dataesr/scientific-paragraphs-categorization huggingface dataset:
Summary While generally speaking, present results on rare B decays are in agreement with SM predictions, measurements of some observables, notably R K and P 5 , show intriguing hints of significant deviation. It is very encouraging to see the excellent reach of LHCb with only Run 1 data, as well as the improvements in the results from the B-factory experiments, which are continuously refining their analysis techniques to obtain more sensitive tests of the SM. This year's CKM conference has also seen the first results on rare B decays to τ leptons, which are interesting in view of the hints of lepton flavour universality violation in decays to muons. Within the next few years, new results from LHCb, ATLAS, and CMS Run 2 data will be supplemented by the arrival of the first results from Belle II and from the kaon experiments KOTO and NA62, as well as by results from the next generation of charged lepton flavour violation experiments.
Moreover, given the limited time and the specific resource environment designed for the shared task, it was hard to get better results using a multilingual approach than using a monolingual approach for resource-rich languages since training new word embeddings requires time. Thus, we processed 69 corpora with monolingual models, and only 13 corpora with our multilingual approach. In what follows we describe the architecture of our system (section 2), our monolingual (section 3) as well as our multilingual approach (section 4). Finally, we compare the results with the baseline provided by UDPipe1. 1 and with the results of other teams (section 5).
Finally, the parser can be trained on monolingual and multilingual data depending on the parameters chosen for training. An overview of the overall architecture is given in Figure  1. Details on word embeddings along with the number of dimensions considered are given below. • Word: randomly generated word embedding (100) • XPOS: language-specific POS (25) • UPOS: universal POS (25) • External embedding1: pretrained word embedding (100) • External embedding2: pretrained word embedding that is replaced with Word (100) • one-hot encoding: one-hot encoding of the language ID (65) Word refers to words taken from the training corpora and used as lexical features with vectorized embeddings in our parser. Both XPOS and UPOS 3 are used as delexicalized features. The content of Word and POS is set randomly when the training phase starts.
Surprise Languages and Kazakh There were four surprise languages provided for evaluation within the CONLL 2017 shared task: Buryat, Kurmanji, North Sámi and Upper Sorbian (all in the Universal Dependency format). Less than 30 sentences were provided for training, and Kazakh also had 30 sentences for training. We divided the training corpus in half: half od the data were set apart for development and never used for training. Word embeddings. The first step for training multilingual model is finding topologically similar languages. Thus, we selected three languages for each surprise language in order to be able to derive multilingual word embeddings. The choice of languages was based on the Word Atlas of Language Structures 5  and on advices from linguists. Bilingual Dictionary. There has been many attempts to build multilingual embeddings  (Ammar et al.
ParTUT corpora We assumed that all ParTUT corpora are related with each other. Thus, we used a similar multilingual approach to parse the ParTUT corpora but we used different compositions of corpora for Corpus LAS UAS Rank(LAS) Rank(UAS) Baseline(LAS) Overall (  81  4. 4 Czech-CLTT, Galician-TreeGal, Slovenian-SST These three corpora have a small number of training sentences. We thus chose to train them together but with different language hot-encoding values.
1  (Straka et al. , 2016)  and our system is 2. 58 LAS in our favor. Our system shows better results in avoiding over-fitting issues. Performance gaps are narrowed when considering only 9 http://universaldependencies. org/conll17/results. html PUD test sets (for example, our system ranked second best for processing English PUD and Russian PUD), which is encouraging for practical applications. Multilingual model. Table  4  shows the results obtained when using the multilingual models on the small treebank dataset (fr partut, ga, gl treegal, kk, la, sl sst, ug, uk). We ranked 4 th , with 54. 78 LAS score on this group of languages. However, in terms of extremely resource-poor languages (surprise languages), we have ranked only 12 th , with 36. 93 LAS score. This is slightly lower than the UDPipe1.
