dataesr/scientific-paragraphs-categorization huggingface dataset:
Computation efficiency The computational accuracy of the method is better illustrated with a figure (see Supplementary Figure  10 ) where we show the time taken to apply MEIF on datasets with number of predictors varying in between 20 to 200. The computations are done in an Intel(R) Core(TM) i7-8550U CPU @ 1. 80GHz 1. 99 GHz processor laptop with 16 GB RAM. MEIF implementations takes around a minute for a data with 200 causal SNPs, as evident in Supplementary Figure  10. Note that the run time depends partly on the parameter settings of the cforest. For the simulations and for Supplementary Figure  10 , we made sure that the number of trees (ntree) is same as the number of predictors in the model. From our simulations and from the real data application, we observed that for large dataset (>500 predictors), we did not gain much by increasing the ntree parameter.
DGRP Cardiac Dataset We validated the applicability of our approach on both cross-sectional and longitudinal datasets by applying epiMEIF on 1) A cross-sectional dataset : Cardiac performance at 1 week (Heartperiod) and 2) a longitudinal dataset : Cardiac aging (Heartperiod at 1-week and 4-week). The epiMEIF method has been developed in the framework of a project aiming to identify the genetic architecture of natural variations associated to cardiac aging in Drosophila. We analysed the cardiac performances in a natural population of young (1 week) and old (4 week) flies from the Drosophila Genetic Reference Panel (DGRP  21  ), a population consisting of 205 inbred lines derived by 20 generations of full-sib inbreeding from inseminated wildtype caught female flies from the Raleigh, USA population.
Whole-genome sequencing data, along with genotype calls, are available for all 205 lines. Contractility and rhythmicity were measured in females, using approximately 2000 flies from 168 DGRP lines at 1 week and 1800 flies from 165 lines at 4 weeks. More details on the phenotype and genotype dataset can be found in Saha et al. ,  25. Here, we have mainly focused on the application of epiMEIF on the natural variations of the Heart period from the above dataset. To alleviate the computation burden, we apply a single locus association test using mixed model (LMM) that first filters the SNP markers that have a significant effect on the phenotype, thereby reducing the size of the genome data for epistasis detection using epiMEIF. Details on data pre-processing steps involving the quality control checks on the DGRP GWAS dataset, followed by the single GWAS application is detailed below.
An additional dataset of 20 DGRP lines was also analysed by us where the cardiac performance was studied following the same approach (with 12 flies observed per line) that were not used in the model building stage. We treat this dataset as our validation dataset and utilized it to test if the genetic interactions predicted from epiMEIF method in the original dataset have an impact on the phenotypic variations in this independent dataset. Pre-processing steps and single GWAS: Genetic variants/genotype data linked to the cardiac phenotypes were identified using the GWAS web tool developed for the DGRP by Mackay  21  (http://dgrp2. gnets. ncsu. edu/). It comprised four million high-quality SNPs amongst which 95% were homozygous. Typical quality assurance procedures involving maintaining the genotype call (GC) rate at 90% for each chromosome, removing SNPs with minor allele frequency (MAF)>0.
0179 (must be present in 3 lines out of 167) were performed to get rid of the poor-quality genotype data. All phenotypes' distributions are treated for outliers using standard outlier treatment i. e. , remove the observations that lie outside 1. 5 * IQR, where IQR, the 'Inter Quartile Range' is the difference between 75th and 25th quartiles. The removed data were validated by the biologists as technical outliers by looking back at the original experiment results. After the QC checks, single marker GWAS using LMM was performed on 2,456,752 SNPs from the DGRP data. The rapid decline in LD locally and lack of global population structure in Drosophila was favourable for GWAS application  21. As mentioned in the main text, the single GWAS act as a filtering step and help narrow the search down to a reduced set of causal SNPs to accommodate computational requirements of epiMEIF.
