dataesr/scientific-paragraphs-categorization huggingface dataset:
This TDIDT algorithm, called RDMT(H * ), is based on REMT  [14]  and is written in Java using the WEKA package. RDMT(H * ) has been tested on artificial and real datasets and compared with other well-known monotone and non-monotone classifiers in terms of classification accuracy. Our analysis shows our classifier can exploit the eventual monotonicity of the dataset: it can compete with non-monotone classifiers in accuracy and, moreover, it is much more robust to non-monotone noise than purely monotone classifiers. The paper is organized as follows. In Section 2, we present some new discrimination measures for attribute ranking that enable to take into account a monotone link between a descriptive attribute and the class. In Section 3, a new binary decision tree classifier is proposed that exploits the proposed rank discrimination measures.
Rank discrimination measures Let us consider a set Ω = {ω 1 ,. , ω n } of objects or alternatives described by a family A = {a 1 ,. , a m } of attributes with finite totally ordered range (also called true criteria in  [6, 7] ), i. e. , for each j = 1,. , m, a j is a function on Ω ranging in X j = {x j1 ,. , x jt j } with t j > 1 and (X j , ≤) totally ordered. We assume a labelling function λ : Ω → C is given, where C = {c 1 ,. , c k } is a set of classes with k > 1 and (C, ≤) also totally ordered. We stress that, for i = 1,. , n, each object ω i can be mapped to a corresponding (m + 1)-tuple (a 1 (ω i ),. , a m (ω i ), λ(ω i )), obtaining a dataset of examples, moreover the product space X = X 1 × • • • × X m forms a lattice (X, ≤) where for each x, y ∈ X, x ≤ y ⇔ x j ≤ y j , for j = 1,. , m.
( We say that the dataset of examples is consistent if and only if for each ω i , ω h ∈ Ω it holds (a 1 (ω i ),. , a m (ω i )) = (a 1 (ω h ),. , a m (ω h )) im- plies λ(ω i ) = λ(ω h ), moreover, it is said to be mono- tone consistent if and only if for each ω i , ω h ∈ Ω it holds (a 1 (ω i ),. , a m (ω i )) ≤ (a 1 (ω h ),. , a m (ω h )) implies λ(ω i ) ≤ λ(ω h ). Recall that each attribute a j ∈ A as well as the labelling function λ determines a partition of Ω, whose elements are denoted, respectively, as {a j = x js } = {ω h ∈ Ω : a j (ω h ) = x js }, s = 1,. , t j , and {λ = c q } = {ω h ∈ Ω : λ(ω h ) = c q }, q = 1,. , k, moreover, the same partitions can be object-wise written, denoting for each ω i ∈ Ω [ω i ] aj = {ω h ∈ Ω : a j (ω i ) = a j (ω h )}, (2) [ω i ] λ = {ω h ∈ Ω : λ(ω i ) = λ(ω h )}, (3) where for each ω h ∈ [ω i ] aj we have [ω h ] aj = [ω i ] aj and, analogously, for each ω h ∈ [ω i ] λ we have [ω h ] λ = [ω i ] λ.
RDMT(H * ) classifier In order to make a comparison of the introduced measures with other proposal present in the literature we wrote a binary decision tree classifier in Java relying on the WEKA package  [30]. Our proposal is essentially based on REMT classifier  [14] , differing from it for the use of a rank discrimination measure H * for splitting instead of the rank mutual information. We call it RDMT(H * ) classifier, where the acronym RDMT stands for Rank Discrimination Measure Tree. RDMT(H * ) is a simple classifier parametrized by the choice of a rank discrimination measure H * between H * G , H * S and H * P and by other three pre-pruning parameters. No post-pruning is executed on the resulting tree, and missing values are not allowed. As common practice in tree induction  [5] , the RDMT(H * ) algorithm is completely specified once are known the following parts: splitting rule, stopping rule and labelling rule.
