dataesr/scientific-paragraphs-categorization huggingface dataset:
Landmark Fitting The library includes methods to fit the pose and shape of a model and perform face frontalisation. This section describes the individual components and how they fit together. 4 The functionality described in this paper is based on v0. 5. 0. Naturally, software evolves, and certain parts might be slightly different in future versions. We encourage users to look at the example app of the most current version. 5 For the sake of brevity, we omit namespaces in the code examples. Where not otherwise indicated, types and functions are either in the std or our library's namespace.
Additionally, a row of zeros is inserted after every third row to accommodate for homogeneous coordinates, and the basis vectors are multiplied with the square root of their respective eigenvalue. The cost function in (3) can be brought into a standard linear least squares formulation. For details of the algorithm, we refer the reader to  (Aldrian and Smith, 2013). This functionality is directly mapped into code. The shape coefficients can be estimated as: The first three parameters are given from the previous step. The vertex_indices are obtained with the supplied landmark annotation metadata and the mapping facilities of the library -we refer to the online material for the full documentation. lambda is an optional regularisation parameter to constrain the optimisation to plausible shapes.
The example code snippets from these sections can be combined into a fully working application with not many more lines than presented here. The pose estimation and landmark fitting run in a matter of milliseconds, while the texture remapping is the slowest component taking around 100 milliseconds (measured on an Intel Core i7-4700MQ). That is, to reduce dependencies of the framework and to make it run on any environment, it is not using OpenGL or any other acceleration technique. The main goals of the library are that it is simple, easy to use, easy to integrate into existing software and extensible. The full documentation, which includes additional functionality not presented here, as well as the complete and documented example application from this section, are available in the library repository.
CONCLUSIONS We presented the Surrey Face Model, a multiresolution 3D Morphable Face Model that is publicly available for non-commercial purposes. The model is available in three different resolution levels and accompanied by isomap coordinates and landmark metadata. We introduced a lightweight headeronly library written in modern C++ that accompanies the model and is actively developed on GitHub at https://github. com/patrikhuber/eos. The software features real-time shape model fitting and face frontalisation functionality and interoperability with OpenCV. In contrast to existing work, the Surrey Face Model is available in multiple resolution levels and is built from racially diverse scans. Furthermore, a model-fitting software is available alongside the model to fit the model to novel images and videos.
Parameter tuning The HMA algorithm relies on a set of correlated parameters. To achieve a reasonable tuning of the parameters, we adopt the Iterated F-race (IFR) method  [5] , which allows an automatic parameter configuration, using the IFR algorithm that is implemented and integrated in the irace package  [23]. Table  2  summarizes the parameters of our HMA algorithm, along with the range of values that were determined by preliminary experiments. Among these parameters, four of them (Psize, α, W, Csize) need to be tuned and the other two parameters (threshold ratio r and tabu timing parameter T ) are adaptively or randomly chosen among the values in the given sets (Sr and St) during the search process. We set the tuning budget to 1000 runs of HMA and each run is given 100 generations. We restrict the training set to contain 8 challenging instances taken from val, egl, C, E and EGL-G sets: val-10D, egl-e3-B, egl-s3-C, C11, E12, E15, EGL-G1-B, EGL-G2-B.
Acknowledgment We are grateful to the anonymous referees for their valuable suggestions and comments which helped us to improve the paper. The work is partially supported by the  PGMO  ( 2014-0024H ) project from the  Jacques Hadamard Mathematical Foundation  and the  National Natural Science Foundation of China  (Grants  61473301 ,  71201171 ,  71501179 ). Support for  Yuning Chen  from the  China Scholarship Council  is also acknowledged.
