dataesr/scientific-paragraphs-categorization huggingface dataset:
The reconfiguration starts with all components running. The reconfiguration scenario requires an update of the database component (Γ bhv = {(mariadb, update), (mariadb, deploy)}) and Γ ports specifies that all ports must eventually return to their initial (active) state. Our method generates a reconfiguration plan in 1. 95 seconds, correctly deducing ) its number of components (size), the number of problems solved by the SMT solver (smt) followed in parentheses by the number of those that were found satisfiable, the total solving time in seconds followed in parentheses by the cumulated time taken by the SMT solver (time), the number of steps in the solution before relaxation of the synchronization barriers (steps), and the number of behaviors executed in that solution (bhvs). missing behaviors for mariadb as well as components affected by the interruption of its service, i.
Related work For models with fixed component life cycles, planning and scheduling techniques have been used to plan reconfigurations  [1, 13]. Pre-established protocols can also be used: while such solutions are in general less flexible, they have desirable features such as decentralized coordination  [14]  or recovery policies  [6]. Comparatively fewer works study the problem of reconfiguration planning in models with programmable component life cycles, such as Concerto. Kikuchi et al. [20]  synthesize reconfiguration plans with a model finder. Unlike us, they assume that all available reconfiguration operations are given in the input of the scheduling problem, which may limit scalability. Operations and reconfiguration goal are encoded in the Alloy specification language, and synthesis is performed by the Alloy Analyzer.
Metis is limited to deployment rather than general reconfiguration, making the computation of dependencies more straightforward. Aelous shares many similarities with Concerto, but lacks intra-component parallelism and asynchronous commands in its reconfiguration language. These features improve the efficiency of reconfigurations but also make them more difficult to plan. Note that these features can also be represented through planning and scheduling problems  [18] , typically solved by approximation. The problem of determining reconfiguration goals (i. e. , the analysis phase) is complementary to the planning problem. Engage  [15]  uses a SAT solver to build a complete target configuration (a set of components to deploy) from a partial specification, based on a hierarchical specification of a distributed software stack.
Hedges play an important role in the management of conversational interaction. In peertutoring, they are notably used by tutors in dyads (pairs of interlocutors) experiencing low rapport to tone down the impact of instructions and negative feedback. Pursuing the objective of building a tutoring agent that manages rapport with students in order to improve learning, we used a multimodal peer-tutoring dataset to construct a computational framework for identifying hedges. We compared approaches relying on pre-trained resources with others that integrate insights from the social science literature. Our best performance involved a hybrid approach that outperforms the existing baseline while being easier to interpret. We employ a model explainability tool to explore the features that characterize hedges in peer-tutoring conversations, and we identify some novel features, and the benefits of such a hybrid model approach.
", conveying that the proposition is 1 https://github. com/AnonymousHedges/HedgeDetection considered by the speaker as subjective. In a further sub-division, Attribution Shields, as in "The doctor wants you. ", the involvement of the speaker in the truth value of the proposition is not made explicit, which allows speakers not to take a stance. As described above,  Madaio et al. (2017)  found that tutors who showed lower rapport with their tutees used more hedged instructions (they also employed more positive feedback), however this was only the case for tutors with a greater belief in their ability to tutor. Tutees in this context solved more problems correctly when their tutors hedged instructions. No effect of hedging was found for dyads (pairs of interlocutors) with greater social closeness.
Computational methods for hedge detection: A number of studies have targeted the detection of hedges and uncertainty in text  (Medlock and Briscoe, 2007; Ganter and Strube, 2009; Tang et al. , 2010; Velldal, 2011; Szarvas et al. , 2012) , particularly following the CoNLL 2010 dataset release  (Farkas et al. , 2010). However, this work is not as related to hedges in conversation, as it focuses on a formal and academic language register  (Hyland, 1998; Varttala, 1999). As noted by  Prokofieva and Hirschberg (2014) , the functions of hedges are domain-and genre-dependent, therefore this bias towards formality implies that the existing work may not adapt well to the detection of hedges in conversation between teenagers. A consequence is that the existing work does not consider terms like "I think," since opinions rarely appear in an academic writing dataset.