It is important to notice that generally, even if the training dataset is monotone consistent, the greedy tree induction with H * S , H * G and H * P does not guarantee a globally monotone classifier. Hence, it is important to investigate if RDMT(H * ) can assure at least a weaker form of monotonicity. Algorithm REMT is shown to guarantee a weak kind of monotonicity that we call rule monotonicity  [14]. Let T = (N, A) be the decision tree generated by the induction procedure, where N = {r} ∪ I ∪ L is the set of nodes (partitioned in the singleton formed by the root r, the set of internal nodes I and the set of leaves L) and A is the set of directed arcs. It is known (see, e. g. ,  [26] ) that each path r l with l ∈ L induces a decision rule R l , thus we denote with R T the set of decision rules generated by T.
Given l 1 , l 2 ∈ L, R l1 and R l2 are comparable (see  [14] ) only in the case they are generated by the same attributes, in this case we say that R l1 < R l2 if and only if attribute values of R l1 are less than R l2. We simply denote with λ(R l ) the label attached to leaf node l. Then we say that T is rule monotone if and only if for each R l1 , R l2 ∈ R T : R l1 < R l2 ⇒ λ(R l1 ) < λ(R l2 ). ( 10 ) In  [14]  it is proven that in the case the dataset is monotone consistent, then algorithm REMT guarantees rule monotonicity. The following proposition states that the same also holds for RDMT(H * )  [23]. Proposition 3. 1 Let D be a dataset of examples {(a 1 (ω i ),. , a m (ω i ), λ(ω i )) : i = 1,. ,.
Experimental analysis The RDMT(H * ) algorithm for monotone decision tree construction has been compared with other well-known monotone and non-monotone classifiers having a WEKA implementation, on classification tasks involving artificial and real datasets. Each test has been executed performing a stratified 10folds cross-validation with the same seed (equal to 1) for the pseudo-casual number generator: the WEKA environment guarantees all the folds are equal for each tested classifier. We used WEKA 3-6-0 since it is the last version providing implementations of the used monotone classifiers. Two non-monotone classifiers have been considered: J48 which is a Java implementation of C4. 5 classifier  [26]  and SimpleCart which is a Java implementation of CART classifier  [5]. For what concerns the monotone classifiers, we used OLM, OSDL and OCC which are, respectively, Java implementations of the Ordinal Learning Model  [5] , the Ordinal Stochastic Dominance Learner  [5]  and the Ordinal Class Classifier  [11]  (this last classifier is a monotone meta-classifier for which we used J48 as basic classifier).
,k a j. Clearly, for k = 10 the corresponding dataset is monotone consistent due to monotonicity of maximum operator. In order to execute a fair comparison between the three measures H * G , H * S and H * P we set maxDepth = 100, measureT hreshold = 0 and percM inSize = 0. 01 for all the tests on artificial data. Indeed, setting measureT hreshold > 0 could favour some measure and penalize the others. Observing Figure  1  one can see that RDMT(H * G ) achieves the best results presenting the highest CCI and K together with the lowest M EA for all k except for k = 9 in which CART has slightly better results. Each one of RDMT(H * G ), RDMT(H * S ) and RDMT(H * P ) behaves systematically better than monotone classifiers OLM, OSDL and OCC also for high values of k in which the degree of monotonicity in the dataset tends to be perfect.
In particular, one would expect much better accuracy results in case of monotone consistency of the dataset, i. e. , for k = 10. These three monotone algorithms seem to appreciate the increment of monotonicity in the dataset but their improvement in accuracy is very small. For  the degrade in performance is much more considerable for H * P. We relate the behaviour of measure H * P to its pessimistic nature which does not allow to exploit a high number of monotone attributes. The given results show that RDMT(H * ) outperforms all the other classifiers for k comprised between 3 and 7, that is in presence of remarkable non-monotone noise. We conclude this section reporting results of tests executed on real datasets taken from UCI  [28]  and WEKA  [30]  repositories. Since OLM and OSDL do not support numeric attributes or examples with missing values, we preprocessed all the datasets, removing all the examples with missing values and discretizing real attributes applying WEKA filter Discretize, moreover, integer attributes have been converted to nominal ones by using WEKA filter NumericToNominal, both contained in weka.
filters. unsupervised. attribute. We collected eight datasets whose description (taking into account the pre-processing phase) is given in Table  2. After a preliminary estimation study, we set pre-pruning parameters to maxDepth = 100, percM inSize = 0. 01, while measureT hreshold = 0. 01 for H * G , measureT hreshold = 0. 05 for H * S and measureT hreshold = 0. 1 for H * P in a way to obtain a good compromise between accuracy and tree size. Table  3  shows results of tests executed on datasets listed in Table  2. Those results essentially highlight a good behaviour of RDMT(H * ) also on real data. In detail, the first four datasets (ERA, ESL, LEV and SWD) are monotone datasets taken from WEKA repository while the other four (CPU, Breast Cancer, Dermatology and Lymphography) are general purpose datasets.
