dataesr/scientific-paragraphs-categorization huggingface dataset:
JADE Autosafing The vast majority of JADE operations occur while the spacecraft is not in contact. As such, it is necessary for JADE software to internally monitor housekeeping telemetry and enter a Fig. 77  MCP rate limiting algorithm safe state if conditions suggest the possibility of hardware damage. Autosafing limits are set higher than the yellow and red limits monitored on the spacecraft in the real-time telemetry stream. Within the LUT, all JSIB monitors have associated critical minimum and critical maximum values. JADE Autosafes if a telemetry point is consistently outside the region bracketed by these values over a specified persistence. Each monitored telemetry point also has an associated subsystem to be safed. For example if a telemetry point associated with safing JADE-E180 only is out of limits for the required persistence, only E180 is safed, and the rest of JADE continues taking data unaffected.
JADE Data Products JADE data files follow the Committee on Data Management and Computation (CODMAC) level descriptions, which are generic for use on science missions and not Juno-specific. The process follows level 1 (the raw data telemetered to Earth from Juno) to level 8 where userorientated information is provided, explaining why particular data is taken at a given time, and if there is any ancillary information associated with the dataset. Table  17  shows the CODMAC levels (different from NASA levels); not all levels are relevant to JADE, e. g. JADE does not provide any level 4 data products since there is no resampling of data. For JADE, the data at different CODMAC levels is as: • Level 1 Raw: All JADE instrument data delivered to Earth in Juno's telemetry stream, exactly as telemetered down.
Temperature Anisotropies and Plasma-β Once densities and temperatures have been deduced by either moment summation or forward modeling, further values can be derived. For instance temperature anisotropy is the ratio of the perpendicular temperature to the parallel temperature. Likewise plasma β is calculated as the ratio of plasma pressure to the magnetic pressure. Level 3 Juno magnetometer data provides B, and combined with the level 5 JADE data of density, n, and temperature, T to compute: β = nk B T B 2 /2μ 0 (4. 8) where k B is the Boltzmann constant and μ 0 is the permeability of free space. Further plasma properties such as Mach numbers, scale lengths, wave and collision frequencies can be calculated from the JADE ion and electron plasma parameters.
Level 6 Ancillary Data Level 6 data aid in understanding JADE data, but are not measured by JADE. For example, JADE data are time-stamped from the onboard Juno spacecraft clock and values included within all level 1 data records. For level 2 data, time is converted to UTC using NASA's Navigation and Ancillary Information Facility (NAIF) SPICE software suite (http://naif. jpl. nasa. gov). NAIF SPICE software is also used to generate all spacecraft orientation, position velocity data. In addition, data from the magnetometer on Juno is used to convert JADE counting data in to pitch-angle distributions.
JADE-E We tested JADE in the LRS, HRS and Burst modes of operations during our initial turn on. The data presented here were collected during LRS in the solar wind. Figure  79  shows a spectrogram of energy versus spin angle taken by JADE-E060 (cf. Fig. 5 ) over a 10-min integration period, which corresponds to ∼10 spacecraft spins. The energy ranges from 100 eV to 100 keV and the spin covers the entire 0 • -360 • rotation. The color codes the electron count rate per bin (∼11. 6 % in energy by 15 • ). Below ∼1 keV the data are clearly ordered by spin phase and provide measurements of the solar wind electrons (core, halo and Strahl). Above ∼1 keV the 2-3 Hz rates are caused by penetrating radiation (cosmic rays) and MCP internal noise; these low background rates indicate that JADE-E is ready to make the important Jovian electron measurements once it enters the Jovian magnetosphere.
Thus, these indexes permit to estimate in a global way an optimal number of clusters for a given method and a given dataset. The best data partition, or clustering result, is in this case the one which minimizes the difference between their values [LAM 04]. Conversely to classical distance-based indexes, they are independent of the clustering process. Moreover, it has been demonstrated in  [LAM 11 ] that straightforward adaptations of these indexes permits to detect degenerated clustering results, whenever those jointly include a small number of heterogeneous or "garbage" clusters with large size and a big number of "chunk" clusters with very small size. Another As regards to this approach, a feature is then said to be maximal or prevalent for a given cluster iff its Feature F-measure is higher for that cluster than for any other cluster.
