dataesr/scientific-paragraphs-categorization huggingface dataset:
Once all MAPs are deployed, each agent is trained to maximize the overall network performance and solves the complex interference problem. Figure  4    V. CONCLUSIONS The emerging 6G architecture will be dominated by operator services enabling dynamic topology changes of the network infrastructure to better adapt to time-varying user traffic needs. Thus, in this paper, we introduce a new service called Coverage Extension as a Service for a dynamic and efficient expansion of the infrastructure by using mobile access points. This service is powered by the framework of DEDICAT 6G project. The context and situation awareness module provides two functionalities: device discovery (i. e. a heat map related to the number of users present at a given time at a designated point) and technology recognition (i.
e. identifying technology and predicting the traffic characteristics for a better spectrum usage and RAT selection). These modules provide essential information for making decisions exploiting MAPs (e. g. how to manage MAPs and its associated users). Then we propose a joint optimization of MAP placement and network operations. This highlights the impact on the network performance of flexible and dynamic networks infrastructures. In future work, DEDICAT 6G framework will be extended with various AIbased approaches to address CEaaS for different use-cases (e. g. , smart highway, smart warehousing, public safety and enhanced experience of temporary events). A particular attention will be paid to the management of dynamic networks. Moreover a experimental proof of concept will be developed to validate the technology enablers.
I. INTRODUCTION In this paper we present the research directions and innovative solutions developed in the European Horizon2020 project Smart4RES (http://www. smart4res. eu) to better model and forecast weather variables and production of renewable energy sources (RES) like wind, solar and run-of-the-river hydro in order to optimise their integration into power systems and electricity markets. Smart4RES gathers experts from several disciplines, from meteorology and renewable generation to market-and grid-integration. It aims to contribute to reach very high RES penetrations in power grids of 2030 and beyond, through thematic objectives including: 1) Improvement of weather and RES forecasting 2) Streamlined extraction of optimal value through new forecasting products, data market places, and novel business models 3) New data-driven optimization and decision-aid tools for market and grid management applications.
This work has been realized in the frame of the Smart4RES Project, a Research and Innovation Horizon 2020 program funded by the INEA agency under Grant Agreement n°864337. The authors are with: 1 : MINES ParisTech -PSL University, Centre PERSEE, Sophia Antipolis, France, name. surname@mines-paristech. fr  4) Validation of new models in living labs and assessment of forecasting value vs costly remedies (i. e. storage) to hedge uncertainties. Forecasting of weather variables and of Renewable Energy Sources (RES) production are both mature disciplines. However, specific weather conditions remain challenging to forecast with existing models, leading to economic impacts on power systems: Figure  1  shows large forecasting errors on the total PV production of Germany, caused by an unpredicted fog event.
III. CONCLUSION Accurate forecasts of weather and variable generation are key for accurate decision-making. High-resolution weather forecasts are developed in Smart4RES thanks to advanced simulation models at large scale (Ensemble NWP) and small scale (LES), and optimally combined distributed weather measurements for solar nowcasting. Collaborative forecasting investigates the improvement associated to local data sharing between distributed RES plants. This data sharing paves the way to a data market where agents exchange measurements, predictions or other types of valuable data. Lastly, Smart4RES proposes data-driven approaches that streamline decision-making by simplifying the model chain of bidding RES production, storage dispatch or predictive management electricity grids. They also provide interpretable hindsight to decision-makers by integrating the decisions of experts (human-in-the-loop) and will be tested in realistic laboratory conditions (software-in-the-loop).
Materials and methods Participants. Fifty-nine healthy right-handed participants took part in the study. All participants reported no history of neurological or vestibular disorders, and normal or corrected-to-normal vision. Five participants did not complete all experimental conditions due to motion sickness symptoms. After the experimental session, datasets related to 16 participants were discarded because they met any of the following criteria that have been recommended for reliable estimate of inhibition latency, also known as the stop signal reaction time (SSRT)  11  : (i) percent inhibition (P Inhib ) on Stop-trials less than 25% or greater than 75%; (ii) percent Go-omission (here named Go failure, GF) greater than 40%; and (iii) SSRT standard estimate (see below for its detailed computation) that is negative or less than 75 ms (ms) (see Supplementary Fig.