In the first four datasets, RDMT(H * ) occupies always one of the first three positions in the rank of accuracy results between the six analysed classifiers: it is in first position for LEV, in second position for ERA and ESL, and in third position for SWD. Notice that in the three cases in which RDMT(H * ) is not in first position the difference in CCI with the first position in the accuracy rank is always strictly less than 1%. The obtained results are clearly a consequence of the chosen parameter setting and a different choice could further increase the performance. Also in the last four datasets, RDMT(H * ) remains always in the first three positions being second (ex aequo with C4. 5) on Lymphography and third in all the other three datasets. Notice that in each case the difference in CCI with the first position in the accuracy rank is always strictly less than 6%.
>From previous discussion we can conclude that our classifier, essentially based on a rank discrimination measure, can compete with more sophisticated ones having also a pruning phase, such as C4. 5 and CART. We want to underline that in all the tested real datasets the best performances of RDMT(H * ) are always obtained by H * G or H * P (in four of the eight cases there is an ex aequo with H * S ) thus these new two measures appear to behave generally better than H * S.
Conclusion In this paper, we presented a rank generalization of Gini discrimination measure and Yuan and Shaw discrimination measure, moreover we introduced directly a third function inspired to the functional structure of the second generalized measure. We also presented a binary tree classifier RDMT(H * ) parametrized by a rank discrimination measure H * and other three pre-pruning parameters. RDMT(H * ) has been implemented in Java using the WEKA package and it has been tested on artificial and real datasets, comparing it with other well-known monotone and non-monotone classifiers also implemented in WEKA. This classifier assures a weak form of monotonicity on the resulting tree, namely rule monotonicity, in the case the dataset is monotone consistent. Our analysis shows our clas-sifier can exploit the eventual monotonicity of the dataset: it can compete with non-monotone classifiers in accuracy and, moreover, it is much more robust to non-monotone noise than purely monotone classifiers.
In this work we systematically study the linear and nonlinear structure formation in chameleon theories of modified gravity, using a generic parameterisation which describes a large class of models using only 4 parameters. For this we have modified the N -body simulation code ECOSMOG to perform a total of 65 simulations for different models and parameter values, including the default ΛCDM. These simulations enable us to explore a significant portion of the parameter space. We have studied the effects of modified gravity on the matter power spectrum and mass function, and found a rich and interesting phenomenology where the difference with the ΛCDM paradigm cannot be reproduced by a linear analysis even on scales as large as k ∼ 0. 05 hMpc -1 , since the latter incorrectly assumes that the modification of gravity depends only on the background matter density.
e. the mass of the scalar field and its coupling to matter as a function of the scale factor. This method works even on fully nonlinear scales  [13, 14]   1  where the screening effects on smaller scales, for instance for galaxy halos, can therefore be powerfully analysed. In this work, we study the nonlinear structure formation using N -body simulations for the general chameleon theories that we define here and which are parametrised by two m(a) and β(a) functions  [13]. Technically, we use a variant of the ECOSMOG code  [23] , which is based on a public adaptive mesh refinement (AMR) code RAMSES  [24] , to solve and evolve the N -body system. Although chameleon theories, even in the nonlinear regime, have been studied extensively in the literature (e. g. ,  [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] ), those studies are mostly for specific models in a very restricted parameter space.
