dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The funds from  Department of Biotechnology (DBT) , and  Indian Council of Agricultural Research (ICAR), Government of India , for establishment of instrumentation facility are gratefully acknowledged. The facility availed from  University grants commission-Special Assistance Program (UGC-SAP ) and  Department of Science and Technology-Fund for Improvement of Science and Technology Infrastructure (DST-FIST ) at  Department of Animal Science, Bharathidasan University  is acknowledged. We thank  Prof. Chen-Chung Liao ,  Proteomics Research Center, National Yang-Ming University, Taipei, Taiwan , for help in mass spectrometry analysis and Prof. S. Parthasarathy,  Department of Bioinformatics, Bharathidasan University, Trichirappalli, India , for providing bioinformatics facility.
GA thanks  University Grants Commission (UGC), New Delhi , for the award of BSR faculty fellowship. BG and PP acknowledges the  MOE Start-Up Grant  from  Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. SM acknowledge the Scientific and Engineering Research Board (SERB),  India, for the award of National Postdoctoral Fellowship  and financial support (File No. PDF/2016/004000 ).
This paper deals with feature selection procedures for spatial point processes intensity estimation. We consider regularized versions of estimating equations based on Campbell theorem derived from two classical functions: Poisson likelihood and logistic regression likelihood. We provide general conditions on the spatial point processes and on penalty functions which ensure consistency, sparsity and asymptotic normality. We discuss the numerical implementation and assess finite sample properties in a simulation study. Finally, an application to tropical forestry datasets illustrates the use of the proposed methods.
We prove that the estimates obtained by regularizing the logistic regression likelihood can also satisfy asymptotic properties (see Remark 2). Our procedure is straightforward to implement since we only need to combine the spatstat R package with the two R packages glmnet and ncvreg. The remainder of the paper is organized as follows. Section 2 gives backgrounds on spatial point processes. Section 3 describes standard parameter estimation methods when there is no regularization, while regularization methods are developed in Section 4. Section 5 develops numerical details induced by the methods introduced in Sections 3-4. Asymptotic properties following the work by  Fan and Li (2001)  for generalized linear models are presented in Section 6. Section 7 investigates the finite-sample properties of the proposed method in a simulation study, followed by an application to tropical forestry datasets in Section 8, and finished by conclusion and discussion in Section 9.
(5. 2) where w i is the value of the weight surface at point i. The estimate ŵi is obtained as suggested by  Guan and Shen (2010). The similarity beetween (5. 1) and (5. 2) allows us to compute the estimates using software for generalized linear model as well. This fact is in particular exploited in the ppm function in the spatstat R package  (Baddeley and Turner, 2005; Baddeley et al. , 2015)  with option method="mpl". To make the presentation becomes more general, the number of dummy points is denoted by nd 2 for the next sections.
3), we obtain E u∈D∩D w(u) log δ(u) ρ(u; β) + δ(u) = D w(u)δ(u) log ρ(u; β) + δ(u) δ(u) du, which is exactly what we have in the last term of (3. 3). In addition, conditional on X∪D, (5. 3) is the weighted likelihood function for Bernoulli trials, y(u ) = 1{u ∈ X} for u ∈ X ∪ D, with P{y(u) = 1} = ρ(u; β) δ(u) + ρ(u; β) = exp -log δ(u) + β z(u) 1 + exp -log δ(u) + β z(u). Precisely, (5. 3) is a weighted logistic regression with offset term -log δ. Thus, parameter estimates can be straightforwardly obtained using standard software for generalized linear models. This approach is in fact provided in the spatstat package in R by calling the ppm function with option method="logi"  (Baddeley et al. , 2014 (Baddeley et al. , , 2015)). In spatstat, the dummy point process D generates nd 2 points in average in D from a Poisson, binomial, or stratified binomial point process.
Coordinate descent algorithm LARS algorithm  (Efron et al. , 2004 ) is a remarkably efficient method for computing an entire path of lasso solutions. For linear models, the computational cost is of order O(M p 2 ), which is the same order as a least squares fit. Coordinate descent algorithm  (Friedman et al. , 2007 (Friedman et al. , , 2010) )  appears to be a more competitive algorithm for computing the regularization paths by costs O(M p) operations. Therefore we adopt cyclical coordinate descent methods, which can work really fast on large datasets and can take advantage of sparsity. Coordinate descent algorithms optimize a target function with respect to a single parameter at a time, iteratively cycling through all parameters until convergence criterion is reached. We detail this for some convex and non-convex penalty functions in the next two sections.
