dataesr/scientific-paragraphs-categorization huggingface dataset:
In that context the WGAN is trained with mock images from simulations. The trained model is then confronted with real observations from the HST. The authors compare then the anomaly scores from both datasets and conclude that the neural network struggles to reconstruct some of the observed galaxies, meaning that they do not exist among the simulated galaxies. Zanisi et al. (2021)  also explores whether anomaly detection approaches with deep learning can be used to compare galaxy images from cosmological simulations to observations. They use however a different approach based on the Autoregressive Model pixelCNN. Similar to GANs, pixelCNN is a generative model which can be use to learn the probability density function of data and generate new samples. However it provides an explicit expression of the likelihood function built in an Autoregressive fashion; that is, the values of a given pixel are determined based on the values of the previous ones.
This is usually very challenging given that neural network models are usually parametrised by a large number of parameters. There is one work in astrophysics attempting this by  Cranmer et al. (2020). The authors train a GNN in a supervised manner to predict the properties of some dataset encouraging a sparse representation by the neural network. They then apply symbolic regression to the trained model (Figure  39 ). The authors show that they are able to recover for example some known Newtonian laws by predicting the movement of particles. More interestingly, they discover a new analytic formula which can predict the concentration of dark matter from the mass distribution of nearby cosmic structures. The formula is learned by applying symbolic regression to a GNN which learned the properties of a Dark Matter only simulation.
Summary of Deep learning for discovery • Deep learning has been explored as a discovery tool. The main motivation is that future big data surveys are too large and too complex for efficient human-based exploration. Deep learning is therefore mainly used for visualisation and anomalyoutlier-detection. The transient astronomy community has been a particularly active field on this front in preparation for LSST. • As opposed to previous applications, these applications rely on unsupervised deep learning. There is a variety of different approaches which have been tested: Autoencoders, Generative Models-GANs, VAEs, Autoregressive Flows. Self-supervised approaches using contrastive learning have also started to be used for this task. • For visualisation, the usual approach is to use deep learning to obtain a low dimensional representation of the data which can be explored more easily.
The most likely reason for the limited constraining power of the CNN analysis comes from the low-resolution of maps used in the analysis (HEALPix nside=1024), but available non-Gaussian information content when baryonic systematics are taken into account is also an open question discussed below. Jeffrey, Alsing, & Lanusse (2021)  performed an analysis of the DES Science Verification (SV) data using a slightly different approach to previous works. Instead of training a CNN for regression, the authors introduced an information loss which explicitly trains the network to compress the input lensing maps into a low dimensional (asymptotically) sufficient statistic, that can further be used for inference with a Likelihood-Free Inference approach. More specifically, they used a Variational Mutual Information lower bound to train the model, which relies on using a Normalising Flow (NF) to approximate the posterior distribution on cosmological parameter from the low dimensional output of the convolutional compressor network, and training both models jointly as to minimise the negative log likelihood of the NF (Figure  30 ).
Once trained under this information loss, the model can be applied to data, and a robust estimate of the posterior was achieved by Neural Likelihood Estimation using the pyDELFI package  (Alsing et al. 2019). Compared to previous papers, this approach has asymptotic optimality guarantees, and does not rely on any Gaussian assumptions for the likelihood of the summary statistic. In this paper, the authors found consistent but tighter constraints with this approach compared to a power spectrum analysis, but the constraints remained very large due to the small size of the SV dataset. One question remains unclear, however, regarding the amount of additional information deep learning can extract over the power spectrum or simpler higher-order statistics, when systematics like baryonic effects are taken into account.
This suite of simulations therefore not only allows the study of the dependency between cosmological parameters and a given set of astrophysical systematics, but also can be used to check the robustness to an assumption of a particular baryonic feedback model (IllustrisTNG or SIMBA). On this dataset, the authors demonstrated that not only does the total matter distribution contain significant cosmological information accessible by deep neural networks, but also baryonic fields such as the H I density  (Villaescusa-Navarro et al. 2021a) , and that even individual galaxies bare some imprints of cosmological parameters  (Villaescusa-Navarro et al. 2022). The question however is: can this information be retrieved under the uncertainties of the baryonic model? In  Villaescusa-Navarro et al. (2020) , the authors illustrated on an analytically tractable toy model that a Neural Network can be trained to optimally marginalise over baryonic effects as long as the training data left the associated parameters free to vary according to a given prior.
