dataesr/scientific-paragraphs-categorization huggingface dataset:
The second method is based on atlases. As in the proposed method, the database of MRI and CT pairs is nonrigidly registered to the target MRI. A global similarity measure, the normalized cross-correlation (NCC), is computed over the full image field of view between each warped MRI and the target MRI to select the most similar atlas to the target. The NCC is defined as  (7)  where is the mean and the standard deviation of image I. The CT corresponding to the selected MRI, called best-atlas CT (baCT), is then converted into attenuation map (baCT -map), as in the proposed method. The quantitative validation consisted of three steps. 1) A pseudo CT was synthesised from each subject's MRI following the proposed method. The pseudo CT (pCT), baCT, and UTE CT were compared to the subject's ground truth CT image at the original resolution, validating the accuracy of the CT synthesis.
D. Results 1) Pseudo CT Accuracy: Following the proposed method, a pseudo CT was generated using only the MRI image of the subject, and then compared to the ground truth CT. The MAE, as defined in  (6) , and the mean error (ME) defined as (8) Fig. 3. For subject 1, from left to right: the acquired CT, the pCT generated by the proposed method, the baCT, and the UTE CT (top); the acquired T1, the difference between the pCT and the CT, the difference between the baCT and the CT, and the difference between the UTE CT and the CT (bottom). were calculated between the ground truth CT and the pseudo CT for every subject in the validation dataset. Contrary to the MAE, which provides information about reconstruction error and deviations from the expected values, the ME provides information about an inherent bias in the methodology.
The MAE and ME were also computed between the best-atlas CT and the ground truth CT, and between the UTE CT and the ground truth CT. The average standard deviation (SD) MAEs and MEs, measured in Hounsfield units, across all the subjects in the database are presented in Table  I. The average absolute error obtained with the pseudo CT method ( HU) is 1. 4 times smaller than the error obtained with the best-atlas method ( HU) and 1. 7 times smaller than the error obtained with the UTE method ( HU). A paired t-test was used to confirm that the improvement is statistically significant when the proposed method was employed instead of the best-atlas and UTE-based methods. Examples of ground truth CT, pseudo CT, best-atlas CT, UTE CT and difference images are presented in Fig. 3  for a representative subject (subject 1) whose results were close to the average results.
2) Attenuation Map Accuracy: The relative mean absolute error between the ground truth -map and the pseudo CT -map -, defined as  (9)  and the relative mean error, defined as  (10)  were used to assess the attenuation map synthesis accuracy. The rMAE and rME were also computed between the best-atlas CT -map and the ground truth CT -map, and between the UTE -map and the ground truth CT -map. The average rMAEs and rMEs, measured in percentages, across all the subjects in the database are presented in Table  I. The average absolute error obtained with the pseudo CT method % is 1. 4 times smaller than the error obtained with the best-atlas method % and half of the error obtained with the UTE  method %. The paired t-test shows a statistically significant improvement with the proposed method. Examples of ground truth CT -map, pseudo CT -map, bestatlas CT -map, UTE -map, and difference images are presented in Fig.
5) Example of PET Reconstruction From Sinogram Data: As a final test for the performance of the method, we used an offline version of the Siemens Healthcare reconstruction software (made available for this project) on PET/MR data for one subject from the validation dataset. The default settings for OSEM (21 iterations of three subsets, Gaussian post-filtering) were used. We reconstructed PET images using the CT, pCT, baCT, and the UTE -maps to correct for attenuation. Results are shown in Fig. 10. The UTE-based method underestimates the quantity of bone which leads to a global underestimation of the activity in the PET image. Compared to the proposed method, the best-atlas method sometimes fails to accurately delineate the bone, such as in the skull (yellow arrows) and sinus areas (green arrow), which results in a local underestimation of the PET activity.
IV. DISCUSSION Following the principle of multi-atlas information propagation, the proposed method synthesizes a CT from an MRI image which is then processed to obtain an attenuation map used in the PET reconstruction. Two separate datasets were used for the optimization and the validation of the method to reduce bias. We compared our solution to a simpler atlas-based method and to a prototype version of a UTE-based method made available to early users of the Siemens Biograph mMR hybrid PET/MR scanners. The results presented in Table  I  demonstrate that the absolute error estimated between the pseudo CT and the original CT is significantly smaller compared to the other methods. Difference images in Fig. 3  reveal that errors are concentrated at the skull/ soft-tissue, skull/air, and soft-tissue/air boundaries, and that the amount of bone is underestimated when the UTE-based method is used.
