dataesr/scientific-paragraphs-categorization huggingface dataset:
FFDNet is a recent image denoising method based on a convolutional neural network architecture. In contrast to other existing neural network denoisers, FFDNet exhibits several desirable properties such as faster execution time and smaller memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. In this paper we propose an open-source implementation of the method based on PyTorch, a popular machine learning library for Python. Code for the training of the network is also provided. We also discuss the characteristics of the architecture of this algorithm and we compare it to other similar methods.
A second reason for using an additive Gaussian noise model is simplicity: it is a reasonable assumption and it makes the problem of denoising easier to approach. In this paper we will only focus on the problem of denoising images contaminated with additive Gaussian noise. FFDNet is designed to work with RGB or grayscale images-and not other types of data such as raw images. Let I be a noiseless image, while Ĩ is its noisy version corrupted by a Gaussian white noise N, then Ĩ = I + N. ( 1 ) Observe that most experiments focus on the case of white additive Gaussian noise (AWGN), but this algorithm can be potentially applied to other types of noise such as spatially varying noise. The rest of the paper is organized as follows. In Section 2 we introduce the FFDNet architecture and training details.
Training Details The training dataset is composed of pairs of input-output patches ( Ĩj , M j ), I j m j=0 which are generated by adding AWG of σ ∈ [0, 75] to the clean patches I j and building the corresponding noise map M j (which is in this case constant with all its elements equal to σ). A total of m = 128 × 8000 patches are extracted from the Waterloo Exploration Database  [14] , where the mini-batch size is 128. The patch size is 64 × 64 and 50 × 50 for grayscale and color images, respectively. Patches are randomly cropped from randomly sampled images of the training dataset. Data is augmented five times by introducing rescaling by different scale factors and random flips 2. In the cases in which residual learning is used, the network outputs an estimation of the input noise F( Ĩ ) = N.
This scheduling strategy is in the same spirit as the one proposed by the authors of FFDNet. It is implemented in the code provided with the use of the milestone parameters. During training, after the number of epochs surpasses the first milestone, the learning rate is divided by 10, and is divided again by 100 after the second milestone is surpassed. The actual values of the these parameters have been chosen heuristically after observing the loss graphs of several trainings. In other words, a learning rate step decay is used in conjunction with ADAM. The mix of learning rate decay and adaptive rate methods has also been applied to other deep learning projects  [29, 25, 26, 30] , usually with positive results. Two datasets are used to evaluate the grayscale and color FFDNet denoisers. The BSD68 database and Set12 databases are utilized for grayscale denoising, while the CBSD68 and the Kodak24 are used for color denoising.
The BSD68 is composed of 68 images of size 481 × 324 from the testing set (testset for short) of the BSD database  [17] , while CBSD68 corresponds to the color version of this image set. The Set12 dataset is a collection of standard test images of sizes either 256 × 256 or 512 × 512. Finally, the Kodak24 (Kodak Lossless True Color Image Suite 3  ) is composed of 24 768 × 512 color images. As for validation during training, all the images from the Kodak24 suite are used as the validation dataset for color denoising, while the Set12 database is used for grayscale denoising. The open-source implementation of the FFDNet method provided with this paper is based on PyTorch  [18] , a popular machine learning library for Python. Testing and training code is available from the IPOL website. Table  2  shows a comparison of P SN R on the two color datasets obtained with the code provided by the authors and the code proposed in this paper.
For each tested configuration we simulated 10 8 point source gamma-rays, in the energy range 3. 0 GeV -330 TeV. The response of the telescope array is then simulated with the sim_telarray program (see  [8]  and  [9] ): while in parallel pointing mode, the pointing of the telescopes is usually unique between  all of them, in the case of divergent mode the pointing direction of each telescope had to be manually set by changing the configuration file which is given as input to sim_telarray. The pointing was obtained thanks to a separate tool, whose final purpose will be to provide for a given layout and direction of the general pointing center, the best individual telescope azimuth, and zenith angle and a multiplicity map. In figure  1  are reported the four configurations that were simulated, each one with a different offset between the telescope pointing directions.
