dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The problem of classification is crucial in many applications. For example, in handwritten digit recognition, a digitized image of the written digit (the input) is processed, and each character must be classified as one of the digits 0-9 (ten classes in all). In a SPAM filter, each message is processed and must be classified as SPAM or ham (not SPAM). The inputs are features of the messages (the frequency of some key words, of capital letters, etc. ). In medicine, inputs such as blood indicators and patients' other pieces of information can be used to decide if a disease is present or absent. The last two examples are instances of binary classification. In general, each classifier is a flexible model that learns from data, in its particular way, using a large database of observations for which the ground truth is known (the training set).
Those methods are based on the mining of rules by the well known Apriori algorithm  [8] , which has been improved since, and has been successfully applied to very large datasets, mining all the possible rules concerning frequent itemsets (sacrificing only small support rules). In the context of classification, class association rules are rules implying a particular category of the classification variable. In  [3] , the authors introduced the first serious classification based on association rules (CBA), by relating classification rules under a precedence order, so that the observations can be classified using, the strongest rules, and if not applicable, a default class, so that the misclassification rate is small  [9, 10]. The strength of the rules is measured by the confidence, as it estimates the probability of the class given the other features of the rule.
SIA was initially developed in the field of Didactics of Mathematics  [12] , and is now being used in a wider range of data and domains  [13] [14] [15] [16] [17]. In this work, it will be compared to other classification methods of widespread use in the literature (Naïve Bayes, radial basis neural networks, decision trees J48 and simple CART) with breast cancer open access datasets from UCI Machine Learning repository, namely Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC)  [18] , through the computation of the confusion matrices: accuracy, precision, sensitivity and specificity. These datasets have been used in order to test classifiers such as in  [2, [19] [20] [21] [22] [23]. In addition, the Haberman dataset has been used to test algorithms in  [24].
The EEG Eye State Data Set (IDs_mapping) has been used in  [25]  for classification of eye state using k-Nearest Neighbors algorithm and multilayer perceptron neural networks models, and also used in  [26] , which proposes a novel EEG eye state identification approach based on Incremental Attribute Learning (IAL). The Cervical Cancer Behavior Risk Data Set (SOBAR) in  [27]  was also used in this study. All the computations in this paper have been conducted by means of the R Statistical Software [28].
Methodology The aim of this paper is to define an accurate and easy to interpret binary classifier, and to show that it is as competitive as other widely used and more complex classifiers. For that aim, a quality measure taken from the Statistical Implicative analysis, described in Section 2. 1, was used. The rationale of the classifier is exposed in Section 2. 2, and the criteria for comparing the performance of the competing algorithms are given in Section 2. 4. In Section 2. 3, the open access datasets used for the test are introduced, and the results of the comparison are shown in Section 3. Finally results are discussed in Section 4.
A New Quality Measure of Association Rules Association rules are endowed with several quality (or interestingness) measures. The most widely used ones are confidence, support, and lift, and they can be easily displayed when rules are mined through the Apriori algorithm  [8] , implemented in the popular arules R package  [29]. There are several other quality measures, each one extracting a slightly different facet of the considered rule  [30] [31] [32]. For the sake of good prediction rates, the confidence is the most important one, since it measures how likely the right-hand side (rhs) of the rule is observed for an individual who is seen to hold the left-hand side (lhs) of the rule. For very large datasets, the computation is an issue, and the Apriori algorithm needs to sacrifice rules affecting a low proportion of the sample, in order to finish in a reasonable time.
All the quality measures are descriptive, therefore subject to a natural variability under the sampling scheme that has led to our dataset. In order to measure the effect of the lhs over the rhs, one can use the statistical inference over the value of the lift of a rule in the whole population. To that aim, the implication intensity of  [11]  is used. Definition 1. Let a and b be two binary variables. The implication intensity of the rule a ⇒ b is the probability that a lack of relationship between a and b produces more counter examples than the ones observed in the sample. Formally, P(N ab > n ab ), where N ab stands for a random variable accounting for the number of counter examples observed in a random sampling, and n ab stands for the number of observed counter examples in the current sample.
8. Choose the m-tuple with best performance (accuracy by default, but any other can be chosen). 9. Return final classifier that uses the m-tuple leading to the best performance. For example, if a variable V has 3 categories a, b and c, one then considers the rules where each category implies the positive class (a → 1, b → 1 and c → 1), and the rules where each category implies the negative class (a → 0, b → 0 and c → 0). If for category a, both of its rules are not significant, it will not be possible to use this variable V to classify an individual for who V = a. Then V is not a significant variable. We have implemented the algorithm in a convenient R package for Linux, freely available in  [35]. It contains the function SIAclassif(), that develops the algorithm, and returns the classifier, as well as the function predict.
