dataesr/scientific-paragraphs-categorization huggingface dataset:
Starting from this latent representation, the initial image size is retrieved by a decoder performing reverse transformations with a cascade of deconvolution and upsampling operations. We also based our model on the above-mentioned category of architectures. We chose a U-Net  (Ronneberger et al. 2015)  with a ResNet18  (He et al. 2016)  as encoder backbone (≃11 millions of free parameters), which is an architecture widely used for image segmentation. Its residual connections preserve of the input's spatial information along the cascade of convolution and downsampling operations thanks to a direct mapping of the output of each layer of the compression arm into the corresponding layer of the decompression arm. We use the architecture implemented in the SMP package 6. The encoder and decoder parts are formed by four blocks, each one is composed by a series of convolution layers, batch normalization layers, rectified linear unit (ReLU) activation, and max pooling layers.
6 The SMP package containing the network architecture used in this paper is available at https://github. com/qubvel/segmentation_models. pytorch. The final layer of the network has a sigmoid activation function to produce a detection map 𝒚 ∈ [0; 1] 𝑀. The detailed description of the architecture, the number of parameters per layer, and the input / output shapes of each layer can be found at the above mentioned link. The network weights are trained from scratch with the samples generated with the procedure described in Sect. 2. 2. 1. Initial weights are drawn uniformly through a He-Kaiming distribution  (He et al. 2015). The SMP package also provides pre-trained weights. Pre-training is performed with the ImageNet dataset (RGB conventional images) either in a supervised, semi-supervised, or weakly-supervised learning fashion  (Yalniz et al.
The training process stops when the accuracy metric AUC F1R (i. e. , AUC under ROC representing the F1R score as a function of the detection threshold 𝜏 at validation time) evolves in less than 2% during the ten previous epochs. The model optimization is performed with the adaptive gradient algorithm AMSGrad  (Reddi et al. 2019)  which is a variant of the Adam  (Kingma & Ba 2014)  optimizer with a longer-term memory of past gradients. The parameters of the optimizer and of the scheduler have been fine tuned on two datasets and are kept constant for all our experiments. In practice, we observed that the optimized values are quite robust with respect to the dataset diversity. The weight decay 8  is fixed at 10 -5 and the initial learning rate is set to 10 -3 with a regular decrease by 10% every 10 epochs.
Pre-processing aspects We adopt a simple patch-based approach, in which we predict the flux of a putative source from a unique (reduced) patch centered on the approximate location of the source. We propose to parameterize the mapping between the input patch and the flux with a CNN trained in a supervised fashion, from the dataset of interest (i. e. , the underlying model is data-dependent, as for the detection stage of this paper). The dataset is first reduced to a single frame, from which the input patches are extracted. This pre-processing consists in three steps. First, the temporal mean is computed and subtracted pixel-wise in order to remove most of the quasi-static speckles. Second, the dataset is derotated by the opposite of the parallactic angles to co-align the signal of the sources.
Third, the dataset is averaged temporally, resulting in a single averaged frame. This last step allows to obtain an efficient training procedure as it reduces the size of the input data by a factor 𝑇. Besides, we observed empirically that this step is beneficial to improve the estimation accuracy as it acts as a simple denoiser: the source signal is constant along time, while residual speckles are not quasi-static after cube derotation, thus canceling out. Keeping         q 𝒓 𝑛 = 𝒓 𝑛 -𝒎 𝑛 , ∀𝑛 ∈ P , (step 1) , ( 𝒓 = 1 𝑇 𝑇 𝑡=1 [D q 𝒓] 𝑡 , (steps 2 and 3). ( ) 14 In this framework, and unlike the detection stage, we do not apply a whitening of the spatial correlations at step 1. Indeed, we observed empirically that whitening the dataset between steps 1 and 2-3 degrades the performance of our model, as illustrated by Fig.