, 2007a). For instance, the coordinate-wise update for the elastic net, which embraces the ridge and lasso regularization by setting respectively γ to 0 or 1, is βj ← S M i=1 ν j z ij (y i - ỹ(j) i ), λγ M i=1 ν j z 2 ij + λ(1 -γ) , (5. 6) where ỹ(j) i = β0 + l =j z il βl is the fitted value excluding the contribution from covariate z ij , and S(z, λ) is the soft-thresholding operator with value S(z, λ) = sign(z)(|z| -λ) + =      z -λ if z > 0 and λ < |z| z + λ if z < 0 and λ < |z| 0 if λ ≥ |z|. (5. 7) The update (5. 6) is repeated for j = 1,. , p until convergence. Coordinate descent algorithm for several convex penalties is implemented in the R package glmnet  (Friedman et al. , 2010). For (5. 6), we can set γ = 0 to implement ridge and γ = 1 to lasso, while we set 0 < γ < 1 to apply elastic net regularization.
Simulation set-up The setting is quite similar to that of  Waagepetersen (2007)  and  Thurman et al. (2015). The spatial domain is D = [0, 1000] × [0, 500]. We center and scale the 201 × 101 pixel images of elevation (x 1 ) and gradient of elevation (x 2 ) contained in the bei datasets of spatstat library in R (R Core Team, 2016), and use them as two true covariates. In addition, we create three different scenarios to define extra covariates: Scenario 1. We generate eighteen 201 × 101 pixel images of covariates as standard Gaussian white noise and denote them by x 3 ,. , x 20. We define z(u) = x(u) = {x 1 (u),. , x 20 (u)} as the covariates vector. The regression coefficients for z 3 ,. , z 20 are set to zero. Scenario 2. First, we generate eighteen 201 × 101 pixel images of covariates as in the scenario 1.
We also fit the oracle model which only uses the two true covariates. All models are fitted using modified internal function in spatstat  (Baddeley et al. , 2015) , glmnet  (Friedman et al. , 2010) , and ncvreg  (Breheny and Huang, 2011). A modification of the ncvreg R package is required to include the penalized weighted Poisson and logistic likelihood methods.
In the following, m ≈ 1600 and nd 2 = 400, 1600, and 6400. Note that the choice by default from the Poisson likelihood in spatstat corresponds to case (c). Baddeley et al. (2014)  showed that for datasets with very large number of points and for very structured point processes, the logistic likelihood method is clearly preferable as it requires a smaller number of dummy points to perform quickly and efficiently. We want to investigate a similar comparison when these methods are regularized. We only repeat the results for κ = 5 × 10 -5 and µ = 1600, and for scenarios 2 and 3. We use the same selection and prediction indices examined in Section 7. 2 and consider only the adaptive lasso method. Table  8  presents selection properties for the Poisson and logistic likelihoods with adaptive lasso regularization.
Application to forestry datasets In a 50-hectare region (D = 1, 000m × 500m) of the tropical moist forest of Barro Colorado Island (BCI) in central Panama, censuses have been carried out where all free-standing woody stems at least 10 mm diameter at breast height were identified, tagged, and mapped, resulting in maps of over 350,000 individual trees with more than 300 species (see  Condit, 1998; Hubbell et al. , 1999 Hubbell et al. , , 2005)). It is of interest to know how the very high number of different tree species continues to coexist, profiting from different habitats determined by e. g. topography or soil properties (see e. g. Waagepetersen, 2007; Waagepetersen and Guan, 2009). In particular, the selection of covariates among topological attributes and soil minerals as well as the estimation of their coefficients are becoming our most concern.
We are particularly interested in analyzing the locations of 3,604 Beilschmiedia pendula Lauraceae (BPL) tree stems. We model the intensity of BPL trees as a loglinear function of two topological attributes and 13 soil properties as the covariates. Figure  2  contains maps of the locations of BPL trees, elevation, slope, and concentration of Phosporus. BPL trees seem to appear in greater abundance in the areas of high elevation, steep slope, and low concentration of Phosporus. The covariates maps are depicted in Figure  4. We apply the regularized (un)weighted Poisson and the logistic likelihoods, combined with adaptive lasso regularization to select and estimate parameters. Since we do not deal with datasets which have very large number of points, we can set the default number of dummy points for Poisson likelihood as in the spatstat package, i.