E. Multilevels One of the most important features of the ECOSMOG code is that it enables adaptive mesh refinements, and to make sure that this part of the code also works correctly we need to test it on the refinements. This is the task of this subsection. The Gaussian test described above provides a good starting point for the multilevel test here, because the density contrast at x = 0. 5 could be made very large by choosing appropriate values for α, which triggers refinements of the simulation meshes. Indeed, when α → 1, the fast change of density field close to x = 0. 5 makes refinements essential to guarantee the high precision. For simplicity, in the test here we only refined the grid once, making this a 'two-level problem', with level 8 (9) representing the coarse mesh (refinement), where 'level 8' means the mesh has 2 8 = 256 cells in each dimension.
VII. SUMMARY AND CONCLUSIONS To summarise, in this paper we have brought together two essential techniques for the systematic studies of the nonlinear structure formation in generic modified gravity theories of the chameleon type: a simple parameterisation scheme which covers all known chameleon theories using only four parameters and a modified version of the ECOSMOG code to run highresolution simulations efficiently. This allows us, for the first time, to get an overall picture about the behaviour of general chameleon-type theories and the part of its parameter space which is relevant for cosmology. The powerful tomography mapping  [13, 14]  enables us to characterise the chameleon theory and its generalisations using only a few parameters. In our case, there are two parameters describing the present value of the scalar field mass (ξ) and its time evolution (r), and another two parameters describing the current value of the coupling strength (β 0 ) and its time evolution (s).
ACKNOWLEDGMENTS ACD is supported in part by  STFC. BL acknowledges supports by the  Royal Astronomical Society and Durham University. HAW thanks the  Research Council of Norway   FRINAT grant   197251/V30  for support and  Durham University  for the hospitality where part of this work was carried out. GBZ is supported by a  Dennis Sciama Fellowship  at the  University of Portsmouth. PB is partially supported by  ANR   BLANC 2010 041301. The simulations and the post-process of the simulation data were performed on the SCIAMA machine at the  University of Portsmouth  and on the COSMA supercomputer at  Durham University.
The calibration of the monitor and lighting conditions, for example, are unknown and reflect the variety of viewing conditions encountered on the web. As a result, data obtained from the Mechanical Turk can contain more variance than data obtained from a lab study. However this reduced control is compensated by the larger quantity of data that we can collect. We provide an evaluation of the Mechanical Turk data by replicating the final design of our experiment in a lab. The lab data show a good agreement with the crowdsourcing data but reveal higher accuracy along the contrast dimension. For each style, three subjects participated in the study and we collected 10 responses per task from each observer 1. All subjects were students (21-26 years of age), novice in computer graphics and unaware of the experimental hypotheses.
The standard deviation in perceived gloss is also stronger on average, with a value of 0. 05 instead of 0. 034 for the realistic case. Summary and Discussion. We have observed two forms of deviations in the subjects' settings: one along the iso-gloss contours, and one across them. The first deviation occurs along the iso-gloss contours where the distributions of subjects' settings are oriented along the diagonal from high-contrast low-sharpness to low-contrast high-sharpness, even for realistic images. The center of the distributions also tend to   I. : Correlation between material sharpness and perceived contrast, for each reference contrast in our dataset (top), and correlation between material contrast and perceived sharpness, for each reference sharpness (bottom). The correlation is significant for all the materials in the Mechanical Turk data but is not significant for the low contrast and sharpness materials in the lab data.
We highlight significant correlations in bold (p-value < 0. 01). move along the iso-gloss contours and away from the extreme materials. This deviation could be due to the fact that when subjects are uncertain, they prefer to avoid the interpretation that corresponds to the ends of the scales in the matching experiment. Fleming et al. [2003]  report a similar bias in their experiment. We conclude from these observations that for mid-gloss materials, subjects confound an increase in contrast and decrease in sharpness with an increase in sharpness and decrease in contrast. We performed an analysis of correlation to further evaluate the dependence between contrast and sharpness. We report in Table I (top) the correlation between material sharpness and the perceived contrast in realistic renderings, for each reference contrast in our dataset.
In the context of image abstraction, that simplify image content by removing spurious details, our results suggest that painterly rendering is more effective than blur to preserve material appearance through the simplification. The data we collected on the Mechanical Turk suggest most of the trends we observe in the lab data but contain a stronger compression along the contrast dimension for all stylizations. We explain this increase in accuracy for perceived contrast in the lab by the fact that the lab environment (calibrated monitor, dark room) offers much more contrast than the one experienced by the Mechanical Turk users on typical desktop and laptop computers. We also observed more variance in the Mechanical Turk data than in the lab data. However, although we have collected approximately the same number of responses in both setups, the variance on each platform can have different interpretations.
