dataesr/scientific-paragraphs-categorization huggingface dataset:
Weakly-supervised learning • Reducing the need for fine-grained annotations by using weaker forms of supervision, such as image-level, point-level or bounding boxes labels. • It enables training on larger datasets, as weak annotations are often easier and cheaper to obtain. • The model's robustness and performance can be highly affected by the quality of the training data. • Designing effective high resolution and accurate model can be challenging with lowlevel annotations. • Model's generalization capability can be hindered by the quality of labels and uncertainties.
For instance, they are being implemented in target classification with SAR imagery  [39]  and scene classification using Sentinel 1&2  [78]. Similar to TL, Semi-SL has demonstrated the highest frequency in the reviewed papers of this study, within the field of remote sensing image classification. In fact, many unannotated samples are available in a vast remote sensing image scene that can be effectively exploited in a Semisupervised process for improving the model generalization and final classified results. This has resulted in numerous advancements over the past few years in the development of DL frameworks that are based on various methods of Semi-SL for classification of remote sensing imagery. Along these lines, deep active learning was developed and investigated for different remote sensing data classification such as RGB  [79] , hyperspectral  [52] ,  [53] , and polarimetric SAR  [50] , or different applications such as crop type mapping  [54].
For instance, Tao et al, (2015) used stacked sparse AE for unsupervised feature learning combined with SVM classifier for Hyperspectral scene classification  [90]. A joint multiple parallel SAE and CAE was presented in  [91]  for unsupervised land cover classification using multi-modal VHR hyperspectral, LiDAR, and digital surface model (DSM) dataset. Radman et al. (2023)  proposed an unsupervised burnt area mapping using Sentinel-1 SAR data, where a CNN model was trained based on combination of a fuzzy c-means clustering and a saliency-guided segmentation  [92].
B. Target, Anomaly, and change detection Anomalies, targets, and changes all follow a similar characteristic in remote sensing datasets, which is their scarce presence compared to the background samples. This makes modelling process so challenging due to the imbalanced distribution of object of interest and the surrounding background. NSDL algorithms can tackle this issue and prevent model failure. Majority of NSDL studies for target detection are focused on TL and Semi-SL, which quantitively is similar to what we observed in studies regarding classification. Various studies investigated TL from pretrained models on large datasets for target detection, such as airplane detection from VHR RGB images using SSD  [93]  or R-CNN  [94]  deep object detectors, which their backbone deep model is a pretrained VGG16.
To address the issues of high complexity and computation cost associated with pretrained models in target detection in VHR imagery, due to their numerous learnable parameters, chen et al. , (2019) presented a compressed deep model  [95]. This model aims to estimate the parameters of the complex model by minimizing the l2 loss between the output of the compressed and complex models. Li et al. , (2022) proposed a CNN with attention transformer model in order to reduce the domain gap between pretrained model on ImageNet dataset and remote sensing dataset for target detection  [96]. A TL framework between satellite multispectral imagery for cloud detection was presented in  [97]. They proposed a convolutional AE model with domain adaptation between Landsat-8 and Proba-V imagery. As mentioned earlier, GANs can improve the model's generalization and robustness with adversarial learning and assist with data augmentation with synthesizing training samples.
using VHR RGB imagery. Xu et al. , (2020) proposed a multi-scale hierarchical active learning algorithm using CNN model for detecting vehicles from large-scale remote sensing dataset  [103]. Xia et al. , (2021)  investigated the incorporation of Semi-SL and CNN for building edge detection  [104]. Liao et al. , (2022)  proposed a joint Semi-SL and TL framework for target detection from SAR imagery  [105]. They trained model with small number of SAR and abundant amount of optical imagery, and then updated the training samples during semi-SL process. The main focus of WSL studies in the field of remote sensing target detection is pixel-level detection of target of interest using low-level annotated data. Li et al. , (2020) presented a WSL convolutional framework for cloud detection from high resolution satellite imagery using image-level annotated data  [48].
