dataesr/scientific-paragraphs-categorization huggingface dataset:
SPM implementation The Statistical nonParametric Mapping (SnPM, version SnPM13; RRID:SCR_002092;  [Nichols and Holmes, 2002] ) toolbox was used to carry out permutation tests in SPM. The "MultiSub: one-sample T-test F I G U R E 1 (Continued) on diffs/contrasts," Compute and Inference modules within SnPM were applied to obtain the final group-level activation maps. Each of the comparison methods described in the previous section were also applied to our permutation results to assess crosssoftware differences for nonparametric inference methods. In addition, we also generated intrasoftware Bland-Altman plots and Dice coefficients to understand differences between the parametric and nonparametric methods applied within each package. These methods were excluded for ds000120, since it was not possible to conduct permutation inference for an F-test within AFNI, and parametric inference was unfeasible in FSL for this study as discussed in the previous section.
| Scripting of analyses and figures AFNI and FSL scripts were written in Python 2. 7. 14 and SPM scripts were written in Matlab R2016b. Scripts were made generalizable, such that the only study-specific differences for each of the analyses in a software package were the raw data and working directory inputs, subject-and group-level analysis templates (as well as a run-level template for FSL), and a unique conditions structure necessary for creating the onset files for the specified study. For each analysis package, a script was written to extract the stimulus timings from the raw data to create event files that were compatible within the software. Subjectlevel analysis templates were batch scripts created for each study containing all processing steps of the subject analysis pipeline for the respective software, with holding variables used where subject-or run-specific inputs were required.
The main script would take the template as an input, and cycling through each of the subjects, replace the holding variables with appropriate pathnames to create distinct batch scripts for each subject. These were then executed to obtain subject-level results for all participants in the study. A Python Jupyter Notebook  (Kluyver et al. , 2016)  was created for each of the three studies. Each notebook harvests our results data from NeuroVault and applies the variety of methods discussed in the previous section using NiBabel 2. 2. 0  (Brett et al. , 2017)  1203654  (Bowring et al. , 2018b). Registration of each subject's functional data onto the anatomy was visually assessed. The mean and standard deviation images of the MNI structural and (mean) functional data (Figure  S1 , note that axial slices are slightly different between software due to different bounding boxes of the images) substantiate that registration was successful in all packages across the three studies.
| Cross-software variability While qualitatively similar, variability in T-statistic values and locations of significant activation was substantial between software packages across all three studies. Comparisons of the thresholded results with the published findings are shown in Figure  1a , with further multi-slice comparisons across software in Figure  1b  (also in Figures  S2, S4 , and S6). The ds000001 study described positive activation in the bilateral anterior insula, dorsal anterior cingulate cortex, and right dorsolateral prefrontal cortex, and negative activation in the ventromedial prefrontal cortex and bilateral medial temporal lobe. In our reanalysis (Figure  1a , left) all three software found activation in these set of regions, with the exception that decreases in the medial temporal lobe were unilateral in FSL and  The original ds000120 study found extensive activations for the main effect of time-the frontal, supplementary, posterior parietal cortex, basal ganglia, prefrontal cortex, ventral striatum, and orbitofrontal cortex all showed significant activation.
Our reanalyses (Figure  1 , right) are consistent with these findings, with the exception that neither AFNI nor SPM exhibited orbitofrontal (OFC) activation (though, the SPM analysis mask had poor OFC coverage). AFNI's F-statistic values look to be generally larger than SPM here (Figure  1b , bottom, z = 0 and z = 12 slices). The unthresholded statistic maps from our reanalyses (Figure  2 , Figures S7, S9, and S11) also show that while extreme values display moderate agreement, there are considerable differences across the brain in each given study. NeuroSynth association analyses conducted on the unthresholded T-statistic maps (Table  2 ) show that the most strongly related term to the activation patterns displayed across all three sets of results was the same: "anterior insula" for each software's ds000001 map, "medial prefrontal" for ds000109, and "visual" for ds000120.
