dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The main goal of CybserSco. Py is to enable the design of augmented MDA experiments in which the imaging settings can be changed in real-time as a function of unsupervised image analysis conducted during the time course of the experiment. CyberSco. Py has been built as a modular web application; in addition to the modularity of the device management, both the image analysis and decision-making algorithm are separate modules that can be adjusted individually and plugged into the communication modules that drive the microscope and its associated components. In that respect, CyberSco. Py aims to push forward "low code" or "no code" strategies, which are becoming increasingly popular, and enable biologists without coding expertise to create complex, event-based routines and workflows using cloud-based web applications.
Creation of automation protocols that contain simple logical, conditional statements such as "IF this THEN that" or "WHILE this DO that" are within the reach of CyberSco. Py and may have an impact for researchers in biology who do not want, nor have the time or expertise, to dive into programming. This goal could be reached progressively by building on the examples proposed here and adding generic scenarios that are driven through the results of real-time image analysis. For example, the scenario developed in this work that acts on a microfluidic chip based on the number of cells could be directly reprogrammed by changing the trigger condition. Image analysis remains the bottleneck of automation and will continue to require expertise in machine learning and coding. However, the pace at which deep learning is being adopted in laboratories and adapted into user-friendly software solutions and online tools  22, 23  suggests that an increasing number of easy-to-use methods to create UNET-trained networks will be developed in the near future and could subsequently be incorporated into CyberSco.
Py. While we modified several classic experiments to demonstrate the potential of event-based microscopy, rethinking automation software for microscopy and including complete management of triggers and event detection may provide other important benefits in quantitative cell biology. To start with, we can use our conditional microscopy framework to better prepare experiments and obtain a level of quality control before starting an experiment. Indeed, we showed that the number of cells may be an important factor when exploring the dynamics of population growth after a metabolic shift. Other processes related to cell-cell communication, metabolic gradients and cell-cell contact inhibition are also likely to be dependent on cell density. To increase experimental reproducibility, it seems reasonable to add a condition (or a set of conditions) on cell density to start a timelapse experiment.
This could be in either a trigger mode, with the software sending a status report to the user through all sorts of classic communication channels (e. g. Slack, Teams, Email, SMS) when something goes wrong or the experiment reaches a given state, or-even better-the microscope could use the output of the image analysis to correct the problem automatically (e. g. , by relaunching an autofocus step). Such simple automation workflows will certainly help to achieve high-quality data, reduce the time and cost of experiments, and improve experimental reproducibility. Another important aspect is the huge amounts of data acquired in conventional, uninformed MDA. In many cases, this is due to the fact that image analysis is performed a posteriori, which requires as much data as possible to be acquired given the constraints of the imaging system and the biological sample.
Our motivation to develop this proof-of-concept software with a simple user-friendly interface was to introduce conditional microscopy to a large audience. While this first step is relatively limited, the initial framework we propose here can be improved and developed further. Ideally, a global effort to develop application programming interfaces (APIs) for lab automation would facilitate the development of no-code workflows that are accessible to all researchers and integrate with commonly used collaborative online tools such as Slack and Teams. We believe that researchers could benefit from such advanced ways of conducting experiments, especially the ability to perform event-based automated imaging. CyberSco. Py is a first step to bring such concepts to the attention of biologists.
Predictions with this model are obtained in around 0. 2 seconds. Cell tracking is performed using both image correlation in real-time and using a simple proximity relationship between the predicted contours. CyberSco. Py framework. A. Architecture. CyberSco. Py is built in Python and uses the web application library Flask to create a web user interface. Microscopy protocols are written into a YAML (human readable data serialization language) file, which can be interpreted by the Python core module of CyberSco. Py, which drives the various components of a IX81 fully automated microscope. The core module also drives a set of fluidic valves that can be used to switch the media flowing into a microfluidic device. A class in Python is associated to each device. Images obtained from the camera are analyzed in realtime by a U-NET deep learning model to segment yeast cells and/or detect specific events, depending on the pre-trained model selected by the user.
545 nm/40 (49905 -ET) with a 500 ms exposure time. Microscopy experiments were carried out in a thermostat chamber set to 30 °C. CyberSco. Py softwareCyberSco. Py is written in Python for the backend and HTML/CSS/ JS for the frontend, connected by a WebSocket channel. Communication to the different devices is made directly through serial communication and whenever necessary, the drivers provided by the vendors or a generic version from the µManager community. CyberSco. Py is installed on computer software that must be equipped with a recent GPU to benefit from U-NET deep learning segmentation of cells. A server can interface several microscopes running CyberSco. Py and be extended with a user manager database and image database management program such as OMERO. The current opensource release of CyberSco.
