dataesr/scientific-paragraphs-categorization huggingface dataset:
Interestingly, our data also showed the largest increase in fixations towards the hips during the 2° foveal vision condition (for both front-and back-views), followed by the face and the breast with front-views, giving this region a pivotal role in the evaluation of female attractiveness. The information collected in this area could relate to the extraction of the curvature of the hip shape and an estimation of hip width, as highlighted by the fixation patterns with constrained vision. In fact, WHR had an effect on the attractiveness ratings only when the peripheral vision was sufficient (in the natural viewing and 4° spotlight conditions), which is concordant with the fact that the whole silhouette is necessary to estimate the WHR  12. Why use peripheral vision when relevant information could be extracted directly? Although information is crucial to adaptive decision-making, only a fraction is actually relevant to the task; even so, individuals tend to encounter information at rates considerably higher than their capacity for full use  10.
Methods Stimuli. We created virtual female bodies with the open source tool MakeHuman, which allows for the flexible morphing of a 3D human body that varies in several dimensions (see Fig. 1 ). Three waist and 2 hip circumferences were used, resulting in 6 different body shapes representing a relatively large range of WHR (0. 67-0. 87) within a reduced range of BMI. To avoid an eventual habituation effect, 3 distinct faces (with different haircuts and facial features) were combined with each body shape, resulting in a total of 18 unique virtual female bodies. All other features were kept constant. The WHR of each 3D stimulus was calculated with the software measures, and the BMI was estimated as Volume * Density/(height) 2 , with density = 1. 043 g/cm  3 37  and the volume (in cm 3 ) of each stimulus was measured using the plugin Viewer 3D of ImageJ.
The linear mixed models were fitted with LMER (package lme4 1. 1-9) using R 3. 1. 1 (R Core Development Team). ∼ + + + + + + + + ( )+ ( ) ( ) Viewing Eye movement analyses. Eye movement data were analyzed using the new version of iMap  15, 17  , which implements a robust data-driven approach based on a Linear Mixed Model (LMM) and a bootstrap clustering method for hypothesis testing. Fixations and saccades were extracted from the raw data by using the default settings in the Eyelink software. Fixation durations were then projected back into the two-dimensional space according to their x and y coordinates at the single trial level. We smoothed the fixation duration map by convoluting it with a two-dimensional Gaussian Kernel function. We then estimated the fixation bias of each condition independently for all the observers by taking the expected values across trials within the same condition.
Y V iewing condition Body orientation Viewing condition Body orientation Fixation duration Observer x xSize y ySize 1 1 4 x y The LMM was fit by maximal likelihood (ML) using the fitlme function from the Statistics Toolbox ™ , Matlab 2013b. Furthermore, the sum of all categorical coefficients in the design matrix were set to zero to allow type III hypothesis testing in analyses of variance (ANOVA). Parametric statistics on the fixed effect and contrasts are performed by the coefTest for LinearMixedModel class in the Matlab software. To control for false positives from pixel-wise hypothesis testing, a multiple comparisons correction was applied by using a bootstrap clustering method  39, 40. Original parametric statistical values were thresholded at a given p value (0. 05/the number of pixels).
Acknowledgements This study was supported by the  Agence Nationale pour la Recherche  ' HUMANWAY' project  ( ANR-12-BSV7-0008-01 ). Support through the  Labex IAST  ( ANR-11-IDEX-0002-02 ) to J. B. is also gratefully acknowledged. This is contribution 2015-245 of the  Institut des Sciences de l'Evolution de Montpellier  ( UMR CNRS 5554 ). We thank two anonymous referees for their constructive comments.
0 ± 0. 1 and contained ∼ 70% of the power, the rest being distributed in an "error beam" extending up to an angular radius of ∼ 40 (see blue solid curve in Fig. 3a  in Sect. 3 below for the beam profile). Online data reduction at the telescope was performed with the BoA software developed for LABOCA  (Schuller 2012 ). Offline data reduction, including baseline subtraction, removal of correlated skynoise and 1/ f noise, and subtraction of uncorrelated 1/ f noise was performed with in-house IDL routines, including tailored versions of the Scanamorphos software routines which exploit the redundancy in the mapping raw data, especially data taken with filled arrays. The Scanamorphos algorithm, as developed to process Herschel observations, is described in depth in  Roussel (2013). To account for the specificities of the observations discussed here, it had to be modified.
