dataesr/scientific-paragraphs-categorization huggingface dataset:
The specific formulas used are the following: We therefore calculated precision, recall and FPR at all three different levels of analysis: (i) at the identification/vocalization level (only for the ZA-Pygar dataset), (ii) at the recording level and (iii) at the dataset level. The metrics calculated at the first level will be referred to as ID_precision, ID_recall and ID_FPR, the metrics calculated at the second level will be referred to as rec_precision, rec_recall and rec_FPR and the metrics calculated at the third level will be referred to as ds_precision, ds_recall and ds_FPR (Table  3 ). It is important to note that, since our categorization criteria imply that one correct identification can override any number of incorrect identifications of the same species within the same acoustic sample, the values obtained for these metrics will be highly influenced by the level of analysis chosen.
2. 5. Factors influencing BirdNET performance Using only the Za-Pygar dataset, we further analyzed how BirdNET performance might be influenced by factors related to the acoustic dataset analyzed (total duration recorded, habitat recorded, passive recorder used, preponderance of biophony over anthropophony and geophony (Bradfer-Lawrence et al. , 2023; see Appendix S6), and number of species vocalizing at the same time) as well as the number of recordings available online for each species as a proxy for the size of the training dataset used by BirdNET. To estimate the influence of the size of the dataset (i. e. , total recording duration) analyzed on birdNET performance, we calculated recall and FPR at the dataset level (ds_recall and ds_FPR) for 23 different subset sizes going from 10 to 230 recordings using a step increase of 10.
For each subset size, we performed 200 random selections of recordings from the whole set of 237 recordings making up the ZA-Pygar dataset and then averaged out their results (i. e. , we generated 200 random subsets of 10 recordings and then averaged out the ds_recall and ds_FPR corresponding to each of these subsets; likewise for all other subset sizes until 230). This number of random selections proved to be sufficient for results to be solidly consistent across multiple iterations of the same analysis. As for the variability of the predictive power of BirdNET across species, we distinguished between two different types of causal factors: (i) factors related to the difficulty inherent in identifying certain vocalizations, such as some acoustic patterns being easier to identify than others, or bird species with vocalizations highly similar to those of other species being harder to identify than species with more idiosyncratic sounds, and (ii) the size and quality of the acoustic data available for each species on the online platforms used as sources of data for the training of the algorithm.
The first type of factor being considered out of scope for this study, we analyzed the influence of species-specific data availability on BirdNET performance. Even though we do not have direct access to the training datasets used by BirdNET, we know that the Xeno-canto platform (Xeno-canto, 2023) and the Macaulay Library of Natural Sounds  (Macaulay, 2023)  were used as sources of audio data for this purpose  (Kahl et al. , 2021). Thus, we used the total number of foreground recordings available on both platforms for each species analyzed as a rough proxy for the species-specific availability of recordings during the training phase of the algorithm. More specifically, we examined the correlation between the number of recordings available on these platforms and BirdNET precision, recall and F1-scores across species.
Average precision scores by species improve considerably by setting higher minimum confidence thresholds (Fig. S5 ), recall scores by species following the opposite trend (Fig. S6 ). When analyzing BirdNET results at the recording level for the ZA-Pygar dataset, we found that the optimal minimum confidence thresholds for maximizing global F-scores for β-values of 1, 0. 25 and 0. 1 are 0. 3, 0. 55 and 0. 65, respectively (Fig. 4 ). The results obtained for each of these thresholds show that, as the minimum confidence score required to accept BirdNET identifications becomes stricter, both TPs and FPs decrease while FNs rise sharply (Fig. S7 ). This causes both the number of species correctly detected and the number of species mistakenly detected to vary considerably depending on the minimum confidence threshold used.
