dataesr/scientific-paragraphs-categorization huggingface dataset:
They can range from m ? 1, m being the dimension of OMEGA or SIGMA matrix, for uninformative priors, to the number of subjects (for OMEGA) or to the number of observations (for SIGMA) in the previous study for very informative prior. Usually, the degree of freedom for informative OMEGAs is calculated with the formula df = 2*[ OMEGA 2 / (SE of OMEGA 2 )] 2 ? 1  [1, 6, 11, 23, 39]. The same formula can be applied for informative SIGMAs. The degrees of freedom pertain to the entire OMEGA block, including the off-diagonal elements. Nonetheless it is a general or vague strength parameter, which lends its strength on all elements to the OMEGA block. If a high degree of freedom is placed on an OMEGA prior block, and that block has 0 off-diagonals whereas the data suggests a strong off-diagonal, the analysis may be compromised.
This resampling is repeated. And then, for each OMEGA, an inverse-Wishart distribution can be fitted to the distribution of the resampled OMEGA: the degree of freedom of the inverse-Wishart distribution is the one that can be reported in $OMEGAPD. Figure  3  summarizes how to code the prior weight in the control file. Non-informative distribution can also be referred to as vague  [12] , because as long as a prior is used, it remains at least slightly informative. Eight out of 32 articles analyzing sparse data reviewed implemented informative priors on all parameters  [5, 10, 12, 15, 23, [29] [30] [31] , 18 implemented informative priors, and/or so-called ''weakly informative'' (when they are associated to 10%  [20]  or 50%  [24, 28]  uncertainty) only on a part of the parameters  [7-9, 13, 16-22, 24, 25, 28, 32-35] , and three implemented uninformative priors on some of the parameters while had informative priors on the rest of the parameters  [4, 6, 11].
The first is the same as the one obtained with models without prior: run with MAXEVAL = 0 NOPRIOR = 1 (without prior). O P can then be calculated as the difference between the reported OFV without constant and O S. • TOTAL DATA POINTS NORMALLY DISTRIBU- TED (N) • N* Once the prior is used in the base model, the total OFV (which includes the prior penalty) can be used in the Likelihood Ratio Tests (LRT), if no change is made to the prior information. Thus, O S should not be used in the LRT, as the prior was involved in the original fitting so O S is not a minimum OFV by itself (not a maximum likelihood position). Therefore, the OFV to be used in comparisons using LRT is the total OFV that is reported in the output of NONMEM (OFV without constant = O S ? O P ).
Impact of varying the prior Milosheska et al. tested the sensitivity of the model parameters  [23]  (i) to the prior specification by varying the prior values by -50% and ? 50%, and ii) to the informativeness of the prior by changing the precision of the prior (SE from -50% to ? 50%). The impact of changing prior values and precision was quantified by the resulting change in the estimate of the impacted parameter. The sensitivity of the model to the weight of the prior was considered acceptable as parameter estimates remained within ± 15% range when the SE varied of ± 50%. If changing the prior value results in identical change in parameter estimates, it means that the new data contains little information about this parameter: the prior is important in the model and it should be carefully defined and trusted.
Differences in parameters between previous population and new population The parameters implemented with priors should be similar in the previous and the new population. Otherwise, the new estimates would be constrained to a biased value, leading to a misfit of the model built with prior. Moreover, the estimation of other parameters could also be impacted. Pharmacometricians used different strategies to verify the hypothesis of similarity between parameters in the previous and in the new populations. Some of these strategies can also be used to characterize the amount of information given by the new (sparse and/or small) dataset compared to the prior information. Brill et al. tested whether the univariate addition of a parameter without prior information showed any significant improvement in OFV for each parameter of the model built with full prior  [7] , that is, testing on each parameter if the addition of a parameter of difference (e.
g. named DIS), estimated on the new data without prior, significantly improved the OFV of the model built with prior. For each parameter, two models are compared with the LRT: one with the parameter estimated with prior (DIS = 0), one with the parameter estimated with prior multiplied by (1 ? DIS). This can be done with automated Stepwise Covariate Modelling (SCM) in PsNÒ [40]: the difference in parameters between the previous data and the new data can be coded DIS = 1, reflecting the difference between the populations of the two datasets  [42]. The difference in OFV between the two models should be compared to an actual significance level that can be computed with Stochastic Simulation and Estimation (SSE) in PsNÒ. If the addition of DIS (estimated on the data only) on a parameter significantly improves the OFV, the parameter differs between previous population and new population.