1 ). Data from the remaining 38 participants (mean age: 37 years old, SD: 10; 19 females-50%) were analyzed. Informed consent for both study participation and publication of identifying information/images in an online open access publication was obtained from each participant. All subjects were paid for their participation in the study. All procedures were approved by the Ile de France XI independent ethics committee. Experimental design. Before the real recordings, participants conducted two training sessions: a first round with the motionless centrifuge to familiarize the subject with the two tasks (100 trials for each task; see below for detailed description of tasks), and a second round to familiarize the subject with rotating centrifuge. Then, participants performed the two tasks successively under three experimental conditions.
Stop-signal tasks. In the stop-signal paradigm, participants perform a choice response time (RT) task that refers to as the Go subtask, in which they respond to a stimulus, that is, the Go stimulus. In a minority of trials (30%), the Go stimulus is followed by a Stop signal that instructs participants to withhold their response  7, 8. In the present work, the two stop-signal tasks (classical and sensorial) were implemented using Matlab 2018b (The Mathworks) and the PsychToolbox  13. Both tasks consisted of 100 trials in the condition Control and 50 trials in conditions Canal and Otolith (where rotation time was limited to 75 s due to technical specifications of the centrifuge), including 70% Go-and 30% Stop-trials. The Go-and Stop-trials were presented in a pseudorandomized and unpredictable manner to the subject.
The BHA used data from the entire group to estimate parameters at the individual level  17. Three main parameters were estimated to characterize the SSRT distribution, given the BHA assumes that SSRTs follow an ex-Gaussian distribution: the μ Stop and σ Stop parameters give the mean and the standard deviation of the Gaussian component and reflect the leading edge and mode of the distribution, respectively; and the τ Stop gives the mean of the exponential component and reflects the tail of the distribution (see Fig. 2a )  18. Two additional parameters were estimated: the percent triggerfailures (TF) that quantifies failures to launch inhibition process in Stop-trials, and the percent Go-failures (GF) that accounts for errors of omission in the Go subtask. Estimating the TF and GF parameters has been shown to be critical to avoid over-or underestimation of SSRT distribution parameters, respectively  [19] [20] [21].
Parameters estimation in BHA relies on Markov Chain Monte Carlo (MCMC) sampling that yields posterior distribution for the model parameters. We refer the reader to the paper from Van Ravenzwaaij et al. (  2018 ) for in-depth coverage of the MCMC sampling method  22. In the present work, BHA and MCMC sampling method were implemented using the R package Dynamic Models of Choice (DMC)  17. We initialized MCMC sampling with 33 chains and 120 iterations for each chain. The effective sample size, which adjusts the actual sample size (33 chains × 120 samples) for redundancy due to autocorrelations, was around 250. We estimated the parameters described above for each condition and each task. Model convergence was assessed by applying both visual checking (MCMC chains should have the appearance of "flat fat hairy caterpillars"), the Gelman-Rubin statistic  23  , and several functions from the DMC package that quantify redundancy (autocorrelation) and the effective number of independent samples  17.
Acknowledgements We thank  M. Guillard ,  C. Peuriere ,  F. Boutet ,  C. Coutant , and  P. A. Barraud  for their help with data collection. We also thank  T. Gallopin  for his helpful comments on an earlier version of the manuscript. This study is part of a project on cognitive neurosciences supported by the  French Military Health Service. The opinions or assertions expressed herein are the private views of the authors and are not to be considered as official or as reflecting the views of the French Military Health Service. We are grateful to two anonymous reviewers for their insightful comments on the manuscript.