Thus the set L c of prevalent features of a cluster c can be defined as: 4 = 5 ∈ 1 | = 67 # ∈1 # 8 4 Whenever it has been exploited in combination with hypertree representation, this technique has highlighted promising results, as compared to the state-of-the-art labeling techniques, like Chi-square labeling, for synthetizing complex clustering output issued from the management of highly multidimensional data [LAM 08]. Additionally, the combination of this technique with unsupervised Bayesian reasoning resulted in the proposal of the first parameter-free fully unsupervised approach for analyzing the textual information evolving over time  [LAM 10b ]. Exhaustive experiments on large reference datasets of bibliographic records have shown that the approach is reliable and likely to produce accurate and meaningful results for diachronic scientometrics studies [LAM 12].
Data extraction and preprocessing The data is a collection of patent documents related to pharmacology domain. The bibliographic citations in the patents are extracted from the Medline database 1. The source data contains 6387 patents in XML format, grouped into 15 subclasses of the A61K class (medical preparation). 25887 citations have been extracted from 6387 patents  [HAL 12 ]. Then the Medline database is queried with extracted citations for related scientific articles. The querying gives 7501 articles with 90% recall. Each article is then labeled by the class code of the citing patent. The set of labeled articles represents the final document set on which the training is performed. The final document set is unbalanced, with smallest class containing 22 articles (A61K41 class) and largest class containing 2500 articles (A61K31 class).
Inter-class similarity computed using cosine correlation indicates that more than 70% of classes' couples have a similarity between 0. 5 and 0. 9. Thus the ability of any classification model to precisely detect the right class is curtailed. A common solution to deal with unbalance in dataset is undersampling majority classes and oversampling minority classes. However sampling that introduces redundancy in dataset does not improve the performance in this dataset, as it has been shown in  [HAL 12 ]. So that bootstrapping of train and test data may solve problems of classification sensibility, stability, scalability and dimensionality but does not improve accuracy computation over the sampled correlations. Conversely, pruning irrelevant features and contrasting the relevant ones has we propose hereafter seems thus to be a good alternative.
Results The different results are reported in tables 1 to 5 and in figure  1. Tables present standard performance measures (True Positive, False Positive, Precision, Recall, F-measure and ROC) weighted and averaged over all classes. For each table, and each combination of selection and classification methods, a performance increase indicator is computed using the DMNBtext True Positive results on the original data as the reference. Finally, as soon as the results are identical for Chi-square, Information Gain and Symmetrical Uncertainty, they are thus only once in the tables as Chi-square results. Table  1  highlights that performance of all classification methods are low on the considered dataset if no feature selection process is performed. They also confirm the superiority of the DMNBtext, SMO and KNN methods on the two other tree-based methods in that context.
Conclusion Feature maximization is a cluster quality metric which favors clusters with maximum feature representation as regard to their associated data. In this paper, we have proposed a straightforward adaptation of such metric, which has already demonstrated several generic advantages in the framework of unsupervised learning, to the context of supervised classification. Our main goal was to build up an efficient feature selection and feature contrasting model that could overcome the usual problems arising in the supervised classification of large volume of data, and more especially in that of large full text data. These problems relate to classes' imbalance, high dimensionality, noise, and high degree of similarity between classes. Through our experiments on a large dataset constituted of bibliographical records extracted from a patents' classification, we more especially showed that our approach can naturally cope with the said handicaps.
Related work Ontologies evolution is often confused with the filed of database schemas evolution. In fact, many issues in ontology evolution are exactly the same as the issues in schema evolution. However, there are several differences between them. Instead of comparing directly the process of evolution, ontologies and database schemas evolutions are generally compared through an analyses of the differences between the ontology and the database schemas. Noy and al. [15]  have summarized this difference by the following points: 1) Ontologies themselves are data to the extent to which database schemas have never been. So, ontologies evolution must consider both the structure and instances of the ontologies; 2) Ontologies themselves incorporate semantics while database schemas do not provide explicit semantics for their data.