Introduction Model-based testing derives concrete test cases from an abstract test model  [5, 17]. Tools derive test sequences from a model that specifies possible test executions and expected results  [1, 7, 8, 13, 17]. We use Modbat  [1]  because it offers an embedded domain-specific language  [19]  that combines extended finite-state machines  [6]  with low-level monitoring code written in Scala  [14]. Our work on testing Java's non-blocking networking (package java. nio) extends previous work where we tested a custom version of that library and found several hidden defects  [3]. That custom library was designed to be compatible with Java Pathfinder (JPF)  [18]  by working with JPF extension net-iocache to backtrack the effects of network input/output  [10]. It stores the effects of network operations in memory, and replays them from previously stored data after backtracking.
During the 1991 eruption of Mount Pinatubo (Philippines), the dissemination of a film from the French volcanologists M. and K. Kraft likely saved many thousands of lives. Recently, the World Organization of Volcano Observatories (WOVO) provides video resources through VOLFilm, a Multilingual and multiplatform films database for resilience to risks from volcanic hazards (http://www. wovo. org/ volfilm-multilingual-and-multi-platform-films-forresilience-to-risks-from-volcanic-hazards. html). Dissemination of information also comprises continuous media slots on PDC risk prevention, preparedness and management taking into account specificities. Community Based Disaster Risk Reduction (CBDRR) should be considered in integrating top-down and bottom-up approaches and as a channel of information and actions between stakeholders.
HEURISTICS FOR GLOBAL PATCH SEARCH There are already a number of efficient patch search techniques which exploit the so-called image coherency: Neighboring query patches, since they overlap, have high chances of having neighboring matches in the database image. Thus knowing the position of a good match for a patch helps in determining good matches for its neighbors. This idea was first applied by  (Barnes et al. , 2009)  to compute a nearest neighbor field (NNF), assigning the closest k patches to each patch in the image. The original algorithm was presented for images but can easily be extended to video. More recent works reported significant improvements (between one and two orders of magnitude) by combining PatchMatch with more classic search data structures such as partition trees (more specifically KD-trees)  (Olonetsky and Avidan, 2012; He and Sun, 2012)  and locality sensitive hashing (Korman and Avidan, 2011;  Barnes et al.
Partition Trees A partition tree is an inductive data structure encoding the position of a set of n points in R d (the database). Once the partition tree has been built it is used to search for the nearest neighbors of a point (the query). Nodes in the tree can either be leaves (also called bins) containing a maximum number of elements, or a split value between two subtrees: the "left" subtree and the "right" subtree. A partition tree splits recursively the data space by applying a simple split at each node (with the exception of the leaves). At each splitting operation, the set of elements in the current subtree is split in two equally sized subsets which are then used to construct the child subtrees. The construction of the tree is fully specified by a split value function and a split function.
ACKNOWLEDGEMENTS This work is supported by the " IDI 2016 " project funded by the  IDEX Paris-Saclay ,  ANR-11-IDEX-0003-02. This Work is also partly founded by  BPIFrance  and  Région Ile de France  in the FUI 18 Plein Phare project, the  Office of Naval research (ONR  grant  N00014-14-1-0023 ).
Materials and Methods Stimuli. In Experiment 1, there were two categories of stimuli, "vocal" and "instrumental". Both sets of stimuli were taken from the RWC database  11  , as for related behavioral studies  10, 12. We computed several acoustic indices for the RWC sound set. First, the harmonic-to-noise ratio  6  (HNR) was calculated using the Harmonicity object's cross-correlation method in the Praat software (www. praat. org). A descriptor of spectral shape and indicator of brightness, the spectral centroid, was computed by estimating the center of gravity of the average pressure variance (mean of half-wave rectified signals, low-passed at 70 Hz) at the output of an auditory filterbank  13. Finally, we used a signal-processing model of cortical processing  8  , to evaluate spectral scale and temporal rate.
