dataesr/scientific-paragraphs-categorization huggingface dataset:
We do not know how strong the e ect of using such an incorrect tree is, but it is probably more important than the e ect of including intra-strain recombinants. For this reason, with the exception of CRF01_AE, we have avoided the inclusion of CRFs in the reference alignment, and we advise users to do the same. In the HIV-genome dataset we have noticed that SHERPAS appears to have some di culties distinguishing CRF01_AE from strain A1 (this can be observed for example in queries 18, 51, 65 in Section C below), which is related to the fact that the relative position of CRF01_AE and the A strains within the reference tree is probably erroneous for at least part of the reference alignment. In the next subsection we discuss how an advanced user may include CRFs in the reference alignment in a clever way, which addresses in a satisfactory way the problems above.
Preprocessing The operations necessary prior to the execution of SHERPAS -i. e. the construction of the reference tree, of the phylo-k-mer database, and of the sequences-to-strains mapping-were the same for all the datasets. We briefly describe them here. Reference trees were constructed from the reference alignments (dataset-specific, see below) using  PhyML 3. 3. 20180214 [5]  using GTR+ +I as nucleotide substitution model. A discrete distribution with 4 rate categories (the default in PhyML) was used to model rate variation across sites. The proportion of invariant sites was estimated from the alignment, as well as the shape parameter -(the latter is done by default by PhyML). The resulting reference trees are included in the Dryad dataset. PhyML was run with the following command: phyml -i <reference_alignment> -m GTR -v e The pkDBs were constructed using RAPPAS2 v0.
1. 3a, using parameter k = 10 (which is not the default value) and threshold parameter Ê = 1. 5 (the default). (See also Sec. 1. 3 above for the meaning of the Ê parameter. ) The resulting pkDBs are included in the Dryad dataset (. rps files). RAPPAS2 was run with the following command: python3 rappas2. py build -s nucl -b <path_to_phyml> -w <output_directory> -r <reference_alignment> -t <reference_tree> -m GTR -k 10 Finally, the. csv files mapping reference sequences to strains were constructed manually, using the information contained in the name of each sequence, to infer the strain it belongs to. These files are also included in the Dryad dataset.
HIV-pol References. The reference alignment that we used for SHERPAS is the one provided with SCUEAL (167 sequences), and is available at https://github. com/spond/SCUEAL/blob/ master/data/pol2009. nex. (Accessed October 2019. ) Queries. The 10,000 synthetic queries and the output of SCUEAL for these queries are accessible here: http://www. hyphy. org/pubs/SCUEAL/SCUEAL%20Files_files/Shuffled. zip. (Accessed October 2019. ) Note that none of the queries contains fragments from strains A3, AE, O, CPZ, although these strains are present in the reference alignment. All other strains in the reference alignment are present in some queries. Running SHERPAS. The reference tree reconstructed from the reference alignment, the. csv file recording the classification of the sequences in the reference alignment, and the.
rps file encoding the pkDB for this dataset are available in the pkDB-HIV-pol directory within the Dryad dataset. SHERPAS was executed with the following command: SHERPAS -o out/ -d pkDB-HIV-pol/DB_k10_o1. 5. rps -q HIV_pol/queries. fasta -g pkDB-HIV-pol/ref-groups. csv Running jpHMM. jpHMM was executed with default parameters for HIV sequences, with the following command:. /jpHMM -s HIV_pol/queries. fasta -v HIV It was also executed with the -Q blat option for speed-up. Out of the several files produced by jpHMM, the output file that was used to read the predictions made by jpHMM is recombination. txt. Note that on this dataset the reference alignment used by jpHMM does not coincide with that used by the other methods. Moreover, as discussed in the main text, strains A and N cannot be recognized by jpHMM, which has a slightly negative impact on jpHMM's accuracy measures on this dataset.
HBV-genome References. We used the reference alignment that is distributed with jpHMM. It can be downloaded at http://jphmm. gobics. de/download. html, and is located at Queries. We downloaded the dataset for nucleotide sequence genomes of "genotype all" from the HBVdb website at https://hbvdb. ibcp. fr/HBVdb/HBVdbDataset?view=/data/ nucleic/alignments/all_Genomes. clu&seqtype=0. (Accessed December 2019. ) This alignment contained 7273 sequences, from which we removed 794 sequences marked as recombinant and 273 sequences that belonged to the jpHMM reference alignment. We also removed 1664 sequences that were estimated to be recombinant by both jpHMM and SHERPAS (in fact some of these turn out to be known recombinants  [12] ). The remaining 4542 sequences are the pre-queries that were used as a basis to build 3000 recombinant queries.
