dataesr/scientific-paragraphs-categorization huggingface dataset:
TF-IDF versus Word embeddings We assessed the reliability of the two learning models cited earlier. This evaluation was to be applied on a data set (20 newsgroups) 20,000 items and categories 20 (1000 items per category). For this test, TF-IDF comes at a score of 58% while the word embeddings gives a score of 98%. This result supported our choice to use the word embeddings as a learning model for our bot.
The intelligence Now that we have a logic model and an initial knowledge base, the challenge is to find a technique that will allow the bot to detect the context of new arriving facts and rank them, and this is exactly when the word embeddings is used. Recent Deep Learning idea is that the approximate meaning of a word or sentence can be represented as a vector in a multidimensional space. The nearer vectors represent similar meanings. To do so, we used Gensim for Python designed to automatically extract topics Semantic documents, as efficiently as possible. Gensim is designed to process raw digital and unstructured text. The algorithms in Gensim, such as Latent Semantic Analysis, latent Dirichlet allocation and random projections, describe the semantic structure of documents. The latter is extracted by examining the statistical models of cooccurrences of words in a corpus of training documents.
CONCLUSIONS We have presented here the different steps and tools combined in order to build a semi-intelligent bot based on machine learning technologies. With a solid foundation of learning, a logic model, a word embeddings with good scores (≈ 84%) and improvements in current and future, we hope to combine the three parts in order to have a functional bot. We use advanced tools and technologies, they are very recent and widespread on the Data Science: the python programming language, jupyter notebook for a complete development environment, Gensim for word embeddings, and advanced tools for natural languages processing such as Clips and CoreNLP Stanford. During the project, we have used namely the methods of classification and clustering, Data Mining and Text Mining, Sentiment Analysis, and evaluations of the quality of classifiers (Reminder, Precision, F-Measure).
Then come the current systems, languages and paradigms for Big Data and Advanced Big Data Analytics, that showed us especially the world of Big Data, many use cases and market opportunities. We have tested some big data architectures, including Hadoop and Spark with Python languages, Java and Scala. As the subject of the project is part of the R&D activities, the ultimate goal was clear and understandable, but in practice, problems on understanding how to achieve the objectives arise. Indeed, understanding how to get the value and signification of a text is not easy. Then follows the difficulty of understanding the functioning of the used methods, which algorithms to use and when to use it. Development work and tests, reading papers and publications of other researchers, and the documentation, all that was done in order to understand the subject, to progress and have good results (≈ 84%), it was not the case at the beginning (≈ 60%).
Canary is the multi-object adaptive optics (MOAO) on-sky pathfinder developed in the perspective of Multi-Object Spectrograph on Extremely Large Telescopes (ELTs). In 2013, Canary was operated on-sky at the William Herschel telescope (WHT), using three off-axis natural guide stars (NGS) and four off-axis Rayleigh laser guide stars (LGS), in open-loop, with the on-axis compensated turbulence observed with a H-band imaging camera and a Truth wave-front sensor (TS) for diagnostic purposes. Our purpose is to establish a reliable and accurate wave-front error breakdown for LGS MOAO. This will enable a comprehensive analysis of Canary on-sky results and provide tools for validating simulations of MOAO systems for ELTs. To evaluate the MOAO performance, we compared the Canary on-sky results running in MOAO, in Single Conjugated Adaptive Optics (SCAO) and in Ground Layer Adaptive Optics (GLAO) modes, over a large set of data acquired in 2013.
In Sect. 3, we introduce the tomographic error in LGS MOAO and describe how its evaluation is achieved for Canary. In Sect. 4, we present an analytic LGS MOAO error breakdown and describe how each term is derived. The variance of the residual phase taken in summing all terms, assumed to be independent, is compared to what we estimate from the truth sensor (TS). We show in Sect. 5 the two methods lead to the same evaluation of the residual phase, with a 99 % correlation over a large set of 4,500 on-sky datasets. We also present an overview of the on-sky Canary performance in single conjugate AO (SCAO), MOAO, and open-loop ground layer AO (GLAO). Finally in Sect. 6, we focus on particular results acquired by Canary running successively in SCAO, MOAO and GLAO modes. Using tools introduced in Sect.
