dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, environmental microbial species are delineated according to a sequence similarity cut-off based on comparisons of SSU rRNA gene sequences to demarcate operational taxonomic units  [52]. Although they do not technically represent species, OTUs composed of multiple sequences can be used to describe novel species, using the provisional designation of ''Candidatus'', when the SSU rRNA gene sequences are sufficiently different from those of recognized species  [53]. In this study, after dataset cleaning and sorting, the reads left for the affiliation were clustered at a 95% identity threshold as proposed by Caron et al. [54]  to delineate eukaryotic taxa. These authors defined this similarity threshold after studying the distribution of intra-and inter-specific variations of the 18S rRNA gene in protistan communities.
Materials and Methods The data originating from simulations and pyrosequencing were processed by a pipeline, referred to as PANAM (Phylogenetic Analysis of Next-generation AMplicons) that is based on publicly available programs. In addition to the phylogenetic analysis, this pipeline allows for the complete analysis of a full pyrosequencing run, including raw data processing, sequence clustering into OTUs and generating phylogenies for the taxonomic affiliation. The description of the procedure is detailed in the following sections (''Processing of raw pyrosequencing reads and OTU picking''; ''Phylogenetic affiliation''; ''Richness and diversity indexes''). It is written in Perl and can be run on Linux. The package comprises a reference sequence database, a taxonomy file and reference profile alignments and can be obtained from http://code.
Processing of raw pyrosequencing reads and OTU picking The pyrosequencing reads can be cleaned according to different methods commonly used in the field of molecular microbial ecology. Pyrosequencing errors can therefore be reduced by removing the primers (e. g. ,  [69] ), defining a minimal score and length of the reads (e. g. ,  [14] ) or removing reads with unidentified bases (Ns). Short sequences and sequences with low-quality scores are removed using PANGEA scripts  [16]  and only sequences with a primer match percentage above a defined threshold are selected using Fuznuc  [70]. Alternatively, other quality filtering methods can be implemented; the platform does not depend upon the filtering approach described above. When several samples are analyzed, the checked sequences are split into different files depending on their bar code or tag.
Phylogenetic affiliation For the phylogenetic affiliation, a dedicated database of reference sequences, verified taxonomy and alignments was built using sequences extracted from the SSURef 108 database of the SILVA project  [72]. For this purpose, all the sequences (16S and 18S rRNA genes) with more than 1,200 bp, quality score. 75%, and a pintail value. 50 were extracted. The sequence quality score defined by SILVA is a combination of the percentages of ambiguities, homopolymers longer than 4 bases and possible vector contaminations, and the pintail value corresponds to the probability that the rRNA sequence is chimeric. The complete database, after filtering according to the criteria above, contains 164,353 sequences (Archaea: 11,092; Bacteria: 131,428; and Eukaryota: 21,833) together with their taxonomy.
For each of the 37 phyletic groups, an outgroup containing one sequence from each other group belonging to the same domain plus 2 external sequences were added to the alignment to root the phyletic tree to be produced and to specify the relatedness of early diverging sequences from the root of the group. To broaden the targeted diversity, the user can add specific environmental sequences to the database and the profiles. Using this dedicated database, the phylogenetic affiliation is carried out following the different stages described in the Figure  5. 1-First, OTUs are compared against the reference database described above with USEARCH  [71]. As this first step does not intend to provide an exact affiliation, but rather to give a first approximation to perform a rapid and accurate phylogenetic analysis, the query sequences are sorted according to the taxonomy of their best hits, whatever their similarity score.
Several files are generated, each containing the reads and their 5 best hits, assigned to one of the 37 specific phyletic groups. 2-After reads have been assigned to phyletic groups, they are aligned to the reference sequences of the corresponding profile alignment for that group using hmmalign from the HMMER package  [73]. Synthetic files, which include the reference sequences and the aligned experimental reads, are generated. 3-Using FASTTREE  [74] , a bootstrapped phylogenetic tree (100 iterations) is built for each phyletic profile, including OTUs associated with their 5 best hits and the reference sequences. The trees are then parsed to generate files containing the taxonomy of the inserted sequences and files reporting the clades that could be identified from reads forming monophyletic groups.