While a minimum confidence threshold of 0. 3 allows for a ds_recall of 0. 65 (i. e. , the correct detection of 65% of the species present in the dataset), a threshold of 0. 55 reduces this Fig. to 0. 55 and a threshold of 0. 65 reduces it further to 0. 5. On the other hand, ds_FPR (i. e. , the proportion of species absent from the dataset that are mistakenly detected by the algorithm) varies even more strongly along with the confidence threshold used, with respective values of 0. 41, 0. 07 and 0. 03. In absolute terms, this translates into 48, 41 and 37 species correctly dectected and 85, 15 and 6 species mistakenly detected, respectively. It is important to note that a non-negligible number of species present in the recordings have only been manually detected a small number of times (Fig.
S8 ). Hence, we deem the estimated predictive power of the algorithm for these species to be highly unreliable. The difference between Figs  S7a  and S7d  suggests that limiting the analysis to species detected -either by BirdNET or by the expert birder-in a minimum of 10 recordings, while filtering out less common species, results in a substantially higher overall performance of the algorithm. More specifically, when analyzing the ZA-Pygar dataset with a confidence threshold of 0. 3, this filtering procedure improves rec_precision from 0. 59 to 0. 71 without compromising rec_recall, which actually remains stable at 0. 32. The results obtained after applying this filter (Fig. S7d ) also suggest that the variability in BirdNET predictive power across species is not as pronounced as what we might infer from Fig.
S7a , since the low number of data points available for many species leads to more variable outcomes. Overall, our analyses yielded highly consistent results across the three datasets studied, with maximal F1-scores (corresponding to minimum confidence thresholds between 0. 3 and 0. 45) ranging from 0. 4 to 0. 52 at the recording level (Table  1 ). This roughly means that, in the best case, we can expect two errors (FP or FN) for each correct BirdNET prediction. BirdNET performance is lower (maximal F1-score of 0. 33 for a minimum confidence threshold of 0. 4) when results are analyzed at the identification/vocalization level (i. e. , 1 correct prediction for every 3 errors).
3. 3. Factors influencing BirdNET performance In order to properly isolate the variables of interest and ensure that the heterogenous identification procedures and recording durations used in the three datasets do not have a confounding effect, only recordings from the ZA-Pygar dataset are included in the following analyses. Regarding the influence of sample size on BirdNET performance, there appears to be a positive linear correlation between ds_F1-scores and the logarithm of the number of recordings sampled (Fig. 5 ; linear regression, adjusted R² =. 539, p <. 001). The same seems to be true for ds_TPR (adjusted R² =. 376, p <. 001) and ds_recall (adjusted R² =. 095, p <. 001) as well, in all cases after controlling for the minimum confidence threshold used. As can be observed in Fig. 5 , ds_FPR shows a pronounced decrease when higher confidence scores are required.
ds_recall, while following the same trend, shows differences of more moderate proportions as a response to increased minimum confidence thresholds. This suggests that a given global recall target can be reached either by setting a low enough minimum confidence threshold or by setting a high minimum confidence threshold and procuring a large enough acoustic dataset, the latter approach resulting in substantially lower ds_FPR values. Other analyses performed suggest that the number of different bird species vocalizing simultaneously seems to have a significant influence on ID_precision (Fig. S9 ; Spearman, r s = 0. 122 and p <. 001) but not on ID_recall and ID_F1-scores (Spearman, r s = -0. 051 and p =. 285 for ID_recall, r s = 0. 017 and p = 0. 72 for ID_F1-score). In contrast, neither the recorder used nor the predominant habitat sampled seem to affect BirdNET performance (Fig.
Our results further suggest that BirdNET precision scores can be improved not only by raising the minimum confidence threshold used, but also by filtering out species not having reached a minimum number of BirdNET detections across the whole dataset (Fig. S5 ). We found, however, that species such as garden warblers (Sylvia borin) and Western Bonelli's Warblers (Phylloscopus bonelli) were identified by BirdNET on a high number of occasions, always incorrectly, and exceeded our minimum occurrence frequency threshold (Fig. S7d ). Non-negligible numbers of FPs for other non-present species suggest caution around their identifications as well. On the positive side, these misidentifications seem to be effectively filtered out by establishing strict enough minimum confidence thresholds (Fig. S7c ).