While promising, this result didn't necessarily imply that this implicit marginalisation would be robust to a change in baryonic model. To investigate precisely this question, Villaescusa-Navarro et al. (  2020 ) trained a CNN on 2D projected density fields from the CAMELS dataset to regress ( m , σ 8 ). They showed that models trained on IllustrisTNG lead to almost unbiased results on SIMBA and vice-versa, implying that in the process of learning a summary statistic that marginalises over baryonic effects, the neural networks are discarding the part of the signal affected by baryons, and are therefore no longer extremely sensitive to the details of the modelling of those effects. This result remains of course limited, but is very encouraging for the analysis of data.
Using this normalisation, we observe several interesting trends. Works using deep learning for galaxy surveys, represent roughly >5% of all works focusing on galaxies. This is not a large fraction but it is still remarkable given the relatively recent emergence of deep learning. They receive on average ∼1. 5 times less citations per publication. It suggests that the impact of deep learning papers remains moderate as compared to the average. We might speculate about possible reasons. One possibility is that most of the works making use of deep learning are thought in preparation of future big data surveys which have not arrived yet (e. g. Euclid, LSST). The works are therefore more a demonstration of feasibility. It could be argued that existing surveys such as DES or SDSS for example are already good targets for data-driven science.
This is certainly true, and as we have seen in this review, there are many works targeting these surveys. However, the sizes and dimensionality of current surveys still allow one to use relatively optimised codes and leave some room for some manual checks. This is also supported by the trends observed when the deep learning papers are divided by topic. The highest citation rate is measured for works focusing on simulation (i. e. emulation of cosmological simulations), which by definition do not require new observational data. Publications focused on unsupervised discovery, which strongly rely on new data being available, present the lowest impact, although they show a strong increase in numbers (Figure  47 ). It could also be that the technology is still too young, and it is just a matter of time that the impact increases.
Small (and biased) labelled datasets A major challenge in applications of deep learning for astronomy is the lack of large enough labelled data sets to train supervised Table  2. Major challenges that deep learning works applied to astronomy might suffer and that will need to be addressed in the coming years. We also provide elements of solutions already being explored along with the corresponding references. The 'all galaxy evolution' group includes an automatic search of all publications in the field of galaxy formation (with or without deep learning). deep learning models. This issue has been highlighted multiple times during the description of works. We have seen in Section 3 that deep learning was first applied to classification of galaxy morphologies. This was mainly triggered by the fact that large samples of labelled images existed from previous citizen science efforts.
A similar behaviour is observed in the radio domain. However, the vast majority of the data are unlabelled preventing the deployment of supervised deep learning methods. Although this issue is common to many disciplines, it is particularly critical in astronomy since the properties of the images change depending on the instrument used. Therefore, a sample of labelled images from one observational dataset cannot directly be exported to another survey, hampering the use of deep learning. We notice however, that large dataset are not always needed. Some works (e. g. Ackermann et al. 2018; Walmsley et al. 2019 ) have demonstrated that reliable results can be obtained with small training samples. This is probably a consequence of the relatively limited complexity of astronomical images compared to natural images.
Nevertheless, reducing the amount of labels is usually desirable for most supervised applications. There exist several types of solutions listed in Table  2. An obvious one is to manually label more data. It is however very time consuming and cannot be done for every new survey. Active learning approaches allow to label only the more informative examples for the deep learning model, hence reducing the needed time. Active learning has been particularly explored in the framework of galaxy morphology (e. g. Walmsley et al. 2020). Another fairly straightforward solution is Transfer Learning. This is usually done by training a model in a similar dataset to the target dataset for which labelled data exist. The neural network weights are then refined with a small sample of labelled examples from the target sample.