Richness and diversity indexes After the cleaning step, richness (Chao1 and ACE), diversity (Shannon) indexes, and coverage are computed for each sample  [77]. Subsequently, sequence library sizes are equalized to avoid biases associated with different sampling depths (e. g. ,  [78] ). Briefly, the same number of sequences (i. e. , the number of sequences in the smallest sample) are randomly sampled from each library, and diversity indexes are calculated for these equalized datasets. After phylogenetic affiliation, Chao1 and the Shannon diversity indexes are computed for levels 2 and 3 from the EMBL classification (e. g. , Stramenopiles and Bacillarophyta ).
Analysis of sequencing data obtained from simulations PANAM was first tested on near full-length sequences with known taxonomy using 5 sets of 1000 sequences randomly picked from the reference database and removed from it for evaluations to be re-affiliated. The reliability of PANAM taxonomic affiliations was evaluated for specificity defined as the proportion of ranks correctly affiliated among the detected ones. A pyrosequencing simulation was also performed with pseudo-reads being generated by clipping the 5 6 1000 full-length sequences datasets from 6 universal forward primers for Eukaryotes  [79]  (Table  S2 ). Clipped sequences were extended 200 and 400 bp from the forward primer positions defined on the Saccharomyces cerevisiae sequence (V01335), thus covering regions with different variability along the 18S rRNA gene.
As emphasized, this pipeline allows taxonomic affiliations within an evolutionary context: its performance was thus primarily compared with that of STAP (Small Subunit rRNA Taxonomy and Alignment Pipeline)  [51] , the phylogenetic affiliation method used in WATERS (Workflow for the Alignment, Taxonomy, and Ecology of Ribosomal Sequences)  [41] , but was also compared with non-phylogenetic methods, including BLAST and the RDP Classifier implemented in MOTHUR  [19]  trained on the near full-length and trimmed sequences of the reference database. The computational load of the phylogenetic analyses using PANAM was also tested with increasingly large datasets to evaluate processing time on a personal computer and to detect any scaling issues.
Analysis of sequencing data obtained from environmental studies The PANAM tree-based method was run on environmental sequences, namely i) a set of environmental sequences originating from published studies on the diversity of protists and belonging to described environmental lacustrine clades of Perkinsozoa and Cercozoa  [6, 7]  and ii) from an environmental survey of the lacustrine protist diversity performed in eight freshwater ecosystems. For this purpose, eight lakes or reservoirs, described in Table 2 (Lakes Anterne, Aydat, Bourget, Godivelle, Geneva, and Pavin, and Reservoirs Sep and Villerest), were sampled once during their thermal stratification (from May to August according to the lake). Water samples from the epilimnion (1 to 5 m) were collected with a Van Dorn bottle at a permanent station (the deepest zone of the lake).
Finally, the amplicons of all of the samples were pooled at equimolar concentrations and pyrosequenced using a Roche 454 GS-FLX system (Titanium Chemistry) by GATC (Konstanz, Germany). The reads, alignments and trees have been deposited in Dryad (http://datadryad. org). The reads used in this study were selected from a full run, separated into bins according to the tags, analyzed by PANAM, using trimming criteria of quality score. 22 and sequence length. 200 bases and clustering into OTUs with a 95% similarity threshold. UNIFRAC metrics  [30]  and a principal coordinate analysis were used to compare the small protist community between the lakes based on phylogenetic information obtained by PANAM using the packages Picante and ade4 implemented in the R software  [80]. To broaden the covered diversity, more specifically regarding the environmental and pyrosequencing datasets processed in this study, and to build phylogenies with more similar sequences for the studied environment, 173 sequences from eukaryotic clades specific to lacustrine ecosystems, defined in previous works (e.
