dataesr/scientific-paragraphs-categorization huggingface dataset:
The final probeset contains 87812 unique probes targeting 1 (73612 probes) or more (14200 probes) potential transcript both in sense and anti-sense. Normalization and statistical analyses performed to get normalized intensity values have been done as in Celton, Gaillard et al [131]. For each comparison and each probe, we retrieved a ratio of the logarithms of the fluorescence intensities (one per compared sample: T0 versus 24hpi or T0 versus 72hpi in our case) and an associated p-value. The applied p-value threshold to determine DEGs (differentially expressed genes) was 0. 05. Through blast analyze, a TAIR accession number (The Arabidopsis Information Resource; https://www. arabidopsis. org/; [132]) has been linked to a majority of apple or pear "probe/corresponding gene" and the couple "TAIR accession/ratio value" and transposable elements.
These 60 nucleotides size increase the probability of finding specific probes on genes with high has then been used to make a global analyze of functional categories observed in the Mapman TCAAGGTTGGTGGACCTCTC) designed on the EF-1α gene (accession AJ223969 for apple and (https://earray. chem. agilent. com/earray/) to generate one probe per gene prediction. Options used were: Probe Length: 60, Probe per Target: 1, Probe Orientation: Sense, Design Options: Best Probe Methodology, Design with 3' Bias. The probeset was then reverse-complemented to generate antisoftware (https://mapman. gabipd. org/homemapman. gabipd. org; [133]). The detailed analyze of DEGs has been done through TAIR and KEGG (https://www. genome. jp/kegg/) databases, and bibliography.
Acknowledgements This project has been supported by the European Reference Network on Rare Congenital Malformations and Rare Intellectual Disability (ERN-ITHACA). Funding: ERN-ITHACA is partly co-funded by the  Health Programme of the European Union. Funding was also obtained from the  European Union 's  Horizon 2020 research and innovation programme  under the  EJP RD COFUND-EJP N•825575.
RECURRENT NEURAL NETWORK Recurrent neural networks, for which the hidden state is computed as h t ← RNN(x t , h t-1 ), are broadly used for temporal data processing. Propagating gradients through the time dimension, however, is known to cause gradients to either vanish (Hochreiter, 1998) or explode  (Graves, 2012). Gated recurrent neural networks, such as Long Short Term Memory (LSTMs)  (Hochreiter & Schmidhuber, 1997)  or Gated Recurrent Units (GRUs)  (Cho et al. , 2014)  alleviate this by introducing additional gates that control the gradient flow through time. These networks process variable length sequences by iteratively applying their parametrization on newly observed data. Hence, extracted features contain expressive high-level information of the input data. However, these networks are often difficult to optimize on smaller datasets.
Results Now that we have defined our model, we assess its validity through extensive experiments on a diverse set of 46 publicly available time series classification datasets. These 46 datasets are used in  (Mori et al. , 2017)  who, fortunately, also report classification accuracy and earliness of compared approaches. They are made available on the UCR repository  (Dau et al. , 2018; Chen et al. , 2015)  and cover a wide range of data modalities, ranging from motion capture through audio to Electrocardiography (ECG) sensor data. The competitors considered here are the best method from  (Mori et al. , 2017)  (denoted SR2-CF2), the pioneering work of  (Xing et al. , 2012)  (ECTS), as well as EDSC  (Xing et al. , 2011) , and RELCLASS  (Parrish et al. , 2013)  methods.
Model selection As stated above, the 46 datasets that are used in these experiments are diverse in nature, which makes the selection of hyper-parameters a critical task. We select the network configuration through three-fold cross validation after 30 epochs of training on pure accuracy. The parameter space was evaluated using grid search. The tuned hyper-parameters for the convolutional model include the number of convolution blocks l ∈ {4, 6, 8}, the number of kernels per convolution d l ∈ {50, 75, 100}, the increase rate of kernel width per layer δ w ∈ {30, 50, 70}. As for the hyper-parameters for the recurrent model, we varied the number of recurrent layers n ∈ {2, 3, 4, 5, 6} and the number of hidden features per layer r ∈ {64, 128, 256, 512}. For both models, we searched with two learning rates η ∈ {0.
