dataesr/scientific-paragraphs-categorization huggingface dataset:
Given a parameter k, this method adds iteratively the most balanced DCPO-pattern to the set of selected DCPO-patterns until the size of the set is equal to k. Fig. 10a  and b  shows experiments performed on DCPO-patterns extracted from quality class sub-dataset IBGN Red by selecting the 50 and the 20 most balanced DCPO-patterns. We observe that the selected DCPO-patterns are more diverse (less redundant) than the k most frequent or the k most discriminant, while they are a good balance between the most frequent and the most discriminant. In addition, increasing the parameter k leads to explore even more the diversity of the overall DCPO-patterns set, with a minimized redundancy. We now present the obtained results.
Results In this section, we provide experiments for three bio-indices available in the Fresqueau dataset and discussed in Section 3. 1. As a reminder, these bio-indices are IBGN, IBD and IPR that correspond to macroinvertebrate, diatom and fish dimensions, respectively. By applying the process on each index, we generate their corresponding biological datasets. According to expert knowledge, for each bio-index we have chosen different time intervals in order to capture sequences that precede a biological sampling. Thus, we select a time interval corresponding to four months before a measurement of IBGN and IPR, and we select a time interval of two months before a measure of IBD. The reason is that diatom populations renew faster than macro-invertebrates and fishes. Then, it is relevant to restrict the interval of physico-chemical sequences on the IBD bio-index.
Overall results We mined DCPO-patterns with a minimum frequency threshold θ of 10% in all biological datasets. We choose this value because it allows us to extract a significant number of DCPO-patterns from the data. Extracting below this minimum frequency threshold is less statistically significant, especially for sub-datasets with a small number of sequences. Fig. 11a, b  and c  shows the number of DCPO-patterns extracted in each quality class sub-dataset. We observe that the number of DCPOpatterns per quality class sub-dataset is unbalanced. In order to highlight this aspect, a logarithmic scale is used on the ordinate axis. For example, 6202 DCPO-patterns are extracted from quality class sub-dataset IBD Yellow while 31 DCPO-patterns are extracted from quality class sub-dataset IBD Green.
For the three bio-indices, the yellow quality class sub-dataset is always the one having the biggest amount of DCPO-patterns, at least two times over other quality class sub-datasets. Furthermore, no DCPOpatterns are found from the IPR green quality class sub-dataset and only one DCPO-pattern is extracted from the IBGN green quality class subdataset. The computation time for each quality class sub-dataset is given by Fig. 12a, b  and c. We observe that the number of DCPO-patterns extracted is very correlated. Indeed, pattern mining approaches are almost linear according to the number of extracted patterns. The maximal computation time is 177. 11 s for the quality class sub-dataset IPR Yellow (3554 patterns) and the minimal one is 0. 046 s for the quality class sub-dataset IPR Green (0 pattern).
Filtered patterns For each biological dataset (IBGN, IBD and IPR), we present two DCPO-patterns extracted from the red and blue quality class subdatasets, i. e. the two extreme quality classes, except for IBD where the orange and the red quality class sub-datasets are merged. These DCPO-patterns are picked from the set of selected DCPO-patterns obtained with the PB_Index measure with a parameter k = 15, i. e. we extracted the 15 most balanced DCPO-patterns. We choose the number of 15 DCPO-patterns empirically since after many tests, we observe that it provides a small set of results easy to analyze and it captures the diversity contained in the data. The set of the 15 most balanced DCPOpatterns per bio-index quality class is accessible at this webpage 3  (in color). For the sake of clarity, DCPO-patterns presented in this paper contain a maximum of four items and two timestamps, but more complex DCPO-patterns are provided on the webpage.
IPR By analyzing fish taxa in a river, this bio-index aims at providing an evaluation of the river quality. For a river station, it measures the gap between the current fish population and the fish population that should be present without human activity impact. Experts also consider parameters such as the watershed surface, the average air temperature and the width and the depth of the river station. An IPR score of 0 means that there is no difference between the measured situation and the ideal situation. As this score increases, the gap between the current situation and the ideal situation is more important. Figs. 13 and 14 provide two DCPO-patterns extracted from blue IPR quality class sub-dataset and red IPR quality class sub-dataset, respectively. The DCPO-pattern in Fig. 13  has a frequency equal to 39.