Connecting protein sequence to function is becoming increasingly relevant since highthroughput sequencing studies accumulate large amounts of genomic data. In order to go beyond the existing database annotation, it is fundamental to understand the mechanisms underlying functional inheritance and divergence. If the homology relationship between proteins is known, can we determine whether the function diverged? In this work, we analyze different possibilities of protein sequence evolution after gene duplication and identify "interparalog inversions", i. e. , sites where the relationship between the ancestry and the functional signal is decoupled. The amino acids in these sites are masked from being recognized by other prediction tools. Still, they play a role in functional divergence and could indicate a shift in protein function.
We develop a method to specifically recognize inter-paralog amino acid inversions in a phylogeny and test it on real and simulated datasets. In a dataset built from the Epidermal Growth Factor Receptor (EGFR) sequences found in 88 fish species, we identify 19 amino acid sites that went through inversion after gene duplication, mostly located at the ligand-binding extracellular domain. Our work uncovers an outcome of protein duplications with direct implications in protein functional annotation and sequence evolution. The developed method is optimized to work with large protein datasets and can be readily included in a targeted protein analysis pipeline.
Algorithm for the identification of inter-paralog amino acid inversions We developed an algorithm to identify the events of inter-paralog inversion in a protein phylogeny using a multiple sequence alignment (MSA) and a phylogenetic tree. The algorithm was implemented as a python package named DIRphy, for the Detection of Inverted Residues in a phylogeny. DIRphy splits the protein sequences of the MSA into four groups according to the organism and ortholog annotations, which can be either provided by the user or automatically done using a tree distance parameter. Based on the matching probabilities of the previous theoretical model, DIRphy calculates a score for each event of "Inversion" and "Species-specific adaptation", representing its probability to occur (see methods for details). However, for this paper, we will mainly focus on the inversion events.
The output of DIRphy is a list of positions above a defined threshold. When the organism grouping is manually selected, the script calculates both the observed and the expected probability of inversion between the specified groups in the given tree. Otherwise, when the organism grouping is not specified, the output table shows the observed probability of amino acid inversions given by the grouping that has the highest probability for that position. In the current version of DIRphy, only a binary paralog classification is allowed. DIRphy is released as an open-source project in Github: https:// github. com/OISTpasca/protein-inversions.
Construction of a fish EGFR dataset and identification of inter-paralog amino acid inversions We tested DIRphy in the phylogenetic tree of the Epidermal Growth Factor Receptor (EGFR) in the fish lineage. First, we filtered 88 fish genomes (taxon 41665) from the European Nucleotide Archive (ENA)  [43]  to obtain a dataset of 167 fish EGFR protein sequences. The dataset included all high-quality (N50 > 1Mb) teleost genomes plus one outgroup before the TSGD (spotted gar). From the phylogenetic analysis of this dataset (S2 Fig), three clear duplication events can be observed. The first, most ancient duplication coincided with the TSGD, around the time of the split with gars (~350 mya), and resulted in the separation of the two copies EGFRa and EGFRb. Two more copies were found in most salmonids, corresponding to the salmon-specific whole-genome duplication around ~80 mya  [44] , and one more copy in goldfish, possibly due to the carp-specific whole genome duplication about ~10 mya  [45].
Inter-paralog amino acid inversions in an extended protein dataset In order to obtain a wider view on inter-paralog inversion events, we tested our algorithm on an extended dataset of 54 duplicated protein trees found in teleost proteomes. The protein trees were selected among those having two proteins copies in at least 80% of Cypriniformes and 60% of other teleosts, and forming two separated clusters. As for the EGFR case, we searched inter-paralog inversion events and specie-specific adaptations between these two clades of fish. We observe again a distribution of scores with an extreme-value shape (Fig  5A ). Comparing inter-paralog inversions events and specie-specific adaptations, we found a little difference between the background distributions. Therefore, the background distribution of both events provides the same reference score for the identification of outliers.
