dataesr/scientific-paragraphs-categorization huggingface dataset:
25 and P < 0. 05 following Bonferroni correction. Next, we analyzed a previously published RMS patient gene expression dataset (35) using the R2: Genomics Analysis and Visualization Platform (http://r2. amc. nl). For each gene signature, we computed the average log fold change in expression between living (status = live) and deceased (status = dead) patients, where the averaging is over all the genes in the signature. Here, we consider both up-regulated and down-regulated genes. However, the down-regulated genes contribute to the average log fold change with minus signs. To obtain a null distribution for this test statistic, we randomly flip the "survivor" and "nonsurvivor" labels when computing average log fold change. We simulate this null distribution to compute the P value for each gene signature.
MBE suggest that effective population sizes may be different in the two species and that H. ismenius may not be at equilibrium. These conditions may further complicate the interpretation of traditional tests to detect gene flow. Thus, we tested whether gene flow occurred during the species demographic history by inferring the demographic history of the species and comparing scenarios with or without gene flow. To do so, we extended the previously developed two-population models implemented in the software DILS Demographic-Inference with Linked Selection (DILS,  Fraïsse et al. 2021 ) in order to consider four populations under four main demographic scenarios and include the possibility of (asymmetric) gene flow between species and/or subpopulations. We modified the coalescent simulation pipeline accordingly and included ABBA-BABA-related summary statistics that are informative to reveal localized gene flow.
1   1A ), were also used when assessing gene flow in ABBA-BABA-related methods (detailed in supplementary table S1, Supplementary Material online). Finally, a set of 12 H. melpomene individuals were used as an outgroup. For behavioral and chemical data H. h. melicerta and H. i. boulleti specimens were collected in Darién (Eastern Panama) and around Gamboa, Colón (Central Panama), whereas H. h. zuleika specimens were collected from Chiriquí (Western Panama) (fig. 1 ). Stocks of these races were reared at the Smithsonian Tropical Research Institute in Gamboa (Panama)  (Huber et al. 2015).
Genotyping and Variant Calling Reads were trimmed using fastp  (Chen et al. 2018) , aligned to the H. melpomene v2. 5 genome with bwa-mem v. 0. 7. 13  (Li 2013 ) and filtered with samtools  (Li et al. 2009) , requiring a minimum quality of 20. Duplicates were removed using picard (http://broadinstitute. github. io/picard/). SNP calling was then performed using GATK V4. 1. 9  (DePristo et al. 2011). Variable and invariable sites across the whole genome were included. Following GATK Best Practices, all sites that did not match the following criterion were marked: MQ < 30, QD < 2, FS > 60, MQRankSum < -20, ReadPosRankSum < 10, and ReadPosRankSum > 10. The data set with variant and invariant sites was used for ABC analyses below. SNPs were then extracted from the data set and used for all remaining analyses.
Phasing and Recombination Rate Estimates Phasing was performed using beagle v. 5. 1  (Browning and Browning 2007 ) with default parameters. LDHat software  (McVean et al. 2002)  was then used to estimate effective recombination rates (ρ = 4. Ne. r where r represents the recombination rate per generation and Ne is the effective population size) along the genome. The genome was split in chunks of 2,000 SNPs with overlapping windows of 500 SNPs to compute recombination rate, and data were then merged together. Recombination rates were averaged into 250 kb windows using a custom Python script. Recombination rate was inferred for each species separately (H. hecale and H. ismenius) as well as for outgroups (H. numata and H. pardalinus) to analyze how conserved the landscape is along the divergence continuum (see supplementary results, Supplementary Material online).
Population Relationships Genetic relationships among individuals were assessed with a PCA using the R package ade4  (Dray and Dufour 2007)  based on a LD-pruned data set, further thinned to keep SNPs spaced at least 500 bp from each other (365 K SNPs). Population treeness was tested using Treemix  (Pickrell and Pritchard 2012)  with the same LD-pruned data set and allowing up to 20 migration events. The model that explained the highest level of variance when adding a migration edge was chosen. Five hundred bootstrap replicates of the best model were performed to obtain robustness of the nodes.
