dataesr/scientific-paragraphs-categorization huggingface dataset:
A Language for Publications The elements of the domain are as follows: PublicationProcess initiated by a research team, PublicationStructure composed by several Researcher, shown Figure  2. The metamodel is split in two parts corresponding, in the resulting concrete syntax, to two different views, a static (structural) view, and a dynamic (process) view. The PublicationStructure holds many papers (Paper), each composed of paragraphs (Paragraph), and each paragraph of reviews (ReviewNote). One Researcher writes many papers and one Paper has several authors. One Researcher writes many paragraphs and reviews them with ReviewNote. The Publication-Process (Figure  3 ) is the root of the process view, that part of the metamodel implements the PDL pattern proposed by  (Combemale et al. , 2007). The two parts of the metamodel are related by two EReferences: and process.
So to obtain the best expressiveness for our graphical language, we need to design it according to the criteria proposed by Moody. (Moody, 2009). These criteria consider, first, the relative position of the graphic symbols that make up the graphical language, and then, stylistic criteria about shapes, icons, colors and line styles. We are interested, in this case, only on the positional criterion. One possible notation is that shown in Figure  5. To specify graphical positional roles for the elements composing a metamodel, we define in Table  2  the Diagraph vocabulary, and the associated semantics. The third column, Abstract Syntax correspondence, refers to the Diagraph metamodel shown Figure  1. We use this language to annotate our metamodel as shown in Figure  4. For example, Paper will be represented as a node, while Progress will be represented by a link.
EXPERIMENT We evaluated the usability of Diagraph through a Master modeling course given at the University of Montpellier II. We asked students to implement several simple graphical languages (see metamodels at http://aigle-2012. googlecode. com/svn/experiment) with each tool. We evaluated the performance of each tool. This process was based on an agility scheme. After an introduction to the overall implementation of the Eclipse EMF and GMF Tooling, two workshops were proposed to build Graphical Domain Specific Modeling Languages based on simple metamodels, each consisting of 6 classes, representing a toy modeling situation, with at least one compartmentalized node, several un-typed relations without any label, and also several typed links carrying labels. The multi-view feature was not processed due to the limited time available.
The metamodels are different for each tool compared, and for each mode (supervised and autonomous). However, they are of comparable complexity in terms of the metamodel structure. The profile of the subjects is a mix of research profile and professional profile. For each experiment, each student restitutes: • A free report outlining the taken steps, including text and screenshots of the experiment in progress. • An assessment questionnaire for the process. At the end of the experiment, an interview was conducted. The students consider that the fact of being based on a annotation language is finally more a disadvantage for Diagraph. They think that using annotations, which are informal metadata, are not adapted to write a formal language. The unavailability of a wizard, a property editor, a code completion mechanism also was a disadvantage for Diagraph.
CONCLUSION We have proposed Diagraph, a modeling framework that helps the design of graphical Domain Specific Modeling Languages -DSML, on the top of Eclipse EMF-GMF. It includes, on the one hand a process, and the other hand a set of annotations to enrich a simple Ecore conformant metamodel with concrete syntax graphical roles, playing in one single model the role of a grammar for graphical languages as EBNF does for textual languages. Taking this grammar as an input, Diagraph automatically generates all EMF-GMF artifacts (intermediate models, diagram editors, skeletons of unit tests). Multi-diagramming is natively supported. Diagraph is non-intrusive: no dependency is left in the target models; the framework is structured in a platform independent layer based on a clean metamodel, defining an abstraction for graphical concrete syntax, and a transformation layer targeting the GMF platform, but also other graphdrawing or diagramming systems: Dot-Graphviz (yet implemented), Graphiti, GraphML (non limitative list, and not yet implemented).