Out of these 3000 queries, 2000 combine fragments from two pre-queries, and 1000 are based on three pre-queries. To generate each query, we proceeded as follows. First, the 2 or 3 pre-queries are chosen at random from the 4542, making sure that they belong to di erent strains. Second, we picked 2X random coordinates within the alignment of prequeries, where X Ø 1 is chosen following a geometric distribution with parameter p = 0. 8. The coordinates are chosen uniformly at random, making sure that no two coordinates are less that 100 positions apart (circularity-wise). This is the same minimal distance that was applied to build the dataset of queries for HIV-pol  [8]. Third, each query is built by combining segments extracted from the selected pre-queries and delimited by the random coordinates.
The parameters that we used in the construction procedure above were chosen so as to loosely reflect the characteristics of inter-genotype HBV recombinants presented in a recent overview  [1]. For example, about 80% of the recombinants involving 2 genotypes in Fig. 2  of that review only have 2 breakpoints, hence the choice of p = 0. 8. The queries are available here: https://github. com/phylo42/sherpas/blob/master/ examples/HBV_all/queries-3000. fasta. Running SHERPAS. The reference tree reconstructed from the reference alignment, the. csv file recording the classification of the sequences in the reference alignment, and the. rps file encoding the pkDB for this dataset are available in the pkDB-HBV-full directory within the Dryad dataset (link above). SHERPAS was executed using the option -c (for circular queries) with the following command: SHERPAS -o out/ -d pkDB-HBV-full/DB_k10_o1.
The length of the resulting queries is on average 8. 9 kbp, and it ranges from 5. 5 kbp to 9. 9 kbp. The longest queries are obtained from pre-queries whose length is close to 1 Mbp (which are common e. g. in group O or when sampled from chimpanzees). The queries are available at https://github. com/phylo42/sherpas/blob/master/examples/ HIV_all/queries-C. fasta. Running SHERPAS. The reference tree reconstructed from the reference alignment, the. csv file recording the classification of the sequences in the reference alignment, and the. rps file encoding the pkDB for this dataset are available in the pkDB-HIV-full directory within the Dryad dataset (link above). SHERPAS was executed with the following command: SHERPAS -o out/ -d pkDB-HIV-full/DB_k10_o1. 5. rps -q HIV_all/queries-C. fasta -g pkDB-HIV-full/ref-groups.
HIV Nanopore reads dataset References. The reference alignment, the reference tree and the pkDB are the same as in the HIV-genome dataset. Queries. We ran NanoSim-H (v1. 1. 0. 4) on the queries from the HIV-genome dataset using the following command: nanosim-h HIV_all/queries-C. fasta -n 100000 -o reads-C. fasta -u 0. 0 --maxlen 9000 --min-len 1000 This generates 100,000 reads, each one from a random query of the HIV-genome dataset. For each one of these sequences, we picked the first (in order of appearance) read in the forward strand that comes from that sequence. The resulting queries are available at https: //github. com/phylo42/sherpas/blob/master/examples/HIV_all/reads-C. fasta. Running SHERPAS and jpHMM. The same commands as for the HIV-genome dataset were used here (replace the name of the file containing the queries).
That is, Recall = TP TP+FN. Specificity (also known as true negative rate): out of all queries that are not inter-strain recombinants, the proportion that are not classified as inter-strain recombinants. That is, Specificity = TN TN+FP. Dataset. Among the 10,000 queries in the HIV-pol dataset, there are 2374 queries that are not inter-strain recombinants. Thus this dataset provides a way to compute not only the recall of a classifier, but also its specificity. Moreover, on this dataset, the results of running SCUEAL were made available by the authors  [8] , which allows us to compare the results of SHERPAS to those of jpHMM and SCUEAL. Since jpHMM is not designed to recognize sequence fragments labelled as A or N, for this experiment we removed all queries from the initial 10,000 that were obtained from sequences from these two strains.
