dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the established method for interpreting the results is to generate events and make predictions for interaction generators using the same signal definition, neutrino flux, and target as the experiment, and comparing the calculations to data. We follow this method throughout this article. The Tensions workshop series attempts to examine how measurements are defined/carried out and make comparisons with a variety of Monte Carlo calculations. The first Tensions (2016) workshop  [12]  discussed difficulties in comparing results with different signal definitions, published comparisons of data against a variety of models, and discussed their model dependence. All cross-section experiments were represented, and generator experts were present. General issues of experiments and modeling were discussed from different points of view, and adjustments to the methods were suggested to the experiments.
One of the interesting studies in the first Tensions workshop was pion production where signal definitions are especially complicated. Each experiment chooses analysis methods and signal definition to best utilize their detector and their view of what measurements are needed. The result is that comparing measurements is a difficult job that must be done via generators which are capable of reproducing a wide range of signal definitions. Event generator codes were able to accurately simulate the signal definitions of the MiniBooNE  [13]  and first MINERvA results  [14]  and as a result compare the same models to each dataset. One of the main conclusions of Tensions 2016 was that the two datasets were largely incompatible with our models as a result of comparison with a variety of calculations.
This article comes from the second Tensions workshop which was held in the summer of 2019 at University of Pittsburgh, USA, as well as further investigation which followed this workshop. For this article, we discuss new measurements from Tokai-to-Kamioka (T2K), MINERvA, and MicroBooNE, which have been published with significant improvements in methodology. We use three recent versions of GENIE  [15] , one recent version of NuWro  [16] , and one recent version of NEUT  [17]. To compare the generators to each other and to experiments' cross-section data, we use NUISANCE  [18]. The emphasis is on CC-inclusive (Sec. VI), CC0π (Sec. VII), and CC1π (Sec. VIII) measurements. We make comparisons of the event generators against recent crosssection data, and present novel comparisons beyond modelto-data comparisons.
The use of generators for interpretation of experimental data introduces additional challenges. Complete coverage of phase space is necessary for use in a full detector simulation, typically leading to inconsistency in regions which are poorly understood or where models overlap. Mechanisms for assessment of systematic uncertainties are also necessary and challenging; these must incorporate all relevant degrees of freedom within models while remaining computationally tractable. An additional generator, NUWRO  [16] , is frequently used as a benchmark. With rapid integration of improved theoretical models and a more consistent handling of certain interaction modes, NuWro has provided both a point of comparison and an avenue for developing new models which are later integrated into GENIE and NEUT.
To reflect the idea of the formation zone, hadrons and mesons produced by interactions other than the (quasi-)elastic scatterings or coherent scattering have their production positions shifted toward the direction of the outgoing particles. For pion FSI, the mean-free paths (MFP) of absorption and inelastic scattering are calculated with the model developed by Oset et al. [61]  below 400 MeV=c. Above 500 MeV=c, the MFP are extracted from pion-nucleon scattering data. In the transition region in between, the fraction of low-energy model decreases linearly from 1 at 400 MeV=c to 0 at 500 MeV=c. The normalizations of the mean-free paths were tuned using pion-nucleus scattering data  [62]. The kinematics are determined using the results of a phase-shift analysis with the medium correction suggested by Seki et al.
E. The MicroBooNE experiment MicroBooNE sits in the booster neutrino beam (BNB) upstream of MiniBooNE. As a smaller detector than MiniBooNE it subtends a smaller angle relative to the neutrino beam direction, and as such has a slightly different energy spectrum, but the difference is very small. MicroBooNE uses the same flux simulation chain and data constraints as MiniBooNE. The MicroBooNE detector is a Liquid Argon Time Projection Chamber with 85 tons of active mass. In the MicroBooNE detector, charged particles leave trails of ionization electrons as they traverse the argon and also create prompt ultraviolet scintillation photons. The electrons drift in an electric field to one side of the detector, where they are detected by a series of sensing wires on three separate planes. The scintillation photons are instead detected by 32 photomultipliers.