The nominal significance threshold of 10 -5 was used to select the SNPs associated with the phenotype  21. Note that if the number of causal SNPs (that needs to be explored for epistasis) is less than 10000, any filtering step can be avoided as the forest algorithm can handle as many as 10000 markers. Apart from the genome data, the DGRP dataset comprised other covariates like Wolbachia pipientis status that is a maternally transmitted endosymbiotic bacterium that infects ~53% of the DGRP lines and often impacts the phenotype of the flies  58, 59. These covariates can be easily accounted for in epiMEIF. The proposed method can simultaneously capture several vital components of genome-wide datasets: 1) The joint effect of the multiple loci via the function  in equation (2). 2) The interaction effects between the variants and in between the variant and "Time" (age in the DGRP dataset) (via the function  in equation(  2 )).
Then the SNPs that are highly correlated (above 90%) and that are the in the immediate neighbourhood of 5 SNPs, which are mapped to the same gene are clustered into the same LD blocks. Only one SNP (least causal/significant) is selected from each LD block for epiMEIF application. This led to 3484 SNPs for the cardiac HP dataset at 1 week and 1682 SNPs for the cardiac aging dataset.
Simulation Study We used genetic features obtained in the form of SNPs from the DGRP. For each simulation experiment, we considered a random subset of 1,000 SNPs with a minor allele frequency (MAF) >0. 1. Using the MAF frequency of each SNP we artificially generated a dataset of 1000 SNPs using Bernoulli distribution and made sure that there is no population structure in the genotype dataset generated. We retained the same phenotype data (Heartperiod) as in the original cardiac dataset where there were 165 lines of flies and each line had approximately 10-12 flies. We wanted to show the effectiveness of our method for both longitudinal and cross-sectional data, so we designed two different simulation models: one for 1-week flies and another for both 1 week and 4-week flies. To simulate the phenotype for week 1 flies, 6-8 SNPs were randomly drawn amongst which four chosen SNPs were considered as causal markers to simulate linear additive effects and further 1 to 3 pairs of SNPs were chosen to contribute epistatic effects.
This is shown in Model 1. To simulate the aging phenotype, we use the model shown in Model 2 where the effect of the SNPs at age 4 weeks is higher than effect of the SNPs at age 1 week. Interaction effects were simulated by taking the component-wise binary product as indicated by the 'int' operator above. This corresponds to interactions between both minor alleles. For the sake of simplicity, we did not consider other interactions (minor-major, major-minor and so on). The resulting vector is multiplied by the simulated effect size. The above model and its parameters are mainly adapted from Stephan et al. ,  60. However, unlike them, we do not consider any parameter separately for capturing the population structure because there is no underlying population structure in our artificial dataset.
and   are assumed to be 0. 375 and 0. 125 respectively following Stephan et al. ,  60. To compare the performance of our method in different genetic settings, we vary the relative number of additive and interaction terms. The goal of the simulation experiments is to check how accurately the epiMEIF can capture the above desired interactions in the different scenarios from the artificial dataset with simulated phenotype and artificially generated genotype data. Apart from the 6-8 SNPs shown above, we randomly choose 30 and 50 other SNPs (from the set of 1000 SNPs) in our simulation scenarios. To make sure that these additional SNPs used in the cforest do not have any marginal effect on the phenotype we ran a single marker GWAS model on the 1000 SNPs and extracted only those SNPs that were not associated with the phenotype.
Amongst these SNPs, we selected 30 or 50 SNPs for our simulations. We conducted 100 simulations for each scenario and for each simulation we run the MEIF 10 times and obtain the interactions and their pooled interaction score from the 10 forests. We fit both the epiMEIF and the Weighted epiMEIF on the simulated cross-sectional data and only epiMEIF in longitudinal dataset. The Weighted epiMEIF was introduced to test how accurately they can capture the interactions around the SNPs that are marginally less significant in the model. Apart from testing the overall power (see Table  1 ) of detecting the true interactions we also tested how often the true interactions are captured as the top-ranking interactions (based on the score) in our simulation scenarios. To evaluate this for each simulation, we rank the interactions obtained from the random forest application based on their score and compute the proportion of times the true interactions are captured across the different ranks.
