dataesr/scientific-paragraphs-categorization huggingface dataset:
The learner perceives multimodal signal, vision and sound in the figure, and learns the associations between the appearance of objects and the sound of their names. It then has to prove its ability to chose the right object among a small set when hearing its name. In practice we use images of objects, motion captures, and recorded spoken utterances in the experiments. doi:10. 1371/journal. pone. 0140732. g001 In the following, each experiment consists in two phases: one training phase during which the system is exposed to multimodal data and an evaluation phase in which we test the success of concept learning. During its training the learning agent observes examples of scenes; each example is a set of one observations in each modality. In each scene only one relevant multimodal concept is present and is observed in several modalities.
Datasets The following experiments involve three raw modalities: motion, sound, and image. Motions. In the experiments we use a dataset of choreography motions demonstrated by a human and recorded through a kinect device. More precisely the dataset contains a total of 1100 records, each presenting one of 10 different gestures that are spanned over one or two limbs. Fig 2 illustrates the kind of gestures that compose the datasets. Typical gestures are the walk gestures, involving the two legs, in which the human demonstrator mimics walking in place, or the left hello in which the demonstrator moves his left arm pretending to say 'hello'. The data is publicly available and presented in more details at Mangin  [33]  and is used by Mangin and Oudeyer  [34, 35]. Speech. The acoustic records used in the following experiments are taken from the Caregiver dataset (see  [36] ), provided by the ACORNS project (see  [37] ).
It is composed of 1000 utterances containing 13 keywords. Each utterance is spoken by 4 speakers in English adult directed speech. In the experiments we only use utterances from one speaker. An example of sentences used in the dataset is "Angus is lazy today. " where the keyword is 'Angus'. Other examples of transcriptions from utterances from the dataset are given in Table  1. Images. Pictures used in the experiments were acquired as frames from an interaction with an iCub robot, through an RGBD sensor (red, green, and blue camera coupled with a depth sensor). The acquisition was performed and is described in more details by Lyubova and Filliat  [38]. Each image contains one visual object and possibly the hand of the operator. In the experiments we use images from a subset of 10 objects each appearing in more than a thousand frames.
Open-source code to reproduce the experiments The implementation of the MCA-NMF and the code used in the experiments presented in this paper are available publicly and openly (BSD license) on http://github. com/omangin/ multimodal. It consists in a set of tools that implement the MCA-NMF system, including the NMF algorithm and code to achieve multimodal learning with it, as well as the scripts that correspond to the experiments presented here and produce their results. It includes the methods to process sound and motion data. Features extracted from the datasets are also publicly available as http://dx. doi. org/10. 5281/zenodo. 29600, http://dx. doi. org/10. 5281/zenodo. 29602, and http://dx. doi. org/10. 5281/zenodo. 29607. The code is meant for the reproduction of the experiment we presented as well as for the development of new experiments based on the same framework.
The main steps are explained in more details below. The perception mechanism first segments the acoustic signal into a sequence of short time windows; it then computes Mel-frequency cepstral coefficients (MFCC) for each window. This transforms the original signal into a sequence of MFCC vectors. Additionally we consider dynamic information on top of the sequence of MFCC vectors of dimension 22; discrete derivatives are computed as defined by Driesen  [50]  and denoted by the Δ operator to form the ΔMFCC and ΔΔMFCC. This transformation is analogous to the delayed velocities introduced previously to represent motions. Then, the system computes three codebooks with a k-means algorithm on a dataset of spoken language, for basic MFCC vectors and their Δ and ΔΔ transformations, as described in Driesen  [50].
Experiments We consider semantic associations between elements of the acoustic, visual, and motion modalities; more precisely we define a semantics as an artificial mapping between acoustic words, visual objects, and gestures. An example of such a mapping is given in Table  2. Each triplet of word, gesture, object forms a semantic concept. The data used to train the MCA-NMF model is composed of sentences, motions, and images; each sentence contains one of the keywords, each motion features one gesture, each image an object. Finally the gesture, the word, and the object from an example belong to the same semantic concept, which implements the cross-situational manifestation of the semantics. In the testing phase of the following experiments the system is presented ten potential referrent, each of a different class among the ten possible semantic classes.