The underlying idea is to first compute some meaningful representations of the data in an unsupervised way and then use the obtained representations to train a supervised network. Because the self-supervised step filters out non-informative features, the amount of needed examples for training is reduced. Hayat et al. (2021)  and  Sarmiento et al. (2021)  have recently explored self-supervised learning for galaxy morphology, photometric redshifts and galaxy kinematics respectively. Contrastive self-supervised learning can also be potentially employed to reduce the gap between simulations and observations as well as Autoencoder representations. In addition to the size of the labelled datasets, a major challenge is how representative are those. A common problem in astronomy is that the sample for which labels are available does not always overlap with the inference datasets.
Benchmarking and deployment A final challenge which has not been discussed much in the literature so far is related to how the different approaches can be robustly compared. As we have thoroughly described in this review, the past years have witnessed an emergence of a large number of deep learning methods applied to a diversity of scientific topics. In many cases, the results are shown for a specific dataset, with a specific configuration, which makes it hard to compare with existing approaches. The ML community has been using since many years, what is called standardised datasets. These are common datasets which are publicly shared and on which any new approach is usually tested. This benchmarking approach has been an important channel for progress in the community. The astronomical one is not used to this type of approach and therefore, with some noticeable exceptions (e.
performed a comparison of several anomaly detection algorithms-isolation forests, on-class SVMs, Gaussian Mixture Models and Local Outlier Factor-to identify outliers in the Zwicky Transient Facility (ZTF). See also the work by Martínez-Galarza et al. (2021) which used decision trees and manifold learning. Pruzhinskaya et al. (2019) uses Isolation Forests as well on a set of features derived from the light curves using interpolation with Gaussian Processes. In the following, we will however focus on efforts relying on deep learning. Villar et al. (2021b) used a Variational Recurrent Autoencoder (VRAE) network described in Villar et al. (2020) to identify anomalous light curves from the simulated PLAsTiCC dataset.
Acknowledgements. MHC acknowledges financial support from the  State Research Agency (AEI-MCINN)  of the  Spanish Ministry of Science and Innovation  under the grants " Galaxy Evolution with Artificial Intelligence " with reference  PGC2018-100852-A-I00 , from the  ACIISI ,  Consejería de Economía ,  Conocimiento y Empleo del Gobierno de Canarias  and the  European Regional Development Fund (ERDF)  under grant with reference  PROID2020010057 , and from  IAC  project  P/301802 , financed by the  Ministry of Science and Innovation, through the State Budget  and by the  Canary Islands Department of Economy , Knowledge and Employment, through the  Regional Budget of the Autonomous Community.
In addition to the molecular biology strategy for SrT assays, we have generated a bioinformatics pipeline targeting the retrieval of the Gibson sequence flanked by two molecular barcodes prior to the alignment to the reference genome, the mapping of genomic features, and the reconstruction of a spatial coordinates matrix associated with the retrieved read counts per transcripts (Figure  1F ). The outcome of this primary analysis is processed by our previously described tool, MULTILAYER, which is able to normalize the spatial read-count levels, identify differen-tially expressed genes in a local context, detect gene co-expression patterns, and perform molecular tissue substructure partitioning. 6, 7  This double-barcoding strategy has been used for manufacturing DNA arrays composed of 2,048 unique DNA probes (32 3 32 spots printed twice in an interstitial manner; Figure  1G ).
Bulk transcriptomics for human brain organoids For Bulk RNA-seq transcriptomics, a human brain organoid cultured during 4 months has been dissociated in TRIzol RNA isolation reagent (Thermo Fisher Scientific 15596026). Extracted RNA has been processed with the NEBNext Ultra II RNA Library Prep Kit for Illumina (E7770). hiPS cells were also processed with TRIzol RNA isolation reagent and NEBNext Ultra II RNA Library Prep Kit. Libraries were sequenced within the French National Sequencing Center, Genoscope (150-nt pair-end sequencing; NovaSeq Illumina). Both bulk human brain organoid and hiPS control sequenced samples were aligned to the human reference genome (hg19; Bowtie 2. 1. 0 under default parameters). Mapped reads were associated with known transcripts with featureCounts. 17  Differential expression analysis between bulk human brain organoid and iPS control readouts was done with DESeq2 R package.
