dataesr/scientific-paragraphs-categorization huggingface dataset:
Generation of a core nuclear decommissioning specific ontology As many of the software tools being integrated into PLEIADES already exist and bring along their own history of terms and concepts, it was concluded that a pure terminology-based approach is not suitable. Hence the idea of a common ontology was developed. An ontology is a collection of concepts and the underlying properties that connect these concepts. PLEIADES has defined an ontology specifically designed to represent nucleardecommissioning projects. The PLEIADES platform and data management will rely on this ontology. The PLEIADES ontology has also been aligned with results from international organizations, ensuring basic compatibility with international knowledge resources (such as communities of practice or databases).
PLEIADES platform architecture specification and development With the objective of increasing interoperability, the PLEIADES platform architecture has been defined and guided by the following criteria: respond in a commonly accepted and flexible way to the technological heterogene-ity of the software tools in the PLEAIDES ecosystem, guarantee that the solution meets data security requirements, while still being demonstrable within the timeframe of the project. The PLEIADES architecture (see Fig. 5 ) aims at maximizing the collaboration between the different software modules. It is expected that this collaboration will not happen through direct communication between the different tools, but rather via data exchange to/from a common database with a specific focus on a system design that will take advantage of the common ontology.
The architecture is divided into the client-side and the server-side infrastructure. The client side includes the interface developed in the domain of each partner's software included in the platform. Expected tools are indicated in Figure  6  (DEMplus R for nuclear, already mentioned in project INNO4GRAPH for example). The server-side infrastructure stores the data and secures the data exchanges. It is divided into the front end (that covers the intelligent features) and the back end (that manages the BIM-like data and ontology-based structured data).
Mid-infrared (mid-IR) observations are powerful in identifying heavily obscured active galactic nuclei (AGN) that have weak emission in other wavelengths. Data from the Mid-Infrared Instrument (MIRI) on board the James Webb Space Telescope provides an excellent opportunity to perform such studies. We take advantage of the MIRI imaging data from the Cosmic Evolution Early Release Science Survey to investigate the AGN population in the distant universe. We estimate the source properties of MIRI-selected objects by utilizing spectral energy distribution (SED) modeling, and classify them into star-forming galaxies (SFs), SF-AGN mixed objects, and AGN. The source numbers of these types are 433, 102, and 25, respectively, from four MIRI pointings covering ∼9 arcmin 2. The sample spans a redshift range of ≈0-5.
The Mid-Infrared Instrument (MIRI) on board the recently launched James Webb Space Telescope (JWST) has the potential to become a game changer in the detection and selection of AGN especially at high redshifts. MIRI has continuous photometry coverage of ∼5-25 μm, reaching an unprecedented sensitivity level about 10-100 times deeper than Spitzer. Besides, thanks to the large aperture size of JWST, the angular resolution of MIRI reaches an FWHM of point-spread functions being ∼0 2-0 8, about 8 times better than Spitzer/ IRAC at similar wavelengths. Indeed, simulations suggest that MIRI is able to capture AGN activity down to a faint level of, e. g. , an Eddington ratio around 0. 01 at z ∼ 1-2 (e. g. ,  Kirkpatrick et al. 2017; Yang et al. 2021b). In this Letter, we take advantage of the MIRI imaging data from the Cosmic Evolution Early Release Science Survey (CEERS;  Finkelstein et al.
2017) , which targets the Extended Groth Strip (EGS) field. We also compile the multiwavelength data available in EGS and perform AGN versus star-forming (SF) classifications with spectral energy distribution (SED) modeling. We produce median SEDs for different source types, respectively, and publicly release these empirical SEDs to facilitate future science. Finally, we investigate the BH accretion density (BHAD) as a function of redshift, based on the MIRI-identified AGN. The structure of this Letter is as follows. In Section 2, we describe and analyze the MIRI imaging data. We also perform AGN selections based on the MIRI and other multiwavelength photometric data. In Section 3, we discuss the physical implications based on our analysis results. We summarize our results and discuss future prospects in Section 4.
