dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and social deprivation index. Based on French population and household income census data, we calculated the average rates of unemployment, industrial workers, immigrants, people without a high school diploma or with a post-secondary qualification and the rate of non-taxable households for each zip code. These data were correlated and pooled in a material and social deprivation index according to the bi-dimensional scale put forward by Pampalon  [11]. Five levels of material and social deprivation were defined: level 1 (the least materially and socially disadvantaged population clusters), level 2 (national average for each variable), level 3 (social deprivation only), level 4 (material deprivation only), level 5 (social and material deprivation, the most disadvantaged geographic codes).
Levels of urbanization. We grouped the zip codes into three categories of urbanization and number of jobs (using geographic data produced by the French national statistical institute [INSEE]): 1) the largest urban areas (�10 000 jobs), 2) their surrounding areas (suburban areas), and 3) other urban, suburban and rural areas. Outcomes. We studied the main adverse outcomes after an OMD available in the PMSI data: maternal mortality in hospital or during transport, stillbirth, neonatal mortality, neonatal hospitalization, newborn hypothermia and newborn polycythemia. Maternal deaths were identified at discharge with vital status upon discharge from hospital or an ICD-10 code O95 ("Obstetric death of unspecified cause") by women who delivered until 42 days after delivery. The time of death was calculated from the date on which the act of delivery was recorded.
Statistical analysis Qualitative variables were expressed as percentages and compared using Pearson's Chisquared test or Fisher's exact test. We used a Somers'd test to evaluate trends over the years. The multivariate analyses were done using regressions based on Generalized Estimating Equations (GEE s ) with a log link function and negative binomial distribution to take into account the data correlations (zip codes and years) and overdispersion. The two contextual variables (deprivation index and levels of urbanization) were tested one by one in the regressions, but the model converged better with both, according to the QIC statistic. Risk factors for maternal mortality and stillbirth were analyzed for all OMD. Adverse neonatal outcomes were only analyzed for births before arrival (BBA). For neonatal adverse outcomes, we performed two sensitivity analyses by including the 9.
6% of DAs for women who could not be linked to the DAs of their babies in the database. We used at first a multiple imputation (MI) method  [12] , according to the repartition of adverse outcomes identified in observed data, i. e. using the distribution of the observed data to estimate a set of plausible values for the missing data. Secondly, for all of the missing newborns (in and out-of-maternity born), we considered that there was no adverse outcome (maximum bias) and we used the information on the covariates of the mothers' DA. The results of the two sensitivity analyses are presented in S1 Table. Statistical analyses were performed using SAS 1 version 9. 4 (SAS institute Inc. , Cary, NC, USA). Statistical significance was defined as a P-value <0. 05. Distances were calculated using CHRONOMAP © for MAPINFO © software and IGN ROUTE 500 1 digital road network.
Characteristics of the population The characteristics of the mothers, pregnancies and newborns are presented in Table  1. From 2012 through 2014, we identified 2,256,797 deliveries at or after 24 WG in mainland France, from the French hospital database. Multiple pregnancies accounted for 1. 61%  (36, 118 ) of all deliveries. From 2012 to 2014, 6,733 (3. 0‰) deliveries took place out-of-maternity, including unexpected out-of-maternity deliveries and home deliveries requiring transfer to a hospital. For these deliveries, we counted 6,622 babies born before arrival and 111 deliveries that occurred in a hospital but not in a maternity unit, 41 of which occurred in maternity units that had closed and were replaced by antenatal consultations centers (Centre Périnatal de Proximité, CPP). Over the study period, we identified a significant increase (P <.
Moreover, since planned at home deliveries are considered to concern very low risk pregnancies, this same cohort study showed that severe morbidity, particularly postpartum hemorrhage, was less frequent and less severe in the women who delivered at home than in controls who delivered in hospital. The same is true for neonatal morbidity. Secondly, the French hospital discharge database is not a specific medical register. Thus, another limitation linked to the source of data concerns parity. Primiparous/multiparous status is not available in our data for OMD. We could not therefore adjust our results on this individual characteristic, which known for influencing the risk of OMD. However, a recent study assessing the metrological quality of hospital discharge data (PMSI) for perinatal indicators showed the reliability of the data used in our study  [10] , and another appraised the quality of hospital discharge data to identify maternal morbidity  [42]  and mortality  [43].
It should be noted, however, that our data only includes hospital mortality since deaths that occur at home are not recorded in the PMSI. Finally, our main strength is that we were able to estimate the frequency of out-of-maternity deliveries thanks to the PMSI database including all deliveries in France. We are also able to assess their impact on maternal mortality and on neonatal morbidity and mortality thanks to the linkage of information regarding the mother and her newborn. These results confirm the findings of previous studies demonstrating that the PMSI can be used as a tool for obstetric and pediatric planning  [44, 45]  because it allows units to be scaled according to the characteristics of the population served (individual characteristics and spatial distribution)  [46] , patient turnover in the context of regionalization and differentiation of care  [47] , the length of stay, and the random nature of unscheduled deliveries  [47] [48] [49].
