dataesr/scientific-paragraphs-categorization huggingface dataset:
VALIDATION DESIGN Group Comparison Group comparisons were first performed to assess whether the proposed surface-based analysis approach was able to accurately detect differences between cognitively normal and diseased populations. More precisely, a point-wise, vertex-to-vertex model based on the Matlab SurfStat toolbox 6 was used to conduct a group comparison in the whole brain. As all our subjects are in the space of the FsAverage template, we can infer a vertex to vertex comparison using data smoothed with a Gaussian kernel of 20 mm. The general linear model was used to control for the effects of age and sex. Statistics were corrected for multiple comparisons using the random field theory for non-isotropic images  (Worsley et al. , 1999). A statistical threshold of p = 0. 05 (at the vertex level) was applied.
Individual Classification Individual classification was performed to assess the ability of the proposed surface-based approach to accurately classify CN Aβ-and AD Aβ+ subjects (ADNI ALL dataset). The PET signals projected onto the cortical surface, in the space of FsAverage, were used as features to feed a linear SVM classifier. To compare surface-based and state-of-the-art volume-based analyses, the PET images in the DARTEL space were also used as features and fed to the linear SVM classifier. The evaluation of the classification performances mainly followed the recent guidelines provided by  Varoquaux et al. (2017). Cross-validation (CV), the classical strategy to maintain the independence of the train set (used to fit the model), and the test set (used to evaluate the performances), was performed.
Group Comparison When performing group comparison on the ADNI ALL dataset, the temporal lobe (especially the medial and inferior temporal gyri), posterior cingulate cortex, precuneus, and parietal and frontal lobes show statistically significant differences between CN and AD subjects (Figure  4 ), as expected  (Herholz et al. , 2002). Results obtained with the voxel-based analysis projected onto the cortical surface are consistent with those of our method: the same regions are identified even though their appearance is less homogeneous when compared to the surface-based approach. FIGURE 3 | FDG PET SUVR projected onto the cortical surface (left hemisphere) for (from left to right) a cognitively normal subject (CN), a patient with Alzheimer's disease (AD), a patient with semantic variant primary progressive aphasia (svPPA), and a patient with logopenic variant primary progressive aphasia (lvPPA).
The first row is the projection in the subject's space. The second row is the same signal for each subject, but warped to FsAverage after smoothing with a 20 mm Gaussian kernel. The fact that both approaches identify the same regions shows that the proposed surface-based approach is able to accurately detect differences between CN and AD subjects in a large dataset. When performing group comparison on the ADNI SUBSET dataset (Figure  5 ), similar (but less extended) regions showed statistically significant differences compared to analysis of the ADNI ALL dataset. This shows that the proposed approach is able to correctly identify differences even in small datasets. Note that the smaller number of subjects compared to ADNI ALL leads to larger p-values and less extensive areas. The semantic variant of PPA is characterized by hypometabolism focused in the bilateral temporal lobes with an emphasis in the left hemisphere  (Jung et al.
Individual Classification Linear SVM classification of (surface-based) vertex features led to a mean balanced accuracy of 91. 36% with a standard deviation of 3. 03% (Figure  8 ). The same classification approach but with voxel features [as done in Samper-González et al. (  2018 ), for the same task and using the same dataset] led to a mean balanced accuracy of 91. 33% with a standard deviation of 3. 33%. These results show that the proposed surface-based approach is able to provide as accurate classification results as a volume-based approach. We assessed the proportion of subjects for whom the two classification results agreed. As mentioned in section Individual Classification, the linear SVM were trained and tested on the same 250 stratified shuffle splits for the vertex features and the voxel features.
DISCUSSION AND CONCLUSION We presented a fully automatic pipeline that enables the analysis of PET data on the cortical surface. We evaluated its performance by performing group comparison and individual classification for two different diseases  (AD and PPA). This evaluation demonstrated that the developed pipeline is able to identify the areas of hypometabolism characteristic of AD and of the semantic and logopenic variants of PPA, and to individually differentiate CN and AD subjects. We also showed that this method does not create spurious differences. The proposed surface-based analysis approach relies on tools from different software packages, mainly FreeSurfer and PETPVC, which are combined to build a pipeline able to automatically perform the pre-processing and projection of PET data on the cortical surface.
