of interest.
The evaluation was first carried out using a supervised approach, assuming a gold standard was available, and then using an unsupervised approach, the extended Regression Without Truth (eRWT)  [3]  to rank all segmentation methods as a function of their performance. Our study presents some similarities with  [2] : both used a public cardiac cMRI database (although not the same) for which contours were delineated by experts and algorithms. In our case, the selected database included controls and patients with different cardiac pathologies. In  [2] , only cMRI acquired on patients were included. Furthermore, both studies used STAPLE to combine different contour results, but they differ in their approach. Indeed, in  [2] , authors proposed to use STAPLE to define a gold standard segmentation based on two fully-automated algorithms and three semi-automated algorithms requiring manual input, while the present study focuses on improving the accuracy of automated segmentation algorithms by combining them with STAPLE to get a accuracy similar to the one achieved by experts i.
Materials and Methods Database This work uses the public database provided by Sunnybrook Health Sciences Center  [10]. This cardiac database was first distributed to the participants in the Cardiac MR Left Ventricular Segmentation Grand Challenge (MICCAI 2009). It includes images from forty-five subjects who were divided into four subgroups: healthy individuals (CTRL, n = 9), patients with hypertrophic cardiomyopathy (HYP, n = 12), patients with heart failure without ischemia (HF-NI, n = 12) and patients with heart failure due to ischemia (HF-I, n = 12). For each examination, about ten short axis slices covering the LV were acquired using a breath-hold, retrospective ECG-gated cine-MRI sequence (twenty cardiac phases per slice, contiguous slices with a slice thickness of 8 mm, FOV = 320 mm, acquisition matrix 256 × 256 with a 1.
Segmentation approaches Eight independent estimates of the LVEF were obtained from three manual contouring methods (M1-M3) provided by three independent experts, and from five automated algorithms (M4-M8). The five algorithms described respectively in  [11] [12] [13] [14] [15]  use different segmentation strategies and various user's interactions. The method M8 was described in  [15]  and is an update of the method  [16]  previously evaluated in  [3]. Endocardial borders were obtained on the end-diastolic and end-systolic phases with all methods. Furthermore, contours for all cardiac phases were provided for the whole database by method M5 and for fifteen subjects by method M6. All methods but M5 included the papillary muscles in the LV cavity. Method M4 was the least automated one, while method M8 was fully automated.
Results obtained by the eight segmentation methods are freely available on https://github. com/frederiquefrouin/Medieval. Using each segmentation method, the mean LVEF value and its associated standard deviation were calculated for each of the four subgroups of subjects. More than 99% of these estimated values ranged from 0. 05 to 0. 85. The twenty-four patients of the studied database with heart failure (HF-NI and HF-I) had a reduced LVEF that was considered as pathological ( 0. 45).
Combination of the segmentation approaches Method. Several segmentation results were combined using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm developped by Warfield et al. [5]. This method was implemented using the version 1. 5. 2 of CRKit, which is the software provided by Warfield's team. The STAPLE framework is based on an Expectation Maximization (EM) algorithm  [17, 18]. It uses several segmentation results and calculates simultaneously a probabilistic estimate of a representative segmentation result and a performance level of each delineation included in the calculation. This performance level is provided by the computation of the sensitivity and the specificity indexes between each input segmentation and the segmentation result. The process is iterated until a stable solution is reached.
Unsupervised evaluation using eRWT Theory. The eRWT approach  [3] , an extension of the Regression Without Truth  [19] [20] [21] , aims at comparing and ranking different methods which estimate a specific biomarker such as the LVEF, the true value Θ p of the biomarker being unknown. Considering P samples (denoted by p, ranging from 1 to P) and K segmentation methods (denoted by Mk, k ranging from 1 to K), each segmentation method Mk yields an estimate θ pk of the biomarker for sample p. The eRWT approach assumes a parametric relationship between the true value Θ p and its estimate θ pk based on three hypotheses: H1:. The statistical distribution of the true value Θ p on the whole database has a finite support. H2:. The estimate θ pk is linearly related to the true value (Eq (1)). The error term ε pk is normally distributed with zero mean and standard deviation σ k.
