dataesr/scientific-paragraphs-categorization huggingface dataset:
Many digital projects are developed worldwide, each reaching to the same conclusion: to create a database and just hope for it to be used is not enough anymore. It has to be conceived in a global perspective of interconnections between different databases handling different aspects. With that in mind, a first connection has been established with the Karnak project,17 aiming to organize and make available textual documentation from the temples of Karnak. 18 Whenever a word is identified from a monument in Karnak, a link can be made toward their database via the kiu19 number, providing a highdefinition photograph of the inscription and all the information associated with it (Figure  12. 5). It seems indisputable that for an effective digital tool in the future, the more connections between the different existing databases the better.
Although it raises another issue concerning the use of a common terminology, which cannot be addressed yet considering the diversity of methods, each one with its own merits. In the digital world, given the endless possibilities, it can be hard not to rush towards the most striking features but to ponder each step in order to give a database strong foundations. It does not prevent the team to plan for the long term and to have many ideas for future developments and new features, taking also into consideration feedback and acknowledging the needs of the community. For example, besides the ongoing reflection on how to model and present the evolving knowledge of ancient Egyptian vocabulary, another feature regularly asked for is the implementation of interactive hieroglyphs,20 enabling the search by glyphs in the database.
Conclusion The idea behind VÉgA, besides updating knowledge on the ancient Egyptian vocabulary, is to create a tool, specifically a digital one, enhancing the work capacity of Egyptologists and giving access to the language to a wider range of people. By bringing on board designers and computer engineers, the Egyptologists wanted to imagine new ways to handle old data. For now, VÉgA is still in its infancy and cannot assume the title of a true lexicographical tool yet. And being not yet complete means it still misses some figure 12. 6 Snapshot of the VÉgA desktop with the list of results for the word "baboon" in the search field (without filter). The list shows all the entries containing this word in the translation, and at least eight of them seem to be variants of the same word, may it start by the letter ꜣ, j or ʿ.
22 Hannig 2003 and 2006. 23 Lesko 2002 -2004. 24  Wilson 1997. martin ing on the period and the region, most significantly concerning the semiconsonants. Thus, it becomes a lot easier to ascertain lexical fields, roots, prefixes and suffixes, alternation between phonemes, etc. Some of them are already well known, but if gathering data is made that easier, it is all the scientific community VÉgA hopes for, to enable further and deeper comprehension of the ancient Egyptian language. To conclude, a word should be said on the economic aspect of such platform, which raised an extensive discussion during the conference, but no truly satisfactory solution could be found at this time. Matter-of-factly, it oscillates between fully open access and charged subscriptions. If the first option seems preferable, it relies on the project benefiting from a secure fund over the years to keep it running, which is under present circumstances less and less feasible.
We present a discourse parsing model for conversation trained on the STAC corpus  (Asher et al. , 2016). We fine-tune a BERT-based model to encode pairs of discourse units and use a simple linear layer to predict discourse attachments. We then exploit a multi-task setting to predict relation labels, which effectively aids in the difficult task of relation type prediction; our F1-score equals or surpasses the state of the art in the approaches we have reimplemented using code from the authors with no loss in performance for attachment, confirming the intuitive interdependence of these two tasks. Our method also improves over other discourse parsing models in the literature in permitting attachments in which one node has multiple parents, an important feature of multiparty conversation.
Only SDRT has been applied to multi party conversation as in the STAC and Molweni datasets  (Asher et al. , 2016; Li et al. , 2020). Given our interest in multiparty conversation, we use SDRT and three versions of the STAC corpus, a set of multi-party chats from an online version of the game Settlers of Catan, in which players trade or otherwise acquire resources in order to build roads and settlements and thereby score victory points. The standard version, S, contains only linguistic (chat) moves made by players; the situated version, S-Sit, integrates descriptions of nonlinguistic events (game moves), represented as elementary event units (EEUs). In S-Sit, both EEUs and EDUs are integrated into discourse structure. We present the third version in Section 2. 1. In SDRT a conversation provides a number of EDUs linked together to form a weakly connected, Directed Acyclic Graph (DAG).