Scale is a measure of spectral shape, with fine spectral details associated with high scales. Rate is a measure of amplitude modulation, with high rates associated with faster fluctuations. We chose to summarize the output of the cortical model with two values, "dominant scale" and "dominant rate", center of gravity of rate and scale, calculated as described by Joly et al  9. High rates or scales may be associated with higher complexity, as they indicate finer spectral details and faster temporal fluctuations. A combination of dominant rate and dominant scale has been shown to distinguish between speech utterances and scrambled or environmental sounds  9. Sounds were selected from the RWC database such that the pitch range and the mean harmonic-to-noise ratios (HNR) for the voices did not differ from those of the instruments.
Matlab (Mathworks, Inc. ) and the Psychophysics Toolbox  14  were used for experimental control and data acquisition. Participants. Twenty-two neurotypical adult volunteers (11 M, 11 F) aged 18-33 years old (M = 25. 5, SD = 4. 3) participated in the study. Eighteen subjects were right-handed, two left-handed, and two ambidextrous. The ethical committee from the University of Glasgow approved the study. All experiments were performed in accordance with relevant guidelines and regulations. All volunteers provided informed written consent beforehand and received payment for participation. fMRI scanning. Images of cerebral structure and oxygenation level (BOLD) were acquired on a 3 T Tim Trio Scanner (Siemens) and a 32-channel head coil at the Centre for Cognitive Neuroimaging (CCNi) of the Institute of Neuroscience and Psychology, University of Glasgow.
Analysis. MRI Preprocessing. Data analysis was performed using Statistical Parametric Mapping (SPM8; Wellcome Department of Cognitive Neurology). Images were realigned to correct head motion with the first volume of the first session as reference. T1-weighted structural images were co-registered to the mean image created by the realignment procedure and used for normalization of functional images onto the Montreal Neurological Institute (MNI) Atlas using normalization parameters derived from segmentation of the anatomical image. Finally, each image was smoothed with an isotropic 8-mm full-width-at-half-maximum Gaussian kernel. General Linear Model. EPI time-series were analyzed using the general linear model (GLM) as implemented in SPM8. For each participant (1 st -level analysis) the localizer and experimental runs were modeled separately.
Acknowledgements TA was supported by  ANR  grants ( ANR-10-LABX-0087 ,  ANR-10-IDEX-0001-02 ) and a grant from the  Ministère de la Recherche. DP was supported by an  ERC  grant ADAM # 295603. CS was supported by a  DGA grant  ( PDH-SMO-1-0808 ). SP and PB were supported by a  European Union Erasmus Mundus mobility fellowship in Auditory Cognitive Neuroscience. SP was also supported by a  Frederick Banting and Charles Best Canada Graduate Scholarships  from the  Canadian Institutes of Health Research. PB was also supported by  BBSRC  grants  BBJ003654/1  and  BB/1006494/1 ;  Fondation pour la Recherche Médicale  grant  AJE201214.
This paper reports on an investigation in classification technique employed to classify noised and uncertain data. However, classification is not an easy task. It is a significant challenge to discover knowledge from uncertain data. In fact, we can find many problems. More time we don't have a good or a big learning database for supervised classification. Also, when training data contains noise or missing values, classification accuracy will be affected dramatically. So to extract groups from data is not easy to do. They are overlapped and not very separated from each other. Another problem which can be cited here is the uncertainty due to measuring devices. Consequentially classification model is not so robust and strong to classify new objects. In this work, we present a novel classification algorithm to cover these problems.
I. Introduction There are two broads of classification technique: supervised and unsupervised one. Supervised classification is the essential tool used for extracting quantitative information based on learning database. All extracted features are assigned by labels examples. It tries to classify objects by measuring similarity between new and learning database. The second technique is based on clusters by measuring two criteria essentially compacity and separation  [3, 2]. It tries to form clusters which are compact and separable the possible maximum. Grouping data is not evident. Firstly clusters are overlapped most of the times. Secondly, data to classify are generally very complex. Moreover, there is not a unique quality criteria to measure the goodness of classification. Generally, validity index is used to measure the quality of clustering.