Finally, the error is significantly smaller with the pseudo CT method compared to the best-atlas CT method and the bias lower. After normalization, the error obtained with the UTE-based method is reduced while it remains alike for the proposed and best-atlas methods. All correction methods introduce a bias but the variance of the bias is significantly lower when the pseudo CT method is used instead of the best-atlas or UTE method, as shown in Table  I  and Fig. 9. The use of the Siemens offline reconstruction software is still at an early stage but already demonstrates that the proposed method improves the results when applied in a clinical context compared to the method currently in use, as shown in Fig. 10. In a T1-weighted MRI image of the head, cerebrospinal fluid, air, and bone have similar intensities but distinct values in a CT image which impedes a one-to-one mapping from the observed MRI intensities to CT-like intensities.
Exploiting the concept of morphological similarity between subjects, as an alternative to the one-to-one intensity mapping, improves the synthesis accuracy in boundary areas such as air and bone in the nasal sinuses. The low intensities of bone and air also make the segmentation of MRI images challenging  [6] -  [12] , even after correction of several artefacts  [10]. The proposed method does not require any segmentation of MRI images and thus allows the synthesis of a continuous valued attenuation map and avoids large misclassifications. However, atlas-based methods also have limitations as relying on a database implies the need of a representative population. But, even if morphological variabilities of the skull are limited, in the case where the target subject presents abnormalities, the nonrigid registration should be able to capture them.
Further experiments with subjects presenting abnormal anatomies are required to validate this expectation. Although large number of registrations can leave room for errors to confound the mapping in a single atlas method, the proposed multi-atlas strategy can compensate for most registration problems by combining the local atlas selection and consensus estimation steps. The more accurate results reached when the proposed method is used, compared to the best-atlas method, demonstrate the advantages of synthesizing CTs at a local scale instead of a global scale. Current limitations of the method are due to the limited anatomical information of CT and T1-weighted MRI images. A database with multimodal data including T1-weighted, T2-weighted, and UTE sequences would improve the registration and the image similarity estimates, and could provide additional information, such as bone density.
Clinical information (patient's gender, age, weight, or ethnicity) could also be used to specify the bone density more accurately  [18]. This paper is focused on brain applications and further experiments are required to validate the method in other regions of the body. The technique could, in theory, be applied to other body parts as long as the morphological variability is represented in the database and the registration between MRI pairs is sufficiently accurate. The software implementing the proposed method will be made available online at the time of publication.
Parameter Optimization: The dataset dedicated to the parameter optimization was composed of 18 brain T1-weighted MRIs and CTs. The T1-weighted MRIs (3. 0 T; TE/TR/TI, 2. 9 ms/2200 ms/900 ms; flip angle 10 ; voxel size 1. 1 1. 1 1. 1 mm ) were acquired on a Siemens Magnetom Trio scanner; the CTs (voxel size 0. 586 0. 586 2. 5 mm , 120 kVp, 300 mA) on a GE Discovery ST PET/CT scanner. 2) Validation: The dataset used for the validation was composed of 41 brain T1-weighted MRIs, CTs, UTE-based -maps and reconstructed PETs, and one PET sinogram. The T1-weighted MRIs (3. 0 T; TE/TR/TI, 2. 63 ms/1700 ms/900 ms; flip angle 9 ; voxel size 0. 53 0. 53 1. 1 mm ), UTE-based -maps (voxel size 1. 562 1. 562 1. 562 mm ) and PET sinogram (radiopharmaceutical: FDG) were acquired on a Siemens Biograph mMR hybrid PET/MR scanner; the CTs (voxel size 0.