In this regard, our results suggesting that 19% of the variance in F1-scores between species can be explained by online recording availability (Fig. 6 ) provide ground for optimism about the future reliability of BirdNET. More concretely, this could mean that the diagnostic capacity of BirdNET for less common species could be considerably improved by enlarging the pool of available acoustic data for these species. We presume that a higher availability of acoustic data could facilitate BirdNET performance through two different mechanisms: (i) by enabling BirdNET to enlarge the size of its training dataset, and (ii) by improving the quality of the recordings comprising its training dataset. Acoustic quality scores are included in the metadata of recordings on Xeno-canto and the Macaulay Library, and higher-quality recordings were prioritized in the selection of training data for the BirdNET algorithm  (Kahl et al.
, 2021). Hence, we would expect the average acoustic quality to be higher in the training datasets of species with larger pools of available recordings to choose from. Insomuch as both higher-quality recordings and larger training datasets could facilitate a better adjustment of the algorithm to the target bird vocalizations, these factors could provide a plausible explanation to the positive correlation observed between species-specific acoustic data availability and BirdNET predictive power. We further found a significant linear correlation between the logarithm of the recording time analyzed and the proportion of recorded species having been correctly detected by BirdNET (Fig. 5 ). A possible explanation to this correlation would be that the short total vocalization time recorded for the least frequent species often results in no correct detections of these species by BirdNET.
Thus, insofar as longer recording times contain a greater number of vocalizations per species in expectation, we should expect larger acoustic datasets to provide more chances for the algorithm to correctly detect less frequent species. We therefore hypothesize that a sufficiently high number of recording hours analyzed could partially compensate for the low recall scores obtained when high minimum confidence thresholds are used. Hence, were the necessary conditions to be met, BirdNET might prove successful in capturing most bird species present in a study site while also ensuring a relatively low number of FPs. These results, in line with recent findings  (Toenies & Rich, 2021; Cole et al. , 2022; Wood et al. , 2021) , are encouraging with respect to the potential of BirdNET to reliably and comprehensively describe bird communities in research projects with large amounts of ecoacoustic data at their disposal.
Acknowledgements We thank  Arndt Hampe ,  Maarten de Groot ,  Thomas Boivin ,  Vojtech Lanta ,  Lyudmila Pukinskaya ,  Frédéric Archaux ,  Thomas Perot ,  Márton Molnár ,  Nickolay Sedikhin ,  Valentin Moser ,  Elina Mäntylä ,  Jan Grünwald ,  Karthik Thrikkadeeri ,  Anders Mårell ,  Mona C Bjørn ,  Becky Thomas ,  Andreas Prinzing  and  Lucian Grosu  for manual bird identifications of the TreeBodyguards European dataset. We are grateful to  Michel Beal ,  Jean-Luc Témoin  ,  Valérie Delage  and  Laurent Tillon  from  Office National des Forêts  for their help while recording the Rambouillet dataset. We also thank  Thierry Feuillet ,  Amandine Gasc ,  Jérémy Froidevaux ,  Kevin Darras ,  Yves Bas ,  Elena Valdés-Correcher ,  Thomas Delattre  for helpful advice and support. We are also indebted to the  CESAB Acoucene  working group for fruitful discussions.
Funding information This work has received financial support from the  French government  through the  National Agency for Research  as part of the  Future Investment Program integrated into France 2030  ( Terra Forma  project,  ANR-21-ESRE-0014  and  PSI-BIOM  project operated by  ADEME) , from the  SpatialTreeP  project ( ANR-21-CE03-0002 ) and from  INRAE Métaprogramme Biosefair ( PARMENIDE  project).
conclusions can be drawn from these metrics at different levels of analysis. In the ZA-Pygar dataset, measuring precision at the identification level and recall at the vocalization level can allow us to estimate what percentage of BirdNET identifications will be correct and what percentage of bird vocalizations will be correctly detected by BirdNET, thus providing us with a fine-grained picture of BirdNET performance. However, since the total vocalization time of any given species varies significantly across different recordings, evaluating BirdNET based on the correctness of each individual identification could result in a small number of recordings with long vocalization times having a disproportionate weight over a greater number of recordings with shorter vocalization times. Thus, calculating precision and recall at the recording level can be an appropriate way of homogenizing the weight of each recording in the final results.
