dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization results In Sect. 4. 3. 1, we ground the performance of the proposed photometry estimation module on the same datasets than the considered ones for the detection stage by resorting to massive injections of synthetic sources. For that purpose, we use the ARE metric (Eq. (  15 )) averaged per angular separation. We also put in perspective the results of the detection and of the characterization stages to ground the global gain brought by the proposed algorithm. In Sect. 4. 3. 2, we propose a fast (and approximate) evaluation procedure suited when large amounts of synthetic sources are considered. Finally, in Sect. 4. 3. 3 we briefly discuss and compare photometry estimations provided by our method with estimations coming from the literature on some known sources (either exoplanets, brown dwarfs or background sources) present in the eleven SPHERE-IRDIS datasets considered in this paper.
Characterization of synthetic sources To assess the performance of our method, we reproduce a real setting, hereafter called reference procedure, in which we estimate the flux of injected synthetic sources representative of (possibly unknown) exoplanets. To do so, we first inject synthetic test sources in the considered dataset, and then extract the patches used for training the model. It is crucial to perform these steps in that order otherwise the model could have the opportunity to learn the residual nuisance component below the test sources, and a bias could be introduced. Following this strategy, Fig. 21  shows the ARE score on the estimated photometry of synthetic sources massively injected with the reference procedure as a function of the their contrast and of their angular separation.
The fact that PACO can perform better than the proposed algorithm is due to the complexity to train a deep model, without leak between the train and the test sets, from a unique dataset of interest. This effect occurs only at short angular separations since this is the region of the field of view where generating multiple non-redundant training samples is the most tricky. As illustrated in Sect. 4. 2. 2, this side effect does not occur in the detection stage of the proposed approach thanks to the included whitening procedure, which removes most of the quasi-static speckles (i. e. , only residual structures non-captured by the statistical model remain). Except these residual structures that we aim to capture by deep learning, each new training set thus contain, before injection of synthetic sources, a quasi-random realization of uncorrelated Gaussian noise.
Efficient (and approximate) evaluation procedure When the massive injection of synthetic sources withing multiple datasets is needed to ground the performance of a built detector, the reference procedure described and applied in Sect. 4. 3. 1 is computationally expensive. The computational bottleneck is related to the need to train a new model for every test dataset containing a dozen of synthetic test sources. As an illustration, 30 different models must be trained to get a test set of only 300 samples. In this context and in a similar fashion to the detection stage (see Sect. 4. 2. 1 and Fig. 14 ), we also propose a fast and approximate version of the reference procedure, that is referred as the proxy procedure in the following. Instead of training a different model for each subset of injected sources, we train a unique model without additional synthetic sources mimicking the behaviour of real sources.
Synthetic sources are injected a posteriori, i. e. , after training. Finally, the trained model is applied on the resulting dataset. The gain in terms of algorithmic complexity (about a factor 2,000 with in average 5 synthetic sources in each subset) is similar to the one brought by the proxy procedure of the detection step. At this stage, we have to measure the ability of the proxy procedure to provide a fair estimate of the real performance that would be achieved with the reference procedure. Figure  23  periments, we considered around 3,300 synthetic sources spread over the whole field of view. We observe that the proxy procedure leads to results very close to the ones provided by the reference procedure, excepted near the star where the proxy procedure is over-optimistic, i. e. a positive bias is present (up to a factor five, at worst).
So, it remains to decide for a suited cut-off in terms of angular separation to switch from the reference to the proxy procedure. For that purpose, we resort to a binary hypothesis test. It performs a paired 𝑡-test  (Kendall et al. 1948) , where the H 0 (null) hypothesis represents the equality between the reference and the proxy procedures while the H 1 (alternative) hypothesis represents better results with the proxy procedure. From the results presented in Fig. 23 (a), we conduct in  Fig. 23(b)  this statistical test on the same datasets. It displays the resulting 𝑝-values as a function of the angular separation, and compares them with a 5% significance threshold. For the prescribed significance level, the two procedures can be considered as equivalent for angular separations larger than 0.