One may refer to [FHK02; HM13; Per; LH97; Bas15a]. Acting on the 6 curves in Figure  1  with the group of 8 isometries S, one indeed obtains the 28 possible curves of Figure  2(b). The sixth type in Figure  1 (f) will be eliminated in the following, since the corresponding piece has a radius of curvature which is too small for the miniature vehicles to be able to ride there (see section 2. 2), which reduces the number of possible paths to 20 (see Figure  2(a) ). In this case, the rule « every curve linking any two distinct points in H i » is to be replaced by « every  curve linking any two distinct non-neighboring points in H i », which, as we will see in the following, nevertheless offers a large number of circuits. The curve Γ is of class C 1 ; indeed, each of the curves Γ i is of class C ∞.
Computational limitations and algorithmic complexity In  [Jen04a] , the number of self-avoiding walks was able to be exactly determined up to N = 71; the author obtained 4 190 893 020 903 935 054 619 120 005 916 paths! In  [CJ12] , the number of self-avoiding polygons could be exactly determined up to N = 130; the author obtains 17 076 613 429 289 025 223 970 687 974 244 417 384 681 143 572 320 polygons! Unfortunately, as noted above, these parallelizable methods could not be implemented here. All of the algorithms presented have been programmed in Matlab R. Two versions were planned: the first is vectorial (and therefore parallelizable), and avoids the use of loops, which is relatively fast. However, the tables used are quickly of a significant size, as well as the total number of circuits to be studied.
Availability of data and material Data from this study is deposited in the repository of the World Wide Antimalarial Resistance Network, which is registered with the Registry of Research Data Repositories (re3data. org). Data can be requested through the WWARN website (http://www. wwarn. org/working-together/sharing-data/data-available-research-community) and approved requestors will be provided with a copy of the original dataset used for the publication.
Feel++ Feel++ stands for Finite Element Embedded Language in C++ and is a C++ library for the solution of Partial Differential Equations using generalized Galerkin methods. It provides a framework for the implementation of advanced numerical methods to solve complex systems of PDE's. The main advantage of Feel++ for the applied mathematicians and engineers community relies on its design based on the Domain Specific Embedded Language (DSEL) approach  [22]. This strategy allows to decouple the difficulties encountered by the scientific community when dealing with libraries for scientific computing. As a matter of fact, DSEL provides a high-level language to handle mathematical methods without loosing abstraction. At the same time, due to the continuing evolution of the state-of-the-art techniques in computer science (e.
g. new standards in programming languages, parallel architectures, etc. ) the choice of the proper tools in scientific computing may prove very difficult. This is even more critical for scientists who are not specialists in computer science and have to reach a compromise between user-friendly interfaces and high performances. Feel++ proposes a solution to hide these difficulties behind a user-friendly language featuring a syntax that mimics the mathematical formulation by using a a much more common low-level language, namely C++. Moreover, Feel++ integrates the latest C++ standard -currently C++14 -and provides seamless parallel tools to handle mathematical operations such as projection, integration or interpolation through C++ keywords. Feel++ is regularly tested on High Performance Computing facilities such as the PRACE research infrastructures (e.
Introduction and Related Work Sentiment Analysis is an important topic in natural language processing, and Aspect Based Sentiment Analysis (ABSA), i. e. detection of sentiments expressed on different aspects of a given entity, constitute a very interesting but quite challenging task  (Liu, 2012; Ganu et al. , 2009). ABSA is a task first introduced at SemEval in 2014  (Pontiki et al. , 2014) , continued in 2015  (Pontiki et al. , 2015)  and now, in 2016  (Pontiki et al. , 2016). Our team has participated to the first edition, with good results on the restaurant domain  (Brun et al. , 2014)  and decided to reiterate the participation in 2016, on the same domain but on English and French, as the challenge has become multilingual. While relatively similar, the task has evolved since 2014: aspect targets and categories are annotated together instead of separately; only opinionated terms (Opinion Target Expressions, OTE) are annotated, and aspect categories are finer grained (12 classes instead of 5), which makes the subtasks even more challenging.