Conclusion and discussion We develop regularized versions of estimating equations based on Campbell theorem derived from the Poisson and the logistic likelihoods. Our procedure is able to estimate intensity function of spatial point processes, when the intensity is a function of many covariates and has a log-linear form. Furthermore, our procedure is also generally easy to implement in R since we need to combine spatstat package with glmnet and ncvreg packages. We study the asymptotic properties of both regularized weighted Poisson and logistic estimates in terms of consistency, sparsity, and normality distribution. We find that, among the regularization methods considered in this paper, adaptive lasso, adaptive elastic net, SCAD, and MC+ are the methods that can satisfy our theorems. We carry out some scenarios in the simulation study to observe selection and prediction properties of the estimates.
Acknowledgements We thank  A. L. Thurman  who kindly shared the R code used for the simulation study in Thurman et al. (2015) and  P. Breheny  who kindly provided his code used in ncvreg R package. We also thank  R. Drouilhet  for technical help. The research of J. -F. Coeurjolly is funded by  ANR-11-LABX-0025  Persyval-lab (2011, project  Oculo-Nimbus and Persyvact ). The research of F. Letué is funded by  ANR-11-LABX-0025   Persyval-lab  (project  Persyvact2 ). The BCI soils data sets were collected and analyzed by  J. Dalling ,  R. John ,  K. Harms ,  R. Stallard  and  J. Yavitt  with support from  NSF   DEB021104 , 021115 ,  0212284 , 0212818  and  OISE 0314581 , and  STRI Soils Initiative  and CTFS and assistance from  P. Segre  and  J. Trani. Datasets are available at the Center for Tropical Forest Science website http://ctfs.
This work was supported by France Life Imaging (grant ANR-11-INBS-0006'), and by the French National Agency for Research (grant NEMRO ANR-14-CE17-0013), Labex CAMI (ANR-11-LABX-0004) and Labex ACTION (ANR-11-LABX-0001-01). 1 ICube-AVR, UMR7357, CNRS, Université de Strasbourg, INSA, Strasbourg, France. firstname. lastname@icube. unistra. fr 2 FEMTO-ST Institute, Univ. Bourgogne Franche-Comté, CNRS, Besancon, France. firstname. lastname@femto-st. fr Preprint version prepared by the authors after review for IEEE Robotics and Automation Letters. The official version can be found on IEEEXplore, searching for the DOI 10. 1109/LRA. 2018. 2863372 modality  [1]. A common problem, however, is that the field- of-view (FOV) of micrometer resolution optical biopsies is too narrow for a proper diagnosis.
D. Implementation The mosaicking algorithm was developed in python, using image processing routines from OpenCV and scikit-image. Normalized cross correlation was used for registering images and estimate dX i i-1 , finding the maximum of the correlation between a template at the center of image I i-1 and image I i. After trying several template sizes, a template of 268x307 pixels (i. e. 3/5 of the original image size) was found to give the best results. In order to avoid side effects, the same process was repeated in the backward direction (i. e. matching a template from I i into image I i-1 ), and the average displacement was taken. This displacement was subsequently corrected for motion artifacts. Finally, image blending was performed by placing all pixels from different images in a common reference frame and taking an average value.
B. Ground Truth and Evaluation Metrics In order to assess the performance of the proposed algorithm, we built a ground truth scenario. We imaged the tissue sample (which, since it was taped on a rigid plate, is assumed to be rigid) using a standard optical microscope. Using a backlight to illuminate the sample, cell nuclei (which are typically seen in Cellvizio images) can be observed. We then used a coarse manual alignment, followed by a refinement using mutual information (the imregister. m function in MATLAB) in order to build a reference trajectory. Since this is a tedious process, n randomly selected images I k , k ∈ [1, n] approximately evenly spaced along the trajectory, were selected for building this ground truth. n was 13 for the circular trajectory, and 25 for the spiral (which is longer).
Rosa chinensis 'Old Blush' homozygous genome has been deposited at DDBJ/ENA/GenBank under the accession PDCK00000000. PacBio raw data under the study accession SRP119907. Rosa chinensis 'Old Blush' heterozygous genome has been deposited under the accession PRJEB24406. Resequencing sequence reads have been deposited in the database as study SRP119986. Hi-C data were deposited under SRA numbers SRR6189546 and SRR6189547 and ChIPseq data under SRA numbers SRR6167310, SRR6167311, SRR6167312 and SRR6167313 and under GEO number GSE109433.