For hyperspectral target detection, Yao et al. , (2021) proposed a Self-SL framework with spectral similarity pretext task. Although anomaly detection basically is an unsupervised process, very few studies could be detected through the reviewed database. Among these almost all the studies were focused on hyperspectral imagery. An investigation of convolutional AE for unsupervised hyperspectral anomaly detection can be found in  [61]. Jiang et al. , (2021)  proposed a GAN-assisted unsupervised hyperspectral anomaly detection framework  [112]. In this work, first a salient map of input data is generated, where the approximate anomalous areas are weakly extracted. Then, based on the approximate anomaly and background pixels, a weakly supervised spectral discriminative GAN model was trained for final anomaly detection.
Rao et al. , (2022)  designed a hyperspectral anomaly detection framework based on TL and contrastive Siamese network  [113]. First, model was trained on a dataset with known labels, where the input signals are similar and dissimilar pairs. Then, the model was fine-tuned using the dataset containing anomalous pixels in order to iteratively detecting anomalies. According to our database majority of the change detection works were focused on USL, which demonstrate the high demand of automated change detection systems during the time. similar to the other applications, first DL unsupervised change detection studies were based on automated feature learning using different AE architectures. Zhang et al. , (2018)  proposed a Siamese-like multilayer perceptron network, where the initial change map was obtained by over-clustering, but the final change map was generated after iterative optimization of the labels  [63].
Dong et al. , (2020)  proposed a GAN deep model for reducing the domain difference between bitemporal dataset in a selfsupervised fashion  [123]. They exploited GAN discriminator as pretext learner in order to predict the input patch's time, which is pre-or post-change in this example. A Self-SL ResUnet framework was presented in  [124]. They used contrastive loss incorporated with dual branch AE in order to suppress effect of seasonal changes between bitemporal input images.
Accordingly, recent studies investigated NSDL methods on different types of remote sensing data such as Lidar point cloud, MS, RGB, and multi-modal (e. g. , SAR-optical) registration. Papadomanolaki et al. (2021)  proposed an unsupervised framework that utilizes a fully CNN model to iteratively reduce the distance between two sources of VHR images until they align  [130]. A GANbased model for SAR-optical registration was presented in  [131]. First, a set of synthetic SAR images were generated from their corresponding optical image. Then, the synthesized SAR images and real SAR images were used as training data for SAR-optical matching. The presence of noise is inevitable in the remotely sensed data. While the power and amount of noise varies in different sensors, HS and SAR images generally show higher level of noise.
VII. CONCLUSION AND PROSPECTS This paper provided a comprehensive systematic review and a meta-analysis of the applications and developments of NSDL methods in the remote sensing field. The results demonstrated the increasing trends in using NSDL methods, which is inevitable with increasing the number of satellites and data providers in the era of big EO data. In this study, we reviewed and analyzed 505 papers that have been published in wellknown peer-reviewed journals since 2015. Subsequently, different features, including deep learning architectures, training data requirements, applications, and accuracies, were extracted from these reviewed articles. Eventually, deeper overview and literature review of NSDL methods and their applications in remote sensing was provided. The summary of the key conclusion including the remaining challenges and future directions is as follows: • This study confirmed that the utilization of NSDL methods is increasing in various fields of remote sensing applications.
The results indicate that these methods offer various solutions for the different levels and scenarios of training data. The main reason can be the availability of a vast amount of remote sensing data from various sensors and satellites, making it nearly impossible to prepare suitable annotated data by humans. Notably, insufficient training data can increase the chance of overfitting in supervised deep learning models. • According to the meta-analysis, majority of studies were based on using VHR and RGB imagery. In addition to the rich spatial features of such data, this can be due to the high availability of VHR and HR images with the increasing development and accessibility of drones, surveillance, or even smart phones' cameras along with the availability of high-resolution satellites. This also has led to the availability of many of these type of datasets as benchmarks.
However, field of remote sensing covers > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < various sensors with wide range spatial and spectral resolutions. Recent technological advances in the high availability of remote sensing satellite imagery with very short revisit times and the advent of cloud-based processing platforms such as Google Earth Engine have increased the demand for remote sensing and geospatial data applications. Consequently, as the need for ground truth data is at its highest point, the number of NSDL studies on free datasets such as Sentinel, and Landsat constellations need to be increased. Additionally, there were considerably fewer studies addressing LiDAR or SAR processing using NSDL. LiDAR and SAR, as active sensors, follow different imaging principles than passive sensors.