Ctapipe analysis The reconstruction of the simulated data has been done using ctapipe  [11] , a framework for the data processing for CTA data which is being developed by the members of the CTA Consortium. Ctapipe is mainly developed using Python and its libraries for scientific computing. The simulated events are loaded and the waveforms for the pixels in the cameras are calibrated with the pedestal value: the time slices are then integrated in order to get the charge per pixel belonging to the camera of each telescope triggered in the event. The final images are cleaned with a two-threshold tailcut method and the moments, called Hillas parameters (from  [12] ), of the resulting elliptical images, are calculated. Given the positions of the telescopes and the parametrization of the ellipse in the camera, a stereo reconstruction can be performed in order to find the impact point of the shower on the ground, its direction and the height at which the Cherenkov signal is maximum (more on ctapipe in  [11]  and in the code repository).
Internet-based system of Space Monitoring Data Center (SMDC) of Skobeltsyn Institute of Nuclear Physics of Moscow State University (SINP MSU) has been developed to predict and analyze radiation conditions in near-Earth space. This system contains satellite measurement databases and operational models and devoted to collect, store and process space weather monitoring data in the near real-time. SMDC operational services acquire data from ACE, SDO, GOES, Electro-L, Meteor-M satellites and use them for forecasting, now-casting and post-casting of space weather factors. This paper is intended to give overview of operational services of SMDC Internet-based system and demonstrate their possibilities and limitations to analyze space weather phenomena and predict radiation and geomagnetic conditions in the near-Earth space during February 14-March 5, 2014.
Two web-sites (http://smdc. sinp. msu. ru) and the modern SMDC space weather site (http://swx. sinp. msu. ru, English version http://swx. sinp. msu. ru/index. php?lang=en) provide access to data on energetic particle fluxes obtained from Russian scientific missions (CORONAS series, Tatyana, Tatyana-2, Vernov,. ) as well as from Russian weather satellites (Electro-L and Meteor-M series). These measurements complement the data from GOES, ACE, DSCOVR, SDO and some other monitoring satellites as well as data from on-ground facilities that usually are used for space weather issues. Space Weather web-site (http://swx. sinp. msu. ru/) gives advanced possibilities for various datasets visualization and cross-comparison. Graphical applications give opportunity to analyze simultaneously data from different sources.
SMDC Space Weather web-site also provides access to operational and forecasting models of SMDC as web-based applications: the solar wind forecast at L1 point. In this application, the velocities of recurrent High-Speed Streams (HSS) are predicted for about of 3-day period based on the coronal hole's parameters; forecast of the solar wind parameters and Interplanetary Magnetic Field (IMF) at the Earth's orbit; plasma and magnetic field measurements at L1 point are used as input for simple propagation model; Dst index forecast in term of the perceptron type of artificial neural networks (ANNs) depending on solar wind measurements at L1 point; forecast of the fluxes of outer RB relativistic electrons with the energy greater than 2 MeV based on perceptron type of ANNs. All forecasts use the measurements collected in SMDC integrated database.
sinp. msu. ru/ and http://swx. sinp. msu. ru. Both web-sites use the same database which works under PostgreSQL. While http://smdc. sinp. msu. ru/ is aimed to give simple access to data collected in SMDC data base, http://swx. sinp. msu. ru web-site is intended to describe current space weather conditions using near real-time information coming from different data sources or from operational models. Particle fluxes measured on-board several Russian weather satellites and scientific space missions can be obtained through http://smdc. sinp. msu. ru/ web-site. There are, mainly, the historical data from the past space experiments. Figure  1  represents the main page of http://smdc. sinp. msu. ru/ web-site. Table  1  shows data availability for some missions presented at this SMDC web-site. Current solar activity, geomagnetic and radiation conditions in the Earth's magnetosphere and heliosphere can be accessed in the near real-time mode using SMDC' Space Weather web-site (http://swx.
