dataesr/scientific-paragraphs-categorization huggingface dataset:
The proof provided by the solver is checked by Isabelle. The focus of our work is the smt tactic, but we also extended Sledgehammer so that it also suggests the new tactic to users. The smt tactic translates the current goal to the SMT-LIB format  [10] , runs an SMT solver, parses the proof, and replays it through Isabelle's kernel. To choose the smt tactic the user applies (smt (z3)) to use Z3 and (smt (verit)) to use veriT. We will refer to them as z-smt and v-smt. The proof formats of Z3 and veriT are so different that separate reconstruction modules are needed. The v-smt tactic performs four steps: 1. It negates the proof goal to have a refutation proof and also encodes the goal into first-order logic. The encoding eliminates lambda functions. To do so, it replaces each lambda function with a new function and creates app operators corresponding to function application.
Sledgehammer suggests the veriT-based reconstruction if it is the fastest tactic that finds the proof; so users profit without action required on their side. We plan to improve the reconstruction of the slowest rules and remove inconsistencies in the proof format. The developers of the SMT solver CVC4 are currently rewriting the proof generation and plan to support a similar proof format. We hope to be able to reuse the current reconstruction code by only adding support for CVC4-specific rules. Generating and reconstructing proofs from the veriT version with higher-order logic  [8]  could also improve the usefulness of veriT on Isabelle problems. The current proof rules  [40]  should accommodate the more expressive logic.
Acknowledgment We would like to thank  Haniel Barbosa  for his support with the implementation in veriT. We also thank  Haniel Barbosa ,  Jasmin Blanchette ,  Pascal Fontaine ,  Daniela Kaufmann ,  Petar Vukmirović , and the previous reviewers for many fruitful discussions and suggesting many textual improvements. The first author has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 713999 , Matryoshka). The second author is supported by the  LIT AI Lab  funded by the  State of Upper Austria. The training presented in this paper was carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www.
Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.
ML models automatically learn useful patterns from training data (including the extracted features from the ECG signals) for which the diagnosis is already known and aim at extracting knowledge into their structures and parameters. The development of automated AF detection has attracted an increased level of attention, since the combination of wearable devices and ML has been seen as a potential solution for an early management of AF in order to prevent adverse events such as stroke. During the last decade, there has been an explosion of AF detection algorithms. Interested readers are referred to  Sörnmo (2018). The availability of open-source ECG databases on the PhysioNet website and through recent CinC/PhysioNet challenges has allowed for the development of novel ML techniques, among which most recently deep learning (DL) approaches.
It should be mentioned that the aim of this paper is not presenting the best model for AF classification but to highlight the benefits of interpretability for building a more compact set of features to provide cardiologists with tangible information in the classification. Accordingly, we introduce an interpretable hand-crafted feature-based model for AF classification. The rest of the paper is organized as follows. In section 2, one first presents the data of interest and the machine learning task what we are performing on these data. Thereafter, in section 3 one reviews the main global and local explanation techniques for hand-crafted feature-based models. In section 4, results of the feature importance techniques and evaluation of the performance and the strength of each technique by feature selection and classification on CinC/PhysioNet 2017 dataset are presented.
Dataset and Feature Extraction This study focused on the analysis of the dataset from the 2017 PhysioNet/Computing in Cardiology (CinC) challenge  (Clifford et al. , 2017) , collected using a mobile health device (the AliveCor device), as it constitutes one of the largest dataset of single lead ECG with heart rhythm annotations. The dataset includes 8528 single lead ECG signals between 9 and 60 s in length, which were sampled at 300 Hz and filtered by a band pass filter. The signals were labeled into four classes: atrial fibrillation (A) (735 samples), normal sinus rhythm (N) (5,050 samples), other rhythms (O) (2,456 samples), and noisy recordings (∼) (284 samples). A set of 56 features are extracted from each individual signal in the dataset based on  Behar et al. (2017). These hand-crafted features were designed for different purposes: (i) assessing the quality of the recording (ii) analyzing the morphology of the ECG (either measuring the QRS width, detecting P waves or assessing the presence of f-waves) and (iii) analyzing the regularity of the RR intervals [either standard HR variability (HRV) measures or specific measures suggested for the detection of AF (Coefficient of sample entropy and Poincare plot)].
