dataesr/scientific-paragraphs-categorization huggingface dataset:
System architecture models The architecture models are developed using KARMA language based on the GOPPRRE ontology. KARMA language enables to create meta-models including UPDM diagrams for formalizing the mission view and operation scenarios; as well as SysML diagrams for formalizing the functional views, logic views and physical structure of assembling processes, as shown in Figure  16. The previously created process solutions, which are stored in Neo4j database, can semantic-driven tradespace framework Fig. 15  Requirement validation process for three requirement items be exported and transformed according to the GOPPRRE ontology definitions. The transformed process solutions can be imported to MetaGraph to automatically generate different SysML diagrams KARMA language. The system architects can reconfigure and modify the parameters of these diagrams.
Finally the modified KARMA models are transformed back to OWL models to feed back to Neo4j database using the same plugin for GOPPRRE ontology. Figure  16 -A represents the operational views of the entire aircraft assembly process based on the meta-model of OV1 (High-level Operational Concept Graphic diagram) in UPDM  (Hause, 2010). Using meta-model of SysML activity diagram  (Friedenthal et al, 2011) , the assembly process option is defined in Figure  16-B. Figure  16 -C represents an operation scenario for 3D simulation whose KARMA syntax is shown in Figure  16 -D  (Lu et al, 2020).
AM that describes the relations between operations through a matrix representation using in-and out-degree. , with the help of attributes of predecessor/successor from DataFrame. 5. Later, the vertices in DAG, i. e. the operation ids in AM, are pruned according to the principle of topological ordering from Kahn's algorithm  (Kahn, 1962) , starting from the last vertex. While each pruning action identifies the interaction between corresponding operations, e. g. , predecessor(s) and successor(s), formed in sequential or parallel order, it also translates and wraps the results of each pruning action into DES engine executable code. 6. After going through the whole AM until only one start operation exists, the DES executable code is generated and cached in a string format. 7. At last, the DES engine receives the translated string code to simulate the corresponding lead-time based on the input process and operation information, with a simulation results cached in DataFrame.
It can be saved in CSV or stored in a dedicated repository for further usage, e. g. , visualization. In the DataFrame, each Series has multiple attributes. As shown in Figure  18 , one of the attributes describes the ID(s) of one or more predecessor operation(s) (Figure  18-a )), based on which the DataFrame is sequenced in proper order and translated into an AM (Figure  18-b )). After pruning operations in AM and wrapping corresponding execution function, the DES executable string as simulation code is generated (Figure  18-c )), the DES engine performs the simulation and the simulation results are generated (Figure  18-d )). Other attributes can be processed together with the simulation results and be further enriched for certain services such as visualization. Following the same approach, the retrieved data about design options are fed into Visual Components 3D simulation platform.
Visualization and user interface The visualization module is developed using a Python library Streamlit. It aims at illustrating the simulation results, synthesized metrics and performance of a target design option, system design analysis, trade-off of different design options, etc. Currently, the visualization module contains four blocks, as shown in Figure  19 : DES Visualization Block, 3D Visualization Block, Trade-off Visualization Block and Design Option Comparison Block. • The DES Visualization Block seeks to depict the significant performances of a simulated design option, including basic statistics, architecture topology and system design analysis. Typical basic statistics include, for example, the number of operations and automation operation rate, which are basically the facts derived directly from the description about the assembly process and operation; Besides, architecture topology illustrate how the operations within this assembly process are connected.
Discussion During this case study, the proposed tradespace framework was implemented to support the aircraft orbital joint process design using the selected toolchain. Some main achievements are summarized as follows: • It verifies the feasibility of using ontology to integrate information and knowledge from different stakeholder during the aircraft manufacturing system design. Different information and knowledge is first captured with application level ontologies which can be integrated under the same upper-level ontologies such as the IOF-Core and BFO used in this study. • It proves the possibility of reusing the knowledge captured in an ontology to help creating new solutions for an manufacturing system design through querying and reasoning enabled by a graph database and relevant algorithms.