IBD This last bio-index concerns the microscopic granularity of the biology. Like IPR and IBGN bio-indices, IBD is the measurement of a score that highlights the viability of the river. Here, the viability is representative of micro-algae populations. It consists in analyzing and counting 400 individuals from a sample. As with IBGN, the calculation is based on a data dictionary providing the polluo-sensitivity and the ability of the species to be in various environments. Figs. 17 and 18 show two examples of DCPO-patterns on this bio-index. The above experiments are performed to show the methodology and some obtained results. We now present a qualitative study of the proposed process.
Using domain knowledge to discretize the data Discretizing data with domain knowledge (SEQ-eau and AFNOR standards) is useful. By extracting DCPO-patterns by quality class subdatasets (biological quality classes), we can highlight and measure the concordance between physico-chemical and biological parameter quality classes. For example, the impact of the physico-chemical parameter PHOS on diatoms has been widely studied  (Coring, 1999; Kelly et al. , 1995)  and is well-known. The river biological quality class for diatoms is often bad when very high PHOS concentrations are measured. Currently, it is not possible to predict precisely its impact on IBD index value. It is just possible to say that a moderate/bad biological quality class, e. g. a yellow, an orange or a red IBD class, is predictable with high probability.
By analyzing some DCPO-patterns, we observe that for each IBD quality class sub-dataset, a PHOS quality class is related. Table  10  sums it up: we can observe that the IBD index quality classes do not correspond exactly with physico-chemical quality classes. IBD quality classes decrease more rapidly than PHOS quality classes. For instance, a yellow IBD is related to a green PHOS and an orange IBD is related to a yellow PHOS. Furthermore, a red IBD is related to a red PHOS, but specifically concerned by a red PHOS combined with a red AZOT and a red MOOX. Then, with IBD, we observe that: (1) the biological status is very sensitive to water disturbances and (2) the combination of multiple bad physico-chemical values produces necessarily degradation of the biological dimension. It leads to new perspectives: suggesting to determine new interval values for physico-chemical parameter quality classes to match with bio-index classes and to evaluate more precisely the impact of the combination of physico-chemical parameters.
Perspectives We applied this generic process on the three bio-indices IBGN, IBD and IPR because they are studied and measured for many years in French river ecosystems. It then gives us a substantial amount of data to explore. However there exist more recent bio-indices that concern the river viability for macrophytes with the IBMR bio-index (AFNOR (Association Française de NORmalisation), 2003) and oligochetes with the IOBS bio-index (AFNOR (Association Française de NORmalisation), 2002). We are interested in analyzing them to discover new knowledge on rivers. Exploring all the biological dimensions is important to measure a global river quality. In addition, since DCPO-patterns are useful to capture the discriminant features for different biological qualities, we wish to extend our process to classification  (Cheng et al.
Frequency: represented by the number of sequences in quality class sub-datasets that support the DCPO-patterns (Section 3. 2). Analysts are interested in the most frequent DCPO-patterns per quality class sub-dataset since high frequency DCPO-patterns are more supported in the dataset than low frequency DCPO-patterns. 2. Discriminance: represented by the generalized growth rate value (Section 3. 2). Analysts are interested in the most discriminant DCPO-patterns per quality class sub-dataset. The more discriminant a DCPO-pattern is in a quality class sub-dataset, the more specific it is in this quality class sub-dataset. 3. Redundancy: in DCPO-pattern mining, it is usual to find DCPOpatterns that carry almost the same knowledge and that describe almost the same set of sequences. Avoiding redundancy in results is important to improve DCPO-pattern analysis, by retrieving an example of each different case found in the data, without retrieving similar cases.
