dataesr/scientific-paragraphs-categorization huggingface dataset:
Application Scenarios of HybroGen Dynamic compilation allows code generation at different times during the execution of a program. Figure  4  presents three moments to generate instructions corresponding to the kernel similar to Figure  1  but in an algorithmic form. In the first case, the code is generated only once and at program initialization. This case illustrates a situation where the generated code is execute more than once, N times in the figure, to amortize the cost of dynamic compilation. In the figure  4  we can see that parameter of the execution, i. e. i, is not used for the code generation but it can use in parameter of the code generated call. We also see the specialization parameter s which is a parameter of the generated function genAdd in this figure. In the second case figure 4 part (2), the code generation takes place just before the execution of the kernel, this maps to the kernel initialization.
Celcius to Fahrenheit The first demonstration is the conversion of degree Celcius to degree Fahrenheit. This example illustrates the case of an expression of mutliple arithmetics operations. We have chosen this code example for its simplicity. The code of the compilette is described with HybroLang language as following: The compilette C2F is called in the main program with different parameters which corresponds to temperature values that we want to convert. This is a typical example of one code generation for several calls to generated code. A more sophisticated version can have data types (int in this example) in parameters and the same compilette can generate code for int, float 16 bits, float 32 bits or whatever. The data value description is very flexible with HybroLang.
Multiplication with Specialization In this part we focus on a small example of using specialization with HybroLang , which illustrates a functionality specific to HybroGen : injected value at run-time. This code is as small as possible to focus on specialization on data at run-time. We have chosen a compilette which computes a multiplication of a value by a constant. This constant } while ( i s F l o a t || ( ! i s F l o a t && ( d i f f > precd ) ) ) ;.
} In this code, we can see two calls to genIterate, which is the function responsible for code generation, the first with float precision and the second with double precision. The computation of one step of iterations corresponds to the call of fPtr1 or fPtr2 where value maps to the previous result, af is the precision that we want to obtain. At the end of the loop, we compute the difference between the current and the previous result to decide if the precision has been reached to change to double precision or to stop the program.
Metrics and Evaluation of HybroGen Flow To evaluate HybroGen compilation flow, the number of Lines of Code (LoC) is a good indicator to evaluate the extra cost to port C code to hybrid HybroLang and C code. Table  1  presents the number of LoC for three applications and for the different parts of the code. Compilette code is written with HybroLang and we can see that it is very small 12 and 14 lines depending on the application. This code is compiled with HybroLang which generates C code specific for an architecture. For all the applications and the two targeted architecture, the number of lines of C code generated is between 96 and 284. This difference can be explained by the number of instructions in the database for each architecture and for the semantic instructions and arithmetic use in the application.
Discussion and working direction This article has presented a new compilation infrastructure called HybroGen. We have shown that our tool is already working on small examples, which was a challenge in term of compilation chain complexity. Our technical targeted metrics are (1) code generation speed and (2) code generation size. As we use the same code generation scheme than deGoal  [10]  we already know that those two metrics are similar and faster and smaller than any JIT compiler. As scientific targets we want to follow two main objectives which are: Scientific support for transprecision: we target to support applications containing runtime transprecision and support scientific transprecision applications. This objective is very useful on hardware platforms which contains many floating point representations.
For example, RISC-V platform from GreenWaves, the GAP9, has support for floating point variants of 8, 16 and 32 bits. RISC-V standard platform has support for 32 and 64 bits while IBM Power8 platform has support for 32, 64 bits. Those platforms are good candidates. Compilation support for non Von Neumann architecture: we also support code generation for "in memory computing" devices  [13]. On those devices the difficulty comes from the fact that there is two synchronized instruction flows to generate. This platform is not in the scope of this article. Metrics to fight with : this article has showed a proof of concept an initial results. We'll continue to improve our HybroGen tool and in the future experiments we will focus on other metrics which are : Speedup for scientific applications : thanks to our run-time optimization we will have speedups that will help scientific applications which need run-time transprecision support.