The tool includes a "zoo"  (Favre, 2006)  of numerous example cases. The Diagraph is released under an open-source license at http://code. google. com/p/diagraph/. Our current work is to design functional models such as eFFBD  (Pfister et al. , 2012) , which are hierarchical decompositions in the sense of IDEF0  (Ross, 1977) , and also are executable models (timed Petri nets), even if the operational semantics is not a part of our research. Abstract syntax and concrete syntax coevolution is realized de facto by Diagraph. As a future work, we would like to master the coevolution of language (abstract and concrete syntax) and existing instances of that language. We also would like to (semi) automatize the generation of a Diagraph grammar, by analyzing patterns in the abstract syntax. Such patterns would drive the concrete syntax: in fact they are concrete-syntax intentions.
We think about integrating the annotation mechanism in the refactoring process of Eclipse, and to improve the editor with code completion. A longterm objective is building concrete syntaxes by abstract syntax patterns recognition, but also, at a different (lower) meta-level, building model by imitating model patterns taken from a thematic repository. Another objective is to improve the megamodel management by splitting the abstract syntax in several metamodels. At last, we would like promoting the Diagraph metamodel at the M3 level.
, 2010). Pairs of corresponding 2D detections of traffic signs are deducted from tracking them in successive images. Two stereo-based approaches called single-camera (stereo from motion) and dual-camera (rigid stereo base) are studied. Traffic sign localization accuracy varies from 1 -3m for singlecamera to 5 -18m for dual-camera. The localization accuracies obtained by aforementioned methods (24cm to a few meters) may be sufficient for road database generation where traffic signs should be associated to road sections. However several applications such as vision based positioning using road landmarks  (Li and Nashashibi, 2010)  and high scale 3D city modeling  (Früh and Zakhor, 2004, Cornelis et al. , 2008)  may require higher accuracies. To tackle this issue  (Soheilian et al. , 2013)  developed an image based system for traffic sign reconstruction reaching subdecimetric accuracy.
-Size: the size of resulting 3D traffic sign should lie in some range (specified by a traffic sign reference document). -Visibility: the resulting 3D traffic sign should face both camera centers. • Similarity constraint: both detected signs should have the same visual characteristics. We evaluated the reconstruction on a large dataset of 4800 full HD images acquired on a 1 km long path which resulted in 1057 2D detections, from which 62 signs were reconstructed. Only one sign from the ground truth was omitted (1 False Negative) and two reconstructions had no counterpart in the ground truth (2 False Positives, against 9 with a greedy heuristic). Seven signs were duplicated. Most of the "ghost" signs from the greedy heuristic of  (Soheilian et al. , 2013)  were eliminated as illustrated in Figure  4 , validating its practical interest.
Results on synthetic dataset for increasing D ref with σ = 4cm, Ncam = 5 small compact clusters that do not necessarily correspond to particles (they might be coincidental with so many lines). Conversely, large values will under-segment (one reconstruction for multiple particles), which lowers both precision and recall. Moreover, high E ref values make the problem more difficult as the consistency graph becomes much denser, so it has much more cliques, which decreases the quality of the result for the maximum clique computation that has a constant time budget, and increases computation time for the local optimality.
We have in mind two scenarios where multiple persistence diagrams arise: Scenario 1: We have a random sample of compact sets K 1 ,. , K n drawn from a probability distribution on the space of compact sets. Each set K i gives rise to a persistence diagram which in turn yields a persistence landscape function λ i. An analogous sampling scenario is the one where we observe a sample of n random Morse functions f 1 ,. , f n from a common probability distribution. Each such function f i induces a persistent diagram built from its sub-level set filtration, which can again be encoded by a landscape λ i. The goal is to use the observed landscapes λ 1 ,. , λ n to infer the mean landscape µ = E(λ i ). Scenario 2: We have a very large dataset with N points. There is a diagram D and landscape λ corresponding to some filtration built on the data.