sinp. msu. ru). As Space Weather resource, this internet service uses data coming from different active space missions, like Meteor-M2, Electro-L2, GOES-14, GOES-15, SDO, DSCOVR and so on. From the other hand, SWX data services allow also to get and analyze historical data collected in SMDC database. Advanced graphical applications give possibility to compare data coming from different data sources. In the rest of this paper we will consider only SMDC' space weather web-site (http://swx. sinp. msu. ru), referred below as SWX. Figure  2  shows its main page representing the current conditions in the Earth's environment: Solar X-ray flux, Kp index of geomagnetic activity, 10 MeV proton flux and subrelativistic electron flux at GEO with 5 min update rate. Now, the data sources are GOES-14, GOES-15 and Electro-L2 satellites as well as Potsdam's Geophysical Center.
SWX web-site collects the data coming from different sources: satellites, on-ground stations, data centers, quantitative models. Special software, Satellite Data Downloading System (SDDS), is developed to collect and process the data. SDDS services convert the primary data coming from different sources into unified format and load data into different tables of data base. There are two ways for users to retrieve data from SMDC storage: either request data directly from their programs using the RESTful API of SDDS or retrieve data interactively using Web-applications. One of the advantages of SMDC data system is that the system allows users to retrieve data from different satellites as a single array of time series with different cadence. This feature is very useful in data analysis and enables physicists to focus on their work and not to spend time on data preparation.
First of all, it reduces the effort in collecting data sets from different sources and converting them into a unified format suitable for further use. The latter work is usually time-consuming and error-prone. Secondly, when various data sets are put together, they give a more precise overall picture of the subject to be studied. Being presented together, these data sets can be used for measurements crosscalibration, to diagnose errors in measurement of the satellite instruments. Operational applications on Space Weather web-site provide real-time analysis and assimilation of experimental data as well as forecasting and post-casting of radiation conditions in near-Earth space. These applications use data from mentioned above spacecraft collected in a single database. Space Weather website represents the space environment physical conditions in the near real-time (page "Space Weather Now", see http:// swx.
sinp. msu. ru/weather. php?lang=en). This page gives access to measured data and also to some calculations in terms of SMDC operational services. Page "Tools" (http://swx. sinp. msu. ru/tools/ida. php?lang=en &gcm=1) contains several applications that give access to data from SMDC database. Interactive data Analysis Tool (IDA) allows to display the data from different sources (satellite measurements, geomagnetic indices) for a given time interval. One can create templates to generate the standard set of the figures. Some predefined templates are accessible on the IDA Tool page. Hourly averaged data are used in this service. Data Visualisation and Analysis Tool (DaVisAT) gives access to satellite and on-ground data with different time resolutions. Sun Viewer (SunView tool) is catalogue of hourly Sun images obtained by SDO/AIA for different wavelengths.
Page "3D Magnetosphere" allows to reproduce magnetospheric magnetic field structure by  Alexeev et al. (2001) ,  Kalegaev (2011)  in the real-time as well as for a given time moment. Page "Models" is intended to give access to the interactive models that can be used for scientific investigations but does not reach the "operational" level. 3 Overall space environment conditions in near-Earth Space Analysis and cross-comparison of data from multi-satellite measurements as well as magnetospheric modeling can provide information concerning the mechanisms responsible for the different dynamical processes taking place in the Earth's magnetosphere. Space environment conditions and its effects during the time period February 14-March 5, 2014 are presented using tools available on the SMDC Space Weather website.
The magnetopause stand-off distance forecast Physical conditions on Earth's orbit can be obtained propagating solar wind parameters from L1 point (where they are measured by ACE or calculated based on Sun observations in terms of the model like that presented in Sect. 4) to the Earth's location. A simple convection delay or "phase front" method described by  Pulkkinen & Rastätter (2009)  was used at SMDC as an operational service to describe solar wind and IMF propagation from ACE position to the Earth environment. Figure  6a -c compares solar wind parameters (IMF Bz, density and velocity), calculated by SMDC model and those from OMNI database. Correlations for results are 0. 93 for density, 0. 88 for Bz and 0. 98 for velocity. Solar wind parameters at the Earth's orbit appear in the OMNI database after half a year.
