dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, the ensuing sets of 40 brain networks served as a basis for feature engineering to automatically learn distinguishing the 18 tasks in the second data half based on network loadings alone. Third, the quantitative models of task-specific network loadings allowed generating a synthetic wholebrain activity map for each experimental task. The correlation values r quantify the voxel-wise similarity between the reconstructed activity map and the average activity map for each task and network decomposition method. This measure of recovery performance indicates the information loss incurred when first expressing activity maps as 40 network loading values and then translating these values back into whole-brain space. The similarity between real HCP task maps (leftmost column) and synthetic model-derived task maps (right columns) indicates that 40 network loadings can well describe task-evoked neural activity patterns in the HCP task battery.
(JPG) S7 Fig. Reconstruction  of ARCHI tasks from different network co-occurrence models. Leftmost column: Average whole-brain activity from 18 experimental tasks of the ARCHI dataset. The voxel-wise mean was computed across the first-level contrast activity maps from 78 participants. Right columns: Four different network models were derived from the task activity during the 18 ARCHI tasks. First, four different network decompositions (i. e. , ICA, sparse PCA, PCA, and factor analysis) were applied to the first half of the HCP task data. Second, the ensuing sets of 40 brain networks served as a basis for feature engineering to automatically learn distinguishing the 18 tasks in the second data half based on network loadings alone. Third, the quantitative models of task-specific network loadings allowed generating a synthetic wholebrain activity map for each experimental task.
Additionally, network and region dictionaries were derived from i) identical task-data as positive test (dark blue), ii) nonidentical task-data (medium blue), iii) resting-state data (light blue), and iv) Gaussian noise as negative test (red). As a second conclusion, network dictionaries derived from non-identical task maps and rest maps are similarly successful in recovering whole-brain activity during divering experimental tasks. This was confirmed by univariate t-tests between the task-wise correlation values r (cf. results section). (PNG) S12 Fig. Recovery  performance of task activity patterns across tasks. Parallel coordinate plots that depict the recovery performance by one line for each of the 18 tasks in both datasets. The network dictionaries were derived from i) identical task-data (dark blue, AT = ARCHI task data, HT = HCP task data), ii) non-identical task-data (medium blue), iii) resting-state data (light blue, HR = HCP rest data), and iv) Gaussian noise (red, DN = data noise), analogous to S11 Fig.
Arrows: A diagnostic subanalysis indicated what rest networks were automatically ranked top-five in distinguishing a given task from the respective 17 other tasks (i. e. , k = 5 analogous to analyses in Figs3 and 4). Although the experimental tasks in the HCP and ARCHI repositories, "story versus math" and "sentences versus computation" were the most similar cognitive contrasts in both datasets. For these four experimental conditions the modelderived task maps are highly similar. Consequently, two independent classification problems in two independent datasets with a six-fold difference in sample size resulted in two independent explicit models that, nevertheless, generated comparable task-specific maps. This indicated that network co-occurrence modeling indeed captures genuine aspects of neurobiology rather than arbitrary discriminatory aspects of the data.
The recovery performance of all 18 tasks (radial columns) is measured by the Pearson correlation between the model-derived task activity maps and the average first-level task map. As an important observation, network dictionaries derived from different tasks and from rest data were similarly successful in recovering whole-brain activity during diverging experimental tasks, while specialized regions achieved much worse recovery performances in both datasets. See S11 and S12 Figs for additional analyses. doi:10. 1371/journal. pcbi. 1004994. g008.
network loading values and then translating these values back into whole-brain space. The similarity between real HCP task maps (leftmost column) and synthetic model-derived task maps (right columns) indicates that 40 network loadings can well describe task-evoked neural activity patterns in the ARCHI battery. (JPG) S8 Fig. Region clustering of task activity. 40 clusters of homogeneous neural activity patterns across tasks and across participants. Spatially constrained ward and spatially unconstrained kmeans clustering were applied to the first half of the task maps from the HCP and ARCHI dataset. Each cluster is depicted in a unique, arbitrary color. This grey-matter atlas of non-overlapping brain regions was used to explain task-evoked neural activity patterns. (PNG) S9 Fig. Reconstruction of HCP tasks from different region co-occurrence models.