Supervised ML Approaches for Rhythm Classification We try with different supervised classifiers such as Support Vector Machine (SVM)  (Cortes and Vapnik, 1995) , Logistic Regression (LR)  (Hosmer et al. , 2013) , Random Forest (RF)  (Breiman, 2001)  and Gradient Boosting (GB)  (Friedman, 2001). Each classifier is trained on the training set and tested on the test set, including the extracted features from ECG signals and their corresponding labels. We also apply a cascaded form of the mentioned classifiers. It could be a way to handle the imbalanced dataset CinC/PhysioNet in which the class N samples are almost two-third of all the recordings  (Behar et al. , 2017). Hence, we try with the cascaded form of the classifiers which are Cascaded SVM (CSVM), Cascaded LR (CLR), Cascaded RF (CRF), and Cascaded GB (CGB).
Hence, in the results, we only report F µ. To assess the classification generalization, we use repeated 10-fold cross validation. In 10-fold cross validation, the dataset is divided into 10-and 9-folds are considered for training and one-fold is considered for the test  (Stone, 1974). Then, the average of values of each measure for the test folds is considered for the evaluation of the classifier. Besides, we apply the non-parametric statistical Wilcoxon test  (Cuzick, 1985)  to assess the statistically significant difference between methods. We also use Receiver Operating Characteristic (ROC)  (Fawcett, 2006)  and Area Under the ROC curve (AUC) for the evaluation of classification. ROC is plotted in a two-dimensional space in which the x-axis and y-axis represent the True Positive Rate (TPR) or R and the False Positive Rate (FPR), respectively  (Sokolova and Lapalme, 2009).
For any feature θ , the decrease in Gini impurity resulting from this optimal split, G θ (τ , T), is stored and accumulated for all nodes τ in all trees T in the forest, in the Gini importance I G (θ ) = T τ G θ (τ , T) (11) The Gini importance I G indicates how often a particular feature θ is selected for a split, and how discriminating it is for the classification. The Gini importance values can be used as values of feature importance  (Guyon and Elisseeff, 2003). The advantage of RF is that, unlike LR, it requires no prior knowledge on the linear separability of the classes. The learning is agnostic and it is much more general and applicable to even large datasets.
The explanation can be obtained using ξ (x) = arg min ψ∈ L(f , ψ, π x ) + (ψ) ( 16 ) where L(f , ψ, π x ) is a measure of how unfaithful ψ is in approximating f in the locality defined by π x. The goal is to minimize the L(f , ψ, π x ) while keeping (ψ) small enough to produce an understandable explanation. LIME only implements a class of linear models as interpretable models ψ(z ′ ) = w ⊤ ψ z ′ and develops a linear model using sampled dataset Z. The dataset Z contains samples z ′ ∈ {0, 1} d ′ drawn uniformly at random from non-zero elements of x ′ , weighted by π x. The labels for the sampled instances z ′ ∈ {0, 1} d ′ are generated by using the main probability function f (z). The function requires an original representation z ∈ R d of a sample, which can be recovered from interpretable representation z ′ ∈ {0, 1} d ′.
So, L(f , ψ, π x ) in (  16 ) is defined as L(f , ψ, π x ) = z,z ′ ∈Z π x (z)(f (z) -ψ(z ′ )) 2 (17) Finally, by using the dataset Z and the optimization in (  16 ), the local explanation ξ (x) for the sample x is provided. As we deal with the extracted features from ECG signals and their corresponding labels, we apply the implementation of LIME for tabular data, through which new samples are created by perturbing each feature individually, drawing from a normal distribution with mean and standard deviation taken from the feature.
(2017)  and  Pyakillya et al. (2017) , for the classification of ECG signals on CinC/PhysioNet dataset. The column #features depicts the number of the most important feature generated by the feature importance techniques which cause the best results in the classification. The best value of each column is highlighted in bold. To make the table readable, we use the abbreviations as LR_RF, PT_RF, RF_RF, SHAP_RF, where the name before "_" shows the name of the feature importance and model agnostic technique and the name after stands for the RF classifier. In the method proposed by  Behar et al. (2017) , a set of 35 features are selected based on the SVM classifiers and a CSVM classification is performed. In the method proposed by  Pyakillya et al. (2017) , a 1-Dimensional Convolutional Neural Network (1D-CNN), whose input is the raw ECG signal of length 10100, is applied for the feature selection and classification.