Qualitative Results In this section we present qualitative results obtained from the recurrent model described in Section 3. 3. 1. First, we want to get an impression on the training process of a model that was previously trained on pure accuracy and is now additionally optimized for earliness. To demonstrate our approach, we selectively chose the TWOPATTERNS synthetic dataset from the UCR datasets. We present quantitative results on all UCR datasets in the next section. Here, we show the accuracy and loss functions on the test dataset during the training process in Fig. 5. Following Eq. (  4 ), the overall loss, shown in Fig. 5(a ) is a α = 0. 8 weighted sum of classification loss in Fig. 5(b ) and earliness loss Fig. 5(c). By observing the P(t) scores of a single input sample at selected epochs in Fig.
Quantitative state-of-the-art comparison In this section we quantitatively compare our early classification method with the other early classification approaches presented in Section 2. As stated above,  Mori et al. (2017)  provide accuracy and earliness results for their and comparative methods in their supplementary material We can hence compare our results on a diverse collection of datasets with a large number of early classification approaches. However, we would like to emphasize that a comparison of these early classification methods requires care in terms of evaluation criteria and choice of earliness parameters. The trade-off between earliness and accuracy is parametrized differently in each approach. We followed the parameters evaluated by  Mori et al. (2017) , as shown in Table  1.
Hence we calculate an α-weighted score similar analogous to the loss function in Section 3. 2. This score is closest to the objective defined to the respective approaches and should be the primary evaluation focus. Since we evaluate this score on a large number of datasets, we decided to use win/loose plots for a first quantitative evaluation. Datasets where our method achieved a higher scores than the competitor are counted to the win category and vice-versa. We illustrate the results in Table  2. Our approach clearly outperforms the early classification models EDSC, RELCLASS, and ECTS on almost all evaluated trade-off factors. However, it seems that  (Mori et al. , 2017)  achieve a better score on many datasets with their non differentiable early stopping heuristic. We further analyze the differences between our approach, SR2-CF2 of  Mori et al.
(2017)  and the RELCLASS  (Parrish et al. , 2013)  method in Fig. 8. Here we show accuracy, earliness and α weighted scores as scatter plots. In this figure, the horizontal axis shows our performance, while the vertical axis is based on scores reported by  Mori et al. (2017)  and  Parrish et al. (2013) , respectively. Each dot in these plots corresponds to a single dataset for a given α value. Colors indicate different target trade-offs (i. e. α values). The diagonal marks equal performance between the approaches. It seems that our approach achieves similar accuracies compared to SR2-CF2, as shown in Fig. 5(d ), but performs the classifications later. This results in an overall score that is slightly worse, as shown in Fig. 8(c ). Even though our evaluated scores are slightly worse than the numbers reported by  Mori et al.
(2017)  we present a method that can be broadly employed, as we demonstrate on two separate classification models, as described in Section 3. 3. In contrast to SR2-CF2, our method is scalable for large datasets, requires only a single model parametrization to process a variable length sequence, and is trained via standard back-propagation and gradient descent. In contrast, SR2-CF2  (Mori et al. , 2017)  trained separately a Gaussian process classifier for each possible sequence length and designed a binary, non-differentiable stopping decision that requires a parametrization using genetic algorithms. Still, our approach clearly outperformed the baseline methods ECTS and EDSC and achieves better scores compared to RELCLASS in Table  2. Comparing to RELCLASS in particular in Fig. 8 , one can observe the variability in terms of earliness of the classification between the approaches.
Influence of the Earliness Accuracy trade-off factor In our final experiment we qualitatively assess the effect of the accuracy-earliness trade-off parameter α for our approach and the SR2-CF2  (Mori et al. , 2017)  method. As stated previously, we specifically designed our loss function, see Fig. 3 , such that the effect of al pha on earliness and accuracy is similar. In Fig. 7 , we show both approaches on the two datasets ECGFIVEDAYS and HAPTICS. The SR2-CF2 method is illustrated in orange, while our approach is shown in blue. Accuracy and earliness for different values of α ∈ {0. 6, 0. 7, 0. 8, 0. 9} are drawn on the axis. As intended, an increasing weight on the classification loss with α ∈ {0. 8, 0. 9}, improves the achieved accuracy, while simultaneously increasing the earliness, as can be observed consistently for both approaches.