Ne values were independently drawn for N anc , N 1 , N 2 , N 3 , and N 4, but shared the same shape parameters  (Roux et al. 2016). Priors were generated using a Python version of priorgen software  (Ross-Ibarra et al. 2008). A mutation rate of 2. 9 × 10 -9 mutations per bp and generation was used, as estimated for H. melpomene  (Keightley et al. 2015).
Random Forest and Neural Networks Model Choice Two approaches were used to perform model choice. First, posterior probabilities were computed using a classic ABC procedure. The "best" 1,000 simulations (out of 1 million) were retained, weighted by an Epanechnikov kernel peaking when Sobs = Ssim and a neural network that performs a nonlinear multivariate regression where the model is a response variable to be inferred. Fifty feed-forward neural networks were used for training with 15 hidden layers in the regression. Ten replicates ABC model choices were performed to obtain the average posterior probability of each model. The R package "abc"  (Csilléry et al. 2012)  was used for the model choice procedure. Because classical ABC may suffer from the curse of dimensionality, especially for a complex model with many summary statistics as here, a random forest approach was also used for model choice (e.
g. ,  Rougemont et al. 2016). This approach has the benefit of directly assessing the proportion of correct model choice by the algorithm. A forest made of 500 trees was constructed, and a linear discrimination analysis was included as additional summary statistics. Computations were performed using ABC-RF package  (Pudlo et al. 2016). All available summary statistics were used to construct a confusion matrix, estimate classification error, and perform the model choice. The IM and AM models were compared among each other and to other models using only the ABC-RF procedure. The whole pipeline is available at https://github. com/QuentinRougemont/ABC_4pop.
Genomic Differentiation and Divergence Analyses to Test for Linked Selection Divergence among species and populations was measured by estimating nucleotide diversity (π), levels of divergence (D XY ), and genetic differentiation (F ST ) in windows of 250 kb along the genome using Python scripts from  Martin et al. (2013). Patterns of heterogeneous genomewide divergence can be due to genetic hitchhiking of neutral alleles linked to selective sweeps  (Smith and Haigh 1974)  or to background selection  (BGS, Charlesworth et al. 1993). These combined effects, referred to as linked selection, reduce polymorphism at sites closely linked to advantageous or deleterious variants  (Payseur and Nachman 2002). The intensity of selection on linked loci is mostly modulated by variation in local recombination rate and by gene density  (Kaplan et al.
i. boulleti on the one hand and between the nonmimetic pair H. h. zuleika and H. i. clarescens on the other hand. In addition, other more distant P1 species were tested, namely H. pardalinus and H. numata as well as H. ismenius telchinia. A 1 Mb block jack-knifing approach was implemented to calculate the mean and variance of D and f d and to test whether D differed significantly from zero. f d statistics were calculated in 10 kb sliding windows along the genome using custom scripts developed by  Martin et al. (2013). To investigate relationships among individuals at regions showing strong f d values, local phylogenies using RAxML-NG  (Kozlov et al. 2019)  were built. A GTRCAT model and 100 bootstraps were used. Analyses were complemented with the D FOIL statistics  (Pease and Hahn 2015) , that enable testing the topology [(P1, P2)(P3, P4)], O), with a potential higher power to test the directionality of introgression than the above test.
ARG Reconstruction at Optix The ARG was estimated in order to investigate variation in population history of optix (population size, topology, allele age, and gene flow) using ARGweaver  (Rasmussen et al. 2014). A constant mutation rate was used along the genome of 2. 9 × 10e -9 mutations per bp and generation, estimated in H. melpomene  (Keightley et al. 2015) , and recombination rate of 1e -8 per bp/generation. Utility scripts provided in  Hubisz and Siepel (2020)  were modified to plot the resulting trees as well as the TMRCA, RTH, π levels, and population size. A large window around optix (660 to 750 kb, with optix being located between 705,604 and 706,407 bp) was used to provide further context. Data set included both variant and invariant sites.
