dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , being unaffected or favoured by connectivity loss;  Lindborg, 2007; Piessens, Honnay, Nackaerts, & Hermy, 2004). Vegetative reproduction is considered an alternative life cycle that allows the persistence of local species without sexual reproduction  Eriksson (1996) , particularly if a population has been isolated  (Eriksson & Ehrl en, 2001). Vegetative reproduction alone without any other traits ensuring long-term persistence (e. g. , persistent seed bank) leads to a certain risk of local extinction  (Honnay & Bossuyt, 2005 ). Yet, 100% of three species that reproduce mostly vegetatively and 92% of 24 species that reproduce either vegetatively or sexually had a transient or short-term persistent seed bank in our study (LEDA database,  Kleyer et al. , 2008). Our findings also contrast with previous studies demonstrating that plants resistant to connectivity loss display high seed germination rates  (Favre-Bac et al.
Acknowledgements We thank  L eo Broudic  and  Mathieu Bijou  for assistance in the field and  Maxime Dahirel  for assistance with the statistical analyses. We also thank the farmers who gave permission for weed surveys to be performed in their fields. This work used landscape databases collected in the LTSER site " Zone Atelier Armorique " and plant traits from the TRY database (http://www. try-db. org). The TRY database is hosted, developed and maintained by  J. Kattge  and  G. Boenisch  ( Max Planck Institute for Biogeochemistry, Jena, Germany ). TRY is currently supported by  Future Earth/bio-DISCOVERY  and the  German centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. Land-cover and hedgerow network maps were established by  Jean Nabucet  ( LETG-Rennes-Costel ) and Kermap.
1. Mutation, the source of genetic diversity, is the raw material of evolution; however, the mutation process remains understudied, especially in plants. Using both a simulation and reanalysis framework, we set out to test the performance of two types of variant callers, generic ones and those developed for cancer research, to detect de novo somatic mutations. 2. In an in silico experiment, we generated Illumina-like sequence reads spiked with simulated mutations at different allele frequencies to compare the performance of seven commonly-used variant callers to recall them. More empirically, we then reanalyzed two of the largest datasets available for plants, both developed for identifying within-individual variation in long-lived pedunculate oaks. 3. Even in plants, variant callers developed for cancer research outperform generic callers regarding mutation recall and precision, especially at low allele frequency.
Generation of mutations To ensure the feasibility of the project and to limit the computational load, a first step is to subsample one or several sequences of user-defined length in the reference genome. The first workflow named generateMutations therefore uses a bespoke R script named sample_genome to generate these subsets. The workflow then takes advantage of the two scripts included in simuG  (Yue & Liti, 2019) , vcf2model. pl, and simuG. pl, respectively, 1) to build a model of heterozygous sites distribution for an haploid reference genome based on was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 21, 2022. ; https://doi. org/10. 1101/2021. 10. 11. 462798 doi: bioRxiv preprint a user-defined set of known heterozygous sites in vcf format and 2) to build the second reference haploid genome comprising a user-defined number of heterozygous sites to accurately represent diploidy.
Typically, the user can define a number of heterozygous sites based on the product of nucleotide diversity (ߨ) and genome length (L). The workflow uses a homemade R script named generate_mutations to spike randomly the reference genome with a user-defined number of mutations which are drawn in a binomial distribution using a user-defined transition/transversion ratio (R). Finally, the workflow takes advantage of InSilicoSeq  (Gourlé et al. , 2019 ) defined with the model option hiseq to generate datasets of mutated and non-mutated in silico Illumina-like sequencing reads using (1) the original reference haploid genome; (2) the reference haploid genome with heterozygous sites, as the workflow was developed for a diploid species; and (3) the reference genome spiked with mutations following user-defined allelic fraction (AF) and depth of sequencing depth (C).