Conclusion and future work This paper proposes a semantic-driven tradespace framework to support aircraft manufacturing system design. It proves the feasibility of using an integrated application ontology to link relevant cross-domain information and knowledge, such as physical assembly system domain knowledge, industrial system requirements, architecture models information etc. The ontology-based graph database can accelerate the generation of alternative design solutions for new assembly systems, which can be further verified through DES and 3D simulations. The simulation results can help industrial architects to perform trade-offs among the possible solutions and make decisions. The results of the case study prove these capabilities of the tradespace framework enabled by the proposed toolchain.
The System Integration Module is enabled by Protégé 1 (both desktop and web version) for application ontology development. The IOF-Core ontology 2 can be imported to Protégé as foundation with new entities being added during knowledge capturing. After the application ontology is created, it will be imported to a graph database hosted on the cloud which can be accessed by different stakeholders. The Microsoft Azure 3 and Neo4j 4 are used as cloud server and the graph database respectively. • The Requirement Management Module is enabled by a customized Requirement Management System (RMS), named OntReq. It supports ontologybased definition of performance requirements based on Top-Level Industrial Requirements (TLIRs). It can import regular office documents and ReqIF semantic-driven tradespace framework.
• The System Integration Module is enabled by Protégé 1 (both desktop and web version) for application ontology development. The IOF-Core ontology 2 can be imported to Protégé as foundation with new entities being added during knowledge capturing. After the application ontology is created, it will be imported to a graph database hosted on the cloud which can be accessed by different stakeholders. The Microsoft Azure 3 and Neo4j 4 are used as cloud server and the graph database respectively. • The Requirement Management Module is enabled by a customized Requirement Management System (RMS), named OntReq. It supports ontologybased definition of performance requirements based on Top-Level Industrial Requirements (TLIRs). It can import regular office documents and ReqIF semantic-driven tradespace framework.
10. every T reset iterations do Reset one of the two discovery policies with the lowest intrinsic reward without the entropy term (i. e. b) ) accumulated over T reset time steps. 11: end while their transitions are Markovian, (ii) filter unnecessary information. Furthermore, XSRL must learn discovery policies which (iii) guide agents quickly through most diverse transitions while avoiding unlearnable ones. Finally, after XSRL pretraining, the state transition estimator ϕ must (iv) provide advantageous inputs to solve unseen control tasks. We evaluate the criterion (i) with the average of the next observation prediction errors on a training dataset and a test dataset. While the former is formed from samples generated during the training process, the latter is carefully designed for each environment, as described in Section 4.
Test Datasets For quantitative performance evaluation of our XSRL algorithm, we use an error measure of the next observation prediction, and for the state-of-the-art RAE baseline, we use an error measure of the next observation reconstruction. To perform those evaluations, we need an appropriate test dataset for each of the three environments described above. To do this, we carefully collected a wide variety of 400 transitions formed of observation-action pairs into a dataset. We generated them in two different ways. In the case of TurtleBot maze, we hand-designed expert trajectories that follow the U-shape of the maze. In the case of InvertedPendulum and HalfCheetah, we executed a policy learned by SAC from the ground truth baseline.
Hardware Details All our experiments are performed on three computers, each containing 40 cores and a Titan xp GPU provided by Nvidia. Figure  4 : Error measure results (the lower the better) obtained on a training dataset (top row) and a test dataset (bottom row) (which is defined for each environment in Section 4. 2. 6), averaged over last 20 epochs across 5 runs (with a random seed). This measure corresponds to the prediction of o t+1 with XSRL, and to the reconstruction of o t+1 with RAE. XSRL (w/ distractor) is performed in TurtleBot Maze with a randomly sampled wall color. In this section, we show the results of our quantitative and qualitative evaluations to validate whether XSRL verify criteria (i), (ii), and (iii) which we defined in Section 4. Fig. 4  reports the results of the error measure obtained on a training dataset and a test dataset (defined in Section 4.