Leftmost column: Average whole-brain activity from 18 experimental tasks of the HCP dataset. The voxel-wise mean was computed across the first-level contrast activity maps from 498 participants. Right columns: Two different region models were derived to explain the task activity during the 18 HCP tasks. First, two different clustering algorithms (i. e. , ward and k-means clustering) were applied to the entirety of the HCP task data. Second, the ensuing sets of 40 coherent activation clusters served as a basis for feature engineering to automatically learn to distinguish the 18 tasks based on cluster loadings alone. Third, the quantitative models of taskspecific cluster loadings allowed generating a synthetic whole-brain activity map for each experimental task. The correlation values r quantify the voxel-wise similarity between the reconstructed activity map and the average activity map for each task and region clustering method.
This measure of recovery performance indicates the information loss incurred when first expressing activity maps as 40 region summary values and then translating these values back into whole-brain space. The frequently low similarity between real HCP task maps (leftmost column) and synthetic model-derived task maps (right columns) indicates that 40 cluster loadings might not well describe task-evoked neural activity patterns in the HCP battery. (JPG) S10 Fig. Reconstruction of ARCHI tasks from different region co-occurrence models. Leftmost column: Average whole-brain activity from 18 experimental tasks of the ARCHI dataset.
This study was supported by the  Deutsche Forschungsgemeinschaft (DFG ,  BZ2/2-1  and  BZ2/3-1  to DB; International Research Training Group IRTG2150), the  German National Academic Foundation (DB) ,  Amazon   AWS Research Grant  (DB), the  START-Program of the Faculty of Medicine, RWTH Aachen (DB) , and the  MetaMRI  associated team (BT, GV). The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 ) hypotheses. In the future, such network co-occurrence signatures could perhaps be useful as biomarkers in psychiatric and neurological research. under grant agreement no. 604102  ( Human Brain  Project).
The SRF is essentially a normalized two-dimensional Fourier transform of the cell's preferred stimuli, indicating the response as a function of stimulus vertical and horizontal frequency (or, equivalently, orientation and overall spatial frequency). It is computed by reverse correlation, as a weighted average of the 2D Fourier transforms of incoming stimuli, weighted by the responses to this stimuli (David and colleagues include a correction term for spectral autocorrelation, which we neglect here for simplicity). We computed the SRF of top-layer cells by exposing them to a number of image patches, taken from the Van Hateren database of whitened images (thought to emulate the output of retinal processing), and then weighting the summed 2D Fourier transforms of these patches by the cell's response (Fig  9 ).
Materials and Methods Here we provide a full description of our model. We provide both descriptive equations, and a snippet of our original Matlab code for reference. The full Matlab source code for our implementation of the model is available online. Our model is composed of two reciprocally-connected square areas (the "top" and "bottom" layers). The bottom layer emulates the properties of visual area V1, while the top layer emulates properties of higher visual areas that receive the top-down attentional modulation (such as V4 and MT). Each area is organized as a regular lattice of locations, with one location above every pixel of the input image, and contain 8 orientation-selective cells at every location. In the bottom layer, the cells at any location are a full complement of orientation-selective edge detectors, implemented as Gabor filters of 8 orientations between 0 and 157.
Phenotypic data To evaluate the impact of flowering habit (PF or SF) through the growing season, we studied patterns of emergence of inflorescences and primary stolons in the 28 genotypes. Newly emerged inflorescences were counted weekly from week 16 (14 April) until week 43 (21 October), a total of 28 weeks, and stolons every 2-4 weeks  (weeks 19, 21, 23, 26, 29, 32, 35, 39, and 43). Inflorescences and stolons were counted when they visually emerged from the crown. After their emergence, inflorescences were tagged and stolons were cut to ease their counting. To study the relationship between the emergences of inflorescences and stolons, the stolon time indexing was retained. Count data were transformed into growth rates (weekly number of either emerged inflorescences or stolons) since the measurement dates for the stolons were unevenly spaced.