Finally this register contains the result of the multiplication. The third parameter maps to the first register in input which is represented by the variable a in the initial code. To summaize, the generation of the code for the multiplication of a by b to transform on two functions of generation as following: h2_sValue_t a = { REGISTER , ' i ' , 1 , 3 2 , 1 0 , 0 } ; h2_sValue_t h2_0 = { REGISTER , ' i ' , 1 , 3 2 , 6 , 0 } ; riscv_genMV_2 ( h2_0 , ( h2_sValue_t ) {VALUE, ' i ' , 1 , 3 2 , 0 , ( b ) } ) ; riscv_genMUL_3 ( h2_0 , h2_0 , a ) ; 4. 4. 2. Back-end Code Generation Using Database Request Generation functions are composed of a conditional structure to select the best instruction. For example, riscv_genMV_2 is used to select instruction for move operation with 2 operands. This function is generated by HybroLang as following: void riscv_genMV_2 ( h2_sValue_t P0 , h2_sValue_t P1 ) { i f ( ( P0.
This macro is generated with SQL request to a database which contains instructions encodage and format for different architectures and variants. The Application Binary Interface (ABI) is also described in the database and requested by HybroLang to build c code. 4. 4. 3. Binary Code Generation at ExecutionFinally, the execution of this compilette on RISC-V architecture produces these instructions:Register t1 and r15 respectively for RISC-V and POWER, contain the specialize value: 3 in this execution. This value is multiplying with instructions mull and mullw by the value in input register a0 or r15. To improve the performances of HybroGen , some passes of optimization are needed to reduce to number of move. For example, the result of the multiplication can be stored in t1 or r3, the ouput register for respectively RISC-V and POWER.
of its capabilities in two directions: (1)  on scientific demonstrators of the transprecision 476 capabilities because it is a challenge for IA applications and (2) on heterogeneity, i. e. 477 the capability to generate binary code at run-time for multiple processors from high 478 performance Power8 up to small RISC-V compute nodes. 479 Our HybroGen infrastructure will be open-source but has not reached a release 480 quality. Nevertheless, we share the code examples version (HybroLang , generated C 481 code) in the following repository https://github. com/oprecomp/HybroLang. Running 482 those code allows reproducing the code generation scenarios described in this article.
Code generation speed : as we can regenerate the binary code very often, it's very important to generate it as fast as possible. HybroGen is designed to generate binary code generator which are very fast because our compiler is able to restrict the code generation to the only instructions that are needed by the application. Code generator size thanks to the previous point, our final code generator are very small (KB order or magnitude), does not rely on external library and can be suited for embedded systems. The table 2 summarized the current supported platforms. Our HybroGen infrastructure will be open source but is not yet ready for a public release. Nevertheless, it is possible to the HybroLang input sources, the output C and.
475 of its capabilities in two directions: (1)  on scientific demonstrators of the transprecision 476 capabilities because it is a challenge for IA applications and (2) on heterogeneity, i. e. 477 the capability to generate binary code at run-time for multiple processors from high 478 performance Power8 up to small RISC-V compute nodes. 479 Our HybroGen infrastructure will be open-source but has not reached a release 480 quality. Nevertheless, we share the code examples version (HybroLang , generated C 481 code) in the following repository https://github. com/oprecomp/HybroLang. Running 482 those code allows reproducing the code generation scenarios described in this article.
Model and simulation setting We used the IPSL-CM5A2 general circulation model, which is built upon the CMIP5 Earth system model developed at the Institut Pierre-Simon Laplace (IPSL), i. e. , IPSL-CM5A-LR  (Dufresne et al. , 2013; Sepulchre et al. , 2020). The IPSL-CM5A-LR Earth system model is composed of the LMDZ atmospheric model  (Hourdin et al. , 2013) , the ORCHIDEE land surface and vegetation model  (Krinner et al. , 2005) , and the NEMO v3. 6 ocean model which includes modules for ocean dynamics (OPA8. 2; Madec, 2016), biochemistry (PISCES;  Aumont et al. , 2015)  and sea ice (LIM2;  Fichefet and Morales-Maqueda, 1997). The atmospheric grid has a horizontal resolution of 3. 75 • longitude per 1. 875 • latitude (96 × 95 grid points) and is divided into 39 vertical levels. For a more detailed description of the model and its different components, the reader is referred to  Sepulchre et al.