We call it the rank-wise power detection across the different simulation scenarios. Supplementary Figure  1  and Supplementary Figure  2  shows the rank-wise power in capturing the interaction of interest for Age 1 and Aging simulations, respectively. Supplementary Figure  1  illustrates that the Weighted epiMEIF can more efficiently capture the true interaction compared to the epiMEIF. Hence, we prefer Weighted epiMEIF for epistasis detection in cross-sectional data. Note that the improvement of Weighted epiMEIF over the epiMEIF is more prominent amongst the top ranks. As already mentioned earlier, for aging data simulations, we have only applied epiMEIF and it performs very well in capturing the interactions with high marginal effect in the top ranks. Though less efficient in capturing interactions with low marginal effect (SNP_1: SNP_2 in Scenario 2 of Supplementary Figure  2 ), the overall power of capturing these interactions still reaches 80-90%.
∑    +     + ( 1 ,  2 , ⋯ ,   ,   ) +   , …………………………………………. equation(2)  ~ (0, ),   ~  (0,   ),  = 1, … ,   = 1, ⋯ , , ∑  =  where•   = [ 1 ,  2 , ⋯ ,    ] is the   × 1 vector of responses for the   observations in cluster ,•  is the  ℎ fixed-effect covariate for cluster. = [    × 1 vector of  ℎ fixedeffect covariate values for cluster , and   = [ 1 ,  2 , ⋯ ,   ]  is the  × 1 vector of  ℎ fixed-effect covariate values over all clusters (e. g. , in the DGRP dataset,   comprises the variants like the date on which fly is dissected). •   is the   ×  matrix of random-effects covariates that we do not intend to include in the cforest. •  1 ,  2 , ⋯   are the marker variables in our study and  capture the time or the longitudinal component of the data (  denotes the time at which   is observed).
Holistic Behaviour Analysis Framework (HBAF): From Sensing to Interpreting Users' Behaviour A virtual agent system should take users' personal situation into account during interactions, for example, by using sensor data collected in the users' daily life. For a coaching application as developed in the Council of Coaches project, a valuable starting point for the conversations is the users' behaviour. Ideally, for a health coaching application an holistic profile would be available. That is, a broad picture of the user as a whole, that includes behavioural aspects on multiple domains. In our use case, domains for which behaviours were measured included physical activity and social interaction. This information was gathered using a smartphone, wearable, a weight scale and a blood pressure measuring device.
This data is securely gathered, after which selected views and summaries are generated. Only this 'derived content' is accessible for the rest of the platform, ensuring that users' sensitive data are exclusively stored in the HBAF. The HBAF detects the user's physical (e. g. , walking), social (e. g. , having a conversation), emotional (e. g. , feeling happy), and cognitive (e. g. , reading a book) behaviours. These classes of information have been selected for their potential to ground opportunities for behaviour change through coaching conversations, although the HBAF's architecture is not necessarily limited to those classes. Furthermore, behavioural data is analysed at different time scales, affording the detection of short-term behaviours (e. g. , sitting on a chair), long-term behaviours (e.
WOOL Web Service: Shared Knowledge Base With Access Authorisation The WOOL Web Service 4 fulfils the role of the Shared Knowledge base (SKB) in the Agents United Platform. As the central 'memory' of the system it stores all necessary information and makes it accessible to all other modules in the platform. This does not only involve immediate memory (at the level of the ongoing conversation), but also longer term memory of sensor data, personal characteristics, coaching goals, and a conversational history per user that spans across recurring interactions. In our use case, a naming scheme for variables is used to store information and distinguish between information that should be remembered between dialogues (e. g. , dialogues that have been completed or a goal that has been set) and information that is stored 4 The Web Service is part of the WOOL Dialogue Platform: www.
woolplatform. eu and used during execution of a dialogue (e. g. , which paths through the current dialogue have been completed). Variables in the last case will be overwritten when the dialogue is held again, whereas other data is retained longer term; also some variables are stored as histories or time series data whereas for other variables only the most recent value is retained. Other examples of such a database exist, there are many more agent applications with some kind of memory storage (e. g. ,  [9, 25] ), but together with the middleware communication protocols employed between components in the Agents United Platform, the SKB is crucial for a fluent information flow between system components, which can be facilitated by a consistent naming scheme.