This work was supported by an  IMPACT  studentship funded jointly by  Siemens  and the  UCL Faculty of Engineering Sciences. The  Dementia Research Centre  is an  Alzheimer's Research UK Co-ordinating Centre  and has also received equipment funded by  Alzheimer's Research UK  and  Brain Research Trust. S. Ourselin  receives funding from the  EPSRC  ( EP/H046410/1 ,  EP/J020990/1 ,  EP/K005278 ), the  MRC  ( MR/J01107X/1 ), the  EU - FP7  project  VPH-DARE@IT  ( FP7-ICT-2011-9-601055 ), and the  NIHR Biomedical Research Unit (Dementia)  at  UCL. This work was also supported by  AVID Radiopharmaceuticals  (a wholly owned subsidiary of  Eli Lilly ) and by researchers at the  National Institute for Health Research University College London Hospitals Biomedical Research Centre  ( NIHR BRC UCLH/UCL High Impact Initiative ).
1 ) 1 Parameter Optimization: The dataset dedicated to the parameter optimization was composed of 18 brain T1-weighted MRIs and CTs. The T1-weighted MRIs (3. 0 T; TE/TR/TI, 2. 9 ms/2200 ms/900 ms; flip angle 10 ; voxel size 1. 1 1. 1 1. 1 mm ) were acquired on a Siemens Magnetom Trio scanner; the CTs (voxel size 0. 586 0. 586 2. 5 mm , 120 kVp, 300 mA) on a GE Discovery ST PET/CT scanner. 2) Validation: The dataset used for the validation was composed of 41 brain T1-weighted MRIs, CTs, UTE-based -maps and reconstructed PETs, and one PET sinogram. The T1-weighted MRIs (3. 0 T; TE/TR/TI, 2. 63 ms/1700 ms/900 ms; flip angle 9 ; voxel size 0. 53 0. 53 1. 1 mm ), UTE-based -maps (voxel size 1. 562 1. 562 1. 562 mm ) and PET sinogram (radiopharmaceutical: FDG) were acquired on a Siemens Biograph mMR hybrid PET/MR scanner; the CTs (voxel size 0.
As regards data assimilation, the AROME model gets its initial condition from a 3DVar algorithm. This observation processing is performed every 3 h over a 3 h time window  (Brousseau et al. , 2011). The background-error covariances (hereafter denoted as the B matrix) used are computed from an ensemble of forecasts. This B matrix is purely climatological in the current operational version  (Brousseau et al. , 2008). The AROME B matrix uses a multivariate formulation based on vorticity, divergence, temperature, surface pressure and specific humidity, which are the analysis control variables, under the assumptions of horizontal homogeneity, isotropy and non-separability. A long model forecast up to 30 h is carried out every 6 h (0000, 0600, 1200, 1800 UTC), whereas at intermediate times a 3 h forecast is made to provide an initial background file to the next analysis.
AROME-NWC system architecture AROME-NWC is based on the AROME configuration. In AROME-NWC, a data assimilation followed by a short-range forecast (6 h) is performed every hour, with lateral boundary conditions refreshed every hour. The atmospheric variables (except hydrometeors) are not cycled, which means the model starts from a recent AROME forecast every time (indeed, the most recent AROME forecast available valid at the initial time). The background state is obtained from an adaptation on to the AROME-NWC grid of the AROME forecast. There are two motives for using a background condition from the host model (the model that provides the boundary conditions). Firstly, for technical reasons we want to be able to launch a AROME-NWC forecast at a specific hour even if the forecast from the previous hour did not run correctly.
Adaptation of the background-error covariances The 3DVar data assimilation system from AROME used in AROME-NWC requires the specification of the B matrix, as already discussed. Since the B matrix represents the average error between the best previous forecast of the atmosphere and the true state of the atmosphere, it could be expected that this B matrix is dependent on the lead time of the background, since it is known that the longer a forecast is, the farther from the observations (hence the true state) it is. For testing purposes, B matrices were computed using an offline ensemble of forecasts, which are run with boundary conditions taken from ensemble forecasts of the global model in use in Meteo-France and with initial conditions generated with an assimilation cycle of perturbed observations  (Brousseau et al.
Therefore, the sensitivity experiment of using a 3 h B or a B computed from an ensemble of forecasts ranging from +2 to +9 h does not show any impact, due to the much larger uncertainties that lie in the data assimilation system and in the way the background-error statistics are computed. There is much more variability due to the chosen sampling period of time for the statistics computation, for example. As for the observation errors, which consist of instrumental and representativeness errors, the same values as in AROME are used.