1a  is therefore not constrained by the ArTéMiS observations and has been arbitrarily set to a small positive value (corresponding approximately to ∼ 5σ) to facilitate the display using a logarithmic intensity scale. To restore the missing large-scale information, we combined the ArTéMiS data with the SPIRE 350 µm data from the Herschel HOBYS key project  (Motte et al. 2010; Russeil et al. 2013 ) employing a technique similar to that used in combining millimeter interferometer observations with single-dish data. In practice, this combination was achieved with the task "immerge" in the Miriad software package  (Sault et al. 1995). Immerge combines two datasets in the Fourier domain after determining an optimum calibration factor to align the flux scales of the two input images in a common annulus of the uv plane.
Acknowledgements. We would like to thank  ESO  and the  APEX staff in Chile  for their support of the  ArTéMiS  project. We acknowledge financial support from the  French National Research Agency  (Grants  ANR-05-BLAN-0215 & ANR-11-BS56-0010 , and  LabEx FOCUS   ANR-11-LABX-0013 ). Part of this work was also supported by the  European Research Council  under the European Union's  Seventh Framework Programme  ( ERC Advanced Grant  Agreement no. 291294 -'ORISTARS' ). This research has made use of data from the Herschel  HOBYS  project (http://hobys-herschel. cea. fr). HOBYS is a Herschel Key Project jointly carried out by  SPIRE Specialist Astronomy Group 3  ( SAG3 ), scientists of the  LAM laboratory in Marseille, and scientists of the Herschel Science Center (HSC).
Segment Neighbourhood Search Take the constrained case (4) which segments the data up to t, for t ≥ k + 1, into k + 1 segments (using k changepoints), and denote the minimum value of the cost by C k,t. The idea of Segment Neighbourhood Search is to derive a relationship between C t,k and C s,k-1 for s < t: C k,t = min τ k j=0 C(y τj +1:τj+1 ), = min τ k   min τ 1:k-1 k-1 j=0 C(y τj +1:τj+1 ) + C(y τ k +1:τ k+1 )   , = min τ k C k-1,τ k + C(y τ k +1:τ k+1 ). Thus the following recursion is obtained: C k,t = min τ ∈{k,. ,t-1} [C k-1,τ + C(y τ +1:t )]. ( 10 ) If this is run for all values of t up to n and for k = 2,. , K, then the optimal segmentations with 1,. , K segments can be acquired. To extract the optimal segmentation we first let τ * l (t) denote the optimal position of the last changepoint if we segment data y 1:t using l changepoints.
That is because its complexity is on average O(n log K). Furthermore, it relies on a few fairly simple operations and hence the constant in front of this big O notation is expected to be quite low. For this reason we believe that Binary Segmentation is good reference point in terms of speed and we do not think it is possible to be much faster. However, unlike the other algorithms, Binary Segmentation is not guaranteed to find the optimal segmentation. For a fuller investigation of the loss of accuracy that can occur when using Binary Segmentation see  Killick et al. (2012). 7. 1 Speed benchmark: 4467 chromosomes from tumour microarrays  Hocking et al. (2014)  proposed to benchmark the speed of segmentation algorithms on a database of 4467 problems of size varying from 25 to 153662 data points.
These data come from different microarrays data sets (Affymetrix, Nimblegen, BAC/PAC) and different tumour types (leukaemia, lymphoma, neuroblastoma, medulloblastoma). We compared FPOP to several other segmentation algorithms: pDPA  (Rigaill, 2010) , PELT  (Killick et al. , 2012) , and Binary Segmentation (Binseg). For such data, we expect a small number of changes and for all profiles we ran pDPA and Binseg with a maximum number of changes K = 52. We used the R system. time function to measure the execution time of all 4 algorithms on all 4467 segmentation problems. The R source code for these timings is in benchmark/systemtime. arrays. R in the opfp project repository on R-Forge: https://r-forge. r-project. org/projects/opfp/ It can be seen in Figure  6  that in general FPOP is faster than PELT and pDPA, but about two times slower than Binseg.