This pipeline is part of the software platform Clinica, under the name pet-surface. The target audience of Clinica is mainly of two types. First, scientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, typically not experts in image processing for all of the involved imaging modalities or in statistical analysis. Second, researchers developing advanced machine learning algorithms, typically not experts in brain image analysis. The main objective of Clinica is to enable reproducible research. This is done by developing pipelines performing data processing and analysis in a standardized way, and that can be run thanks to simple command lines. A downside of this approach is its limited customization options. In particular, it is not possible to specify a different reference region for the PET intensity normalization.
However, this option could easily be added to future releases if the need arises. Note that the vast majority of the operations presented in this study was conducted with Clinica (see section Validation design). Because Clinica relies on Nipype to build pipelines, the processing can be parallelized over subjects, reducing the total time needed for the computation. Running pet-surface for a FIGURE 8 | Distribution of the balanced accuracy across 250 runs obtained when classifying CN Aß-vs. AD Aß+ subjects (ADNI ALL dataset) using a linear SVM fed with either vertices (obtained with the proposed method) or voxels. In both cases, no smoothing was applied. subject takes approximately 1 h on an Intel R Core TM i7-4870HQ CPU @ 2. 50GHz. We mainly rely on the mris_expand function of FreeSurfer rather than on vol2surf.
cognitively normal subjects demonstrated promising results on a large dataset, obtaining the same balanced accuracy as a standard volume-based approach (surface-based: 91. 36%, volume-based: 91. 33%), with a reduced standard deviation across runs (surface-based: 3. 03%, volume-based: 3. 33%). When performing group comparison on the ADNI ALL , ADNI SUBSET , and CAPP LOGOPENIC datasets, we showed that the proposed approach was able to detect the expected differences between control and diseased subjects, and that patterns of statistical differences were consistent between the surface-based and a standard volume-based method. However, we observed that the accuracy of the proposed approach can be limited when applied to subjects with severe focal atrophy, such as subjects with svPPA. This is related to the difficulty of extracting surfaces and generating accurate parcellations in these areas.
This approach is particularly interesting for multimodal analyses as data extracted from different modalities can be analyzed in the same way (e. g. , cortical thickness from MRI and metabolism from FDG PET). By applying it to a large number of control subjects and patients with AD and two variants of primary progressive aphasia, we showed that the proposed surface-based approach was able to identify areas where hypometabolism was expected when comparing control and diseased subjects, and that these findings were consistent with those of a standard volume-based approach. By performing individual classification tasks on a large dataset of control and AD subjects, we showed that our method performed equally well than a standard voxel-based approach. The proposed pipeline, which is part of the publicly available Clinica platform (under the name pet-surface), should enable the joint analysis of PET and MRI data on the cortical surface.
ACKNOWLEDGMENTS Data collection and sharing for this project was funded by the  Alzheimer's Disease Neuroimaging Initiative (ADNI)  ( National Institutes of Health  Grant  U01 AG024904 ) and  DOD ADNI (Department of Defence  award number  W81XWH-12-2-0012 ). ADNI is funded by the  National Institute on Aging , the  National Institute of Biomedical Imaging and Bioengineering , and through generous contributions from the following:  AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation ;  Araclon Biotech ;  BioClinica, Inc. ; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc. ; Cogstate;  Eisai Inc. ;  Elan Pharmaceuticals, Inc. ;  Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd  and its affiliated company  Genentech, Inc. ; Fujirebio;  GE Healthcare ;  IXICO Ltd.