SED Modeling Setup We employ CIGALE v2022. 1  (Boquien et al. 2019; Yang et al. 2020 Yang et al. , 2022) )  to perform SED modeling based on the broadband photometry described in Section 2. 1. The adopted CIGALE parameters are summarized in Table  1. For star formation history (SFH), we use a standard delayedτ module (sfhdelayed in CIGALE). We allow the e-folding time and stellar age varying from 0. 5-5 Gyr to 1-5 Gyr, respectively. We employ  Bruzual & Charlot (2003; bc03)  for the simple stellar population (SSP) module, assuming a  Chabrier (2003)  IMF with a solar metallicity (Z = 0. 02). We also include the nebular module (Villa-Vélez et al. 2021) for emission from the HII regions. For the dust attenuation, we adopt dustatt_modified_starburst in CIGALE, which is primarily based on  Calzetti et al.
(2000)  extending to short wavelengths (91. 2-150 nm) with  Leitherer et al. (2002). The allowed range of color excess is E(B -V ) = 0-1 (see Table  1 ). For galactic dust emission, we use the dl2014 module  (Draine et al. 2014). The dust luminosity numerically equals the attenuated luminosity of starlight, because CIGALE strictly follows energy conservation. The  Draine et al. (2014)  recipe models the dust emission with two components, a diffused emission and a photodissociation region (PDR) emission associated with star formation. The two components share the same mass fraction of polycyclic aromatic hydrocarbon (PAH) compared to total dust, and we allow three different values of it (see Table  1 ). The major difference between the two components is the radiation field. The diffuse dust is radiated by the minimum radiation parameter (U min ) set by the user, and the PDR's radiation field ranges from U min to a maximum fixed value of = U 10 max 7.
1); MRS can provide secure spectroscopic redshifts for our high-z AGN candidates, essentially testing our conclusion of the BHAD excess compared to X-ray measurements (Section 3. 2). An alternative approach to probe AGN at high redshifts is mining the existing JWST spectroscopic database (e. g. , NIRSpec Multi-Object Spectroscopy and NIRCam grism slitless spectroscopy), many of which target high-z galaxy candidates. Based on the spectra, the presence of AGN can be identified by searching for broad emission lines and/or narrow high-ionization lines (e. g. ,  Cleri et al. 2023; Kocevski et al. 2023). The spectroscopic selections can potentially probe AGN at the highest redshifts. For example, the recent work of  Larson et al. (2023)  reported a broad-line AGN at z = 8. 679, the earliest BH discovered so far.
Acknowledgments We thank the referee for helpful feedback that improved this work. We acknowledge the hard work of our colleagues in the CEERS collaboration and everyone involved in the JWST mission. G. Y. , K. I. C. , and E. I. acknowledge funding from the  Netherlands Research School for Astronomy (NOVA). K. I. C. and V. K. acknowledge funding from the  Dutch Research Council (NWO)  through the award of the  Vici Grant   VI. C. 212. 036. C. P. thanks  Marsha  and  Ralph Schilling  for generous support of this research. T. A. H. is supported by an appointment to the  NASA   Postdoctoral Program  (NPP) at  NASA Goddard Space Flight Center , administered by  Oak Ridge Associated Universities  under contract with  NASA. This work acknowledges support from the  NASA/ESA/CSA  James Webb Space Telescope through the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Incorporated , under  NASA  contract  NAS5-03127.
The control optimization process includes two loops: a fast evaluation loop and a slow learning loop, see figure  9. The fast evaluation loop is managed by the ControlDesk software and Simulink. For our study, the evaluation loop operates at the sampling frequency (250 Hz). For each control law tested, the time series of the actuation command and the hot-wire signal are recorded and post-processed with MATLAB. The slow learning loop includes the post-processing of the control and the control law update; it is automated with Python and MATLAB scripts. Finally, the whole control unit is supervised by a PowerShell script that automates all the steps of the control optimization.
The starting point of gMLC is MLC based on linear genetic programming  (Brameier & Banzhaf 2006, LGP). Following the genetic programming terminology, the candidate solutions are also referred to as individuals. Like the MLC method, gMLC makes no assumptions on the structure of the relationship between the inputs and the outputs of the controller. The optimal solution needs, however, to be computable, meaning it can be expressed by a finite number of mathematical operations with finite memory. Indeed, the candidate solutions are internally represented by matrices inherited from linear genetic programming. Each matrix resembles a computer program that unequivocally codes a control law. Each line of the matrix is an instruction pointing to basic operations (+, -, ×, ÷, cos, sin, tanh, etc. ) and registers containing constant random numbers and variables (a 1 , a 2 , a 3 , etc.