The data obtained by these sensors are often noisy and full of clutters, which makes their interpretation a cumbersome task. Consequently, preparing manual annotation data from active sensors would be a timeconsuming task with a low confidence level. This emphasizes the importance and applicability of NSDL for such data for preprocessing such as denoising, despeckling, and registration and remote sensing applications such as anomaly or change detection. • Presenting novel and diverse customized deep models have become more feasible with the growing variability of new DL architectures, such as the family of Attention mechanisms and Transformers, GANs, a variety of AE models, as well as various loss functions, such as contrastive and adversarial losses. For instance, AEs due to their capability in automated feature extraction can increase the discriminability of complex remote sensing classes in latent sub-space.
Attentions showed their capability by highlighting the salient areas especially, in solving WSL problems. Furthermore, GANs were useful for data augmentation and balancing the training data in highly imbalanced problems such as change detection. However, as also discussed in  [6] , improving the quality and reliability of the generative models in order to presenting diverse and accurate simulated data would be some of the remaining challenges that need to be carefully considered and addressed in future works. Furthermore, while designing a complex model for non-supervised problems in remote sensing may be effective, it is imperative to pay attention to the efficiency and robustness of the developed model. For instance, as discussed in  [41] , model collapse in deep Semi-SL models on remote sensing data is a common challenge.
Thus, it is essential for future studies to focus on this issue by probably providing a comprehensive analytical and theoretical perspective, as well as designing more efficient frameworks based on feature engineering, efficient architecture or novel loss functions. • As shown in the meta-analysis, NSDL methods were wellreceived in remote sensing studies such as classification and change detection. Although these objectives are the main applications of remote sensing data, NSDL methods have high potentials to be explored in other applications. In general, there is still much more to explore in multi-modal image registration, or fusion, image or point cloud denoising, hyperspectral unmixing, and time-series anomaly detection. • The majority of NSDL methods rely on a small number of annotated training data, and as such, the accuracy and reliability of this data is of utmost importance.
• Currently various sources of data are available, which can be leveraged for NSDL model training. Thanks to the increasing availability of free satellite datasets, a massive amount of unlabeled Earth observation data is currently available. Another source of data can be open data with geo-tags from the internet and social media. While supervised methods do not effectively exploit this abundant pool of unlabeled data, the main challenge for NSDL modelling can be filling the domain gap in the presence of a pool of available multi-modal data. Thus, domain adaptation from another time or spatial or electromagnetic domain for TL, and unsupervised representation learning and then fine-tuning in a Self-SL framework will play important roles in the majority of future developments.
e Side view and f Top view of the AlphaFold2 model of the full ExbB-ExbD complex, generated using the NMR structure (PDB ID 8PEK, this work) as a template. In this model, the dimeric organization of the periplasmic domain of ExbD (pink and light pink) imposes the alignment of its helices in similar positions and orientations, and locking both Asp 25 sidechains in hydrogen bonds with Thr 218 from ExbB. In the top view, the periplasmic domain of ExbD is not visible due to its location above the clipping plane. We proceeded our structural study with ExbD Sm, peri as NMR fingerprint spectra indicated that ExbD Ec, peri suffers from line broadening due to conformational exchange dynamics, while the S. marcescens protein revealed high-quality spectral data (Figure  S1-S2 ). The solution structure of ExbD Sm, peri was determined as detailed in the Methods and resulted in a swapped homodimer (Figure  1c  and Table  S3 ).