Characterization of real sources In this section, we compare the photometry estimations obtained with the tested algorithms on some real known sources present in the considered datasets. When available, we also compare them with measurements published in the literature that have been obtained either with the SPHERE Data Center implementations of TLOCI and PCA or with ANDROMEDA (for HD 95086 b only). For PCA (VIP), the reported uncertainties are computed from the residual combined image (i. e. , after subtraction of the on-axis PSF, derotation, and stacking) in an annulus at the same angular separation than the source of interest. For PACO, the photometry is estimated by accounting for the spatial correlations of the nuisance component. As concerns the proposed algorithm, and as for its detection stage, the control of the uncertainties is an intricate task given that the core of the deep model works as a black-box.
For that reason, we did not produce estimations of the standard-deviation, which is left for future work. For the experiments we conduct with PCA (VIP), PACO and the proposed method, we use the same pre-reduction of the raw observations and the same off-axis PSF template that has been measured prior the science observations. It is not ensured that these specificity also hold for photometry estimates extracted from the literature, which could induce (unknown) systematic variations that are not taken into account. Similarly, given the variability of the observing conditions between different observations, absolute comparisons between multiple epochs of the same target should be done with caution. Table  8  reports the photometry estimations on known real sources. Even in the absence of absolute ground-truth, we can make some relative comparisons for a given dataset and for a given source.
In that view, photometry estimates produced by the proposed method are compatible with published and/or obtained results with PCA (VIP) and PACO for almost all sources. The largest discrepancy is for CC5 of HIP 88399  (2018-04-11) , where a factor six lies between the estimates from PCA (VIP) and the proposed algorithm. Very likely, the PCA (VIP) estimate is not reliable as the source is located at large angular separation, i. e. in an area of the field of view where PCA (VIP) is prone to large errors (see Fig. 21 ). Besides, PACO and the proposed method lead to quite close estimations, which also supports the previous claim. Photometry estimates of CC-12 that we identified from HD 95086 datasets in Sect. 4. 2. 3 are consistent among the different algorithms. They are also consistent between the two epochs where CC-12 has been detected.
For CC-11 that we identified from the same datasets, we note a discrepancy by about 40% between the estimates obtained (i) by the proposed method, and (ii) by the other tested algorithms. However, this two groups of estimations are consistent between the 2015 and 2018 epochs. The discrepancy could be attributed to the presence of a bright background source (denoted by CC-8 in Fig. 20 ) that could lead to an overestimation of the photometry with algorithms that do not rely on a training step with synthetic sources. In any case, these two candidate point-like sources should be considered with caution. Table  8. Estimated photometry on some known real sources present in the considered datasets. For HD 95086, the (candidate) point-like sources are denoted as in Fig. 20. For HIP 88399 (respectively, HIP 72192), point-like sources are denoted by CC1 to CC5 (respectively, CC1 and CC2) through increasing angular separations.
CONCLUSION We have described the key principles of a new algorithm for detecting and characterizing point-like sources at high contrast from ADI observations. The detection stage combines the statistics-based model of PACO with deep learning in a three step procedure: (i) the data are centered and whitened using the PACO framework, (ii) a CNN is trained to detect synthetic sources from the pre-processed images, and (iii) a detection map is inferred. While the CNN itself works as a black-box approach, the proposed method encompasses prior domain knowledge such as the apparent motion of sources and the expected shape of the exoplanetary signal inside the ADI datasets. More importantly, the proposed detection approach capitalizes on the statistical model of the nuisance component embedded in PACO to improve the stationarity and the contrast during a pre-processing step.
Once a candidate source has been identified, its photometry can be estimated using a dedicated deep learning module, also trained in a supervised fashion. Tested on eleven SPHERE-IRDIS datasets, the proposed detection method performs better than standard algorithms of the field like PCA as well as PACO in terms of precision-recall trade-off. The detection sensitivity is improved by a factor between two and five in average with respect to PACO for the whole range of angular separations. This gain in even more important compared to baseline methods as cADI or PCA. Interestingly, we showed experimentally that the proposed approach is able to (mostly) reach the fundamental detection sensitivity driven by the photon noise limit for angular separations above 0. 5". This corresponds to an optimal extraction of the sought signals in that regime of separations.