2  of the main text. A number of observations can be made here. First, while SCUEAL and jpHMM result in good (Pareto-optimal) combinations of specificity and recall, it is interesting to note that on this dataset jpHMM has better (100%) specificity, while SCUEAL has better recall. Previously reported results on other datasets do not appear to confirm this observation  [13] , but this is not surprising because those results were on a di erent but related classification problem, namely typing whole sequences, i. e. assigning them to their strain of origin. Moreover SCUEAL implements a non-deterministic optimization heuristic, with better results expected if SCUEAL is left to run for longer times. As for SHERPAS, window size has a clear e ect, with small windows favoring recall, and larger windows favoring specificity.
If the two best-scoring branches in SHERPAS-full are both from X, then X will be present in the output partition, irrespective of the value of the threshold. This is why the specificity for SHERPAS-full cannot be increased up to 100% by increasing its threshold. Coming back to the question that motivated this experiment -namely the use of SHERPAS as a first screening tool for inter-strain recombinants-finally note that very high recall can be achieved with both SHERPAS-reduced and SHERPAS-full by using small window sizes and low thresholds. On this dataset, these parameter settings result in recall that is substantially higher than jpHMM, and at least as good as SCUEAL (up to 99. 0% for SHERPAS-full, and up to 99. 4% for SHERPAS-reduced, vs. 98. 8% for SCUEAL), for a fraction of their running times.
C Annex: Some illustrated outputs To provide readers with a feeling of how similar the results of SHERPAS and jpHMM are, here we show an illustration of the outputs of SHERPAS-full (with default parameters) and jpHMM on the first 100 queries out of the 3000 in the HIV-genome dataset. For each query, three bars show respectively: (top) the true composition of the query, with di erent strains represented by di erent colors; (mid) the output of SHERPAS-full, (bottom) the output of jpHMM. Black regions are those that are left unassigned (N/A) by the recombination detection tool. Each colored fragment is drawn to scale. Below the three bars, we also report the color coding for the strains present in at least one of the three bars. It is helpful to look at these results together with those reported in Table  4  in the main text.
The reference data and their preprocessing. 1. 2 Probability of a k-mer at a position in the reference tree and alignment. 1. 3 Phylo-k-mers and their probability score. 1. 4 The full pkDB. 1. 5 The reduced pkDB. The algorithm at a glance. 2. 2 The main procedure. 2. 3 Heap property and update. 2. 4 Converting scores into likelihoods. 2. 5 Producing the output. 2. 6 Complexity analysis. Accuracy of the reference tree. 3. 2 Absence of recombination. 3. 3 Dealing with circulating recombinant forms. 3. 4 Monophyly of the strains. 5. 2 HIV-pol. 5. 3 HBV-genome. 5. 4 HIV-genome. 5. 5 HIV Nanopore reads dataset. 5. 6 Experiment on the specificity/recall trade-o.
Funding and conflicts of interest This work was publicly funded through  ANR  (The  French National Research Agency)  under the  Investissements d'avenir programme  with the reference  ANR-16-IDEX-0006. B. L. is employed by  Spygen , a company specialising in aquatic and terrestrial biodiversity monitoring using environmental DNA. N. R. is supported by a doctoral fellowship from the  French Ministère de l'Enseignement supérieur, de la Recherche et de l'Innovation. We also received support from  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ).
1 1 3 Assumptions on the reference data 3. 1 11331 The reference data and their preprocessing. 1. 2 Probability of a k-mer at a position in the reference tree and alignment. 1. 3 Phylo-k-mers and their probability score. 1. 4 The full pkDB. 1. 5 The reduced pkDB. The algorithm at a glance. 2. 2 The main procedure. 2. 3 Heap property and update. 2. 4 Converting scores into likelihoods. 2. 5 Producing the output. 2. 6 Complexity analysis. Accuracy of the reference tree. 3. 2 Absence of recombination. 3. 3 Dealing with circulating recombinant forms. 3. 4 Monophyly of the strains. 5. 2 HIV-pol. 5. 3 HBV-genome. 5. 4 HIV-genome. 5. 5 HIV Nanopore reads dataset. 5. 6 Experiment on the specificity/recall trade-o.