The inconclusive interpretations based on the current world neutrino oscillation data justify an active program of new short baseline oscillation searches near reactors and accelerators. Some of these are already taking data  [20] [21] [22] , while others are currently being commissioned, or under construction  [23, 24]. The SoLid experiment, short for "Search for oscillation with a 6 Li detector", is a new generation neutrino detector, based on plastic scintillator technology, currently in operation at the BR2 research reactor at the SCK • CEN in Mol, Belgium. The experiment will perform an oscillometric measurement of electron anti-neutrino disappearance as function of the anti-neutrino energy and interaction distance, at a baseline between 6 and 9 meter. The SoLid data will be interpreted in terms of additional sterile neutrinos related to mass eigenstates corresponding to ∆m 2 ∼ 1 eV 2.
Each detection plane is lined on the inside with 2cm thick black high density polyethylene (HDPE) to improve the moderation and reflection of neutrons created at the edges of the detector. The outer front and back surface of each detection plane is capped with a 2mm thick black HDPE sheet. The optical fibres protrude from the edges of a detection plane into a hollow aluminium frame where they are coupled on one end to a multi-pixel photon counter (MPPC) from Hamamatsu type S12572-050P using optical grease. The other end of the fibre is mirrored with a thin aluminium tape. The position of the MPPC and mirror alternates between adjacent fibres to ensure a more uniform light response throughout the detector. The first full scale prototype submodule of SoLid, named SM1 hereafter, consists of 9 collated detection planes to amount to a final configuration of 16×16×9 detection cubes, read out by a network of 32 × 9 fibres connected to one MPPC each.
The data trigger logic is implemented in the FPGA of each GLIB board and is based on the time coincidence of PA pulses with an amplitude exceeding a pre-set threshold. This threshold was set to 6. 5 PA in order to maintain a sustainable data rate. The time coincidence was imposed by requiring at least one horizontal and one vertical fibre pulse to exceed the amplitude threshold within a coincidence window of 3 samples. When a trigger occurs, a 256 sample waveform is stored from each channel that exceeds the threshold. Additionally, a random periodic trigger was used to read out waveforms from all detector channels. Since each MPPC has a unique breakdown voltage, each channel is biased individually to achieve a uniform energy response across the detector. To achieve this, the gain of each channel is measured by identifying the first and second pixel avalanche peaks in the amplitude spectrum of each channel, collected with random triggers.
w. e. )) and not effective in shielding the detector from cosmic rays. In the winter of 2014-2015 the prototype detector was installed in front of the reactor core as shown in Fig. 5  and commissioned during the last days of a reactor cycle. At the end of February 2015 the reactor was shut down for a 1. 5 year-long overhaul of its Beryllium fuel core matrix. During this shut-down period, background data was collected, as well as some source calibration data. A list of data sets taken with the prototype can be found in Tab. 2. Figure  6  shows various trends found in the data, for both the reactor on and off data taking periods. The identification of the various objects is explained in later sections. It can be seen that the rate of reconstructed cosmic muons is stable over this period, which indicates that the timing of the detector is stable.
The RMS of the deposited energy per path length of reconstructed muons crossing the detector over a period of 3 months was measured to be less than 1%, proving the stability of the gain and energy response of the detector. The reactor shut-down is indicated in the power profile, which is correlated with the dropping rate of triggered waveforms exceeding the thresholds. The rate of reconstructed neutron signals shows a very small decrease after the reactor shut-down, implying a small reactor induced neutron background. Data taking dead-time was monitored continuously and was found to be negligible.
06(syst)) %. Note that the systematics come from the uncertainty on the 6 Li content of the 6 LiF:ZnS(Ag) screens. On the other hand, det was determined using calibration data with neutrons emitted by an AmBe source, placed either in the front (Position 1) or in the back (Position 2) of the detector, and a neutron selection based on a PID value higher than 10. The values of det were taken from the number of neutrons that were identified in each cube, divided by the expectation coming from simulations. The distribution of all values of det obtained for cubes receiving a sufficient amount of signal hits during the two calibration runs are shown in Fig. 11. The values obtained from both runs are in good agreement and combined to obtain a final average across the detector of det = (5. 51 ± 0. 02(stat) ± 1.
