dataesr/scientific-paragraphs-categorization huggingface dataset:
The new GWAS Atlas  (73) , in contrast, focuses on plants and domesticated animals. Another new resource CausalDB  (74)  focuses on applying fine-mapping tools to try to sift true causal variants from GWAS data while TSEA-DB (75) offers an interesting tissue-specific view of GWAS traits and phenotypes. All efforts to link genome variation to phenotype benefit from complete and balanced representations of species diversity and so the arrival of the PGG. Han database (  76 ) is welcome, focusing as it does on the Han Chinese, a group hitherto under-represented in population genomics data. Among resources for drug design it is worth highlighting the popular Therapeutic Target Database  (77)  which reports a number of new features including target regulators (both miRNA and TF), target-interacting proteins and information targets of patented therapeutic agents.
TDR Targets  (78)  focuses particularly on drug design to address neglected tropical diseases and incorporates new data into a network representation to allow, for example, whole-genome target prioritization and exploration of drug repurposing. It also contributes a stylish cover image to this Issue. Two other heavily used databases, ClinVar  (79)  and DisGeNET (80) also contribute Updates, each featuring expanded content and a new web interface. As ever, cancer databases are well-represented with new contributions to the field including CancerTracer (81), a resource for studying and intrapatient tumor heterogeneity that features data from 1500 patients, including patient-specific tumor phylogenetic trees, and DNMIVD (82) which has a wide range of functions regarding links between DNA methylation and cancer.
A major returning plant database is PlantRegMap (83) which exploits the information in its associated database of plant transcription factor binding sites PlantTFDB to help predict the functional regulatory maps of dozens of plants. A single paper (84) reports updates to both AraPheno and AraGWAS Catalog databases that include RNA-Seq and knockout mutation data for the model organism Arabidopsis thaliana. The Malvaceae, which include such important crops as cotton and cacao gain their own dedicated functional genomics database MaGenDB (85) which includes over 300 diverse omics datasets and a customized genome browser. Also very much orientated toward agricultural purposes is MBKbase (86), a plant molecular breeding knowledgebase. It includes germplasm information and genomic, population sequencing, phenotypic and gene expression data.
The final section, with databases not easily lying within the earlier categories, contains the usual intriguing and eclectic mix. Proteomics research is covered by two major returning databases. ProteomeXchange (87) covers changes made at each of its member organizations, now--with the addition of iProX and Panorama Public--totaling six. Pro-teomicsDB (88)--which also encompasses diverse data such as gene expression and, in the new release, protein turnover information--now supports organisms other than the original human, with A. thaliana leading the way. The popular MatrisomeDB database  (89)  covering proteomics of the extracellular matrix is appearing here for the first time with a new version tripling the number of datasets of the original. Elsewhere, FoldamerDB (90) addresses peptidic foldamers, non-natural oligomers with defined solution structures that mimic the behavior of natural macromolecules and have potential in areas as diverse as antimicrobial therapy and materials science.
NAR ONLINE MOLECULAR BIOLOGY DATABASE COLLECTION For this 27th release of the NAR online Molecular Database Collection (as usual freely available at http://www. oxfordjournals. org/nar/database/c/), in our ongoing pro-cess to provide and up-to-date resource, over the last year we have updated 305 entries, added 65 new resources and deleted 125 discontinued databases, bringing the total collection to 1637 databases. This is a never ending task, as once this publication sees the light, there may be listed resources going down which we will not detect until our next scheduled review during the year, at which point those database owners will be informed. Ignored requests or lack of action will result in databases listed as obsolete being deleted in future updates of the collection. We encourage authors to submit their updates to XMF at xose.