6. More quantitatively, the absolute error of estimation is increased, whatever the angular separation, by a factor between three and five. Besides, keeping a whitening step for photometry estimation does not allow to obtain better results than PACO for most of the field of view. This is expected as the whitening distorts the shape and the norm of the source signal, thus hampering the recovery of its flux. The detection algorithm is not subject to this constraint as its task is to determine whether a source is present or not, regardless of its flux. This fact illustrates that deriving a quantitative result (as a flux estimate) is a more complex task than providing a qualitative result (as related to the presence or to the absence of a source) with our algorithmic setting. We can expect that building the model from several datasets of observations (instead of a single one in this work) would relax these constraints.
The results are averaged azimuthally for sources of flux drawn uniformly between 1 × 10 -6 and 3 × 10 -5. The mean ARE (denoted by ARE) averaged over the angular separation is also reported as an overall measure of the performance. The known real sources were masked out and were not considered. Datasets: the eleven SPHERE-IRDIS datasets considered in this work, see Sect. 4 for the recording logs. to over-fitting (due to the increase in terms of model complexity), especially in the absence of a whitening procedure preventing memorization of the nuisance structures by the network. From an input patch ( 𝒑 [ 𝑝] ∈ R 𝐽 , the network produces a single scalar 𝛼 [ 𝑝] ∈ R + , representing the estimated source's photometry.
All the observations are performed with the apodized Lyot coronagraph  (Carbillet et al. 2011 ) of the VLT/SPHERE instrument. (a) Since the EIDC aimed to perform a blind benchmark, information that would allow to identify the datasets are not known. (b) This dataset was recorded with the star-hopping technique recently available for the VLT/SPHERE instrument  (Wahhaj et al. 2021 ) and its analysis is not reported yet. We do not exploit the dataset associated to the observation of the reference star. The dataset associated to the target star (HD 95086) is processed like all other datasets considered in this paper. (c) This dataset is only used for additional experiments conducted in Appendix A2. Ti cards. The pre-processing step is highly parallelized, and it is run on CPUs.
Datasets description and reduction strategies For our comparative analysis, we have selected 15 datasets recorded with the SPHERE-IRDIS instrument. Three of the 15 SPHERE-IRDIS datasets were extracted from the exoplanet imaging data challenge (EIDC) initially designed to ground the detection performance of existing post-processing algorithms for high-contrast imaging  (Cantalloube et al. 2020). These datasets are used as a sanity check to assess the ability of the proposed algorithm to detect injected sources at moderate levels of contrast. To study in more details the performance of the proposed method, we selected 12 additional datasets, mostly part from the SHINE survey of the SPHERE-IRDIS instrument  (Desidera et al. 2021; Langlois et al. 2021; Vigan et al. 2021). They were obtained by the observation of the following stars: -HD 95086, a A8III type star of the Carina constellation, hosting an exoplanet discovered by direct imaging with the SPHERE instrument  (Rameau et al.
The diversity in the experienced observing conditions is quite representative of the SPHERE observations. The raw observations were pre-reduced 10  with the DRH pipeline  (Pavlov et al. 2008 ) of the SPHERE instrument, which performs thermal background subtraction, flat-field correction, anamorphism correction, compensation for spectral transmission, flux normalization, bad pixels identification and interpolation, frame centering, true-North alignment, wavelength calibration, astrometric calibration, and frame selection. These operations are complemented by custom routines implemented in the SPHERE data center  (Delorme et al. 2017) , in particular to improve bad pixels correction. Finally, the SPHERE data center combines the pre-reduced observations and delivers the calibrated ADI datasets we consider in this work.
4. 2 with the cADI, PCA, and PACO algorithms, (see Sect. 1 for their respective principle). For cADI, we have re-implemented the original method  (Marois et al. 2006 ) based on a full-frame estimation of the off-axis PSF and of the S/N map, i. e. , without angular-specific processing. We have also used the refined implementation of cADI available in the VIP package  (Gonzalez et al. 2017) , which includes a protection angle strategy accounting for a minimal field rotation between successive images when building the off-axis PSF in order to limit the self-subtraction effect. After computation of the offaxis PSF, a S/N map is derived by accounting for an annular-based estimation of the noise in the residual images. We also applied the VIP implementation of the PCA-based algorithm combined with the same protection angle strategy and the annular-based computation of the S/N.
e. , it is not optimized), and the photometry is estimated at this groundtruth position. When estimating the photometry of real sources, both the astrometry and the photometry are optimized by the different algorithms. Table  4. Mean results of AUC for ROCs giving the TPR as a function of the FDR. The scores are averaged over the eleven SPHERE-IRDIS datasets considered in this paper, see Sect. 4 for the observation logs. Only the 59 known real sources present in these datasets were considered. Figure  7  of  (Flasseur et al. 2022 ) display the corresponding ROC from which these mean results were aggregated.