Motion and spoken utterances The first experiment (also detailed in  [35] ) compares the various evaluation methods, that is the alternative modalities of comparison and metrics. In this experiment the learner is trained on two modalities: demonstrated motions and spoken utterances. It also validates the capabilities of the MCA-NMF learner to acquire multimodal concepts that appear in a cross-situational way in training data. Table  3  presents classification success rates for various combinations of comparison modalities and metrics. These results demonstrate that the system is capable of learning aspects of the semantic associations. If the system is trained on a dataset where no semantic association exist between the two modalities (such a dataset can be obtained by choosing a random motion and a random utterance for each demonstration), it generally scores around 0.
11 (Note that this is not 0. 1 because the distribution of sound examples from the Caregiver dataset is not exactly uniform. ). Additionally the result shows for example that the sound modality, mainly because of its very high dimension, is not a good choice for the comparison, specially when the comparison is performed with the Euclidean metric. Indeed the choice of the metric to use is highly dependent on the nature of the data in the modality; therefore, using the internal representation is a way to only use one metric. Therefore the following experiments focus on performing the comparison on the internal representations, using cosine similarity as a metric. The main interest of proceeding that way is that the comparison is the same, regardless of what the test and reference modalities are.
Training In the second sentence (top right), the correct meaning is guessed by the system but it is not located specifically in the sentence. Because the dataset used for training is rather small in comparison to the variety of word combinations, many non-keywords appears only or most often together with the same keyword. In this case the word 'join' and the phrase 'join us' are always observed together with 'daddy', while the occurrences of 'does' are more spread among the various keywords. The grammar used to generate the utterances is described concisely by Altosaar et al. [36]  and in more details by  Driesen [50]. This situation is similar to the case of natural sentences where contexts highly modify the distributions of words and may explain such delocalization of the perceived meaning of sentences.
In particular this provides an example where the system achieves teleological understanding, which here means guessing the general meaning of a sentence, without compositional understanding. The third sentence (bottom left) is very specific because it is one of the rare ambiguous sentences of the dataset. Indeed the sentence 'Angus takes off his shoe. ' features both the keyword 'Angus' and the keyword 'shoe'. Furthermore the learner is capable in this sentence to recognize both words and localize them roughly at the start and end of the utterance. Finally, the fourth sentence illustrates the many cases where there are false positives for other meanings or (not in the example) no clear meaning is found by the learner in the sentence. Emergence of localized representations of concepts. In previous experiments we evaluated the learner on concrete tasks that emphasize its ability to relate information from one modality to another.
Basal XYR1 formation is unaffected by the xyr1 ΔA2294 mutation Despite the strong reduction of the degree and rate of nuclear uptake in the strain harbouring the xyr1 ΔA2294 allele, the cytosolic level of the truncated XYR1 remained low but comparable to that in the parental strain (Figure  7A ). In order to rule out that this would be an artefact caused by autofluorescence of T. reesei, we repeated these experiments under microscope settings optimized to completely eliminate cellular background auto-fluorescence detection (see Methods). Under these stringent detection settings, any fluorescence signal must emanate from GFP only. As (See figure on previous page. ) Figure  2  XYR1 structure and structural consequences of the truncated XYR 1-780. (A) Predicted coiled-coil domains in XYR1 using COILS, which compares a sequence to a database of known parallel two-stranded coiled-coils and derives a similarity score from which the probability that the sequence will adopt a coiled-coil confirmation is calculated.
The terminal 16 amino acids (L765-F780; light red) encode a non-sense sequence that finds no BLASTp hit in the T. reesei genome. (C) 3D-reconstruction of XYR1. RaptorX protein structure prediction software (raptorx. uchicago. edu) identifies three functional domains in XYR1, resembling DNA-binding domain (DBD), fungal-specific transcription factor domain (FSTFD) and acidic-activation domain (AAD), perfectly matching our manual annotation (B). Five of the seven α-helices comprising the AAD are missing in the truncated XYR1 1-780 protein (highlighted in blue). The closest structural orthologs are the Zn 2 Cys 6 -type transcriptional regulators Ppr1 and Gal4 from S. cerevisiae, both known to be active as non-symmetrical homodimers  [63]. shown in Additional file 5: Figure  S4 , the fluorescence intensity of cytosolic GFP-XYR1 1-780 was the same under XYR1 inducing and non-inducing conditions (1 mM and 65 mM D-xylose, respectively), and was also similar to that measured in T.