To overcome these bottlenecks towards a genome reference, we obtained a homozygous genome that we sequenced with a long-read sequencing technology. We developed an original in vitro culture protocol combining fine-tuned starvation, cold stress and hormonal treatments to induce R. chinensis 'Old Blush' microspores to switch from gametophyte to sporophyte development. This approach allowed microspores to initiate divisions, form homozygous cell clusters, and develop embryogenic callus from which homozygous plantlets could be regenerated (Supplementary Notes 2; Supplementary Fig. 1 ). The homozygous rose line was sequenced on the PacBio RS II platform. An 80x sequencing coverage was obtained with 40 single-molecule real-time cells. Preliminary assembly of the rose data with a single assembler generated several hundred of contigs, illustrating the challenge of assembling plant genomes despite long-reads data10,11.
A key step in improving the contiguity of the assembly is the detection and the filtering of spurious edges in the graph of overlaps. The assembler CANU implements filter parametrization at the read level, leading to more accurate and contiguous assemblies12. We developed a software called til-r, which implements similar and alternate heuristics to clean the graph of overlaps of the FALCON assembler (Supplementary Fig. 2 )13. Then, we used CANU to perform a meta-assembly of six complementary raw assemblies generated by CANU and FALCON/ TIL-R (Supplementary Notes 3, see URLs section ). The final assembly was composed of 82 contigs for an N50 of 24Mb, increasing the contiguity metrics of a simple assembly by threefold and demonstrating the power of meta-assembly approaches (Supplementary Fig.
2 ). The seven pseudo-chromosomes were built by integrating 86. 4% of the 25,695 markers of the K5 rose high-density genetic map14. A large fraction of the assembly (97. 7%, 503Mb) was oriented with Pearson's correlation coefficients ranging from 0. 986 to 0. 996, illustrating the high congruence between sequence and genetic data. The genome structure and quality was confirmed by the mapping of Hi-C chromosomal contact map information data (Figure  1 ; Supplementary Fig. 3 ). With very few remaining gaps and high consistency between genetics and sequence data, the rose genome assembly is one of the most contiguous obtained so far for a plant genome. The rose genome encodes 36,377 inferred protein-coding genes and 3,971 long non-coding RNAs. Annotation assessment with the Plantae BUSCO v2 dataset15 identified 96.
5% complete gene models. BUSCO analyses using assembled heterozygous genome of R. chinensis 'Old Blush' (Supplementary Notes 4) identified 93. 5% complete genes (Supplementary Data 1). Based on transcriptomic data from pooled tissues, 207 miRNA precursors were predicted. Transposable elements (TE) spanned 67. 9% of the assembly, 50. 6% being LTR retrotransposons (Supplementary Notes 5, Supplementary Fig. 4 ; Supplementary Table  1 ). The web portal RchiOBHm-V2 (see URLs section) provides access to the reference genome integrating annotations, polymorphisms, transcriptomic data and the first rose epigenome on rose petals (Supplementary Notes 6). Comparative genomic investigation allowed us to assess rose paleohistory within the Rosaceae family (Supplementary Notes 7). Conserved gene adjacencies identified an ancestral Rosaceae karyotype (ARK) consisting of 9 protochromosomes with 8,861 protogenes (Supplementary Fig.
7 ). Data mining of the rose genome combined with in-depth biochemical and molecular analyses of volatile organic compounds (VOCs) permitted identification of at least 22 biosynthetic steps in the terpenes pathway that have not been characterized in the rose, two among which have never been characterized in other species (Supplementary Notes 9; Supplementary Fig. 7 ). To study the relationships between color and scent pathways, we performed biochemical and molecular analyses on cyanidin, whose glucosylated derivatives represent more than 99% of total anthocyanin pigments21, and on germacrene D, a VOC produced in petal cells of R. chinensis 'Old Blush' (Supplementary Data 3). Our analyses suggest a coordinated biosynthesis of these two compounds achieved through the miR156-SPL9 regulatory module.