A predictive transient two-phase flow rotodynamic pump model has been developed in the Code for Analysis of THermalhydraulics during an Accident of Reactor and safety Evaluation (CATHARE-3). Flow inside parts of the pump (suction, impeller, diffuser and volute) is computed according to a one-dimensional discretisation following a mean flow path. Transient governing equations of the model are solved using an implicit resolution method and integrated along the curvilinear abscissa of the element. This model has been previously qualified at the component scale by comparison to an existing experimental database. The present study aims at extending the validation at the system scale: a whole experimental test loop is modelled. The ability of the transient pump model to predict flow rate, head and torque as a function of time during a 1-s pump fast start-up is evaluated.
Introduction Hydraulic systems containing rotodynamic pumps often involve complex flow transients, especially in the context of nuclear power plant safety evaluations where accidental scenarios are investigated (Chenaud et al. [1] ). The purpose of the present paper is to develop a predictive rotodynamic pump model to be integrated in a thermal-hydraulic system simulation computational code. The corresponding scientific system scale software is the Code for Analysis of THermalhydraulics during an Accident of Reactor and safety Evaluation (CATHARE-3), which is used as a reference for nuclear safety analyses in France. The rotodynamic pump model to be integrated in the system code needs to be able to predict the performance of hydraulic machines coupled to a global system composed of pipes and capacities in fast transient operation.
[3] ). Safety studies of such reactors lead to the necessity of simulating long time (hours, even day) two-phase flow system transients with a computational (CPU) time smaller or equal to the real time of the transient (Tenchine et al. [4] , Miettinen  [5] ). This constraint imposes that the pump model to be developed in the CATHARE-3 code respects a low CPU time cost. The first and traditional method to model rotodynamic pumps with this code consists in a 0D approach, in which the pump is located at a point of the circuit and non-dimensional head and torque performance curves are provided as input data (Mauger et al. [6, 7] ). Associated momentum and energy contributions are taken into account via source terms in the balance equations. This traditional model is not predictive and cannot be used for Generation IV two-phase flow applications.
For this reason, it was decided to implement a predictive transient, two-phase flow rotodynamic pump model in the CATHARE code and qualify results at component scale (pump) and system scale (reactor) with respect to available experimental data (Matteo et al. [8] [9] [10] [11] [12] [13] ). The global strategy of the CATHARE-3 one-dimensional pump model development project and the first pump-scale verification and validation results were presented by Matteo et al. [8, 9]. The associated main aims are:.
Experimentation of the pump led to a reconsideration of the nominal point as Q N = 23 m 3 /h, H N = 42. 8 m and N N = 2900 rpm, which is used in the following simulations. A vaneless diffuser of constant width (7 mm), whose inlet and outlet diameters are, respectively, 206 and 240 mm, is located downstream the impeller. A simple diverging volute is located downstream the diffuser, as shown in Figure  4 , and is then connected to the discharge pipe. More information on DERAP pump geometric data is available in the work of Duplaa  [15]. In the present study, only single-phase characteristics are used to qualify the predictive 1D-pump model. The two-phase measurements will be used in a further study dedicated to two-phase qualification of the model. One-dimensional (1D), three-dimensional (3D) or point (0D) hydraulic elements can be associated together to represent a whole facility.
Modelling of the Pump and the Complete Circuit Resolution is made using a implicit scheme which allows large variations of the time step during the computation and low CPU time cost. The governing equations discretised for the 1D module are given below. The 1D mesh allows one direction for liquid and gas velocities but two possible ways (positive or negative). z and k, respectively, correspond to the curvilinear abscissa and the phase index (k = L for the liquid and k = G for the gas). Phase mass balance: ∂ ∂t Aα k ρ k + ∂ ∂z Aα k ρ k V k = k AΓ + S k (1) Phase momentum balance: ∂ ∂t Aα k ρ k V k + ∂ ∂z Aα k ρ k V 2 k + Aα k ∂P ∂z + Ap i ∂α k ∂z +A k βα(1 -α)ρ m ∂V G ∂t - ∂V L ∂t + V G ∂V G ∂z -V L ∂V L ∂z = A k ΓW i -A k τ i -(C k χ f + AKα k ∆z )τ k +Aα k ρ k g z + R(1 -α k ) 4 p i ∂A ∂z + SM k (2) Phase energy balance: ∂ ∂t Aα k ρ k (h k + V 2 k 2 ) + ∂ ∂z Aα k ρ k V k (h k + V 2 k 2 ) -Aα k ∂P ∂t = +Aα k ρ k V k g z + χ c q w,k + Aq k,E + A k Γ(h k + W 2 i 2 ) + SE k (3) Mass transfer term: Γ = χ c /Aq w,i -q L,i -q V,i h V -h L (4) Equations (  1 )-(  3 ) are solved for each phase (liquid and gas) in classical CATHARE-3 computations.