Raw tomographic error Tomographic reconstruction introduces a tomographic error when compared to single conjugate AO, to be included in the MOAO residual phase variance. In addition, the calibrated reconstructor, applied on a given data set on-sky, is usually different from the MMSE one given by Eq. 10, because the turbulence may evolve between the calibration time and the acquisition time. Moreover, the identification of the model parameters assumes the turbulence is composed by a few discrete layers, which introduces additional errors. We define e(t) as the error between the on-axis measurement and its tomographic reconstruction from the off-axis measurements : e(t) = S on (t) -RS off (t), (14) with R the on-sky tomographic reconstructor. The tomographic error can be derived by computing the trace of the error covariance matrix C ee : C ee = (e(t) -e )(e(t) -e ) t.
ERROR BREAKDOWN IN MOAO The design of future AO-assisted MOS for ELTs will require detailed numerical simulations of AO systems with a very large number of degrees of freedom. To prepare those simulations, it is necessary to validate the current modelling of the MOAO systems and therefore to build an accurate WF error breakdown. Canary provides two sources of performance diagnostics : the residual phase as measured by the TS and the sky point spread function (PSF) as delivered by the imaging camera Camicaz. We propose two methods to evaluate the WF variance σ 2 ε as observed by the science camera. The first one as the independent terms (IT) method gives σ 2 ε from Canary telemetry data by splitting the residual phase into a sum of terms assumed to be uncorrelated. We compare the IT approach with the TS method, as the direct evaluation of σ 2 ε based on the TS measurements.
ANALYSIS OF THE JOINT NGS PLUS LGS TOMOGRAPHY PERFORMED ON-SKY We focus in this section on the Canary performance to target the improvement of the atmosphere compensation, achieved thanks to both tomographic reconstruction and LGS. We compare on-sky results acquired during what we call a script. A script is five data recordings of the same sequence made of three successive AO modes observation. A sequence lasts 45 s, 15 s per AO mode. This duration has been chosen to enable us to compare Canary performance, running in different AO modes, in similar atmospheric conditions. We compare on-sky performance achieved by SCAO, GLAO and MOAO with and without LGS. We use the letters "N" and "L" following the AO modes to define respectively NGS and LGS sensors. For instance, "MOAO4L3N" means Canary was compensating the turbulence in MOAO mode using the four LGS and three NGS.
In the following, we will present three of those scripts, numbered 274, 292 and 275. In Table  4 , we report observation conditions we have identified during those scripts. The seeing and outer scale values comes from the Learn&Apply algorithm. The wind speed has been retrieved from the full width at half maximum of the slope measurement temporal auto-correlation function, after subtraction of the noise contribution. This method provides an integrated value of the wind speed along the turbulence profile. These scripts have been selected because of their relatively close observing conditions. The observed asterism was A47 for scripts 274 and 275 and A53 for 292  (33).
Script 274 : SCAO/MOAO 4L3N/GLAO 4L3N In Fig. 7 , we show three PSFs averaged over five H-band images acquired during script 274 in SCAO, MOAO 4L3N and GLAO 4L3N, the September 13th 2013 night. Thanks to the tomographic reconstruction, we make the SR improving from 12. 9% in GLAO to 21. 0% in MOAO, corresponding to a wave-front error reduction by an order of 190nm rms between the two modes, using Eq. 42. The best SR is obtained by SCAO with a value of 28. 3%. In Fig. 8 , we highlight the evolution of both analytic and IR image SR with time. Analytic SR have been derived from Eq. 43 using the residual phase variance developed in Eq. 24 (IT method). The figure displays the five samples per AO mode, acquired successively and interlaced between each other. By interlacing different AO modes, we minimize the impact of the possible turbulence non-stationarity during the script.
If a particular event happens during the script, we should observe it with each mode if it lasts for at least 45 s. This ensures that we are able to compare AO modes with nearly similar observational conditions. Figure  8  shows that we are in the most able to reproduce analytically the SR in a satisfactory way. The evolution of the IR image SR with time is well reproduced, as the absolute value and the difference between modes. However, we have an under-estimation of the SR that becomes more severe when the SR becomes higher, especially for SCAO, thus implied by the Parenti approximation according to Fig. 6. Note from the same figure, the Maréchal approximation would give worse reproduction of low SR. For further performance analysis, we present in Fig. 9  top, in the left panel, a comparison between the turbulent profile on which the sky reconstructor has been calibrated and the post-retrieved one on the script 274    LGS/NGS=×, MOAO 3 NGS=.