Acknowledgments This study is part of the  TANGO  project (L'offre territoriale de soins hospitaliers en France metropolitaine: de la prise en charge hospitalière des grossesses pathologiques à celle des nourrissons de moins d'un an, analyse des bases nationales du PMSI pour les hospitalisations se rapportant aux naissances 2012-2015). This study has received aid from the  "Direction Generale de la Sante" (DGS) , the  "Caisse Nationale de l'Assurance Maladie des Travailleurs Salaries" (CNAMTS) , and from the  Inserm  and the  INSERM Plan Cancer , within the framework of the call for projects launched by the IRESP in 2014. The authors thank  Suzanne Rankin  and  Sonia Bechraoui-Quantin  for reviewing the manuscript. The authors thank  Gwenaelle Periard  for building the manuscript.
The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos.
The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
Since the trigger algorithm is not fully efficient in identifying all signal hits, a larger time window than the one defined for the triggered hits is saved for further analysis. Assuming a triggered event with t first as the time of the first triggered hit and t last as the time of the last triggered hit, all photon hits in each PMT in a time window [t firstt marg , t last + t marg ] are recorded, where t marg is defined by the maximum amount of time that a photon propagating in water would need to traverse the whole detector. As a result, the total time window of triggered neutrino events in KM3NeT/ORCA is about 3 µs. A summary with detailed information about the simulated data for KM3NeT/ORCA is shown in Tab. 1.
For a two-layer network and thus two functions f (1) and f (2) (thesymbol of f is neglected from this point on) this is: f (x) = f (2) ( f (1) (x)). (3. 1) Here, f (1) refers to the first layer in the network and f (2) to the second. The first layer of a neural network is called the input layer, the intermediate layers are called the hidden layers and the last layer is called the output layer. Event type E gen spectrum N trig [10 6    [17]. N trig is the number of events that remain after triggering. Atmospheric muons have been simulated with the MUPAGE package  [18]. Random noise events have been simulated conservatively with a 10 kHz single rate per PMT with additional n-fold coincidences (600 Hz two-fold, 60 Hz three-fold, 7 Hz four-fold, 0. 8 Hz five-fold and 0. 08 Hz six-fold  [16] ).
The number of parameters in CNNs can be further reduced with the aid of pooling layers, which reduce the dimensionality of the layer outputs by selecting or combining the neuron outputs  [19, 20]. For the training of a neural network, the data are split up into a training, validation, and test dataset. The network is trained on the training dataset and validated by applying the network to the validation dataset. Once the training is finished, the network is applied to the test dataset to determine its performance. If the value of the loss function is significantly greater for the training dataset than for the validation dataset, this is called overfitting with respect to the training dataset. The smaller the size of the training dataset, the higher the probability that the network will focus on the peculiarities of individual input images instead of generalising generic image features.
Data pre-processing For each hit in a simulated event, the PMT identifier, i. e. the relative coordinate of the hit PMT in a DOM, is recorded. Additionally, the time at which the PMT signal crosses the discriminator threshold, and the measured ToT, are stored. However, the ToT value itself is not used as input for the CNNs. In order to feed this four-dimensional event data to a CNN, the hits can be binned into rectangular pixels, such that each image encodes three spatial dimensions (XYZ) and the time dimension T.
The weights of the neural network are updated after one batch of images is passed through the network. This is known as batch gradient descent. The batch size is defined as the number of images contained in one batch. The batch size in the training for all presented CNNs is generally set to 64 and the learning rate, i. e. the step size in the Adam algorithm for the update of the weights is annealed exponentially. The training of all presented networks has been executed at the TinyGPU cluster at the RRZE computing centre 2. It consists of 32 nodes with 4 GPUs each. The GPUs are either Nvidia GTX1080, GTX1080Ti, RTX2080 Ti, or Tesla V100. All CNNs in this work have been trained with  CUDA 10 [29]. In order to train the networks, an open-source software framework called OrcaNet  [15]  has been developed, which is intended as a high-level application programming interface on top of Keras  [14] , specifically suited to the large datasets that frequently occur in astroparticle physics.
A mistakenly classified atmospheric muon, e. g. , classified as a random noise event, has the same effect on the total loss as an atmospheric muon classified as a neutrino. No regularisation techniques, such as dropout, are added to the network. The training dataset is large enough, cf. Sec. 6. 3, so that virtually no overfitting occurs, i. e. the training-phase loss is of the same order as the loss during the validation phase.