As mentioned above, as the slip factor correlation called ""present work"' is a generalisation of Stodola's one for mixed-flow pumps, both are superimposed in Figures  9  and 10. It was expected in the case of the DERAP radial pump. Experimental measurements provide a mechanical torque measured on the shaft, whereas the model predicts a hydraulic torque. Thus, the two values cannot be directly compared. However, assuming the friction torque as independent of flow rate (Duplaa  [24] ), they can be estimated at the nominal flow rate and removed on the whole flow rate range. It can be seen as an efficiency applied on the motor torque data to obtain an estimated experimental hydraulic torque, plotted in red cross in Figure  10 , and which is very close to the value predicted by the model. In the following, the modelling of the fast startup transient using the CATHARE-3 code is presented.
These two pressure evolutions are well predicted by the model by comparison to experimental pressure evolutions from Duplaa et al. [15]. The CATHARE code method seems to be adapted to simulate fast transients such as this startup. The robustness of the code was shown previously by simulating a transient affecting a High Temperature Gas Reactor turbine by Saez et al. [41]  (but using another turbomachinery model of the CATHARE code).
Funding: This work was mainly supported by the  Generation IV nuclear reactor program  of the French alternative energies and  Atomic Energy Commission (CEA). Industrial and academic partners are also involved:  Framatome, Electricite De France (EDF) ,  CEntre Technique des Industries Mecaniques (CETIM) ,  ArianeGroup , and the  Fluid Mechanics Laboratory of Lille (LMFL). The APC of this paper was funded by  Euroturbo.
We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.
, 2018)  and human activity recognition  (Nweke et al. , 2018; Wang et al. , 2018)  to acoustic scene classification  (Nwe et al. , 2017)  and cyber-security  (Susto et al. , 2018). In addition, the diversity of the datasets' types in the UCR/UEA archive  (Chen et al. , 2015b; Bagnall et al. , 2017)  (the largest repository of time series datasets) shows the different applications of the TSC problem. Given the need to accurately classify time series data, researchers have proposed hundreds of methods to solve this task  (Bagnall et al. , 2017). One of the most popular and traditional TSC approaches is the use of a nearest neighbor (NN) classifier coupled with a distance function  (Lines and Bagnall, 2015). Particularly, the Dynamic Time Warping (DTW) distance when used with a NN classifier has been shown to be a very strong baseline  (Bagnall et al.
HIVE-COTE is currently considered the state-of-the-art algorithm for time series classification  (Bagnall et al. , 2017)  when evaluated over the 85 datasets from the UCR/UEA archive. To achieve its high accuracy, HIVE-COTE becomes hugely computationally intensive and impractical to run on a real big data mining problem  (Bagnall et al. , 2017). The approach requires training 37 classifiers as well as cross-validating each hyperparameter of these algorithms, which makes the approach infeasible to train in some situations  (Lucas et al. , 2018). To emphasize on this infeasibility, note that one of these 37 classifiers is the Shapelet Transform  (Hills et al. , 2014)  whose time complexity is O(n 2 • l 4 ) with n being the number of time series in the dataset and l being the length of a time series.