Py can be found on GitHub (https://github. com/Lab513/CyberSco. Py)Acquisition, segmentation and trackingImage acquisition is preceded by an autofocus algorithm that optimizes the quality of the segmentation, as well as the sharpness of the object under scrutiny. Cell segmentation is achieved via a machine-learning algorithm based on the U-NET architecture. Twenty images of cells at different positions and containing different numbers of cells were taken to produce the training set. This dataset was then augmented as it is classicaly done. (see supplementary information).
(2010). Redshift and luminosity distances (fifth and sixth column, respectively) are taken from NED, assuming H 0 = 67. 8 km s -1 Mpc -1 , Ω matter = 0. 308, Ω vacuum = 0. 692. The NASA/IPAC Extragalactic Database (NED) is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology. The X-ray illumination of the accretion disc makes one implicit prediction. Since most of the X-rays will be absorbed by the disc, they will add to its heating. This externally supplied energy will appear in the form of thermal emission at UV/optical wavelengths, which should be variable (since X-rays are highly variable in AGN) and correlated with the X-rays, with some delay. Kammoun et al. (2021a, hereafter K21)  studied in detail the response of a NT disc to X-ray illumination by a point-like source, located above the central BH, taking into account all relativistic effects, and using the latest X-ray reflection models.
Quasar micro-lensing variability sample We consider the quasar sample of  Morgan et al. (2010) , which consists of 11 objects with accretion disc microlensing measurements using multi-epoch light curves. We added half-light radius measurements for four objects from  Cornachione & Morgan (2020). The sources and data are listed in Table  1. Black hole mass (M BH ) measurements are taken from the literature. They are based on the observed C iv and Mg ii line widths (Hβ for RXJ 1131-1231), and locally calibrated virial relations for black hole A11, page 2 of 10 masses. The statistical uncertainties on the BH mass measurements are quite small, but the errors are dominated by systematic uncertainties. We assumed a systematic uncertainty of 0. 3 dex (e. g. Vestergaard & Peterson 2006)  for all the BH mass measurements listed in Table  1.
The measurements are taken from  Chen et al. (2012) , who presented the results from the analysis of Chandra monitoring data of six gravitationally lensed quasars, which also belong in our sample (namely: QJ 0158, HE  0435, SDSS 0924, SDSS 1004 , HE 1104 , and Q 2237). These authors measured the X-ray luminosity (corrected for magnification) in various bands. We chose to compare the model with the measurements in the 10-50 keV band, in order to minimize any complications due to the (possible) presence of absorption, either in the host or the lensing galaxy. Figure  6  shows that the model predictions are fully consistent with observations. We stress that the model X-ray luminosity plotted in this figure is the one that is needed to heat the disc so that the disc size and luminosity remains consistent with the observations.
In addition,  Kammoun et al. (2021b)  assumed f col = 2. 4, while we assume the colour-correction factor of  Done et al. (2012). We are currently developing a new code to fit the optical/UV time lags, for any combination of the X-ray heating source, spin, and colour-correction term. These results will be published soon. Our preliminary results show that we can fit the optical/UV time-lags assuming X-ray disc illumination and the same assumptions we adopt in this work. A11, page 9 of 10 Our results offer strong support to the X-ray disc illumination hypothesis in AGN. We plan additional tests of the disc X-ray illumination hypothesis by considering broadband AGN SEDs (in particular, SEDs that include short wavelength data). Although it is not straightforward to use KYNSED to fit SEDs for individual AGN if they have been constructed using contemporaneous data (see the discussion in Sect.
Acknowledgements. We thank  H. Netzer , the referee, for his helpful and constructive comments that helped us improve the manuscript significantly. I. E. P. acknowledges support by the project " Support of the international collaboration in astronomy (ASU mobility )" with the number:  CZ. 02. 2. 69/0. 0/0. 0/18-053/0016972. ASU mobility is co-financed by the  European Union. M. D. thanks for the support from the  GACR  project  21-06825X  and the institutional support from RVO:67985815. E. S. K. acknowledges financial support from the  Centre National d'Etudes Spatiales (CNES).
The architecture of the MTSP project (Figure  1 ) is organized around three main tasks: -Automation procedures (OCA) -The real time data processing and archiving procedures (OCA) -The telescopes and data acquisition system (OP) The assembly of three telescopes (two Hα 6563 Å channels for solar activity, and a CaII K 3934 Å magnetic proxy) is built by OP. A new housing and equatorial mount have been installed at Calern observatory (1270 m, OCA, Figure  2 ). The instruments (Figure  3 ) will be integrated mid 2022, then tested and commissioned in 2023 for systematic and automatic observations. Data will be freely available to the international community, without any delay, both for operational and scientific purpose. MSTP comes with a specific database (located at Nice computer centre with 100 TB of disk storage), with possible access through the BASS2000 solar database, and will offer later virtual observatory services.