Discussion In this work, we propose a novel, end-to-end trainable mechanism that produces a probabilistic decision that can be used to prematurely stop evaluating a sequence of observations if enough classification-relevant characteristics have been observed. It is broadly applicable to any sequential model that evaluates a hidden representation at each time t. We demonstrated the applicability by implementing our mechanism on two different sequential classification models in Section 3. 3. We were able to compare the accuracy and earliness of our method to other early classification approaches on a diverse set of 46 datasets that enclose various types of use-cases for time series classification. These results have shown that our method was better or comparable to methods in the state-of-the-art in early classification.
Compared to the best early classification model of  Mori et al. (2017)  we achieved similar accuracies, but with later classifications, thus resulting in a worse overall score. Nevertheless, we argue that our approach is methodically simpler, more scalable to large datasets and easier to deploy in existing sequential classification methodologies. Further we do use a single model parametrization for a variable length sequence and train our stopping rule end-to-end. In contrast,  Mori et al. (2017)  used a different classification model parametrization at every time step, and were forced to employ genetic algorithms to train their models due to a non-differentiable cost function. Also, we would like to emphasize that we modified our training procedure and loss function to be able to compare to  Mori et al.
8 evertheless, cohort studies (Table  S1 ) faced other methodological challenges. The long prodromal PD phase may induce reverse causation because nonmotor and motor symptoms could influence statin use before diagnosis. 9, 10 Thus, long follow-ups, allowing lagged analyses while retaining a sufficient number of cases, are necessary to decrease the risk of reverse causation; this issue was rarely addressed in previous studies. 11, 12 4] [15] [16] [17] [18] [19]  Most studies based on electronic databases  11, 14, [18] [19] [20]  did not adjust for major confounders (eg, smoking, physical activity). 21 Last, lipophilic statins cross the blood-brain barrier more freely than hydrophilic statins,  22  and only some studies distinguished them. 11, 12, 14, 18, 20, 23 e examined whether statins (overall, lipophilic, hydrophilic) are associated with reduced PD incidence in a cohort of French women followed for 15 years, while attempting to overcome the limitations described previously, in particular the incomplete consideration of reverse causation.
Participants E3N is a French cohort study of 98,995 women, born between 1925 and 1950, recruited in 1990, and affiliated with a French national health insurance plan covering mostly teachers (Mutuelle Générale de l'Education Nationale). 24 articipants completed a self-administered questionnaire on lifestyle and medical history at baseline and every 24 to 36 months thereafter. A total of 11 waves of data are available (last, questionnaire Q11-2014; average response rate of 80%). Since January 1, 2004, drug and medical consultations claims databases were available for 95% of women alive in 2004. Women who stopped responding to questionnaires can be followed in these databases in order to assess statin use. Causes of death for women who died were available. All participants signed informed consent, in compliance with the rules of The French National Commission for Data Protection and Privacy, which approved the study.
Statin Use We used drug claims databases to assess statin use (prescription-only medications) during the follow-up (January 1, 2004 to December 31, 2018). These databases include detailed information on drugs, doses, and dates of purchase. Statins were identified by their Anatomical Therapeutic Chemical (ATC) codes (ATC-C10AA/C10BA/ C10BX). To account for differences in recommended daily doses for different statins, doses were converted to defined daily doses (DDD). 25 e first considered ever use of any statin and then distinguished lipophilic (atorvastatin, fluvastatin, simvastatin) from hydrophilic statins (pravastatin, rosuvastatin). 22 o assess the role of cumulative duration and dose of treatment, for each statin claim, we extracted information on date of purchase, number of tablets delivered, and dose of each tablet.