A new STAC dataset: STAC-Squished Like other recent work in discourse parsing, we develop a model trained on S and detail our results on S in Section 4. But S is not ideal for two reasons. First, we discovered that it contains duplicated data points due to a mistake in the data extraction script. There are 488 duplicated EDUs, corresponding to 60 duplicated dialogues. Second, and more seriously, there are rhetorical incoherences in S. When we examined relation labels, we found, with  Badene et al. (2019) , that some were not coherently used, because S lacks essential nonlinguistic (game) information  (Asher et al. , 2016). Figure  2  provides an example. The exchange on the right, taken from S, is somewhat incoherent; the Continuations (dark green) from 282-283 and 283-289 make little sense, but were included in order to make a complete DAG.
To do this, we treated each sequence of EEUs as one block without any internal structure. That is, we collapsed the sequence into a single EEU. Table  1  reflects differences between the various STAC corpora in numbers of EDUs, EEUs, Relation instances over only EEUs (R-E) and mean distances between attachments of linguistic units. We have made available the new S-Sq (squished) corpus at https://github. com/zineb198/LineBert.
Our Model S contains a set of multi party dialogues, each consisting of a set of n EDUs [e 1 , e 2 ,. , e n ]. Our model BERTLine, is a simple but efficient discourse parsing model with two components: one for inferring attachments and one for inferring relation labels. Like  Shi and Huang (2019)  and  Wang et al. (2021) , we compute a standard unlabeled attachment and labeled attachment score for evaluation. Attachment. BERTLine's attachment module has an encoder-decoder architecture. Encoding for each EDU pair is obtained by finetuning a BERT model on the task of predicting if two EDUs are attached. We used BERT because it takes the position IDs of tokens as input, which is useful for the pair encodings. BERTLine has access only to the content and the speaker/emitter of the EDUs. [CLS] vectors furnish embeddings h of EDU pairs.
Results Table  2  shows that BERTLine is competitive on S with the most advanced model of  Wang et al. (2021)  and even beats its relation labeling score. We provide both the reported scores of  Shi and Huang (2019) ,  Liu and Chen (2021)  and  Wang et al. (2021)  and the scores that we got by rerunning their code using the same gold input from S with our evaluation metric, which explicitly considers EDUs with multiple parents. 1 Discrepancies in reported scores, also noted in  Wang et al. (2021) , may depend on choices of what and how to evaluate but also on machine hardware. With regard to the different versions of STAC, BERTLine's overall scores improve between S and and S-Sit in Table  3 , although there is a dramatic drop in linguistic only attachments (A-L) for S-Sit. This is due to the presence of many easy to predict attachments and relation instances between adjacent EEUs, which drown out performance on harder, longer distance EDU attachments.
Interestingly, our score also improves considerably with S-Sq, and linguistic-only attachment suffers a much smaller drop. We hypothesize that this is because we have lowered the median distance between EDUs and rectified the imbalance between EDU and EEUs by deleting long series of EEUs. BERTLine has a far simpler architecture than the neural models whose code we were able to rerun. It is essentially a local model that uses a minimal amount of message passing from nodes to potential neighbors, a technique from graph neural nets also used to encode entire graphs in  Wang et al. (2021). BERTLine's main advantage comes from the explicit pair encoding of EDUs. While broader contextual information and structural constraints on DAGs like those in  Perret et al. (2016)  can undoubtedly improve scores for discourse attachment and relation labeling, we have sought to show that the efforts of complex architectures to harness this extra information have failed to lead to substantial gains.
How to more successfully exploit it is our main research topic for the future. Shi and Huang (2019)  (DS), Liu and Chen (2021) (Hier) and  Wang et al. (2021)  (GNN) on the standard STAC dataset S, as well as the scores we were able to obtain using their models or retraining from their code using our evaluation metric (reported/recomputed). BERTLine (BL) beats the state of the art for relation label prediction on S with a score of 56. 25. We also provide BL's results on the squished STAC dataset S-Sq with great performance on relation labeling.