Discussion We have shown how the bootstrap can be used to give confidence bands for Bubeknik's persistence landscape and for the persistence silhouette defined in this paper. We are currently working on several extensions to our work including the following: allowing persistence diagrams with countably many points, allowing T to be unbounded, and extending our results to new functional summaries of persistence diagrams. In the case of subsampling (scenario 2 defined in the introduction), we have provided accurate inferences for the mean function µ. We are investigating methods to estimate the difference between µ (the mean landscape from subsampling) and λ (the landscape from the original large dataset). Coupled with our confidence bands for µ, this could provide an efficient approach to approximating the persistent homology in cases where exact computations are prohibitive.
We propose to enhance that approach by estimating a weighting coefficient for each individual channel. This enables distinguishing between, e. g. , habituation trends which differ for distinct groups of channels thus reflecting different trial-to-trial variabilities of the underlying sources-something which is not possible in the approach of de Munck et al. [11]. We apply the proposed method to simulated data as well as an illustrative real MEG data set from an auditory experiment, and discuss its limitations in the Discussion. http://www. biomedical-engineering-online. com/content/13/1/75.
Material Real MEG data were acquired from a healthy male volunteer (age 42, normal hearing), who is a member of the Special Lab Non-invasive Brain Imaging. The subject was exposed binaurally to 500-ms long 1-kHz sine tones at 90 dB SPL. The difference between the onsets of the consecutive stimuli was 2 s. The data were sampled at the frequency of 1017. 25 Hz and filtered between 0. 5 and 400 Hz. 160 trials were extracted out of 224 as artifact free. They were offset corrected for each channel with respect to the mean magnetic field over the 200-ms long prestimulus baseline time window.
Acknowledgments The authors would like to thank the anonymous reviewers as well as  Dr Reinhard König  and  Prof. Wojciech Kordecki  for their critical comments which helped to significantly improve the manuscript. The authors acknowledge support from the  German Academic Exchange Service (DAAD)  within its project based personnel exchange programme (PPP) between Germany and Poland, 2013-2014. C. Sielużycki was supported by the  German Research Foundation  and is now supported by the  Fondation pour la Recherche Médicale  (FRM). P. Kordowski is supported by the  Foundation for Polish Science : International PhD Projects Programme co-financed by the  EU European Regional Development Fund.
e. , the message QU IT takes any IRC implementation to the initial state. The EFSM A that has been extracted from the RFC specification  [16]  is an initialized EFSM that has four states, 47 transitions, 12 inputs, 25 outputs, 6 context variables and 17 input and output parameters. After limiting the number of states by B = 9 and deriving the FSM F SM sim (IRC) with 34 inputs, a test suite has been derived as a transition tour of the obtained FSM  [8]. This test contains 265 input sequences with the total length of 1164 inputs counting QUIT input as the reset. The test was downloaded into the data base of the software T ester  [17]  for testing a free available implementation ngIRCd (version 16) that is widely used as a server IRC implementation. The software ngIRCd was downloaded from the developer web site and was compiled by A.
e. , a clock variable  [9] , has been derived. Since a context variable is a clock variable, a method presented in  [19]  has been used for simulating such extended machine in order to obtain an equivalent FSM where for the sake of simplicity, only the part that is responsible for getting files from the server has been modelled. A test suite was derived as a transition tour of the obtained FSM. Experiments with two implementations supporting TFTP, namely, class T F T P Server defined in the commons -net -2. 0. 0 library developed by Apache and atf tpd Linux server developed by Jean-Pierre Lefebvre are reported in  [9]. Some mismatching has been detected between these implementations and the protocol specification. In the T F T P Server an acknowledgement with the unsent packet number has been ignored while the atf dp inplementation replies to acknowledgements incrementing their numbers that does not match the protocol specification.
Introduction Classification is an important and useful statistical tool in all scientific fields where decisions have to be made. Depending on the availability of a learning data set, two main situations may happen: supervised classification (also known as discriminant analysis) and unsupervised classification (also known as clustering). Discriminant analysis aims to build a classifier (or a decision rule) able to assign an observation x in an arbitrary space E with unknown class membership to one of k known classes C 1 ,. , C k. For building this supervised classifier, a learning dataset {(x 1 , z 1 ),. , (x n , z n )} is used, where the observation x ℓ ∈ E and z ℓ ∈ {1,. , k} indicates the class belonging of the observation x ℓ. In a slightly different context, clustering aims to directly partition an incomplete dataset {x 1 ,.