Statistical Analysis Analyses were performed using SAS 9. 4 (SAS Institute Inc. , Cary, NC) and Stata16 (StataCorp, College Station, TX). Two-tailed P values ≤0. 05 were considered statistically significant. Participant characteristics (2004) are described overall and according to statin use and PD status at the end of follow-up. A nested case-control study was used to describe statin use and medical consultations trajectories prior to PD diagnosis in cases and matched controls and to assess whether trajectories changed in cases compared  with controls during the prodromal PD phase (Supplementary Methods). To estimate the association between statin use and PD incidence, we used the Cox proportional hazards regression model for time-varying covariates with age as the time scale. 34 The strength of the associations is quantified through hazard ratios (HRs; 95% CIs).
E3N participants are educated and motivated women who provide high-quality information in questionnaires with high response rates. 24 It is also generally considered that representativeness is not essential for estimating associations. 47, 48 We acknowledge that we included only women, which hampers generalizability to men; however, women represent an understudied population in PD research, in whom additional studies are needed. 49 2] [53]  Second, our analyses with a 5-year lag are based on 524 incident PD cases (80 ever used statins); ours is the largest prospective study in women to date, as larger studies were retrospective and based on healthcare databases. However, results of subgroup and doseeffect analyses need to be interpreted cautiously due to the small number of exposed PD patients; we were not able to perform sensitivity analyses using lags >5 years or to examine dose-response relations for individual molecules.
Acknowledgments: This work was realized with data of the E3N cohort (INSERM) and supported by the  Mutuelle Générale de l'Education Nationale (MGEN) ,  Gustave Roussy Institute , and  French League against Cancer  for the constitution and maintenance of the cohort. This work has benefited from State aid managed by the  National Research Agency (ANR)  under the program "Investment in the future" bearing the reference  ANR-10-COHO-0006  and under the program " Young researcher " bearing the reference  ANR-18-CE36-0006-01 , as well as a subsidy from the  Ministry of Higher Education, Research and Innovation  for public service charges bearing the reference N 2102918823, 2103236497, and  2103586016 , and from  IRESP (Institut de Recherche En Santé Publique). The authors acknowledge all women enrolled in the E3N cohort for their continued participation.
M. C. obtained a research grant from the  ANR. A. E. has obtained research grants from  Plan Ecophyto (French Ministry of Agriculture ) and  France Parkinson Association. The work reported in this article was performed during A. F. 's term as a visiting scientist at the  International Agency for Research on Cancer. The authors declare no other relationships or activities that could appear to have influenced the submitted work. Funding agencies: This project was funded by the  Michael J. Fox Foundation for Parkinson's Research  and the  France Parkinson Association.
From the literature review, it emerges that limited quantitative data of local and instantaneous film thickness inside minichannels are available. Extended databases covering a wide range of operating conditions are needed for the development/assessment of correlations and for the validation of computational fluid dynamics simulations  [32]. In the present study, liquid film thickness measurements are performed with HFE-7000 during vertical downflow condensation inside a 3. 38 mm inner diameter channel at 40 • C saturation temperature. Two different test rigs, respectively located in Padova (Italy) and in Toulouse (France), have been used: the first consists of two counter-flow tube-intube heat exchangers separated by an adiabatic borosilicate glass tube for film thickness measurement; the second is made of a diabatic sapphire tube.
Experimental apparatuses and data reduction In the present paper, liquid film thickness data of HFE-7000 obtained in two independent setups are compared at the same working conditions with the aim of checking the agreement between the experimental datasets taken using two different techniques. The two experimental setups are respectively located at the Laboratory of Two-Phase Heat Transfer of the Department of Industrial Engineering of the University of Padova (Italy) and at Laboratory on Plasma and Conversion of Energy (LAPLACE) of Université Paul Sabatier in Toulouse (France). In both cases, the condensation heat transfer test section displays an inner diameter of 3. 38 mm and is designed to allow the simultaneous measurement of liquid film thickness and heat transfer coefficients. In the experimental device located in Padova (Italy), the film thickness measurements, together with flow pattern visualizations, are performed in an adiabatic glass window, where the working fluid flows at the desired thermodynamic conditions (set in a previous heat exchanger in which the condensation heat flow rate is removed by using cooled water).