F1 Attach   Shi and Huang (2019)  were the first to obtain significant discourse parsing results using a neural approach on the corpus S. They attempted to capture incremental and contextual effects in their model by training a supplemental Structured Encoder that incrementally updates attachment paths (sequences of parent-child EDUs). However,  Wang et al. (2021)  showed that the model obtains similar scores with or without the Structured encoder; the encoder didn't capture what it intended. Moreover, they implemented this method with Python 2 and Tensorflow 1. 3, which are not in use anymore. Liu and Chen (2021)'s model encodes EDUs using a pre-trained RoBERTa model  (Liu et al. , 2019)  and a bi-GRU cell to capture contextual information but limits the size of the input dialogues. It uses two linear layers for link and relation prediction.
Limitations Some of the limitations of this work are the lack of diversity in expert-annotated discourse data sets on multiparty and situated dialogue. Since current data sets come from forums and chat messages, we still have to see how our model behaves in a spoken conversation context. More investigation efforts must be made in order to better analyze and evaluate S-Sq. While it has a more predictable structure due to nonlinguistic elements, it also seems at first glance to contain better suited semantic relation labeling for the linguistic elements. We need to do an in-depth error analysis on BERTLine's performance on the three versions of the STAC corpus. We would also like to investigate training on one corpus and then running the model on the other corpus, and we would like to do the same with the Molweni corpus.
The maturity of numerical simulations now allows to study the mechanisms of the noise sources on various complex configurations. Moreover, numerical simulation methods can be sufficiently accurate to predict the noise generated by such configurations. In order to take the next step in the maturity of numerical prediction, these NIRs must be accurately evaluated and modeled. Experimental data based on academic configurations are therefore needed to validate the new tools and numerical models. One promising NRT is the use of a fairing in front of the landing gear to reduce the noise of this system 1. The present study aims at collecting an experimental database (pressure drop and turbulence characteristics) of several fairing solutions in order to have validation test cases for CFD simulation and thus develop new models for such complex geometries.
The sampling rate is 4Hz and 80 samples are acquired. When the SVMtec scanner is out of range for high pressure drop samples, only the Digiquartz sensor is used on all the pressure taps. A two-component fringe-mode LDV allows the measurement of the axial U and vertical W velocity components in almost the entire volume of the test section  3. The emitting optics produce an elliptical measurement volume whose little axis can be as small as 50 microns. Flow is seeded with amorphous silica particles. The LDV signal is unevenly sampled due to the random arrival of particles in the measurement volume. A reconstruction method  2, 4  is used to resample the raw data at a constant rate. Signals are processed using the in-house ONERA software, ASSA  5. A minimum sampling rate of f m = 15kHz measurements per second is ensured, for each velocity component, and more than 200,000 samples are acquired so that statistical convergence of the mean velocity is largely ensured.
e. , define 𝑐 𝑖𝑗 as a normalized version of the empirical tractography-derived SC or eSC), leading to only excitatory interactions between ROIs; and (ii) in the second one the 𝑐 𝑖𝑗 values are assigned by the corresponding entries of the hybrid rsSC connectomes, leading to both excitatory and inhibitory interactions between ROIs. The delays 𝜏 𝑖𝑗 were calculated as 𝜏 𝑖𝑗 = 𝐿 𝑖𝑗 /𝑉 , where 𝐿 𝑖𝑗 (mm) is the average tract (path) length of the streamlines connecting regions 𝑖 and 𝑗, and 𝑉 (m/s) is an average velocity of signal propagation. In this particular dataset the exact path lengths are not available, hence we used instead the euclidean distance between nodes as proxies. The euclidean distance has been used in the literature in the construction of structural networks (see e. g. [64] ) and found to closely follow the trends obtained by anatomical tract-tracing studies.
Furthermore in  [65] , the authors showed that such networks also strongly correlate with MRI tractography-based networks. The matrix L = 𝐿 𝑖𝑗 can thus be used to calculate the delays 𝜏 𝑖𝑗 in the coupling, which can be expressed as 𝜏 𝑖𝑗 = 𝜏 • 𝐿 𝑖𝑗 /⟨𝐿 𝑖𝑗 ⟩, where 𝜏 = ⟨𝐿 𝑖𝑗 ⟩/𝑉 is the global (or mean) delay. In Eq. (  8 ) the self-connections were excluded by setting the diagonal elements in the matrices eSC/rsSC and L to zero (i. e. 𝑐 𝑖𝑖 = 𝐿 𝑖𝑖 = 0 respectively). In Figure  1 (A), we show the empirical SC matrix (weights of the node-to-node connections) for a Non Carrier subject from the dataset with 80 nodes (ROIs). Figure  1(B)  depicts the corresponding to this SC and subject rsSC matrix calculated as described earlier. Note that the hybrid rsSC contains negative entry values as opposed to SC one that is restricted to having only positive values.