The creation of the interestingness measures for evaluating the quality of the association rule -based knowledge plays an important role in the post-processing of the Knowledge Discovery from Databases. More and more interestingness measures are proposed by two approaches (subjective assessment and objective assessment), studying the properties or the attributes of the interestingness measures is important in understanding the nature of the objective interestingness measures. In this paper, we focus primarily on the objective interestingness measures to obtain a general view of recent researches on the nature of the objective interestingness measures, as well as complete a new classification on 109 selected objective interestingness measures on 6 criterions (independence, equilibrium, symmetry, variation, description, and statistics).
Introduction The process of knowledge discovery from databases (KDD)  (Fayyad et al. , 1996)  is usually divided into three main stages: preprocessing, processing or forming knowledge patterns (mining), and post-processing those patterns. The evaluation of the interestingness or the quality of the patterns found in the processing stage is always one of the contents attracting researchers. During the last decade, the research community in the KDD field recognized the postprocessing stage to evaluate the interestingness or the quality of the knowledge patterns generated from the processing stage to be a complex and important part of the KDD process (Silberschartz and Tuzhilin, 1996;  Liu et al. , 1999; Hilderman and Hamilton, 2001; Tan et al. , 2004). For solving this problem, most of approaches are based on the creation of the interestingness measures.
New efficient clustering quality indexes Jean-Charles Lamirel, Nicolas  Dugué, Pascal Cuxac  Abstract-This paper deals with a major challenge in clustering that is optimal model selection. It presents new efficient clustering quality indexes relying on feature maximization, which is an alternative measure to usual distributional measures relying on entropy, Chi-square metric or vector-based measures such as Euclidean distance or correlation distance. First Experiments compare the behavior of these new indexes with usual cluster quality indexes based on Euclidean distance on different kinds of test datasets for which ground truth is available. This comparison clearly highlights altogether the superior accuracy and stability of the new method on these datasets, its efficiency from low to high dimensional range and its tolerance to noise.
I. INTRODUCTION Unsupervised classification or clustering is a data analysis technique which is increasingly widely-used in different areas of application. If the datasets to be analyzed have growing size, it is clearly unfeasible to get ground truth that permit to work on them in a supervised fashion. The main problem which then arises in clustering is to qualify the obtained results in terms of quality. A quality index is a criterion which makes possible to decide which clustering method to use, to fix an optimal number of clusters and also to evaluate or develop a new method. Many approaches have been developed for that purpose as it has been pointed out in  [25]   [31]  [27]  [1]. However, even if recent alternative approaches do exist  [4]   [13]   [14] , the usual quality indexes are mostly based on the concepts of dispersion of a cluster and dissimilarity between clusters.
Computation of the latter criteria themselves relies on Euclidean distance. Most popular such indexes are the Dunn index  [9] , the Davis-Bouldin index  [6] , the Silhouette index  [28] , the Calinski-Harabasz index  [5]  and the Xie-Beni index  [32]. They implement the afore mentioned concepts in slightly different ways. Consider a dataset D made of n data points, and C, a partition in k clusters of the dataset : C = (C i , • • • , C k ). The Dunn index (Equation  1 ), that has to be maximized, identifies clusters which are well separated and compacts. It combines dissimilarity between clusters and their diameters to estimate the most reliable number of clusters: DU = min 1≤i<j≤k Diss DU (C i , C j ) max m=1,••• ,k Diam(C m ) (1) The dissimilarity distance usually used as the numerator is often an exhaustive dissimilarity measure between all Jean-Charles Lamirel is with the Synalp Team, LORIA, Nancy, France (email: lamirel@loria.
As an example, in their reference paper, Milligan and Cooper  [25]  compared 30 different methods for estimating the number of clusters. They classified CH and DB in the top 10, with CH the best but their experiments only used simulated data described in a low dimensional Euclidean space. The same remark can be made about the comparison performed in  [31]  or in  [7]. However, Kassab et al. [16]  used the Reuters test collection to show that the aforementioned indexes are often unable to identify an optimal clustering model whenever the dataset is constituted by complex data which need to be represented in both highdimensional and sparse description space, obviously with embedded non-Gaussian clusters, as is often the case with textual data. The silhouette index is considered one of the more reliable indexes among those mentioned above, especially in the case of multidimensional data, mainly because it is not a diameter-based index optimized for Gaussian context.