Looking at the 2p2h prediction, NEUT, NuWro, and G18_10a all utilize the Valencia 2p2h model, but we see up to 20% difference. This difference could come from binding-energy implementation and final-state interaction modeling. GENIE G18_02a uses Empirical MEC for the 2p2h model, yet still produces similar 2p2h predictions for MicroBooNE and T2K, but smaller prediction for MINERvA. This is likely due to both models being tuned to MiniBooNE CCQE-like data, but handling the neutrino energy scaling differently. This suggests that different datasets could be used to constrain the energy dependence of the models. In T2K, 2p2h predictions are similar for each generator except for G18_02a, which is 27% higher because it uses a different 2p2h model (Empirical) than the others and different nuclear model (RFG).
The small overlap region where a direct comparison is possible between MINERvA and T2K is for events with 1 < p μ < 2 GeV=c. MINERvA 's muon measurements are limited to a range in muon angle between roughly 0. 9 < cos θ μ < 1. 0 where T2K also has coverage, and both cover a nearly full angular range for pions. Thus, the range where the two datasets can be compared is limited to the first bin of p μ for MINERvA (Fig. 26 ), and the two highestmomentum bins for T2K (lower right panel of Fig. 22 ). In this limited region, the two results are largely consistent with each other, and the generator-data comparisons are consistent for the two, which is reassuring. However, we note that the ðq 0 ; ⃗ qÞ phase space after the signal definition cuts are included for T2K and MINERvA are notably different, even with the W < 1.
Although no direct comparison between datasets was possible due to differences in signal definition, there was clearly a more rapid evolution of the cross section in neutrino energy for the calculations than was observed in the data. Later calculations at the time  [146]  were unable to resolve the differences. The first publication of pion production results from T2K  [133]  provides significant new information with a more modern treatment of uncertainties, at a similar neutrino energy to MiniBooNE. Attempts at comparing MINERvA and T2K measurements run into similar issues as MINERvA and MiniBooNE comparisons. Each measurement seeks a result that is objective and reproducible. However, detector technology, geometry, and selection sculpt the particle acceptances differently, imparting potential model dependence to the data.
However, no additional changes in the phase-space restrictions on particle kinematics in the signal definition were made. Separately, all the models changed due to more complete fitting to neutrino H/D data  [50, 147]. The main effect is to make the change with beam energy smaller and all the models are now in better agreement with MINERvA data. Agreement of generators with both datasets is reasonable for pion kinematics for the quoted uncertainties. On the other hand, all generators have similar systematic problems describing the Adler angle variables presented by T2K. The G18_02a, NuWro, and NEUT predictions for the single-pion production measurements are broken down by mode for T2K and MINERvA Q 2 and pion momentum/kinetic energy signal definitions in Figs. 30  and 29. Although T2K data extend to higher pion energies, both peak at the same energy within the resolutions provided as both are dominated by Δ production processes.
It is difficult to make a quantitative assessment of the agreement of data and calculations with regard to energy dependence. The calculations have similar agreement with both T2K and MINERvA data according to χ 2. In addition, the average cross section of both datasets for overlapping kinematics (Sec. VIII C) have a ratio very similar to that of the calculations. The generators are also very similar in lepton kinematics, and differ mostly in hadron kinematics. These observations all give confidence that the energy dependence of the calculations matches that of the data within the accuracy of the data. Beyond comparisons with calculations, a more detailed examination of the T2K and MINERvA analyses was made. Both measurements have restricted muon angle ranges, much more so for MINERvA. MINERvA also has a significant hole in the pion angle acceptance around 90°, and for higher-momentum pions.