, 2009)  ). Then we only focused on the simulated mutations, and therefore excluded from the analyses the known heterozygous sites provided by the user thanks to the vcf file for GATK, freebayes, Somatic Sniper, and Strelka2 using BEDTools subtract  (Quinlan & Hall, 2010)  or directly within the variant caller for Mutect2 and VarScan. In silico experiment We used the generateMutations workflow to generate 1000 mutations in the oak genome with varying biological and sequencing parameters. To ensure consistency between the in silico experiment and the reanalysis of empirical data, we used the reference genome "Qrob_PM1N'' of Quercus robur 3P from Bordeaux, ENA accession number PRJEB8388 was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
947). We used known heterozygous sites from the reference genome  (Plomion et al. , 2018)  to simulate back one thousand heterozygous sites (ߨ = 0. 01 , L = 1 Mb , N = ߨ x L = 10 ସ ). We used varying values of transition/transversion ratio (R = [2, 2. 5, 3]), allelic fraction (AF = [0. 05, 0. 1, 0. 25, 0. 5]), and sequencing depth  (C = [25, 50, 100, 150, 200] ), resulting in 60 simulated datasets of mutated and associated base reads (3R x 4AF x 5C). We then used the detectMutations workflow to detect (recall) spiked mutations with every variant caller (Mutect2, freebayes, GATK, Strelka2, VarScan, Somatic Sniper, and MuSe). Using known spiked mutations, we assessed the number of true positive (TP), false positive (FP), and false negative (FN) for each variant caller to detect mutations and each combination of biological and sequencing parameters.
Oak data reanalyses We re-analyzed publicly available oak data from two projects led by Bordeaux, France  (Plomion et al. , 2018)  and Lausanne, Switzerland (Schmid-Siegert et al. , 2017) (SRA PRJNA327502 and ENA PRJEB8388, respectively). We then used the best-performing variant caller based on our in silico investigation, Strelka2, and the variant caller for mutation detection from the original publication to compare the results, i. e. , GATK with Best Practices for Swiss data (Schmid-Siegert et al. , 2017) and Mutect2 for French data  (Plomion et al. , 2018). The Swiss data comprised 2 libraries of medium sequencing depth (60X) representing one lower and one upper branch. The French data comprised 3 libraries of high sequencing depth (160X) representing 3 branches (lower, mid, and upper).
All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which this version posted April 21, 2022. ; https://doi. org/10. 1101/2021. 10. 11. 462798 doi: bioRxiv preprint noted that this strategy could have removed some true somatic mutations. We used these raw datasets to identify the mutations from the original studies after realigning the megabase containing the mutation on the 3P genome using BLAT  (Kent, 2002). For both datasets, we finally kept candidate mutations with (1) a read depth for both the normal and mutated samples between half and two times the mean sequencing depth (30-120X and 80-320X for Swiss and French datasets, respectively), (  2 ) an absence of the mutated allele in the normal sample, (3) a minimum of 10 copies of the mutated allele in the mutated sample and (4) an allelic frequency <0.
5. In addition, Strelka2 calculates an empirical variant score (EVS) based on a supervised random forest classifier trained on data from sequencing runs under various conditions, which provides an overall quality score for each variant  (Kim et al. , 2018). We took advantage of the EVS to define a conservative set of candidate mutations for both datasets, hereafter referred to as the EVS datasets. Given that the proportion of the genome falling within the sequencing depth boundaries used for the detection (i. e. between 50 and 200% of the mean sequencing depth) varies depending on the dataset, we weighted the observed number of mutations by the proportion of the genome satisfying the sequencing depth criteria to provide a more accurate and comparable estimate of the real total number of mutations.
2 ). For allele frequencies over 0. 25, all variant callers perform similarly well, except for freebayes, which identified many false positives. Over the 80 tested parameter combinations, Strelka2 was the best performing variant caller for various allelic frequencies and sequencing depths (in 57/80 simulated datasets , with an average recall of 0. 95 for a precision of 0. 98, Fig. S2-4  and Table  S1  and S2 ) and the second fastest caller (Fig. S9 ). was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. We further investigated the performance of the best performing variant caller, Strelka2, on two empirical datasets on pedunculate oak  (Schmid-Siegert et al. , 2017; Plomion et al. , 2018)  in comparison to the variant callers used in the original publications, i.