The 99 th percentile of 0. 07 shows that the number of inversion events resemble the one from the simulated evolution experiment rather than the EGFR example. In our reduced dataset, most proteins have less than 5 positions with a high score (Fig  5B ). However, two proteins display an unusually high number of inter-paralog inversion events, similarly to the case of EGFR extracellular domain. While most proteins of similar length showed an average of 5 inversions, in EGFR extracellular domain we observed 19 inversions, while in spotted gar's reference ID W5MLG3 and W5M4B2 we observed 18 inversions (Fig  5C ). The two proteins respectively correspond to human orthologs KANK2 and JAK2. At least half of the positions do not appear to have a substantial modification of the amino acid physicochemical properties (e.
Conclusions In conclusion, we have observed an event in paralogs that lead to the inversion of functional residues. This new event that we named inter-paralog inversion has been described by a theoretical model and validated by literature and bioinformatics studies. Inter-paralog inversions are a distinct case of parallel evolution. While parallel amino acid replacements have been observed to be fixed at a rate comparable to the one of neutral replacements  [52] , we observe few inter-paralog inversions in the datasets we analyzed. Previously, an attempt to detect parallel and convergent evolution in large protein families with many duplications was already made by Von Der Dunk & Snel  [53]. However, our study focuses on the distinct case (asymmetric divergence) where the diverging paralogs converge to the same phenotype.
In such a case, neutral substitutions related to the ancestral relationship of a protein can be filtered out to better search for functional adaptations. To the best of our knowledge, we are the first to identify and describe the inter-paralog amino acid inversion event. These inversions are potentially exploited for functional divergence and, if missed, might lead to the wrong classification of proteins to functional groups. We provide a general tool, named DIRphy, to identify interparalog inversions in a large protein dataset. DIRphy can be easily integrated in an existing pipeline of protein annotation to improve functional annotation and provide the positions that might have been overlooked by other functional site prediction methods. On a large-scale analysis, DIRphy has the potential to provide insights into which pathway is affected by the inter-paralog amino acid inversions.
Fish dataset To test the inter-paralog inversion score, we generated a fish genome dataset. First, we downloaded all genomes from the European Nucleotide Archive (ENA) belonging to taxon 41665 (Actinopterygii). Through this method, we obtained 88 fish genomes. Next, we downloaded the pre-annotated fish EGFR protein sequences from the ENSEMBL database  [57]  and used them to build an HMM profile with the HMMER package  [58]. The HMM profile was used as a query in Augustus package suite  [59]  to search for EGFR related genes in the fish genomes. We then filtered out interrupted CDS, sequences clustering with other ErbBs in a phylogenetic tree, and sequences with an aberrant branch length in the non-synonymous codon tree. After this procedure, we obtained 167 fish EGFR protein sequences.
Simulated evolution We ran a simulated evolution experiment using an in-house pipeline based on the Pyvolve python package  [68]. The pipeline simulated an evolutionary pathway of 5000 random amino acids on the fish EGFR phylogenetic tree, using the same model of evolution and evolutionary rates that were used to construct the tree (JTT with rate heterogeneity)  [69] [70] [71]. The simulation generated an output alignment that was used to run the DIRphy pipeline, to calculate the base probability of an inter-paralog inversion.
Bootstrap To perform the bootstrap, we used an in-house Matlab script. We selected from the fish EGFR DIRphy prediction the 19 sites with a score higher than the 99 th percentile of the simulated evolution scores. We calculated a phylogenetic tree for the full alignment and the inverted sites alignment using the neighbor-joining algorithm  [72]  and the BLOSUM80 matrix  [73]. The similarity distance between trees was calculated using the method described in Nye et al. [50]. We then performed 500 bootstrap alignments for each set of alignment lengths: full, 250, 100, 20. We excluded columns with 90% or more gaps and repeated the sampling whenever one sequence did not have at least one non-gap position. For each bootstrap alignment, we generated a tree, then calculated the distance to the full tree and inverted sites tree.