Pseudomolecules building Pseudomolecules were built by anchoring the 82 contigs to the K5 SNP genetic linkage map14 using the ALLMAPS software38. Four chimeric breakpoints were identified and corrected by identifying the primary contigs in which the problematic regions were not merged. Three chimeric breakpoints were absent in CANU assemblies and the fourth was absent in all primary assemblies. Finally, ALLMAPS was applied on the corrected metaassembly enabling the building of seven pseudomolecules corresponding to rose haploid chromosome number by anchoring and orienting 97. 7% of the contigs (503Mb) based on 86. 4% of the genetic markers. The final assembly consists of seven pseudo-chromosomes, the mitochondrial and chloroplast genomes plus 46 unanchored contigs spanning 11. 2 Mb (Supplementary Fig.
Annotation of protein-coding genes and lncRNAs Gene models were predicted using a fully automated and parallelized pipeline egn-ep (see URLs section) that carries out probabilistic sequence models training, genome masking, transcript and protein alignments computation and integrative gene modeling by the EuGene software43 (release 4. 2a). The configuration of the egn-ep pipeline is detailed in Supplementary Notes 5. The inferred mRNAs were assessed by BUSCO v215 which found 1,389 complete, 23 fragmented and 28 missing gene models (96. 5%, 1. 6% and 1. 9% respectively). 36,377 genes were retained after the removal of annotated repeated elements (see below). Correspondence between gene models in homozygous and heterozygous annotations were established by best reciprocal hits (Supplementary Table  7 ; Supplementary Data 1).
Functional annotation of protein-coding genes The protocol described by Schläpfer et al44 was used to annotate enzymes and build the metabolic network. Two cut-offs were modified to increase stringency, BLAST e-value cutoff lowered to 10 -5 and pathway-prediction-score set to 0. 3 in pathway-tools. Nineteen pathways considered as false positives were removed. A MetExplore instance45 gives access to the network (see URLs section). Protein coding genes were annotated by integrating five sources depending on their expected accuracy. Priorities were successively given to i) a search of reciprocal best hits with the 218 Rosaceae proteins tagged as "reviewed" in the UniProt database (90% span, 80% identity)46, ii) the description of the 8,512 previously annotated enzymes, iii) transcription factors and kinases identified (2,414 and 1,885 respectively) by ITAK47, iv) the 3,954 transcription factors identified by PlantTFCat48, v) the InterPro analysis matching 31,853 proteins49.
De novo transposable element and repeat annotation The pseudo-chromosomes were deconstructed into "virtual" contigs by removing stretches >11 undefined bases (Ns) to exclude gaps. We generated 2,742 « virtual » contigs with a N50 of 22 Mb for a total length of 515 Mb. TEdenovo pipeline50,51 from the REPET package v2. 5 (see URLs section) was used to detect transposable elements (TEs) in these contigs and to build a consensus sequence for each TE family using a minimum of 5 sequences per group. A library of 28,545 consensus, classified according to structural and functional features (similarities with characterized TEs from RepBase database v21. 0152 and domains from Pfam27. 0), was generated. After removing redundancy and filtering consensus classified as satellites (labelled SSR) and unclassified consensus constructed with <10 copies in the genome, a library of 8,226 consensus was used to annotate TE copies in the whole homozygote genome using the TEannot pipeline with default parameters53.
Genetic structure and genome segmentation Illumina data mapping and SNP calling was performed as described in Supplementary Notes 8. The number of homozygote and heterozygote variants by sliding windows of 1 Mb was computed for each genotype using functions of the bedtools suite (bedtools makewindows, bedtools intersect, bedtools groupby)56 on genic SNPs. To compute the density of variants per window, the number of variants was divided by the number of informative sites (mapping coverage between 5 and 60 for the fourteen resequenced species and between 50 and 300 for the heterozygote Old Blush genotype). We use the term variants in tetraploid species to refer both to allelic differences and differences between homeologues (i. e. , between genes of different sub-genomes). Due to vegetative multiplication of rose cultivars, limited recombination has occurred after hybridization, and introgressed fragments should be of large size.
If the genomes or sub-genomes involved in hybridization events have a different distance with the reference genome, genomic regions with different introgression histories should display different levels of variant density in resequenced hybrid cultivars. We used the changes in variant density in the genotypes FRA, GIG, HUM, MUT and SAN to segment the genome into 35 intervals (ranging from 2 to 56 Mb). The genomic boundaries were defined as the start of the windows corresponding to the inflexion points in density files. For each of the thirty-five genome segments, the genetic structure was inferred on biallelic SNPs with no missing data and not overlapping with repeated elements. Principal component analyses57 were performed with the glPCA function of the adegenet package (version 2. 0. 1) 58.