Data Description Our approach has been tested using six widely used datasets: Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC), Wisconsin Prognosis Breast Cancer (WPBC), Haberman's survival (Haberman), EEG Eye State Data Set (IDs_mapping) and Cervical Cancer Behavior Risk Data Set (SOBAR)  [18]. The WBC dataset relates the malignancy of tumors (2 for benign, 4 for malignant) to 10 attributes like clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli, mitoses, and class. It has 683 records with a class distribution of 444 negative and 239 positive records. The WDBC dataset relates the diagnosis (M = malignant, B = benign) to ten cell nuclei features, extracted by computer vision diagnostic system: radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry and fractal dimension  [36].
For each image, as it contains several cells, three values of each feature are kept: the mean value, the standard error, and the mean of the three largest values. In summary, there are 569 records with 30 input features, and a class distribution of 212 positive and 357 negative records. In the WPBC dataset, the outcome (R = recurrent, N = non-recurrent) is related to time (recurrence time, for recurrent, and disease-free time, for non-recurrent), as well as to features such as in the WDBC dataset, and tumor size and lymph node status. In summary, there are 194 records on 30 input features with 46 positive and 148 negative records. The Haberman dataset reflects a study on the survival of patients who had undergone surgery for breast cancer. It contains 306 instances and 3 attributes (the age of patient at the time of operation, the year the patient underwent surgery and the number of positive auxiliary nodes detected) as well as the survival status class (1 = the patient survived 5 years or longer, and 2 = the patient died within 5 years).
It gives 81 positive and 225 negative records. EEG Eye State classification is important and useful to detect humans' cognition state. The dataset includes 14 continuous EEG measurements. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analyzing the video frames. A '1' indicates the eye-closed and '0' the eye-open state, with an amount of 6723 positive and 8257 negative records. The Cervical Cancer Behavior Risk Data Set contains 18 explanatory attributes regarding ca cervix (1 = has cervical cancer, 0 = no cervical cancer) in 72 instances, with 21 positive and 51 negative records [37].
Classifier Comparison Criteria To evaluate our approach, we have compared our results to those of the different classifiers (J48, Naive Bayes, svmRadial, CART and K-nearest neighbors). All machine learning algorithms used for the comparison in this paper were conducted using RWeka  [38]. The RWeka package provides a collection of functions that give R access to the machine learning algorithms in the Java-based Weka software package  [39]. The level of effectiveness of the classification model is calculated with the number of correct and incorrect classification in each possible value of the variable being classified in the confusion matrix  [1, 40]. The entries in the confusion matrix have the following meaning in the context of our study: TP is the number of true positives (the number of correct predictions that an instance is positive), TN is the number of true negatives (the number of correct predictions that an instance is negative), FP is the number of false positives (the number of incorrect predictions that an instance is positive) and FN is the number of false negatives (the number of incorrect predictions that an instance is negative).
Results As stated in Section 2. 4, the decision was made to use 5-fold cross-validation. The datasets were randomly divided into five groups. Fixing one of such groups, all methods were fitted to all the instances in the other groups. Then, all fitted methods were asked to classify the instances in the fixed group, from which the confusion matrix and the four performance criteria were computed. Repeating the process for each of the five groups and averaging the obtained values, the results shown in Table  2  and represented for ease of interpretation in Figure  1  are obtained. These results are discussed in Section 4. When the classes are unbalanced, some classes may not appear in some training/test sets, and it leads to undefined values of the performance criteria (because of the fractions 0/0).
The classifier has been implemented in an R package, and it can be freely downloaded from  [35]  and used as follows under Linux: • Place the Clasif. zip file in a local folder (that we denote as path_to_file) • Run install. packages(path_to_file, repos = NULL, type="source") in the R Console. The package will be installed from source. • Run library(Classif) in the R Console • Run Classif() in the R Console. It provides a comfortable windows interface where the user can pick the dataset file and select the number of votes without writing code. Its output is the comparison among the classifiers that we show in Table  2. The object rules (that the user can print just typing rules in the R Console) contains the classifying rules and the accuracy for each possible number of votes.
• Internally, the function SIAclassif() is the one that performs all the steps of our algorithm, according to the implifidence threshold and the maximum number of votes. It computes the implifidence of all the rules, it selects the significant variables, and it computes all the combinations of groups of significant variables and the resulting classification for each one, taking the one which maximizes the accuracy (by default, or any other criterion specified by the user). It prints a sentence explaining the rule for classification. As an example, the result for the WBC dataset is shown in Appendix A.