The numerical implementation uses an optimization function implemented in MATLAB (R2012a, The Mathworks, Inc. ). The figure of merit F Mk chosen to rank the methods Mk is defined as the expected value of the square error between the true value of the parameter Θ p and its estimated value by a given method (Eq (2))  [22]. F Mk ¼ E½ðY À a k Y À b k À ε k Þ 2 : ð2Þ If the statistical distribution of Θ p is a Beta distribution, the figure of merit can be expressed analytically by Eq (3): F Mk ¼ ða k À 1Þ 2 mðm þ 1Þ ðm þ nÞðm þ n þ 1Þ þ 2 a k À 1 ð Þb k m m þ n þ b 2 k þ s 2 k : ð3Þ To set the shape parameters of the Beta distribution (μ and ν), we started from the values chosen in  [3]  (μ = 4 and ν = 5) and refined these initial values so as to minimize the sum of the K figures of merit. Final values of the μ and ν parameters were set to 2.
85 and 3. 40 respectively. These slight modifications of the Beta distribution compared to that used in  [3]  did not yield substantial changes in the ranking of the methods, as already shown in  [3]. The final ranking of methods was based on a bootstrap process  [23]  running on the database of P values θ pk generating N (N = 1000) θ p n k values. From each drawing n, P values p n were drawn from the 45 samples. From these θ p n k values, the K figures of merit F n Mk were computed using the previously described optimization. The non-parametric Kruskal-Wallis test  [24]  was applied to the N × K values of F n Mk to test the equality of the median among the K methods. When it was not equal, each pair of methods was tested, using a Bonferroni correction with a Type I error equal to 5%  [25]  to determine the significantly different pairs.
Use of STAPLE to combine endocardial LV segmentations The aim of this work was to evaluate the efficiency of the STAPLE algorithm  [5]  to estimate a clinical biomarker, the LVEF, from a segmentation resulting from the combination of different independent segmentation algorithms. To demonstrate it, a collection of segmentations applied to the MICCAI 2009 cardiac MRI database was used. For the forty-five cases of this database eight segmentation methods were available, including delineations provided by three independent experts, and five delineations obtained using five automated LV segmentation algorithms. As the LVEF is a primordial biomarker, the paper primarily focused on results obtained for this parameter. The database had the advantage of including a large variety of cardiac diseases (with normal or reduced LVEF) and control subjects.
In other words, could a combination of different automated segmentation results yield better results than the ones from each individual method? The question was challenging since several evaluation studies  [2, 7]  already showed that the STAPLE output strongly depends on the number and on the quality of the inputs used to create the combined segmentation. Assuming that the automated methods incorporate different strategies, we tested whether their combined use could actually help in improving segmentation results on a whole database. All possible combinations of three, four and five automated segmentations were thus systematically tested. As in  [2] , the STAPLE version that was used provided results that did not depend on the size of the background, i. e. the region of interest surrounding the left cavity.
Furthermore STAPLE was tested on both 2D and 3D data, and better results were obtained when applying STAPLE in 2D mode, slice by slice. This seems to be due to the large anisotropy of the initial data and to the 2D strategy used by the experts and most of the automated algorithms to delineate the contours. To assess the segmentation results, a visual inspection of the contours of all STAPLE segmentation results superimposed onto the MR images was first performed. This visual assessment showed that in most cases, the STAPLE algorithm was able to correct, in every slice, too loose or too tight delineations obtained from automated methods. Supervised and unsupervised statistical evaluations were then performed to assess the results obtained using each STAPLE combination of three, four and five automated methods.
Extension to other cardiac function parameters The present work mainly focused on the estimation of the LVEF value. As this parameter is derived from both end-diastolic volumes (EDV) and end-systolic volumes (ESV), additional tests have been performed to extend our study to five other clinical parameters: the left ventricular end-diastolic volumes and end-systolic volumes, the stroke volume (SV = EDV -ESV), the epicardial volume (EpV) defined at the end-diastolic phase, and the myocardial mass (MM = 1. 05 × (EpV -EDV)). Epicardial borders, obtained on the end-diastolic phases for all methods except M7, and used to study the EpV and the MM parameters, are also available on https://github. com/frederiquefrouin/Medieval. The eRWT approach was performed for each clinical parameter with specific settings (see S1 Table ).