Unlike OMNI database, SMDC gives possibility to get solar wind data near the Earth in real time. SMDC service makes it possible to calculate in real time the magnetopause stand-off distance on the base of some existing magnetopause model (http://swx. sinp. msu. ru/apps/rss/index. php?gcm=1). Taking into account that solar wind has to spend about 1 h (it depends on solar wind speed value) to reach the Earth's magnetosphere from L1, the calculations from  Kuznetsov & Suvorova (1998).
Patra et al. (2011)  provide Dst predictions using physical WINDMI model based on the calculation of ring currents in the magnetosphere-ionosphere system, using input data from ACE spacecraft. Revallo et al. (2014)  proposed Dst index 1 h ahead forecasting model based on artificial neural networks (ANNs) combined with an analytical model of the SW -Earth's magnetosphere interaction. SMDC implements a Dst index prediction service that uses scientific models based on ANN technology  (Dolenko et al. , 2005; Myagkova et al. , 2017). SMDC operational service gives online 1 h prediction of Dst index using in-situ ACE spacecraft measurements of solar wind and IMF parameters in terms of ANN approach (http://swx. sinp. msu. ru/models/dst. php?gcm=1 &lang=en). Results of the Dst predictions calculated during period on 14 February-5 March, 2014 are shown in Figure  9.
Solar proton events The time profiles of energetic proton fluxes during February 14-March 5, 2014 are shown in Figure  3d. Two events were observed during the period studied: on February 20 and February 25, 2014 (ftp://ftp. swpc. noaa. gov/pub/indices/SPE. txt). The first SEP was associated with a solar flare at  The second SPE was associated with a solar flare starting at 00:39 UT and peaking at 00:49 UT on February 25 (X4. 9; S12E82; AO11990; 103 pfu). The second SEP event was described in detail in the article  (Lario et al. , 2016). Russian spacecraft Meteor-M and Electro-L series have SINP MSU scientific instrumentation on-board to measure SEP fluxes. These data can complement measurements of ACE and GOES spacecraft that are the main source of data about radiation conditions in near-Earth space for space weather centers.
Discussion The main goal of this study is to reveal strong and weak aspects of SMDC Space Weather services and demonstrate their possibilities to describe the different manifestations of space weather. Time interval of February 14-March 5, 2014, as noted above, was rich in various phenomena on the Sun, in the solar wind and in IMF and consequently in the Earth's magnetosphere. SMDC tools give possibility to analyse measurement data from different sources as well as modelling data simultaneously in a single workspace. Great abundance of space weather phenomena registered during this time interval gives a good opportunity to reveal advantages and disadvantages of SMDC Space Weather applications. Let us list some phenomena and physical processes that took place in near-Earth space during the studied period of high solar activity: The SW pressure pulse under northward IMF on 15 February 2014 caused the magnetopause inward motion and magnetospheric compression up to ~6 R E.
Small pressure pulse under southward IMF on 23 February caused a moderate geomagnetic storm (Dst max À50 nT) and electron flux decrease mainly at outer L-shells. Classical time profile of solar energetic proton event is observed at GEO on 25 February. Small pressure pulse under southward IMF caused a moderate geomagnetic storm (Dst max ~À90 nT) on 27-28 February 2014. The magnetopause moved inward up to ~6 R E. Electron fluxes at GEO showed Dst-like variations. Huge amount of measurement data available during prolonged period of solar and geomagnetic activity gives opportunity for a complete analysis of the various magnetospheric phenomena occurring in Earth environment. SMDC software and databases allow to collect and process data coming from different sources: satellites, ground stations, operational models, neural networks.