Neutron capture time The AmBe calibration data is also used to perform a study to estimate the neutron capture time. This is the time that a neutron needs to thermalise and gets captured by a 6 Li atom. A neutron loses energy in the detector by scattering from a nucleus, mostly H and C in the case of SoLid, and in doing so it creates proton recoils. The time correlation between a prompt signal corresponding to a proton recoil and the delayed 6 Li neutron capture provides us with a characteristic time that is related to the thermalisation and capture processes of the neutrons, shown in Fig. 12. A clean neutron enriched sample was selected from the AmBe calibration runs, and an off-time window is used to model the background in the data. The neutron capture time of (91. 56 ± 1. 07) µs is estimated from data, which is in very good agreement with (91.
1 and monitor the long-term stability of data taking with the detector. Muon rates and angular distributions are used to validate the cosmic ray models and simulation of the material structures in and around the detector used by the experiment. Timedelayed signals following a tagged muon validate the timing and pulse shape discrimination based particle identification, and will allow for a data driven estimation of the time-correlated background components. This will in turn allow the development of dedicated selection criteria to reduce the contributions of these backgrounds. To estimate muons and muon-induced backgrounds, simulations of single muons were generated in a rectangular surface above BR2 using the analytical flux of Guan et al. [29]. The propagation of muons, through the surrounding material, was performed using G 4.
A detailed description of the BR2 containment building and the prototype was input in G 4, shown in Fig. 13 , and the events that hit the detector were processed by a standalone code that simulates the effect of the read-out system. On the other hand, atmospheric neutrons were generated using the model of Gordon et al. [30]. Large samples of muons and atmospheric neutrons were produced using the recipe described above and they were analysed using the SoLid off-line software. This procedure allowed us to estimate the flux and energy distributions of muons, spallation and atmospheric neutrons in the prototype.
On the other hand, spallation neutrons were identified by searching for identified neutrons in a time window of 1 -1001 µs after a tagged muon. Again, a time window shifted by 1 ms was employed for background estimation. The on-and off-time ∆t µ-n distributions are shown in Fig. 17  (left). A combined fit with an exponential function and a flat background (Fig. 17 , right) yields the value of τ n = 89. 81 ± 2. 63(stat) µs for the neutron capture time on 6 LiF:ZnS(Ag). This value, the uncertainty of which is statistically limited, is compatible with the measured capture time using AmBe data described in section 4. 4. Both measurements, combined with our knowledge on the decay properties of muons and the thermalisation of neutrons in PVT, confirm the purity of the particle identification and the validity   of the timing of the detector electronics and readout.
Reactor data As mentioned earlier, the BR2 reactor overhaul and maintenance schedule coincided with the deployment of our submodule prototype. This allowed for only a small dataset to be collected of 50 hours under stable running conditions of the detector during reactor operations, as indicated in Tab. 2. Given the small neutron trigger and detection efficiency reported in section 4. 3, the expected number of observed anti-neutrino events in the data run is 10 ± 1(stat) which is unlikely to result in any statistically significant anti-neutrino excess in the data. The data can nevertheless be used to validate the assumptions made in the simple background model described above. Compared to the current estimation of the accidental backgrounds, time-correlated backgrounds are the biggest concern for the success of the full-scale experiment.
Predicted anti-neutrino rate The accurate prediction of the flux and energy spectrum of electron anti-neutrinos is an involved process, subject to intense debates  [1, [6] [7] [8] [9] [10] 18]. The strategy of the SoLid collaboration is to use state-of-the-art methods to predict the anti neutrino flux and spectra, based on a detailed 3D model of the BR2 core coupled to MCNPX/CINDER'90  [32]  as a starting point to produce the fission rates. In addition, the MURE code will be used to track the burn up of the fissile products in the core as prescribed by  [33]. The SoLid experiment will have the option of computing a conversion spectrum based on Huber  [6]  and Mueller  [34] , supplemented by off equilibrium corrections provided by MURE  [33] , or to use predictions using the summation method developed by the Nantes group  [35, 36].