We provide the coefficients of the other styles computed from the lab and Mechanical Turk data as supplemental materials. We interpolate between these coefficients for brush sizes that are not in our dataset. Figure  1  illustrates our prediction of perceived materials for various styles. For this figure we used the data collected on the Mechanical Turk that are more representative of the viewing conditions under which this paper is likely to be viewed. The standard error of estimate of our model varies among styles between 0. 032 and 0. 039 for c and d and reflects the variance observed in the subjects' settings. Our model gives a coefficient of determination r 2 that varies between 0. 5 and 0. 8 for c and d on our lab data. The averaged L2 distance between the predictions of our model and the corresponding mean subjects' settings is equal to 0.
Acknowledgments. We thank the anonymous reviewers for their comments that improved the quality of this paper. We are especially grateful to the reviewer who suggested to subtract the contribution of the realistic settings to compensate for response bias. We also thank  Martin S. Banks  for fruitful comments on this work and for giving us access to his lab facilities. Finally we thank all the subjects who participated in this study. This work was funded in part by  NSF  grants  1011832 ,  0924968 ,  1016920 , equipment and software from  Intel , Adobe and NVIDIA, an  Okawa Foundation Research Grant , and by the  Inria-UC Berkeley associate team CRISP.
EPJ Web of Conferences as well as on the physics of different transport processes like rotation. We present here a grid of stellar models computed with the code STAREVOL by  [9] , including effects of rotation and thermohaline mixing on global asteroseismic quantities for a large range of masses (0. 85 M to 6. 0 M ) at different metallicities (Z = 0. 0001, 0. 002, 0. 004, and 0. 014).
The project CROMEcross-border mobility for electric vehiclesis the first European field demonstration introducing a standardised and interoperable cross-border charging infrastructure network in the French-German Upper Rhine region. The main aim of the project, funded by different Ministries on both sides of the border, is to create and test a safe, seamless, user-friendly and reliable mobility with electric vehicles between France and Germany. Fully public interoperable charging stations have been implemented ensuring easy access and charging of EVs all over the French and German CROME area. Solutions are developed and tested about charging services enabling simplified authentication, identification, billing as well as charging spot availability and reservation. The achieved CROME market place enables data exchanges and roaming between different operators ICT back-end systems based on the Bosch service brokering platform.
Feedback and dissemination The The acceptance analysis, as part of the evaluation concept (cf. Fig. 7 ), consists of repeatedly questioning the users and fleet managers of the EVs by online surveys with different focuses (expectations, first experiences and adoption of EVs), as well as of face-to-face interviews with some users and workshops with fleet managers; this qualitative study aims to understand the rationale of the individuals (ways of thinking and behaving) concerning EVs and the charging infrastructure, to consider their original questions, and to collect data from the observation of their mobility routines in professional sites. Additionally, technical data on trips such as speed and acceleration is collected by using data loggers within the car and smartphones additionally. The latter allows to track the GPS signal and make it possible to conduct a short survey before each trip, asking e.
Acknowledgments We would like to thank  Anne-Sophie Fulda  and  Axel Ensslen  for substantial support for this paper. This work was supported under grants from the  German Federal Ministry of Economics and Technology (BMWi) [Grant  01ME12002 ] and  Bundesministerium für Verkehr, Bau und Stadtentwicklung in Germany  and from  Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME) in France.
UML is a widely used notation, and formalizing its semantics is an important issue. Here, we concentrate on formalizing UML state machines, used to express the dynamic behaviour of software systems. We propose a formal operational semantics covering all features of the latest version (2. 4. 1) of UML state machines specification. We use labelled transition systems as the semantic model, so as to use automatic verification techniques like model checking. Furthermore, our proposed semantics includes synchronous and asynchronous communications between state machines. We implement our approach in USM 2 C, a model checker supporting editing, simulation and automatic verification of UML state machines. Experiments show the effectiveness of our approach.