We evaluated the effectiveness of the global explanation techniques by feature selection and classification, while validation of local approaches is more difficult as additional feedback is provided for each sample but does not impact the classification results. Reviewing the local explanation techniques may help the cardiologists to gain trust in the automated diagnosis, as it can confirm or infirm that the automated model is looking at a characteristic of the signal that makes clinical sense. For example, if a model focuses on high RR variability for highly artifact signals, cardiologists can discard the decision. Similarly, end-users can have more trust in a model that locally focuses on the QRS width for the detection of PVCs. Unlike, LIME which perturbs data around an individual prediction to build a model, SHAP computes all permutations globally to get local accuracy.
CONCLUSION Machine learning has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for Atrial Fibrillation (AF) detection. Providing an explanation for the decision made by CADx is considerable from cardiologists' point of view. In this paper, a range of interpretability techniques has been applied to hand-crafted features based ML models for heart rhythm classification particularly AF detection. We tested different global and local explanation feature importance techniques. We validated the impact of the techniques by applying feature selection according to the obtained feature importance and classification to the public short electrocardiography (ECG) dataset of CinC/PhysioNet. It has been shown that each feature importance technique results in different feature rankings, depending on their characteristics and assumptions.
Significant differences were found between the dose response-curves of healthy African and European donors. To validate these data, three other cohorts of healthy donors living in three different African countries were included. The donors of African ancestry living in Europe demonstrated a response that was intermediate relative to the other groups. These in vitro data underscore the necessity to establish a specific dose-response curve for each biological dosimetry laboratory. Overall, these data raise the possible role of ethnicity, as well as environmental factors, in the response to genotoxic stress.
3 µg/ml each) was added to the slides, and they were subsequently denatured on a hot plate at 80 • C for 3 min and incubated in the dark for 1 h at room temperature. After hybridization, slides were washed (3 × 5 min) with 70% formamide/10 mM Tris pH 7. 2 and then in 50 mM Tris pH 7. 2/150 mM NaCl pH 7. 5/0. 05% Tween-20 (3 × 5 min). After washing with PBS, the slides were counterstained with DAPI and mounted with PPD. Images of metaphase cells were acquired using automated acquisition module Autocapt software (MetaSystems, version 3. 9. 1) using a ZEISS Plan-Apochromat 63 × /1. 40 oil and CoolCube 1 Digital High Resolution CCD Camera. The analysis was performed using ChromScore software (Cell Environment).
Statistical Analysis A script in R R was developed according to the IAEA recommendations  (IAEA, 2011). The curves generated in this study are based on generalized linear models using the glm function of the standard Stats package, computing the maximum likelihood estimation, given the count of aberrations following a quasi-Poisson distribution. The curves were fitted as a quadratic function alpha * x + beta * x 2 + C. The standard error of the mean (SEM) is shown and the dose-response curve is plotted in gray. The uncertaintly of the aberration count is plotted as error bars according to the exact Poisson 95% confidence intervals  (Johnson et al. , 2005). Uncertainty bounds of the dose given by the aberration count can be estimated using Merkle's method, combining the variance-covariance matrix of the fitted function and the confidence intervals of the aberration counts.
Software-Defined Networks (SDN) According to the Software-Defined Networking paradigm, computer networks can be split in three planes: management, control and data planes as depicted in Figure  1. The data plane is composed of the forwarding devices (switches, routers, etc. ). The control plane is responsible for sending commands to the forwarding devices in order for them to apply the required networking policy. The management plane is represented by network management applications such as those related to traffic engineering, mobility management and wireless communications, security, and reliability. These applications are implemented using network programming languages and they interact with the control plane thanks to an open northbound Application Programming Interface (API). The control plane controls the forwarding devices via an open southbound API such as OpenFlow  [22].
Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.
866 for Biarritz and 0. 858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.
Introduction Databases containing information on past storm characteristics and their impacts on the coast are essential for the disaster-risk-reduction process. They enable scientists and coastal stakeholders to better understand the storm hazard in a specific area, to identify potential trends, and most importantly to assess coastal risks (present or future) through their use in the development and validation of early warning systems  [1, 2]. In these databases, storm impact is mostly represented as a qualitative variable with different categories. The different categories of storm impact are called "regimes" and are defined according to the Sallenger's scale  [3]. This scale was originally derived to classify storm impact intensity based on the relation between wave-induced maximum water level and topographic elevations of the different sections of a natural beach.