Preparation of training, validation and test data For the training of the background classifier, the simulated data from Tab. In order to balance the datasets with respect to their class frequency, one could split the data into 50% neutrino and 50% non-neutrino events (25% atmospheric muons + 25% random noise). Considering that the neutrino sample has the lowest number of events (about 22. 8 × 10 6 ), one would have to remove a significant fraction of the 23. 3 × 10 6 generated random noise events, and of the 65. 1 × 10 6 atmospheric muon events for a class-balanced data splitting. On the other hand, based on the RF background classifier, it can be expected that the final accuracy of the classifier should be close to 99%. Therefore, a balanced splitting of the data into 50% for each class is not necessary, in order to avoid a local minimum during the training process.
The following data splitting is used: 1/3 neutrino events, 1/3 random noise events and 1/3 atmospheric muon events, and hence the final class balance is 1/3 neutrino events and 2/3 non-neutrino events. Using this data splitting and considering the number of MC events summarised in Tab. 1, the size of the used training dataset is larger compared to a 50/50 split. The fractions of different neutrino flavours and interaction types is kept as indicated in Tab. 1. This rebalanced dataset is then split into 75% training, 2. 5% validation, and 22. 5% test events, which is a trade-off between maximizing the training dataset and retaining sufficient statistics for performance evaluations. Additionally, the events that have been removed to balance the dataset (mostly atmospheric muons) are added to the test dataset.
Performance and comparison to Random Forest classifier The performance of the CNN background classifier is evaluated using the training and validation cross-entropy loss  [20]  of a specific classifier, the softmax classifier  [20] , as a function of the number of epochs, and is shown in In order to compare the CNN performance to the RF background classifier, the same test dataset is used. A pre-selection of these events is carried out to reduce the fraction of atmospheric muon and random noise events to a few percent of all triggered events. For atmospheric muons this is achieved by selecting only events for which the reconstructed particle direction is below the horizon, i. e. which are up-going events in the detector. Furthermore, the events must have been reconstructed with high quality by the KM3NeT/ORCA maximum-likelihood reconstruction algorithms for either track-like or shower-like events.
Finally, events reconstructed by the maximum-likelihood-based algorithms as originating from outside of the instrumented volume of the detector are removed. In total, the pre-selected test dataset consists of about 3. 3 × 10 6 neutrino, about 6 × 10 4 atmospheric muon and about 4 × 10 4 random noise events. This selection is used for all of the following performance evaluations. In order to get a first impression of the CNN-based background classifier, the distribution of the neutrino class probability is investigated for all three event classes (neutrinos, atmospheric muons, random noise), cf. Based on the results shown in Fig. 4 , it can be seen that the rate of random noise events misclassified as neutrino-induced events is significantly lower than for atmospheric muons. Using the predicted probability for an event to be classified as neutrino-induced, a threshold value p can be set to remove background events.
In order to quantify the performance of the CNN background classifier, the metric shown below is used to investigate the fraction of remaining atmospheric muon and random noise events for a given threshold value p. The atmospheric muon or random noise contamination, and the neutrino efficiency, are defined as: C µ/RN (p) = N µ/RN (p) N total (p) , (6. 1) ν eff (p) = N ν (p) N ν,total. (6. 2) Here, N µ/RN is the number of atmospheric muon or random noise events, whose probability to be a neutrino-induced event is higher than p, while N total (p) accounts for the total number of events, after the same cut on p. Regarding the neutrino efficiency, N ν (p) is the total number of neutrinos in the dataset whose neutrino probability is greater than the threshold value p, while N ν,total is the number of neutrinos in the dataset, without applying any threshold.
Preparation of training, validation and test data In order to train the CNN track-shower classifier, only simulated neutrino events are used. The total neutrino dataset is rebalanced such that 50% of the events are track-like (ν CC µ ) and 50% are shower-like. The shower class consists of 50% ν CC e and 50% ν NC e. Additionally, the dataset has been balanced in such a way that the ratio of track-like to shower-like events is always one, independent of neutrino energy. The rebalanced dataset is then split into three datasets with 70% training, 6% validation, and 24% test events. In total, the training dataset contains about 1. 3 × 10 7 events. Using a Nvidia Tesla V100 GPU, it takes about one and a half weeks to fully train this CNN track-shower classifier.
Performance and comparison to Random Forest classifier The evolution of the cross-entropy loss  [20]  during the training is shown in Fig. 8. Even though the validation cross-entropy loss is within the fluctuations of the training loss curve at the end of the training, some minor overfitting occurs. The reason is that during the application of the trained network on the validation dataset, no neurons are dropped by the dropout layers, contrary to the training phase. Therefore, the validation loss should be lower than the average training loss, if no overfitting is observed. This can be seen by investigating the training and validation loss curves in the earlier stages of the training process, e. g. between epoch 0 and 3. Here, the validation loss is typically found at the bottom of the training loss curve.