III. Related works Many researches are done about fusion in classification. Most of them is about either clustering  [4, 5, 17, 12, 13]  neither classification  [15, 19]. Some researches deploy combination to improve classification performance. Other one deploy fusion to construct a new classifier such as neural network based on belief function  [7]  or credal K -N N  [6]  or credal decision tree  [8]. In  [7] , the study presents a solution to problems bound in bayesian model. Conditional densities and a priori probabilities of classes are unknown. They can be estimated from learning samples. The estimation is not reliable especially if the set of learning database is small. Moreover, it can not represent very well uncertainty connected to class membership of new objects. If we dispose of few labeled examples and we have to classify new object which is very dissimilar of other ones uncertainty will be big.
Classic decision trees bases on probabilities. They are not always suitable to some problems like uncertainty. Uncertainty of inputs and outputs can not be modelled very well by probability. Moreover, a good learning database is not always available. The research proposes an extension to a previous study dealing with decision tree solving two class problem. It is based on belief function. The new study aims to treat multi-class problem by combining decision trees (two class problem) using evidence theory. In  [11] , two supervised classifier are combined which are Support Vector Machines and K-Nearset neighbors. Combination aims to improve classification performance. Each of them has disadvantages. SV M for example depends strongly on learning samples. It is sensitive to the noise and the intruder.
After that, results are unified thanks to vote technique. Combination was used to analyze multi-sources images. Fusion was needed because sources are heterogeneous. In  [18] , many techniques of clustering collaboration are presented. It differs by the type of result. Result can be a unique partition of data or an ensemble of clustering results. For the first type of result, fusion techniques of classification are used. For the second, multi-objective clustering methods are used. They try to optimize simultaneously many criteria. At the end of process, the set of results is obtained. It is the best result that compromises between the criteria to be optimized. Concerning fusion between clustering and classification many researches deploy clustering in the learning phase of supervised classification  [10, 16, 20].
IV. Fusion mechanism This work is an improvement of a previous one. The former  [9]  was established to combine clustering and classification in order to improve their performance. Both has difficulties. For clustering, we have essentially problems of complex data and index validity. For classification, we have problem of lack of learning database. We used belief function theory to fuse. We respect the three steps of combination process: modelling, combination and decision. Our frame of discernment is: 2 Θ , Θ. = {q j ; j. = 1,. , n} where n number of classes q j found by the supervised classifier. For modelling step, both sources must give their beliefs in the classes. Unsupervised source gives as outputs clusters. The classes are unknown for it. How can the clustering source give their beliefs for it? To do that, we look for the similarity between classes and clusters.
An object situated in the center of a cluster is considered more representative of the cluster than another one situated in the border for example. The coefficient α i is defined as (v i is the center of cluster C i ): α i = e -x-vi 2 (19) • Classification (supervised source): We used the probabilistic model of Appriou: m j s (q j )(x k ) = α ij R s p(q i |q j ) 1 + R s p(q i |q j ) (20) m j s (q c j )(x k ) = α ij 1 + R s p(q i |q j ) (21) m j s (Θ)(x k ) = 1 -α ij (22) q i the real class, α ij reliability coefficient of the supervised classification concerning class q j. Conditional probabilities are computed from confusion matrices on the learning database: α ij = max p(q i |q j )(i ∈ 1,. , n) (23) R s = max q l (p(q i |q l )) -1 (i, l ∈ 1,. , n) (24) Step 2: combination Use of conjunctive rule equation 4 Step 3: decision Use of pignistic criteria equation  15  Three improvements are aiming in the present paper: noise, missing data (uncertain data) and lack of learning database.
V. Experimental study In this section, we present the obtained results for our fusion approach between supervised classification and unsupervised classification. We conduct our experimental study on different databases coming from generic databases obtained from the U. C. I repository of Machine Learning databases. We did experiments on data. We make data missing. Also, we inject noise with different rates and we take a little sampling database (10%). The aim is to demonstrate the performance of the proposed method and the influence of the fusion on the classification results in a noisy environment and with missing data. The experience is based on three unsupervised methods such as the Fuzzy C-Means (FCM), the K-Means and the Mixture Model. For the supervised methods, we use the K-Nearest Neighbors, credal K-Nearest Neighbors, Bayes, decision tree, neural network, SVM and credal neural network.