Extractions were not stopped. Elution samples were analyzed with gas chromatography coupled to mass spectrometry (GC-MS) with a Bruker Autosampler SCION SQ 436-GC, using a nonpolar Rtx-5 MS fused silica capillary column (0. 32 mm i. d. , 30-m long, 0. 25-µm-thick film) and helium as the gas carrier. Volumes of 0. 5 µL were injected automatically in a splitless mode with the injector temperature at 250 °C (see  Mérot et al. 2015  for more details). Peak area was used as a measure of compound abundance relative to the concentration of dodecane in the sample. Since chemical receptor sensitivity is unknown for these butterflies, low-concentration peaks were not excluded from our descriptions. However, peaks that were too low for quantification were treated as missing data for a given extract. Quantification values were log-transformed [log 10 (x + 1)], allowing comparison of peaks with large differences in abundance  (Lecocq et al.
Statistical Analysis of Chemical Data Chemical similarities and differences between species and sexes were visualized with heatmap graphs on the relative abundance of the compounds present in each tissue separately, using Matlab R2012a Student Version. Since chemical bouquets are likely to be perceived as composite signals, multivariate analyses were performed on the concentration of all compounds present in each tissue separately, where each compound is treated as a variable. For each tissue, analysis of compositional similarity of chemical cocktails between species was performed with sexes pooled as well as separating by sex, using nonsupervised NMDS ordination implemented in the vegan  (Oksanen et al. 2017 ) packages in R. Prior to NMDS, the Bray-Curtis distance matrix of the chemical composition was computed.
The appropriate number of dimensions was chosen for each data set by plotting a "scree diagram" (stress vs. number of dimensions). The significance of chemical differences between species or sexes was assessed by a discriminant analysis on the Bray-Curtis similarity matrix, using a perMANOVA  (Anderson 2001)  implemented in the vegan package  (Oksanen et al. 2017) , with 1,000 permutations. Whenever categories were significantly different, partial least squares discriminant analysis (PLS-DA) was applied to the data set using R packages mixOmics and DiscriMiner to identify variables (i. e. , compounds) most diagnostic of a given category. The indval index, which gives the probability of association between a compound and a given category, was also estimated by computing indicator values that suggest which compounds are indicative of a given category.
For that purpose, the indval function in the R package labdsv (Roberts 2012) was used, following  Heuskin et al. (2014). Finally, a comparison of the per-compound concentration was performed with a Kruskal-Wallis test coupled with a pairwise Wilcoxon rank-sum test and Bonferroni correction for multiple testing. To facilitate the identification of the compounds, linear retention indices (LRI) were calculated for each compound relative to the retention times of a mix of alkane standards (C10-C26). So far, a partial number of compounds were tentatively identified by comparison of retention indices and mass spectra with those of authentic reference standards (Sigma-Aldrich Corporation), with published databases (Wiley Registry of Mass Spectral data-7th edition and NIST Chemistry WebBook; NIST Chemistry WebBook 2015) and with compounds previously identified  (Estrada et al.
Acknowledgments We thank  Adriana Tapia ,  Moisés Abanto ,  Catherine Brunton ,  Cristóbal Ríos ,  Andrés Orellana ,  Benjamin Rice  and  Elizabeth Evans  for help with butterfly collection and breeding; the  Autoridad Nacional del Ambiente (Panamá)  for collection permits;  Ene Leppik ,  Centina Pinier  and  Claire Mérot  for help with chemical analyses;  Florence Piron-Prunier  for assistance with molecular work;  Platforms Genouest , Genotoul, and the  Montpellier Bioinformatics Biodiversity (MBB)  for calculation time;  Claire Lemaitre ,  Fabrice Legeai ,  Nicola Nadeau  and  Violaine Llaurens  for advice and discussions; EPHE for hosting Barbara as a PhD student. We are most grateful to the three anonymous referees whose in-depth reviews improved this work. This work was funded by the  European Research Council  Grant  StG-243179  ( MimEvol ) and  Agence Nationale de la Recherche  grant  ANR-18-CE02-0019-01  ( Supergene ) to MJ; and by a  STRI  fellowship to BH.