Instructions are also almost absent ("I think you have to add ten to both sides. "), a strong limitation for the study of conversational hedges since it is in requests (including tutoring instructions) that indirect formulations mostly occur according to  Blum-Kulka (1987). Prokofieva and Hirschberg (2014)  also note that it is difficult to detect hedges because the word patterns associated with them have other semantic and pragmatic functions: considering "I think that you have to add x to both sides. " vs "I think that you are an idiot. ", it is not clear that the second use of "I think that" is an hedge marker. They advocate using machine learning approaches to deal with the ambiguity of these markers. Working on a conversational dataset,  Ulinski et al. (2018)  built a computational system to assess speaker commitment (i.
e. at which point the speaker seems convinced by the truth value of a statement), in particular by relying on a rulebased detection system for hedges. Compared to that work, our rule-based classification model is directly detecting hedge classes, and we employ the predictions of the rule-based model as a feature for stronger machine learning models, designed to lessen the impact of the imbalance between classes. We also consider apologies when they serve a mitigation function (we then call them Apologizers), as was done by the authors of our corpus, and we also use the term subjectivizers as defined below, to be able to compare directly with the previous work carried out on this corpus. As far as we know, only  Goel et al. (2019)  have worked with a peertutoring dataset (the same one that we also use), and they achieved their best classification result by employing an Attention-CNN model, inspired by  Adel and Schütze (2017).
They then engaged in another social period, and concluded with a second tutoring period where the other student was assigned the role of tutor. Audio and video data were recorded, transcribed, and segmented for clause-level dialogue annotation, providing nearly 24 000 clauses. Nonspeech segments (notably fillers and laughter) were maintained. Because of temporal misalignment for parts of the corpus, many paraverbal phenomena, such as prosody, were unfortunately not available to us. Since our access to the dataset is covered by a Non-Disclosure Agreement, it cannot be released publicly. However the original experimenters' Institutional Review Board (IRB) approval allows us to view, annotate, and use the data to train models. This also allows us to provide a link to a pixelated video example in the GitHub repository of the project 2.
Data annotation: The dataset was previously annotated by  Madaio et al. (2017) , following an annotation manual that used hedge classes derived from  Rowland (2007)  (see Table  1 ). Only the task periods of the interactions were annotated. Comparing the annotations with the classes mentioned in the related work section, Subjectivizers correspond to Relational hedges  (Fraser, 2010) , Propositional hedges and Extenders correspond to Approximators  (Prince et al. , 1982)  with the addition of some discourse markers such as just. Apologizers are mentioned as linguistic tools related to negative politeness in  Brown and Levinson (1987). Krippendorff's alpha obtained for this corpus annotated by four coders was over 0. 7 for all classes (denoting an acceptable inter-coder reliability according to  Krippendorff (2004) ).
The dataset is widely imbalanced, with more than 90% of the utterances belonging to the Not hedged class. In reviewing the corpus and the annotation manual, however, we noticed two issues. First, the annotation of the Extenders class was inconsistent, leading to the Extenders and Propositional hedges classes carrying similar semantic functions. We therefore merged the two classes and grouped utterances labeled as Extenders and those labeled as Propositional hedges under the heading of Propositional hedges. Second, the annotation of clauses containing the tokens "just" and "would" (two terms occurring frequently in the dataset that are key components of Propositional Hedges and Subjectivizers but that are not in fact hedges in all cases) was also inconsistent, leading to virtually all clauses with those two tokens being considered hedges.
We therefore re-considered all the clauses associated with any of the hedge classes, as well as all the clauses in the "Not hedged" class that contained "just" or "would". The re-annotation was carried out by two annotators who achieved a Krippendorff's alpha inter-rater reliability of. 9 or better for Apologizers, Subjectivizers, and Propositional hedges before independently re-annotating the relevant clauses. An example of a re-annotation was removing "I would kill you!" from the hedge 2 https://github. com/AnonymousHedges/HedgeDetection classes.
