dataesr/scientific-paragraphs-categorization huggingface dataset:
These methodologies are increasingly computationally expensive and in many cases intractable for sets of large, complex genomes, which at this time have only been overcome by substantially reducing reconstruction resolution  [26] [27] [28]. Other methods attempt to reconstruct a parsimonious sequence ordering in the ancestor based on orthologous sequence adjacencies in extant genomes, under the assumption that genomic rearrangements are unlikely to result in the same sequence organization several times independently. These methods can be applied to different types of markers, typically either alignable sequence blocks or individual genes, and are appropriate for small  29  and large genomes such as vertebrates or plants  30, 31. However, it is unclear whether current methods can provide high-resolution reconstructions and scale to the large genomic resources available in comparative genomics databases.
We show that AGORA is efficient, flexible and scales to integrate hundreds of large genomes, to reconstruct their common ancestors at every node in the species phylogeny with relatively modest computational costs. Along with the open-source algorithm, all precomputed ancestral genome reconstructions are publicly available in the Genomicus  34, 35  database (https://www. genomicus. bio. ens. psl. eu/genomicus) and benefit from the full browsing and comparative genomics tool infrastructure of the database. The database is regularly updated since 2010 to reflect reference genome improvements and represents a perennial resource for high-quality, high-resolution ancestral genomes for the molecular evolution community across disciplines and model phylogenetic clades. https://doi. org/10. 1038/s41559-022-01956-z to other state-of-the-art ancestral genome reconstruction methods  33.
AGORA can be used with other markers than protein-coding genes, such as conserved non-coding elements; however, due to unreliability of phylogenetic trees for those sequences, we recommend limiting the reconstructions to the order of protein-coding genes for best performance. AGORA can also be used iteratively to assemble blocks of markers and scaffold them over several rounds of reconstruction into larger contiguous ancestral regions (CARs). We propose several workflows customized for different clades and applications as part of the AGORA package (Supplementary Fig. 1 ). In this study, to demonstrate the capabilities of AGORA, we used two datasets from distant eukaryotic clades, with different numbers of species, genes and variable gene tree reliability: (1) a dataset of 93 vertebrates and 5 outgroups and their 23,528 gene trees, including a total of 1,814,614 extant protein-coding genes, and leading to the reconstruction of 81 ancestral genomes; and (2) a dataset of 58 plant genomes and 8 outgroups, corresponding to 48 ancestral genomes (Methods, Supplementary Data 4 and Supplementary File 1).
3a ). We also find that the vast majority of clade-relevant benchmark universal single-copy orthologue (BUSCO)  43  reference sets are present as single-copy genes in our inferred ancestral gene sets (Fig. 3b ). In addition, we also confronted our inferred ancestral gene contents for seven key vertebrate ancestors to those calculated by Ancestral Genomes, another effort to estimate the ancestral gene content, but not gene order, at different evolutionary nodes  29. Ancestral Genomes relies on the PANTHER database  44  and therefore uses an independent set of extant genomes and gene trees. AGORA and Ancestral Genomes both inferred highly similar gene contents for the same ancestors (Fig. 3c ). 4 ), where a region on the third chromosome is expanded to highlight the evolution of gene organization with respect to the Amniota genome (c).
AGORA also fared better by including more species and more recent assemblies than DESCHRAMBLER. Altogether, these results support that the gene-based reconstruction algorithm of AGORA is highly consistent with current ancestral reconstruction methods, while providing a notable increase in resolution for the study of local genomic events. We further tested the robustness of AGORA to varying input datasets by reconstructing an alternative Boreoeutheria ancestral genome using gene families from hierarchical orthology groups built with OMA  48  , a completely different gene orthology inference pipeline from Ensembl Compara. Both reconstructions were remarkably convergent with over 96% similarity (Supplementary Information, 'Comparison between Ensembl Compara and OMA hierarchical orthology groups' and Supplementary Fig.