Fig. 1  The MTSP project architecture. The green box is located at Calern station, and the database at Nice observatory. Observations will also be available through the BASS2000 data centre with additional Virtual Observatory (VO) services. Real time images (JPEG) together with FITS scientific data-sets will be freely delivered to the international community. 2. 1 Automation procedures MTSP is the first automatic solar telescope assembly available in France (other solar instruments are under human control). The telescopes (Figure  3 ) and housing (Figure  2 ) are under the supervision of a control/command computer (Figure  1 ). This PC is interconnected with other slave machines, such as the data acquisition computer (which handles cameras, filters, motor focus, scintillometers, thermal units), the solar tracking computer (which drives the equatorial mount) and the House Keeping (HK) automation.
Real time data processing and archiving procedures Raw 12 bits data are collected by the acquisition PC and transmitted to the control computer. There are two data levels, N0 and N1. N0 are raw data written in FITS format. For that purpose, FITS keywords with exhaustive data descriptors are simply added and N0 images are directed to the Nice archive via the fast network link between the observing site and the data centre. N1 is the first level of scientific data. For each image, the Sun radius and centre are detected and the solar disk is centred in the 35 arcmin FOV. A rotation is then applied in order to present the North up. The dark current is subtracted. Corrections of optical distortions are applied, if needed. A 8 bits JPEG real time image is derived and is immediately uploaded into the database.
Keywords are added to scientific images to form FITS data-sets, which will be transferred after observations (by night), so that the delay for scientific data will be about 12 hours. The query system of the Nice database will offer, for each channel, individual images or large data-sets (zip or tar. gz files) that will be built on line using the time interval provided by the requestor. The database is dimensioned to store all observations of cycle 25 and will start to operate in 2023. In parallel, the query system will be incorporated to the solar BASS2000 data portal. Progressively, it will offer virtual observatory services, so that links to other wavelengths will be offered together with MTSP data (such as EUV from SDO/AIA or radio from Nancay Radioheliograh). Observations will start with two operating modes: a standard one in the case of low activity level (60 s cadence), and a fast mode (10-15 s) in the case of high level.
Conclusion The MTSP project is dedicated to the survey of fast evolving events in the chromosphere at the source of solar activity, such as flares and coronal mass ejections. Large flares often occur after the solar maximum (2025 for the present cycle) during a few years. MTSP, with an outstanding cadence of 10-15 s, has also the major goal to investigate Moreton waves, which are extremely fast and rare phenomena associated with largest flares. Such events are difficult to detect in the chromosphere, so that only a few cases have been studied. With systematic, fast and multi-channel observations (two Hα and one CaII K telescopes), MTSP will increase the chances to catch such phenomena. Data could be combined to SDO/AIA observations of the low corona at slower cadence (45 s) in several EUV channels.
MTSP will operate automatically at Calern observatory (1270 m) under good seeing and climatic conditions. High cadence observations will be freely delivered, without any delay, to the international community through a dedicated database located at Nice computer centre. MTSP will cover cycle 25, from 2023 to, at least, the end of the present decade, where new generation solar synoptic networks could start, such as the Next Generation GONG project  (Hill et al. , 2019)  or the Solar Physics Research Integrated Network Group (SPRING,  Gosain et al. (2018) ). MSTP will support Solar Orbiter (ESA) and Parker Solar Probe (NASA) operations in the coming years. Fig. 20  Simulation of the correction factor to apply to the measurements of the polarization rate with a Lorentzian filter, as a function of the FWHM ( Å), for various CWL shifts (solid, dashed, dotted lines for respectively -0.
Acknowledgements We thank the anonymous referee for useful suggestions and comments. We are indebted to  Y. Bresson ,  C. Renaud  (OCA),  J. -M. Rees ,  C. Blanchard  (OP) and the technical teams of OP and OCA for their assistance. We are also grateful for financial support to Paris and Nice observatories, the  Direction Générale de l'Armement , the  IDEX Plas@Par , the  Programme National Soleil Terre (PNST/INSU/CNRS) , the  DIM ACAV (Ile de France Region)  and the  IDEX UCA/JEDI.
To read the paper please go to IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore. Detection of objects is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substantial progress. However, because of the large variety of object scales, densities, and arbitrary orientations, the current detectors struggle with the extraction of semantically strong features for small-scale objects by a predefined convolution kernel. To address this problem, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed model adopts single-shot detector in parallel with a lightweight image pyramid module to extract representative features and generate regions of interest in an optimization approach.