2. 6) on each of the three environments. This error measure corresponds to the prediction error of the next observation for XSRL and the two ablations (XSRL-random and XSRL-MaxEnt); it corresponds to the reconstruction error of the next observation for RAE  [Ghosh et al. , 2019]  (following a random exploration) and RAE-explor (following an effective exploration, as explained in Section 4. 1). We observe on the two environments, TurtleBot Maze and HalfCheetah, that the error measure for XSRL is higher than that for RAE and RAE-explor on both training and test datasets. This does not correspond to a poor exploration performance of XSRL but to the objective function which is more complicated than RAE. Indeed, all information in the next observation that cannot be predicted from the current time step is ignored as it is the case for random distractors or too complex information from the transition model, which tends to increase the prediction error.
In addition to these qualitative and quantitative comparisons, the better performance of XSRL exploration is also confirmed by the quantitative evaluation of the prediction error measure displayed in Fig. 4. This measure reaches its lowest value with XSRL exploration, followed by XSRL-MaxEnt and finally XSRL-random which is by far the worst strategy. Apart from the comparative study of our XSRL exploration, we observe that an effective exploration improves the generalization performance of RAE models. Indeed, the quantitative evaluation of the observation reconstruction (see Fig. 4 ) shows a smaller error on the test dataset with RAE-explor (which is trained with an efficient exploration as explained in Section 4. 1) than with RAE. Thus, training on diverse transitions improves the generalization performance of observation reconstruction with RAE.
Discussion Experimental results show that the proposed XSRL algorithm builds state representations that are the only ones to perform well on the three unseen RL tasks. We see the link between the generalization performance of XSRL with respect to the next observation prediction (see Table  4 ) and the transfer performance of its pretrained state estimator (ϕ) to a new RL system (see Table  4 ). More specifically, the good prediction performance on a test dataset of state embeddings pretrained with XSRL guarantees their good transfer performance to new RL tasks. As we have seen, the generalization performance on the test dataset strongly depends on the exploration efficiency (see Fig. 4 ), which is better with XSRL than with its two ablations. Moreover, our exploration allows agents to reach transitions far away from their initial states and much faster than the policy entropy maximization and random strategies (see Fig.
7 ). Hence the link between fast exploration of various transitions and good generalization. However, the strategy of maximizing the policy entropy works well for learning policies with extrinsic rewards in the RL context. In particular,  Eysenbach and Levine [2021]  show in a navigation task with obstacles that it allows to increase the exploration diversity of an agent and thus to more efficiently bypass obstacles than with a random exploration where he would remain blocked. In contrast to XSRL, our results showed that the good reconstruction performance of state embeddings pretrained with RAE did not always guarantee good transfer to RL tasks. Specifically, no matter how much RAE models minimize the reconstruction error on the test dataset of TurtleBot Maze, RL systems will perform poorly with inputs from this type of representation.
Finally, we analyze the results of SAC+XSRL across the three different continuous control tasks. While in Turtle-Bot Maze and InvertedPendulum SAC+XSRL obtains performances equal to those of SAC+ground truth, in HalfCheetah its performance is lower (0. 82) and close to SAC+RAE (0. 85). Moreover, this poorer performance is accompanied by a failure during the pretrainings of XSRL models, since they obtain abnormally high prediction errors on the training datasets. Indeed, comparing this prediction performance on the most similar environment InvertedPendulum (because it does not present perceptual aliasing) we have in Table  4 : 4. 2 on InvertedPendulum, 27. 6 on HalfCheetah. Furthermore, as we have already discussed, XSRL exploration allows in HalfCheetah to keep the robot in constant motion, whereas random exploration cannot because the robot quickly gets stuck in a lying position 4.
Funding This work has been sponsored by the  Labex SMART  supported by  French  state funds managed by the  ANR  within the  Investissements d'Avenir program  under references  ANR-11-LABX-65  and  ANR-18-CE33-0005 HUSKI , and by the  FET  project  VeriDream  that has received funding from the  European Union 's  H2020-EU. 1. 2. 2. research and innovation program  under grant agreement No  951992. We gratefully acknowledge the support of  NVIDIA Corporation  with the donation of one Titan Xp GPU used for this research.