IX. MINIBOONE DATASETS MiniBooNE produced a series of key measurements: CCQE and CCQE-like  [152] , CC1π þ [13], CC1π 0 [153], NCπ 0  [154] , and NC elastic (NCEL)  [155]. These datasets have had a long-lasting impact on the field of neutrino cross-section physics, as they pioneered important approaches in measuring neutrino cross sections with intense, modern beams. MiniBooNE developed the presentation of double-differential results with high-statistics datasets, and a broad signal definition (only based on final-state properties which gives reduced model dependence). Additionally, they set standards regarding the information made available to describe and support each analysis in data releases that have now become a regular part of a modern neutrino cross-section publication. The results spawned extensive theoretical development of models to explain the data, and experimental efforts to assess uncertainties on models.
g. , T2K, MicroBooNE) at a similar neutrino energy regime. One major appeal of the MiniBooNE results was the uniformity of acceptance of the detector, but improvements to T2K reconstruction and detectors will overcome this limitation. In addition, the statistical precision of MINERvA has surpassed or is approaching that of MiniBooNE. It is clear that the MiniBooNE NCEL and π 0 (NCπ 0 ) production measurements are of great value because of large statistics, excellent efficiency uniformity, and up-todate error analysis. NCEL is a forward-folded result, and they are published for neutrino and antineutrino enhanced running, each with a full covariance matrix. Both channels are important and cover reactions that are otherwise very poorly known. They should be employed with confidence for comparison with calculations and can be used with the same confidence as most other modern datasets.
Since these results are still unique, we have chosen not to include them in the present work where the emphasis is on comparing different datasets. Datasets such as CCQE-like and CC1π þ should be used with care as the estimated uncertainties did not include the full correlation information. As these data are still of high quality, we urge the collaboration to reanalyze the events using more modern methods. For now, any comparison of calculation with theory should include at least one other data set if one of these MiniBooNE measurements is used; the Appendix includes plots of the MiniBooNE data which can be qualitatively compared to the other datasets discussed in earlier sections of this document.
01. The different inclusive datasets seem to give contrasting conclusions when compared with various generators. For T2K, NEUT does a better job describing these data, and NuWro predicts a smaller cross section in the forward region of the lepton compared to the other generators there. It also consistently predicts a larger peak at intermediate muon momenta, while these differences are not vis NuWro. For MicroBooNE, GENIE predictions have similar values of χ 2 for all three models, while the value from NEUT is the highest. All generators seem to have equal understanding of A dependence in general. For MINERvA measurements, NuWro gives the lowest χ 2 compared with other predictions. However, all event generators show disagreement at low available energy and the peak of the distributions for the regions between 2p2h and RES.
In particular, the lack of published correlation matrices for some MiniBooNE datasets presents a major difficulty. Quantitative model comparisons based on goodness of fit to these data should not be attempted unless great care is taken to address the statistical implications of the missing bin-to-bin correlations  [159]. We feel that a reanalysis of this important dataset should be undertaken using modern methods. Provisional qualitative conclusions based on model comparisons to the MiniBooNE CC0π and CC1π measurements should be confirmed with more rigorous testing against modern measurements. The MiniBooNE NC elastic and NC1π 0 results are not subject to the same caveats: both provide high-quality measurements with full covariance matrices of otherwise poorly known cross sections. Models may be compared to the NCEL and NC1π 0 data with the same level of confidence as typical modern measurements.
APPENDIX: ADDITIONAL MINIBOONE QUALITATIVE COMPARISONS Given the concerns raised in Sec. IX we provide comparisons to MiniBooNE data in a limited way for this paper. We note specifically that a lack of correlations may lead to incorrect conclusions being drawn from the data when a naive bin-by-bin comparison is made. But, there is no established procedure to overcome this shortcoming for the currently available data. Only qualitative comparisons are provided, and a full interpretation should rely on other datasets presented (e. g. , T2K, MINERvA, MicroBooNE). Here we discuss for this specific study CCQE-like and CC1π þ results. The first measurement, CCQE-like, included only the final-state muon in the signal definition. The CC1π þ measurement introduced a novel way to identify positively charged pions in a Cerenkov detector through their inelastic scattering signature.
