dataesr/scientific-paragraphs-categorization huggingface dataset:
In figure  5. 11c we also show the retrieved trace obtained while constraining the fundamental spectral intensity to the measured one. The retrieval quality is slightly worse (1. 78% error), which is expected as explained before. Finally, in the last step of the retriever module, we allow the user to propagate the retrieved pulse through a chosen amount of common optical materials (air, fused silica, KDP…) and to observe the resulting temporal shape. This allows a quick (but manual) feedback with the experiment: exactly how much material should be added to obtain the shortest pulse on-target? How can the measured spectral phase best be compensated? In figure  5. 11e, inserting 0. 50 mm of glass gives the optimal pulse duration (5. 88 fs FWHM). The retrieval with free or fixed fundamental spectra gives very similar pulse profiles for this dataset.
Conclusion We have presented the capabilities of the PyMoDAQ-Femto module, which allows to simulate and retrieve PNPS traces. The possibility of quickly visualizing the shape of various traces is particularly helpful to understand measurement techniques for short femtosecond pulses. Besides its educational value, this open-source software can also be used in real conditions with experimental data, as we demonstrate in a hollow-core fiber post-compression experiment. When set up in combination with PyMoDAQ, it allows to measure femtosecond pulse reliably and transparently to the user, regardless of the used hardware. We hope that the large flexibility in pre-processing and retrieving options encourages its use in diverse situations, and stimulates collaborative contributions to the software.
The most challenging part is the wavefront distortions introduced by the sampling system. In the HPLS case, these distortions were separately measured using an auxiliary laser in a double pass configuration, which was sent through a high quality, small aperture beam splitter placed in front of the wavefront sensor through the telescopes and back reflected with a high flatness mirror back on the same optical path to the wavefront sensor. The recorded distortions were below a tenth of a wavelength in our case and they are now subtracted from the raw wavefront data in order to provide accurate information on the pulse wavefront after compressor. On the temporal and spectral characterization side, a similar situation appears. The pulses at the exit of the compressors pass through a specially designed flat spectral transmission leaky mirror and through the compressor window.
S ð3Þ x 1 ; t 2 ; x 3 ð Þ¼ Z 1 À1 dt 1 Z 1 À1 dt 3 S ð3Þ t 1 ; t 2 ; t 3 ð Þ e Àix (e) Example of a typical plot ω probe vs. T obtained as a result of a pump-probe experiment. (f) Pictorial representation of the matrix dataset obtained with a 2DES experiment; the two frequency axes ω 1 and ω 3 are obtained by Fourier transforming the delay times t 1 and t 3. The evolution of the 2D (ω 1 , ω 3 ) maps is followed along t 2. Coherent Multidimensional Spectroscopies.
Acknowledgements The author wishes to thank her colleagues from the  Laboratoire d'Optique et Biosciences , and is very grateful to  Manuel Joffre  for his invaluable help in this work. More detailed information on femtosecond pulses can be found in the textbooks referenced in the bibliography, and the first sections of this document are largely inspired by courses dispensed by  Manuel Joffre  and available on https://cel. archivesouvertes. fr/cel-00092964v4. I also recommend the courses of the previous  Ecole Femto  with special thanks to  Frédéric Druon ,  Marc Hanna , and  Ammar Hideur.
Acknowledgements It was an honor for me to prepare this lecture, thank you to the organizers. Part of this document was "directly inspired" by the PhD works of  Sophie Kazamias ,  Sameh Daboussi ,  Fabrice Sanson ,  Yann Mairesse ,  Antoine Comby , and a review by  Thierry Ruchon  on attosecond science. I want to thank all the  French community of HHG. I am proud to be one of them. I hope this work will help young researchers take an active part in the progress of this field of science. I want to warmly thank  Kristal Bontemps  and  Alok Pandey  for their help in editing equations and figures and correcting my English. Thank you also to the reviewers who helped increasing the quality of the document. High-Order Harmonic Generation (HHG):  From Concept to Applications Acknowledgements Extreme Light Infrastructure Nuclear Physics (ELI-NP) Phase II , is a project co-financed by the  Romanian Government  and the  European Union  through the  European Regional Development Fund  and the  Competitiveness Operational Programme  ( 1/07.