They fixed the parameter that had a marked reduction in uncertainty and estimated LS (life span), the parameter that had no decrease in uncertainty: LS was well estimated without prior (low RSE). This illustrated that the data did contain information about LS. Moreover, LS estimate was close to the reference value: this information was in agreement with the prior value. To compare the pharmacokinetic parameters distributions between adults and children, Perez-Ruixo et al. used a ''parametric bootstrap approach'' on a dataset of 12 children (12 samples per children)  [26]. They compared the estimates of the model built with prior to the theoretical distribution of parameters that would have been obtained if adults and children had the same parameter distributions. This theoretical distribution was obtained with stochastic simulation (with uncertainty) and estimation.
First, they simulated 1000 (new) pediatric datasets with uncertainty, using the parameters distributions of a nonparametric bootstrap of the (reference) adult model with allometric scaling. Then, they estimated the parameters of the model built with prior on each of the 1000 simulated datasets. The distribution of these estimated parameters constituted the theoretical distribution. The estimates of the fixed effect parameters of the model built with prior was within the 95% confidence interval of the theoretical distribution, which confirmed the similarity of the pharmacokinetic parameter point estimates between adults and children. On the contrary, the estimates of the between-subjects and residual variability of the model built with prior were out of the 95% confidence interval of the theoretical distribution, which did not confirm the similarity of PK parameter Fig.
4  Case example of the plot proposed by Tsamandouras et al. [11] , data from  [43]. The estimate of THETA(CL) in the model built with prior was 12. 7 L. h -1 (black line). THETA(CL) in the reference model was 9. 89 L. h -1 and its standard deviation 3. 71 L. h -1 (dotted lines) distribution between adults and children. The authors proposed this approach to detect differences in the distribution of PK parameters between adults and children. However, this approach is questionable, since the prior constrains the estimation of pediatric parameters to be similar to the adult estimates. If there were differences in PK parameters between the children and the adult dataset, it would be difficult to find them as the parameters estimates on the children dataset are already constrained by the adult values.
In that article, what is interpreted by the authors as a difference in PK parameter distribution (OMEGA) could actually be a difference in PK parameters compensated by an inflated parameter distribution due to the constrained bias in PK parameter estimates. In cases where the model on new data can be built without prior (e. g. priors are used to stabilize the model and to avoid flip-flop kinetics), its final parameter values can be compared with those of the model built with prior  [14, 35]. In cases where the previous data are available, the similarity in PK parameter distribution between populations can be assessed by comparing the results of models estimated without prior on the pooled data stratified by two different approaches  [27]. The first stratification is an arbitrary dichotomization that stratifies the pooled dataset by population (previous and new populations).
The second stratification is a random dichotomization implemented by the MIXTURE subroutine in NONMEM, which identifies two subpopulations with different PK parameters. If the previous and the new populations are identified as subpopulations in the random dichotomization, the previous and the new populations are different: the model on the new population should not integrate prior from the previous population. If the subpopulations identified in the random dichotomization are not consistent with the arbitrary split, i. e. there are individuals from both the previous and the new population in each subpopulation, it is assumed that previous and new data are part of the same population: the variability in PK parameters can be described by covariates. In this case, it is possible to estimate on the new data a model built with prior from the previous population.
Assessment of the impact of covariates implemented a priori After implementation a priori, the impact of covariates can be assessed in terms of effect on the parameter, for example graphically plotting parameter versus covariate  [7, 9, 12]. The physiological plausibility has to be taken into account. To test if the covariate is still significantly influencing the model on the new population, it is unclear how the OFV can be used for comparison between models: the LRT cannot be used to compare models directly with DOFV when changes are made in the PRIOR information  [14]. Krogh-Madsen et al. chose to compare OFV on the data (O S ). The two models built with prior (one using the previous model without covariate and the other using the previous model with one covariate) were run on the new dataset.
Search for new covariates Stepwise covariate modelling Stepwise covariate modelling is questionable when using the PRIOR subroutine. In some articles, covariates were added on parameters estimated with priors using the classical stepwise covariate modelling with forward inclusion and backward deletion (for example, p \ 0. 05, that is a threshold of DOFV = 3. 84 in the hypothesis that the DOFV is Chi-square distributed)  [5, 19, 23]. In practice, specific considerations should be kept in mind when searching for covariates on parameters that are estimated with priors: -with prior information on THETA, the typical values of the parameters are constrained to be close to the one of the reference model: if the covariates were not similarly distributed in the previous and the new population, the covariate should be centered around its median in the previous dataset.
Alternatively, the median covariate of the new dataset could be used, but the THETAP shall be adjusted accordingly, and it is important to take into account that the uncertainty of the parameter, which depends on the normalization, can be biased. -with prior information on ETA, the introduction of a covariate would decrease less the inter-individual variability than if the inter-individual variability was estimated on the new dataset. As much as possible, covariate search on model parameters estimated with priors should not be performed: if the new data is statistically too weak to support a PK/PD parameter, even on a base model, then the statistical power is likely too low to support a covariate analysis on that parameter. Instead, one should rather search for covariates only on parameters without prior  [44].