, 2017 ) evaluated 18 TSC algorithms on 85 time series datasets, none of which was a deep learning model. This shows how much the community lacks of an overview of the current performance of deep learning models for solving the TSC problem  (Lines et al. , 2018). In this paper, we performed an empirical comparative study of the most recent deep learning approaches for TSC. With the rise of graphical processing units (GPUs), we show how deep architectures can be trained efficiently to learn hidden discriminative features from raw time series in an end-to-end manner. Similarly to  Bagnall et al. (2017) , in order to have a fair comparison between the tested approaches, we developed a common framework in Python, Keras  (Chollet, 2015)  and Tensorflow  (Abadi et al. , 2015)  to train the deep learning models on a cluster of more than 60 GPUs.
In addition to the univariate datasets' evaluation, we tested the approaches on 12 Multivariate Time Series (MTS) datasets  (Baydogan, 2015). The multivariate evaluation shows another benefit of deep learning models, which is the ability to handle the curse of dimensionality  (Bellman, 2010; Keogh and Mueen, 2017)  by leveraging different degrees of smoothness in compositional function  (Poggio et al. , 2017)  as well as the parallel computations of the GPUs  (Lu et al. , 2015). As for comparing the classifiers over multiple datasets, we followed the recommendations in  Demšar (2006)  and used the Friedman test  (Friedman, 1940)  to reject the null hypothesis. Once we have established that a statistical difference exists within the classifiers' performance, we followed the pairwise post-hoc analysis recommended by  Benavoli et al.
(2016)  where the average rank comparison is replaced by a Wilcoxon signed-rank test  (Wilcoxon, 1945 ) with Holm's alpha correction  (Holm, 1979; Garcia and Herrera, 2008). See Section 5 for examples of critical difference diagrams  (Demšar, 2006) , where a thick horizontal line shows a group of classifiers (a clique) that are not significantly different in terms of accuracy. In this study, we have trained about 1 billion parameters across 97 univariate and multivariate time series datasets. Despite the fact that a huge number of parameters risks overfitting  (Zhang et al. , 2017)  the relatively small train set in the UCR/UEA archive, our experiments showed that not only DNNs are able to significantly outperform the NN-DTW, but are also able to achieve results that are not significantly different than COTE and HIVE-COTE using a deep residual network architecture  (He et al.
-We propose a unified taxonomy that regroups the recent applications of DNNs for TSC in various domains under two main categories: generative and discriminative models. -We detail the architecture of nine end-to-end deep learning models designed specifically for TSC. -We evaluate these models on the univariate UCR/UEA archive benchmark and 12 MTS classification datasets. -We provide the community with an open source deep learning framework for TSC in which we have implemented all nine approaches. -We investigate the use of Class Activation Map (CAM) in order to reduce DNNs' black-box effect and explain the different decisions taken by various models.
Time series classification Before introducing the different types of neural networks architectures, we go through some formal definitions for TSC. Definition 1 A univariate time series X = [x 1 , x 2 ,. , x T ] is an ordered set of real values. The length of X is equal to the number of real values T. Definition 2 An M -dimensional MTS, X = [X 1 , X 2 ,. , X M ] consists of M different univariate time series with X i ∈ R T. Definition 3 A dataset D = {(X 1 , Y 1 ), (X 2 , Y 2 ),. , (X N , Y N )} is a collection of pairs (X i , Y i ) where X i could either be a univariate or multivariate time series with Y i as its corresponding onehot label vector. For a dataset containing K classes, the one-hot label vector Y i is a vector of length K where each element j ∈ [1, K] is equal to 1 if the class of X i is j and 0 otherwise.
The behavior of these non-linear transformations is controlled by a set of parameters θ i for each layer. In the context of DNNs, these parameters are called weights which link the input of the previous layer to the output of the current layer. Hence, given an input x, a neural network performs the following computations to predict the class: f L (θ L , x) = f L-1 (θ L-1 , f L-2 (θ L-2 ,. , f 1 (θ 1 , x))) (1) where f i corresponds to the non-linearity applied at layer l i. For simplicity, we will omit the vector of parameters θ and use f (x) instead of f (θ, x). This process is also referred to as feed-forward propagation in the deep learning literature. During training, the network is presented with a certain number of known input-output (for example a dataset D). First, the weights are initialized randomly  (LeCun et al.