Illumina genome sequencing of T. reesei QM9136 Chromosomal DNA from T. reesei QM9136 was prepared as described previously  [5]. Fragment libraries were prepared according to the TruSeq® DNA Sample Preparation Guide from Illumina (www. illumina. com). These libraries were then loaded onto the cluster generation station for single molecule bridge amplification using the Standard Cluster Generation kit from Illumina. The slide with amplified clusters was then subjected to sequencing on the Illumina Genome Analyser I (GAI) for single reads using the 36 cycle Sequencing Kit version 1 from Illumina. The whole genome sequence of T. reesei QM9136 was deposited at the Sequence Read Archive (SRA; http:// www. ncbi. nlm. nih. gov/sra) under the accession number SRX059777.
Sequence alignment and analyses Illumina short reads from QM9136 were mapped onto the T. reesei genome (http://genome. jgi-psf. org/Trire2/), using the Maq 0. 6. 6 software solution  [55]. Mapping was done with two maximum mismatches. InDels and SNVs were also identified using Maq 0. 6. 6. Homozygous mutations were selected and filtered on genomic context (complexity = 1, uniqueness > 15. 8, GC percentage between 0. 31 and 0. 74). Each mutation was manually checked using the Integrative Genome Viewer and compared to sequence of the RUT lineage (Figure  1 ) to remove the false mutation coming from initial QM6a error sequencing. Large structural variations were searched by using the BreakDancer software  [56]  and filtered on the reads number covering the genomic variations. We evaluated the location of SNVs and deletions according to gene annotations using the "filtered models" from the JGI website.
A Nikon Plan Apo VC 60×/1. 2 water immersion objective lens was used, and laser intensity and dwell time during image acquisition were kept to a minimum to reduce photobleaching and phototoxic effects. Brightfield images were captured simultaneously with a Nikon C1-TD transmitted light detector mounted behind the condenser turret. Images were recorded with a maximum resolution of 1024×1024 pixels and saved as TIFF. Apart from display range adjustments, images were not subjected to further manipulation. Intensity measurements were performed with the MacBiophotonics ImageJ work package available at (www. macbiophotonics. ca/ software. htm).
All assays were carried out as triplicates in a 96-well plate format covered with optical tape, including no-template controls. Measurements with the housekeeping gene translation elongation factor 1 (tef1) were performed for reference calculation and data normalization. Determination of the PCR efficiency was performed using triplicate reactions from a dilution series of cDNA (1; 0. 1; 0. 01; 0. 001). RT-qPCR primers, amplification efficiency and R-square values are given in Table  4. Amplification efficiency of each sample mRNA was then calculated from the given slopes in the iQ5 Optical system Software v2. 0 and fold-changes in gene expression were calculated using REST Software  [61]. All samples were analysed in at least two independent biological experiments with three RT-qPCR replicates in each run.
Bioinformatic in silico analyses of XYR1 Functional domains were analysed by Pfam domain search  [15]. Coiled-coil regions were identified using COILS  [16] , and Protparam  [18] ) was used for the determination of general physicochemical properties of XYR1. The 3D-functional domain structure of XYR1 was determined by the RaptorX protein structure prediction software (raptorx. uchicago. edu; [17] ).
Acknowledgments Work in the  Vienna Laboratory  was supported by grants from the  Austrian Science Fund   P-23202  and  I-1249  to CPK, and  P24219  to BS. The work conducted by the  U. S. Department of Energy Joint Genome Institute  is supported by the  Office of Science of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The IBENS high-throughput sequencing facility is a member of the " France Génomique "  consortium  ( ANR-10-INBS-0009 ). Work in IFPEN and ENS has been funded by the  Tuck Foundation (Enerbio fund ,  TriSys  project), as well as fellowship to FB). The authors are grateful to  Dr. Christa Ivanova  for providing the T. reesei QM9414Δtku70 strain, and the expert help of  Dr. Lukas Hartl  is also gratefully acknowledged.