The short cut-off impact The cut-off time selection is crucial: on the one hand the forecast model needs as many observations as possible to perform well, but on the other hand the forecasters, especially in a nowcasting context, need to have the model outputs as soon as possible. For the AROME-NWC configuration, the aim is to have the model deliver its forecasts 30 min after the analysis time. To do so, the cut-off time was set to 15 min. Even with the analysis time window being (-45 min, +15 min), that cut-off time value is rather short compared with the usual cut-off time for the AROME configuration: 3 h (associated with a (-90 min, + 90 min) time window). The observations loss of AROME-NWC is assessed by comparing the number of data received in the information processing centre with a 15 min cut-off and with an infinite cut-off time (Figure  7 ).
In this paper we present an analysis strategy and a dedicated tool to determine the exclusion confidence level for any scenario involving multiple heavy extra quarks with generic decay channels, as predicted in several extensions of the Standard Model. We have created, validated and used a software package, called XQCAT (eXtra Quark Combined Analysis Tool), which is based on publicly available experimental data from direct searches for top partners and from Supersymmetry inspired searches. By means of this code, we recast the limits from CMS on new heavy extra quarks considering a complete set of decay channels. The resulting exclusion confidence levels are presented for some simple scenarios with multiple states and general coupling assumptions. Highlighting the importance of combining multiple topology searches to obtain accurate re-interpretations of the existing searches, we discuss the reach of the SUSY analyses so as to set bounds on new quark resonances.
In this work we are therefore trying to approach the question: given the parameter space describing the extra quark sector in a BSM model and the experimental data from searches of new XQs (or indeed others), would it be possible to find allowed or excluded parameter regions without performing dedicated simulations? In order to obtain an answer to this question two major issues should be solved first: the creation of a pre-loaded data-base of existing experimental analyses to be used for this task and a large amount of MC data. These two issues are the most time-consuming part for such analyses and the focus of this paper is precisely to provide a way to optimise such tasks by means of an automated tool in order to considerably simplify the comparison. We therefore built a code, named XQCAT 2 (eXtra Quark Combined Analysis Tool), that allows the user to quickly determine the exclusion Confidence Level (eCL 3 ) of any given scenario with multiple XQs: a scenario is basically defined by providing the masses and BRs of each new quark of a given species.
The efficiencies of the various signal channels for the many included searches are encoded in the database of XQCAT, so that no additional MC generation is needed in order to calculate the eCLs. The signal channels are characterised by the final states and a set of masses of the XQs. All in all, from a basic input of the code consisting of only masses and BRs of all new XQs, the tool provides the eCL of the specific spectrum combining data from different experimental searches. Given the publicly available results at this time, in this preliminary study we chose to use one published CMS analysis 4 based on the entire 8 TeV dataset optimised for a single vectorlike XQ coupled to third-generation quarks  [34]. By definition, one would expect that this search is not suited to a re-interpretation when considering coupling to light-flavour quarks and so, in addition, we use a set of four inclusive topological SUSY searches  [35] [36] [37] [38] [39] [40]  whose generic jets + 0,1,2 lepton signature should be sensitive to final states with light quarks.
The results in this paper are therefore a realistic estimate on the weakest limits that could be achieved on multiple XQ scenarios while new experimental searches and interpretations are in progress. The paper is organised as follows. In Section 2, we describe the analysis strategy for the XQCAT code, including the generation of the database of efficiencies and the calculation of the eCLs. In Section 3 the validation of the code is discussed in detail and tests are thoroughly illustrated in simple cases. In Section 4, we use the XQCAT code to provide new constraints on scenarios with multiple XQs. In Section 5, the interplay and complementarity between direct XQ and SUSY searches is discussed. Section 6 contains the conclusions and emphasises the prospects of applying the code to other existing searches and other new physics scenarios.
General approach In our model-independent approach, the analysis of scenarios with multiple XQ states that can decay into any SM quark together with a Higgs or gauge boson is addressed by a dedicated computing tool called XQCAT. Starting from the QCD pair production followed by the decays of the new heavy quarks, allowed to mix with any SM quarks via Yukawa interactions, one can estimate the number of signal events that survive the selection cuts for any given signature and specific search strategy, that is finding the respective efficiencies for each subprocess that contributes to the given final state. We have simulated signals for each subprocess for different values of the masses of the XQs and have then used an analysis code to evaluate the respective efficiencies for each subprocess contributing to each individual signature and the respective search.