3A ). Adding Strelka2 recommended filtering based on empirical variant scores yielded the most conservative dataset with a similar number of mutations between both studies and a 2 to 7-fold increase compared to the original number of mutations. Due to lack of access to biological material from the original studies, conclusions were drawn from this list of conservative candidate somatic mutations (but see supplementary note S4 for a discussion regarding validation of mutations). The distribution of allelic frequencies of detected mutations partly explains differences among detection methods (Fig. 3A ), with Strelka2 and Mutect2 detecting mutations with lower allelic frequencies than the candidate mutations presented in the was not certified by peer review) is the author/funder. All rights reserved.
No reuse allowed without permission. The copyright holder for this preprint  (which this version posted April 21, 2022. ; https://doi. org/10. 1101 /2021. 10. 11. 462798  doi: bioRxiv preprint original publications, especially for the Plomion et al. ,  (2018)  study that used higher sequencing depths. Based on the set of conservative mutations detected by Strelka2 (EVS), we then explored annotations and mutation spectra in both datasets (Fig. 3B-C ), which have rarely been explored in model plant species (but see first evidence based on mutation accumulation lines in Arabidopsis in  Weng et al. , 2019)  and never in the wild. The proportions of mutations found in different genomic regions (e. g. genic, intergenic) were highly correlated between both original studies and proportional to the representation of the genomic regions, supporting a random distribution of mutations throughout the genome (Fig.
We then introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. We design a training procedure to obtain a flexible and lightweight stochastic prediction model compatible with sequence-to-sequence recurrent neural architectures. Experimental results on 3 different datasets show that our method DVMS outperforms competitors adapted from the selfdriving domain by up to 37% on prediction horizons up to 5 sec. , at lower computational and memory costs. Finally, we design a method to estimate the respective likelihoods of the multiple predicted trajectories, by exploiting the stationarity of the distribution of the prediction error over the latent space. Experimental results on 3 datasets show the quality of these estimates, and how they depend on the video category.
• We introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. The latent variable is designed to modulate the function connecting the past to the future. Each sample of the latent variable leads to a different plausible future. We design a training procedure to obtain a flexible and lightweight stochastic prediction model compatible with sequence-to-sequence recurrent neural architectures. Experimental results on 3 different datasets show that our method DVMS outperforms competitors adapted from the self-driving domain by up to 37% on prediction horizons up to 5 sec. , at lower computational and memory costs. • We design a method to estimate the respective likelihoods of the multiple predicted trajectories, by showing that the distribution of the prediction error over the latent space has some stationarity, which we exploit.
Experimental results on 3 datasets show the quality of these estimates, and how they depend on the video category. Sec. 2 presents recent work on point-wise and uncertainty-aware prediction of head motion, as well as relevant work on trajectory prediction from the domains of robotics and autonomous driving. Sec. 3 formulates the prediction problem we tackle, positions formally the contribution in the framework of 360°streaming optimization, and motivates the approach with analysis of head motion data. Sec. 4 (i) provides necessary background on deep generative models, (ii) presents our DVMS stochastic prediction model, emphasizing its generality and exemplifying it with a simple recurrent architecture, and (iii) presents experimental results of DVMS on 3 datasets. Sec. 5  shows how to exploit the latent space to estimate trajectory likelihoods, and provides experimental assessment.
Analysis of the need for multiple prediction in head motion data We now analyze the need for multiple prediction from two perspectives: from the data only, and from the performance of a given predictor on this data. We consider the test data of the MMSys18 dataset, described in Sec. 4. 4. In what follows, past (resp. future) trajectories are considered over a horizon of 1 sec. (resp. 5 sec. ) as done in recent work  [10, 52]. We consider the orthodromic distance defined in Sec. 4. 4 between pairs of trajectories. The distance is normalized with the mean length of both trajectories. Every normalized orthodromic distance is augmented with the cosine distance between the last past segments of the pair, to consider orientations of the trajectories as well. We first investigate how the distance between past  trajectories relates to the distance between their corresponding true futures.