Unigram and bigram: We count the number of occurrences of unigrams and bigrams of the corpus in each clause. We used the lemma of the words for unigrams and bigrams using the nltk lemmatizer  (Loper, 2002)  and selected unigrams and bigrams that occurred in the training dataset at least fifty times. The goal was to investigate, with a bottomup approach, to what extent the use of certain words characterizes hedge classes in tutoring. In Section 5 we examine the overlap between these words and those a priori identified by the rules. Part-of-speech (POS): Hedge classes seem to be associated with different syntactic patterns: for example, subjectivizers most often contain a personal pronoun followed by a verb, as in "I guess", "I believe", "I think". We therefore considered the number of occurrences of POS-Tag n-grams (n=1, 2, 3) as features.
We used the spaCy POS-tagger and considered POS unigrams, bigrams and trigrams that occur at least 10 times in the training dataset. LIWC: Linguistic Inquiry and Word Count (LIWC)  (Pennebaker et al. , 2015)  is standard software for extracting the count of words belonging to specific psycho-social categories (e. g. , emotions, religion). It has been successfully used in the detection of conversational strategies  (Zhao et al. , 2016a). We therefore count the number of occurrences of all the 73 categories from LIWC. Tutoring moves (TM): Intelligent tutoring systems rely on specific tutoring moves to successfully convey content (as do human tutors). We therefore looked at the link between the tutoring moves, as annotated in  Madaio et al. (2017) , and hedges. For tutors, these moves are (1) instructional directives and suggestions, (2) feedback, and (3) affirmations, mostly explicit reflections on their partners'comprehension, while for tutees, they are (1) questions, (2) feedbacks, and (3) affirmations,  mostly tentative answers.
Classification models The classification models used are presented here according to their level of integration of external linguistic knowledge. Rule-based model: On the basis of the annotation manual used to construct the dataset from  Madaio et al. (2017) , and with descriptions of hedges from  Rowland (2007) ,  Fraser (2010)  and  Brown and Levinson (1987) , we constructed a rule-based classifier that matches regular expressions indicative of hedges. The rules are detailed in Table  7  in the Appendix. LGBM: Since hedges are often characterized by explicit lexical markers, we tested the assumption that a machine learning model with a knowledgedriven representation for clauses could compete with a BERT model in performance, while being much more interpretable. We relied on LightGBM, an ensemble of decision trees trained with gradient boosting  (Ke et al.
, 2017). This model was selected because of its performance with small training datasets and because it can ignore uninformative features, but also for its training speed compared to alternative implementations of gradient boosting methods. Multi-layer perceptron (MLP): As a simple baseline, we built a multi-layer perceptron using three sets of features: a pre-trained contextual representation of the clause (SentBERT; Reimers and Gurevych (  2019 )) ; the concatenation of this contextual representation of the clause and a rule-based label (not relying on the previous clauses) ; and finally the concatenation of all the features mentioned in section 4. 2, without the contextualized representation. LSTM over a sequence of clauses: Since we are working with conversational data, we also wanted to test whether taking into account the previous clauses helps to detect the type of hedge class in the next clause.
While exploring the Shapley values associated to each clause, we observed that features like tutoring moves are extremely informative for a very small number of clauses (therefore not significantly influencing the overall performance of the prediction), and more or less not informative for the rest. Inferring the global importance of a feature as a mean across the shapley values in the dataset may not be the only way to explore the behavior of gradi-ent boosting methods. It might be more useful to cluster clauses based on the importance that SHAP gives to that feature in its classification, as this could help discover sub-classes of hedges that are differentiated from the rest by their interaction with a specific feature (in the way that some Apologizers are characterized by an "oh"). We also note that the explanation model is sensitive to spurious correlations in the dataset, caused by the small representation of some class: for example, "nine" (n=7) and "four" (n=20) are positive predictors of Apologizers.
g. , negative feedback followed by a smile). Regarding the SHAP analysis, most of the features that are considered as important are coherent with the definition of the classes (I for subjectivizers, sorry for apologizers, just for propositional hedges). However, we discovered that features like utterance size can also serve as indicators of certain classes of hedges. A limitation of SHAP is that it makes a feature independence assumption, which prompts the explanatory model to underestimate the importance of redundant features (like pronouns in our work). In the future we will explore explanatory models capable of taking into account the correlation between features in the dataset like SAGE  (Covert et al. , 2020) , but suited for very imbalanced datasets. In the domain of peer-tutoring, we would like to be able to further test the link between hedges and rapport, and the link between hedges and learning gains in the subject being tutored.