The probability of infection is greater during the third trimester but the risk of the fetus developing major clinical signs is greater early in pregnancy. Pregnant women are often unaware of risk factors for congenital toxoplasmosis. Offering prenatal education could mean that women adopt simple behavioral measures to avoid toxoplasmosis. This review included two randomized controlled trials (involving 5455 women). Data could not be combined because each trial measured effectiveness in different ways. One study was from Canada and involved 432 women randomly assigned to a 10-minute presentation during a prenatal class about toxoplasmosis prevention or to a usual prenatal class. Losses to follow-up were high and 285 completed the post-test questionnaire in the third trimester. Only 5% of the intervention women recalled having obtained information on toxoplasmosis prevention during prenatal classes.
Data collection and analysis For methods used in the previous version of this review, see Di Mario 2013. For this update, no new reports were identified for assessment but we assessed the quality of evidence of the existing studies using the GRADE approach as outlined in the GRADE Handbook. In order to assess the quality of the body of evidence relating to the following outcomes for the two comparisons: 'educational prenatal classes compared with usual prenatal classes and brochure and an audiotape compared with standard care'. 1. Rate of congenital toxoplasmosis. 2. Rate of toxoplasmosis seroconversion during pregnancy. 3. Pregnant women's knowledge of risk factors for acquiring toxoplasmosis infection. 4. Pregnant women's awareness of the importance of avoiding toxoplasmosis infection during pregnancy.
Seroconvertion rate during pregnancy in the intervention period was compared with the seroconversion rate in the baseline period, when no intervention on toxoplasmosis prevention was in place (from 1979 to 1982)  (Breugelmans 2004 ). The Pawlowski 2001 study used a multifaceted intervention including providing information to pregnant women, refresher training for health professionals, media campaigns, and the training of biology teachers in secondary schools, over six years and their effectiveness was assessed. It was impossible to obtain the full text of the study conducted in Cuba (Molé 1992): data retrieved from the abstract indicate that an educational intervention about how to avoid toxoplasmosis infection during pregnancy was delivered to seronegative pregnant women. All pregnant women were enrolled at their first prenatal visit.
Other potential sources of bias Since the prenatal class instructors of the experimental and the control group in Carter 1989 were the same, it is not possible to exclude some contamination, even if such contaminations are usually not an issue in cluster trials  (Torgerson 2001) , and in any case, it would have acted in the sense of reducing the effect. The total sample, considering the unit of randomization, was low (26 prenatal classes in the intervention group and 26 prenatal classes in the control group). It is not clear from the paper if a statistical analysis was conducted to take into account the effect of intracluster correlation. The authors did not report raw data (number or proportion): only the P value for differences was reported. We also contacted the authors for the original data but we did not receive a reply.
Primary outcomes The second trial included in the review (Wallon 2006) reported the following results: 1. seroconvertions for toxoplasmosis detected during the study did not differ between groups (risk ratio (RR) 1. 70, 95% confidence interval (CI) 0. 56 to 5. 21; participants = 3949; one study) (Analysis 1. 1): there were 13 cases out of 2591 pregnant women (0. 5%) in the intervention group and four cases out of 1358 pregnant women (0. 3%) in the control group (P = 0. 35). 2. The data above have not been adjusted for cluster design and so it is reasonable to assume the confidence intervals have been underestimated.
Niches and Phylogeny We collected 20,800 occurrence points from herbaria that mainly represent the Southern Hemisphere and Asia (see table A1 in app. A; tables A1-A4 available online) and 27,000 occurrences that cover North America and Europe from the Global Biodiversity Information Facility (GBIF; www. gbif. org; for details, see table  A2 ). These data cover the entire geographic range and ecological conditions occupied by the Danthonioideae  (Humphreys and Linder 2013; Linder et al. 2013 Linder et al. , 2014)). After removing species that were not in the phylogenetic tree, our database included 209 of the 281 species. We used ArcGIS 9. 3  (ESRI 2008)  to exclude unreliably georeferenced points (e. g. , occurrences in oceans) and to overlay the occurrences with 19 WorldClim bioclimatic layers  (Hijmans et al.