The gain brought by the characterization stage of the proposed approach is more moderate but non-negligible. The absolute error of photometric estimation is reduced by a factor two with respect to PACO for sources of contrast up to 10 -6 located above 0. 5". Nearer the star, the advantage is on average either to PACO or to the proposed approach, depending on the dataset and of the source location. The gain brought by the characterization module of the proposed approach could allow to further constrain the physical properties of detected exoplanets, usually performed by fitting atmospheric models to the extracted photometry. Based on the analysis of these results, several lessons can be drawn regarding the deployed methodology. As a major point, this work illustrates the feasibility to build a complex model (with millions of parameters) of the nuisance component through supervised deep learning from a single ADI dataset of observations.
Our results also emphasize that deriving a flux estimate is a more complex task than providing a qualitative result related to the presence or to the absence of a source with our hybrid modeling of the nuisance component. In particular, we illustrate numerically that the whitening process is detrimental for source characterization (hence, it is not applied) because it modifies both the shape and the amplitude of the exoplanetary signature. In the absence of the whitening procedure, a shallower architecture should be used to avoid over-fitting. We are currently working on the extension of the proposed algorithm for the joint processing of multi-spectral datasets such as the ones provided by the SPHERE-IFS instrument using the angular plus spectral differential imaging technique. Besides, we are currently investigating the three main limitations of the proposed approach: (i) the lack of control of the uncertainties, (ii) its task-dependence which is not adapted to reconstruct spatially resolved objects like circumstellar disks, and (iii) the data-dependence of the learning procedure which does not take benefits from multiple observations to build a more general and robust model of the nuisance component.
Given the increased computational burden of the proposed detection approach with a GSM model by a factor between 10 and 30 (which represents the typical number of iterations needed to reach convergence of the estimators (A1)), we recommend, for practical reasons, to use the standard version of deep PACO embedding a multi-variate Gaussian model, as defined in Sect. 2. 1. 1. This is also the choice we made for the presentation of the results in the main core of this paper. The alternative version of the algorithm embedding a GSM model can be reserved to refine, in a second step, the reduction of some datasets with ambiguous detections. When comparing with state-of-the-art detection algorithms, we use for PACO the version embedding a GSM model to compare fairly the proposed method against the best setting of existing methods.
√ 𝒂 computed on the fly. This strategy explicit rewards the deep model to perform better than PACO. Table  A2  compares, with the same procedure as described in Sect. 2. 2. 3, the detection performance in terms of precision and recall of the proposed algorithm using either the whitening procedure described in Sect. 2. 1. 2, and the whitening procedure described in this Appendix. It shows that for the eleven SPHERE-IRDIS datasets we study in details in this work, the variant approach accounting for the transformation induced by the whitening process on the off-axis PSF leads only to a slight improvement of the overall detection performance. This is due to the fact that none of the eleven considered datasets was recorded under bad observing conditions. Figure  A1  gives a qualitative comparison between the two approaches for a dataset of HR 8799 (see Sect.
4 for the recording logs) impacted by the wind-driven halo effect. The variant procedure described in this appendix allows to avoid numerous evident false alarms occurring when the normalization of the pre-processed frames is omitted. Compared to the whitening procedure described in Sect. 2. 1. 2, the variant described in this appendix has a computational burden increased by a factor 4 × 𝐾, i. e. by typically a factor between 200 and 500 for the VLT/SPHERE instrument. For practical reasons, we recommend to use the standard whitening procedure by default, as defined in Sect. 2. 1. 2. This is also the choice we made for the presentation of the results in the main core of this paper. The alternative version of the algorithm accounting for the whitening of the offaxis PSF can be reserved to refine, in a second step, the reduction of some datasets for which obvious and numerous false alarms are experienced at inference time.