Flipper: Interaction Management and Conversational Intent Planning The Interaction Manager is the conversationally-aware glue between various components, providing the 'beat' to which the other components in the think layer run. The Interaction Manager has been built using the Flipper system  [32, 33] , an information state based conversation engine that choreographs other modules involved in producing the conversation. Addressing a single topic can involve a complex dialogue of moves, and a single 'move' utterance might require multiple social conversational moves (including turntaking and back-channelling), to be delivered properly. Flipper is used to orchestrate this process and, while doing so, keeps track of when moves have been delivered, whether they were interrupted, or not performed.
In our use case, for example, when setting a physical activity goal, we want to know when certain coaching information is delivered and when the user has agreed on a certain goal (e. g. '7,500 steps'). This process of negotiating a suitable goal requires choreographed conversational moves between an activity coach and a social coach. The latter may respond to the activity coach with statements such as "Group activities are a great way to motivate you to reach your physical activity goal. " As interaction manager, Flipper communicates with most other modules as illustrated in Figure  2 : Flipper (1) requests, from the TSE, new topics to be addressed; (2) controls the dialogue engine to successively determine possible next conversational moves and generate corresponding multi-modal utterances using information from the SKB; (3) requests the SAIBA behaviour realisers to produce selected utterances on the multi-agent embodiments; (4) receives user responses via the GUI, which it communicates back to the dialogue engine.
WOOL 5  is built as an adaptation of the Yarn language  6  and models dialogues as transition graphs that link agent statements and user replies. This dialogue engine and language provide a lightweight way to, for example, include an initial version of dialogues for testing, or to include single use dialogues (such as the scripted first introductions in our use case). The editor supports the dialogue development process through an intuitive GUI for authoring and testing dialogues. Once a dialogue has been started with the user, the user's response to a statement by an agent determines what the next agent-statement should be. This process is repeated until the end of the dialogue has been reached.
GRETA & ASAP: Multi-Modal Behaviour Realisers The setup of the Agents United Platform ensures that conversational content can be delivered via any SAIBA-compliant multi-modal behaviour realiser  [13]. Currently, two embodied virtual agent platforms are fully integrated: GRETA  [17]  and ASAP  [37]. In our use case, various agents from both platforms (as also previously shown in Figure  1 ) delivered the coaching content in an integrated virtual environment, where agents held conversations between each other and the user. 2. 6. 1 Virtual Embodiments via the GRETA Platform. Of the two virtual agent platforms, GRETA  [17]  is the most advanced with respect to the autonomous nonverbal behaviour generation that accompanies agents' utterances. The GRETA agents' behaviours are driven at three different levels: first, by indicating its communicative intentions using the Function Markup Language FML-APML  [16] ; second, from a semantic analysis of the agent's utterance  [22] ; and third, from the prosody  [41].
2). The inclusion of multiple agents in one environment required agents to be added as independent entities. Therefore, we added a hierarchical structure to GRETA. At the highest level, we added an environment manager that knows about the objects and their location. It can contain various objects, in particular agents. Each agent is controlled by its own 'CharacterManager' module that contains its information: the 3D aspect, voice, behaviour lexicons and baseline  [15]. Feedback mechanisms ensure the information flow between the components. They allow an agent to know where another agent is located and what it is doing. Furthermore, we have integrated GRETA within Unity  7. The integration was done using a TCP-IP message controller where Unity acts as the server and GRETA as the client. As soon as an animation has been computed within GRETA, it sends it frame by frame to Unity.
Section 4 illustrates the performance of getsf on a large variety of images that were obtained with different telescopes in a wide spectral range, from X-rays to millimeter wavelengths. Section 5 describes all strengths and limitations of getsf. Section 6 presents a summary of this work. Appendix A discusses inaccuracies of the surface densities and temperatures, derived by spectral fitting of the images. Appendix B describes the single-scale spatial decomposition that is used by getsf in its processing steps. Appendix C gives details on the software. In this paper, images are represented by capital calligraphic characters (e. g. , A, B, C) and software names and numerical methods are typeset slanted (e. g. , getsf ) to distinguish them from other emphasized words. The curly brackets {} are used to collectively refer to either of the characters, separated by vertical lines.