Acknowledgements The author thanks all students and colleagues who contributed to the results mentioned in this lecture:  J Duboisset ,  P. Gasecka  ( SHG, CARS, 2PF );  C. Rendon-Barraza ,  N. Kumar Balla  (SHG in metal nanoparticles);  M. Hofer ,  H. B. De Aguiar ,  S. Gigan  (polarization in scattering media).
The LO is aligned in the same direction as the emitted signal (defined by phase-matching conditions) so that the measured quantity indeed results from the interference between the signal and the LO (heterodyne detection). (e) Example of a typical plot ω probe vs. T obtained as a result of a pump-probe experiment. (f) Pictorial representation of the matrix dataset obtained with a 2DES experiment; the two frequency axes ω 1 and ω 3 are obtained by Fourier transforming the delay times t 1 and t 3. The evolution of the 2D (ω 1 , ω 3 ) maps is followed along t 2.
Time series Figure  6  shows the time series of the global mean fraction of cloud-free scenes for the NN, the EUMETSAT IASI-L2 and the CIRS-LMD cloud masks, derived from  Metop-A and Metop-B between 2008 (2013 for Metop-B)  and 2020. Currently, the CIRS-LMD data are only available until 2019. Results are presented for land and sea observations together and separate. Both the NN and the CIRS-LMD cloud products show an excellent stability over time, with no visible trends and a very good consistency between the two Metop instruments (absolute difference lower than 1 % on average for the NN). In contrast, the L2 cloud-free time series reveal sharp discontinuities coinciding with version changes. In addition, a clear positive trend is also visible between 2012 and 2020, likely due to the CO 2 concentration increase in the atmosphere.
found  an association between accelerated cortical thinning and attentional impulsivity, particularly in the DPFC region identified as being related to cannabis use  [1]. In total, this literature suggests that accelerated prefrontal thinning resulting from cannabis use is likely to be a negative prognostic factor for mental health outcomes. Bayesian causal network (BCN) modeling is a method for estimating the directional relationships between a set of differentially related variables  [15]. This approach models the relationships among variables as a directed acyclic graph using the conditional dependencies among variables. The structure of a BCN can be estimated through use of structure learning algorithms. These algorithms apply an emerging understanding of how directional relationships are predicated on conditional dependence to determine the BCN that best represents the joint probability distributions of a dataset  [16].
From this measure, the trait sensation seeking was selected for BCN modeling, as it was the trait with the largest bivariate relationship with cannabis use at age 19 (r = 0. 29 at age 19). Pubertal Development Scale. Pubertal status was assessed using the pubertal development scale, a self-report measure completed by the participant. This measure has been shown to have good reliability and to correspond with accepted self-report and biological measures of pubertal development  [25]. Socioeconomic status. A composite of socioeconomic status (SES) was derived by aggregating: Mother's Education Score, Father's Education Score, Family Stress Unemployment Score, Financial Difficulties Score, Home Inadequacy Score, Neighborhood Score, Financial Crisis Score, Mother Employed Score, and Father Employed Score in a manner consistent with prior work in the IMAGEN dataset  [26].
Demographic questionnaire. Children's age, sex, and handedness were measured using a demographic questionnaire completed by their parent/ guardian. Structural MRI. Structural magnetic resonance image (MRI) data were acquired with a 3-dimensional T1-weighted magnetization prepared gradient echo sequence based on the one used in the Alzheimer's Disease Neuroimaging Initiative protocol  [27]. These T1 images were processed using the CIVET pipeline (v2. 1. 0) on the CBRAIN platform using Compute Canada. In the current project, average cortical thickness was extracted from the regions of interest identified in Albaugh et al. that showed a dose-dependent response to cannabis use. This region spanned the left and right dorsomedial and dorsolateral prefrontal cortices and is hereafter referred to as the dorsal prefrontal cortex (DPFC).