Data compilation Simulation results were compared to MATR changes ( MATR) documented by proxy-data records (Table  S1  in the Supplement). We compiled published MATR and CMMT proxy data from various research fields: paleobotany (macrofossils and palynology), geochemistry (isotopic measurements on various material) and paleontology. The data were selected to range from the Lutetian (47. 8 Ma) to the end of the  Rupelian (27. 8 Ma). This large time interval allows the representation of seasonal temperature changes parallel to the long-term cooling of the Eocene. The inclusion of data from the middle Eocene allows a comparison with simulations testing the effect of a pCO 2 lowering alone, before AIS formation at the EOT. It is justified by the presence of paleobotanical records suggesting a strengthening of the seasons already from the Lutetian to the Priabonian (e.
Code availability. LMDZ, XIOS, NEMO and ORCHIDEE are released under the terms of the CeCILL license. OA-SISMCT is released under the terms of the Lesser GNU General Public License (LGPL). The IPSL-CM5A2 code is publicly available through svn, with the following command lines: svn co http://forge. ipsl. jussieu. fr/igcmg/svn/modipsl/branches/ publications/IPSLCM5A2. 1_11192019 (IPSL Climate Modelling Centre, 2022a), modipsl cd modipsl/util;. /model IPSLCM5A2. 1 The mod. def file provides information regarding the different revisions used, namely -NEMOGCMbranchnemo_v3_6_STABLErevision666-XIOS2branchs/xios-2. 5revision1763 -IOIPSL/srcsvntags/v2_2_2 -LMDZ5branches/IPSLCM5A2. 1rev3591 branches/publications/ORCHIDEE_IPSLCM5A2. 1. r5307rev 6336-OASIS3-MCT2. 0_branch(rev4775IPSLserver). The login/password combination requested for the first use to download the ORCHIDEE component is anonymous/anonymous.
Data availability. The key climatological outputs of the simulations are stored in the PANGAEA database: https://doi. pangaea. de/ 10. 1594/PANGAEA. 930422  (Toumoulin et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-18-341-2022-supplement. Author contributions. YD, AT and JBL conducted the modeling experiments. AT compiled proxy data, analyzed the model results, generated figures and tables, and drafted the paper. AT, YD, DT and JBL discussed the paleoclimate results. LK contributed to the paleobotanical context and AL and GDN to the paleogeographic and geological context. All authors have provided critical feedback and contributed to the final paper. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests.
Acknowledgements. We thank the  CEA/CCRT  for providing access to the HPC resources of TGCC under the allocation  2018-A0030102212 , and  2019-A0050102212  made by  GENCI. Agathe Toumoulin  and  Guillaume Dupont-Nivet  acknowledge the support of the  ERC MAGIC  under grant  649081. Yannick Donnadieu  and  Delphine Tardif  acknowledge support from  ANR AMOR  (grant no. ANR-16-CE31-0020 ). The authors acknowledge  Cheng-Sen Li  for sharing a translation of the reference Budantsev (1997). We acknowledge the use of Ferret (https://ferret. pmel. noaa. gov/Ferret/, last access: 10 November 2021) and RStudio software (https://www. rstudio. com/, last access: 10 November 2021) for analysis and figures in this paper. We sincerely thank  Alberto Reyes  for his editorial handling and two anonymous reviewers for their interesting comments that improved the quality of this article.
Phosphoproteomic Data for the Bioinformatic Workflow The bioinformatic workflow input consisted of a list of UniProt accession numbers (AC) of proteins identified as differentially phosphorylated or enriched (i. e. , targets) between experimental conditions that lead to SYK perturbation. For MCF7  [17]  and DG75  [19]  (SYK-positive) cell phosphoproteomics, SYK was inhibited by incubating cells with its pharmacological inhibitor piceatannol or vehicle. For the SYK-negative MDA-MB-231 cell line (called "MDA231" in this study), we compared the phosphoproteomes of parental and inducible SYK-EGFP-expressing cells  [18]. For DG75 and MDA231 cell phosphoproteomics, the phosphopeptides were obtained after trypsin-digestion prior to two steps purification on an anti-phospho-tyrosine affinity column and on polymer-based metal ion affinity column as previously described  [18, 19].