For LightGBM, a "square root of the square root of the inverse class proportion" correction was selected. Neural models were trained using AdamW as an optimizer  (Loshchilov and Hutter, 2018) , and used a reduced feature vector, obtained with the application of PCA (d init = 1800; d = 100 ; 99. 8 % of the information is conserved). No significant performance differences were observed between the original vector and the reduced vector for training the models. To compute the SHAP values mentioned in the paper, we kept one split to perform the 5-split of the dataset, and leave 1 split to validate and early stop the model, in order to avoid overfitting. A complete configuration of hyperparameters used for each model is reported in the GitHub repository with the code of the paper: https://github.
A New Immunophenotyping Approach All published reports in the literature used classical flow cytometry to analyze the immunophenotype of MPAL cases. Panels obviously differ from center to center, but basically, the markers recommended in the literature  [5, 34•]  have been increasingly applied, allowing for a better assessment of published series. We took the opportunity of preparing this review to explore how MPALs' immunophenotype could be interpreted by new artificial intelligence/machine learning tools such as the FlowSOM software  [35• ]. The latter has been developed in the R Bioconductor environment for a fast hierarchical clustering of cell subsets with shared immunophenotypic features  [7•, 35•]. Our group has published on the use of this tool for the detection of minimal residual disease in AML [36•], to characterize blastic plasmacytoid dendritic cells neoplasms (BPDCN)  [37] , to investigate erythroid differentiation in normal bone marrow  [38]  and in patients with clonal or non-clonal anemia  [39].
The MSTs were created using FlowSOM after compensation check and normalization of MFI to lymphocytes as described previously  [36 • , 37-39]   In the progenitor area (bermudes,  [41] ), a small yet undescribed subset of Tdt + /MPO + /CD19 -/CD79a -/DR -cells represented over 6% of leukocytes. This peculiar subset could be related to the current hypothesis of leukemia-driving events at an early ill-differentiated stage of hematopoietic maturation  [8]. In this typical case of B/My MPAL, the unsupervised clustering analysis revealed a higher number of coexisting subsets than disclosed by the classical FCM analysis. In other MPAL cases tested similarly (manuscript in preparation), this diversity appeared even more marked. This might explain the necessity to combine ALL and AML-specific chemotherapy regimens in some patients.
Based on 10 polymorphic microsatellite markers, we evaluated genetic diversity, population structure and gene flow amongst six groups of feral cats located at a maximum of 10 km from known petrel colonies. We used feral cat capture-mark-recapture (CMR) data and SECR models to estimate the density and the home range of feral cats near the well-studied Barau's Petrel breeding colony of Grand Bénare. We determined their density and general activity (movements and detection probabilities) in relation to the types of habitat used (trail vs. vegetation cover). Finally, genetic data and behavioural data were combined to propose an adapted feral cat control strategy.
The means of allelic richness amongst areas were compared using pairwise Wilcoxon's signed rank tests with Bonferroni correction. Observed heterozygosity (H O ), unbiased expected heterozygosity estimated according to Nei (1978) (H E ) and Wright's F-statistics (F IS ) according to the method of  Weir and Cockerham (1984)  were calculated for all and each population using GenAlEx 6. 5  (Peakall and Smouse 2012). Deviations from Hardy-Weinberg equilibrium (HWE) were tested for each of the six areas using the package pegas 0. 13 (Paradis 2010) using R 3. 2. 0 (R Core Team 2021), with the exact test based on 10 3 Monte Carlo permutations.