Note that system. time is inaccurate for small times due to rounding, as pointed out in Figure  6  middle and right. For these small profiles we also assessed the performances of FPOP, PELT and Binseg using the microbenchmark package and confirmed that FPOP is faster than PELT. For these small problems we were surprised to observe that FPOP exhibited about the same speed as Binseg (microbenchmark results figure not shown).
Hence for a fixed size signal we expect the runtime of PELT to improve with the underlying number of changes. Based on Section 6 we expect FPOP to be more efficient than PELT and pDPA. Thus it seems reasonable to expect FPOP to be efficient for the whole range of K. This is what we empirically check in this section. To do that we simulated Gaussian signal with 200000 data points and varied the number of changes and timed the algorithms for each of these signals. We then repeat the same experience for signals with 10 7 and timed FPOP and Binseg only. The R source code for these timings is in benchmark/systemtime. simulation. R in the opfp project repository on R-Forge: https://r-forge. r-project. org/projects/opfp/. It can be seen in Figure  7  that FPOP is always faster than pDPA and PELT.
Interestingly for both n = 2 × 10 5 and 10 7 it is faster than Binseg for a true number of changepoints larger than 500. 7. 3 Accuracy benchmark: the neuroblastoma data set Both PELT and pDPA have been applied to this benchmark by first defining β = λd i for all i ∈ {1,. , n}, and then choosing the constant λ that maximises agreement with the annotated regions in the training set. Since FPOP computes the same segmentation as PELT, we obtain the same error rate for FPOP on the neuroblastoma benchmark. As shown on http://cbio. ensmp. fr/ ~thocking/neuroblastoma/accuracy. html, FPOP (fpop) achieves 2. 2% test error. This is the smallest error across all algorithms that have been tested.
Systemic Autoimmune Diseases, a group of chronic inflammatory conditions, have variable symptoms and difficult diagnosis. In order to reclassify them based on genetic markers rather than clinical criteria, we performed clustering of Single Nucleotide Polymorphisms. However naive approaches tend to group patients primarily by their geographic origin. To reduce this "ancestry signal", we developed SNPClust, a method to select large sources of ancestry-independent genetic variations from all variations detected by Principal Component Analysis. Applied to a Systemic Lupus Erythematosus case control dataset, SNPClust successfully reduced the ancestry signal. Results were compared with association studies between the cases and controls without or with reference population stratification correction methods.
SNPClust amplified the disease discriminating signal and the ratio of significant associations outside the HLA locus was greater compared to population stratification correction methods. SNPClust will enable the use of ancestry-independent genetic information in the reclassification of Systemic Autoimmune Diseases. SNPClust is available as an R package and demonstrated on the public Human Genome Diversity Project dataset at https://github. com/ThomasChln/snpclust.
Introduction The PRECISESADS project aims at reclassifying Systemic Autoimmune Diseases (SADs), a group of chronic inflammatory conditions characterized by the presence of unspecific autoantibodies in the serum and serious clinical consequences, based on genetic and molecular biomarkers rather than clinical criteria. SADs affect 1% of the global population  [1]  and have limited treatment options and difficult diagnosis. The diseases studied in PRECISESADS are Systemic Lupus Erythematosus (SLE), Systemic Sclerosis, Rheumatoid Arthritis, Sjögren's Syndrome, Primary Antiphospholipid Antibody Syndrome, and undifferentiated cases. Several technological platforms are used to generate biomarker data from patient samples, to obtain as much information as possible about the genetic and molecular mechanisms involved.
The technologies include Single Nucleotide Polymorphisms (SNPs) microarrays, which measures hundreds of thousands of common genetic variations in a population, gene expression and protein microarrays, mass spectrometry for metabolic profiling, or flow cytometry, as examples. The methodological approach is to first analyze data from each technological platform individually and then to merge relevant features from each platform to build the final classifier. Here we present the results of the preparatory work for SNP clustering analysis performed on a test dataset. Familial and twin studies have estimated a 50% genetic component in SADs  [2, 3]  and Genome-Wide Association Studies (GWAS) have found several loci associated with SADs  [4]. However, genome-wide clustering of SNPs is known to primarily group patients by ancestry prior to disease relevant features  [5, 6].