Phrases related to the task paradigm used in each study ("goal" for ds000001, "theory mind" for ds000109, "visual" for ds000120) were found across all software's activation patterns, alongside a range of common anatomical terms. 3 ). The Bland-Altman plots comparing percentage BOLD change maps (Figure  S13 ) are more conclusive, showing a clear trend for SPM to report larger effect estimates than the other two packages. Figure  3b  presents the Bland-Altman plot comparing unthresholded F-statistic images for ds000120, which has a very different appearance due to F-statistics being nonnegative. The corresponding Bland-Altman plot comparing partial R 2 values (Figure  S14 ) for this study is similar in shape. Broadly speaking, while there are no gross differences in sensitivity, there is a slight tendency for AFNI's extreme statistics to exceed FSL's and SPM's, and SPM's to exceed FSL's, most evident in ds000109.
684; it is notable that across all studies, the AFNI/SPM dice coefficients are consistently the largest. Spill over values, given by the grey values beneath the dice coefficients in Figure  4 , are generally largest for SPM comparisons. They are particularly prominent in the negative activation plot for ds000001, where there is at least 30% spill over for all parametric pairwise comparisons, the largest being 39% for SPM/FSL. Recalling that these values are the percentage of activation which occurred within one package that was outside the other package's analysis mask, this is likely due to the fact SPM consistently had the smallest analysis mask out of the three packages, while FSL had the largest. In our ds000001 reanalyses, SPM's group-level analysis mask was made up of 175,269 voxels, while AFNI's had 198,295 voxels and 251,517 for FSL.
For ds000001 increases, FSL permutation obtained no significant results, thus generating Dice coefficients of zero; for ds000109 decreases, only AFNI and FSL parametric obtained a result and hence only one coefficient is displayed. Dice coefficients are mostly below 0. 5, parametric-nonparametric intrasoftware results are generally higher; ds000120's F-statistic results are notably high, at 0. 684, perhaps because it is testing a main effect with ample power [Color figure can be viewed at wileyonlinelibrary. com] The nonparametric Bland-Altman plots (Figure  6 ) show substantial spread qualitatively similar to the parametric ones (Figure  3 ), and correlations between statistics maps are similar for nonparametric in congruence with the parametric comparisons (Table  3 ). EC  The Dice coefficients comparing the thresholded permutation test and parametric inferences are generally the best of any (Figure  4 , 3-element lower diagonal).
Rather, we feel that the key contribution of our work is the quantitative measurement of intersoftware differences on common datasets. Our finding that exceedingly weak effects may not generalise across packagesevidenced across all three of our analyses-is the primary take-home message of this work. While larger effects were found to be more robust-demonstrated by the similar Neurosynth association analysis results that suggest some alignment in the final qualitative conclusions that can be drawn from all three software's statistical maps-we stress that our analyses have been conducted under particularly favourable conditions: The use of studies with a strong, primary effect and extensive efforts made to harmonise the three analyses. Because of this, at best our results present an optimistic view of intersoftware disparities.
While a deeper analysis on the differences between topological aspects of images across software would be valuable, there are inherent difficulties in this approach, such as identifying corresponding features between maps when the number and size of activations reported are variable across software. It is notable that the level of variation in our analysis results also fluctuated across the datasets we analysed. This is highlighted in our Dice comparisons, where the ds000001 Dice coefficients are considerably smaller than ds000109 for both the inter and intrasoftware comparisons. The relatively poor performance of ds000001 may be due to the smaller sample size for this study (16 vs. 21 for d000109), as well as the particular inference method used. For ds000001, group-level inference was conducted using a cluster-forming threshold of p <.
The reasons for this are exhibited most clearly by our ds000109 analyses; the visual slice comparison of our replications of the main figure from the original study in Figure  1a , shown alongside the publication figure itself, look remarkably similar and could lead to the conclusion that each package's results highly agree. It is only when analysing these results over the whole brain, that we discover broad differences in these activation patterns, for example, positive activation identified in the auditory cortex in FSL that was not reported by AFNI and SPM, and significant deactivation determined only by AFNI and FSL. At the start of our investigations, we selected a common set of preprocessing steps to be applied within each software package across all studies regardless of whether they had been used in the original analysis.