However, the model can be overfitted, i. e. it is too specialised to the observed data. Such as in classification approaches, the solution is to split the observation data into two datasets: one for calibration (training data) and the other for validation (validation data). Usually the partitions' distribution is between 75%/25% and 80%/20% (respectively for training/validation). If the observed dataset is small, Crooks (2018) suggest to use cross-validation, which train and test the model multiple times using random train and test partitions. However, it is not always possible in a user-centred approach because it is particularly time-consuming for the experts. The experts could help, correcting the model when the data is not covering local minima, but such interventions must remain highly targeted.
User-defined Validation Measures For each application, the experts identify a list of domain specific measures that will be used to validate the model. These measures are used to evaluate the distance between simulation and reality. Several validation measures may be identified, and combined, to provide an overview of the realism of the simulation. If field data is available, these measures can be used to process a distance between simulation results and real data. In our case study related to urban growth simulation, these measures must describe the buildings generated by the model. They can be compared to real buildings observed in satellite images, for example. The table 1 lists several spatial measures identified by our expert (a geographer). The table  2  shows whether these measures can be applied at a micro-level (e.
For example, such an approach has been followed in the RePast platform, through the Repast Simphony Statecharts  (Ozik et al. , 2015). However, it is not a full programming language. It is just a finite state machine that can set agent's statuses, and thus trigger behaviours written in others languages (e. g. ReLogo, Java,. ). Two types of visual programming do exist. Instructions can either be represented with blocks (like Scratch) or with nodes in a graphical representation (like flowcharts in node programming languages). For example, Figure  5  illustrates a behaviour rule "look for food" described in the NetLogo language to simu-late ant foraging  (Wilensky, 1997). The same rule is translated in the Scratch language in Figure  6. Of course, such manual modification of behaviour rules still time-consuming and difficult.
Figure1: The structure of the ODD+D protocol. Grey boxes indicate new elements compared to the ODD protocol, and the number in brackets shows the added questions (Müller et al. , 2013). calibration step. The first question that comes in such process (fig. 2) is: what do we rely on for calibration? Wilensky and Rand (2015)  talk about "microvalidation" when the calibration is done on the agent attributes individually and "macrovalidation" when it is done by analysing the global dynamics of the system. However, the model can be overfitted, i. e. it is too specialised to the observed data. Such as in classification approaches, the solution is to split the observation data into two datasets: one for calibration (training data) and the other for validation (validation data). Usually the partitions' distribution is between 75%/25% and 80%/20% (respectively for training/validation).
If the observed dataset is small, Crooks (2018) suggest to use cross-validation, which train and test the model multiple times using random train and test partitions. However, it is not always possible in a user-centred approach because it is particularly time-consuming for the experts. The experts could help, correcting the model when the data is not covering local minima, but such interventions must remain highly targeted. Calibration of a model based on natural processes or human behaviour can be tricky. It depends on lots of explicit and implicit parameters that are in interaction. These parameters may have lots of possible values, and even an infinite number of values for some. Therefore, the parameter search space can be very colossal. Crooks (2018)  introduces two kinds of calibration: "qualitative"and 'quantitative".
We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results. In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.
2014; Frieman et al. 2007 ) has five bands and the Catalina survey  (Drake et al. 2011 ) has only one band. Then, a time series of brightness changes is generated over time, called the light curves. Afterwards, the light curve is fed to a machinelearning classifier to determine the class of the object. Among all the methods developed to perform such a classification,  Möller & de Boissière (2020)  introduced a model called SuperN-Nova: a supernova photometric classification framework that uses a recurrent neural network (RNN;  Rumelhart et al. 1985; Hochreiter & Schmidhuber 1997; Cho et al. 2014)  to classify different types of supernovas such as SNIa, SNIb, SNIIP, and others using only light curves. The proposition yields good results because while Bayesian neural networks (BNN) are known to be robust to overfitting and can easily learn from small datasets, they are still significantly more complex than standard neural networks and computationally expensive.