Then, the restrictions must be considered in the ontology evolution process; 3) Ontologies  are decentralized by nature so their content and usage are often more complex than a database schemas. Ontologies evolution has been influenced by the research on schema evolution database  [16]  but it is a different area of research having its own characteristics. The first proposed methods in the literature  [17, 4, 18]  have presented ontologies evolution process in general but they are considered as the basis of most current works. Thus, Hartung and al. [19]  have studied the evolution and the difference between two versions of the same ontology. This work provided a COnto-Diff tool which can detect different basic changes, however, it has not presented any processing for inconsistencies. Khattak and al.
Conclusion and future work In this paper, we presented the use of the graph grammars to formalize and implement the ontology changes. We proposed an a priori approach of inconsistencies resolutions to adapt ontologies and preserve their consistency. The use of AGG allowed a simple definition of rewriting rules and it presented many advantages. Two programs were developed OWLToGraph and GraphToOWL to automate the back and forth process of transformation of the ontologies to graphs. They allow the user to work and avail the benefits of graph grammars even if his ontologies are defined by another representation language. Many perspectives can be identified. Firstly, it is important to extend the study for the complex ontology changes. It would also be interesting to exploit ontology changes to define a formal approach of ontologies composition knowing that the composition is a combination of some basic changes (AddClass, removeClass, AddAxiom, RemoveAxiom, etc.
The prompt fission gamma spectra and multiplicities are investigated through the Monte Carlo code FIFRELIN which is developed at the Cadarache CEA research center. Knowing the fully accelerated fragment properties, their de-excitation is simulated through a cascade of neutron, gamma and/or electron emissions. This paper presents the recent developments in the FIFRELIN code and the results obtained on the spontaneous fission of 252 Cf. Concerning the decay cascades simulation, a fully Hauser-Feshbach model is compared with a previous one using a Weisskopf spectrum for neutron emission. A particular attention is paid to the treatment of the neutron/gamma competition. Calculations lead using different level density and gamma strength function models show significant discrepancies of the slope of the gamma spectra at high energy.
These lacuna in the evaluated library motivate us to study more deeply the fission fragment de-excitation process through the Monte Carlo code FIFRELIN  [3, 4]  which is developed at the Cadarache CEA research center. In this paper, two different models implemented to simulate the de-excitation process of the fragments are exposed. The prompt gamma multiplicity and spectra coming from these simulations are compared and discussed. Finally, the discrepancies between simulation using different models of level density (ρ(E, J, π)) and gamma strength function ( f XL ( γ )) are highlighted as well as the influence of a variation of the fragments total angular momentum.
Model 1: Uncoupled neutron and gamma emission 2. 1 Model description The first model of decay cascade that have been implemented in the FIFRELIN code is based on the assumption that all the prompt neutrons are emitted before the prompt gamma and the conversion electrons. While the excitation energy of the fission fragment is higher than a limit energy, a neutron is emitted with its kinetic energy in the fragment frame sampled from a Weisskopf spectrum. The limit energy is determined as follows: E limit = S n + E rot (J), E rot = J(J + 1) 2 2kJ rig where the rotational energy E rot (J) is a function of the spin J of the fragment, and of a fraction k of its rigid body moment of inertia J rig. It is assumed that each neutron emission decreases the total angular momentum of the fragment by /2.
When the limit energy is reached, the gamma decay takes place. Its simulation is based on the knowledge of a complete level scheme of the post-neutron fragment up to a given continuum beginning energy. This energy is chosen so that the level density is equal to 10 5 levels per MeV. Below that continuum energy, a complete discrete level scheme is determined using a process already described in Ref. [4]. Experimental levels coming from the RIPL-3  [5]  database are taken into account. Above the continuum beginning energy, the detail of discrete levels is no more computed and the decay algorithm relies on average nuclear properties over 1 keV energy bins. Knowing the level scheme and the initial excited level i of the post-neutron fragment, the code is able to calculate the decay probability of every possible transitions to a reachable level or bin f.