For consistency with prior work using BCN modeling in the IMAGEN dataset  [18] , we used a principal components analysis to create a single variable for cortical thickness in both hemispheres. All variables (cortical thickness, cannabis use, other substance use, age, puberty, polygenic risk score for cannabis use, childhood trauma, socioeconomic status, handedness, and ADHD symptoms) were residualized for site and sex. This single variable was derived from the first principal component. This was done for consistency with recent work using BCN modeling focused on alcohol and brain morphometry in the IMAGEN dataset  [18]. This was done for DPFC thickness at age 14 (eigenvalue = 1. 8; percent of variance explained = 92%) and the change in DPFC from ages 14 to 19 (eigenvalue = 1. 7; percent of variance explained = 87%).
Polygenic risk score. In calculating a polygenic risk score (PRS) for cannabis, genotypes were imputed using the 1000 Genomes Project phase 3 reference panel for Europeans using the Michigan Imputation Server  [28]. SNPs that did not meet quality control criteria (Minor Allele Frequency < 0. 01; Genotype Call Rate < 95%; Hardy-Weinberg Equilibrium < 1 × 10 -6 ) were excluded. Genetic variants imputed with lower accuracy (R 2 < 0. 6), insertion/deletions and palindromic SNPs were excluded, resulting in 5,183,147 SNPs. The PRS was calculated based on a genomewide association study (GWAS) conducted in an independent sample. This GWAS described the genetic correlates of total lifetime frequency of cannabis use using data from the International Cannabis Consortium, UK Biobank, and 23andMe datasets  [29].
Then BCN analyses were conducted using the package bnlearn  [16]  in R following a template of the analyses conducted in Robert et al. [18]. Code for this study is available at https://github. com/owensmax/ Cannabis_Bayesian_Networks. First, a list of "blacklisted" pathways was set (Supplementary Table  2 ), which are pathways that cannot exist for logical reasons (e. g. , age 19 cannabis use affecting sex at birth). Then the structure learning algorithms were applied to derive directed acyclic graphs representing the directional relationships between variables. To derive statistics on the BCN models, the algorithms were run 10,000 times with bootstrap re-sampling (i. e. , samples drawn randomly with replacement) for samples 100% of the size of the entire dataset. From these bootstrapped BCN models a summary model is derived aggregating their results.
These algorithms apply an emerging understanding of how directional relationships are predicated on conditional dependence to determine the BCN that best represents the joint probability distributions of a dataset  [16]. In score-based algorithms, the presence and direction of the relationships between variables are determined by testing numerous, randomly generated potential BCN models to evaluate which configuration of directed edges has the best model fit  [31, 32]. In constraint-based algorithms, the specific tools used to determine the directional relationship between variables are statistical independence tests, which are combined with known rules about how the joint probability of three variables provides information about their directional relationship  [32, 33]. In hybrid algorithms, key elements of score-based and constraint-based algorithms are combined to leverage the strengths of each approach.
g. , ecological momentary assessment of cannabis use) the effect sizes seen in bivariate analyses may have been larger. However, a correlation of 0. 17 is not insignificant either, particularly when compared to other meaningful effect sizes found in similar datasets  [39]. Prior research has estimated normative cortical thinning of around 1% annually, which comes out to around 0. 03-0. 06 mm per year  [8] [9] [10] [11]. Results of the current study were highly consistent with this prior literature, as the average percent reduction in cortical thickness from ages 14 to 19 was 3. 6% (i. e. , 0. 12 mm reduction in thickness). In cannabis-using participants, the average percent reduction was 4. 4% (0. 14 mm reduction in thickness), compared to an average reduction of 3. 1% in cannabis abstinent participants (a 0.
