dataesr/scientific-paragraphs-categorization huggingface dataset:
Strong features were: the sentence's term weight, length and similarity sum. Schlesinger et al. (2008)  used a rule-based sentence splitter and six-gram tokenization to process Arabic texts. The authors outlined the lack of resources to accomplish these two tasks. Motivated by  Douzidia and Lapalme's (2004)  good evaluation results, authors use original Arabic texts rather than English machine translated texts, a unigram language model and signature terms to score sentences. Once top ranked sentences are extracted, the system replaced Arabic sentences with the corresponding machine translated (MT) sentences. El-Haj et al. (2011a)  proposed two Arabic text summarization systems: AQBTSS, a query-based Arabic single-document summarizer, and ACBTSS, a concept-based summarizer. The first, AQBTSS, attempts to fit the generated summary to a specific Arabic user's query while the second, ACBTSS, attempts to fit it against a bag-of-words representation of a certain concept.
The two systems use a vector space model to score sentences. Interestingly, for the concept-based summarizer, the author dressed a list of 10 concepts with the corresponding most frequent terms. This was defined on the basis of a 10,250 Arabic newspaper articles corpus with approximately 850 documents per concept. On the basis of this work, the KALIMAT 1. 0 3 corpus was recently released; it is a free and publically available dataset of 20,291 newspaper articles which fall into six categories: culture, economy, local news, international news, religion, and sports. It contains the corresponding single-and multi-document extractive summaries. A full NER, POS and morphological analysis is also available for all of the dataset articles. Sc ¼ a 1 Sc lead þ a 2 Sc title þ a 3 Sc cue þ a 4 Sc tf:idf ð1Þ Other works try to understand the source text and rely on a deep analysis of the source text.
This system is different from the early summarization systems  Barzilay and McKeown (2005), Marcu, 2000; Radev and McKeown (1998)  and was motivated by better results and robustness of statistical techniques. The TAC MultiLing 2011 workshop  (Giannakopoulos et al. , 2011)  strengthened this new trend and made a multilingual dataset of seven languages available, including Arabic. The workshop allowed systems (which were not originally developed for Arabic except for  El-Haj et al. (2011b) ) to produce multi-document news summaries for seven different languages, including Arabic. A hierarchical latent Dirichlet allocation-based system  (Liu et al. , 2011)  ranked first and the El-Haj et al. (  2011b ) system ranked third. These efforts continued during the ACL multilingual 2013 workshop  (Giannakopoulos, 2013).
It uses basic Arabic NLP resources (sentence segmentation, tokenization and root extraction) and attempts to address information diversity without omitting sentence relevance along the sentence scoring and extraction process. In fact, it first uses a discriminant analysis method, mRMR  (Peng et al. , 2005) , to score terms. Second, it uses the resulting mRMR scores to rank sentences according to the discriminant power of their terms. We experiment with our method on single-and multi-document Arabic datasets: EASC and TAC 2011 multilingual datasets under different configurations.
Maximizing relevance requires selecting features, which represent at best the entire dataset. The authors accomplished this in two steps: 1. Applying a classification method to find different classes of observations. 2. Then, they opted, again, for a mutual information score between each feature and the classification variable resulted from one. To use mRMR, we need as an input, a matrix, where each column represents a feature and each row represents the corresponding observations. Every observation belongs to a specific class; thus, each row of the input matrix is attached to a class number c (the value of the classification variable). In this approach, we try to select the top m and m < n features with a maximum relevance (mutual information {feature f i , classification variable c}, and at the same time, with a minimum redundancy (mutual information between two features f i , f j ).
Text preprocessing In our system, the sentence is the extraction unit and the term is the scoring unit, and it becomes necessary to perform preprocessing of the source document. Here, we need to run the following classic steps: For Arabic texts, these basic NLP tasks are problematic. Indeed, sentence segmentation is a difficult task because of non-capitalization and minimal punctuation  (Farghaly and Shaalan, 2009). Indeed, it is common to find an entire paragraph without punctuation in Arabic texts. We usually use Arabic coordinators such as ‫"ﻭ''‬ wa and ‫"ﻑ''‬ fa instead of the ''. ". Unfortunately, as far as we know, there is no Arabic sentence splitter that considers all of these particularities. We rely on a naive ''. " -based splitter. This choice was forced but appropriate because in the two datasets we used, sentences were separated by ''.