GNSS Data Processing Among other multidisciplinary networks, OVPF maintains 24 permanent GNSS stations, one of the most dense networks at an active volcano. Raw data are processed using PPP method by the Gipsy-Oasis software  (Desai et al. , 2014)  providing daily solutions in the ITRF2008 reference frame. The typical standard deviations over 5 year period has been estimated at a station located 15 km from the summit and not affected by volcano deformation (REUN, Figure  1a ), and equal to 5. 2, 4. 8, and 11. 2 mm for eastern, northern, and vertical components, respectively. We removed horizontal tectonic motion from the time series using linear velocity trend estimated from this station, with values of +18 mm/yr eastern and +12 mm/yr northern. In order to increase the signal-to-noise ratio of GNSS observations, we compute displacement trends over a 7 day moving-window.
In view of these promising results, we implemented this method operationally as an extension of the GNSS module in the WebObs system  (Beauducel et al. , 2020) , an integrated web-based system for data monitoring and network management implemented in 15 observatories worldwide. The module was initially developed with a simple isotropic point source  (Beauducel et al. , 2014 (Beauducel et al. , , 2019)) , and we added the possibility of setting a pCDM source and associated parameters for real-time modeling. This has been especially useful during recent crisis managements at La Soufrière de Guadeloupe  (Moretti et al. , 2020)  and PdF.
In our context, the mock components can be used for several purposes, e. g. , for increasing test coverage, for security testing, or for testing new systems made up of reusable components during the development activity. To reach these purposes, our approach combines model learning, quality metrics evaluation and mock generation. Model learning is used to infer models, which encode the behaviours of every component of a communicating system and its architecture. On these models, we evaluate 6 quality metrics mostly related to Auditability, Testability and Dependability. These metrics allow to classify components into 4 categories: "Mock", "Test", "Test in Isolation" and "Code Review". We finally propose model-based algorithms to help generate and execute mocks. This approach has been implemented as a tool chain available in  [18].
λ denotes a labelling function mapping each vertex v ∈ V to a label λ(v) Our approach is structured into 3 main steps, illustrated in Figure  1. A model learning technique is firstly applied to a given event log collected from a system denoted SUT. For every component c 1 of SUT, it generates one IOLTS L(c 1 ) expressing the behaviours of c 1 along with one dependency graph Dg(c 1 ) expressing how c 1 interacts with some other components of SUT. The second step computes quality metrics on these models, and assists the developer in the component classification under the categories: "Mock", "Test", "Test in Isolation", "Code Review". Once the mockable components are identified, the third step helps the developer in the mock generation by means of the IOLTSs produced previously. It is worth noting that mocks often increase test coverage along with the generation of more logs, which may be later used to generate more precise IOLTSs and re-evaluate metrics.
Some quality attributes can now be automatically evaluated for all the components of SUT. By means of these attributes, we propose to classify the components into 4 categories "Mock", "Testable", "Testable in Isolation", "Code Review", to help developers dress their test plan. To select relevant quality attributes, we firstly studied the papers dealing with the use of mocks for testing, e. g. ,  [10, 11, 21, 3, 16, 12, 2, 23, 22]. In particular, we took back the conclusions of the recent surveys of Spadini et al. [22, 21] , which intuitively report that developers often use mocks to replace the components that are difficult to interpret, complex, not testable, or those that are called by others (e. g. , external components like Web services). Then, we studied some papers related to Testability  [7, 19, 8, 9]  and Dependability  [20, 5].
But in the present work, we have models inferred by a model learning technique. They generalise what we observed about SUT, but may expose more behaviours than those possible (over-approximation) or may ignore behaviours that can occur (under-approximation). As a consequence, we shall talk about fuzzy metrics in the remainder of the paper. We hence measure quality with the 6-tuple Acc f , U nd f , InDeps f , OutDeps f , Obs f , Cont f. This notion of fuzzy metric, albeit unusual, reinforces the fact that the quality measurement may evolve as we gather more data by testing SUT and updating the models. Table  1  summarises our literature study and our interpretations of the relationships of a component with the four component categories studied in the paper with respect to quality metric measurements.