In MOAO and GLAO, we have used the IT approach to get the residual phase variance, while in SCAO we have used the TS approach. data sets. These results are from an average of fifteen profiles, each of which has been retrieved on five layers on each data set. We obtain (by post processing) 75% of turbulence at the ground (below the first kilometer), against 70% expected by the sky reconstructor (computed 20 min. before the script was run). In addition, the 11 km altitude layer observed during the script was quite well identified in the reconstructor. However, the observed 16 km altitude layer was partially identified thanks to the high altitude layers, between 14 and 18 km, as expected by the reconstructor. Finally, it seems that our MMSE reconstructor was still relevant for the turbulence profile encountered during the script.
Script 292 : SCAO/MOAO 4L3N/GLAO 4L3N We discuss here the results from script 292. In the same way as script 274, we compared SCAO, MOAO and GLAO, but with a mis-calibration of the MMSE reconstructor. We illustrate in Fig. 9  the turbulence profile used to compute the MOAO reconstructor, compared to the one retrieved during the script 292. According to Table  4 , there was one hour between the calibration and the script. During this time, strong altitude layers were appeared at 10. 5 and 13. 5 km, accounting together for 35 % of the total turbulence and these are not expected by the MOAO reconstructor. The global observation conditions, between script 292 and 274, was quite similar as given in Table  4. In addition, Fig. 9  allows us to compare error breakdown between these two scripts, to highlight that we obtained the same level on each error term except for tomography.
We get 285 nm rms during script 292 against 185 during script 274 for MOAO. These values are comparable since the turbulence profiles during observations, as shown in Fig. 9 , were largely similar, with 60-70% of ground turbulence and the rest of the energy distributed into two altitude layers. The 215 nm rms difference in tomographic error is thus due to the mis-calibration of the tomographic reconstructor. It has led to a decrease of the IR image SR according to Table  4. Vertical error distribution plots given in Fig. 9  also illustrate that the MOAO reconstructor has been miscalibrated. However it still performs much better than the GLAO one, especially at high altitudes. For instance at the 11 km layer, we get similar performance in GLAO during the two scripts 274 and 292, with 275 nm and 285 nm of error respectively.
We underline the tomographic errors of MOAO in script 292 are very significant for the two high altitude layers at 11 and 13. 5 km with 140nm and 235 nm respectively. Much poorer performance is shown when compared to the high altitude layers of script 274. The MOAO reconstructor has to be calibrated several times each night. It is thus mandatory to propose innovative techniques to compute and update a MMSE reconstructor for large number of degrees of freedom in order to prepare the E-ELT. Fast computation and data transfer are required. Several works are on-going on that, in particular using GPUs  (15; 16).
Script 275 : SCAO/MOAO 4L3N/MOAO 3N Script 275 was obtained directly after script 274. The turbulent profile did not evolve significantly between the two scripts. We were comparing SCAO with MOAO, with and without LGS. We aim to evaluate the impact of using LGS on the tomographic reconstruction for the Canary case. We report in Fig. 10  the error breakdown we get during this script. In MOAO 4L3N, we get the same errors compared to script 274 in Fig. 9 , as we expect since the observational conditions were similar according to Table  4  and the turbulent profile was similar as well. In addition, we get very similar error values between MOAO 4L3N and MOAO 3N. The main difference comes from the tomographic error with 260 nm rms against 185 nm rms respectively in MOAO 3N and MOAO 4L3N. ' Overall, we see an increase of nearly 4% in IR image SR (see Table  4 ) when introducing LGS.
Between the two error computation approaches (analytic and TS based), we get a correlation of 99 %, for all AO modes. We have detailed the Canary performance on particular data sets. We have showed the LGS can increase the SR by up to 4% compared to the using only the NGS, while the MOAO tomographic reconstructor allows a gain of 8. 5 percentage points of SR, compared to GLAO compensation. We have also discussed the vertical error decomposition of the tomographic error, evaluating the gain brought by MOAO in altitude compared to GLAO, while the GLAO nulls the error in the ground layer. Moreover, we have highlighted the static aberrations in MOAO that must be carefully calibrated and monitored during the observations. that allows us to rewrite Eq. 49 as follows : G xx (∆ ijpql ) = Considering D φ (ρ) = (φ(r) -φ(r + ρ)) 2 as the phase Structure Function (SF), we have the final expression of the x-axis slopes covariance : G xx (∆ ijpql ) = 1 2d i d j dv i dv j -2D φ ∆ ijpql + d i -d j 2 x + (v i -v j )y + D φ ∆ ijpql + d i + d j 2 x + (v i -v j )y +D φ ∆ ijpql - d i + d j 2 x + (v i -v j )y.