Observation of 214 Bi decay In order to measure the 214 Bi induced background, further denoted as BiPo, dedicated selection requirements are applied to the data collected when the reactor was off. The following selection requirements are chosen to select a pure sample of BiPo events: • Events with a muon preceding the ZnS signal are vetoed when ∆t = t(n-like)-t(µ) < 1. 5 ms. • The ZnS and PVT signal are required to be observed in the same cube, ∆r(E M, nlike) = 0. • The time between the ZnS and PVT signal is required to be less than 1 ms, ∆t = t(nlike)t(E M) < 1 ms. • The energy of the PVT signal is required to be 0. 6 < E pr ompt < 3 MeV. The upper limit is justified by the end-point energy Q β. The distribution of ∆t is shown in the left panel of Fig. 23. The distribution is fitted in the range between 20 and 1000 µs with the analytical function: f (∆t) = c 1 × exp(- ∆t τ 1 ) + c 2 × exp(- ∆t τ 2 ) + c acc (8.
Conclusion and outlook The SoLid collaboration designed and constructed a full scale prototype module of a new reactor neutrino detector with a per-mille level of control on its proton content. The prototype was deployed and commissioned at the intended location of a large scale detector of 1. 6 ton, where it demonstrated excellent operational stability over a period of several months. The commissioning of the module demonstrates the equalisation and calibration of the individual channel energy response up to a level of 3% and an energy resolution of 20% for 1 MeV electrons interacting in the plastic scintillator, that can be improved to 14% by means of several design improvements. Calibration runs taken with gamma and neutron sources demonstrate the possibility to exploit pulse shape discrimination for particle identification.
The analysis of cosmic ray data demonstrates the excellent muon tracking capabilities of the detector which can be used to calibrate the energy response and monitor its stability over time. The prototype collected useful data during reactor operations, albeit with suboptimal trigger performance due to unexpected features in the readout electronics. Event reconstruction and analysis tools were developed to perform physics analysis with the collected data. Using this data, accidental and time-correlated backgrounds are measured in realistic conditions. The spatial segmentation, a unique feature of the SoLid detector, is shown to be a powerful tool in reducing both accidental and time-correlated backgrounds. The background model, which is completely data driven, shows its applicability to the IBD analysis.
The components of the time-correlated background are analysed using dedicated selections and are extrapolated to the IBD search region, indicating that fast neutron induced backgrounds dominate the experimental uncertainties in the current set-up. These results are taken into account in the shielding strategy of the full-scale detector. Based on the observations made with the prototype module, the SoLid collaboration adapted a set of design changes for the full scale experiment. These include a new design of the front-end electronics with far superior noise-tolerances, and an improved light collection to achieve a better energy resolution. In addition the final detector will contain twice the amount of 6 LiF:ZnS(Ag) screens to increase the neutron detection efficiency. The detector will also be cooled to an ambient temperature of 5 • C to reduce the dark count rate, in combination with a passive shielding to reduce the cosmic and reactor induced backgrounds.
Acknowledgements This work was supported by the following funding agencies:  Agence Nationale de la Recherche  grant  ANR-16CE31001803 ,  Institut Carnot Mines ,  CNRS/IN2P3  et  Region Pays de Loire, France ;  FWO-Vlaanderen  and the  Vlaamse Herculesstichting, Belgium ; The U. K. groups acknowledge the support of the  Science & Technology Facilities Council (STFC) ,  United Kingdom ; We are grateful for the early support given by the  sub-department of Particle Physics at Oxford and High Energy Physics at Imperial College London. We thank also our colleagues, the administrative and technical staffs of the SCK • CEN for their invaluable support for this project. Individuals have received support from the  FWO-Vlaanderen  and the  Belgian Federal Science Policy Office (BelSpo)  under the IUAP network programme; The  STFC Rutherford Fellowship program  and the  European Research Council  under the European Union's  Horizon 2020 Programme  ( H2020-CoG)/ERC Grant  Agreement n.