Detection of known real sources A first classical sanity test to evaluate the detection performance of a post-processing algorithm is to study qualitatively its ability to redetect real known sources initially detected with different algorithms, and possibly from different datasets. We present detailed results for one dataset (HD 95086, 2015-05-05, see Table  3 ) selected among the eleven SPHERE-IRDIS observations we consider because HD 95086 is the star having the larger number of known real sources in the SPHERE-IRDIS field of view. Results obtained for the ten other datasets are given in supplementary material. Figures  9  and 10  give detection maps produced with the five tested algorithms. The detection threshold is set to 𝜏 = 5 for the algorithms producing a S/N map (i. e. , cADI, cADI (VIP), PCA (VIP), PACO), and to 𝜏 = 0.
This claim is also supported by the PACO detection map that displays a few false alarms in the same area. Figure  11  gives a more quantitative analysis of the previous results through ROCs representing the TPR as a function of the FDR (see Sect. 2. 2. 3 and Eq. (  13 )) for the same dataset of HD 95086 (2015-05-05). This type of representation gives a comparison of the precisionrecall trade-off reached by each method, regardless the detection quantity (S/N or pseudo-probability) they produce. These curves are obtained by counting the number of true positives (TPs), and false alarms (FAs) for the full range of possible detection thresholds, i. e. 𝜏 ∈ [0; 1] for the proposed method, and 𝜏 ∈ [min( 𝒚); max( 𝒚)] for cADI, PCA, and PACO. Table  4  presents averaged results over the eleven SPHERE-IRDIS datasets we consider in this study, and the detailed scores for each dataset are given in Table  1  of the supplementary material.
Detection of synthetic sources In this section, we evaluate quantitatively the detection performance of the proposed method with ROCs and contrast curves built from massively injected synthetic sources. As a first step, we apply the proposed detection algorithm on the three SPHERE-IRDIS datasets from the public EIDC data challenge  (Cantalloube et al. 2020 ). The resulting detection maps are given in Fig. 13. Using the same procedure than  Cantalloube et al. (2020)  to benchmark 22 post-processing algorithms (including cADI, PCA, and PACO), we compute the F1R score at the set detection threshold (𝜏 = 0. 5), and ROCs for the TPR and the FDR scores, as defined in Eqs. (  13 ), by varying the detection threshold. The AUC is then computed from ROCs. Table  5  summarizes the results we obtained with the proposed algorithm.
As a purpose of comparison, we also report the PACO results that have been published in  Cantalloube et al. (2020). It emphasizes the interesting precision-recall of the proposed approach that performs, on these datasets, on par with or better than the 22 post-processing algorithms considered in the EIDC data challenge. However, these results should be taken with caution since they are based on a few datasets, with only six injected sources at relatively bright levels of contrast. Besides, several algorithms (including PACO) are also able to detect the injected sources without any false alarm in the field of view for a sufficiently large detection threshold. At this stage, the better performance of deep PACO in terms of false alarms rejection (quantified by the AUC FDR metric) are mostly explained by the fact that the proposed algorithm produces detection maps with almost binary values.
It can be noted that this effect is also encountered for all the algorithms of the EIDC data challenge producing the same type of outputs like RSM  (Dahlqvist et al. 2020)  or SODINN  (Gonzalez et al. 2018). To ground in details the precision-recall trade-off of the proposed detection algorithm, it is necessary to build consistent ROCs and contrast curves by resorting to massive injections of synthetic sources -unknown at training time but that we aim to detect at inference time-for various levels of contrast. For that purpose, the most realistic procedure, hereafter called reference procedure, consists in (i) splitting the whole set of synthetic sources in small subsets, (ii) injecting synthetic sources of one subset in the dataset of interest so that injected fake sources mimic the behaviour of real (possibly unknown) sources, (iii) training the detection model with the procedure described in Sect.