This was to maximise the comparability of the results while being consistent with best practices within the community. However, several complications arose during our analyses. For ds000001, orientation information was missing from seven of the subject's structural and functional scans. Because the source DICOM files were no longer available, it was not possible to retrieve the original position matrices. As a consequence of this, the structural and functional images were misaligned, resulting in suboptimal coregistration during our analyses. Additionally, a bug in the event-files induced during data conversion to the BIDS standard had resulted in some of the event timings being lost. Thanks to the cooperation of BIDS and OpenfMRI these problems were solved; a revised dataset (Revision: 2.
, 2018). Further work could consider the factorial expansion of all possible combinations of preprocessing, first level modelling, and second level modelling, akin to previous efforts in assessing reproducibility over a number of pipelines  (Strother et al. , 2002). Due to the restrictive requirements of this investigation-the necessity for published task-based fMRI data using analysis methods compatible in AFNI, FSL, and SPM-the three studies analysed here were found to be the only datasets hosted on OpenfMRI suited to the aims of our investigation. Of the datasets that were not used, the most common reasons for exclusion were that no publication was associated to the data, that the sample size of the study was too small, or that custom software or region of interest analysis had been used as part of the analysis pipeline which was not feasible across the three software packages.
Nevertheless, a greater sample of studies will need to be replicated across the packages to gain a more comprehensive understanding of the variability between software and validate the results found here. With increasing access to population neuroimaging studies, where thousands of fMRI subject data are available, a future study could test for nonzero software-related variation by splitting a large dataset (e. g. , UK Biobank  [Alfaro-Almagro et al. , 2018] , N > 10,000) into smaller subsets to generate an extensive collection of replication analyses across the three packages. This may allow for the creation of a null-distribution from which differences between software results could be assessed in terms of statistical significance and confidence intervals, expanding on the raw concrete differences between T-statistics maps highlighted in this effort.
As simulation techniques become more advanced, there is also the potential for creation of synthetic subject-level fMRI data as a ground truth to which each software package's results could be compared  (Ellis et al. , 2019). Of the datasets we did use, subject data were missing from both the ds000109 and ds000120 datasets. For ds000109, while 29 young adults were scanned for the false belief task, only 21 were present in the dataset; for ds000120, we analysed 17 subjects instead of 30 used in the original study. These analyses therefore should not be compared like-for-like with the published results, and have substantially less statistical power than the original studies. Overall, our sample sizes for the three datasets analysed (ds000001, ds000109, ds000120) are 16, 21, and 17, respectively.
While small, these sample sizes are fairly representative of a typical functional neuroimaging study over the past two decades-between 1995 and 2015, the median sample size of an fMRI study increased steadily from 8 to 22  (Poldrack et al. , 2017). This increase has continued, and a review of 2017 publications found a median sample size of 33  (Yeung, 2018). Hence while our datasets are important for judging previous work, a future comparison exercise with larger datasets would be a valuable addition to the literature. We have kept many parameters fixed in our analyses, such as the use of nonlinear registration for all software packages, and the addition of motion regressors in all our design matrices. How changes in these variables influence the analysis results warrants further investigation; for example, while we decided to fix a 2 mm cubic voxel size in all packages (since this is the default in FSL, SPM), a recent study found that alterations in this parameter can significantly impact statistical inference  (Mueller et al.
For a qualitative assessment of whether similar activation patterns were displayed between packages, a NeuroSynth (RRID:SCR_006798, T A B L E 1 Software processing steps ***. nidm. zip" Implementation of each of the processing steps (ds000001, ds000109, ds000120) within AFNI, FSL and SPM. a The @SSWarper program was ran on each subject prior to afni_proc. py for brain extraction of the anatomical image, and to apply the nonlinear warp of the anatomy to MNI space. b ds000120 only. c Image calculator was used to create bain mask from grey matter, white matter, and CSF images; see text. BOWRING ET AL. http://neurosynth. org) association analysis was conducted on each software's unthresholded statistic maps. These analyses performed a cognitive decoding of the unthresholded statistic image with images in the NeuroSynth database, to find the words or phrases most strongly associated with the activation patterns found in the statistic map.