ACKNOWLEDGEMENTS The IMAGEN consortium includes:  Tobias Banaschewski, M. D. , Ph. D. ; Gareth J. Barker, Ph. D. ; Arun L. W. Bokde, Ph. D. ;  Sylvane Desrivières, Ph. D. ;  Herta Flor, Ph. D. ;  Antoine Grigis, Ph. D. ; Hugh Garavan, Ph. D. ; Penny Gowland, Ph. D. ;  Andreas Heinz, M. D. , Ph. D. ; Rüdiger Brühl, Ph. D. ; Jean-Luc Martinot ,  M. D. , Ph. D. : Marie-Laure Paillère Martinot, M. D. , Ph. D. /Eric Artiges, M. D. , Ph. D. ; Frauke Nees, Ph. D; Dimitri Papadopoulos Orfanos, Ph. D. ,  Herve Lemaitre, Ph. D; Tomáš Paus, M. D. , Ph. D. ;  Luise Poustka, M. D. ,  Sarah Hohmann, M. D. ,  Sabina Millenet, PhD. ;  Juliane H. Fröhner , MSc;  Michael N. Smolka ,  M. D; Henrik Walter, M. D. , Ph. D. ;  Robert Whelan, Ph. D. ;  Gunter Schumann, M. D. ImagenPathways  " Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways " is a collaborative project supported by the This work was funded by  NIH/NIDA   T32DA043593  and  R01DA047119.
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ) and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.
We present BlendHunter, a proof-of-concept deep-transfer-learning-based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic Canada-France Imaging Survey (CFIS)-like images. We compare the performance of this method to SEP (a Python implementation of SExtractor) as a function of noise levels and the separation between sources. We find that BlendHunter outperforms SEP by ∼15% in terms of classification accuracy for close blends (<10 pixel separation between sources) regardless of the noise level used for training.
Blended sources make up a significant fraction of the observed sources in survey images: >30% for the Dark Energy Survey (DES,  Samuroff et al. 2018) , >50% (up to >60% and >70% for the Deep and UltraDeep layers, respectively) for Hyper Suprime-Cam (HSC,  Bosch et al. 2018) , and >60% for Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST,  Sanchez et al. 2021). Simply removing all the blends that have been identified would significantly reduce the sample size and could potentially lead to entangled biases in the shear correlation function  (Hartlap et al. 2011). Additional  In the spirit of reproducible research, all code and data needed to reproduce the results in this paper have been made publicly available on GitHub (https://github. com/CosmoStat/BlendHunter) without any restrictions.
2020) , the separation of low-surface-brightness galaxies from artefacts  (Tanoglidis et al. 2021) , the classification of planetary nebulae (Awang  Iskandar et al. 2020) , and the deblending of galaxy images  (Arcelin et al. 2021). This work introduces a proof-of-concept deep transfer learning approach, called BlendHunter (BH), for the automated and robust identification of blended sources in single-band galaxy survey data. This method incorporates a convolutional neural network (CNN) trained on a large database of natural images and a fully connected layer for classification. Simple parametric models are used to train the fully connected layers and the learned weights are in turn used to identify blended sources in more realistic images. This paper is organised as follows. The following section introduces the properties of the training and testing data.
Given the size of the postage stamps, we expect that standard source-extraction software, such as SExtractor, will be easily able to identify and separate sources near the borders but may struggle as the sources get closer together. To better highlight this specific problem, we additionally separate blends into two categories: 'close blends' and 'distant blends'. We define close blends as postage stamps in which the two sources are separated by less than ten pixels and distant blends as those in which they are separated by ten or more pixels. To avoid any spurious correlations, blended images are produced by simulating the galaxies individually and then artificially combining them. For simplicity, we only consider blends consisting of two sources. Real images may well contain cases involving more than two sources as well as various artefacts that would make classification more complicated.