However, like the Dunn and Xie-Beni indexes, its main defect is that it is computationally expensive, which could represent a major drawback for use with large datasets constituted by high-dimensional data. There are also other alternatives to the usual indexes. For example, in 2009, Lago-Fernãndez et al. [18]  proposed a method using negentropy which evaluates the gap between the cluster entropy and entropy of the normal distribution with the same covariance matrix, but again their experiments were only conducted on two-dimensional data. Other recent indexes attempts were also limited by the researchers' choice of complex parameters  [31]. Our goals were manifold: to get rid of the method-index dependency problem and of the sensitivity to noise issue, and to deal with high-dimensional context while avoiding computation complexity and parameter settings.
II. FEATURE MAXIMIZATION FOR FEATURE SELECTION Feature maximization is an unbiased measure which can be used to estimate the quality of a classification whether it be supervised or unsupervised. In unsupervised classification (i. e. clustering), this measure exploits the properties (i. e. the features) of cluster associated data. Its principal advantage is thus to be totally independent of the clustering method and of its operating mode. Consider a partition C which results from a clustering method applied to a dataset D represented by a group of features F. The feature maximization measure favours clusters with a maximal feature F-measure. The feature Fmeasure F F c (f ) of a feature f associated with a cluster c is defined as the harmonic mean of the feature recall F R c (f ) and of the feature predominance F P c (f ), which are themselves defined as follows: F R c (f ) = Σ d∈c W f d Σ c∈C Σ d∈c W f d F P c (f ) = Σ d∈c W f d Σ f ∈Fc,d∈c W f d (7) with F F c (f ) = 2 F R c (f ) × F P c (f ) F R c (f ) + F P c (f ) (8) where W f d represents the weight of the feature f for the data d and F c represents all the features present in the dataset associated with the cluster c.
It is an indicator value which is proportional to the ratio between the F-measure F F c (f ) of a feature in the cluster c and the average F-measure F F of this feature for the whole partition  1. It can be expressed as: G c (f ) = F F c (f )/F F (f ) (12) The active features of a cluster are those for which the contrast is greater than 1. Moreover, the higher the contrast of a feature for one cluster, the better its performance in describing the cluster content. Below we give an example of the operating mode of the method, on the basis of a toy-dataset encompassing two classes (Men (M), Women (F)) described with 3 features: Nose Size, Hair Length, Shoes Size. Figure  1  shows the source data and how the F-measure calculation of the Shoes Size feature operates in the Men class. As shown in Figure  2 , the second step consists in calculating the average F-measure of each feature over the clusters,    9 ) and notation F (x,.
III. EXPERIMENTAL DATA AND PROCESS To objectively evaluate the accuracy of our new indexes, we used several different datasets of varying dimensionality and size for which the optimal number of clusters (i. e. ground truth) is known in advance. Part of the datasets came from the UCI machine learning repository  [3]  and is more usually exploited for classification tasks. The 4 selected UCI datasets represent mostly low to middle dimensional datasets and small datasets (except for PEN dataset which is large). The ZOO and SOY datasets which includes variables with modalities are transformed into binary files. IRIS is exploited both in standard and in binarized version to obtain clearer insight into the behavior of quality index on binary data. VERBF is a dataset of French verbs which are described both by semantic features and by subcategorization frames.
The ground truth of this dataset has been established both by linguists who studied different clustering results and by a gold standard based on the VerbNet classification, as in  [29]. This binary dataset contains verbs described in a space of 231 Boolean features. It can be considered a typical middle size and middle dimensional dataset. The R8 and R52 corpora were obtained by Cardoso Cachopo from the R10 and R90 datasets, which are derived from the Reuters 21578 collection  3. The aim of these adjustments was to only retain data with a single label. Considering only monothematic documents and classes that still had at least one example of training and one of test, R8 is a reduction of the R10 corpus (the 10 most frequent classes) to 8 classes and R52 is a reduction of the R90 corpus (90 classes) to 52 classes.