The test dataset contained SNP microarray data from 4,212 European SLE patients and 1,221 European controls. After quality control, Minor Allele Frequency (MAF) filtering, and tag SNP selection  [8] , PCA was performed on 379,190 SNPs from 5,433 patients. For each of the 100 first principal components, strong contributing SNPs were selected and SNP-dense regions were summarized by haplotypes. A total of 261 SNPs were selected and 331 haplotypes inferred. On the SNPClust selected dataset, the clustering signal due to ancestry was significantly reduced. The performance of SNPClust was compared to GWAS standard approaches and reference population substructure correction methods  [9, 10]. SNPClust was shown to enrich the selection of ancestry-independent sources of genetic variation associated with the phenotype, and hence propose more robust candidate biomarkers.
Ancestry clusters As expected, PCA applied on the input dataset (without any prior feature selection or data transformation) grouped patients by the country of origin of the samples, discriminating Northern from Southern Europeans in the first principal component and Eastern from Western Europeans in the second (Fig  1 ). This ancestry signal can also be seen in most of the 10 first principal components. The most important non-ancestry-based source of genetic variation in the PCA appeared on principal component 3 (see below), thus confirming that the ancestry signal is much stronger than clinically relevant signals in clustering approaches.
Benchmark GWAS have found several loci associated with SADs. However such approaches are impaired by population substructures that can generate false-positives or domestic association signals  [4]. In order to evaluate the information conserved by SNPClust and compare it to existing population stratification correction methods, a GWAS was performed on the input dataset, without or with subpopulation structure correction (see methods), and compared with the results of a GWAS performed on the SNPClust selected dataset. Compared with the GWAS performed directly on the input dataset, the GWAS performed on the SNPClust-processed dataset selected much less SNPs with a nominal p-value < 0. 05: 97,066 vs. 222. In addition, the initial selection of SNPs by SNPClust reduced the impact of the multiple testing correction: 59% of SNPs on the input dataset with p < 0.
Discussion The ancestry information is contained in many SNPs across the genome, and may therefore be present in clinically relevant SNPs, in particular in auto-immune diseases where the HLA locus is involved. Therefore removing simply the main known ancestry-informative markers may lead to the removal of clinically relevant SNPs while preserving many SNPs carrying small bits of ancestry information. Approaches considering the first principal components to adjust associations  [10]  can also result in loss of clinically relevant information because not all the first principal components are associated with ancestry. SNPClust overcomes these limitations in two steps. First the major contributors to a large number of the first principal components are considered, therefore selecting the markers explaining most of the variance in the dataset.
This has the property of preserving the largest sources of genetic variation. Then, the SNPs that could be considered as haplotypes due to their correlation and spatial proximity are summarized. This reduces the relative importance of ancestry information present in many SNPs while preserving the information conveyed by the haplotypes. At the same time, it also reduces the multiple-testing problem. In the dataset of 379,190 SNPs from SLE patients and controls, the first principal components were associated with ancestry. After application of SNPClust 592 markers remained and the first principal components were not associated to the centers. Therefore the ancestry information and the number of markers were strongly reduced. This method is also interesting for GWAS, due to the enrichment of ancestry-independent markers tested and the reduced multiple testing problem.
When compared to genomic control, 42% less SNPs were associated with the diseases but 10 times more outside the HLA locus. Compared to Eigenstrat, SNPClust had more statistical power and a higher ratio of associations outside HLA. Therefore SNPClust is useful to find several associated loci without being overwhelmed by strong signals from one single locus such as HLA. SNPClust will be applied to the SNP array data generated in the PRECISESADS project and will possibly enable the use of ancestry-independent genetic information in the reclassification of SADs. It is available as an R package and demonstrated on the public Human Genome Diversity Project dataset  [6]  at https://github. com/ThomasChln/snpclust.
Analysis set Observations with missing value rates above 3% and SNPs with missing value rates above 1% were excluded. An additive genetic model is then used (AA = 0, AB = 1, BB = 2) and SNPs with MAF below 5% were excluded to remove rare variants, which are more prone to genotyping errors. In addition, in order to decrease the required computation time and memory usage, redundant SNPs were removed by applying TagSNP (r 2 > 0. 8, window of 500,000 base pairs). The missing values were imputed by random sampling of each SNP. Then each SNP was centered and scaled to unit variance. A total of 5,433 patients and 379,190 SNPs were selected for analysis. This dataset defines our analysis set.