τ τ θ 1 1  J J L -= s s x We used the integrated completed likelihood (ICL) criterion  (Rigaill et al. , 2012) , a model selection criterion dedicated to the segmentation objective, to determine the number of flowering phases. For each number of flowering phases, the following quantity was computed: ICL | J J J L d T H J = - - = 2 2 log ( ) log ( ; ), x S X x (1) where L L J J ( ) ( , ; ) x sx s = ∑ θ ^ is the likelihood of the all the possible segmentations in J flowering phases of the observed multivariate series x, d J is the number of free parameters of a J-flowering-phase model and H J P P ( ; ) ( )log ( ) S X x S s X x S s X x s | | | = =- = = = = ∑ is the entropy of the segmentation S in J flowering phases for the observed series x. The principle of this penalized likelihood criterion consists in making a trade-off between an adequate fitting of the model to the data (given by the first term in Eqn 1) and a reasonable number of parameters to be estimated (controlled by the second term in Eqn 1).
We also visualized the posterior probabilities of entering flowering phase j at time t, P S j S j J t t ( , ; ), = = - -1 1|x and the posterior probabilities of being in flowering phase j at time t, P S j J t ( ; ), = |x for each time t and each flowering phase j for J-flowering-phase models of interest (see illustrations in  Guédon, 2013). These posterior probability profiles computed using the smoothing algorithm  (Guédon, 2013)  are particularly useful for assessing change-point position uncertainty. Multiple change-point models and associated statistical methods are implemented in the StructureAnalysis package that is part of the OpenAlea platform (freely available at http://openalea. gforge. inria. fr).
Genetic maps, QTL detection and genetic effect of one marker linked to the QTL The initial population, to which the 26 genotypes and the two parents 'Capitola' and 'CF1116' belonged, comprised 213 individuals. Female and male genetic maps of this population were previously developed using data obtained from the cross 'Capitola' × 'CF1116'  (Rousseau-Gueutin et al. , 2008; Lerceteau-Köhler et al. , 2012). QTL detection was performed by composite interval mapping  (Zeng 1993 (Zeng , 1994) )  using QTL Cartographer software  (Basten et al. , 1997)  separately for each parent as previously described by  Lerceteau-Köhler et al. (2012). A total of 1000 permutations of the phenotypic data were performed for each trait to determine a P<0. 05 logarithm of odds (LOD) significance threshold level: from 5.
2 to 9. 3 for female data and from 5. 4 to 9. 1 for male data. QTL analyses were performed separately for the number of inflorescences or stolons newly emerged during the considered period. The genetic effect of the marker EMFv020_146 linked to the QTL (H and A denote, respectively, presence and absence of this marker) was studied on the entire segregating population 'Capitola' × 'CF1116', for which the flowering observed from September to October or November has been recorded for six consecutive years, from 2004 to 2009.
Acknowledgements The authors thank  Marie-Noëlle Démené  and  Christophe Rothan  for fruitful discussions and  Philippe Chartier  for supervising the experimental assay. This work was supported by  Région Aquitaine  and was carried out under the auspices of the  EU   FP7-KBBE-2010-4 Project  (Grant Agreement no. 265942 ). The authors gratefully acknowledge the participation of  Sadia Schafleitner  and  Alain Bonnet  in help in phenotyping and Invenio for taking care of the assay.
This chapter introduces statistical learning and its applications to brain-computer interfaces. We begin by presenting the general principles of supervised learning and discussing the difficulties raised by its implementation, with a particular focus on aspects related to selecting sensors and multisubject learning. This chapter also describes in detail how a learning approach may be validated, including various metrics of performance and optimization of the hyperparameters of the considered algorithms. We invite the reader to experiment with the algorithms described here: the illustrative experiments included in this chapter may be reproduced using a Matlab/Octave toolbox 1 , which contains the implementation details of the various different methods.