Both panels refer to same subject presented in Figure  2  with the lines indicating the corresponding linear fit in each case. In Figure  4  we present a statistical analysis for all subjects per category, i. e. 38 non-carriers (A) and 38 carriers (B). For each subject we considered the 5 maximum values of correlation coefficients between eFC and sFC using SC and rsSC matrices for the simulated time series respectively (circles in Figures  2(A), (D) ) and produced boxplots. We then used the t-test to compare the respective mean values. The difference in the respective mean values of the two datasets is found to be statistically significant with very small 𝑝-value (𝑝 ≤ 0. 0001) for both non-carriers (Figure  4(A) ) and carriers sets (Figure  4(B) ). The difference in the respective mean values of the two datasets is statistically significant measured by the t-test with very small 𝑝-value (𝑝 ≤ 0.
g. minimum/maximum values of the colorbars of panels Figure  2(B), (C), (E), (F )) for Non Carriers (A) and Carries (B) respectively. Both sFC (simulated with rsSC/SC) perform rather well in capturing the positive correlations observed in the empirical BOLD signals. However, note that the simulated time series obtained with SC (left red boxplots) do not adequately capture the negative correlations that occur in the empirical time series (left light green boxplots). On the other hand, BOLD signals generated with rsSC (left light blue boxplots) yield negative correlations much closer to the empirical ones. This trend is present for both Non Carriers and Carriers datasets. Next, to further explore the advantage in using the hybrid rsSC matrices beyond Static Functional Connectivity matrices, we sought out to perform a similar PSE analysis for Dynamic Functional Connectivity, which allows us to capture switching trends in the resting-state activity.
Our testing sample is an APOE 𝜀4 dataset  [60]  that contains both structural (traditional SC and rsSC) and functional (BOLD signals and FC matrices) for two groups of cognitively-normal subjects, namely Carriers and Non Carriers (control). Our ultimate goal was to numerically compare the performance of oscillator models in producing simulated FC when using traditional SC versus rsSC (see e. g. [18, 40, 61, 21] ) which define the pair-wise coupling weights (𝑐 𝑖𝑗 ) in the Kuramoto model Eq. (  8 ) in fundamentally different ways (see Figure  1 ), with traditional SC only permits attractive coupling. As the tract lengths were not available, we considered the euclidean distances between ROIs in the Desikan atlas  [59]  to account for the mean delay parameter (𝜏 ) in the Kuramoto model. Overall, we found that there are important advantages in using hybrid rsSC as it can produce BOLD sequences and synthetic FC that follow well the general trends of the empirical BOLD time series and empirical FC (Figures  2  and 4 ).
r. t. positive versus negative BOLD correlations. Despite the fact that in general both sFC (simulated with rsSC/SC matrices) perform rather well in capturing the positive correlations observed in the empirical BOLD signals, only the rsSC ones can effectively produce negative correlations closely matching those occurring in the empirical BOLD signals (see also  [75] ). This result was demonstrated in Figure  6  and was observed for both Non Carriers and Carriers datasets. Our study has a few limitations. First, we restricted ourselves to a specific frequency band during simulations and thus future studies should further explore different ranges of frequencies in the Kuramoto model, e. g. either in different Hz ranges or extracted directly from the empirical BOLD signals per node and per subject (see e.
Acknowledgments We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the  European Union 's  Horizon 2020 research and innovation programme  through the  ICEI  project under the grant agreement No. 800858. In particular, we acknowledge the access to the JUSUF supercomputer at the  Jülich Supercomputer Centre. TM would like to thank the  Institut Henri Poincaré  for its support through the ' Research in Paris' programme  and hospitality in the period during which part of this work took place.