1. Frequency: represented by the number of sequences in quality class sub-datasets that support the DCPO-patterns (Section 3. 2). Analysts are interested in the most frequent DCPO-patterns per quality class sub-dataset since high frequency DCPO-patterns are more supported in the dataset than low frequency DCPO-patterns. 2. Discriminance: represented by the generalized growth rate value (Section 3. 2). Analysts are interested in the most discriminant DCPO-patterns per quality class sub-dataset. The more discriminant a DCPO-pattern is in a quality class sub-dataset, the more specific it is in this quality class sub-dataset. 3. Redundancy: in DCPO-pattern mining, it is usual to find DCPOpatterns that carry almost the same knowledge and that describe almost the same set of sequences. Avoiding redundancy in results is important to improve DCPO-pattern analysis, by retrieving an example of each different case found in the data, without retrieving similar cases.
For instance, in pharmacometrics and in particular pharmacokinetic-pharmacodynamic (PKPD) applications, linear models are usually not sufficient to satisfactorily model data. Consequently, nonlinear mixed-effects models have been largely adopted  [29, 19, 5, 33]  even though they involve computationally taxing calculations when performing maximum likelihood, a stumbling block until recently. However, strategies such as the Stochastic Approximation EM (SAEM) algorithm  [16] , implemented in the Monolix software and R  [23] , have recently led to significantly faster methods for not only linear mixed-effects but also nonlinear mixed-effects models. Next let us consider the event risk itself, modeled by a hazard function λ, which characterizes the distribution of the time-to-event process. The hazard function may be constant or vary as a function of time.
SAEM is extremely fast and has already been used to help treat a large range of real-world problems including Hepatitis C treatment outcomes  [30] , longitudinal data analysis  [27] , parameter estimation in HIV models  [3, 18] , bioequivalence crossover trials  [10] , and much more. The Monolix software provides a general implementation of SAEM that can be easily extended by the user to new modeling challenges. SAEM has also been implemented in the R package saemix and the Matlab statistics toolbox as nlmefitsa. m. It turns out that in this framework, computation of p(y i |ψ i ) for the various cases (repeated events with interval censoring, right-censored time-toevents, joint models, etc. ) is a critical modeling step. In the following section, we therefore explicitly calculate this pdf for a wide range of cases.
The model requires estimation of 15 parameters including 6 random effects variances, yet takes only a few second to converge to systematically accurate parameter estimates. In Web Appendix C, we also briefly present model diagnotic tools, even though this is beyond the scope of the present paper. For each scenario, the SAEM algorithm was used with M = 100 simulated datasets for computing the parameter estimates ( θm , 1 ≤ m ≤ M ). To assess statistical properties of the proposed estimators for each parameter, percentage-wise relative estimation errors (REE m , 1 ≤ m ≤ M ) were computed: REE m = θm -θ | θ | × 100. Using the REEs, the relative bias (RB) and relative root mean square errors (RRMSE) were computed for each parameter in each scenario: RB = 1 M M m=1 REE m RRM SE = 1 M M m=1 REE 2 m.
Example 1 This first example demonstrates that an accurate estimation of the interpatient variability of the hazard function requires observation of multiple events. We consider a basic RTTE model under the mixed-effects framework with constant hazard (See  [15] ) for each individual i = 1, 2,. N , expressed as λ i (t) = λ i (13) log (λ i ) ∼ N log(λ), ω 2. ( 14 ) The goal in mixed-effects modeling is then to estimate the population value λ and variance ω 2. We assume that events are observed between time t 0 = 0 and time t end = 12. Furthermore the event times are assumed to be exactly known. We are thus in the situation described Section 3. 2. 1 with observed and right censored events. The conditional distribution of the observations is given in equation 7. The 100 datasets with 120 individuals in each were simulated under nine different scenarios with λ ∈ {0.
Example 2 The goal of this experiment is to shown that interval censoring should be properly taken into account in order to avoid estimation bias. Using the same basic model as the previous example, the simulation scheme is the following: • M = 100 datasets with N = 1000 subjects in each. • The true parameter values are (λ , ω ) = (0. 5, 0. 5). • The event process is single-event (e. g. , death). • Events are interval or right-censored. The intervals are contiguous and of length ∆. • Observations occur between t 0 = 0 and t end = 24. Here, the events are interval or right-censored. If we incorrectly take this information into account, for instance by considering that the event has happened at the interval midpoint, decreasing estimation quality and bias are introduced as the length ∆ of the intervals increases.