The R8 and R52 datasets, with respective size of 7674 and 9100 documents, and associated bag of words description spaces of 1187 and 2618 words, can be considered as large and high dimensional datasets. The summary of overall datasets characteristics is provided in Table  I. We exploited 2 different usual clustering methods, namely k-means  [24] , a winner-take-all method, and GNG  [11] , a winner-take-most method with Hebbian learning. For text and/or binary datasets we also used the IGNGF neural clustering method  [20]  which has already been proven to outperform other clustering methods, including spectral methods  [29] , on this kind of data. We have reported on the method that produced the best results in the following experiments. As class labels were provided in all datasets and considering that the clustering method could only produce approximate results as compared to reference categorization, we also used purity measures to estimate the quality of the partition generated by the method as regards to category ground truth.
With higher dimensions, these indexes were generally unable to provide any correct estimation. This phenomenon has already been observed in previous experiments with Davis-Bouldin (DB) and Calinski-Harabasz (CH) indexes  [16]. Davis-Bouldin (DB) performed slightly better than average on low dimensional data. Our PC index was found to perform significantly better than average on low dimensional data but remains a better low dimensional problem estimator than a high dimensional one. Help from passive features somehow seems mandatory to estimate an optimal model in the case of high dimensional problems. Hence, the EC index which exploited both active and passive features was found to have from far the best performance, with low (Table  II ) or high dimensional data (Table  III ). According to our evaluation criteria, this index only returns wrong results in the case of the PEN dataset.
However, even in this case its estimation (model of size 9) is still in the close neighbour of the optimal one (model of size 10). Additionally, the EC and PC indexes, were both found to be capable of dealing with binarized data in a transparent manner, which is not the case of some   of the usal indexes namely the Xie-Beni (XI) index, and to a lesser extent, Calinski-Harabasz (CH) and Silhouette (SI) indexes. One potential explanation is that binarization process introduces some sparsity that is better dealt by our indexes than by those which use Euclidean distance. Interestingly, on the UCI ZOO dataset, the results of noise sensitivity analysis presented in Table IV underline the fact that noise has a relatively limited effect on the operation of PC and EC indexes. The EC index was again found to have the most stable behavior in that context.
The Figure  5  presents a parallel view of the different trends of EC value on non noisy and noisy clustering environment, respectively. It shows that noise tends to lower the index value in an overall way and to soften the trends relatively to changes in k value. However, the index is still able to estimate, either the optimal model in the best case, or a neighbour model in the worst case. The usual indexes do not work as well at all in the same context. For example, the Silhouette index firstly delivered the wrong optimal k values on this dataset before getting out of range when the noise reached 20% on clustering results. The Davis-Bouldin (DB) and Dunn (DU) indexes were found to shift from a correct to a wrong estimation as soon as noise began to appear. In all our experiments, we observed that the quality estimation depends little on the clustering method.
A new version of this environmement working on full-text of the papers is up to now under developpement in the context of the ISTEX 5  project. A demonstrator of this new environment is already available online  6  and described in Dugué et al  [8]. Figure  6  identifies the different steps of the proposed diachronic analysis process  7. In this particular experiment, we first constitute a dataset of bibliographic data from the ISTEX project. The ISTEX database is queried to extract papers related to research in medical care between years 1996 and 2010. This results in a dataset of 9779 papers. A basic indexing is exploited on the full text of the documents in order to produce content metadata (single and multi-words indexing terms). The resulting index size is 8347 terms with freq. > 4. A random walk algorithm (here Walktrap  [26] ) exploiting the relationship between the different extracted terms and the publication years is used to extract meta-periods (  [30] ).