). The N inst lines of the matrix are then read linearly yielding the control commands as outputs of the first registers. We refer to  Li et al. (2019)  for more information on the internal representation of the control laws. The gMLC algorithm starts with a broad exploration of the control law space with a Monte Carlo sampling (MCS) phase. The MCS generates N MCS random matrices that represent the first set of individuals. The individuals are evaluated and added to the database of all individuals. Then, the algorithm alternates between exploration phases carried out by genetic programming and exploitation phases performed by downhill simplex iterations until a stopping criterion is reached. The role of exploration is to locate new and better minima in the space of control laws with stochastic recombination of the best-performing individuals.
The stochastic recombination is achieved with the genetic operations crossover and mutation. This exploration is much like the evolution phase in the LGP method, however, in the case of gMLC, the concept of population that evolves through generations is generalized by considering all the individuals evaluated so far and stored in the database. Thus, during the exploration phase new individuals are generated by recombining the best among all the previously evaluated individuals. This ensures that no Each exploration phase is followed by an exploitation phase. This step exploits the local gradient information to slide down towards the neighbouring minimum. This is carried out with a variant of downhill simplex for infinite-dimensional spaces introduced by  Rowan (1990)  and referred to as downhill subplex.
After each downhill simplex iteration, the simplex is updated by replacing the least-performing individuals. The downhill simplex steps are iterated until at least N p individuals are generated. The newly generated individuals are then added to the database of all individuals. We emphasize that all the new individuals belong to the subspace defined by the original N sub individuals. The algorithm returns the best-performing control law once the stopping criterion is reached. Otherwise, a new iteration of exploration and exploitation is carried out. The stopping criterion may be a performance threshold or a total number of evaluations when the testing budget is limited. We note the critical intermediate phase of reconstruction between each exploitation and exploration occurrence. Indeed, the new individuals generated by the downhill simplex are linear combinations of individuals without a matrix representation, which is essential for genetic recombination during the exploration phase.
Thus, a matrix reconstruction is performed for each linearly combined individual by solving a secondary optimization problem. The goal is to derive a matrix that translates into a control law that has the same response as the linearly combined one. Such a problem is similar to a surface fitting problem, which we solve with linear genetic programming. The reconstruction phase builds a matrix representation for the linearly combined individuals so that genetic operation can be performed. For more information on the gMLC algorithm, we refer the readers to Cornejo Maceda et al. (2021). Figure  6  schematically illustrates the different phases of the gMLC algorithm and the learning principle in the control law space. The MATLAB implementation of gMLC employed for this study is freely available from the website https://github.
Closed-loop control of the narrow-bandwidth regime In this section, we describe the best control law derived by gMLC that mitigates the self-sustaining oscillations of the mixing layer for the narrow-bandwidth regime. In the following, the notations for the control law, cost and standard deviation associated with the learning on the narrow-bandwidth regime are marked by the superscript I. Figure  10  depicts the cost for the 1000 evaluated individuals during the optimization process. The individuals from the MCS and exploration phases are sorted following their cost, as there is no direct causal relationship between two successive individuals, while the individuals generated during the exploitation phase are depicted in the order of their evaluation as each individual depends on the previous one.
The dynamics of intermittency is chaotic  (Lusseyran et al. 2008 ) with the appearance of long time scales that are demanding from the point of view of machine learning. The goal is again to stabilize the flow by reducing the oscillations of the mixing layer, but this time in the case where two modes compete, as described in § 2. 5. This constitutes a challenging problem as gMLC needs to learn a control law able to control two modes simultaneously. For the gMLC optimization, the same parameters as for the narrow-bandwidth regime have been employed, see § 4. 2. In the following, the notations for the control law, cost and standard deviation associated with the learning on the mode-switching regime are marked by the superscript II. Figure  14  depicts the cost of the individuals evaluated along the learning process.