Ø Essex Arabic Summaries Corpus (EASC) For single-document summarization, we use the Essex Arabic summaries corpus  (El-Haj et al. , 2010). The dataset contains 153 documents from Wikipedia, AlRai and AlWatan newspapers 5. The dataset contains 10 main topics: art and music, education, environment, finance, health, politics, religion, science and technology, sports and tourism. For each document, five model extractive summaries are available. These model summaries were generated by native Arabic speakers using Mechanical Turk 6. Users were asked to select sentences focusing on the main idea of the source document. The model summaries size does not exceed 50% of the source document's size. The dataset is available in two encodings: UTF-8 and ISO-Arabic.
Ø MultiLing Pilot 2011 Dataset For the multi-document summarization task, we used the Text Analysis Conference (TAC) 2011 MultiLing Pilot dataset  (Giannakopoulos et al. , 2011). It is a parallel multilingual corpus of seven languages: Arabic, Czech, English, French, Greek, Hebrew and Hindi. The creation of the corpus started by gathering an English corpus, and it contains 10 document sets with 10 documents for each set. The original news articles were extracted from the WikiNews 7  website. Each document set describes one event sequence: the 2005 London bombing or the Indian Ocean Tsunami. Texts in other languages, including Arabic, have been translated by native speakers of each language. For each document set, three model summaries are provided by fluent speakers of each language (native speakers in most cases).
Unfortunately, we did not find any peer summary results. Thus, we compare our summaries to the baseline lead sentences. The baseline lead sentences extract the first n sentences of each document; n is equal to the model summary's size. For the EASC dataset, we have five model summaries per document. Consequently, for each document, we produce five baseline summaries; then, we compute the average of their ROUGE-1 and ROUGE-2 scores. Because of the nature of dataset documents (news and Wikipedia articles), it turns out to be a strong baseline to beat. Indeed, news articles follow the inverted pyramid style 8 and address the most important information first. Wikipedia articles usually begin with a lead section 9 , which serves as an introduction to the article and a summary of its most important aspects.
This example is chosen to illustrate that controlled friction is an interesting alternative in terms of tactile feelings generation. The challenge is however in modelling the friction and to deduce appropriate control laws. A feature of stick-slip vibration is a saw tooth displacement time evolution with stick and slip phases clearly defined in which the two surfaces in contact stick respectively slip over each other  [7]. From the later definition a relative similarity may be perceived between the modelling of stick-slip vibration and the texture in figure  1. In the chapter is presented the approach we propose using Matlab-Simulink-Stateflow® to deal with our structure as a finite state structure.
5]. Here is an example of numerical values set: k n =9. 7 MN/m, F n =10 N, m=0. 0723 kg, d n =200 N/ms -1 , h=0. 004 m. A MATLAB function, similar to the one in Appendix may be used to calculate the quantities of the example above, to be supplied to Simulink interfaces. In Figure  12  are shown the time evolution of normal reaction R n : for low vibratory amplitude, the normal reaction does not get null but is modulated at the excitation frequency and according to the vibratory amplitude. For higher vibratory amplitudes Figure  12  shows an annulment of R n and thus an intermittent contact. The impact of the switch in transitions contact-separation-contact is remarkable. For a purpose, in steady state the exciting frequency is taken equal to 35 kHz in this simulation. For the purpose of verification this simulation was compared to the one performed in  [36].