ACKNOWLEDGEMENTS We thank the anonymous referee for her/his careful reading of the manuscript as well as her/his insightful comments and suggestions. This project is supported in part by the  European Research Coun-cil (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme (COBREX ; grant agreement  n°885593 ). The work of TB and JP was supported in part by the  Inria/NYU collaboration , the  Louis Vuitton/ENS chair on artificial  intelligence and the  French government  under management of  Agence Nationale de la Recherche  as part of the  Investissements d'avenir program , reference  ANR19-P3IA0001  ( PRAIRIE 3IA Institute ). The work of JM was supported in part by the  ERC  grant number  714381  ( SOLARIS  project) and by  ANR 3IA MIAI@Grenoble-Alpes  ( ANR-19-P3IA-0003 ).
DATA AVAILABILITY The raw data used in this article are freely available on the ESO archive facility at http://archive. eso. org/eso/eso_archive_main. html. They were pre-reduced with the SPHERE Data Centre, jointly operated by OSUG/IPAG (Grenoble), PYTHEAS/LAM/CESAM (Marseille), OCA/Lagrange (Nice), Observatoire de Paris/LESIA (Paris), and Observatoire de Lyon/CRAL (Lyon, France). The resulting preprocessed datasets will be shared based on reasonable request to the corresponding author.
Examples of estimated spatial covariance matrices are displayed in squares for four regions of interest pickled at about 0. 5 (pink), 1. 0 (green), 1. 5 (purple), and 2. 0 (orange) arcsec. It results from this pre-processing step centered and whitened observations from which our detection and characterization models are built by supervised deep learning, see Fig. 2. Dataset: HIP 72192 (2015-05-10), see Sect. 4 for the recording logs.
The experiments were conducted by resorting to massive injections of synthetic sources, and known real sources were excluded of this study. The scores are detailed for the eleven SPHERE-IRDIS datasets considered in this work, see Sect. 4 of the main paper for the observation logs. Algorithm (1), (2), (3), (4), and (5) stands respectively for cADI, cADI (VIP), PCA (VIP), PACO, and the proposed deep PACO algorithm. For each range of angular separations, the best results are emphasized in bold.
observations 𝒓 ∈ R 𝑁 ×𝑇 observations 𝒓 ∈ R 𝑁 ×𝑇 strategy for known sources (training only)𝑛 (𝒓 𝑛 -𝒎 𝑛 ) s. t. L 𝑛 L ⊤ 𝑛 = C -1 𝑛 , ∀𝑛 ∈ P (default) or concat. 𝑛 𝒉 ⊤ C -1 𝑛 (𝒓 𝑛 -𝒎 𝑛 ) 𝒓 ∈ R 𝑁 ×𝑇 pre-processed observations 𝒓 ∈ R 𝑁 ×𝑇 ▶ generation of the training set input pre-processed observations 𝒓 ∈ R 𝑁 ×𝑇 pre-processed observations 𝒓 ∈ R 𝑁 ×𝑇 pre-processing update on injection arcs 𝑀 photometry estimates 𝛼 ∈ R + deep PACO: Combining statistical models with deep learning for exoplanet detection and characterization in direct imaging at high contrast supplementary material Olivier Flasseur, Théo Bodrito, Julien Mairal, Jean Ponce, Maud Langlois, Anne-Marie Lagrange This supplementary material complements the corresponding main paper by presenting detailed results for SPHERE-IRDIS datasets we considered in this work.
Since the notion of observing conditions is relative and can be characterized by several metrics (e. g. , Strehl ratio, coherence time, air mass, etc. ), we did not find quantitative values for these measures indicating the strict use of the variant version described in Appendix A2. This large effort would require the processing of hundreds of datasets, which is left for future work. Qualitatively, we observed that datasets impacted by a bright wind-driven halo, displaying the same apparent motion than the objects of interest, are more subject to lead to an increased false alarm rate without the pre-processing described in Appendix A2. MNRAS 000,1-28 (2023).
These modules were highly similar within canonical frequency bands and comprised anatomically contiguous regions up to the high-gamma frequency band. Finally, we used Neurosynth meta-analysis decoding  ( Yarkoni et al. , 2011 )  to reveal that the observed modules comprised cortical regions exhibiting shared cognitive functions, suggesting that these modules correspond to brain systems with specific functional roles. Hence, the modules identified might serve the regulation of balance between segregation and integration of neuronal activity through phase-synchronization.