A scalable framework to integrate genomes across phylogenies A major strength of AGORA resides in its ability to compute the gene order of every ancestor in a phylogeny using different subsets of the same extant genome comparisons. In a context where new species genomes are being sequenced with increasing speed and accuracy, comparative genomics need methods that can integrate evolutionary information along the species tree and across lineages without relying on a single extant genome as reference. Using the legacy architecture of the Genomicus synteny database  34, 35  , which is updated with every new release of the Ensembl database, we tested how our methodology scales with the number of extant reference genomes available as well as their quality (Supplementary Fig. 13 ). Ensembl Compara v.
101 included the reference sequences of 264 vertebrate species and five outgroups, for a total of 5,539,325 extant protein-coding genes organized into 62,478 gene trees. Using this information, AGORA reconstructed a total of 265 ancestral genomes along the species tree in 6 h and 50 min on a Linux machine with four central processing units and approximately 80 GB of random access memory (Supplementary Data 2). Therefore, AGORA is computationally inexpensive and can be run on a desktop machine for small-to-medium datasets. However, AGORA can also be parallelized and is optimized for usage on a computing cluster for large applications and database updates. Overall, the quality of key ancestral genomes increases as new extant genomes are included in the database (Supplementary Fig. 13 ). The introduction of high-quality reference genomes in under-represented clades over time has contributed to the reconstruction of previously inaccessible ancestors, such as Strepsirrhini, the ancestor of lemurs, bushbabies and lorises, and more recently Chiroptera, the ancestor of bats.
Interestingly, we observed that even the inclusion of low-contiguity, fragmented genomes markedly improves ancestral genome reconstructions. For instance, including low-contiguity genomes more than doubles the median value (G50; Methods) for the reconstructed Amniota genome (Supplementary Information, 'Impact of low-contiguity assemblies'). This is likely because different reference genomes are generally assembled independently and assembly errors rarely produce the same erroneous gene arrangements from one genome to the next. Because AGORA only considers conserved gene adjacencies as potentially ancestral, the additional information from correctly assembled scaffolds offsets the noise introduced by assembly errors, which are discarded as not conserved. Therefore, we argue that the inclusion of low-cost, fragmented reference genomes in comparative genomics databases serves a purpose beyond gene-based analyses.
We selected 73 well-reconstructed ancestors and their 74 extant descendants (15 birds and reptiles, 41 mammals, 18 fish; Methods) from the Genomicus Vertebrates database v. 102, which contains a total of 269 extant and 265 ancestral genomes. We then compared consecutive genomes on all 131 branches of the phylogenetic tree, representing a combined time of about 5 billion years of independent evolution, and traced gene adjacencies that were rearranged on each branch (Methods). In total, we identified 5,749 rearrangement breakpoints that occurred along the 131 branches (average rate 1. 17 breakpoint per million years), most of which are intrachromosomal. We also identified 1,370 interchromosomal rearrangements (translocations, fusions or fissions) with an average rate of 0. 28 rearrangement per million year (Fig.
Finally, we took advantage of the unique standpoint provided by ancestral genomes to investigate which gene-to-gene interval is the most conserved in all bony vertebrates. Scanning the Euteleostomi ancestral genome, we selected the gene adjacency with the strongest support from 8,173 pairwise genome comparisons used to reconstruct this genome. The adjacency between the ELK3 and CDK17 genes is ancestral to bony vertebrates and remains conserved in 187 out of 192 descendant genomes available in the Ensembl 106 database (Fig. 6c ). Interestingly, ELK3 and CDK17 coexpress sense-antisense messenger RNA transcripts in mouse neuronal cells  59. Additionally, ELK3 introns contain enhancer sequences that putatively target the CDK17 promoter  60. Potential complex regulatory functions may be associated with this locus because a sense-antisense transcript produced in the same cells can lead to double-stranded RNA, and in this case, also overlap the ELK3 coding exon.