Nutritional Composition and Portion Sizes Nutritional composition and portion sizes were calculated from the 2008-2010 National Adult Nutrition Survey (NANS) database, which consists of detailed dietary intake data for 1500 Irish adults  [36]. The nutritional composition of the 157 food items, listed in the Food4Me FFQ, was derived from the wider list of corresponding foods within the NANS database. From these, the most frequently consumed foods were identified and used to calculate the composition of the list of foods in the Food4Me FFQ. For example, for all pizzas consumed in the NANS dataset, the nutritional composition per 100g was computed for the three most frequently consumed and the mean of these was then calculated to give a single nutritional composition for pizza. The nutritional composition for the NANS dataset was analyzed using WISP (Tinuviel Software, Anglesey, UK).
WISP uses the 5 th and 6 th editions of McCance and Widdowson's The Composition of Foods, plus all nine supplemental volumes to generate nutrient intake data  [37, 38]. In addition, the nutritional composition dataset was modified previously and updated to include recipes of composite dishes, generic commercial foods, new foods on the market, and current manufacturers' information. For additional foods unique to a specific European country in the Food4Me study, such as Greek baklava, the relevant national food composition database was used. For the calculation of portion sizes, the food codes for each of the frequently consumed foods identified from the NANS database were merged and recoded into a single food code for each food item listed in the FFQ. Using the newly assigned food code, PASW Statistics version 18 (SPSS Inc Chicago, IL, USA) was used to calculate the 25 th , 50 th , and 75 th percentiles of daily intake, which represent small, medium, and large portion sizes of these foods when consumed in the free living population.
Dietary Intake Analysis Printed EPIC-Norfolk questionnaires were coded using the specified template format and cross-checked before sending to Strangeways Research Laboratory (University of Cambridge) for processing using FETA software  [40]. The nutritional composition database used in the EPIC-Norfolk FFQ is based on the revised and extended 5 th edition of McCance and Widdowson's The Composition of Foods plus supplemental volumes  [41]. The Food4Me FFQ nutritional intake data was generated automatically by the online Food4Me programmed system, as described above. For the purpose of the current study, consumption of dietary supplements was not included in the analyses. Data were imported into SPSS for analysis and descriptive statistics were computed to describe the general characteristics of participants.
Overview of the Study Population A total of 177 participants were screened to participate in the study with 159 eligible for inclusion. Following initiation of the study, 27 participants dropped out, as shown in Figure  2. Reasons for dropouts included external commitments, for example, holidays and technical issues. A further 19 participants were excluded from the analysis: 16 had >4 weeks between completion of the two FFQs and 3 reported energy intakes >4500 kcal per day with the Food4Me FFQ (considered to be unrealistically high)  [42]. The final data set, therefore, consisted of 113 participants as illustrated in Figure  2. The results presented here are for the dietary assessment of 67 females (59. 3%) and 46 males (40. 7%) who completed both the printed EPIC-Norfolk FFQ and online Food4Me FFQ in random order.
Third, the differences we observed between the two methods in relation to daily nutrient intakes may have been related to variances in the nutritional composition databases utilized in both methods, as the nutritional composition database used to calculate daily nutrient intakes is more up to date than that used for analysis of the printed EPIC-Norfolk FFQ  [41]. The nutritional composition database used in the EPIC-Norfolk FFQ is based on the revised and extended 5 th edition of McCance and Widdowson's The Composition of Foods plus supplemental volumes, while the nutritional composition data for the Food4Me FFQ is based on the 6 th and 5 th editions of McCance and Widdowson's The Composition of Foods plus all nine supplemental volumes  [38, 41].
Measuring systems with component spins misaligned with the orbital angular momentum is outside of the scope of this project. However, this study does include systems with component spins that are both aligned and antialigned with the orbital angular momenta, and we will evaluate the ability of aLIGO and AdV to distinguish such systems from one another. The standard technique for observing BBH mergers involves matched-filtering data taken from gravitational-wave observatories against "template" waveforms that should closely match potential astrophysical signals  [17, 18, 19]. The observable BBH waveform includes the signal from the inspiral of the two black holes, as well as their merger and the resulting black hole's ringdown. Search templates must include all of these features  [20, 21]. As an alternative to matched-filter searches, a number of algorithms exist to perform searches for unmodelled gravitational-wave signals  [22, 23, 24].