On the other hand, discriminative techniques directly build the classification rule from the learning dataset. Among the discriminative classification methods, kernel methods  [13]  are probably the most popular and overcome some of the shortcomings of generative techniques. Kernel methods are non-parametric algorithm and can be applied to any data for which a kernel function can be defined. A kernel K : E × E → R is a positive definite function such as every evaluation can be written as K(x i , x j ) =< ϕ(x i ), ϕ(x j ) > H , with x i , x j ∈ E, ϕ a mapping function (called the feature map), H a finite or infinite dimensional reproducing kernel Hilbert space (the feature space) and < •, • > H the dot product in H. An advantage of using kernels is the possibility of computing the dot product in the feature space from the original input space without explicitly knowing ϕ (kernel trick)  [13].
Intrinsic dimension estimation The estimation of the intrinsic dimension of a dataset is a difficult problem which occurs frequently in data analysis, such as in principal component analysis. A classical solution in PCA is to look for a break in the eigenvalue scree of the covariance matrix. This strategy relies on the fact that the jth eigenvalue of the covariance matrix corresponds to the fraction of the full variance carried by the jth eigenvector of this matrix. Since, in our case, the class conditional matrix M i shares with the empirical covariance operator of the associated class its largest eigenvalues, we propose to use a similar strategy based on the eigenvalue scree of the matrices M i to estimate d i , i = 1,. , k. More precisely, we propose to make use of the scree-test of Cattell  [5]  for estimating the class specific dimension d i , i = 1,.
Benchmark study on quantitative data We focus here on the comparison of pgpDA with state-of-the-art methods. To that end, two kernel generative classifiers are considered, kernel Fisher discriminant analysis (KFD)  [19]  and kernel QDA (KQDA)  [9] , and one kernel discriminative classifier, support vector machine (SVM)  [24]. The Gaussian kernel is used once again in the experiments for all methods, including pgpDA. Since real-world problems are considered, all the hyper-parameters of the classifiers have been tuned using 5-fold cross-validation. Six data sets from the UCI Machine Learning Repository (http://archive. ics. uci. edu/ml/ ) have been selected: glass, ionosphere, iris, sonar, USPS and wine. We selected these data sets because they represent a wide range of situations in term of number of observations n, number of variables p and number of groups k.
The USPS dataset has been modified to focus on discriminating the three most difficult classes to classify, namely the classes of the digits 3, 5 and 8. This dataset has been called USPS 358. The main feature of the data sets are described in Table  3. Each data set was randomly split into training and testing sets in the hold-out ratio hr given in Table  3. The data were scaled between -1 and 1 on each variable. reported results have been averaged over 50 replications of the whole process. The average classification accuracies and the standard deviations are given in Table  4. Regarding the competitive methods, KFD and SVM provide often better results than KQDA. The model used in KQDA only fits "ionosphere", "iris" and "wine" data, for which classification accuracies are similar to or better than those obtain with KFD and SVM.
For the parsimonious pgpDA models, except for M 7 and M 8 , the classification accuracies are globally good. Models M 1 and M 4 provide the best results in terms of average correct classification rates. In particular, for the "USPS 358" and "wine" data sets, they provide the best overall accuracies. Let us remark that pgpDA performs significantly better than SVM (for the Gaussian kernel) on high-dimensional data (USPS 358). In conclusion of these experiments, by relying on parsimonious models rather than regularization, pgpDA provides good classification accuracies and it is robust to the situation where few samples are available in regards to the number of variables in the original space. In practice, models M 1 and M 4 should be recommended: intrinsic dimension is common between the classes and the variance inside the class intrinsic subspace is either free or common.