DATA AVAILABILITY STATEMENT The data that support the findings of this study are available in the NITRC Software Comparison Project -Task fMRI repository at http:// scp_taskfmri. projects. nitrc. org/. These data were derived from the following resources available in the public domain: OpenfMRI ds000001 dataset, https://www. openfmri. org/dataset/ds000001/; OpenfMRI ds000109 dataset, https://www. openfmri. org/dataset/ds000109/; OpenfMRI ds000120 dataset, https://www. openfmri. org/dataset/ ds000120/.
Modeling Numerical simulations were performed to compute the reflectance (R), external quantum efficiency (EQE), internal quantum efficiency (IQE) and J-V curves of the device under dark and illumination conditions. For this purpose, an in-house MATLAB code solving the fully-coupled semiconductor (Poisson and continuity) equations in one dimension was developed. A special attention was paid to the heterojunction modeling (especially the heterojunctions induced by the window layer and the BSF) by using double-valued mesh points for the quasi-Fermi levels at hetero interfaces  [13]. In addition to bulk recombination, interface Shockley-Read-Hall (SRH) recombination involving surface recombination velocities (SRV) for electrons and holes was implemented. The optical generation was calculated using a transfer-matrix-based method, involving optical properties (n, k) taken either from literature (GaSb  [14] ) or from experimental ellipsometry data (SiO2 and AlGaAsSb  [5] ).
Acknowledgments This work was partly supported by the  French program  on « Investments for the future » managed by the  National Agency for Research  under contracts  ANR-10-LABX-22-01-SOLSTICE  and  ANR-11-EQPX-0016  ( Equipex EXTRA)  and the  H2020 program  through  MSCA ITN PROMIS (GA 641899 ).
This article proposes a new approach for gas identification, this approach relies on applying supervised learning methods to identify a single gas as well as a mixture of two gases. The gas is trapped in a gas discharge tube, it is then ionized at a relatively low pressure using an HV transformer. The images captured after the ionization of each single gas is then captured and transformed into a database after being treated in order to be classified. The obtained results were very satisfying for SVM as well as for LVQ. For the case of identification of a single gas, the learning rate as well as the validation rate for both methods were 100%. However, for the case of mixture of two gases, a Multi-Layer Perceptron neural network was used to identify the gases, the learning rate as well as the validation rate were 98.
However, LVQ can also be trained without labels by unsupervised learning for clustering purposes. [3]  3 Results and Discussion: The database obtained from the response of the mono gas (images captured after ionization of each gas) is of dimension (Mx4). The first three columns correspond to the three variables RGB characterising each sample xi (pixel). The forth corresponds to the class of the sample yi. M is the number of samples. The data are three-dimensional, hence tracing the points xi in a 3D space gives us an idea about the linear or non-linear separability of the data. The repartition of this data in 3D space shows us that the data is totally linearly separable, hence the usage of artificial intelligence or SVM approaches are well adequate to this problem.
Identification by SVM: Since the problem is a multi-class problem (4 classes), we used the tools of SVM multiclasses. We chose the one against one algorithm. We are searching for the number of hyperplans. Using each time as input the samples of the two classes to separate. To classify a new spectrum (identify the gas corresponding to it), a majority vote on the six classifiers is implemented. As we saw when viewing the database, the data is perfectly linearly separable. Therefore, the application of hard-margin SVMs is sufficient to find the optimal hyperplanes that perfectly divide this data into four classes. Then the matrix of quadratic coefficients Q is generated. The criteria to minimise is written in the quadratic program as: min ଵ ଶ ߙ ் ܳߙ + ݂ ் ߙ (1) Respecting the constraints: ‫ݕ‬ ் ߙ = 0 (2) and 0≤ ߙ ≤C (3) With: ݂ = ‫ݕݐ݅݊ݑ(-‬ ‫ݎݐܿ݁ݒ‬ ‫݂‬ ‫݊݅ݏ݊݁݉݅݀‬ (ܰ × 1)) and ‫ܥ‬ = ∞ The command "quadprog" in Matlab permits us to solve the minimisation problem with constraints.