955 A20-15 https://doi. org/10. 1017/jfm. 2022. irretrievably lost. The best individuals to recombine are selected following their cost function. The selection is carried out by the tournament method with a tournament size equal to 7 for 100 individuals, following the Duriez et al. (2017) recommendation. The tournament size is scaled with the number of individuals in the database in order to keep a 7/100 ratio; N p new individuals are built at each exploration phase by recombining the best individuals of the database.
Acknowledgements. The authors thank  N. Deng  and  L. R. Pastur  for fruitful discussions and enlightening comments, and  Maxime Forte  of the  Multi-Physics Department for Energy (DMPE)  of  ONERA  for the equipment loan and the technical exchanges on the DBD actuator. We are particularly grateful to the anonymous referees for their constructive advice and for proposing additional corroborating experiments. Funding. This work is supported by the  National Science Foundation of China (NSFC)  through grants  12172109  and  12172111 , by Guangdong province, China, via the  Natural Science and Engineering  grant  2022A1515011492 , by the  Shenzhen Research Foundation for Basic Research, China , through grant  JCYJ20220531095605012 , by the  French National Research Agency (ANR)  via  FLOwCON  project 'Contrôle d'écoulements turbulents en boucle fermée par apprentissage automatique' funded by the  ANR-17-ASTR-0022 , the  iCODE Institute research project of the IDEX Paris-Saclay  and by the  Hadamard Mathematics LabEx (LMH)  through the grant number  ANR-11-LABX-0056-LMH  in the ' Programme des Investissements d'Avenir '.
, 2015; Eng et al. , 2019; Lubeck et al. , 2014; Zhang et al. , 2021). The adult fruit fly Drosophila melanogaster is a widely used model for cell biology, neurobiology, physiology and behavior  (Sokolowski, 2001; Grenier & Leulier, 2020; Benton, 2022). The recent completion of the entire adult fly cell atlas (FCA)  (Li et al. , 2022) , and the aging fly cell atlas  (Lu et al. , 2023)  provided an in-depth annotation of various cell types, however without information about the localization of these cells or their mRNAs. In addition, several single-cell clusters were left unannotated although marker genes were identified. Here we applied Molecular Cartography (Resolve Biosciences) to create a FISH-based spatial transcriptomics dataset of the entire head and body of adult Drosophila melanogaster flies.
While embryonic development has already been studied using SRT  (Wang et al. , 2022) , we present the first high-throughput single-molecule FISH-based SRT dataset of the various tissues of the adult fly. We compare the data with existing RNA atlases, using the spatial data to validate annotations in the body and identify unknown clusters in the brain. Furthermore, we use the data to reveal subcellular mRNA localization patterns in the flight muscle cells and regionalized RNA expression in the head. Expression patterns of 100 genes in the brain and 50 genes in the body can be visualized interactively using TissUUmaps  (Solorzano et al. , 2020)  at www. spatialfly. aertslab. org.
Spatial transcriptomics allows the identification of cell types in the body The genes for the body datasets were selected to cover a wide range of different cell types. The expression of elav and Syt1 is marking neurons, both in the central and peripheral nervous system at the expected locations (Fig. 2a ). The glial marker repo shows the location of glial cells across the body (Fig. 2a ). The expression of different trypsins is unique to the digestive system. Interestingly, alpha-and betaTrypsin show distinct patterns, with betaTrypsin localized on the inner side of the gut, while alphaTrypsin is localized more distally, suggesting a subcellular localization of these trypsin isoform coding mRNAs to apical or basal regions of the gut enterocytes (Fig. 2a ). Using the fat body marker AkhR and the oenocyte marker FASN2, different populations of fat tissue and oenocytes were identified in the abdomen of the fly at the expected locations.
2e ). Our SRT data confirm this co-expression, showing the labeling of the main gland cells in the abdomen (Fig. 2f ). Next, we performed a more detailed comparison of our SRT data with the FCA scRNA-seq. We calculated the correlation between genes in both SRT and scRNA-seq to find if co-expression is maintained in both datasets (see Methods). In general, gene-gene correlations between SRT and scRNA-seq match significantly (r=0. 69, p<1e-100), with no major disagreements between the two (Fig. S4a-b ). This indicates that in general SRT data corroborate results obtained from scRNA-seq. To study gene-gene relations in more detail, we devised an algorithm that calculates for each gene its colocalization with other genes (see Methods) (Fig. S5a-b ). This colocalization matrix was then clustered to find groups of genes that are co-expressed.