Conclusions We have proposed a machine learning algorithm for classification based on association rules, using a rather novel quality measure, the implifidence (see Section 2. 1), and a majority vote among a set of significant rules. Our approach has been tested using six well known open access datasets, and its results have been compared with four other well established algorithms for classification (Naive Bayes, Radial basis neural networks, Decision trees J48 and simple CART), using 5-fold cross-validation in order to keep away from overfitting. As can be seen in Figure  1 , our proposal gets a top or second position among the five classifiers in four of the six datasets, with regard to accuracy, precision and sensitivity. Our model is ranked top or second in only two of the datasets regarding specificity.
Different techniques are available to assess stress, including heart rate, blood pressure  [20] , and biochemical markers such as cortisol  [21]. However, these techniques are di cult to implement routinely and do not directly evaluate emotions. The emotional environment generated during a simulation session can affect training. Therefore, we chose an innovative technique to assess emotional impact. NEOTROPE has created the complete Affect-tag system, which is a mobile solution for measuring affective and cognitive reactions using physiological data  [22]. After highlighting the feasibility of HFS training for the BN consultation  [18] , this prospective study objectively evaluated the emotional contribution of HFS as an effective tool to develop clinical pro ciency using the new method of Affect-tag.
ACKNOWLEDGMENTS The work was supported by a grant from AMGEN and the ALAIR-AVD. We thank "Textcheck" for assistance with English-language editing. The English in this document has been checked by at least two professional editors, both native speakers of English. For a certi cate, please see: http://www. textcheck. com/certi cate/ZG4M9S Ethics approval and consent to participate Institutional Review Board Statement: N°2019-1 Consent for publication Not Applicable.
Optical modelling RCWA is a frequency-domain computation tool that can perform a full, rigorous electromagnetic analysis of layered structures. All absorption and transmission spectra were calculated using the freely available RCWA package S 4. [34] he spectra displayed in Figures 4A-4D were calculated at normal incidence and the map shown in Figure  4E  was obtained with angles of incidence ranging from 0 to 15 •. Dielectric constants of silicon nitride have been measured using ellipsometry, see Figure  S1. Their accurate determination is less straightforward for TiO 2 , considering the low thickness of the layer and the inhomogeneity of the first monolayers. Meanwhile, the anatase bandgap stands within the wavelength range where optical resonances are expected, leading to strong spectral variation of its extinction coefficients and uncertainties.
We have chosen to consider a model material determined through fitting spectrophotometry measurement of the flat non structured reference. A pre-existing set of n, k values of TiO 2 was fitted to describe the system as accurately as possible. For that purpose we created a fitting program using the RCWA package previously mentioned. For each wavelength, the value of k associated to TiO 2 was incrementally varied until the simulated transmission matched the measured transmission of the flat reference. The dielectric constants of TiO 2 obtained after the fitting process are given in Figure  S2. The angle-resolved transmission map of the metasurface was obtained using a contour plot (XYZ) where X = angle of incidence, Y = wavelength, Z = simulated transmission. In order to highlight the photonic bands, the transmission spectra were simulated with a TiO 2 extinction coefficient k artificially reduced by a factor of ten compared to the extinction coefficient used in the other simulations.
Photocatalytic set up (A) close up of the photoreactor (B) light was switched on and maintained until stabilization of the concentration (step 3). All products and intermediates (NO-NO 2 -NO X ) were analyzed continuously using Model 42i (Thermo Scientific), analyzer which is a chemiluminescent device for the continuous and real-time measurement of nitrogen oxides in ambient air. A C K N O W L E D G M E N T SThis work was funded by the IDEXLYON from Université de Lyon, Scientific Breakthrough project IPPON within the Programme Investissements d'Avenir (ANR-16-IDEX-0005). Samples were fabricated in the Nanolyon and NanoSaintEtienne technological facilities, with the support of CNRS-Renatech+ and the FEDER CARAT project (Région Auvergne Rhône-Alpes). FDTD simulations were performed on the Newton computer cluster facilities operated by PMCS2I at Ecole Centrale de Lyon.
F I G U R E 6 6 Photocatalytic set up (A) close up of the photoreactor (B) light was switched on and maintained until stabilization of the concentration (step 3). All products and intermediates (NO-NO 2 -NO X ) were analyzed continuously using Model 42i (Thermo Scientific), analyzer which is a chemiluminescent device for the continuous and real-time measurement of nitrogen oxides in ambient air. A C K N O W L E D G M E N T SThis work was funded by the IDEXLYON from Université de Lyon, Scientific Breakthrough project IPPON within the Programme Investissements d'Avenir (ANR-16-IDEX-0005). Samples were fabricated in the Nanolyon and NanoSaintEtienne technological facilities, with the support of CNRS-Renatech+ and the FEDER CARAT project (Région Auvergne Rhône-Alpes). FDTD simulations were performed on the Newton computer cluster facilities operated by PMCS2I at Ecole Centrale de Lyon.