As algorithms mature, ancestral genomes such as those presented in this study could become enriched with many more features, including non-coding sequences such as ancestral repeat elements, non-coding RNA genes or regulatory elements, and serve as organizational maps for reconstructed  65  or fossil nucleotide sequences. Reaching this goal could alleviate some limitations of gene-based ancestral reconstructions by providing a much-increased resolution. Because genome sequencing costs continue to decrease, reference genomes are becoming widely available for model and non-model species alike. At the time of writing, the NCBI database accounts for a total of 8,505 eukaryote, 32,172 bacterial and 1,909 archaeal whole-genome sequencing projects and dedicated efforts such as the Vertebrate Genome Project 66 promise to deliver extensive phylogenetic coverage across many clades.
Data collection Genes and gene trees were downloaded from Ensembl v. 92 (ref. 68) and Ensembl Plants v. 41 (ref. 69). Ensembl v. 92 gene trees were edited for poorly supported duplication nodes as described previously  70  , as part of the standard build procedure for the Genomicus synteny database. Of note, this step only marginally improves ancestral genome reconstructions and is not a prerequisite to use AGORA. The species trees for the extant and ancestral genomes from Ensembl v. 92 and Ensembl Plants v. 41 are described in Supplementary File 1.
Comparison between AGORA and DESCHRAMBLER eutherian ancestor We compared AGORA's v. 92 eutherian reconstructions to DESCHRAM-BLER's  33  (300 kb resolution: APCF_hg19_merged. map from http:// bioinfo. konkuk. ac. kr/DESCHRAMBLER/). Because DESCHRAMBLER uses segments of the human genome as units of the reconstruction and was based on the hg19 genome assembly, we converted those regions to their protein-coding gene content and selected the genes still found in Ensembl v. 92 and descendants of ancestral boreoeutherian genes. The Oxford grid plot was generated with the AGORA src/misc. compareGenomes. py script in 'matrix' mode.
Vertebrate evolutionary dynamics Ancestral genomes reconstructed by AGORA from Ensembl v. 102 were filtered to retain the most contiguous reconstructions, resulting in 73 ancestral genomes with G50 > 230 and L70 < 40. Conserved syntenic blocks between successive ancestral genomes in internal branches, and between ancestral genomes and their extant descendant in terminal branches, were computed with PhylDiag  72. Ends of blocks corresponding to likely evolutionary breakpoints were identified using ad hoc scripts. Orthologous genes between successive genomes were also compared in terms of their assignation to scaffolds or chromosomes larger than 200 genes using AGORA's src/misc. compareGenomes. py script in 'printOrthologousChrom' mode. Groups of at least 20 genes relocating to more than 1 chromosome in a descendant genome, and inversely groups of at least 20 genes from 2 or more ancestral chromosomes relocating on the same descendant chromosome, were considered interchromosomal rearrangements.
Acknowledgements We thank  P. Vincens  for the coordination of computing resources and  A. Peres  for computer code engineering. This work was supported by grants from the  French Government  and implemented by the  Agence Nationale de la Recherche (ANR)  ( ANR-07-GANI-008-01 GENOVERT ,  ANR-10-BINF-01-03   ANCESTROME ,  ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02 PSL * Research University ) to H. R. C.
Data availability Ancestral genomes have been precomputed for approximately 200 vertebrate (depending on the release), 41 plant and 222 fungi genomes and are available on the Genomicus database FTP server (ftp://ftp. bio. ens. psl. eu/pub/dyogen/genomicus/). These ancestral genomes can also be explored visually within the Genomicus  35  synteny browser (http:// www. genomicus. bio. ens. psl. eu/genomicus). Ancestral genomes and the data used in this article for analysis are archived on a Zenodo repository (https://doi. org/10. 5281/zenodo. 7479507)  74.
In comparison with former modalities, landmark data, a topological representation of the human body or face using key-points, appears to be less computationally expensive, and more robust in front of intricate backgrounds and with respect to variable conditions including viewpoints, scales and motion speeds  [10]. An efficient way to analyze datasets composed of 2D/3D landmark observations is to consider their shapes independently of undesirable transformations; the resulting representation space of landmark data is non-linear. Accordingly, we represent 2D/3D landmarks in the Kendall shape space  [11]  that defines shape as the geometric information that remains after location, scaling and rotational effects are filtered out. A sequence of landmarks is then modeled as a trajectory on this space.