SMDC services provide crosscomparison of the data obtained from different satellites and from different operational models as well. In general, SMDC operational models presented in this paper describe well measured parameters of the near-Earth's space environment. Simultaneous analysis of experimental data and calculations allow to better understand the mechanisms of Sun-Earth coupling. On the other hand, the measurements-modelling cross-comparison shows some drawbacks or disadvantages of the models that should be eliminated in the future versions of the models. The analysis of SDO/AIA images allows to determine the sources of solar wind variations at Earth orbit. It was shown that the forecast of the recurrent solar wind streams based only on the coronal hole parameters gives underestimated values of solar wind maximal velocities at L1 point.
The interaction of several SW streams can lead to an increase in their geoeffectiveness. Among the future tasks on SMDC services development is the inclusion into the automatic online solar wind speed forecast scheme of the model that can predict speed and arrival time taking into account parameters of the CME. The comparison of the ANN RB forecasting model output with the empirical data at GEO showed that the response of the relativistic electron flux on the high-speed stream of solar wind is not always predicted correctly. We assume that among the inputs of the artificial neural network, some additional parameters responsible for the dynamics of the fluxes of relativistic electrons in the geostationary orbit should be taken into account. One of the main goals of this paper has been to reveal properties of SMDC services that could be improved in the future.
Conclusion SMDC of Moscow State University provides mission support for Russian satellites and gives operational analysis and forecasting of radiation conditions in space. SMDC Space Weather web-site (http://swx. sinp. msu. ru/) gives access to current data about the level of solar activity, geomagnetic and radiation state of Earth's magnetosphere and heliosphere in the near real-time mode. The scientific models of space environment factors have been converted into operational engineering services. They are implemented as space weather Webapplications that provide forecasts of geomagnetic and radiation condition in near-Earth space. The SMDC web-site provides: three to five days ahead forecast of the parameters of high-speed solar wind streams based on the coronal hole' parameters; short-term forecast of solar wind parameters and IMF at the Earth's orbit by ACE (or DSCOVR) measurement; online prediction of Dst index 0.
5-1. 5 h ahead using ANN by the parameters of SW and IMF measured by the ACE spacecraft; middle-term forecast of relativistic electron fluxes of the outer RB; instant structure of geomagnetic field lines for a given time on the base of the magnetospheric paraboloid model A2000 using measurements of solar wind and IMF as input parameters. SMDC Space Weather services were used to describe and analyze space weather phenomena that took place during the period from February 14 to March 5, 2014. Prolonged period of high level solar and geomagnetic activity gives opportunity to demonstrate how well SMDC services describe the different aspects of space weather like solar proton events, geomagnetic storms and outer RB variations. Cross-comparisons of the data obtained from different data sources, both satellites and models, make it possible to validate the existing operational services and understand better the ways for their future improvements.
Acknowledgements. This study was supported by  Russian Scientific Foundation  Grant No  16-17-00098. We have used the CME catalog that is generated and maintained at the CDAW Data Center by  NASA  and  The Catholic University of America  in cooperation with the  Naval Research Laboratory. SOHO is a project of international cooperation between  ESA  and  NASA. Also this paper uses data from the SEEDS CME catalog. The  SEEDS  project has been supported by  NASA   Living with a Star Program  and  NASA   Applied Information Systems Research. We are grateful for the opportunity to use the results of the simulation obtained by the  DBM  models. The authors thank data holders for SDO/AIA, SOHO/LASCO,  ACE ,  Van Allen  Probes, NOAA/POES and GOES as well as geomagnetic indices data. The authors thank data services CDAW, CACTUS and OMNIWeb.
Introduction Many multi-objective optimization problems (MOPs), such as in finance, logistics/transportation, engineering design and life science, are complex. Indeed most of the practical and academic MOPs are NP-complete. Moreover they are CPU time and memory consuming. Even if the use of multi-objective evolu-5 tionary algorithms (MOEAs) allows to decrease the computational complexity of the algorithms, this complexity remains important for a great majority of MOPs in multiple domains of applications, in which the constraints and the ✩ This work is part of a project that has received funding from the European UnionH2020 research and innovation programme under grant agreement No 692286. objective functions associated to the problem are resource intensive and the size of the decision and/or objective space is important.