It was therefore difficult to assess the physical fidelity of the results. Third, the NINJA-1 data set contained stationary noise with the simulated signals already injected into the data. Since the data set lacked the non-Gaussian noise transients present in real detector data, it was not possible to fully explore the response of the algorithms in a real search scenario. Finally, the data set contained only 126 simulated signals, this precluded detailed statistical studies of the effectiveness of search and parameter estimation algorithms. Despite these limitations, the NINJA-1 project lead to a framework within which to perform injection studies using waveforms as calculated by the full nonlinear general theory of relativity, established guidelines for such studies (in particular a well-defined format for the exchange of NR waveforms  [41] ), and clarified where further work was needed.
These newly produced NR waveforms were compared to the most recent calibrated analytical models, finding that the loss of event rates due to modeling is below 3%  [43]. In this paper we study the ability of the search algorithms used in the last of the Initial LIGO and Virgo science runs to observe numerically-modelled BBH waveforms from the set of 60 waveforms submitted to the NINJA-2 project. This is done using data taken during LIGO's sixth and Virgo's second science runs and recoloring that data to the sensitivities expected from early observation runs of aLIGO and AdV. There are a wide range of search and parameter estimation algorithms available within the GW astronomy community: those that were used in past analyses of detector data, old algorithms that have been updated and re-tuned following the experience gained in those analyses, plus many new algorithms under development.
Parameter estimation algorithms have come a long way since the first NINJA project. Previously these analyses were unable to estimate the parameters of high mass systems accurately due to the use of inspiral-only models on data with little measurable inspiral. We show that these tools are now capable of reliably providing parameter estimates for both low and high mass systems. For all but one injection the masses and spins of the black holes were recovered within the estimated 95% credible regions. The remaining injection suffered small systematic biases due to non-Gaussian features present in the noise, the modeling of which is an ongoing endeavor. We find that strong intrinsic degeneracies between the masses and black hole spins  [47, 48]  make it difficult to constrain the masses well, for 3 of the signals the presence of a neutron star cannot be ruled out.
5 rad. Since we do not have access to exact waveforms we define "accuracy" by convergence of the numerical waveforms as resolution and waveform-extraction radius are increased. We also require at least five orbits of numerical data in order to ensure robust blending with the PN portion. No requirements were placed on the hybridization itself, although it is known that hybridization can introduce significant errors  [52, 34, 53]. It was decided to limit NINJA-2 to systems without eccentricity, and with black-hole spins parallel or anti-parallel to the orbital angular momentum. This last condition avoids precession, which we do for two reasons; (i) precession greatly complicates waveform Mass ratio q and dimensionless spins χ i of the NINJA-2 hybrid waveform submissions. Reproduced from  [42].
phenomonology and we prefer to first tackle a simpler subset which still maintains the main features of binary evolution and merger; and (ii) at the start of NINJA-2 the precessing-binary parameter space had been sampled by only a handful of numerical simulations. Waveforms were submitted in the format described in  [41] , and data was provided as strain decomposed into spherical harmonics of weight -2. Groups were encouraged to submit modes beyond (l, m) = (2, ±2) and many did so. However the techniques to validate these higher modes are a current research topic. In order not to delay the NINJA-2 project it was decided to validate only the (2, ±2) modes in  [42]  and employ only these modes for the first NINJA-2 analysis. Different groups employed different codes, as well as different methods for solving initial conditions, dealing with singularities, evolving Einstein's equations, and extracting gravitationalwave information.
Label As with the initial science runs, we expect data taken from these detectors, in the absence of gravitational-wave signals, to be neither Gaussian nor stationary. It is important that search pipelines demonstrate an ability to deal with these features. To simulate data with advanced detector sensitivities and with realistic non-Gaussian and non-stationary features, we chose to use data recorded by initial LIGO and Virgo and recolor that data to the predicted early sensitivity curves of aLIGO and AdV. The data we chose to recolor was data taken during LIGO's sixth science run and Virgo's second science run. The procedure for producing such recolored data was accomplished in the following steps, which were conducted separately for the two LIGO detectors and Virgo. • Identify a two-month duration of initial detector data to be recolored • Measure the power spectral density (PSD) for each distinct section of science mode data using the PSD estimation routines in the lal software package  [91].