3) The proposed architecture is applied to 3D landmarkbased motion analysis, namely action recognition and gait recognition, as well as 2D landmark-based motion analysis, namely facial expression recognition. We report state-of-the-art results on five large scale publicly available datasets: NTU-RGB+D and NTU-RGB+D120 for 3D action recognition, CMU Mocap dataset for 3D gait recognition, and CK+ and Oulu-CASIA datasets for 2D facial expression recognition. The rest of the paper is organized as follows. In Section 2, we briefly review existing research on action recognition, gait recognition, facial expression recognition and geometric deep learning. Section 3 describes geometric modeling of landmark configuration trajectories on Kendall's shape space. In Section 4, we introduce the proposed geometric deep architecture, KShapeNet.
Manifold-based geometric deep learning approaches extend deep architectures to Riemannian manifolds, interpreted either as feature spaces  [12] ,  [64] ,  [13]  or the human body shape, i. e. , the human body is viewed as a manifold  [14] ,  [15]. Huang et al. proposed several networks on non-linear manifolds. In  [12] , they introduced the first network architecture to perform deep learning on the Grassmann manifold. They presented competitive results on three datasets of emotion recognition, action recognition and face verification. Along similar lines, an architecture on the manifold of SPD matrices was proposed in  [64] , and similar experimental evaluation proved the effectiveness of this approach. Recently, the same authors proposed an architecture on Lie groups with application to skeleton-based action recognition  [13].
3D human action recognition In Section 5. 1. 1, we first describe the datasets and experimental settings used to validate our architecture for the action recognition task. For the demonstration of KShapeNet efficiency, an ablation study is presented in Section 5. 1. 2 with a discussion of the impact of intermediate layers, i. e. , the transformation layer and logarithmic map layer (projection to tangent space). Then, in Section 5. 1. 3, we compare the performance of action recognition based on the KShapeNet architecture to state-of-the-art approaches on the same datasets. We conclude in Section 5. 1. 4 with a comparison and discussion of pre-processing techniques, effects of nuisance variation, different variants of the transformation and projection on tangent space layers. The implementation code for KShapeNet will be publicly released upon acceptance of the paper.
Datasets and implementation settings We evaluate the effectiveness of KShapeNet on two large scale state-of-the-art datasets, NTU-RGB+D and NTU-RGB+D120. NTU-RGB+D (NTU)  [68]  is one of the largest 3D human action recognition datasets. It consists of 56,000 action clips of 60 classes. 40 participants were asked to perform these actions in a constrained lab environment, with three camera views recorded simultaneously. Kinect sensors estimated and recorded the 3D coordinates of 25 joints in the 3D camera's coordinate system. For standard assessment, we utilize two state-of-the-art protocols: cross-subject (CS) and cross-view (CV). In the CS protocol, the 40 subjects are split into training and test sets (20 subjects each) made up of 40,320 and 16,560 samples (sequences), respectively. In the CV protocol, we select the samples from cameras 2 and 3 for training, and the samples from camera 1 for testing.
Thus, the training set consists of the front and two side views of the actions, while the test set consists of the left and right 45 degree views. For this protocol, the training and test sets contain 37,920 and 18,960 samples (sequences), respectively. NTU120-RGB+D (NTU120)  [69]  is an extension of NTU60. It is the largest RGB+D dataset for 3D action recognition with 114,480 skeleton sequences. It contains 120 action classes performed by 106 distinct human subjects. NTU120 was built using 32 collection setups; over different setups, location and background were changed. Specifically, in each setup, three cameras were used at the same time to capture three different horizontal views for the same action sample. For this dataset, the two protocols used for evaluation are cross-subject (CS) and cross-setup (Cset).
For the CS setting, half of the 106 subjects are used for training and the rest for testing. For the Cset evaluation, all of the samples with even collection setup IDs are chosen for training, and those with odd setup IDs are used for testing, i. e. , 16 setups are used for training and the other 16 setups are reserved for testing. For the KShapeNet implementation, we set the number of frames to p = 100, and the batch size to 64 for the NTU dataset and 32 for the NTU120 dataset. To estimate the model's parameters, we use cross-entropy loss and set the number of epochs to 30. The Adam optimizer is adapted to train the network, and the initial learning rate is fixed to 1 × 10 -4 for both datasets. For training, we used a machine with a processor speed of 3. 40 GHz, memory of 32 GB and an NVIDIA GTX 1070 Ti GPU.