WONDER-2012 4 Conclusion In that work, the two main fragment de-excitation models implemented in the FIFRELIN code are described. The second model, based on a Hauser-Feschbach treatment of the de-excitation, requires no more hypothesis on the evolution of the fragment spin during the neutron emission. Moreover, this model provides a better description of the neutron/gamma competition. The gamma observables obtained with the two different models are compared to experimental data and we show that the underestimation of the gamma spectrum predicted by the two models is probably due to an underestimation of the post-neutron fragments total angular momentum. The level density and strength function models used for our decay cascade simulations rules the slope of the high energy part of the gamma spectrum.
We present a semi-analytical formalism capable of handling the coupling of electromagnetic sources, such as point dipoles or freepropagating fields, with various kinds of dissipative resonances with radiation leakage, Ohmic losses or both. Due to its analyticity, the approach is very intuitive and physically-sound. It is also very economic in computational resources, since once the resonances of a plasmonic or photonic resonator are known, their excitation coefficients are obtained analytically, independently of the polarization, frequency or location of the excitation source. To evidence that the present formalism is very general and versatile, we implement it with the commercial software COMSOL, rather than with our in-house numerical tools.
The net benefit is the derivation of a versatile and practical method for normalizing the QNM, which is suitable for any software and in particular commercial electromagnetic solvers. We illustrate how to calculate normalized QNMs with COMSOL Multiphysics (http://en. wikipedia. org/wiki/COMSOL_Multiphysics). The manuscript is organized as follows. Section 2 is a reminder of important results obtained in  [10]. It is presented here for the sake of self-consistency. In Section 3, we consider the general problem of the calculation and normalization of QNMs. Although the calculation is not an original task in itself, it is often conducted with dedicated softwares by specialists of computational electrodynamics. Here, we show how to calculate them with standard tools, taking the example of COMSOL Multiphysics for which we provide a model sheet.
Theoretical background Throughout the manuscript, the exp(iωt) notation is adopted for the harmonic fields (consistently with the COMSOL software). We are interested in the response of a resonant optical system that consists of structured absorbing and dispersive media with open boundaries. The system is characterized by the position-and frequency-dependent permittivity and permeability tensors ε(r,ω) and µ(r,ω). Note that the materials can be anisotropic and magnetic; the sole assumption is that the materials are reciprocal ε Τ = ε and µ Τ = µ, where the superscript denotes matrix transposition. The QNMs supported by such a resonant system are electromagnetic field distributions ( ) m m , E H   that are solutions of Maxwell's equations in the absence of source, m m m m ( , ) , i ω ω ∇ × = - E μ r H     (1a) m m m m ( , ) , i ω ω ∇ × = H ε r E     (1b) and that satisfy outgoing wave boundary conditions (the Sommerfeld radiation condition at |r| → ∞).
Calculation and normalization of the QNMs In this Section, we propose a simple and versatile technique to calculate and normalize the QNMs. We believe that the approach can be implemented with various (if not all) numerical software implementing the outgoing wave conditions for |r| → ∞. For the sake of illustration, we use the COMSOL Multiphysics software and its RF toolbox for which we provide a model sheet for the nanorod geometry of Fig. 1   [14].
Pole calculation To calculate the pole, one needs to iteratively solve Eqs. (  5 ) and (  6 ) for several values of ω that progressively converge towards ω . In our simulations, we use a Drude model, ( ) 2 2 Au ε 1 p i ω ω ωγ = - - , with ω p = 1. 26 × 10 16 s -1 and γ = 1. 41 × 10 14 s -1 , to define an analytical continuation of the gold permittivity for complex frequencies. The values of ω p and γ are fitted from tabulated data in  [16] , and ( , ) ω μ r is simply equal to the permeability of the vacuum. Actually, COMSOL does not handle complex frequencies and we use a trick relying on effective dielectric and magnetic tensors to artificially mimic complex frequencies with real ones, see details in Appendix 1. The trick is likely to be usable with any software that considers real frequencies and complex permittivity and permeability.