For MCF7 cell phosphoproteomics, the phosphoproteins were obtained after purification on an anti-phospho-tyrosine affinity column as previously described  [17]. Thus, the MCF7 dataset also included the proteins that physically interact with phosphorylated proteins in a SYK-dependent manner. To normalize the datasets, we selected only proteins the phosphorylation level of which was increased in the SYK-positive condition to allow the comparison with the DG75 dataset that contains only positive targets. In agreement with Naldi and colleagues  [20] , we added a posteriori the direct SYK phosphorylation of ezrin in the MCF7 and MDA231 arrays, and of E-cadherin and alpha-catenin in the MCF7 array. The complete list of SYK targets used as input is provided in Supplementary Material (Table  S1 ).
Signaling Network Comparison We embedded the selected pathways in a prior-knowledge signaling network for each cell line, as previously described  [20, 21]. We then compared different topological parameters using the NetworkAnalyzer tool from Cytoscape (version 3. 7. 1) [27, 28]  (http: //www. cytoscape. org/ (accessed on 18 February 2021)). We removed isolated nodes and we considered the networks as directed. The topological parameters have been described previously  [29]. We used the Cytoscape DyNet plug-in to align and compare the SYK networks  [30]  with the following parameters: initial layout of Prefuse Force Directed Layout; networks treated as directed; find corresponding nodes by name (UniProt ID); find corresponding edges by their source/target (we selected minimal edge properties in order not to discriminate them according to their database of origin).
Signal Propagation Analysis We used our recently developed Phos2Net software to extract the shortest path subnetworks for each cell line  [21]. Reconstructing prior-knowledge networks solely from SYK target-enriched signaling pathways has the advantage of selecting paths with greater confidence. However, we found that this reduced the number of reachable targets and created a bias in the comparison of the extracted subnetworks. Therefore, to focus our study on the intrinsic experimental dataset parameters, we used the same prior-knowledge network to analyze the signal propagation from SYK to its targets. We chose the "All pathways" selection mode to extract them from the same prior-knowledge network reconstructed from the components of all the KEGG and Pathway Commons databases, irrespective of their enrichment.
Comparison of the SYK Signaling Networks in Breast Cancer and Burkitt Lymphoma Cell Lines To better understand the molecular determinants of the SYK kinase pro-and antitumor activities, we analyzed existing tyrosine phosphoproteomic datasets that identified SYK targets in different cell types. Specifically, we used data obtained from the human breast cancer cell lines MCF7 and MDA231, in which SYK acts as a tumor suppressor  [17] [18] [19] , and from the human Burkitt lymphoma cell line DG75, in which SYK plays a pro-oncogenic role  [19, 22]. After post-treatment procedures and normalization of the original phosphoproteomic data (detailed in the Section 2), we used our bioinformatics pipeline to select SYK target-enriched signaling pathways, reconstruct signaling networks from elements of these pathways and select relatively small subnetworks that connect SYK to its targets  [20, 21].
Comparison of the SYK Signaling Networks in Breast Cancer and Burkitt Lymphoma Cell Lines To better understand the molecular determinants of the SYK kinase pro-and antitumor activities, we analyzed existing tyrosine phosphoproteomic datasets that identified SYK targets in different cell types. Specifically, we used data obtained from the human breast cancer cell lines MCF7 and MDA231, in which SYK acts as a tumor suppressor  [17] [18] [19] , and from the human Burkitt lymphoma cell line DG75, in which SYK plays a prooncogenic role  [19, 22]. After post-treatment procedures and normalization of the original phosphoproteomic data (detailed in the Materials and Methods section), we used our bioinformatics pipeline to select SYK target-enriched signaling pathways, reconstruct signaling networks from elements of these pathways and select relatively small subnetworks that connect SYK to its targets  [20, 21].
We compared the results obtained for the breast cancer and Burkitt lymphoma cell lines at different stages of this analysis (Figure  1 ). (A) Cell lines and the corresponding phosphoproteomic analysis strategies used by the authors of the quoted studies to identify SYK targets. We analyzed these phosphoproteomic data to bootstrap the reconstruction of comprehensive networks of SYK downstream signaling in breast cancer and lymphoma cells. (B) Workflow of the network reconstruction and signal propagation analysis (according to  [20, 21] ) and comparison of the results obtained in the three cell lines at different steps of this pipeline. To start exploring the datasets, we first identified SYK targets common to at least two cell lines. The majority of the targets were cell line-specific (Figure  S1A ).