Analytical ultracentrifugation (AUC) For sedimentation velocity experiments, ExbD Sm, peri and ExbD Ec, peri at concentrations of 20 µM, 100 µM and 1000 µM were loaded into 3 mm or 1. 2 cm centerpieces and centrifuged overnight at 42000 rpm in a Beckman Coulter Optima AUC centrifuge operating with an AN60-Ti rotor. Fitting of the data using a continuous size distribution c(S) model was conducted via SEDFIT 15. 1 with a confidence level (F-ratio) of 0. 95. From the fit, sedimentation coefficients at zero concentration in the buffer (50 mM sodium phosphate, 50 mM NaCl, pH 7. 0) could be calculated as well as molecular weights estimated. 34  Hydrodynamic parameters of the closedstate ExbD Sm, peri were calculated with Hydropro. 35  For the closed-state the NMR-structure determined in this study was used, the sedimentation coefficient of monomeric ExbD was calculated from PDB ID 2PFU.
A mixing time of 120 ms was used for all NOESY spectra. For the detection of hydrogen bonds, a 3D BEST-TROSY HNCO spectrum with a total N-CO INEPT transfer period of 133 ms was acquired. 39  Spectral data were referenced to 2,2-Dimethyl-2-silapentane-5-sulfonate (DSS), processed with NMRPipe 40 and in the case of NUS reconstructed using SMILE  41. Visualization of spectra, peak picking and resonance assignments were conducted using CcpNmr version 2. 5  42  and version 3  43. The final resonance assignment of ExbD Sm, peri was deposited to the Biological Magnetic Resonance Data Bank (BMRB) under the accession code 34826. 15  N-R1 and -R2 relaxation rates were measured by pseudo-3D experiments ( 1 H, delay, 15 N). For 15 N-R1 measurements the relaxation delays were 0, 20 70, 150, 200, 300, 400, 550, 700, 900, 1100, 1500, 1800 and 2000 ms in random order with a recycle delay of 5.
50  Briefly, 20 independent ARIA runs were carried out using the same input data but different random number seeds, generating varying random initial conformations and velocities for the molecular dynamics simulated annealing protocol. Subsequently, cross-peaks that remained active (i. e. , those retaining at least one assignment possibility) were collected at the conclusion of 12 out of the 20 ARIA runs. For each active cross-peak, assignment possibilities from each individual ARIA run were combined to generate a new list of consensus distance restraints. Lastly, a single iteration ARIA run was conducted using the consensus distance restraints as input, resulting in the final consensus structure ensemble. The final structural ensemble of ExbD Sm, peri and the restraints were deposited to the Protein Data Bank (PDB) under the accession code 8PEK.
The crystallization plates were subsequently maintained at 18 °C in an automated RockImager1000 (Formulatrix, Bedford, MA, United States) imager to monitor the growth of crystals. The best crystals were obtained in 10% w/v glycerol and 3 M ammonium sulfate. The crystals were cryoprotected by soaking them into the crystallization solution supplemented with 20% glycerol as a cryoprotectant before freezing in liquid nitrogen. Subsequently, diffraction data were acquired at cryogenic temperatures (100 K) on the PROXIMA-1 beamline at the SOLEIL synchrotron facility (St Aubin, France) and processed with XDS 52 through XDSME (https://github. com/legrandp/xdsme)  53.
X-ray structure determination and refinement AlphaFold2 multimer  54  was used to generate initial models of the ExbD-TonB peptide complex. On that basis, crystal structures were determined by molecular replacement with Phaser  55. The final models were generated via an iterative process that involved manual model building using Coot  56  and refinement in reciprocal space with REFMAC 57 and Phenix 58 All data collection details as well as model refinement statistics are summarized in Table  S3. The ExbD-TonB peptide complex structure was deposited to the Protein Data Bank (PDB) under the accession code 8P9R.
ACKNOWLEDGEMENTS This work was supported by the  French Agence Nationale de la Recherche  ( ANR  Energir  ANR-21-CE11-0039 ), the  INCEPTION program « Investissement d'Avenir  grant  ANR-16-CONV-0005  and the  Equipex CACSICE  ( ANR-11-EQPX-0008 ). We acknowledge NMR, Biophysical and Crystallography platforms of C2RT at the  Institut Pasteur  for their help and assistance. The 800-MHz NMR spectrometer and the optima AUC of the  Institut Pasteur  were partially funded by the  Région Ile de France  ( SESAME 2014 NMRCHR  grant no  4014526 ) and DIM one health, respectively. We thank  Michael Nilges  for his constant support and fruitful discussions. Molecular graphics and analyses were performed with  UCSF ChimeraX , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University.