Molecular dynamics We generated the models for the molecular dynamics simulations using alpha fold 2. 0 multimer  [74]. The sequence of Oryzias latipes (olat) and Sinocyclochelius anshuiensis (sans) EGFRa and EGFRb extracellular domains were extracted from the MSA used in the previous analyses. The sequences for the EGF ligands were extracted from the ENSEMBL database and confirmed using manual alignments and the sequences reported in Laisney et al. [41]. The models were cut of the disordered regions (pLDDT score < 50) at the N-and C-terminal of the proteins. We performed the molecular dynamic simulations using the same parameters and scripts as in our previous paper  [75]. In short, we used Gromacs 2020. 1  [76]  and charmm36--mar2019 force field  [77]. We solvated and neutralized the system with NaCl atoms in a dodecahedral box, then we performed energy, temperature, and pressure equilibrations.
Extended dataset The extended dataset was obtained from 70 reference fish proteomes in UNIPROT database  [81]. We used BLAST  [82]  to find all hits in each proteome using as query the duplication outgroup (Lepisosteus oculatus) proteome. The top 5 hits from each proteome were used to generate a MSA with MAFFT and a phylogenetic tree with IQTREE. Then, we selected 54 trees in which a clear and even duplication event with at least 80% of Cypriniformes and 60% of other Actinopterygii were present in two copies and evenly split in two groups. This dataset was then used in the subsequent analysis. S7 Fig. Molecular dynamics of two fish EGFR duplicates in complex with the cognate ligand. Four simulations of 100 ns of the alpha fold model of EGFRa and EGFRb in complex with EGF were performed for Oryzias latipes (olat) and Sinocyclocheilus anshuiensis (sans) proteins.
These BN formulations are all based on discrete variables: continuous variables (e. g. , IM) are discretized into interval sets in order to be combined with variables that have discrete states by default (e. g. , damage states of a component). More recent efforts have focused on solving this issue by improving data structures in discrete BNs and reducing memory storage via conditional probability matrices  (Byun et al. 2019 ). This matrix-based BN framework has been further generalized by  Byun and Song (2021)  to be applied to multi-state systems. Thanks to the decomposition into minimum link sets (MLSs) and the identification of super-components (i. e. , sub-sets of components that are only in series or parallel),  Applegate and Tien (2019)  have also proposed a computationally efficient approach that has the capacity to deal with interdependent infrastructure systems.
Therefore, pending the development of ad hoc BN algorithms that are able to address some of the scalability issues, it is proposed here to adopt a more pragmatic approach based on a sampling inference algorithm (i. e. , Monte-Carlo Markov-Chain sampling). This procedure, implemented in the OpenBUGS software  (Lunn et al. 2009) , has the benefit of avoiding some of the scalability issues and of considering different types of variables (continuous and discrete). The objective here is not to introduce new inference algorithms, but rather to exploit state-of-the-art techniques in order to demonstrate the use of BNs in an operational capacity during the rapid response phase. To this end, the following features are put forward in the proposed loss updating procedure: • Joint estimation of the probabilistic distribution of damage to residential buildings and connectivity loss of a road network; • Integration of two types of observations (i.
Proposed approach to update losses from observations This section describes the procedure that is put forward to update losses in a rapid response context, by making use of various types of field observations. First, the main principles are outlined, along with the datasets and models that are required for the updating. Then, the process to conceptualize the physical road network into an abstract Super-Network is detailed. Finally, the implementation of the BN in the OpenBUGS software  (Lunn et al. 2009 ) is discussed.
Main principles of the loss updating procedure The proposed loss updating procedure, summarized in Fig. 1 , is articulated around two main types of data: • 'Static' data, which includes the conventional models and datasets that are needed to perform conventional risk assessment: knowing the characteristics of the earthquake Fig. 1  Proof-of-concept of the procedure for the rapid earthquake loss assessment of built areas and infrastructure systems (magnitude and location), a GMM is applied to estimate the IM distributions at the locations of the exposed elements, accounting for soil amplification factors. Vulnerability models and fragility curves are also used to predict the damage probabilities of exposed elements, given the IM distributions. Finally, in the case of a road network, the knowledge of the graph topology is also needed in order to access system performance measures such as the network connectivity between two points  (Argyroudis et al.