Declaration of Competing Interest K. F. D. has received financial support to attend educational meetings from Cytokinetics during the conduct of the study, personal fees from AstraZeneca, consulting fees from Us2. ai and research grants to his institution from AstraZeneca and Boehringer Ingelheim outside the submitted work. J. J. V. M. has received funding to his institution from Amgen and Cytokinetics for his participation in the Steering Committee for the ATOMIC-HF, COSMIC-HF, and GALACTIC-HF trials and meetings and other activities related to these trials; has received payments through Glasgow University from work on clinical trials, consulting, and other activities from Alnylam, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardurion, Dal-Cor, GlaxoSmithKline, Ionis, KBP Biosciences, Novartis, Pfizer, and Theracos; has received personal lecture fees from the Corpus, Abbott, Hikma, Sun Pharmaceuticals, Medscape/Heart.
The study and prediction of space weather entails the analysis of solar images showing structures of the Sun's atmosphere. When imaged from the Earth's ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures.
In  [12] , an additional C-GAN outputs the denoised image from predicted shadow mask, while pix2pix of  [4]  outputs the cleaned image directly. We explore the applicability of these denoising methods for the new application of removing cloud shadows from solar imagery. We evaluate sparse representation-based and deep learning-based approaches, and compare against  [1, 2]  (Section 4). Inspired by  [4, 12] , we use a combination of U-Net and C-GAN (Section 3) to obtain the new SoTA for this task. The methods are comparatively assessed on two imaging modalities. To support these experiments, we introduce two new datasets of Sun images with artificial and real clouds.
[14]. In case of fast enough cloud motion, due to the slit movement alongside the motion of clouds, cloud spots may be shifted along the scanning process, creating straight lines on the image (denoted as "streaked" texture). Slower cloud motions result in the "fluffy" texture that is customary of many terrestrial clouds. The "streaked" texture may provide more challenges to denoising methods, due to having different spatial scales in different directions. Fig. 3  illustrates samples of streaked and fluffy textures with different cloud cover thicknesses and streak orientations. The cloudy images don't have cloud-free counterparts for training DNNs. Thus, we introduce a dataset of real (initially cloud-free) images with added synthetic cloud shadows. Using the cloud coverage level metric of  [1] , we selected reasonably cloudfree images in the same time period as above.
Choice of DNN output We compare the three possible outputs of our DNNs, i. e. direct cleaned image and shadow masks to be used as in Eqs. 3 (transmittance) and 4 (residual). Table  1  reports the metrics on the Ca-II synthetic dataset. For both DNNs, Eq. 3 is less stable than Eq. 4 during training, with more noisy loss curves. This more unstable training results in a poor convergence. Poorer results are observed in the sensitive cases of strong cloud coverage (i. e. low values in I ini ) where more subtle adjustments are needed in the mask's values to correct for the clouds. Predicting a residual mask M for Eq. 4 performs slightly better than the direct prediction of Îclean , which, in rare cases, created artefacts that may be confused with solar structures. The better performance of Eq. 4 is in line with other works on residual prediction.
Comparative evaluation of the methods We compare the proposed DNNs against the SoTA  [1, 2]  and the sparse approximation method of  [9]  as non-learning baselines. [1]  requires a clean temporal neighbour image (within 24 hours), which may not be always available. We consider these cases a failure and report their numbers. The first reflectance step of  [9]  is skipped, as reflection plays no role in solar imaging. We use the PSNR, SSIM and RMSE metrics to assess the quality of the predicted shadow-free images. For  [1] , the metrics are computed over non-failure cases. For the DNNs, they are averaged over 10 runs. Table  2  presents results on the synthetic cloud datasets and Fig. 3  illustrates how the algorithms perform on real/synthetic clouds. [1]  obtains the worse results on both imaging modalities.