Classification of networks: the Add Health dataset We now consider network data which are nowadays widely used to represent relationships between persons in organizations or communities. Recently, the need of classifying and visualizing such data has suddenly grown due to the emergence of Internet and of a large number of social network websites. Indeed, increasingly, it is becoming possible to observe âĂĲnetwork informationsâĂİ in a variety of contexts, such as email transactions, connectivity of web pages, protein-protein interactions and social networking. A number of scientific goals can apply to such networks, ranging from unsupervised problems such as describing network structure, to supervised problems such as predicting node labels with information on their relationships. We investigate here the use of pgpDA to classify the nodes of a network.
To our knowledge, only a few kernels (see  [29]  for more details) have been proposed for network data and the regularized Laplacian kernel is probably the most used. This kernel is defined as follows: let X be a symmetric n × n socio-matrix where X ij = 1 if a relationship is observed between the nodes i and j and X ij = 0 in the opposite case. Let D be the diagonal matrix where D ii indicates the number of relationships for the node i, i. e. , D ii = n j=1 X ij. The regularized Laplacian kernel K is then defined by: K = L + νI n -1 , where L = I n -D -1 2 XD -1 2 is the normalized Laplacian of the network, ν is a positive value and I n is the identity matrix of size n. The social network studied here is from the National Longitudinal Study of Adolescent Health and it is a part of a big dataset, usually called the âĂĲAdd HealthâĂİ dataset.
The data were collected in 1994-95 within 80 high-schools and 52 middle schools in the USA. The whole study is detailed in  [12]. In addition to personal and social information, each student was asked to nominate his best friends. We consider here the social network based on the answers of 67 students from a single school, treating the grade of each student as the class variable. Two adolescents who nominated nobody were removed from the network. We therefore consider a whole dataset made of 65 students distributed into 5 classes: grade 7 to grade 11. We first selected by cross-validation the kernel parameter on a learning sample and the threshold parameter for the intrinsic dimensions was set to 0. 2. The most adapted value for ν was 4 and this gives on average 96. 92% of correct classification for the test nodes.
Classification of categoretical data: the house-vote dataset We focus now on categorical data which are also very frequent in scientific fields. We consider here the task of clustering (unsupervised classification) and therefore the pgpEM algorithm. To evaluate the ability of pgpEM to classify categorical data, we used the U. S. House Votes data set from the UCI repository. This data set is a record of the votes (yea, nay or unknown) for each of the U. S. House of Representatives congressmen on 16 key votes in 1984. These data were recorded during the during the third and fourth years of Ronald Reagan's Presidency. At this time, the republicans controlled the Senate, while the democrats controlled the House of Representatives. Figure  5  shows the database where yeas are in indicated in white, nays in gray and missing values in black.
The first 168 congressmen are republicans whereas the 267 last ones are democrats. As we can see, the considered votes are very discriminative since republicans and democrats vote differently in almost all cases while most of the congressmen follow the majority vote in their group. We can however notice that a significant part (around 50 congressmen) of the democrats tend to vote differently from the other democrats. To cluster this dataset, we first build a kernel from the categorical observations (16 qualita- feature subspace of the two groups. Figure  7  presents these visualizations. The observation of these two plots confirms the fact that republicans voted more homogeneously than democrats in 1984 since there is no clear concentration of points on both plots for the democrats.
Classification of mixed data: the Thyroid dataset In this final experiment, we consider the supervised classification of mixed data which is more and more a frequent case. Indeed, it is usual to collect for the same individuals both quantitative and categorical data. For instance, in Medicine, several quantitative features can be measured for a patient (blood test results, blood pressure, morphological characteristics,. ) and these data can be completed by answers of the patient on its general health conditions (pregnancy, surgery, tabacco,. ). The Thyroid dataset considered here is from the UCI repository and  contains thyroid disease records supplied by the Garavan Institute, Sydney, Australia. The dataset contains 665 records on male patients for which the answers (true of false) on 14 questions have been collected as well as 6 blood test results (quantitative measures).