Ablation study To validate the effectiveness of the proposed framework and highlight the impact of each processing block, we performed an ablation study by gradually adding 1) the logarithmic map block (projection to tangent space), and 2) the transformation layer. Table  1  reports the results of this study on the NTU and NTU120 datasets. In the first row, labeled "Baseline", we report the results generated by the deep network (CNN-LSTM used in KShapeNet) using input data represented directly on Kendall's pre-shape space (without moving to the linear tangent space), and without the optimization over rigid or non-rigid transformations. The baseline architecture provides satisfactory results. However, they are not competitive with respect to those produced by stateof-the-art approaches. The second row of Table  1  depicts the results achieved by adding the transformation layer to the baseline architecture.
The transformation layer adopted here considers optimization over non-rigid transformations using the angle-based variant. Further discussion about the choice of this configuration is presented in Section 5. 1. 4. Compared to the "Baseline" results, the transformation layer improves recognition performance by 4. 5% for CS and 0. 3% for CV on the NTU dataset, and by 1. 2% for CS and 0. 3% for Cset on the NTU120 dataset. As explained in Section 4, this configuration of the transformation layer optimizes over nonrigid transformations, hence urging the network to find the best local rotations that are applied to the skeleton joints within each landmark configuration; this justifies the improvement in action recognition accuracy. In the third row of Table  1 , we present the results obtained by only adding the projection to tangent space block, via the logarithmic map, to the baseline model.
Linearization via tangent space projection provides significant improvements in recognition performance, increasing from 85. 1% to 94. 1% for the CS protocol on the NTU dataset. The increase in accuracy is due to a new skeleton landmark configuration representation in the Euclidean tangent space, allowing for the definition of a linear metric between skeleton landmark configuration shapes. In the fourth row of Table  1 , we report the final results produced by the KShapeNet framework, including both the logarithmic map block and the non-rigid transformation layer. KShapeNet results in a significant improvement over the baseline model, and most importantly, further increases recognition accuracy over the two models with individually added components (logarithmic map block or transformation layer).
The combination of both components empowers the network to properly discriminate action classes. For instance, for the CS protocol on the NTU dataset, the accuracy increase due to the additional transformation layer was only 4. 5% and the increase due to the logarithmic map block was only 9%. However, the addition of both components increased recognition accuracy by more than 11%. Accordingly, we conclude that the efficiency of KShapeNet is not only due to the advanced feature extraction capacity of the CNN-LSTM network, but equally due to the convenient data representation of skeleton landmark configuration shapes in the linear tangent space, and the optimization over local rotations.
Comparison to state-of-the-art approaches In this section, we compare the performance of the proposed framework to state-of-the-art approaches on the two datasets, NTU and NTU120. Table  2  reports recognition results of stateof-the-art approaches on the NTU dataset, and compares them to the result generated by KShapeNet. In this table, we distinguish between three classes of action recognition methods: deep learning methods, Riemannian methods, and hybrid (deep Riemannian) methods; our framework, KShapeNet, falls into the third category. The results demonstrate that KShapeNet consistently outperforms deep learning (leveraging CNNs and RNNs), Riemannian and even hybrid approaches. Indeed, our method outperforms the best of these state-of-the-art approaches by 7. 3% and 0. 1% on the CS and CV settings, respectively.
Comparing to the hybrid method presented in  [13] , which incorporates the Lie group structure into a deep network architecture using rotation mapping layers, our approach increases recognition accuracy by more than 35%. Table  3  compares KShapeNet recognition accuracy to state-ofthe-art approaches on the NTU120 dataset. KShapeNet achieves competitive recognition results under the Cset protocol, and outperforms the top competitor (MS-G3D Net) under the CS protocol by 3. 7%.