Conclusion The PRIOR subroutine is a valuable approach to analyze sparse/rare data or estimate mechanistic-based models in an easy way and acceptable run times. Even if this subroutine has been available in NONMEM for years, its use is still uncommon: few articles are available and a lot of questions remains. Anyway, some recommendations can ease the future use of this function and limit the risk of misuse. First of all, the choice the reference model is critical: it can be either carefully selected or combined from several models. Regardless of the reference model, it is best to test the robustness of the final model, e. g. with external VPC. In order to specify the prior weight, the usual approach is to retain the model with priors on the least parameters and with the lowest informativeness on some parameters, but that still allows for a good estimation.
From this table, we define an algorithm to define evolution sequences by architectural element, then we determine the sequential patterns that define the recurrent evolution sequences. We define another algorithm that, based on a database of evolution sequences, determines the evolution rate of architectural elements and the participation rate of actors in evolution operations. We explain our approach through a study on two examples of component-oriented software architecture evolution. In this study, we are interested in the evolution of the structure of architecture. This paper is organized as follows: Bellow related work on software architecture evolution, software architecture evolution planning and prediction solutions and knowledge extraction from data (data mining) is presented. In Section 2, we present the case study on which the approach will be applied.
Software Architecture Evolution Planning and Prediction Solutions In  [29] , the authors proposed a method and the accompanying tool set to aid the user in selecting the "best" evolution alternative. The user specifies the desired types of evolutions and inputs the initial architecture (also specified in xADL). The proposed tools, first, convert the initial architecture to a graph and, then, fetch architectural changes (from the repository) whose categories match the desired evolutions. Next, the evolution alternatives are generated with a graph transformation tool. Quality attributes are evaluated for each evolution alternative based on the impact of different architectural changes. Finally, the user can query and score the evolution alternatives according to the desired structural and path properties.
Extracting Knowledge from Data (Data Mining) Extracting knowledge from data (data mining) has been used in many areas to find patterns to solve decision-making or future projection problems in companies. We then present some work done in this direction. Agrawal et al. [13] , introduced the sequential pattern discovery problem. From a database of client transactions, they define a sequence database where each sequence represents all the items purchased during a transaction. It was a question of discovering all the sequential patterns with a specified minimum support. They defined three algorithms to solve the problem including the AprioriAll, AprioriSome and DynamicSome algorithms. In 1996, the same team proposed an improvement of the previous result with three modifications as follows: First, they add time constraints that specify a minimum and/or maximum time period between adjacent elements of a pattern.
Evolution Model An evolution style captures a characteristic way of evolving all or part of a software architecture. Previously, a meta-model of evolution style was defined  [27]. We extend it to define an evolution style as a process (Figure  3 ) by specifying the role, the architectural element and the operation. Thus, the extended meta-model (Figure  3 ) answers the following questions: What? (what is evolving?) through the ArchitectureElement package, who? (who did it?) through the Actor concept, when? (when to evolve?) from the TimeEvolution concept and how? (how to make it evolve?) through the concepts Header, Competence, Action ands Impact. All the concepts Header, Competence, Action, Impact define the operation. Through the class diagram in Figure  3  bellow, we highlight the concepts and relations of our model that we call MSAES.
The concept EvolutionStyle is the core of our model, it encapsulates what allows to describe and apply an operation of evolution to an architectural element. It consists of two complementary parts: A header and a competence. The Header class describes the signature of the operation. The competence class is split into Action and Impact. Action is a procedure or function of evolution that focuses on the evolving architectural element. It describes an implementation unit corresponding to the header. The Impact class specifies the evolution styles that will be impacted by the execution of the currently defined style. It allows to establish a relationship between the evolution styles. The Actor concept defines the actor, either a natural person or a program that triggers the operation. The ArchitectureElement package including the evolving element and its category allows to model and reify any significant element of an evolving architecture.
After expressing all the evolution styles of the evolving architecture with SAES, a large amount of Evolution data is obtained, it can be use to extract the sequential evolution patterns of software architectures, discover the architectural elements change rate and the actors participation rate in evolution operations in order to plan and predict all possible paths towards the An + 1 architecture. Below we use SAES to express all evolution styles for A 1 evolving to A 3 on the two defines examples.