e. close to textbook style. Qbricks builds on best practice of formal verification for the classical case and tailor them to the quantum case: we bring a new domain-specific circuit-building language for quantum programs, namely Qbricks-DSL, together with a new logical specification language Qbricks-Spec and a dedicated Hoare-style deductive verification rule named Hybrid Quantum Hoare Logic. Especially, we introduce and intensively build upon HOPS, a higher-order extension of the recent path-sum symbolic representation, used for both specification (parametrized) and automation (closure properties). To illustrate the opportunity of Qbricks, we implement the first verified parametric implementations of several famous and non-trivial quantum algorithms, including the quantum part of Shor integer factoring (Order Finding -Shor-OF), quantum phase estimation (QPE) -a basic building block of many quantum algorithms, and Grover search.
Procedure: 1 Major quantum programming languages such as Quipper  [33] , Liqui|⟩  [73] , Q#  [69] , ProjectQ  [68] , Silq  [10] , and the rich ecosystem of existing quantum programming frameworks  [60]  follow this hybrid model and provide dedicated features for interacting with the quantum memory and well-suited for implementing quantum algorithms. They usually embed these features within a standard classical programming language, with forth (send quantum instructions) and backs (get measurement results) between the classical control loop (classical computer) and the quantum part (co-processor). Such circuitbuilding quantum languages are the current consensus for highlevel executable quantum programming languages. 1. 3  The problem with quantum algorithms. The core of a quantum algorithm -the interaction with the quantum co-processor-is usually provided in the form of Figure  2.
One can cite the approach of Boender et al. [12]  or Qwire  [58, 63]. These however lack automation and deeply mix code and specification. Moreover, the underlying matrix semantics impedes scalability of the approach -so far it came only with small case-studies: coin_flip  [61] , teleportation  [12] , and (recently) Deutsch-Josza 1  algorithm  [37]. Recently, Amy  [1, 2]  developed a powerful framework for reasoning over quantum circuits, the path-sums symbolic representation. Thanks to their good closure properties, reasoning with path-sums is well automated and can scale up to large problem instances (up to 100 qubits). Yet, the method is not parametric and only addresses fixed-size circuits. Another explored direction tackles the formalization of quantum programs with classical control, where the input/outputs from the quantum co-processor are taken as oracles.
Proposal and contributions. We propose Qbricks, the first formal verification environment for circuit-building quantum programs, featuring clear separation between code and proof, parametric specification and proof, high degree of proof automation and allowing to encode quantum programs in a natural way, i. e. close to textbook style. Qbricks builds on best practice from formal verification for the classical case (separation of concerns, flexible logical specification language, proof automation, domain-based specialization) and tailors them to the quantum case. More precisely, while relying on the general framework of deductive verification, we tailor it to the specific needs of quantum, bringing several key innovations along the road. Qbricks builds upon the circuit-building, first-order functional language Qbricks-DSL together with the logical specification language Qbricks-Spec.
While minimal, Qbricks-DSL can express implementations of existing non-trivial quantum algorithms and Qbricks-Spec is expressive enough to offer higherorder specification (parametrized circuit production). The two key cornerstones behind Qbricks are: (1) a new Hoare-style proof system, called Hybrid Quantum Hoare Logic (HQHL) dedicated to circuit-building quantum languages, and (2) the new higher-order path-sums (HOPS) symbolic representation of quantum states, extending path-sums Amy  [1]  to the parametric case while keeping good closure properties -HOPS prove extremely useful both as a specification mechanism and as an automation mechanism. In the end, we bring the following contributions. Framework. A programming and verification framework, that is: on one hand, a core domainspecific language (Qbricks-DSL, Section 5) for describing families of quantum circuits, with enough expressive power to describe parametric circuits from non-trivial quantum algorithm; on the other hand, a logical, domain-specific, specification language (Qbricks-Spec, Section 6), tightly integrated with Qbricks-DSL to specify properties of parametrized programs representing families of quantum circuits.
Deductive verification for quantum programs. Deductive program verification In more details, for any function f , annotations by precondition pre and postcondition post are to be understood as a contract for the implementation: the programmer commits to ensuring that the output of f satisfies post for any input satisfying pre, which, in essence, translates into ∀x. pre(x) → post(f (x)) Suppose that the function f is defined, specified and verified. And suppose one defines and specifies a further function д, using a call f (a) to f. This generates a new proof obligation. To fulfill it, the already verified proof obligation for f is assumed as an hypothesis. This compositional reasoning method is particularly well-suited to the compositional nature of circuits. 4. 2 Rational for the design of Qbricks.