Analysis pipeline to identify modules in connectomes of phase-synchronization We combined pre-surgical SEEG recordings from epileptic patients with state-of-the-art methods, to identify modules in connectomes of phase-synchronization. We recorded resting-state LFP data from each patient using a common reference in white matter, distant from the putative epileptogenic zone. We re-referenced the LFP activity of each gray-matter SEEG contact to its closest white-matter contact, which we have demonstrated to preserve undistorted phase reconstruction while minimizing volume conduction  ( Arnulfo et al. , 2015a ). We filtered the recorded LFP data using 18 narrow-band Finite Impulse Response (FIR) filters ( Fig. 1  A) from 2. 5 Hz up to 350 Hz with line-noise suppressed using band-stop filters at 50 Hz and harmonics.
Data acquisition We recorded SEEG data from 67 participants affected by drugresistant focal epilepsy and undergoing pre-surgical clinical assessment. For each participant, we inserted 17 ± 3 (mean ± SD) SEEG shafts into the brain, with anatomical positions varying by surgical requirements. Each shaft had between 8 and 15 platinum-iridium contacts, each contact being 2 mm long and 0. 8 mm thick, with inter-contact distance of 1. 5 mm (DIXI medical, Besancon, France). We acquired 10 min eyesclosed resting-state activity from each participant, via a 192-channel SEEG amplifier system (Nihon Kohden Neurofax-110) at a sampling frequency of 1 kHz. We obtained written informed consent from participants prior to recordings. We obtained ethics approval for the study from Niguarda "Ca' Granda " Hospital, Milan, and we performed the study according to WMA Declaration of Helsinki -Ethical Principles for Medical Research Involving Human Subjects.
Pre-processing We performed re-referencing, filtering and artefact removal of the SEEG data, before estimating the connectome of phase-synchronization. We originally recorded data from all contacts with a monopolar referencing scheme. We subsequently re-referenced activity from each gray-matter contact to the nearest white matter contact as identified by GMPI (gray matter proximity index). We have previously demonstrated the utility of this referencing scheme in studying phasesynchronization, since phase relationships between contacts are well preserved  ( Arnulfo et al. , 2015a ). We only analysed activity from graymatter contacts after re-referencing. We filtered activity from each graymatter contact using FIR filters (equiripples 1% of maximal band-pass ripples) into 18 frequency bands, with center frequencies (   ) ranging from 3 to 320 Hz (excluding 50 Hz line-noise and harmonics).
We estimated the connectome of phase-synchronization as the group-level phase-synchronization between every pair of 148 regions in the Destrieux brain atlas for which we had at least one SEEG contact-pair. We then thresholded the connectome by retaining the estimated strengths of only the top 20 percentile of connections, setting all others to 0. We performed this thresholding as a means of emphasizing the topological organization of the connectome  ( Rubinov and Sporns, 2010 ). We also determined the robustness of our results to the specific choice of percentile threshold, by also identifying modules on connectomes thresholded by retaining the strengths of the top 10 and top 30 percentile of connections. Since we did not have complete recording coverage of the brain with SEEG, we had insufficient data to estimate phase-synchronization of all connections in the group-level connectome.
We chose the Louvain method due to its superior performance in accurately identifying network modules compared to alternative community detection methods  ( Lancichinetti and Fortunato, 2009 ) , and its superior performance, when combined with consensus clustering, in recovering modules in incomplete brain networks  ( Williams et al. , 2019 ). We used the implementation of the Louvain method in Brain Connectivity Toolbox  ( Rubinov and Sporns, 2010 ). We applied the Louvain method to left and right hemispheric regions separately, since the low number of interhemispheric connections might confound the identification of modules. To identify modules while accounting for missing values in the grouplevel connectome matrix, we first generated 5000 variants of the connectome wherein we replaced each missing value with a randomly sampled (with replacement) existing value from the group-level connectome.