Funding information This research was partially funded by the  Helmholtz Association  through the  Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain ". This project was also received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 945539  ( Human Brain Project SGA3 ). TM and AL were also supported by the  Labex MME DII  ( ANR-11-LBX-0023-01 )  French national  funding program. Additionally, LZ and AL were partially supported by  NIH   RF1MH125928  and  R01AG071243. Moreover, LZ was also partially supported by  NSF   IIS 2045848.
Data and code availability statement The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. The TVB code used for the simulation of the brain network dynamics can be found in this repository: https://gitlab. jsc. fz-juelich. de/metaopt/rssc_ simulations_tvb. git. The scripts for generating rsSC are available here: https://github. com/ iforte2/hybrid-connectome. Regarding the statistical tests used in the manuscript, standard python scripts were used (written by the authors).
Data Processing The raw sequence data was demultiplexed using deML v. 1. 1. 3  (Renaud et al. 2015)  and bamtools v. 2. 5. 1  (Barnett et al. 2011 ) and converted from BAM to fastq format using   (Quinlan and Hall 2010). Reads were trimmed for adapter content and quality using trimgalore v. 0. 6. 5 (Krueger 2019), a wrapper tool around fastqc and cutadapt, using --fastqc --retain unpaired. Sequence quality and adapter removal were confirmed with FastQC v. 0. 11. 9  (Andrews 2010). Quality-and adapter-trimmed reads were aligned to the T. fasciculata reference genome v. 1. 0 (GenBank Assembly accession GCA_029168755. 1, Groot  Crego et al. , 2023)  using bowtie2 v. 2. 3. 5. 1  (Langmead and Salzberg 2012)  with the --very-sensitive-local option to increase accuracy. Low-quality mapped reads were removed, alignments were sorted by position using samtools v.
1. 15. 1  (Li et al. 2009) , and PCR duplicates were marked using MarkDuplicates from PicardTools v. 2. 25. 2 (Picard Toolkit 2018). Relatedness between accessions was inferred with kinship coefficients in KING v. 2. 2. 6  (Manichaikul et al. 2010) , both for confirming technical replicates and to infer unexpected relatedness. Samples with an average coverage below 2x or a first degree relationship (consistent with full-sib or direct parent-offspring relationships; i. e. , kinship > 0. 177) were removed from subsequent analysis (Supporting Table  S3 ). Next, variants were called using a dedicated pipeline available at https://github. com/giyany/TillandsiaPhylo. In brief, we used GATK HaplotypeCaller v. 4. 1. 9. 0 followed by joint calling with GenotypeGVCFs  (Poplin et al. 2018). The resulting variant call file (VCF) was filtered to exclude indels and any SNPs 3bp or closer from indels using bcftools v.
Phylogenetic Tree Inference We inferred phylogenetic relationships for all samples using both a maximumlikelihood and a coalescent-based method. We included a coalescent-based method in order to explicitly account for gene tree incongruence, which may result in high support for an incorrect topology  (Kubatko and Degnan 2007). Gene tree incongruence further provides insight into molecular genome evolution, including the extent of incomplete lineage sorting and other genomic processes such as hybridization and introgression  (Galtier and Daubin 2008; Wendel and Doyle 1998). First, a concatenated matrix was prepared by converting a gVCF into a phylip file with vcf2phylip  (Ortiz 2019). A maximum-likelihood tree was inferred on a dataset of concatenated SNPs with IQ-TREE v. 1. 6. 12, using IQ-TREE's ModelFinder to select the best-fitting model and including an ascertainment bias correction  (Nguyen et al.
1. 1. 0  (Junier and Zdobnov 2010). ASTRAL-III v. 5. 7. 8 (hereafter: ASTRAL) was then used to infer a species tree, measuring branch support as posterior probabilities  (Zhang et al. 2018). To explore gene tree discordance, we used phyparts to calculate quartet support for the main, alternative and second alternative topologies as well as the number of concordant and discordant bipartitions on each node with a cutoff of ten for informative bootstrap values (-s 10;  Smith et al. 2015). Gene tree discordance was visualised with phypartspiecharts. py (available from https://github. com/mossmatters/MJPythonNotebooks).