We show in the Table  2  the obtained classification rates before and after fusion for the new mechanism. The data shown are: Iris, Abalone, Breast-cancer, car, wine, sensor-readings24 and cmc. The first ones (before fusion) are those obtained with only supervised methods (K-Nearest Neighbors, credal K-Nearest Neighbors, Bayes, decision tree, neural network, SVM and credal neural network). The learning rate is equal to 10%. We show in the Table  3  the obtained classification rates before and after fusion for the new mechanism for missing data. The data shown are: Iris, Abalone, wine, sensor-readings24 and cmc. The first ones (before fusion) are those obtained with only supervised methods (K-Nearest Neighbors, credal K-Nearest Neighbors, Bayes, decision tree, SVM and credal neural network).
We introduce a constituency parser based on a bi-LSTM encoder adapted from recent work (Cross and Huang, 2016b; Kiperwasser and Goldberg, 2016) , which can incorporate a lower level character bi-LSTM  (Ballesteros et al. , 2015; Plank et al. , 2016). We model two important interfaces of constituency parsing with auxiliary tasks supervised at the word level: (i) part-of-speech (POS) and morphological tagging, (ii) functional label prediction. On the SPMRL dataset, our parser obtains above state-of-the-art results on constituency parsing without requiring either predicted POS or morphological tags, and outputs labelled dependency trees.
Morphological information is crucial for phrase structure parsing of morphologically rich languages  (Seddah et al. , 2013; Björkelund et al. , 2013; Crabbé, 2015). Most multilingual parsers use a morphological tagger as the first step of a pipeline approach. As a first auxiliary task, we perform morphological analysis (prediction of the POS tags and of additional language-specific morphological attributes such as case, tense). We compare the resulting model to a pipeline approach. As the second auxiliary task, we predict the functional label that links each word to its head. Overall, we evaluate to which extent these auxiliary tasks can both improve parsing and enrich the output of the parser. This paper makes the following contributions: 1. We introduce a single greedy parser which does not need predicted POS tags or morphological tags at inference time, and yet outperforms the best published results on the SPMRL dataset  (Björkelund et al.
In those three models, the input to the sentencelevel bi-LSTM is the concatenation of a word embedding and a character-based embedding. In a second set of experiments, the input to the sentence-level bi-LSTM is either a word embedding (TOK) or the concatenation of a word embedding and embeddings for each available morphological tag (TOK+MMT), predicted by a morphological tagger  (Mueller et al. , 2013, MarMoT). We compare the latter setup with an additional functional prediction as auxiliary task (TOK+MMT+D). This last model will give upper-bound accuracies against which we can compare the model with all auxiliary tasks (TOK+CLSTM+M+D), which is the focus of the paper. Data We evaluate our models on the SPMRL dataset  (Seddah et al. , 2013). This dataset contains constituency and dependency treebanks aligned at the word level for 9 morphologically righ languages.
Results and Discussion Results on development and test sets are presented in Table  1. First we observe that our baseline (TOK+CLSTM) is nearly as accurate as the best published results on the SPMRL dataset. The use of morphology as auxiliary tasks (TOK+CLSTM+M) improves the baseline by 0. 5 F1 on average on the test sets. While being greedy, and needing neither predicted POS nor morphological tags, the resulting parser outperforms the product of grammar and reranker combination of  Björkelund et al. (2014). Furthermore, on average, it is only 0. 5 F1 behind the model which uses predicted morphology as input to the bi-LSTM (TOK+MMT). Across languages, the performance difference between the two models can be partly explained by the difference in tagging accuracy (Table  2 ). The TOK+CLSTM+M model matches MarMoT tagging results for several languages, but is not as good overall.