ACKNOWLEDGEMENTS This work was supported by  CNRS ,  INSU, Observatoire de Paris and Université Paris Diderot-Paris 7 in France ,  Science and Technology Facilities Council and Univ. of Durham in UK , and  European Commission  ( Fp7 Infrastructures 2012-1 , OPTICON Grant  312430 ,  WP1 ). These projects have also received the support of the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ) funded by the  "Investissements d'Avenir" French Government programme , managed by the  French National Research Agency (ANR). This work was also funded by the  UK Science and Technology Facilities Council , grants  ST/K003569/1  and  ST/L00075X/1. The raw data used in this publication is available from the authors.
20), polycrystalline Fe and Fe 86. 8 Ni 8. 6 Si 4. 6 , Fe 85 Si 15 and Fe 3 S alloys  [33] [34] [35]. Conventional Mo ¨ssbauer spectroscopic data, electron microprobe analyses and NRIXS data collected on the basaltic glass at ambient conditions were reported elsewhere  20. Metal iron and the iron-based alloys were examined by X-ray diffraction and most were in a bodycentred cubic structure at ambient conditions, except for Fe 3 S, which was in the tetragonal structure. The reported F h i values for Fe 92 Ni 8 , Fe 85 Si 15 and Fe 3 S are based on newly acquired NRIXS data combined with previous data from Lin et al. 34, 35  , who did not report the F h i values for Fe 92 Ni 8 and Fe 85 Si 15. For previously acquired data, the uncertainties on the F h i values are large due to the relatively narrow energy scans acquired, as those studies focused on the determination of the Debye sound velocity for seismological applications.
Each high-pressure data point was measured at least 20 times and as many as 47 times. Solid lines: linear fits to the data for the basaltic glass, Fe and iron-rich alloys. Note the large shift in F h i value of the basaltic glass (a) at B30 GPa, which corresponds to structural changes in the glass  29, 37. Earth's core is unlikely to contain much H and C  27, 28  and the most probable alloying elements, in addition to Ni, are O, Si and S. Our results agree with the conclusion by Shahar et al. 25  that FeO alloy would induce a negligible shift in the d 56 Fe value of the mantle (Fig. 4 ). An important conclusion of the present work is that other alloys of Fe-S, Fe-Si and Fe-Ni-Si would also produce minimal isotopic fractionation in the silicate mantle relative to the bulk Earth. The inferred core-mantle Fe isotopic fraction at B40-60 GPa and B3,000-4,000 K for O, Si and S alloys is approximately five to ten times smaller than the observed Fe isotope composition for MORBs (d  56  Fe of B þ 0.
34, 35. The oF4 values for Fe85Si15 and Fe3S (corresponding to solid symbols in Fig. 1c, d ) at B24 GPa from this study are in agreement with those (corresponding to half-filled symbols in Fig. 1c, d ) calculated by using the SciPhon software with previous NRIXS data, demonstrating the reproducibility of NRIXS results. Black dash-dotted lines represent no iron isotopic fractionation between Fe-rich alloys and pure iron. core formation in those planetary bodies are inferred to be B6 GPa-1,900 K for Mars  49  , B0. 0001 GPa-1,873 K for Vesta  49  and o0. 5 GPa-2,173 K for the angrite parent body  50. Under these conditions, we calculate that as on Earth, the equilibrium iron isotopic fractionation should be small (o0. 02%). Even if significant S is present in cores of these bodies, the results for Fe 3 S indicate that the fractionation would be minimal.