ACKNOWLEDGMENTS The authors are grateful to the  University of Pittsburgh Center for High Energy Physics, Astronomy, and Cosmology (PITT PACC)  for support during the workshop. This manuscript has been authored by  Fermi Research Alliance, LLC  under Contract No. DE-AC02-07CH11359  with the  U. S. Department of Energy, Office of Science, Office of High Energy Physics. The authors acknowledge support from  U. S. Department of Energy  under Contracts No. DE-AC02-05CH11231 , No. DE-SC0007914 , No. DE-SC0008475 , No. DE-SC0021407 , and No. DE-SC0015903  and the  National Science Foundation  under Grant No. PHY-2047665. This work was supported by a grant from the  Science and Technology Facilities Council. We acknowledge the support of  CNRS/IN2P3, France. The work was in part supported by a  Cottrell Postdoctoral Fellowship , from the  Research Corporation for Scientific.
Data Collection Patients registered in the asthma community on the Carenity platform and living in Canada, France, Germany, Italy, Spain, the UK, or the USA were invited by email to participate in an online patient survey. The questionnaire was conducted online between from February 1, 2021 to April 5, 2021. Patients were included in the study if they completed all elements of the baseline questionnaire. The collected data included self-reported sociodemographic and medical profiles, as well as information about the patients' perception of severe asthma, its control, and its management. The ad hoc questionnaire comprised single-choice, multiple-choice, sliding-scale, or free-text answers. The questionnaire was designed by Carenity, and subsequently reviewed by one expert in severe asthma. Additionally, as the questionnaire was programmed in local languages for the different Carenity worldwide platforms, it was also validated by native speakers to ensure compliance for each country involved.
Statistical Analysis Statistical analyses were performed using the statistical software R Studio (v. 3. 5. 0, The R Foundation, Vienna, Austria). Demographics and clinical characteristics were summarized using descriptive statistics. Summary statistics for continuous variables include number, mean, and standard deviation (SD). For categorical variables, numbers and percentages of patients are reported. Estimates are presented with 95% confidence intervals. The p values were calculated using ANOVA, chi-squared, or Wilcoxon methods, with a confidence interval of 95%; a p value\0. 05 was considered statistically significant.
The study was conducted online and no healthcare practitioners were involved in the patient's recruitment. The survey falls under the scope of market research and does not fall under the scope of regulations regarding observational studies nor research implying human beings. For this reason, submission to an ethical committee was neither required nor sought. Study was performed in accordance with the Helsinki Declaration of 1964, and its later amendments. Informed consent regarding participation and publication of outcomes were obtained from all individual participants included in the study. The participants' privacy and confidentiality were guaranteed following health care personal data protection laws and regulations (GDPR, General Data Protection Regulation). Data Availability. The datasets generated during and/or analyzed during the current study (except personal data) are available from the corresponding author on reasonable request.
ACKNOWLEDGEMENTS Funding. Sponsorship for this study and  Rapid Service Fee  were funded by  Sanofi and Regeneron. Medical Writing and Editorial Assistance. Medical writing assistance was provided by  Dr. Florence Boulme  ´on behalf of Carenity. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published. Author Contributions. All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by  Emilie Pain ,  Alexis Bombezin-Domino  and  Camille Graff. The first draft of the manuscript was written by  Florence Boulme , and all authors commented on previous versions of the manuscript.
Online assessment of the tumor pathology during surgery is an important task to give feedback to the surgeon who can be more liberal or conservative in the resection based on the input. While there are methods that perform metabolomics-based online tumor grade prediction, their performance and model complexities are limited by the small dataset sizes. Here, we propose a pathway-informed deep learning model, PiDeeL, to perform survival analysis simultaneously for a better prognostic assessment. We show that incorporating the pathway information into the model architecture and using the multitask learning framework reduce the model complexity and enable us to use deeper architectures with better performance. We show that PiDeeL improves the grade prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve (AUC-ROC) by 3.