It struggles to preserve solar structures when their spatial scale is similar to the scale of clouds in at least one direction, as often happens in case of streaked textures. It is also less robust to strong cloud thickness. [2]  also struggles with the spatial scale of streaked clouds. [9]  fails to recover the underlying solar structures and results in poor contrast. The proposed DNNs outperform non-learning methods on all datasets. On synthesised images, the DNN's respective metrics are not significantly different. Upon visual inspection of artificial and real images, FS produces a slightly more uniform contrast across the solar disk, while the C-GAN's discriminator, focusing on small patches, allows more diversity of contrasts. However, FS tends to over-compensate strong clouds resulting in a lower contrast there, while C-GAN benefits from enforcing a patch-wise visually pleasant output.
It is worth noting that in our real-cloud dataset, most clouds are streaked, while the DNNs are trained on the fluffy synthetic textures. Both DNNs generalised well to this new cloud texture. While, in some cases, faint streaks remain from the cleaned images, this happens for the strongest clouds and generally doesn't prevent the restoration of underlying solar structures. C-GAN performs slightly better than FS in this particular case of strong streaked clouds. Both DNNs provide a detailed restoration of ARs, with the aforementioned contrast differences. Filaments suffer from a lower contrast, likely due to their dark appearance making them harder to distinguish from clouds. This is confirmed by the thicker and therefore blurrier ones being the most affected (erased) by this contrast reduction.
Acknowledgments This work was funded by  ANR  grant  N°ANR-20-CE23-0014-01. One of the authors (JA) thanks  CNES  ( Centre National d'Etudes Spatiale , the  French space agency)  for its support in this project. Acknowledgments. This work was funded by  ANR  grant  N°ANR-20-CE23-0014-01. One of the authors (JA) thanks  CNES  ( Centre National d'Etudes Spatiale , the  French space agency)  for its support in this project.
For each cloud-free image, a new cloud shadow was composed by summing a random number (in range [2, 3]) of duplicates of the basic texture with horizontal and vertical random resizing between 50-100% of the original size, random flips (around one or two axes), and random transparencies (in range [0. 1, 0. 4]). It was then overlapped to the cloud-free image. This procedure provides realistic images of fluffy and semi-transparent cloud shadow contamination. The Ca-II and H-α synthetic datasets comprise respectively 319 and 367 pairs of shadow/shadow-free images, split into 179/44/96 and 205/51/111 training/validation/testing pairs. The combination of our real and artificial cloud datasets provides a variety of streaked and fluffy cloud textures. They are publicly available 1 , along with the pretrained cloud-removal DNNs 2.
As a proof-of-concept, a mini-CROP-seq library, including validated guide RNAs (gRNAs) targeting six previously identified lncRNA regulated by hypoxia and/or associated with bad prognosis  [12]  a well as the two master transcription factors of the hypoxic response (HIF1A and HIF2/EPAS1) and negative control guides, was used. To optimize analysis of fine-tuned regulations in this dataset, we have developed a supervised autoencoder (SAE) neural network  [27] , where we relax the parametric distribution assumption of classical VAE. It leverages on the known cell labels, corresponding to the received gRNA, and a classification loss to incite the latent space to fit the true data distribution. We first validated the approach on HIF1 and HIF2/EPAS1 knock-down, showing a good sensitivity to detect the known temporal switch between both regulators.
Supervised Autoencoder Neural network framework In this section, we provide the background of the supervised autoencoder (SAE) neural network  [27] , and the structured sparsity projection method for selecting features. Figure  1  depicts the main constituent blocks of our proposed approach. Note that we added a "soft max" block to our SAE to compute the classification loss. Let X be the dataset in , and Y the labels in {0,. , k}, with k the number of classes. Let Z ∈ be the encoded latent vectors, X ∈ the reconstructed data and W the weights of the neural network. Note that the dimension of the latent space k corresponds to the number of classes. The goal is to compute the network weights W minimizing the total loss which includes both the classification loss and the reconstruction loss. To perform feature selection, as biomedical datasets often present a relatively small number of informative July 10, 2023 5/19 features, we also want to sparsify the network, following the work proposed in  [30] ,  [31] ,  [32]  and  [33].