This makes it possible to re-sample the images on a larger pixel scale and to convolve them with a different PSF in order to mimic observations from another instrument. Finally, the galaxies have been fitted by either a Sérsic profile or a bulge + disc profile for which the bulge is represented by a De Vaucouleurs profile  (de Vaucouleurs 1948)  and the disc by an exponential profile (see  Mandelbaum et al. 2012, for details). The subset of processed COSMOS data has been made available through the Galsim software package  (Rowe et al. 2015). GalSim is widely used in the astrophysics community to simulate and manipulate galaxy images and was used extensively in several weak lensing challenges, such as GREAT3  (Mandelbaum et al. 2014). The package provides all the tools necessary to work with COSMOS data.
Parametric model training data In order to keep our training samples as simple and generic as possible, we use a series of parametric models derived from fits to the COSMOS sample described in Sect. 2. 2. This constitutes a range of simulated galaxies with different sizes, shapes, and ellipticities. The parametric models are then convolved with the CFIS-like PSF described in Sect. 2. 3. Each image corresponds to a 51 × 51 pixel postage stamp with a convolved galaxy model at the centre. For half of the sample, a second galaxy model is placed at a random position within the postage stamp to produce blends according to the definition provided in Sect. 2. 1. We pad all of the images with zeros (7 pixels in every direction) to avoid issues when the secondary source is close to the border of the postage stamp.
The VGG-16 network The blocks are separated by max-pooling (i. e. , down-sampling) layers. Max-pooling is performed over 2 × 2 windows with a stride of 2. The five blocks of convolutional layers are followed by three fully connected layers. The final layer is a soft-max layer that outputs class probabilities. The full network architecture used is shown in Fig. 1. The VGG-16 network was chosen for the work presented here for several reasons. Firstly, the network can be implemented with weights pre-trained on the ImageNet database  (Deng et al. 2009 ) in order to save computation time and resources. The diversity of this data set has allowed the network to learn a variety of generic image features applicable to most imageclassification tasks. Finally, VGG-16 has already been applied in a variety of astrophysical problems, including galaxy morphology classification  (Wu et al.
Convolutional layers In the first phase, the we simply initialise the convolutional layers (i. e. , the first 13 weight layers) with the pre-trained ImageNet weights. As mentioned in the previous section, the purpose of using pre-trained weights is to save processing time. Therefore, this part of the network is not trained with our data and can be seen as a simple feature extractor. As ImageNet weights are used, the VGG-16 network expects RGB images as inputs. Therefore, we rescaled the simulated images to the range i ∈ Z : 0 ≤ i ≤ 255 and the monochromatic pixel values were repeated across the three RGB channels. The final images are saved as portable network graphics (PNG) files which are fed into the network. Given the relatively small amount of training data, we additionally implemented data augmentation in order to obtain more diversified features from the convolutional layers of the network.
SExtractor benchmark We compared the performance of BlendHunter with SExtractor, as the latter is the most widely used tool in the community for identifying and handling blended sources in astronomical images. The objective was to test the reliability of our approach compared to the state of the art. Specifically, we made use of the SExtractor Python wrapper SEP  (Barbary 2016 ) for our tests. We note that SEP does many things beyond blend identification and many of these steps cannot easily be isolated; however we tried to make the comparison as fair and consistent as possible. SEP implements a multi-thresholding technique to decompose detected sources into subsources (when possible). This method takes two input parameters: the number of bins to decompose the light profile and the minimum contrast value between the main peak and a given subpeak.
Conclusions We present a proof-of-concept deep transfer learning approach for the automated and robust identification of blended sources in galaxy survey data called BlendHunter. This technique uses convolution layers pre-trained on natural images from Ima-geNet, thus significantly reducing the time required for training. The fully connected layers are then trained on simple parametric models derived from COSMOS data for various levels and realisations of Gaussian random noise. Comparison with the community standard SEP (a Python implementation of SExtractor) on 70 sets of 8000 parametric models demonstrates that BlendHunter is significantly more sensitive to blends when the images are very noisy or when galaxy pairs are very close (<10 pixels). In the low-noise distant blend regime, the performance of BlendHunter appears to be consistent with that of SEP.