Indeed, various supervised machine learning models have already been explored in this context to provide automated feedback with regard to pathological classification  [29, 30]  of tissue specimens resected during glioma surgery. The dataset used for these studies  [31]  consists of HRMAS NMR spectra of 565 glioma and healthy control tissue samples. It is relatively small despite being the largest of its kind which prohibited the use of complex models and forced the models built to be shallow. This suggests room for improvement in the performance of the feedback pipeline. From a methodological perspective, the applicability of deep learning models in small data regimes can be improved by making informed architectural choices to simplify models. Incorporating biological prior information may limit the connectivity of the neurons in a model and simultaneously (1) alleviate overfitting when available dataset sizes are small, and (2) aid model interpretability regardless of the dataset size.
We show that this sparse but deep model (PiDeeL) achieves better performance compared to the state-of-the-art model in pathological classification and also provides survival prediction with a c-index of 68. 7%. We analyze the most important metabolites and pathways for the reasoning of the model. We release the code at https://github. com/ciceklab/PiDeeL and the dataset at https://zenodo. org/record/7228791.
Dataset The data used in this study has been collected at the University Hospitals of Strasbourg. The details of data collection and ethics statement are shared in Section 1. 1 of the Supplementary Text. The age, tumor subtype, and grade distribution and details are also available in Supplementary Figure  1. From the acquired ERETIC-CPMG spectra of 565 glioma and healthy control samples, we prepare a sample set by taking out the control samples and the samples without survival information (event and duration). Our sample set consists of 384 glioma samples which contain 299 (77. 9%) aggressive and 85 (22. 1%) benign tumors. Among the glioma patients in our sample set, 269 (70. 1%) of them were reported deceased before the acquisition (until January 2021) of the dataset, whereas 115 (29. 9%) of them were not.
Note that, the ERETIC-CPMG HRMAS NMR FID spectra of 384 glioma samples are released at https://zenodo. org/record/7228791 with their corresponding sample IDs. To prepare our FID spectra for automated metabolite quantification, we apply the same preprocessing pipeline in  [30]. The details of preprocessing method with the parameters used can be found in Section 1. 1. 3 of the Supplementary Text.
Problem Formulation Please see the Section 1. 3 of the Supplementary Text for the automated metabolite quantification. The feature vector for sample i, M (i) , is a 37-dimensional vector [ M (i) 1 , M (i) 2 , M (i) 3 ,. , M (i) 37 ] ∈ R 37 where M (i) j corresponds to the predicted level for the j th metabolite. On the other hand, the class labels for sample i are the event variable, survival duration and tumor malignancy, denoted with m is set to 1 if sample i is from an aggressive tumor, and 0 otherwise. We train survival analysis and pathological classification models based on this formulation. The feature vector we use is the same in the two tasks. However, for survival analysis, we use Y For the survival analysis task, we refer to Cox's baseline hazard function λ(t, M (i) ) for sample i in the dataset, where t is the time.
The proposed loss function of these articles is the negative log partial likelihood of Equation 3. We define the loss function below with our formulation: l(β) = - i:Y (i) e =1 ( h β ( M (i) ) -log j:Y (i) d >t e ( h β ( M (j) )) ) (5) We fit Cox-PH, RSF, and DeepSurv models to our data to get a baseline. We train 2, 3, and 4-layer DeepSurv models with 64 neurons in hidden layers and ReLU activation as the nonlinearity function. We have a single neuron in the last layer to output the predicted risk scores of samples. The trained models on our dataset of size N can be summarized as follows: X (1:N ) = ReLU (F C (64) ( M (1:N ) )) (6) h (1:N ) = F C (1) (X (1:N ) ) (7) where X represents the output of hidden layers, h (i) represents the predicted risk score, F C (•) represents a fully-connected layer with (•) neurons and ReLU represents the rectified linear unit.