Principle to Extract Sequential Patterns of Software Architectures Evolution A first functionis defined, which starting from the Table  1 , associates with each architectural element, the corresponding evolution sequence. The function named Sequence (Algorithm 1), retrieves the table (Table  1 ) sorted on the Category, TimeEvolution and Element columns and associates with each architectural element the corresponding evolution sequence. It provides as an output an equivalent of Table  2. The second function named SequenceSupport (Algorithm 2) allows to compute and associate to each candidate sequence its support, it takes as input the candidate sequences defined from Table  2  (output of the previous function), then computes and associates to each sequence its total number of appearance among all the other candidate sequences.
Discussion and Conclusions In the literature, we found two works similar to that proposed in this paper. These are  [29, 30]. Even if the two works and ours propose a solution to automate the software architecture evolution process, there is still a big difference with respect to the final objectives and the methodologies used to achieve the objectives. According to  [29] , the user must specify the desired types of evolution and introduce the initial architecture (specified in xADL). Then the evolution alternatives are generated with a graph transformation tool. According to  [30] , the user must specify the initial architecture and the target architecture. Then, using an automatic planner the path (in terms of transition architectures) by which the initial architecture can evolve towards the target architecture is generated.
It is worth mentioning here that we use CVT variant of MAP-Elites  [29]  that uses centroidal voronoi tesselation to discretize the task-space into the user specified number of homogeneous geometric regions. In CVT Map-Elites, the number of cells remains fixed irrespective of the dimensionality of the task-space, making it scalable to a very high dimensional task-space. Execute π * and record data in D 7: Update transformation models T with D 8: end while.
However, we ablate the learning of transformation model. Thus, it assumes that one of the repertoire will perfectly match to the test object/damage. However, this assumption is not true since we do not include the repertoire that matches with the test object/damage. All the simulated experiments were implemented in python (see APPENDIX). For both the tasks, we used the pybullet physics simulation library  [33]. For comparison with RTE  [13]  in the hexapod task we used the author provided code https://github. com/resibots/ chatzilygeroudis_2018_rte. For the GP model, we used gpy library  [34]. A video of the experiments is available here : http://tiny. cc/aprol_video.
A. Object Pushing with a Robotic Arm The goal here is to push objects of various shapes and sizes to different goal locations in a minimum number of steps. In this task, we assume that the robot has access to its model (so that it can be controlled in Cartesian space) and to the center and orientation of the object from a vision system (for instance, a QR code on the object). However, the robot does not have any knowledge about the shape and size of the objects. The objective is to adapt to push these objects of unknown shapes and guide them to the target position. Elementary policy: We encode the elementary policy (open loop controller) of the robot with 2 parameters in [0, 1]. These two parameters specify a straight line connecting two (2) During execution, the end effector follows the straight line connected by these two points using inverse kinematics of the arm.
As a consequence, reusing data from different platforms that are obtained through different methods, sensors and protocols, has become a major challenge. The standardization of the information for semantic interoperability of heterogeneous datasets is a key Phenotyping is a major issue for wheat agriculture to meet the challenges of adaptation of wheat varieties to climate change and chemical input reduction in crop. The need to improve the reuse of observations and experimental data has led to the creation of reference ontologies to standardize descriptions of phenotypes and to facilitate their comparison. The scientific literature is largely under-exploited, although extremely rich in phenotype descriptions associated with cultivars and genetic information. In this paper we propose the Wheat Trait Ontology (WTO) that is suitable for the extraction and management of scientific information from scientific papers, and its combination with data from genomic and experimental databases.
We describe the principles of WTO construction and show examples of WTO use for the extraction and management of phenotype descriptions obtained from scientific documents. milestone  [2]. An ontologie, as defined in  [3] , is designed to represent the knowledge from one domain by concepts (or classes), relationships among these concepts and instances of these concepts. Therefore ontologies have long been identified as a critical tool for managing information systems in the fields of integrative plant biology, genetics and phenomics  [4] : among others Gene Ontology  [5, 6]  defines gene functions, biological processes and cellular components ; the Plant Ontology (PO) Database  [7, 8]  developed by the Planteome Project is a community resource for plant structure and developmental stages controlled vocabulary and annotations  [9].
g. , grain, plant). CO distinguishes specific traits for 31 economically important plant species. Their vocabularies have reached different stages of development, ranging from pearl millet (52 variables) to wheat, the richest, with 498 variables. Beside observation and experimental data, scientific literature is a significant source of genetic and phenotypic information on plants  [14, 15]. Automatic information retrieval and information extraction have been acknowledged as major challenges in Life Science for assisting manual biocuration, either to assess experimental or inferred data quality or to fill databases with complementary information  [16, 17]. However, most work focuses on molecular biology, functional and comparative genomics resource development, and phenotypic-related human health, as the Biocreative Track III interactive text mining task in 2012  [18].