The research leading to these results has received funding from the European Community's Seventh Framework Programme [FP7/2007-2013] under grant agreement no. 288175 (CERTAINTY) and no 257414 (ASCENS). We generalize previous works  [2] [3] [4]  by removing the requirement that interaction between processes be expressed pairwise, and also by applying to BIP  [6] , a framework from which efficient distributed code can be generated. In contrast, the model of concurrency in  [2] [3] [4]  requires shared memory read-modify-write operations with a large grain of atomicity. The full paper, including proofs for all theorems, is available on-line, as is our implementation of the method.
Implementation and Experimentation LDFC-BIP, (∼ 1500 LOC Java) implements our method for finite-state BIPsystems. Pseudocode for LDFC-BIP is shown in Figure  4. checkDF(B, Q 0 ) iterates over each interaction a of (B, Q 0 ), and checks (∃ ≥ 0 : LDF C(a, )) by starting with = 0 and incrementing until either LDF C(a, ) is found to hold, or D a has become the entire system and LDF C(a, ) does not hold. In the latter case, LDF C(a, ) does not hold for any finite , and, in practice, computation would halt before D a had become the entire system, due to exhaustion of resources. locLDFC(a, ) checks LDF C(a, ) by examining every reachable transition that executes a, and checking that the final state satisfies Definition 18. The running time of our implementation is O(Σ a∈γ |D a a |), where a is the smallest value of for which LDF C(a, ) holds, and where |D a a | denotes the size of the transition system of D a a.
Martens and Majster-Cederbaum  [14]  present a polynomial time checkable deadlock freedom condition based on structural restrictions: "the communication structure between the components is given by a tree. " This restriction allows them to analyze only pair systems. Brookes and Roscoe  [11]  provide criteria for deadlock freedom of CSP programs based on structural and behavioral restrictions combined with analysis of pair systems. No implementation, or complexity bounds, are given. Aldini and Bernardo  [1]  use a formalism based on process algebra. They check deadlock by analysing cycles in the connections between software components, and claim scalability, but no complexity bounds are given. We compared our implementation LDFC-BIP to D-Finder 2  [8]. D-Finder 2 computes a finite-state abstraction for each component, which it uses to compute a global invariant I.
Facial Expression Processing: From the video of the user's face, we tracked smiles and head movements (e. g. , nods, shakes, tilts) in every frame. We used the Shore Framework  [22]  to detect faces and facial features in order to distinguish smiles. The classifier was trained using the Adaboost algorithm with sample images, considering smiling and neutral as the two binary classes. The features from all over the face were used for boosting. The outcome of the classifier was a normalization function that projected the score onto a range, [0,100]. Thus, each face image was scored from 0 to 100 in smile intensity, where 0 and 100 represented no smile and full smile, respectively. We evaluated this smile recognition system using the Cohn-Kanade dataset  [23] , which includes 287 images of 97 individuals from the United States.
Out of these images, 62 were labeled as happy, where happiness is defined as smiling at various levels. Our testing of the smile analysis module for classifying the images in this dataset yielded precision, recall, and F-score values of. 90,. 97, and. 93, respectively. In addition, we tested the smile module on the JAFEE 178-image dataset  [24]  of happiness, sadness, surprise, anger, and disgust, including 29 instances of happy faces from 10 Japanese women. The results from the testing with the JAFEE dataset yielded precision, recall, and Fmeasure values of. 69, 1, and 0. 81, respectively. Head Nod and Shake Detection: Detecting natural head nods and shakes in real-time is challenging, because head movements can be subtle, small, or asymmetric. Our implementation tracked the "between eyes" region, as described by Kawato and Ohya  [25].
Prosody Analysis: For prosody analysis, we automatically recognize pauses, loudness, and pitch variation, which serve as a measure of how well the speaker is modulating his or her speech, as these are useful for assessing expressivity. In order to recognize pauses, we used a minimum pitch of 100 Hz, a silence threshold of -25 dB, minimum silent interval duration of. 5 seconds, and minimum sounding interval duration of. 5 seconds. Pitch was calculated using acoustic periodicity detection on the basis of an accurate autocorrelation method. To extract prosodic features, we developed an application programming interface (API) using the low level signal processing algorithms included in the Praat  [26] , an open source speech processing toolkit. Speech Recognition: For real-time speech recognition, we used the Nuance speech recognition software development kit  [27].