Due to the quality of results demonstrated by this study, which shows a clear improvement in clinical parameter estimates using the STAPLE combinations compared to the initial automated segmentation algorithms, it becomes feasible to use automated segmentation algorithms and get stable and reliable results. Finally our approach was dedicated to LV segmentation, but it could be easily extended to other organs. One practical interest of such an approach is that it could help in reducing the number of manual delineations which are very time-consuming to collect, especially for databases including a large number of cases. Furthermore, we believe that the high performance obtained by the combination is due to the complementarity of the different segmentation approaches and that results could be less interesting when tuning parameters of a single approach.
Conclusion This work aimed at determining whether combining different segmentation results using the STAPLE algorithm could yield a final segmentation as reliable as that of an expert. This approach was tested in the framework of the estimation of left ventricular ejection fraction on the MICCAI 2009 cardiac cine MRI database. Both supervised and unsupervised evaluations showed that in most cases, the cardiac function parameters were better estimated using the STAPLE approach than using individually the segmentation methods used to create the STA-PLE result. Moreover, the STAPLE segmentation results provided, in most cases, similar estimates to the ones obtained based on manual delineations performed by an expert. The results show that combining different independent automated segmentation methods using the STA-PLE approach yielded segmentations that were as accurate as those provided by expert delineating the left ventricular cavities.
0 eV), to appear in the beam. The level of contamination was checked by comparing data with the RILIS lasers ON (data containing both nickel and contaminants in the beam) with data in laser OFF mode (only contaminants). From this comparison a beam purity of at least 99% 66 Ni was determined. The positively charged nickel beam was extracted from the ion source by applying a 30-kV electrostatic potential and was subsequently sent through the general purpose separator, resulting in a 66 Ni beam which was injected in REX-TRAP  [42]. In this Penning trap the beam was accumulated during 30 ms and cooled by interactions with the buffer gas present (usually Ne or Ar). This bunch of ions was thereafter transferred to REX-EBIS, the electron beam ion source, where the ions were brought to a higher charge state (16 + ).
Also data from stable beam reaction experiments [e. g. , 22 Ne(d,p) 23 Ne] was used to improve the quality of the calibration. During the calibration process it was found that the full energy signal of the E detector depends on the position of the hit along the strip. All full-energy signals were hence corrected for this problem with parameters extracted from the measurement with the α source, using the relationship E corrected = E measured /[1 -(0. 5x)A], with A = 0. 035 and x the normalized position along the strip (x = [0,1]). The global energy resolution of protons emitted in the (d,p) reaction detected by the E-E telescopes was determined by the combination of intrinsic detector resolution, position uncertainty, beam-spot size, energy losses, and angular dependence of the particle kinematics, and was of the order of 1300 keV full width at half maximum (FWHM).
This was needed as the decaying nuclei traveled at speeds around 0. 08c while emitting γ rays, leading to Doppler shifts of the emitted wave lengths. The position of all clusters was determined with high accuracy by analyzing the data from the 22 Ne(d,p) 23 Ne reaction with known incoming energy and by measuring the Doppler shift of the 1017-keV line for each segment. The signals from the Miniball array were digitally handled by a series of digital gamma finder (DGF) modules, with an energy range of nearly 8 MeV. Energy calibration and efficiency determination were done using 152 Eu and 207 Bi sources. For the high-energy part of the spectrum, data from the β decay of a stopped 11 Be beam (T 1/2 = 13. 76 s), including transitions up to 7. 97 MeV, were used  [49]. The total photopeak efficiency for 1-MeV γ transitions was found to be 5.
3 , where the corresponding γ spectra are shown for both prompt and random proton-γ timing conditions. The data in the random spectrum are limited and only contain a doubly humped structure around 1039 keV, the dominant transition in the β decay of 66 Cu (note that no γ rays are emitted in the β decay of 66 Ni)  [52] , in which shape is from the Doppler correction procedure. Traces of the most intense prompt transitions, Compton background of the 1039-keV transition and radiation from the REX-accelerator, are also observed. After the event building and calibration of the raw, detected signals, the kinematic reconstruction of the events was performed. In the case of the γ rays detected by Miniball, the add-back procedure was performed by summing γ -ray energies detected within the same cluster.