Applications to observed regions The multiscale, multiwavelength source-and filament-extraction method presented in Sect. 3 was very extensively tested using ∼ 40 images that were observed with different instruments and both ground-based and orbital telescopes during the past two decades. Multiwaveband observations of star-forming regions obtained in the Herschel Gould Belt Survey  (André et al. 2010)  and HOBYS  (Motte et al. 2010 ) key projects, as well as the most recent interferometric images observed in the ALMA-IMF program  (Motte et al. , in prep. ) , played an important role in validating getsf. The new extraction method has demonstrated very good results in ALMA benchmarks  (Pouteau et al. , in prep. )  on images, created from a magnetohydrodynamic (MHD) simulation of a star-forming region  (Ntormousi & Hennebelle 2019 ) that was populated with model cores and processed by the casa task simobs  (McMullin et al.
2007 ) to resemble the real ALMA observations  (Louvet et al. , in prep. ). Furthermore, getsf has A&A proofs: manuscript no. getsf been applied to source extraction in 15 regions of the ALMA-IMF program and 12 infrared dark clouds of the ASHES survey  (Sanhueza et al. 2019; Li et al. 2020). However, the most significant and definitive testing and validation of extraction tools is achieved with simulated benchmarks for which everything is fully known about their components. The second paper (Men'shchikov 2021, submitted) presents a quantitative analysis of getsf extractions using several variants of the new benchmark (Sect. 2) and old benchmark (Papers I and III). Sections 4. 1-4. 8 illustrate the performance of getsf on nine images obtained with different telescopes: XMM-Newton, the Galaxy Evolution Explorer (GALEX), Hubble, Spitzer, Herschel, the Atacama Pathfinder Experiment (APEX), the James Clerk Maxwell Telescope (JCMT), and ALMA from the X-ray domain to the millimeter wavelengths.
21 ), including substantial numbers of negative areas and artifacts from the data reduction and map-making algorithms. The separated S λ component shows all source-like peaks very clearly, even those that are hardly visible in the original image, because getsf is able to distinguish sources from the elongated filamentary shapes. Many of the sources overlap each other, therefore they require deblending for accurate measurements. The background B λY of filaments is fairly low, hence its subtraction enhanced the visibility of filaments in F λ only little. Nonuniform small-scale fluc-A&A proofs: manuscript no. getsf tuations in S λ were effectively equalized in the detection image S λD by the flattening algorithm. The source catalog contains measurements of 124 sources, 91 of which are selected as acceptably good by Eq.
The missing large-scale emission of the SCUBA-2 image helped getsf expose the many relatively faint, narrow filaments within the wide, massive ISF. In the F λ component, getsf identified 267 and 199 simple, non-branching segments of the filaments (Sect. 3. 4. 5) at 450 and 850 µm, respectively, on transverse scales of 28 and 39. This is similar to the existence of narrow sub-filaments on small scales within the resolved Taurus, Aquila, and IC 5146 filaments (Fig. 13 ) and consistent with the recent ALMA observations of ISF  (Hacar et al. 2018 ). 13 https://www. canfar. net/storage/list/ AstroDataCitationDOI/CISTI. CANFAR/16. 0008/data 4. 8. Star-forming cloud W 43-MM1 W 43-MM1 was observed with the 12 m array of the ALMA interferometer (baselines 13-1045 m) in the 233 GHz band centered at 1300 µm  (Motte et al.
Strengths In contrast to the other methods, getsf extracts sources and filaments simultaneously by combining available information from all wavebands. Its flexible multiwavelength design enables handling of up to 99 images, not necessarily all of them observed in different wavebands. The maximum number of images is arbitrary, representing the largest two-digit integer number used in the output file names; the code can be updated to use a higher value if required for some applications. Any subset of the input images that is deemed beneficial for the detection purposes can be used to detect the sources and filaments, whereas measurements of the identified structures are provided for all input images. In a nonstandard application, the method can also be employed with the position-velocity cubes if they are split into separate images along the velocity axis (getold was used in this way by  Shimajiri et al.