(B) Workflow of the network reconstruction and signal propagation analysis (according to  [20, 21] ) and comparison of the results obtained in the three cell lines at different steps of this pipeline. To start exploring the datasets, we first identified SYK targets common to at least two cell lines. The majority of the targets were cell line-specific (Figure  S1A ). The largest shared subgroup included 90 proteins identified in both MDA231 and DG75 cells. Approximately 50% of these 90 proteins contained the same phosphorylation site(s), suggesting a similar impact of SYK. We then focused on SYK targets specific to each cell line. We investigated whether they were enriched in specific GO biological processes, but none showed a statistically significant enrichment. We observed the same lack of enrichment in GO terms for all the other SYK target subgroups (i.
KEGG Pathways Are Differentially Enriched in SYK Targets in the Breast Cancer and Burkitt Lymphoma Cell Datasets To thoroughly compare SYK signaling networks in these three cell types, we asked whether the identified SYK targets belonged to specific signaling pathways, using the KEGG pathway database  [23] [24] [25]  and the Fisher's exact test to assess the enrichment and classify them. The two breast cancer cell lines MCF7 and MDA231 did not cluster, and the distance between cell lines was closer for the MCF7 and DG75 cell lines than for the MCF7 and MDA231 cell line (Figure  S1B ). To find signaling pathways that were differentially enriched in SYK targets in the three cell lines, we sorted them in function of the mean of the enrichment score in the three cell lines and calculated the deviation from this mean value for each line (Figure  S1C  and Table  S2 ).
We manually reviewed the signaling pathways with the highest differential enrichments and selected those in which the SYK targets were relevant to the functionality of the signaling pathway. To facilitate viewing, we colored proteins of the KEGG signaling pathways according to the SYK target list(s) they belonged to (DG75, MCF7 and/or MDA231 cell dataset), and we included for each KEGG signaling pathway a table that summarizes the main data for each SYK target. These files are available in an open-access searchable directory (10. 5281/zenodo. 4091051). The B-cell receptor signaling pathway was the most enriched amongst the SYK targets in the DG75 cell line (Figure  2  and Table  1 ). SYK targets identified in MCF7 or MDA231 cells were not specific to this pathway and were involved in many other signaling pathways unrelated to lymphocyte-specific immunoreceptors.
Amongst the DG75 cell-specific SYK targets identified in the B-cell receptor signaling pathway, the Bruton's tyrosine kinase (BTK) protein tyrosine kinase is essential for B-lymphocyte development, differentiation and signaling  [31]. In DG75 cells, BTK is phosphorylated on the Y551 residue, indicative of its catalytic activity  [32]. Other signaling pathways enriched in DG75 cell-specific SYK targets included T-cell receptor signaling (Figure  S2 ), Fc gamma receptor-mediated signaling (Figure  S3 ), phagocytosis (Figure  S4 ), and Th1 and Th2 cell differentiation (Figure  S5 ). These pathways seem to be specific of cell types distinct from a B cell line, but the proteins and the protein interactions of these pathways are shared by many cell types. Table  1. SYK targets from the different datasets present in the KEGG B-cell receptor signaling pathway.
The SYK Signaling Networks Display Similar Topological Parameters To compare the SYK signaling networks in the three cell types, we selected the signaling pathways that were most enriched in SYK targets from each dataset. We combined their components to create the SYK networks specific to each cell line using our Phos2Net bioinformatics tool. In these networks, nodes represent the proteins, and edges the interactions between these proteins. The DG75, MCF7 and MDA231 cell networks were similar, with a central core characterized by high density of hyperconnected nodes (Figure  S12A-C ). This feature was absent in a network generated from a dataset of proteins randomly selected (Figure  S12D ). The random network has been generated from the UniProt database. In addition, the node connectivity distribution showed a better power law correlation in the DG75, MCF7 and MDA231 cell networks than in the random network (Figure  S13  and Table  S3 ).