Spermatogenesis across eleven species We generated snRNA-seq data for the testis from ten species that cover the three major mammalian lineages and include key primate species, representing all simian (anthropoid) lineages (Fig. 1a ): eutherian mammals (representatives for 5 of the 6 extant ape lineages, including humans; rhesus macaque, an Old World monkey; common marmoset, a New World monkey; mouse), marsupials (grey short-tailed opossum), and egg-laying monotremes (platypus). Corresponding data were generated for a bird (red junglefowl, the progenitor of domestic chicken; hereafter referred to as 'chicken'), to be used as an evolutionary outgroup. The dataset overall consists of 34 libraries, with one to three biological replicates per species. We refined and extended existing Ensembl  28  genome annotations across all species based on RNA-seq data generated for bulk-testis samples (Supplementary Tables 1, 2; Methods), to ensure optimal read-mapping and prevent biases in downstream cross-species analyses.
1a-e ). The close evolutionary relationship of the seven primates in our study, which diverged between ~2-43 million years ago, enabled the direct integration of datasets across these species and thus the identification of sub-cell types that correspond to intermediate events during spermatogenesis (Fig. 1a ). For all species, we traced the highly dynamic gene expression programs underlying spermatogenic differentiation and key molecular events, thus also identifying a host of novel marker genes (Extended Data Fig. 1g ; Supplementary Table  4 ). To obtain an initial overview of cell type relationships across species, we performed a principalcomponent analysis based on pseudo-bulk cell type transcriptomes (Fig. 1b ). The first principal component (PC1), explaining most variance in gene expression, orders the spermatogenic cell types according to the progression of spermatogenesis for all species (Fig.
Genome and transcript isoform annotation Given that the quality of genome annotation differs substantially between the studied species and given the specific and widespread transcription of the genome in the testis  56  , we refined and extended previous annotations from Ensembl  28  based on testis RNA-seq data. Specifically, akin to the procedure described in references  4, 8  , we used previous extensive stranded poly(A)-selected RNA-seq datasets  4, 57  (100 nt, single-end) for human, macaque, mouse, opossum, platypus, and chicken, and generated and utilized stranded poly(A)-selected RNA-seq datasets (159 nt, single-end) for chimpanzee, gorilla, gibbon and marmoset. For each species, we downloaded the reference genome from Ensembl release 87 (ref. 28 ): hg38 (human), CHIMP2. 1. 4 (chimpanzee), rheMac8 (rhesus macaque), C_jacchus3.
Raw reads processing CellRanger v3. 0. 2 was used for platypus and chicken, and CellRanger v2. 1. 1 for the other species in line with the used Chromium chemistry. The CellRanger mkref function was used with default settings to build each species reference from genomic sequences and customized extended annotation files (Supplementary Table  1 ). Given that pre-mRNA transcripts are abundant in nuclei  64  , exons and introns features were concatenated as described in the CellRanger v2. 1. 1 documentation for considering intronic and exonic reads for gene expression quantification. The CellRanger count function was used with default settings to correct droplet barcodes for sequencing errors, align reads to the genome, and count the number of Unique Molecular Identifiers (UMI) for every gene/barcode combination.
Integration of datasets From the previously filtered UMI count tables, we created Seurat objects for every sample independently, normalized the data and identified the top 10,000 most variable genes. Next, for each species independently, we applied the Seurat 65 anchoring approach using FindIntegrationAnchors and IntegrateData functions with 20 principal components to integrate all datasets together into a single Seurat object correcting for the batch effect. For each integrated species-specific Seurat object, we normalized (NormalizeData function) and scaled the data (ScaleData function) and performed a PCA (RunPCA function). Louvain clusters were calculated using FindNeighbors and FindClusters functions (parameters: dims = 1:20, 1:20, 1:20, 1:20, 1:20, 1:20, 1:20, 1:17, 1:8, 1:10, 1:10 and resolution = 0.