Recently, this approach has been extended to the estimation of storm impact intensity at an engineered beach backed by a seawall  [4]. Due to the extreme and episodic nature of storms, databases covering a long period of time are necessary. Observed data about storm characteristics such as tide, wave, and wind are abundant and have been collected routinely for decades. In addition, numerous reanalyses and hindcasts are available for these variables. On the contrary, data on storm impacts are more sparse and mostly come from archives  [5] [6] [7]  or insurance data  [8, 9]. A few examples of storm impact databases are: the RISC-KIT database, which contains storm impact information for nine study sites in Europe  [10] ; the SurgeWatch database  [5]  for the UK; and a database for the Basque coast  [7].
Even though archives and insurance provide information, there are some limitations including the heterogeneity, the incompleteness of the data sources, and the consequent amount of work needed  [7]. A solution to routinely create a storm impact database could be to use images provided by coastal monitoring stations that are now widely used worldwide to survey and study coastal processes. In recent decades, video monitoring systems have proven to be valuable assets in the study of the coastal zone due to their cost-efficiency and their ability to a provide continuous stream of data including intense storm conditions. Video monitoring systems are generally composed of one or several cameras operated by a monitoring software such as Argus  [11] , HORUS, Kosta (www. kostasystem. com, accessed on 1 May 2021) or Sirena  [12].
Then, the CNN is trained to classify timestacks into storm impact regimes using the annotated dataset. During the training process, the CNN learns to simultaneously classify the images and which features to detect in order to achieve the best classification accuracy. Once the neural network has learned on the training dataset, it can be used to routinely analyze the timestacks produced by the video monitoring system and therefore create incrementally a qualitative storm impact database. This second methodology, based on a self-learning algorithm (CNN), allows for more automation compared to the first methodology because it does not require site-specific calibration  [17]. Another advantage of CNNs is their transfer learning potential. Transfer learning consists in using knowledge gained on a first task, which usually involves a large number of data, and applies it to a different task  [28]  with smaller number of data.
This technique has already been employed in coastal engineering domain  [14, 29]  and usually results in faster training and better accuracy. In the case of storm impact recognition, where images of extreme storm impact regimes are rare by nature, this method can significantly improve the performances of CNNs. Moreover, it is reasonable to think that knowledge acquired at one site can be used to improve the performances on another site. This could be a non-negligible asset for the application of the method to a new site. This paper aims to demonstrate the high potential of CNN methods to constitute a storm impact database using timestacks images provided by coastal video monitoring stations. Different methods are tested using images collected at two study sites. The best practice and the transferability of knowledge gained at one site to another are studied.
Timestack Image Preprocessing The ground truth dataset was built by labeling the 8172 images. There are two methods to annotate the images: by hand or in a semi-automatic way. The annotation by hand is the most straightforward but also the most time-consuming method. The semi-automatic method consists of two steps. First, the position of the waterline is extracted automatically by segmenting the image using Otsu's thresholding method  [16]. Then, the storm impact regime is identified by comparing the position of the waterline with the one of the defense infrastructure. This method is faster than the annotation by hand; however, it still requires an operator because it is not always robust and highly depends on the lightning conditions of the image. To employ this method, the position of the defense infrastructure in the image must be known.
Like the GPB, the beach of Zarautz is also exposed to highly energetic waves and storms coming from the WNW and NW directions. The seawall backing 70% of the beach has an along-shore elevation varying between 6. 5 m in the western part and 8 m in the center of the beach. This seawall serves as a defense infrastructure for the buildings near the beach, and overtopping events are common at high tide during winter storms. A video monitoring station, like the one used on the GPB site was installed in 2010. The station has 4 cameras of 1. 4 Megapixels. Two of the cameras are equipped with 12 mm lens and have a panoramic view, and another 2 equipped with 25 mm lens cover with more resolution the mean high and low tide coastline positions. For the Zarautz dataset, 4 transects are monitored by the camera covering the supra-tidal beach with higher resolution (Figure  4 ).