To do so, for each timestamp of each video in the dataset, we consider all pairs of users, and select 100 pairs per video with the closest past trajectories. Every pair of users yields the distance between both past trajectories, and the distance between both respective true future trajectories. Fig. 1 -left represents the scatter plot of both distances for every pair. We observe that for 100 pairs of closest past trajectories, there is a non-negligible number of pairs whose distance between future elements is at least twice the distance between their past elements. Also, we observe that for close past elements, more distant futures are produced, on this dataset, for explorationtype videos PortoRiverside and PlanEnergyBioLab. Specifically, for PortoRiverside, 71% of points are above the y = x line and 23% of points are above y = 2x.
Experimental settings. Datasets: We consider 3 datasets of 360°videos with head motion traces: • For all of these datasets, we use the same split as described in the supplemental material from by Romero et al. [52] , such that there is no overlap between the videos in the train and test sets for PAMI18 and CVPR18 as well as no overlap between the users for MMSys18. Additionally, we do not make predictions for the first 6 seconds of the video with any of the considered competitors, as done by Romero et al. [52]  to skip the user's initial exploration phase. Metrics: When it comes to evaluating the quality of the multiple predictions, the major challenge is that several plausible futures may correspond to a single input, but the datasets provide only a single ground-truth future. The best way to assess (P1) is therefore to check if the known ground truth is covered by one of the few predictions, while the others can efficiently explore the search space to cover the futures of close inputs.
Experimental results. Prediction error: Fig. 8 -left represents, for the MMSys18 dataset, the orthodromic distance and the BMS metric of DVMS for K ∈ {1, 5}. The shaded area represents the 95% confidence interval. We observe that DVMS achieves performance similar for K = 1 as Deepposition-only, which is the state of the art it builds on, and close to VPT360, hence meeting (P2). For (P1), we observe for K = 2 a 28% reduction in prediction error at prediction step s = 3sec. with DVMS, compared to the single prediction competitors Deep-position-only and VPT360. For higher K, the error reduction increases, and tends to saturate for K = 4 then K = 5. DVMS hence meets both (P1) and (P2) on this dataset. Still for the MMSys18 dataset, Fig. 8 -right, compares the performance of DVMS with the MANTRA-adapted competitor.
We first notice that for every K = 1,. , 5, DVMS yields a lower prediction error than MANTRA-adapted. Also, we observe that MANTRA-adapted does not match the state of the art performance of Deep-position-only for K = 1. For every value of K, DVMS yields lower prediction errors than MANTRA-adapted. At s = 3sec. , for K = 1,. , 5, DVMS achieves a prediction error lower than MANTRA-adapted by 26. 82%, 26. 77%, 32. 01%, 34. 05% and 36. 20%, respectively. The results on the CVPR18 and PAMI18 datasets are presented in Tables  1  and 2. VPT360 does not appear because its performance is not available for these datasets over H = 5sec. Tables  1  and 2  show that DVMS achieves similar performance as Deep-position-only for K = 1, and outperforms MANTRA-adapted for every value of K, on both datasets. Constructing futures by combining past with future pieces from the training set does not seem sufficient for MANTRA-adapted to produce diverse enough futures, compared with DVMS which instead modulates the initial state of the sequence decoder with a random component.
The results on all 3 datasets therefore show that DVMS is able to produce diverse predictions (P1), outperforming the multiple prediction competitor MANTRA-adapted, while providing similar performance to single prediction when K = 1 (P2). Fig. 9  shows qualitative examples of multiple trajectory prediction. It shows similar past trajectories of two different users, yielding   Hardware used to train and test the methods is a Nvidia RTX 3080 with 10GB of video RAM on a station with 128GB of RAM. Table  3  shows that DVMS and MANTRA-adapted have significantly less weights than both single prediction methods Deep-position-only and VPT360. While DVMS has more neural network parameters than MANTRA-adapted, the execution time to generate a trajectory at test time is 14% less than MANTRA-adapted. This is due to MANTRAadapted having to do memory lookup.