5, 0. 5, 0. 2, 0. 5, 0. 5, 0. 5, 0. 5, 0. 5, 0. 5, 0. 3, 0. 5, respectively, for human, chimp. , bonobo, gorilla, gibbon, macaque, marmoset, mouse, opossum, platypus and chicken). The UMAP embedding coordinates were calculated using the RunUMAP function (parameters: dims = 1:20, 1:20, 1:20, 1:20, 1:20, 1:20, 1:20, 1:17, 1:10, 1:10, 1:10 and min_dist = 0. 3, 0. 3, 0. 1, 0. 1, 0. 3, 0. 3, 0. 3, 0. 1, 0. 2, 0. 3, 0. 6 respectively for human, chimp. , bonobo, gorilla, gibbon, macaque, marmoset, mouse, opossum, platypus and chicken). All primate datasets were merged together using the LIGER 67 (v0. 5. 0) integration tool. A LIGER object was created using the createLiger function based on primate 1:1 orthologues from Ensembl release 87, and normalized with normalize, selectGenes and scaleNotCenter functions with default settings.
Higher values show a more efficient translation of transcripts, while lower values indicate relative translational repression. For Fig. 2j  (and Extended Data Fig. 3f, g ), we used time-and tissuespecificity indexes of expressed genes across somatic and germ cells in testis. As described in ref. 4  , tissue and time specificity indexes are calculated from RNA-seq data across organs and developmental stages. Both indexes range from 0 (broad expression) to 1 (restricted expression). The indexes were obtained from ref. 4. For each nucleus, we plotted the median index across expressed genes. In Fig. 2k , we plotted the percentage of genes causing infertility when knocked-out (out of 3,252 knockouts, 173 of which caused infertility). Tested genes for infertility and associated phenotype information were downloaded from the IMPC database  32.
Gene expression trajectories along spermatogenesis We compared gene expression trajectories along spermatogenesis across primates using human, chimpanzee, bonobo, gorilla, gibbon, macaque and marmoset (based on Ensembl 87 orthologues), and across amniotes using human (as a representative of primates), mouse, opossum, platypus and chicken (based on Ensembl 100 orthologues). To compare robustly expressed genes, we used genes that are expressed in at least 5% of the cells in at least one cluster in all considered species. We used the mfuzz package  76  (v. 2. 44. 0), an unsupervised soft clustering method, to cluster gene expression trajectories along spermatogenesis (eight cell types in primates; four cell types in amniotes) across species using Dmin and mestimate functions to estimate the number of clusters and the fuzzification parameter (Extended Data Fig.
Trajectory conservation score We calculated a global trajectory conservation score across species for each 1:1 orthologous gene set. For a given orthologous gene set, this score corresponds to the log-transformed probability that all members fall into the same mfuzz trajectory cluster as: 𝐶𝑜𝑛𝑠𝑒𝑟𝑣𝑎𝑡𝑖𝑜𝑛_𝑠𝑐𝑜𝑟𝑒. = 𝑙𝑜𝑔 2 (4 5 𝑃. ,8,9 ) 9∈< 8∈= where g corresponds to a given orthologous gene set, c to all mfuzz trajectory clusters (1 to 9 for primates, 1 to 12 for amniotes), s to all species (human, chimp. , bonobo, gorilla, gibbon, macaque, and marmoset for primates; human, mouse, opossum, platypus, and chicken for amniotes), and Pg,i,j to the probability that the gene g of the species j falls into the cluster i. A higher conservation score indicates a greater global trajectory conservation. As a proof of concept, we plotted the trajectory conservation score for conserved and changed trajectories which revealed a significant higher conservation score for conserved trajectories (Extended Data Fig.
6b ). In each tubular area, the number of cells was counted manually using the NDP. view2Plus software (2. 8. 24). Then, the pixels occupied by red staining were quantified and the expression level for each cell type was calculated by dividing the stained pixels by the number of cells. For each picture, the stained pixels for each cell type were normalized by the total amount of stained pixels. Cell type and testis-specific genes per chromosome Testis-specific genes were obtained from previously generated RNA-seq data  4  of adult organs (RPKM ≥ 1 in testis and RPKM < 1 in brain, cerebellum, heart, kidney, and liver). Among these, cell-type specific genes were studied for each chromosome. Somatic cell type specific genes were identified using the FindAllMarkers function (parameter: only. pos = TRUE, min.