Only the strong contributors have null uncertainty, therefore SNPs with a null classification uncertainty were selected. If SNPs were selected from more than 8 chromosomes, the model was fitted with an additional mixture until the condition was satisfied. If the condition was not satisfied after 4 iterations, i. e. with 5 mixtures, no SNPs were selected from that principal component (Algorithm 1). Haplotype summarization. In a second step, in order to summarize large loci, for each principal component, selected SNPs on a same chromosome and closer than 1,000,000 base pairs away were summarized by haplotypes, pairs of binary values. Haplotypes were inferred with the SHAPEIT software  [17]  which uses hidden Markov models and has linear complexity with the number of SNPs. Haplotypes were then grouped in 2 classes by Gaussian mixture models.
Benchmark To evaluate the SNPClust algorithm, we performed a GWAS on the SNPClust selected dataset and on the input dataset with and without population stratification correction. First, a generalized linear model with a binomial error distribution was fitted to each SNP and haplotype to predict the disease of patients. Type 2 Analysis of Variance was then applied to obtain p-values. Multiple testing correction was performed with FDR, and results were compared with the outcome of our method. Then, two population stratification correction methods were tested, genomic control and Eigenstrat.
Analysis of molecular data PowerMarker software version 3. 25  (Liu and Muse 2005)  was used to calculate the number of alleles and values of genetic diversity and PIC  (Botstein et al. 1980 ) of each locus. Genetic distances between each pair of cultivars were measured by calculating the shared allele frequencies  (Jin and Chakraborty 1993). The Neighbor joining dendrogram was generated using the DARwin software based on the genetic distance calculated using PowerMarker software. The genetic structure was analyzed by performing PCoA (Principal Coordinates analysis) implemented in the program GenAlex 6. 5  (Peakall and Smouse 2012).
This can be done using machine learning techniques, which 'learn' from the data they encounter. Supervised machine learning allows a given algorithm to be built and optimised using an existing training dataset for which the true tumour geometry and location is known (the 'ground truth'), in order for it to make the right decisions to achieve optimal performance for cases in which the outcome is not known. This includes methods such as K Nearest Neighbours  (Anbeek et al 2005 , Lyksborg et al 2012) , Support Vector Machine (Iordanescu et al 2012, Jayachandran and Dhanasekaran 2013) and Artificial Neural Networks  (Bankman 2000 , Reyes-Aldasoro 2000) , which have been used in the literature for the segmentation of medical imaging by classifying voxels into different categories. The main advantages of such techniques are their high predictive power and their ability to adapt to any given dataset.
Description of the model In this work, we present ATLAAS 6 : an Automatic decision Tree-based Learning Algorithm for Advanced Segmentation. The ATLAAS predictive segmentation model is designed to select the most accurate PET-AS method for optimal segmentation of a given PET image. The best segmentation method is chosen from a list of advanced PET-AS algorithms built into the system. A list of the PET-AS methods used for this work is given in this section. When segmenting a new image, ATLAAS computes for each PET-AS algorithm its predicted segmentation accuracy using a number of parameters extracted from the target image. The prediction is done using a model consisting of DTs built during the training of ATLAAS. The predictive model is built on a large dataset of PET images of tumours, with the parameters describing the tumour uptake varying within a certain range.
The training dataset needs to be large enough to cover a large variety of clinical scenarios and provide accurate classification. Any data for which the ground truth is known can be used (i. e. phantom images, simulated images or clinical data with histopathology reference). This is known as the 'training dataset'. Figure  1  depicts the workflow used in the training phase and in the clinical application of ATLAAS. The following parameters describing the target object uptake were identified as classifiers of the DT learning method: V: target object volume (ml) TBRpeak: Ratio between the target object's SUVpeak, calculated as the mean value in a 1cm 3 sphere centred on the maximum standardised uptake value (SUV) in the target object, and the background SUV, calculated as the mean intensity in a 0.