Bioinformatics processing Primary analysis has been performed with our in-house developed tool SysISTD (SysFate Illumina Spatial transcriptomics Demultiplexer: https://github. com/SysFate/SysISTD). SysISTD takes as entry paired-end sequenced reads (fastq or fastq. gz format), and two TSV files, the first one containing the sequence of the molecular barcodes associated to the rows or column in the printed arrays and the second file presenting the physical position architecture of the spatial barcodes. SysISTD searches for the Gibson sequence (regex query), then for two neighboring barcodes. Paired-reads presenting these features were aligned to the human genome (hg19) with Bowtie2, and known transcripts were annotated with featureCounts. 17  As outcome, SysISTD generated a matrix presenting read counts associated to physical coordinates in the array in columns and known transcripts ID in rows.
To focus the downstream analysis to the physical positions corresponding to the analyzed tissue, we used an in-house R script taking as entry an image of the DNA array scanned with the TRICT filter after the reverse transcription step. This image reveals the presence of the fiducial borders and the cDNA within the tissue. Specifically, we upload to R a croped image within the fiducials (imager package) and we use the ''px. flood'' function to retrieve the pixels associated to the tissue. Finally we applied a pixel to gexel coordinates conversion prior to cross this information with the outcome of SysISTD. The ''tissue-focused'' matrix presenting read counts associated to spatial coordinates in columns, known transcripts in rows was processed with our previously described tool MULTILAYER. 6, 7  For human brain organoids processed in Figure  1 , tissue partitioning has been performed with R4 contiguous gexels per pattern; Tanimoto Similarity threshold: 25%; while for the 3-dimensional tissue reconstruction in Figure  3 , tissue partitioning has been performed with R3 contiguous gexels per pattern; Tanimoto Similarity threshold: 10%.
To compare contiguous sections, the ''batch option'' of MULTILAYER has been used, allowing to compute spatial classes in addition to spatial communities. Spatial classes, Spatial communities and tissue relationships were visualized within Cytoscape. 3-dimensional view of upregulated genes across sections were generated with an in-house R script (library ''rgl''). Prior visualization, spatial gexels across sections were aligned relative to the center of the left human brain organoid (L-hBG) and the angle between the horizontal axis and a vector between the centers of the L-hBG and the R-hBG. Pseudo coordinates were computed by rotating tissues by the aforementioned angle and by centering the zero coordinate to the center of the L-hBG. Cell/tissue type enrichment analysis has been performed with EnrichR 18 with the ARCHS4,  19  CellMarkerAugmented 20 and Panglao  21  databases.
ACKNOWLEDGMENTS We thank all members of the team  SysFate  for contributing to the discussion of this project and the Genoscope sequencing platform for their technical support. This work was supported by the institutional bodies  CEA ,  CNRS , and  Universited'Evry-Val d'Essonne ; the  Genopole Thematic Incentive Actions funding  ( ATIGE-2017 ); and the '' Fondation pour la Recherche Medicale '' (FRM; funding  ALZ-201912009904 ), as well as the  Institut National Du Cancer  (INCa: Funding  2020-181 ).
All raw datasets generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO: http://www. ncbi. nlm. nih. gov/geo/) under accession number GSE223020. Furthermore, processed spatial matrices as well as the corresponding scanned images are available herein: Mendeley Data: https://data. mendeley. com/datasets/ptv86nczbv. d Primary analysis has been performed with our in-house developed tool SysISTD (SysFate Illumina Spatial transcriptomics Demultiplexer: https://github. com/SysFate/SysISTD; Zenodo: https://doi. org/10. 5281/zenodo. 8180576). d Any additional information required to reproduce this work is available from the lead contact. Report Continued Data and code availability REAGENT or RESOURCE d SOURCE IDENTIFIER dTTP solution Thermo Fisher Sientific R0171 dCTP-Cy3 Cytivia PA53021 20X SSC buffer Sigma Merk S6639 Para-formaldheyde Life Technologies 28908 PBS Life Technologies 70011-036 Cadre genetique, 25 mL (1,0 3 1.