The distribution of physical parameters used in these blind injections was not intended to represent any physical distribution. Instead, the injections were chosen to test the ability to recover BBH systems across a wide range of parameter space. We describe the results of searches for these blind injections in section 6 and of parameter estimation studies on these signals in section 7. As well as these blind injections, a large number of (non-blind) simulated signals were subsequently analyzed to obtain sufficient statistics to adequately evaluate the sensitive distances at which the NR waveforms could be detected in the early aLIGO and early AdV simulated data sets. For each of the 60 NR waveforms given in Table  1  and used in results in section 8, a set of ∼ 42000 simulated signals was generated, necessarily with the same mass ratio and spins as the provided NR waveform.
The total mass was chosen from a uniform distribution between 10 and 100 M. The simulations were distributed uniformly in distance, however they were not injected beyond a distance where they could not possibly be detected. The mass-dependent maximum distance that we chose to use is given by D max = M 1. 219M 5/6 175 Mpc. (3) Here 1. 219M is the chirp mass of a (1. 4 + 1. 4)M binary system. The factor of M 5/6 describes, to leading order, how the SNR of the inspiral-only portion of a compactbinary merger at fixed distance will scale with mass when the inspiral is bandwidthlimited  [95, 19]. 175 Mpc is chosen because it is larger than the distance at which it would be possibly to detect a (1. 4 + 1. 4)M binary merger with the early noise curves. Table  3. The details of the blind injections that were added to the NINJA-2 datasets prior to analysis.
Coherent WaveBurst For the NINJA2 cWB analysis it was decided a priori to search for GW bursts in the entire available times during which all three detectors were operating (17. 9 days) and to discard the remaining times. First the search was performed on a total of 12,000 time-lagged observation times, accumulating 563. 7 years of effective background live time. The background events that survived the data quality and analysis selection cuts (i. e. cc > 0. 7 and Λ < 0. 4) were used for calculation of the significance of candidate Table  4. The cWB search and follow-up results. The Event Labels correspond to those of each blind injection given in Table  3. This association is based on the time of the candidates relative to the time of the injections. M denotes the total mass. The false alarm probability, FAR, and false alarm probability, FAP, of each event are estimated by comparison with the empirically-calculated background distribution of the corresponding network of detectors.
To more rigorously determine what effect mismatch between templates and signals may have on detection sensitivity, we perform a large scale injection campaign with the NINJA-2 waveforms in section 8. Initially we used 100 time shifts to identify candidate events. All of the coincident events associated with the blind injections were louder than all background in the 100 time shifts. These were the only events to be louder than all background. Using 100 time shifts we could only bound the FAR of the events to 10 yr -1 , which is not small enough to claim a detection. To improve our estimate, we performed as many 5 s time shifts as possible in the NINJA-2 dataset. This is the same method that was used for the blind injection described in  [46]. Two blind injections were found in all three detectors: Event 2 in the high-mass search and Event 3 in the low-mass search (before category 3 vetoes were applied).
This measurement is then rescaled by a factor of 2. 26 to account for the fact that the observatories do not have equal power to all orientations and sky locations  [135]. We note that the obtained results agree well with this prediction, and fall between the early aLIGO and early AdV predictions when the two diverge. This is expected, as detection in ihope is dominated by the sensitivity of the second most sensitive detector operating at the time  [44]. For times when at least two observatories were operating, the NINJA-2 dataset approximately consists of 50% of time when only one of the LIGO detectors and Virgo were operating and 50% of time when both LIGO detectors were operating, including time when Virgo is operating and when it is not. As the early aLIGO sensitivity does not drop below the early AdV sensitivity for the mass range considered, we expect the obtained sensitivity curve to lie roughly in the middle of the two predictions, and this is what we observe.