Above 70 GPa, the Raman spectra of the diamond anvils were collected for use as a pressure gauge before and after each measurement  55. The pressure was cross-checked with the previously characterized equation of state of Fe and Fe-Ni-Si alloy  33. SciPhon software and data analysis. We applied a new approach based on moment calculations of NRIXS scattering spectra S(E) using the SciPhon software, as this method allows a better assessment of measurement uncertainties and potential systematic errors than using moments of the phonon density of states  32, 41. In the quasiharmonic lattice model, the third moment R 3 of a measured NRIXS spectrum S(E) is used to calculate the mean force constant F h i of iron bonds in the samples (in N/m)  32, 41  : F h i¼ M E R ' 2 R 3 ¼ M E R ' 2 Z þ 1 À 1 E À E R ð Þ 3 S E ð ÞdE; ð3Þ where M is the mass of the nuclear resonant isotope ( 57 Fe in this study), E is the energy difference between incident X-ray and the nuclear resonance E 0 (in meV) and E R ¼E 2 0 =2c 2 is the free recoil energy (that is, 1.
956 meV for the E 0 ¼ 14. 4125 keV nuclear transition of 57 Fe). Within the harmonic approximation, the b-factors and the equilibrium isotope fractionation between two phases A and B at high temperature (4500 K; ref. 32) can be calculated from F h i given above using the following relations  20, 32 F h i B À F h i A T 2 ; ð5Þ where D 56 Fe eq B À A is the permil difference in isotopic ratios ( 56 Fe/ 54 Fe) of phases A and B at equilibrium (that is, if those phases were juxtaposed and the isotopes of iron partitioned between them according to the laws of equilibrium thermodynamics), k is Boltzmann's constant, : is the reduced Planck constant and T is temperature. The mean force constants oF4 of iron bonds in the samples were calculated from NRIXS spectra using the SciPhon software, which includes a correction for non-constant baseline.
Acknowledgements We thank  L. Dubrovinsky  for providing the 57 Fe-enriched Fe  86. 8 Ni 8. 6 Si 4. 6 alloy sample and  Y. Fei  for providing the 57 Fe-enriched Fe 3 S sample. We thank  J. Yang  for experimental assistance and  I. Kuang  for assisting with editing the manuscript. J. -F. L. acknowledges support from the  US National Science Foundation Geophysics and CSEDI  Programs as well as the  Center for High Pressure Science and Technology Advanced Research (HPSTAR). HPSTAR is supported by  NSAF  (Grant Number  U1530402 ). N. D. acknowledges support from  NSF  ( Cooperative Studies of the Earth's Deep Interior ,  EAR150259 ; Petrology and Geochemistry,  EAR144495 ) and  NASA (Laboratory Analysis of Returned Samples ,  NNX14AK09G ; Cosmochemistry,  OJ-30381-0036A  and  NNX15AJ25G ). M. R.
acknowledges support from the  French ANR  ( 2011JS56 004 01 , FrIHIDDA). W. B. acknowledges the support from the  Consortium for Materials Properties Research in Earth Sciences (COMPRES) , the  National Science Foundation (NSF)  through Grant Number  DMR-1104742. We acknowledge GSECARS and  HPCAT of the Advanced Photon Source  for use of the diffraction and ruby facilities. This research used resources of the  Advanced Photon Source , a  US Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. We thank three anonymous reviewers for providing very constructive comments and suggestions.
Background: Systems-of-systems (SoS) are alliances of independent and interoperable software-intensive systems. SoS often support critical domains, being required to exhibit a reliable operation, specially because people's safety relies on their services. In this direction, simulations enable the validation of different operational scenarios in a controlled environment, allowing a benchmarking of its response as well as revealing possible breaches that could lead to failures. However, simulations are traditionally manual, demanding a high level of human intervention, being costly and error-prone. A stimuli generator could aid in by continuously providing data to trigger a SoS simulation and maintaining its operation. Methods: We established a model-based approach termed Stimuli-SoS to support the creation of stimuli generators to be used in SoS simulations.
Stimuli-SoS uses software architecture descriptions for automating the creation of such generators. Specifically, this approach transforms SoSADL, a formal architectural description language for SoS, into dynamic models expressed in DEVS, a simulation formalism. We carried out a case study in which Stimuli-SoS was used to automatically produce stimuli generators for a simulation of a flood monitoring SoS. Results: We run simulations of a SoS architectural configuration with 69 constituent systems, i. e. , 42 sensors, 9 crowdsourcing systems, and 18 drones. Stimuli generators were automatically generated for each type of constituent. These stimuli generators were capable of receiving the input data from the database and generating the expected stimuli for the constituents, allowing to simulate constituent systems interoperations into the flood monitoring SoS.