Conclusion Dissipative resonances are essential ingredients for many physical effects and devices. We have presented a numerical formalism that is able to analytically handle the coupling between optical resonances and various kinds of sources, such as Dirac electrical dipoles -Eqs. (  3 ) and (  9 )or free-propagating fields -Eqs. (  13 ) and  (14). The formalism encompasses radiation leakage and Ohmic losses. It has been implemented with the COMSOL software, but the implementation is very versatile and can be made with many other in-house or commercial software. We emphasize that the new normalization procedure of Section 3. 3 is much simpler and more general than the one initially presented in  [10]. For the sake of simplicity, the formalism has been tested on a very simple example, a gold nanorod in a uniform dielectric background.
Sample preparation and GC-MS analyses GC-MS analyses of amino acids to determine the 13 C-labeling (see File S1 for further details) were carried out as described by Zamboni et al. [34]. Briefly, after 5 generations of exponential growth in the presence of 20% [1-13 C] glucose, cells were collected, proteins were extracted and submitted to acidic hydrolysis, extracts were derivatized with methoxamine and Nmethyl-N-(trimethylsilyl) trifluoroacetamide and injected on a GC-MS (Agilent 6890N gas chromatograph, time-of-flight mass spectrometer Pegasus III). Peak integration was performed using LECO Pegasus software. Because automated peak integrations have been occasionally unreliable, manual controls and/or corrections were systematically performed for each metabolite. Five independent samples were collected along a 1.
13 C Metabolic Flux Analysis. Here, we took advantage of the software OpenFlux, which is a computational tool based on the Elementary Metabolite Units (EMU) framework  [72, 73]  to analyse 13 C-labeling data. The EMU framework used here for the simulation is provided in Table  S1. OpenFlux calculates fluxes from Mass Isotopic Distribution (MID, or isotopic pattern) of amino acids. [ 13 C-1] glucose was used here as the labeling substrate and allowed us to determine the flux-ratio between the pentose phosphate pathway and glycolysis (PPP/Glyc ratio). In fact, when [ 13 C-1] glucose was metabolized by the PPP, the 13 C label was lost by decarboxylation, while the 13 C label was incorporated into amino acids when it enters glycolysis. We recognize that reversible triose phosphate isomerase and aldolase activities could lead to a 13 C-labeling in the C-6 atom position in fructose-1, 6-bisphosphate and then possibly in hexose phosphates  [74].
4), it was possible to calculate 'intrinsic energy production' (ATP), reducing power (NADH, NADPH) and gas exchange (O 2 , CO 2 ). The total CO 2 evolution rate was computed from catabolic CO 2 production (glycolysis, TCA, PPP) and anabolic production as: c MODEL CO2 ~vANA CO2 zv CAT CO2 Then, this value was compared with our experimental CO 2 production rate to verify the validity of the carbon-flux distribution (see section Results 4). In other words, experimental CO 2 evolution was an independent data point not used as a constraint to calculate carbon-flux distribution. We assumed that NADPH requirement and production should have been equal since the metabolism was in steady state. If the production of NADPH was more important than the need, excess NADPH might have been converted to NADP + , thereby reducing NAD + into NADH, by nicotinamide nucleotide transhydrogenase.
Acknowledgments The S. coelicolor strains came from the  John Innes Centre (Norwich)  and were a gift from  MJ Bibb and JP Gomez-Escribano. The authors would also like to thank  Monique Bolotin Fukuhara  for her investment and continuous encouragement from the beginning of this work,  Maxime-Vergne  for FITRS analyses,  Sylvie Lautru  for additional HPLC analyses and  Michae ¨l Dubow  for proofreading of the manuscript.