0 cm) Thermo Fisher Scientific AB0576 Bovine Serum Albumine Sigma Merk A9418-10G SDS buffer Sigma Merk 71736 NEBNext multiplex oligos for New England Biolads E7335 illumina index primer set 1 SPRIselect beads Beckman Coulter B23318 Triton 100-X Sigma Merk 93443-100ML DAPI solution Thermo Fisher Scientific 62248 Glycerol Sigma Aldrich G5516-100ML Experimental models: Cell lines Human BJ primary fibroblast ATCC CRL-2522 Oligonucleotides BCr generic primer: Sigma This paper SSSSGACTCGTAATACGACTCACTATAGGG WSNNWSNNVNNNNNNNNACATTGAAGAA CCTGTAGATAACTCGCTGT BCc generic primer: Sigma This paper NBAAAAAAAAAAAAAAAAAAAAWSNNWSN NVNNNNNNNNACAGCGAGTTATCTACAG GTTCTTCAATGT 2 ND strand synthesis primer: Sigma This paper GTTCAGACGTGTGCTCTTCCGATCTG GGGGGGGGH Amplification adapter seq 1 primer: Sigma This paper GTTCAGACGTGTGCTCTTCCGATCT Amplification adapter seq2 primer: Sigma This paper TACACTCTTTCCCTACACGACGCTCTTCCG ATCTGACTCGTAATACGACTCACTATAGGG Software and algorithms SysISTD This paper GitHub -SysFate/SysISTD; Zenodo; https://doi.
org/ 10. 5281/zenodo. 8180576 Multilayer Moehlin et al. , STAR Protoc. https://github. com/SysFate/MULTILAYER; Zenodo; 2021 Sep 14; 2(4):100823. 7 https://doi. org/10. 5281/zenodo. 8180632 https://doi. org/10. 1016/j. xpro. 2021. 100823 Scanning Software Microvisioneer manualWSI Microvisioneer Other DNA pico litter spotter Scienion Sciflexarrayer S3 Cell Reports Methods 3, 100573, September 25, 2023 e2.
The increasing accumulation of pharmaceuticals in aquatic ecosystems can degrade the freshwater quality and endanger human health. In our work, we will discuss the removal of three pharmaceuticals that are most detected in aquatic environments: ketoprofen (KTP), diclofenac (DCF), and sulfamethoxazole (SMX). The adsorption process removed these toxic pollutants on a bio-sourced activated carbon (AC) prepared from walnut shells (Ws) from the Atlas El Kabir -Marrakech region. MEB, elemental mapping, EDX, FTIR, and BET surface area characterized the properties of prepared activated carbon. The activated carbon obtained had a high surface area (2256 m 2 /g). The experimental design methodology to optimize the adsorption process using the AZURAD® software. This approach was applied to remove three pharmaceutical pollutants in an aqueous solution by AC-Ws.
Experiment design The principal aim of the experimenters is to study the parameters influencing a phenomenon and to find the optimal conditions. More concretely, this study was carried out to model the percentage of adsorption (which we seek to maximize) of three pharmaceuticals (DCF, SMX, and KTP) by varying all input parameters. A design of the experiment was developed using AZURAD ® software. For this purpose, we modeled the percentage of adsorption of PPs as a function of pH dose (variable X1), temperature (variable X2), and AC-Ws dose (variable X3). The experimental design and its variables are presented in.
Fu et al. introduce the R package SSDbain, which can be used to calculate the sample size required to evaluate hypotheses using the Approximate Adjusted Fractional Bayes Factor (AAFBF) for one-way ANOVA models, as implemented in the R package bain. The Bayesian ANOVA, Bayesian Welch's ANOVA, and Bayesian robust ANOVA are available. Using the R package SSDbain and/or the tables provided in this paper, researchers in the social and behavioral sciences can easily plan the sample size if they intend to use a Bayesian ANOVA. Including a neural level of analysis, Ayabe et al. study the development of diagram use competence following the provision of task-appropriate instruction. They focused on.