The saturation temperature T sat is computed from the measured saturation pressure at inlet/outlet of the test section and using REFPROP 10. 0  [36]. The uncertainty analysis has been performed following the JCGM  [37]  guidelines. Considering the present experimental database, the maximum expanded uncertainty on vapor quality (coverage factor k = 2) is equal to ± 0. 017 at G = 20 kg m -2 s -1. Additional information on the experimental setup and the procedure for data reduction and uncertainty analysis is reported in Azzolin et al. [34]. Before starting the experimental test runs, HFE-7000, which is a low pressure fluid, undergoes a degassing procedure in order to remove non-condensable gases that may be dissolved in the liquid phase. The fluid is heated up and vaporized by means of electric resistors and the resulting mixture of vapor and non-condensable gases migrates to a vacuum cylinder which is cooled down by ice.
Comparison between the two experimental datasets Fig. 3  reports the comparison between the mean liquid film thickness obtained in the two independent experimental setups at G = 20 kg m -2 s -1 and G = 30 kg m -2 s -1 as function of the vapor quality. Such values are intended as the average of all the instantaneous film thickness values measured during the acquisition time (equal to 3. 1 s for the experimental tests in Padova and to 10 s for those performed in Toulouse). The acquisition frequency is set to 1000 Hz and 400 Hz for the tests in Padova and in Toulouse, respectively. The interpolation curve obtained from the measurements performed in Toulouse (France) is also plotted to better follow the evolution of the average thickness as a function of vapor quality. As shown in Fig. 3 , in the experimental setup used in Padova the film thickness values at G = 20-30 kg m -2 s -1 are measured only at small vapor quality since in the first heat exchanger, before the glass tube, the superheated vapor was almost fully condensed to allow heat flow rate and vapor quality measurements with low uncertainty.
Fig. 3  shows that very similar film thickness values are obtained in the two experimental test benches. Moreover, it is interesting to note that the film thickness measurements conducted in Padova (Italy) involve an adiabatic glass section placed between two diabatic parts, while in Toulouse (France) liquid film thickness measurements are directly performed in a diabatic tube where the phase change is present. Notwithstanding the smaller heat flow rates obtained with Toulouse setup and the different experimental procedures, no deviations between the two experimental databases can be observed. In order to carry out a fair comparison between the two experimental datasets, a statistical and spectral analysis of the instantaneous liquid film thickness data is also performed. Indeed, even if the mean liquid film thickness is the same for both cases, the amplitude and frequency of the interfacial waves could be different.
In Fig. 4  the probability density function (PDF) of liquid film thickness values of HFE-7000, measured in the two different experimental setups, at mass flux equal to 20 kg m - s -1 and vapor quality x = 0. 08-0. 10 and x = 0. 31 are reported. The same comparison is reported in Fig. 5  at mass flux equal to 30 kg m -2 s - and vapor quality x = 0. 27 and x = 0. 55. The PDFs are calculated considering about 3100 and 4000 instantaneous film thickness values for Padova and Toulouse datasets, respectively, recorded during the acquisition time. An interval for each class equal to 10 μm is chosen for the calculation of the PDF. At G = 20 kg m -2 s -1 (Fig. 4 ) no significant difference between the PDFs obtained in Padova and in Toulouse can be observed. The range of detected film thickness values is comparable in the two cases, as well as the most probable film thickness value (between 60 and 70 μm for x = 0.
08-0. 1, between 50 and 60 μm for x = 0. 31). As expected, the peak film thickness is observed at smaller values with the increasing vapor quality. As already shown in Fig. 3 , the deviation between mean film thickness values in the two experimental setups is small (+2% at x = 0. 08-0. 1, -2% at x = 0. 31 comparing data taken in Toulouse to those of Padova). The same can be argued also for G = 30 kg m -2 s -1 , as shown in Fig. 5. The PDFs of liquid film thickness data taken in Toulouse and in Padova display similar shapes for both the considered vapor quality values (x = 0. 27 and x = 0. 55). The deviation between the mean film thickness values measured in Toulouse and in Padova never exceeds 3% for the considered working conditions. The calculated δ mean value does not represent the most probable film thickness in the PDFs at G = 20 kg m -2 s -1 and G = 30 kg m -2 s -1.