This module is also able to generate a model corresponding to the task to be performed, whose components are available in a base intended for this purpose (stored in the BMT(R) base, cf. below). Indeed, two bases are available  [22] ,  [27]  • The Base of Specifications of Agents (BSA) allows the storage of the specifications of the interface agents. It contains the definition (for each agent) of the sets E (set of the possible events), C (set of the conditions), R (set of the resources), Acv (set of the visible actions: such as the action of the user using the mouse or the keyboard, the reaction of the interface by the posting of new windows and/or change of their contents), Acn (set of the actions which are not visible to the user, relating to the interactions between interface agents). The data stored in BSA is intended to be exploited by the module of task model generation (taking the form of agent Petri nets).
Specifically, a classifier trained on an imbalanced dataset may acquire a bias in favor of the majority class, resulting in an overoptimistic accuracy. This motivates the use of an alternative performance measure, the balanced accuracy, which removes this bias from performance evaluation. We recently proposed a solution to the three above limitations using Bayesian hierarchical models for mixed-effects inference on classification performance. In particular, we introduced the beta-binomial model and the normal-binomial model for inferring on both accuracies and balanced accuracies  (Brodersen et al. , 2012a). Both models use a fully Bayesian framework for mixed-effects inference, are based on natural distributional assumptions, and enable more accurate inferences than the two conventional approaches described earlier.
The twofold normal-binomial model for inference on the balanced accuracy Seemingly strong classification accuracies can be trivially obtained on datasets consisting of different numbers of representatives from either class. For instance, a classifier might assign every example to the majority class and thus achieve an accuracy equal to the proportion of test cases belonging to the majority class. Thus, the use of classification accuracy as a performance measure may easily lead to optimistic inferences  (Akbani et al. , 2004; Brodersen et al. , 2010a Brodersen et al. , , 2012a;; Chawla et al. , 2002; Japkowicz and Stephen, 2002). This has motivated the use of a different performance measure: the balanced accuracy, defined as the arithmetic mean of sensitivity and specificity, or the average accuracy obtained on either class, φ : ¼ 1 2 π þ þ π - ð54Þ where π + : = σ(μ + ) and π -: = σ(μ -) denote the (population) classification accuracies on positive and negative trials, respectively.
Application to synthetic data We examined the statistical properties of our approach in two typical settings: (i) a larger simulated group of subjects with many trials each; and (ii) a small group of subjects with few trials each, including missing trials. Before we turn to the results of these simulations, we will pick one simulated dataset from either setting to illustrate inferences supported by our model (Fig. 5 ). The first synthetic setting is based on a group of 30 subjects with 200 trials each (i. e. , 100 trials in each class). Outcomes were generated using the univariate normal-binomial model with a population mean (logit accuracy) of μ = 1. 1 (corresponding to a population mean accuracy of 71%) and a relatively high logit population precision of λ = 4 (corresponding to a population accuracy standard deviation of 9.
We repeated the above analysis on a sample dataset from a second simulation setting. This setting was designed to represent the example of a small group with varying numbers of trials across subjects. 9 Such a scenario is important to consider because it occurs in realworld applications whenever the number of trials eligible for subsequent classification is not entirely under experimental control. Varying numbers of trials also occur, for example, in clinical diagnostics of diseases like epilepsy where one may have different numbers of observations per patient. Classification outcomes were generated using the univariate normal-binomial model with a population mean logit accuracy of μ = 2. 2 and a low logit population precision of λ = 1; the corresponding population mean accuracy was 87%, with a population standard deviation of 11.
2% (Fig. 5f ). Comparing the resulting posteriors (Figs. 5g-j ) to those obtained on the first dataset, several differences are worth noting. Concerning the population parameters (Figs. 5g, i ), all estimates remained in close agreement with ground truth; at the same time, minor discrepancies began to arise between variational and MCMC approximations, with the variational results slightly too precise (Figs. 5g, i ). This can be seen best from the credible intervals (Fig. 5h , black). By comparison, an example of inappropriate inference can be seen in the frequentist confidence interval for the population accuracy, which does not only exhibit an optimistic shift towards higher performance but also includes accuracies above 100% (Fig. 5h, red ). Another typical consequence of a small dataset with variable trial numbers can be seen in the shrinkage of subject-specific inferences (Fig.