We also propose a solution for the missing observations problem, which demonstrates a significant improvement in the accuracy of the model. To illustrate the performances of our model, we tested it with actual data from the SDSS survey  (Holtzman et al. 2008; Sako et al. 2014; Frieman et al. 2007 ). In Sect. 2, we describe the dataset that we used in our work. Section 3 introduces our architecture ConvEntion and describes the role of each component of the model. In Sect. 4, we present the results of our work with some statistics about the performance and some comparisons with other architectures used for image time series classification. Finally, in Sect. 5, we present our conclusions and perspectives on this work.
Database description The Sloan Digital Sky Survey (SDSS;  Holtzman et al. 2008; Frieman et al. 2007 ) is a very ambitious and successful largescale survey program using a dedicated 2. 5-m telescope at Apache Point Observatory, New Mexico, equipped with photometric and spectroscopic instruments that have released images, spectra, and catalog information for several hundred million celestial objects. The dataset used in this paper was collected during the SDSS Supernova Survey  (Sako et al. 2014) , one of three components (along with the Legacy and SEGUE surveys) of SDSS-II, a three-year extension of the original SDSS that operated from July 2005 to July 2008. The Supernova Survey is a time-domain survey, involving repeat imaging of the same region of the sky every other night, weather permitting.
The images are obtained through five wide-band filters  (Fukugita et al. 1996 ) named u ′ , g ′ , r ′ , i ′ , and z ′ , simplified as u, g, r, i, and z in the following, which corresponds to an effective mid-point wavelength of u (365 nm), g (475 nm), r (658 nm), i (806 nm), and z (900 nm). The survey region observed repeatedly over three years is a 2. 5-degree-wide stripe centered on the celestial equator in the Southern Galactic Cap that has been imaged numerous times in the last twenty years, allowing for the construction of a big image database for the discovery of new celestial objects. Most of the sources have included galactic variable stars, active galactic nuclei (AGN), supernovae (SNe), and other astronomical transients, all of which have been processed to generate multi-band (ugriz) light curves.
The imaging survey is reinforced by an extensive spectroscopic follow-up program that uses spectroscopic diagnostics to identify SNe and measure their redshifts. Light curves were evaluated during the survey to provide an initial photometric type of the SNe and a selected sample of sources was targeted for spectroscopic observations. A141, page 2 of 10 Bairouk, A. , et al. : A&A proofs, manuscript no. aa44657-22 Fig. 1. Sample of some objects present in our dataset. Each image in filter g/i corresponds to a different observation with the same filter. In order to investigate the classification from images rather than light curves, we acquired the images from the public SDSS dataset through their platform. Our dataset contains many types of supernovas (see Table  1  and  Sako et al. 2014). The label of "unknown" mainly represents very sparse or poorly measured transient candidates, "variables" have signals spanning over two seasons, and "AGNs" have a spectral signature.
The three other classes are supernovae of types Ia, Ib/c, and II. Among supernovae, the typing is performed from spectroscopy or from the light curve using different machine learning techniques (see  Sako et al. 2014). We grouped the non-Ia supernovas because our focus in this study only on the Ia type for their interest in cosmology as standard candles and also because of the small number of non-Ia with spectral signatures. The very small class of three SLSN bright objects has been added to the non-Ia supernovae. Figure  1  shows an example of astronomical image time taken from the SDSS dataset.
Challenges Most of the astronomical dataset suffers from a number of problems that should be dealt with before feeding it to the classification algorithm. Among difficulties contributing to the challenging nature of AITS, we can mention class imbalance (as shown in Table  1  of our dataset). In particular, we can clearly see that the classes we have are not balanced where the number of samples for variables is much bigger than SNIa. This imbalance significantly impacts machine learning models due to their higher prior probability, which means they tend to overclassify the larger class(es). As a result, instances belonging to the smaller class(es) are more likely to be misclassified than those belonging to the larger class(es). Another problem that impacts the model is missing bands. Indeed, each time an image is acquired in an AITS it is captured through one filter among a set of up to five or more channels.