, 1998b) , although a robust alternative would be to take a pre-trained model on a source dataset and fine-tune it on the target dataset  (Pan and Yang, 2010). This process is known as transfer learning which we do not study empirically, rather we discuss the transferability of each model with respect to the architecture in Section 3. After the weight's initialization, a forward pass through the model is applied: using the function f the output of an input x is computed. The output is a vector whose components are the estimated probabilities of x belonging to each class. The model's prediction loss is computed using a cost function, for example the negative log likelihood. Then, using gradient descent  (LeCun et al. , 1998b) , the weights are updated in a backward pass to propagate the error.
In other words, each time stamp has its own weight and the temporal information is lost: meaning time series elements are treated independently from each other. For example the set of weights w d of neuron d contains T × M values denoting the weight of each time stamp t for each dimension of the M -dimensional input MTS of length T. Then by cascading the layers we obtain a computation graph similar to equation 1. For TSC, the final layer is usually a discriminative layer that takes as input the activation of the previous layer and gives a probability distribution over the class variables in the dataset. Most deep learning approaches for TSC employ a softmax layer which corresponds to an FC layer with softmax as activation function f and a number of neurons equal to the number of classes in the dataset.
Three main useful properties motivate the use of the softmax activation function: the sum of probabilities is guaranteed to be equal to 1, the function is differentiable and it is an adaptation of logistic regression to the multinomial case. The result of a softmax function can be defined as follows: Ŷj (X) = e A L-1 * ω j +b j K k=1 e A L-1 * ω k +b k (3) with Ŷj denoting the probability of X having the class Y equal to class j out of K classes in the dataset. The set of weights w j (and the corresponding bias b j ) for each class j are linked to each previous activation in layer l L-1. The weights in equations (  2 ) and (3) should be learned automatically using an optimization algorithm that minimizes an objective cost function. In order to approximate the error of a certain given value of the weights, a differentiable cost (or loss) function that quantifies this error should be defined.
Thus, applying several filters on a time series will result in a multivariate time series whose dimensions are equal to the number of filters used. An intuition behind applying several filters on an input time series would be to learn multiple discriminative features useful for the classification task. Unlike MLPs, the same convolution (the same filter values w and b) will be used to find the result for all time stamps t ∈ [1, T ]. This is a very powerful property (called weight sharing) of the CNNs which enables them to learn filters that are invariant across the time dimension. When considering an MTS as input to a convolutional layer, the filter no longer has one dimension (time) but also has dimensions that are equal to the number of dimensions of the input MTS. Finally, instead of setting manually the values of the filter ω, these values should be learned automatically since they depend highly on the targeted dataset.
For example, one dataset would have the optimal filter to be equal to [1, 2, 2] whereas another dataset would have an optimal filter equal to [2, 0, -1]. By optimal we mean a filter whose application will enable the classifier to easily discriminate between the dataset classes (see Figure  2 ). In order to learn automatically a discriminative filter, the convolution should be followed by a discriminative classifier, which is usually preceded by a pooling operation that can either be local or global. Local pooling such as average or max pooling takes an input time series and reduces its length T by aggregating over a sliding window of the time series. For example if the sliding window's length is equal to 3 the resulting pooled time series will have a length equal to T 3 -this is only true if the stride is equal to the sliding window's length.
Another type of normalization was proposed by  Ulyanov et al. (2016)  to normalize each instance instead of a per batch basis, thus learning the mean and standard deviation of each training instance for each layer via gradient descent. The latter approach is called instance normalization and mimics learning the z-normalization parameters for the time series training data. The final discriminative layer takes the representation of the input time series (the result of the convolutions) and give a probability distribution over the class variables in the dataset. Usually, this layer is comprised of a softmax operation similarly to the MLPs. Note that for some approaches, we would have an additional non-linear FC layer before the final softmax layer which increases the number of parameters in a network.