Preparation of training, validation and test data In order to train the CNN event regressor, again only simulated neutrino events are used. Similar to the track-shower classifier, the dataset consists of 50% track-like events (ν CC µ ) and 50% shower-like events (25% ν CC e , 25% ν NC e ). Additionally, the total dataset has been split again into a training, validation and test dataset. As for the track-shower classifier, the training data makes up 70%, the validation dataset 6% and the test dataset 24% of the total rebalanced dataset. The track and shower classes have not been balanced to be independent of neutrino energy, cf. Sec. 7. 3, in order to get a larger training dataset compared to the track-shower classifier. In total, the training dataset then contains about 14 × 10 6 events. The MC energy of ν NC e events is set to the visible energy during the training, since the network cannot infer how much energy is carried away by the outgoing neutrino in the neutral current interaction.
Loss functions and loss weights Before training the CNN event regressor, suitable loss weights need to be defined in order to scale the losses to the same magnitude. For example, the neutrino energy in the dataset ranges from 1 GeV to 100 GeV, while the components of the direction vector range from -1 to 1. Thus, the impact of the energy loss would be overwhelmingly large compared to the loss that is contributed by the direction reconstruction. Consequently, one needs to scale the individual losses of the components of ì ξ reco and their estimated uncertainties with a loss weight, such that all single losses are of the same order of magnitude at the start of the training. However, it has been observed that the CNN does not start to learn during the first few epochs of the training, if the individual losses of the energy, direction and vertex are of the same scale.
Error estimation The CNN is used to reconstruct the regression uncertainty of all reconstructed quantities, as explained in Sec. 8. 2. For the following performance investigation the event pre-selection, based on the current KM3NeT/ORCA reconstruction pipeline, is not applied, i. e. all triggered events of the simulated test dataset are used. In order to evaluate the goodness of the uncertainty reconstruction for any event quantity, e. g. energy or direction, the events are binned according to their estimated uncertainty value, σ reco. For the event distribution in each bin, the standard deviation, σ true , of the residuals of the reconstructed and true MC values |ξ recoξ true | is calculated. The distribution of σ true versus σ reco is shown in Fig. 18  (left) for the energy reconstruction of ν CC e events.
Acknowledgments The authors gratefully acknowledge the compute resources and support provided by the  Erlangen Regional Computing Center (RRZE). Significant parts of this work are based on the soon to be published doctoral thesis "Sensitivity studies on tau neutrino appearance with KM3NeT/ORCA using Deep Learning Techniques" by  M. Moser. The authors acknowledge the financial support of the funding agencies:  Agence Nationale de la Recherche  (contract  ANR-15-CE31-0020 ),  Centre National de la Recherche Scientifique (CNRS) ,  Commission Européenne (FEDER fund and Marie Curie Program) ,  Institut Universitaire de France (IUF) ,  LabEx UnivEarthS  ( ANR-10-LABX-0023  and  ANR-18-IDEX-0001 ),  Paris Île-de-France Region, France ;  Shota Rustaveli National Science Foundation of Georgia  (SRNSFG, FR-18-1268), Georgia;  Deutsche Forschungsgemeinschaft (DFG) ,  Germany ; The  General Secretariat of Research and Technology (GSRT), Greece ;  Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Università e della Ricerca (MUR) ,  PRIN 2017 program  (Grant  NAT-NET 2017W4HA7S ) Italy;  Ministry of Higher Education, Scientific Research and Professional Training, Morocco ;  Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO) , the  Netherlands ; The  National Science Centre, Poland  ( 2015/18/E/ST2/00758 );  National Authority for Scientific Research (ANCS) ,  Romania ;  Ministerio de Ciencia, Innovación, Investigación y Universidades (MCIU) :  Programa Estatal de Generación de Conocimiento  (refs.
Conclusion In this work, the first application of deep convolutional neural networks to the reconstruction and classification of simulated neutrino events in the neutrino detector KM3NeT/ORCA has been reported. Detailed MC datasets have been suitably pre-processed to generate pixelated high-dimensional image input for the training of CNNs. CNNs have been designed to separate neutrino events from background, and track-like from shower-like neutrino events. In addition, CNNs have been used to reconstruct the direction, energy, and interaction point of the neutrinos, and to estimate the uncertainty on each of the reconstructed quantities. Comparisons to the performance of classical machine-learning approaches to classification and maximum-likelihood reconstructions show the competitive and in some cases already superior performance of the employed CNNs, though there are still many aspects that can possibly be optimised.