g. , transition removal, transition duplication, action alteration, etc. With security testing, the Mock model might be automatically generated from L(c1) by injecting sequences of transitions expressing attack scenarios. If we take back our example of Figure  3 , the quality metrics given in Table  4a  reveal that d1 and d2 are good candidates as mockable components. Figure  4b  shows a mock example for d1. This IOLTS was written by hands from the IOTS L(d1) of Figure  3. It aims at experimenting G with unexpected and high temperature values. Component Acc f U nd f InDeps f OutDeps f Obs f Cont f Mock d1 1 1 1/2 1/2 1 0 X+ d2 1 1 1/2 1/2 1 1 X+ G 1 1 1/2 1/2 0 0 X ( A Mock runner is a generic piece of software in the sense that its design and implementation depend on the type of system considered.
Compared to the classical leaky integrator model, we found a better match between our probabilistic agent and the behavioral responses, both for the anticipatory eye movements and the explicit task. Furthermore, by controlling the level of preference between exploitation and exploration in the model, we were able to fit for each individual's experimental dataset the most likely level of volatility and analyze inter-individual variability across participants. These results prove that in such an unstable environment, human observers can still represent an internal belief about the environmental contingencies, and use this representation both for sensory-motor control and for explicit judgments. This work offers an innovative approach to more generically test the diversity of human cognitive abilities in uncertain and dynamic environments.
To our knowledge, such algorithm was not yet available, and we will here provide with an exact analytical solution by extending previous results from  [58]  to the binary nature of data in the Binary Switching model presented above (see Eq 1). In addition, the proposed algorithm is biologically realistic as it uses simple computations and is online, that is, all computations on the sequence may be done using solely a set of variables available at the present trial, compactly representing all the sequence history seen in previous trials. We will also provide a computational implementation and a quantitative evaluation of this algorithm. Then, we will present the analysis of experimental evidence to validate the generalization of previous results with this novel switching protocol. In order to understand the nature of the representation of motion regularities underlying adaptive behavior, we collected both the recording of eye movements and the verbal explicit judgments about expectations on motion direction.
Still, as for the latter, there is only one parameter h ¼ 1 t which informs the BBCP model that the probability bias switches on average every τ trials. Moreover, note that the resulting operations (see  Eqs 4, 7, 8, 11 and 12)  which constitute the BBCP algorithm can be implemented online, that is, only the state at trial t and the new datum x t 0 are sufficient to predict all probabilities for the next trial. In summary, this prediction/update cycle exactly inverts the binary switching generative model and constitutes the Binary Bayesian Change-Point (BBCP) detection model. Quantitative analysis of the BBCP detection model. We have implemented the BBCP algorithm using a set of Python scripts. This implementation provides also some control scripts to test the behavior of the algorithm with synthetic data.
In a second analysis, we quantitatively estimated the squared Pearson correlation coefficient and the mutual information between the raw data and the different models, both as computed on the whole data or for each observer individually (see insets in  Fig 4). The respective values for the whole dataset (r 2 = 0. 486 and MI = 0. 729) and across participants (r 2 = 0. 459 ± 0. 104 and MI = 0. 707 ± 0. 134) are slightly higher than that found by  [48]  and  [11]  for anticipatory pursuit measures gathered across experimental trial blocks with fixed direction biases and significantly better than that estimated with the true probability (r 2 = 0. 381 ± 0. 083 with p = 0. 002 and MI = 0. 562 ± 0. 107 with p = 0. 002) and for that estimated by the leaky-integrator model (r 2 = 0. 366 ± 0. 089 with p = 0.
002 and To analyze the relation between these behavioral data with the predictions made by models, we first looked at the variability of all these measures conditioned on the predicted probability and gathered over 5 equal partitions of the [0, 1] probability segment. For the 12 participants, we collected an estimate of (A) the amplitude of anticipatory pursuit (aSPEM) and (B) the bet score value. As a regressor, we have used the true probability (P true ¼ x t 1 , blue color), and the probability bias estimates obtained with a leaky integrator (P leaky , orange color) and by the BBCP model (P BBCP , green color). We display these functional relations using an error-bar plot showing the median with. 25 and. 75 quantiles over the 5 partitions. This shows a monotonous dependency for both behavioral measures with respect to the probability, close to a linear regression, but with different strengths.