5j ). In comparison to the first setting, there are fewer trials per subject, and so the shrinkage effect is stronger. In addition, subjects with fewer trials (red) are shrunk more than those with more trials (blue). Thus, the order between sample accuracies and posterior means has changed, as indicated by crossing black lines. Restoring the correct order of subjects can become important, for example, when one wishes to relate subject-specific accuracies to independent subject-specific characteristics, such as behavioral, demographic, or genetic information. The primary advantage of VB over sampling algorithms is its computational efficiency. To illustrate this, we examined the computational load required to invert the normal-binomial model on the dataset   iance). (e) The benefits of a mixed-effects approach in subject-specific inference can be visualized (cf.
(f-j) Same plots as in the top row, but based on a different simulation setting with a much smaller number of subjects and a smaller and more heterogeneous number of trials in each subject. The smaller size of the dataset enhances the merits of mixed-effects inference over conventional approaches and increases the shrinkage effect in subject-specific accuracies. shown in Fig. 5a. Rather than measuring computation time (which is platform-dependent), we considered the number of floating-point operations (FLOPs), which we related to the absolute error of the inferred posterior mean of the mean population accuracy (in percentage points; Fig. 6 ). We found that MCMC used 4000 times more arithmetic operations to achieve an estimate that was better than VB by no more than 0. 13 percentage points.
Application to a larger number of simulations Moving beyond the single case examined above, we replicated our analysis many times while varying the true population mean accuracy between 0. 5 and 0. 9. For each point, we ran 200 simulations. This allowed us to examine the properties of our approach from a frequentist perspective (Fig. 7 ). In the first setting (Fig. 7 , top row), each simulation was based on synthetic classification outcomes from 30 subjects with 200 trials each, as described in the previous section. One instance of these simulations is shown as an example (Fig. 7a ); all subsequent plots are based on 200 independent datasets generated in the same way. We began by asking, in each simulation, whether the population mean accuracy was above chance (0. 5). We answered this question by computing p-values using the following five methods: (i) fixed-effects inference based on a binomial test on the pooled sample accuracy (orange); (ii) fixed-effects inference based on a binomial test on the average sample accuracy (violet); (iii) mixed-effects inference using VB (solid black); (iv) mixed-effects inference using an MCMC sampler with 100,000 samples (dotted black); and (v) random-effects inference using a t-test on subject-specific sample accuracies (red).
(d) Comparison of accuracy of subject-specific estimates, using different inference methods. (e) Example of a smaller dataset (sampled from the same model as in Fig. 5f ). (f-h) Same analyses as above, but based on smaller experiments. As can be seen from Fig. 7b , the first method violates this requirement (fixed-effects analysis, orange). It pools the data across all subjects; as a result, above-chance performance is concluded too frequently at small test sizes and not concluded frequently enough at larger test sizes. In other words, a binomial test on the pooled sample accuracy provides invalid inference on the population mean accuracy. A second important property of inference schemes is their sensitivity or statistical power (Fig. 7c ). An ideal test (falsely) rejects the null with a probability of α when the null is true, and always (correctly) rejects the null when it is false.
In the presence of observation noise, such a test is only guaranteed to exist in the limit of an infinite amount of data. Thus, given a finite dataset, we can compare the power of different inference methods by examining how quickly their rejection rates rise once the null is no longer true. Using a test size of α = 0. 05, we carried out 200 simulations for each level of true population mean accuracy (0. 5, 0. 6, …, 0. 9) and plotted empirical rejection rates. The figure shows, as expected, that a Binomial test on the pooled sample accuracy is an invalid test, in the sense that it rejects the null hypothesis too frequently when it is true. This effect will become even clearer when using a smaller dataset (see below). 10  Finally, we examined the performance of our VB algorithm for estimating subject-specific accuracies (Fig.