Study of the stationarity of error distribution in the latent space of DVMS We first analyze how err k u,v ,t evolves over t = t st ar t : T , for given test videos v and users u from the MMSys18 dataset. The MMSys18 test set is made of 5 videos of 4 categories  [4, 52] : exploration (PortoRiverside and PlanEnergyBioLab), moving focus (Turtle), static focus with camera motion (WaterPark), static focus without camera motion (Warship). Fig. 10 -left shows an example for video PortoRiverside and user u = 56. We observe that, for every t ∈ {t st ar t : T }, the future trajectory y 3 t :t +H produced by latent value z 3 consistently yields the lowest prediction error. Fig. 10 -right shows that, for video Turtle and user u = 28, the lowest error is consistently produced by latent value z 2. A v i j = 1 U K U u=1 K k =0 err k u,v ,i -err k u,v ,j 2 , (i, j) ∈ {t st ar t ,.
,T } 2 (10) Second, as Fig. 10  only shows examples obtained for specific choices of (v, u), we investigate how representative these cases are. To do so, we define a latent stationarity matrix (LSM) per video v defined in Eq. 10, where U is the total number of user traces per video. For every user u, the main term in the summation represents the difference, between timestamps i and j, in how the error is distributed in the discrete latent space Z K. Fig. 11  depicts such error differences as heatmaps for all the five videos of the test set of the MMSys18 dataset. First, we observe that, when the timestamps i and j are equal, error difference is null, which is expected. Also, it is interesting to observe that the closer i and j, the lower the error distance. This shows that the results illustrated above for specific (u, v) pairs are general: the prediction error yielded by z k varies more or less slowly over time (depending on the videos).
Such stationarity may hence be exploited to produce error (i. e. , likelihood) estimates. Similar qualitative results hold on the other datasets, but we do not show these maps owing to space limitation. However, estimation results on all 3 datasets are shown in Sec. 5. 3. Second, it is interesting to observe that the level of stationarity/speed of variation of the error produced by every z k , for k ∈ {1, K }, depends on the video category. For both exploration-type videos PortoRiverside and PlanEnergyBioLab, the error difference for a given timestamp difference is significantly higher than for the focus-type videos. The stationarity of latent errors is hence lower in exploration videos, and error estimates from the past timestamps should yields better estimates in Focus videos, which we investigate in the next section.
Results on trajectory likelihood estimation Datasets: The results below are obtained on the same 3 datasets as those considered in Sec. 4. 4. Metrics: We measure the quality of the trajectory likelihood/error estimate with its Pearson correlation coefficient with the ground truth. Indeed, as the motivation for the present contribution is to benefit from such estimates in a stochastic formulation of resource optimization (see Sec. 3. 2), we want to evaluate how much are the produced estimates linearly correlated with the ground truth error. If the correlation coefficient was 1, we would obtain the true likelihood. For any past window size r , for every tuple (v, u, t), we are interested in how the corresponding K samples (err k u,v ,t , err k u,v ,t (r )).
Investigating how the LSM can be a novel characterization for different videos, and possibly different users, is left for future work. In this work, we develop a useful application of such characterization for deep learning-based predictors. For the test sets of the PAMI18 and CVPR18 datasets, Fig. 13 -left and 14-left show that there is a moderate significant correlation of the estimates with the ground truth errors. For the test set of the MMSys18 dataset, there is a moderate to low correlation, the significance being low possibly owing to the very low number of videos in the test set (only 5). We also observe that the correlation generally increases with r for the MMSys18 and PAMI18 datasets in Fig. 12  and 13 (reaching maximum level for r ≥ 2 sec. and r ≥ 3. 5 sec. , respectively), while for the CVPR18 dataset in Fig.