For choosing the optimal number of clusters, two criteria were used; the log likelihood given K (L(K);  Pritchard et al. 2000)  and the second-order rate of change of mean log-likelihood (∆K;  Evanno et al. 2005). Both criteria were calculated using STRUCTURE HARVESTER online Web server  (Earl and vonHoldt 2012). CLUMPAK software  (Kopelman et al. 2015)  was used to find the optimal individual alignments of replicated cluster analyses and to visualise the results. Population structure was also explored by integrating spatial coordinates of samples using a Bayesian model executed in a Markov Chain Monte Carlo, as implemented in the R package Geneland 4. 0. 5  (Guillot et al. 2005 (Guillot et al. , 2008)). The geographical information was used to detect spatial delineation of genetic discontinuities, where the number of area units is treated as an unknown parameter.
A burn-in period of 20,000 iterations followed by 10 5 iterations for each run was used. The default values were used for all other parameter settings. Migration rate estimates were obtained by choosing the run with lowest Bayesian deviance, measured by the assignment values (Dassign;  Faubet and Gaggiotti 2008). Posterior mean and mode migration rates and 95% high density predictive interval (HDPI) were estimated using the package HDInterval  (Kruschke 2011)  in R. Estimates of recent migration rates and approximate 95% confidence interval (CI) were also explored by the Bayesian approach as implemented in BayesAss 3. 0. 4 (Wilson and Rannala 2003). Five runs were first performed by changing the number of seeds (s = 10, 100, 500, 750 and 1000) to obtain a suitable convergence. The number of iterations was 10 6 , of which 10 5 were burn-in and the sampling frequency was 100.
Mixing parameters were 0. 6 for allele frequencies, 0. 9 for inbreeding coefficients and 0. 5 for migration rates. The final run consisted of the same mixing parameters and 100 numbers of seeds. Spatially explicit capture-recapture study CMR data of feral cats were obtained during a single season of camera trap survey at Maïdo (Fig. 1C ). This site was selected for: (i) the presence of scats and direct observations of feral cats, (ii) its proximity to a monitored Barau's Petrel colony (about 5 km) where feral cat predation is known to occur  (Faulquier et al. 2009 ), (iii) the proximity of trails which were supposed to maximise the feral cat detection probability  (Meek et al. 2014; McGregor et al. 2015)  and (iv) the technical feasibility. We deployed and geo-referenced with GPS (Garmin 64 s; 5 m accuracy) 20 camera-traps (9 Scoutguard-MG882K-12mHD, 10 Bushnell Trophy Cam HD and 1 Reconyx HC600 Hyperfire) from 17 February to 25 April 2016 (68 days).
2012; McGregor et al. 2015; Lavery et al. 2020; Palmas et al. 2020). A sampling occasion lasted one day (24 h, hereafter named "trap-night";  Otis et al. 1978; Wang and Macdonald 2009). For each sampling occasion, individuals were photographed ("captured"), identified ("marked") and "released". All feral cats previously identified and re-sighted were considered as a recapture. A capture event was defined as all pictures of unique individuals within a 30-min time period  (Di Bitetti et al. 2006). Cameras were checked every 10 days to download data from memory cards and replace batteries. No feral cats were captured for the genetic study in the CMR study area during and 6 months before the behavioural study. However, from September to December 2015, four feral cats were captured at 5. 9 to 7.
8 km from the nearest camera trap. The trapping effort (in trap-nights) was calculated by adding for each camera the number of days where each camera was active over the study period. The capture efficiency (in number of capture events/100 trap-nights) was calculated by dividing the number of feral cat capture events for all cameras divided by the total trapping effort and multiplied by 100  (Palmas et al. 2020 ). Since we designed a short study period (to avoid emigration, immigration or mortality) and we did not consider kitten pictures in the dataset (to avoid recruitment), we applied SECR models that require a demographically closed population. These models assume no emigration or immigration, no mortality and no recruitment during each trap session  (Otis et al. 1978; Efford 2004 ). The matrix of spatially explicit histories of capture-recaptures was constructed for each feral cat by linking each capture of each individual with the coordinates of the camera and with the occasion.