Supervised statistical learning The goal of supervised learning is to construct a predictor function that assigns a label to any given example; this predictor function is constructed from labeled examples that provide a basis for this training process. The predictor function is obtained by optimizing a certain criterion, which includes a term for the empirical risk (the behavior of the function on the given examples) and a regularization term (which guarantees the behavior of the function on new examples). Matlab/Octave toolbox: https://github. com/rflamary/mltool. This section will describe the principles of supervised statistical learning, as well as the two most important algorithms in the current state of the art. The reader should refer to Lotte et al. [LOT 07] for a more detailed account of the state of the art.
Among the 18 datasets used in the last BCI competitions [BLA 04, BLA 06, TAN 12], LDA and FDA were used in nine of the methods with the best performance in the classification category. Logistic regression, a classification method that is also used for BCIs [TOM 07], introduces a logistic cost function (Table  9. 1). Although this technique is less widely used than LDA or SVM, it performs excellently for linear classifications, particularly at higher dimensions.
Support Vector Machines (SVM) Support Vector Machines or SVMs are obtained by minimizing a criterion of type equation [9. 3], where the empirical risk term is the hinge loss (Table  9. 1 and Figure  9. 2). The most common regularization term is the ℓ 2 regularization term, which effectively maximizes the margin, i. e. the minimum distance between the points and the separating hyperplane. Linear SVMs are a widely used category of classifiers for BCIs [RAK 08, KAP 04]; they have produced state-of-the-art results in detecting event-related potentials [RAK 08, LAB 10] and motor imagery [SCH 05a]. Specifically, SVMs were the best performers in six of 18 of the datasets of the three latest BCI competitions, in particular for the most recent datasets [BLA 04, BLA 06, TAN 12]. Note that SVMs may be extended to train nonlinear classifiers using the kernel trick, which replaces the scalar product with a similarity function and thus generates a more complex separating boundary.
Performance metrics This section presents a selection of the performance metrics used to evaluate and compare the advantages of different hypotheses or algorithms, considering the cases of classification and regression separately. These performance metrics, which must always be estimated from data distinct from the training data (see section 9. 4 [BLA 06, BLA 04]), test the generalization capacity of the trained hypotheses. Readers can refer to Schlögl et al. [SCH 07] for a more complete treatment of the topic of performance metrics.
Estimation of the performance metric As mentioned earlier, the performance of a classifier f (•) on the training data is ultimately not what we are interested in; by increasing the complexity of f (•), it is always possible to construct a classifier that performs perfectly on the known data. The end-goal is the capacity of generalization of f (•), that is to say its performance on future data. We can attempt to evaluate the general performance of f (•) by partitioning the available data into training data, which is used to optimize f (•), and test data, which is used to estimate the performance of f (•) in general. In doing so, we assume that the test data extracted from the available dataset is a good representation of the future data that the classifier will need to predict. If there is plenty of available data, estimating the performance metric is not particularly difficult: there are sufficient data to train a classifier f (•), and there are sufficient (other) data for estimating the performance of f (•).
A series of hold-out processes is then performed by taking each of the subsets as the testing dataset, and the remaining data as the training dataset. As above, the performance is estimated by the average of the performances on the K testing subsets. Compared to random sampling, cross-validation reduces the variance of the estimate (each datapoint is included exactly once in the testing dataset). When the number of available datapoints is very low, we may in particular choose K equal to the number n of points: cross-validation is then equivalent to the leave-one-out (LOO) method, which performs training on all datapoints except one, saving the final point to test the trained classifier. The choice of K involves establishing a compromise between the bias and the variance: as K increases, the bias of the estimate decreases (given some weak assumptions [HAS 01]) but the variance increases, because the various training datasets are strongly correlated.
Bootstrapping Bootstrapping constructs the training data using n uniform samples with replacement from the n available datapoints. Certain points can therefore be present multiple times in the training data, and others may be omitted; on average, the fraction of non-selected points is 37% of the available data. Classifier performance estimation is performed on the whole of the available dataset; as above, the performance is averaged over multiple different samples for the training data. This method of estimation is less optimistic than LOO; the bias is similar to the bias of cross-validation with K = 2 [HAS 01]. Despite this bias, performance rankings of hypotheses using bootstrapping are usually considered reliable.