2005 ) at a resolution of 30″. These data (40,331 occurrences and associated bioclimatic data) are deposited in the Dryad Digital Repository: http://dx. doi. org/10. 5061 /dryad. 1dc8q (Wüest et al. 2015). In order to describe the climatic space available on each continent, we sampled the terrestrial parts of the world with a lattice of 0. 57 spacing, sampling the same bioclimatic data layers at the same 30″ resolution. This provided a similarly sized data set (62,000 points) to the combined Danthonioideae occurrence points. The areas we defined here (hereafter, continents; see fig. 1a ) are Africa, Australia, New Zealand (including the Pacific Islands), Southeast Asia (including Papua New Guinea, Indonesia, and the Philippines), Northern Asia, Europe, South America, and North America (including the West Indies, Panama, and countries further north).
Climate Availability and Niche Evolution 000 We reduced the 19 WorldClim variables using the outlying mean index (OMI) ordination  (Doledec et al. 2000)  implemented in the ade4 package  (Dray and Dufour 2007)  in R (R Development Core Team 2012) in order to obtain uncorrelated climatic niche dimensions. We preferred OMI over other ordination techniques because it does not assume any particular distribution of a species' response along environmental gradients and does not weigh the observation site by species richness or abundance, assumptions that might not be valid for intercontinental niche comparisons. From the transformed axes (hereafter, OMI axes), we extracted the minimum, mean, and maximum of the scores as niche parameters for each species. In order to account for possible uncertainties resulting from the initial data (specimen misidentifications, inaccurate georeferencing), we used the fifth and ninety-fifth percentiles of observations along OMI axes as the minima and maxima.
Acknowledgments This study was funded by  Swiss Science Foundation  grant  31003A-107927  to H. P. L. A. A. is supported by grants from the  Swedish Research Council  ( B0569601 ) and the  European Research Council  under the  European Union'sSeventhFramework Programme  (FP/2007-2013,  ERC Grant Agreement 331024 ). R. O. W. received support from the  Swiss Science Foundation  (grant  PBZHP3-147226 ). We would like to thank  A. Humphreys  for collaboration and valuable comments on earlier versions of this work,  J. M. de Vos  and  P. B. Pearman  for valuable discussions, and  G. Litsios  for help with the analyses. We are grateful to two anonymous reviewers, Associate Editor  C. Baer , and  Editor S. Kalisz  for constructive comments and suggestions that greatly improved this article. R. O.
We pay special attention on the hybrids ones, providing for the first time, a set of hybrid calibrations for different morphological types and stellar masses. We also analyze the dependence with the color (SDSS gr), axial ratio and ionized-gas attenuation. This analysis is the starting point for a series of papers in which we will study how the SFR in the Local Universe is distributed across galaxy components (bulge, disks, nuclei) and bidimensionally. Ultimately, we are interested in knowing how the local SFR density is spatially distributed over galaxies and how these results would compare to similar future studies at high redshift. This paper is organized as follows: in Section 2 we describe the reference sample used in this article, in Section 3 we describe the data and the analysis apply to the data, in Section 4 we discuss our results, and finally, in Section 5 we summarize the main conclusions.
As this is a random sub-selection of the mother sample based only on visibility along the observing period, this should be representative in terms of galaxy properties of the entire CALIFA mother sample. Fig. 1 : From left to right. Distributions of the r-band absolute magnitude, half light radius and log(M /M ). The white histograms correspond to the complete CALIFA sample (939 galaxies), and the grey-filled areas correspond to our sample (380 galaxies). A visual inspection of these histograms together with the perform of the K-S test probability show that our sample is representative in terms of galaxy properties of the entire CALIFA sample. (white areas in Figure  1 ) and the galaxies involved in this study (grey-filled areas). We use a K-S test to check whether the two data samples come from the same distribution.