Primary Biliary Cirrhosis Data This well known dataset comes from a study conducted by the Mayo Clinic from 1974 to 1984. The study includes 158 patients who received Dpenicillamine and 154 who received a placebo. Patient survival is the outcome of main interest. By the end of the study, 140 patients had died and 172 were still alive. Several biomarkers, including serum bilirubin, were measured during the study. A total of 1945 measurements of serum bilirubin were made available. Various joint models for this data are proposed in  [24]. All of these assume a linear mixed-effects model for the longitudinal data. We will show that our approach provides a straightforward extension to more general nonlinear mixed-effects models. Following  [24] , we used the following model for the serum bilirubin: m i (t) = c 0,i + c 1,i t + c 2,i t 2 , with log b ij = m i (t ij ) + aε ij.
Epileptic seizure counts In this study, all recruited patients were on standard anti-epileptic therapy and completed a 12 week baseline screening phase. Thereafter, patients were randomized to parallel treatment groups receiving placebo or active treatment (gabapentin 0. 45, 0. 6, 0. 9, 1. 2 and 1. 8g). Overall, time profiles from 788 patients were included in the database. Data consisted of baseline daily counts of epileptic seizures measured over 12 weeks followed by 12 weeks of active treatment. Several count data models have been proposed, including a mixture of two Poisson models  [22]  and a hidden Markov model  [7]. Such models assume that the probability function of the number of seizures is piecewise-constant over time. We propose to extend this approach by considering seizures as interval-censored events.
To be able to implement SAEM for joint models in the afore-mentioned range of cases, we have for each derived precise expressions for the conditional likelihood of the observations given the individual parameters. In a series of simulation studies, we have shown that the SAEM algorithm converges for joint models in a matter of seconds or minutes rather than hours or days. As a consequence, we can also quickly estimate the Fisher information matrix, the observed likelihood and the individual parameters. SAEM for joint models is intuitively implemented in the Monolix software: in order to pass from nonlinear mixed effects modeling to joint modeling, all that is required of the modeler is to provide the parametric form of the hazard function. We have illustrated this by performing joint modeling in two real examples: survival data and repeated time-to-event data.
A Monte-Carlo study. All these results so far have been obtained with only one simulated dataset. We now present a Monte-Carlo study to give stronger evidence that the SAEM algorithm is very efficient for estimating the population parameters in this complex joint model. We simulated 100 replicates of the same trial, using the same design and the same population parameters, and estimated the populations parameters for each simulated trial. Initial values were randomly chosen around the true values. Figure  7  displays the distribution of the 15 estimated population parameters obtained with SAEM. We see that the PK parameters and the residual errors parameters can be estimated very accurately with this design. Estimation of the hazard model is less precise, mainly because the variability of the parameters α and β is difficult to estimate.
The application and testing of the three methods for the Norwegian datasets are described in detail in  Fleig (2011)  and only the main points are reviewed here. Atmospheric pressure data were obtained from the NCEP/NCAR reanalysis project  (Kalnay et al. , 1996)   (0-20 • E, 55-70 • N) for the 1000 hPa level (Z1000) and the 700 hPa level (Z700) at UTC at a spatial resolution of 2. 5 • × 2. 5 •. Precipitation data for the period 1970-2008 for 175 stations distributed across Norway were extracted from the "European Climate Assessment & Dataset" (ECA&D, 2011) database. These stations and the time period were selected out of the 368 available stations for the region, such that no station had more than 50 days with missing data. The locations of the precipitation stations and the grid of geopotential heights are presented in Fig.