These groups match with the major cell types, while also highlighting genes expressed in male reproduction to be specifically localized. In addition, this analysis method grouped neuronal and muscular genes into the same cluster, consistent with the close proximity between these two cell types  (Fig S5c). In conclusion, cell-type-specific marker genes can be used for the unambiguous identification of cell types in SRT data. Furthermore, we highlight both high specificity and high reproducibility across adult fly body sections in our data. Together, this dataset represents the first SRT dataset for the adult fly body. Class assignment shown on the right, based on maximum score. Genes used: Neurons: elav, Syt1, Sh, acj6, ey, VAChT, Gad1, VGlut, nAChRalpha7; male repr. : Awh, eyg, svp; epithelia: grh, alphaTry, betaTry, hth; heart: tin, Hand.
, 2018; Li et al. , 2022; Pech et al. , 2023). First, we checked the expression of C15, acj6, Oaz, caup and unpg, known markers for olfactory projection neurons (OPNs). OPNs are neurons that relay information from the antennal lobe to downstream processing centers including the mushroom body and the lateral horn. The co-expression of all markers is only detected in the most anterior samples, corresponding to the location of OPN cell bodies (Fig. 4b ), again showing the precision of our method. Next, we investigated the expression of markers for perineurial glia (repo, moody, Indy, Vmat), which form the blood-brain barrier (BBB) and find them at the periphery of the brain, the expected location (Fig. 4c ). We compared gene-gene relationships, by calculating gene-gene correlations in the spatial dataset and the single-cell dataset (Fig.
Similarly, the neuropeptides Ilp2 and Pdf show a weak overlap with the DAPI staining, instead forming patterns (Fig. S7e ). Thus, SRT can recapitulate the location of known cell types in the fly brain by using established marker genes and additionally identify expression at novel locations for some of these. Compared with other organs, the cell type diversity of the fly brain is extremely complex. A key challenge in scRNA-seq is the annotation of clusters to cell types. In our scRNA-seq dataset, there are 188 distinct clusters, of which only 83 are annotated today. Recent efforts to map the fly brain through EM and connectomics studies have identified 4,552 morphological cell types  (Schlegel et al. , 2023). SRT can provide a bridge between scRNA-seq studies and morphology-based studies. As such, it becomes possible to annotate unknown clusters based on the spatial localization of their marker genes.
To pave the way towards such integration, we focused on unannotated clusters in our brain dataset. It is important to note that current neuronal nomenclature is based on neuropils (axons and dendrites) and not on the location of the neuronal nuclei. Therefore, we used the established neuropil nomenclature to describe the location of the detected nuclei, but this does not necessarily mean that the axons of these cells also project there. To identify the location of unknown clusters in the brain, we started with Fer1 expression, as it is a strong marker for cluster 120 in our scRNA dataset  (Li et al. , 2022). Using SRT we show conclusively that these cells are located in the central brain, in the ventral gnathal ganglion and saddle, and the superior intermediate protocerebrum (Fig. 4d ). Similarly, we show that AstC expression (cluster 122) is limited to the dorsal part of the central brain, consistent with its expression in DN1 and DN3 neurons  (Díaz et al.
, 2019)  (Fig. 4d ). Next, we investigated the expression of otp (clusters 30 and 62), which marks nuclei close to the lateral horn and the superior neuropils (Fig. 4e ). Finally, we found the expression of vg (cluster 20) to be limited to the boundary region between the optic lobe (OL) and the central brain (CB) (Fig. 4e ). These results confirm that combining scRNA-seq with SRT technologies can indeed identify defined locations of formerly unknown cell clusters in the fly brain. Next, we wanted to find genes enriched to specific spatial locations in our brain SRT data. To do so, we assigned mRNA molecules to spots using a similar 5 µm resolution grid as done above for the body data. By performing unsupervised clustering using all mRNA localizations from all datasets together, we identified 23 clusters (Fig  4f ).