To address these issues, this paper proposes a feature image pyramid network that naturally encodes rotation equivariance (REFIPN) to generate semantically strong scale-wise feature maps for object detection in RSIs. In this model, we introduce a feature-merged framework that enhances the context information to improve the small-scales object detection accuracy. More specificly, we improved context information of the feature maps by combining SSD  [10]  with the proposed light-weight image pyramid module. In our network, a spatial attention module and a feature fusion module are designed to normalise the weights and enrich the context information. In addition, we propose filter CNNs which equivariance under various rotations to obtain rotation equivariance of CNNs with extra weight sharing.
To minimize the model's size while maintaining its robustness against rotations, we only propagate the highest amplitude values obtained from Λ feature maps. In the REFIPN (see Fig. 1(c )), we first down-sample an input image to build an image pyramid module, in which all the stages of the network is featurized. Next, scale-wise features of the pyramidal module and region of interests are integrated into the SSD features (SSD adopts convolution filters), in a spatial attention module, with the aim of increasing the discriminative ability. Also, we utilize a feature integration module to combine the features from different layers. Each layer of the proposed model only keeps the highest amplitude value of Λ which appears as a 2D vector field to propagate it to the other layers in the network.
The main contributions of our work are threefold. • We propose a novel object detection model by introducing a feature image pyramid network and integrating multilevel features to obtain more semantically strong features and improve the accuracy of detection. • We transform the convolution operator by encoding rotation equivariance and shift to deal with the complicated object deformation in RSIs. • We conducted a comprehensive ablation study to demonstrate the impact of each proposed module on detection results. The remainder of this paper is organized as follows. Section II presents the related work. Section III describes the proposed framework, including feature image pyramid network and rotation equivariance that is encoded in the convolution, and feature learning. The experimental results on two datasets are reported in Section IV to validate the performance of the proposed model.
As shown in Fig. 2 , REFIPN contains two key modules: the conventional SSD network plus the light-weight image pyramid module (LIPM) for generating semantically strong features. Similar to SSD, VGG-16 is adopted as the backbone and we insert several progressively smaller convolution layers to improve feature extraction. Differing from SSD, REFIPN contains a LIPM's layer in different layers of SSD by using an attention module. Then, the features of each layer are fused with the features of the previous layer using a fusion module.
IV. EXPERIMENTS AND ANALYSIS In this section, we describe our experimental details, such as the datasets used in our experiments, followed by evaluation metrics, and we perform various experiments to evaluate the performance and efficiency of our proposed model. In addition, an extensive ablation study is conducted to evaluate the performance of each proposed module. We used a Tesla V100 GPU to implement and evaluate the networks. The model is implemented using Keras 2. 1. 2, and TensorFlow 1. 3. 0 GPU as the backend deep learning engine.
A. Datasets and Evaluation Metrics DOTA  [58]. It is a large RSI dataset used for object detection which comprises of 2806 images with different size ranges (800 × 800 to 4000 × 4000) and 15 classes of objects with different orientations, and scales. NWPU VHR  -10 [26]. This is a 10 class and manually annotated dataset that contains 650 optical RSIs, in which 565 images were obtained from Google Earth and have various sizes (533×597 to 1728×1028 pixels). We randomly selected 70% of the original images to form the training set, 10% as the validation set, and the rest as the testing set. For the DOTA dataset in the training phase, we split the images into the 400 × 400 pixels sub-images with 200 pixels overlap between the neighboring sub-images. In the testing phase to evaluate the effect of input size in the detection results, we prepare two sets of the testing dataset.
Image patches of 400×400 and 800×800 pixels are cropped from the test set images with 200 and 400 pixels overlaps respectively. We also use the multi-scale technique in this process  [5]. More precisely, first we rescale the original images by 0. 8×, and 0. 4× before splitting, and then take all the patches as the training and testing sets. Since DOTA contains large number of images, we randomly selected 19,853 patches for training, 1836 (400 pixels) and 1570 (800 pixels) patches for testing. For the NWPU VHR-10 dataset, the quantity of images is insufficient for training, to increase the training set, we perform rotation, rescaling, and mirroring. Furthermore, as the number of the entities in each class was unbalanced, we use different techniques for each class to balance the number of objects.
Similar to DOTA for the NWPU VHR-10, we rescale the original images by 0. 5× and 1. 5× before splitting, and we prepared two test set images (400 × 400 and 800 × 800 pixels) for evaluating the detection performance of state-ofthe-art approaches over different input sizes. In addition, to evaluate the performance of the state-of-the-art models in the estimation of objects orientation, we prepare another dataset by using the plane and car categories of the original DOTA dataset. We crop a 80 × 80 square patch around each plane and car, according to central location of the bounding box. These cropped patches are used for training and testing of the baseline models. 834 planes and 729 cars are used for training and 160 planes and 130 cars for testing. For evaluating the performance of our proposed detector, the frames per second (FPS) and mean average precision (mAP) are used as evaluation metrics.