2, and (iv) applying the trained model to the dataset containing the synthetic sources injected in step (ii). Steps (ii) to (iv) are repeated for all subsets of synthetic sources. This procedure simulates the real situation when we face a new dataset with real unknown exoplanets that we aim to detect at inference time. Due to the Table  5. Detection scores: F1R at detection threshold 𝜏 = 0. 5 (the higher, the better), AUC under the ROC representing the TPR as a function of 𝜏 (the higher, the better), and AUC under the ROC representing the FDR as a function of 𝜏 (the lower, the better). Scores reported for PACO are extracted from the EIDC data challenge  (Cantalloube et al. 2020) , and deep PACO scores are computed with a similar procedure for the three SPHERE-IRDIS datasets from the EIDC. - (a) Metrics can not be computed since there is no injected source in this dataset.
computational burden of step (iii), repeating this full procedure for all subsets of synthetic sources that we aim to detect at inference time is not realistic to build ROCs and contrast curves. To circumvent this issue, we resort to a proxy procedure: instead of training a different model for each subset of injected sources, we train a unique model without synthetic sources mimicking the behaviour of real (possibly unknown) sources. Synthetic sources are injected a posteriori, i. e. , after completing the training step (iii), and the trained model is applied on the resulting dataset. Figure  14  illustrates the principle of the reference and proxy procedures. The proxy procedure leads to an improvement in terms of algorithmic complexity by a factor equal to the number of subsets (typically 2,000, with in average 5 synthetic sources in each subset), that is determinant to be used in practice.
At this stage, we still need to show that the proxy procedure we defined leads to reliable results so that our comparisons with state-of-the-art algorithms are fair. In particular, we aim to show that the model resulting from the proxy procedure is not prone to over-fitting induced by an imperfect separation between training and testing data. In the proxy procedure, over-fitting could possibly occur if the model partially memorizes the nuisance component that is seen by the network without the injected sources we aim to detect at inference time. Figure  15  shows examples of detection maps obtained with the procedure of reference described previously and its proxy version on the three datasets of HD 95086  (2015-05-05, 2018-02-23, 2021-03-11)  considered in this work. In these experiments, we considered more than 700 synthetic sources spread over the whole field of view.
IRDIS 1 IRDIS 2 IRDIS 3 mean Following the previously defined proxy procedure, we present detailed results obtained on HIP 88399  (2018-04-11) , which is a SPHERE-IRDIS dataset representative of the mean results we obtained over the eleven ones we consider in this work. Results for the ten other datasets are reported in the supplementary material. Figure  16  shows detection results on a sample of 10,000 synthetic sources in a diagram contrast versus angular separation for PACO and the proposed method. Each synthetic source is classified as missed, true, or false detection using the detection thresholds defined in Sect. 4. 2. 1. For PACO, setting the detection threshold at 𝜏 = 5 corresponds, in average, to a realistic control  (Flasseur et al. 2018a (Flasseur et al. ,b,c, 2020a) )  of the Table  6.
Comparison between the reference procedure and its proxy version for evaluation of the detection performance of the proposed algorithm. Synthetic sources are classified as true, missed, or false detections using a detection threshold at 𝜏 = 0. 5. Mean detection results are averaged for the three datasets of HD 95086  (2015-05-05, 2018-02-23, 2021-03-11)  probability of false alarms (PFA) at 5𝜎 (i. e. , PFA ≃ 3 × 10 -7 ). While the PFA should theoretically be controlled by the other algorithms producing a S/N map (cADI and PCA), we have shown in previous works  (Flasseur et al. 2018a (Flasseur et al. ,b,c, 2020a) )  that the contrast curves are over-optimistic for these algorithms (i. e. , there are significantly more false alarms than expected) due to a miss-modeling of the nuisance component.