Separation quality BSS Eval of MATLAB was used to evaluate the above method. It gives three standard metrics for source separation: signal-to-distortion ratio (SDR), signal-tointerference ratio (SIR), and signal-to-artifact ratio (SAR)  [33]. SDR is a global measure of the quality of source separation that encompasses the two other metrics, SIR indicates how well the target source is separated from interference, and SAR indicates how well the target source retains sound quality after separation. sc-NTF was compared with p-NTF and f-NTF. 2ch-stereo signals were obtained from the 'Signal Separation Evaluation Campaign' Web site (SiSEC 2008  [34] ). More specifically, we used development data from the underdetermined speech and music mixture task. These 2ch-stereo sources contain a number of instruments placed independently in a 2-D field.
Computational cost As mentioned earlier, the omission of the calculation of the channel matrix Q should be a great advantage for sc-NTF. A simple experiment was conducted by measuring the runtime on an Intel Core i7 (2. 80 GHz) processor for three NTF methods implemented in MATLAB code. It should be noted that the code was not particularly optimized by incorporating external libraries written in, for instance, C language. Instead, the built-in functions automatically provided by MATLAB, such as division and log functions, were used. The conditions of the experiment were the same as those for the quality evaluation  described in Section 5. 1 (the number of channels J = 2, the number of frequency bins K = 513, and the number of frames L = 314). Figure  8  shows that p-NTF takes almost four times as much computational power as the other NTF methods.
A large set of features was explored, including lexical, syntactic, structural, contextual and linguistically informed features (such as polarity, verb classes, production rules and word pairs). Although most of the research studies have been done for the English language, some efforts focused on relation identification in other languages including French  (Muller et al. , 2012) , Chinese  (Huang and Chen, 2011) , German  (Versley, 2013) , and Modern Standard Arabic (MSA)  (Al-Saif and Markert, 2011). Al-Saif and Markert (2011)  proposed the first algorithm that identifies explicitly marked relations holding between adjacent Elementary Discourse Units (EDU) within the PDTB model. In this paper, we extend Al-Saif and her colleague's work by focusing on both explicit and implicit relations that link adjacent as well as non-adjacent units within the SDRT, a different theoretical framework.
Among these features, some have been successfully employed for explicit Arabic relations recognition such as al-masdar, connectives, time and negation (cf. Al-Saif and Markert, 2011). Others however are novel for the Arabic language and include contextual, lexical as well as lexico-semantic features, such as argument position, semantic relations, word polarity, named entities, anaphora and modality. We investigate how each feature contributes to the learning process. We report on our experiments in fine-grained discourse relations' identification as well as in mid-level relations' and top-level class identification. We compare our approach to three baselines that are based on the most frequent relation, discourse connectives and the features used by  Al-Saif and Markert (2011). Our results are encouraging and outperform all the baselines.
This project annotated 60 texts from the MUC 6 and MUC 7 data sets and only experts in the theory did the annotation. The Annodis corpus combined two perspectives on discourse: a bottom-up view that incrementally builds a document discourse structure from EDUs, and a top-down view that focuses on the selective annotation of multi-level discourse structures. The bottom-up approach resulted in the annotation of 86 documents (short Wikipedia articles as well as news articles) with a total of 3199 EDUs and 3355 relations. As far as we know, the Discourse Arabic Treebank corpus (D-ATB) corpus is the first effort toward building recursive and complete discourse structures of Arabic texts (cf. Sections 4 and 5).
The data Arabic Treebank (ATB) v3. 2 part3  (Maamouri et al. , 2010b)  consists of 599 newswire stories from Annahar News Agency. There are a total of 339,710 words/tokens before clitics are split and 402,291 words/tokens after clitics are separated for the Treebank annotation. Each document in this corpus is associated to two annotation levels. First a morphological and parts of speech level and then the syntactic Treebank annotation that characterizes the constituent structures of word sequences and provides categories for each non-terminal node. We have randomly selected 90 documents from ATB. Our aim was to manually annotate each document with complete discourse coverage according to the cognitive principles of SDRT (cf. Section 2). The annotation of our corpus required three steps: (a) the elaboration of a new hierarchy of discourse relations, (b) the definition of the annotation manual and (c) the manual annotation of our corpus following the annotation guidelines as defined in the manual.