Molecular biology, gene iden<fiers and phylogeny Gene idenBfiers, sequences and methods for RNA probe DNA templates and cis-regulatory DNA generaBon are described in Table  S1. Genes and cis-regulatory regions were named according to the ascidian community nomenclature  29. PutaBve Tfap2 binding sites were mapped on CesA loci using FIMO (hap://memesuite. org/tools/fimo)  30  with matrices collected from the Jaspar database  31  and the GCCN3/4GGC moBf  32. The sites were mutated by making the following changes G->C and C->G in order to keep the GC content unchanged (the validity of these changes was confirmed using FIMO). Wild type and mutated versions of the Phmamm. CesA promoter were synthesized as gBlocks (IDT). Genomic regions that were amplified from genomic DNA using PCR or gBlocks were placed upstream of the Ciinte.
In Molgula, Gh6 was found epidermal but without regionalized expression. It is thus unlikely to regulate local tunic expansion into fin blades. While this observaBon could stem from an inherent divergence in this species which belongs to a very fast-evolving genus, a beaer candidate would be a gene with conserved regionalized expression in all ascidians that make fin blades (Fig  S1 ). Tunicate genomes encode a number of glycosyl hydrolases (annotated for Phallusia in the CAZY database  43  ), and one of them might fulfill the requirements for our above model for fin elongaBon. We are currently searching for genes coding for cellulase expressed in the epidermis during late embryogenesis in different ascidian species. Cellulose-related HGT-acquired genes in ascidians: dynamic evolu<onary history and developmental system driR.
Acknowledgements We thank  G. Diaz  ( Port-Vendres ) and staff ( M. Fuentes , divers and boat crew) at the marine staBons of  Banyuls-sur-Mer and Roscoff (French  node of the  European research infrastructure EMBRC)  for providing animals. We would like to acknowledge the  BioPiC imaging facility (Sorbonne Université/CNRS, Banyuls-sur-mer ) for access to microscopes. We acknowledge  H. Yasuo  for the gim of cDNA clones,  A. McDougall  for sharing the CRISPR protocol before publicaBon and  R. Chowdhury  for establishing and teaching the 'Femtojet Phallusia injecBon' procedure in our team.
coli cells; (iii) the intrinsic adjuvant properties of T5 CLPs, (iv) the capacity of the decoration protein pb10 or its pN domain alone to tolerate the fusion with large size Ags and finally (v) the well-controlled and highly efficient anchoring of a high copy number of the displayed Ag. Further investigations will establish the potential of this platform to protect against different pathogens. or Ova (Ovalbumin, GenBank accession number J00895) were cloned in pET28b: (i) full-length pb10 for pmC and pO, (ii) the N-terminal capsid binding domain of pb10 (pN, first 73 amino-acids) for pNmC or pNO and (iii) the C-terminal domain of pb10 (pC, amino acids 74 to 164) for pCO. All constructs included a His-Tag in frame with the mCherry or Ova C-terminal end. The pET28-pmC, -pNmC, -pO and -pNO vectors were constructed using a ligation-free technology (FastCloning method according to Li et al  38  or with the Quick-Fusion Cloning Kit from Biotool), while pET28-pCO was constructed by using the golden-gate seamless assembly method based on the type IIS restriction endonuclease according to Engler et al.
Acknowledgements This work was supported by the  Centre National de la Recherche Scientifique  and the  University Paris-Saclay. L. L. C. , E. V. and N. D. received a fellowship from the  French Ministère de l'Enseignement Supérieur et de la Recherche. L. L. C. received an additional fellowship from the  Ligue Nationale contre le cancer. L. R. -C received a fellowship " Becas Don Carlos Antonio Lopez " from the  Paraguayan Ministry of Education. This work benefited from the Cryo-EM platform of I2BC, supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-05 ] and member of IBISA. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INSB-05-02   &  project  ID 160  to P.