We observe that deep PACO can reach for some datasets (see e. g. HIP 88399 (2018-04-11) in Fig. 17 , and HIP 88399 (2015-05-10) as well as HIP 88399 (2016-04-16) in the supplementary material) at large separations the best achievable detection limit driven by the photon noise, which corresponds to an optimal unmixing between the signal of the sources of interest and the nuisance component. Near the star, an important gap remains (by a factor 5 to 10) between the actual performance and the theoretical lower limit, which a sign of a lack of angular diversity in this area. In practice, the gap between the actual contrast and the lower bound limit depends on several characteristics such as the quality and the stability of the observing conditions, the total amount of parallactic rotation, the number 𝑇 of temporal frames, etc.
, as illustrated in Figs. 21 and 22 of the supplementary materials. Reducing this gap at close separation could be addressed by investigating ways to perform the training step of our model from various datasets for which the presence of sources at similar locations is very unlikely. This adaptation requires specific developments that are left for future work. Figure  18  focuses on the comparison between PACO and the proposed deep PACO algorithm. It represents the TPR of deep PACO for synthetic sources missed by PACO (i. e. , for S/N ≤ 5) as a function of the angular separation and of the PACO's S/N of detection. For instance, on this dataset about 65% of sources below 0. 5 arcsec with a S/N of detection between 3. 5 and 4. 0 with PACO are detected with deep PACO (i. e. , above the detection threshold 𝜏 = 0.
5). Similarly, more than 86% of sources between 2. 0 and 4. 0 arcsec with a S/N of detection between 3. 5 and 4. 0 with PACO are detected with deep PACO (above the detection threshold 𝜏 = 0. 5). For these two exam-   ples, achieving the same TPR with PACO would require to decrease the detection threshold at the price to an increase of the FPR up to a mean factor of 800. Figures  23  and 24  of the supplementary material give similar type of representation than Fig. 18  for the ten other SPHERE-IRDIS datasets analyzed in this work. Figure  19  gives ROCs representing the TPR as a function of the FDR for the HIP 88399 (2018-04-11) dataset. There results are obtained with the 10,000 synthetic sources considered for results presented in Fig. 16. The results are split in four different angular separation ranges: [0; 2], [2; 4], [4; 6], and [6; 7] arcsec.
Identification of candidate background point-like sources In this section, we take the example of a joint analysis of datasets of HD 95086 considered in this work to illustrate the ability of the proposed approach to detect candidate faint point-like sources. Figure  20  shows the detection maps obtained with PACO and the proposed deep PACO approach on the 2015 and 2018 observations of HD 95086. It emphasizes that two candidate point-like sources (respectively denoted CC-11 and CC-12) are detected with PACO at a S/N above 5 only for one of the two epochs (in 2018, which is also one of the best SPHERE-IRDIS observations of this star) while they remain just below the classical detection threshold at 5𝜎 on the 2015 epoch. These two point-like sources are detected with deep PACO on the same two epochs.
Based on their astrometry, these two faint point sources are co-moving with the other background stars seen in the projected field of view of the instrument, so that they could be background stars too 11. These candidate point-like sources are not reported in the last detailed study of the HD 95086 architecture based on the analysis of ten SPHERE-IRDIS datasets (including the 2015 epoch but not the 2018 epoch) with classical post-processing algorithms (i. e. , cADI, PCA, TLOCI), see Fig. 2  of  Chauvin et al. (2018). The analysis of more recent and better datasets (including the 2018 one) in the SHINE survey  (Langlois et al. 2021)  of the SPHERE instrument allows to identify by visual inspection (i. e. , not based on a strict measure of S/N above the classical detection threshold at 5𝜎) CC-11 while CC-12 has not been identified yet.
3  and 4  of the supplementary material. This observation is consistent with the fact that the achievable contrast is worse for this dataset than for the 2015 and 2018 observations, likely because the observing conditions were quite average for the 2021 observations and that only half of the total integration time was spent on the target star (the 2021's epoch being recorded with the starhopping technique, and the resulting reference dataset remaining not exploited in this paper). PACO and deep PACO detection maps from the 2015 and 2018 epochs also display a few blobs, circled in red, above the detection threshold that are not consistently detected on multiple epochs. These detections are located very near the borders of the (extended) field of view. They are likely artifacts due to the lack of temporal samples in this area to estimate the model parameters.