Notes and References A more detailed discussion of the subject following the same pathway can be found in  [135]. The associated website http://www. gipsa-lab. grenoble-inp. fr/˜ioandore. landau/identificationandcontrol/ provide matlab and scilab functions for system identification as well as simulated and real input/output data for training. For a general coverage of system identification see  [166, 223]. Chapter 6.
It is possible, however, to implement the filter without normalizing the numerator coefficients. 10 These filters can be computed using the functions filter22. sci (Scilab) and filter22. m (Matlab R ) to be downloaded from the book website. b z0 = 4 T 2 s + 4 ζ num ω 0 T s + ω 2 0 ; b z1 = 2ω 2 0 - 8 T 2 s b z2 = 4 T 2 s -4 ζ num ω 0 T s + ω 2 0 (7. 72) a z0 = 4 T 2 s + 4 ζ den ω 0 T s + ω 2 0 ; a z1 = 2ω 2 0 - 8 T 2 s a z2 = 4 T 2 s -4 ζ den ω 0 T s + ω 2 0 γ = b z0 a z0 β 1 = b z1 b z0 ; β 2 = b z2 b z0 (7. 73) α 1 = a z1 a z0 ; α 2 = a z2 a z0 Remark: for frequencies below 0. 17 f s the design can be done with a very good precision directly in discrete-time. In this case, ω 0 = ω 0den = ω 0num and the damping of the discrete time filters H S i and P F i is computed as a function of the attenuation directly using Eq.
The parameters to be optimized are x ak et x bk. For the discrete time cases α k and β k can be chosen as: α k = β k = z 0 -z -1 1 -z 0 z -1 k , where z 0 is the time constant of the parametrization (which can be adjusted). Using the parametrisation and the constraints indicated above an (RS) controller with desired properties can be obtained by convex optimization. For more details on the optimization procedure see  [148, 147]. The Matlab R toolbox Optreg provides the appropriate routines for specifying the constraints and finding the optimal controller. The method will be used in Chapter 10 for active damping.
Evaluation of the Complexity For complexity evaluation, the measure of the Task Execution Time (TET) in the xPC Target environment will be used. This is the time required to perform all the calculations on the host target PC for each method. Such process has to be done on each sample time. The more complex is the approach, the bigger is the TET. One can argue that the TET depends also on the programming of the algorithm. Nevertheless, this may change the TET by a factor of 2 to 4 but not by an order of magnitude. The xPC Target MATLAB environment delivers an average of the TET (AT ET ). It is however interesting to asses the TET specifically associated to the controller by subtracting from the measured TET in closed-loop operation, the average TET in open-loop operation. The following criteria to compare the complexity between all the approaches are defined.
The algorithms for identification in closed-loop take into account the fact that all data are available. The simulated adaptive feedforward compensation schemes in self-tuning regime operates like in real-time, i. e. , the algorithms ignore the availability of the data over the entire time horizon. It is supposed that models for the secondary and the reverse paths are available. Section 6. 3 gives details on how these models can be obtained. Measured data on the real system for w(t) and x(t) will be used instead of the identified model of the primary path. These data have been obtained using as excitation to the system a PRBS with shift register of length N = 16 and frequency divider p = 1. Around 82 sec of real time data have been obtained. 3 ets begin with the identification of the feedforward compensator using closedloop input matching algorithms (see Section 9.
However, this is not the case for all close gene pairs. For instance, the gene AP2si117 with only one exon occurred in the same cluster with the genes AP2si45, AP2si55, AP2si95, AP2si97, AP2si131, AP2si132 and AP2si121 which displayed more than eight exons. In addition, to investigate the motifs shared by related proteins in the different families, the MEME motif search tool was employed and the motifs found were then subjected to SMART annotation and confirmed in Pfam database. In total, 15 conserved motifs were identified, lengths ranging from 11-50 amino acids (Additional file 5). The motifs 1, 2, 3 and 5 specifying the AP2 domain were identified in all the 132 AP2/ERF proteins while the motif 12 related to B3 domain was found in the four RAV genes (Additional file 6). However, the remaining motifs were unidentified when searched by SMART and Pfam databases.