These efficiencies are then stored in the database of XQCAT The most general scenario we consider covers the case of the following XQ species which can be simultaneously present in the generic model: n X X 5/3 + n T T 2/3 + n B B -1/3 + n Y Y -4/3 (2. 1) where the sub-script indicates the electric charge. Therefore, T and B are up-and downlike quark partners, respectively, and X and Y are exotic particles with charge 5/3 and -4/3, respectively. We limit our analysis to these four species of XQs because they are the only ones which can couple directly to SM quarks. The new states are then assumed to decay promptly, otherwise their signatures would be that of long-lived heavy objects or of bound states thereof, depending on their lifetime  [41]. Assuming general mixings between XQs and SM quarks, we consider the following complete set of decays: • X → W + u i , • T → W + d i , Zu i , Hu i , • B → W -u i , Zd i , Hd i , • Y → W -d i , where i = 1, 2, 3 labels the SM family.
Generation of the efficiency database The simulation of QCD pair production for each quark and for each mass has been performed with MadGraph5, v. 1. 5. 8  [44]. The subsequent decays of the XQs into SM quarks and bosons have been computed with BRIDGE, v. 2. 24  [45]. PYTHIA, v. 6. 4  [46]  has been used for decays of SM particles and subsequent hadronization and parton shower. The detector simulation has been performed by Delphes2, v. 2. 0. 2  [47]  with suitable detector card for CMS (analogous card for ATLAS will be used in a next upgrade of the code). Since the XQ pair production is a QCD process, jet-matching has also been considered and appropriate matching parameters have been chosen for each mass of the XQs. Therefore, the processes simulated in MadGraph5 have been: pp → Q Q + {0, 1, 2} jets (2.
The cut-and-count approach is therefore the most accurate technique we can adopt with fast simulation tools, and the validation will be performed selecting subsets of bins that reproduce as closely as possible the experimental results. For the purpose of our present analysis, we have implemented two different kind of searches (limiting to CMS studies for the moment, ATLAS searches will be included in the following version of the code): • Direct search of XQs We implemented the CMS analysis B2G-12-015  [34] , at √ s = 8 TeV with a 19. 5 fb -1 dataset, for a pair produced T quark that mixes only with third-generation SM quarks and can decay to W + b, Zt or Ht with variable BRs. The CMS collaboration presents the 95% CL lower limits on the T quark mass for different combinations of its BRs using six mutually exclusive channels: two single lepton (single electron and single muon), three di-lepton (two opposite-sign and one same-sign) and one tri-lepton channel, all containing tagged b-jets in the final state.
98 fb -1 2011 dataset at √ s = 7 TeV. We have also included the updated α T  [39]  and same-sign  [40]  searches at 8 TeV, with 11. 7 fb -1 and 10. 5 fb -1 , respectively. It has been verified that the selected searches are uncorrelated and, therefore, it is possible to statistically combine them, yielding 95% CL bounds at 7 TeV (combination of 4 searches), 8 TeV (combination of 2 searches) and 7+8 TeV (combination of 6 searches). A validation of the uncorrelation of the SUSY searches has been performed in  [49]  and relies on the following facts: -The searches have been selected based on the number of leptons in the final state. Since the veto requirements are looser than the acceptance requirements for all the analyses performed, it is guaranteed that considering a 0-lepton, 1lepton, 2-lepton SS and 2-lepton OS search, events are non-overlapping.
Validation of the limit code The limit code has been tested by computing the expected and observed limits using the information provided in the experimental search documentation. This test allows us to determine any discrepancies between the statistical method used in our approach and the one in the CMS analysis. The resulting eCLs and 95% mass bounds computed with XQCAT considering the combination of all channels or of the multi-lepton channels only are shown in Tab. 1. The uncertainty on the signal events has been assumed to be 20%. The results of this test show two different effects: 1. due to a different analysis technique, we are not able to reproduce the mass bounds considering the single lepton-channels in combination with the multi-lepton channels; 2. considering only the multi-lepton channels we can reproduce the experimental expected (observed) mass bounds with a discrepancy of -8% (-6%).