Identification of Two Gases using an MLP Neural Network: In our case (four gases), there exist six possible cases for mixtures of two gases which are (CO2,O2), (CO2,Ar), (CO2,He), (O2,Ar), (O2,He), and (Ar,He). For every case of mixture of two gases, the total RGB of all possible mixtures ranging from (99% Gas1, 1% Gas2) to (1% Gas1, 99% Gas2) was provided as an input database to the Matlab neural network toolbox (nftool). The output corresponding to the total RGB for mixtures of (CO2,O2), (CO2,Ar), (CO2,He), (O2,Ar), (O2,He), (Ar,He) are 1100, 1010,1001,0110,0101,0011 respectively. The neural network was trained on 70% of the input samples. Validation and testing were done on 15% of input samples. The learning rate as well as the validation rate were 98. 59% and 98. 77% respectively. The figure below shows a Matlab GUI that takes the measured values of RGB as an input, and uses the neural network to identify the gases in the mixture.
Conclusion and Perspectives As a result, a new approach for gas identification using SVM and LVQ was studied and tested on four different gases. This approach allows us to identify only a mono gas, the results showed a 100% learning rate for SVM and LVQ, which is considered to be promising. In addition, an MLP neural network was used in order to identify the gases in a mixture of two gases, the learning rate as well as the validation rate were 98. 59% and 98. 77% respectively. It will be more interesting in future work to develop an advanced algorithm capable to identify several gases (more than two) in a mixture. This requires a much bigger database that provides more information for the machine allowing it to identify gases in a mixture. The proposed approach is essential to go further in identifying gases in a mixture.
The use of different proteases individually or in combination creates a unique set of peptides. Performing multiple digestions can increase overall confidence in protein identification, if cleaving sites are different. It is not straightforward to determine which combination of proteases will lead to a set of peptides suitable for MS/MS analysis. However, the cost of some proteases does not allow for easily trying multiple combinations in order to avoid redundancy of cleaving sites. To this end, few software that predict cleavage sites of proteases in protein sequences have been developed. Among those, the most commonly used are PeptideCutter from ExPASy Server (4) and a module integrated in MaxQuant  (5). PeptideCutter performs a digestion using one or multiple proteases, among a total list of 38, and provides detailed results, including positions of cleavage sites, peptide sequences, lengths and masses.
Last but not least, in PeptideCutter it is not possible to input novel protease definitions, which is not adapted to the increasing number of new or more   (10). Values depend on temperature, ionic strength and the microenvironment of the ionizable group. specific proteases (denoted as 'Sequencing Grade' or SG). Depending on the company manufacturing those SG proteases, specificity and definition can change. Hence, it is important for the user to easily adapt the software by including novel definitions of proteases. The alternative, MaxQuant, partially overcomes some of those issues. The user can input new protease definitions by specifying between which amino acids cleavages occur. Unfortunately, this definition is not sufficient to properly define some proteases. For example, definition of trypsin in MaxQuant lacks some exceptions, e.
g. it is defined as cleaving after K or R, but not before P (see Table  1  for amino acid designation). However, it has been reported that although most of the times P blocks the cleavage when found after K, this is not true when K is preceded by W: a cleavage occurs after K in 'WKP' motif  (6). Currently, it is not possible to create such rules in MaxQuant. In this paper, a novel software developed to predict proteases-induced cleavage sites on sequences is presented, Rapid Peptides Generator (RPG), overcoming most issues of existing programs. First, RPG computes an accurate estimation of the molecular weight and isoelectric point of each generated peptide. Second, RPG takes into account miscleavages and importantly, it assigns to each proteases a probability of miscleavage event. Third, RPG introduces two distinct digestion modes.