dataesr/scientific-paragraphs-categorization huggingface dataset:
A. 10 SOURCE_FIND The SOURCE_FIND software is described in detail in AMI Consortium:  Franzen et al. , (2011) , where it is applied to the 10C survey of radio sources at 16 GHz. The software is capable of identifying and characterising sources in radio synthesis maps with varying noise levels and synthesised beams, and includes a straightforward and accurate method for distinguishing between point-like and extended sources over a wide range of SNRs. It is part of the standard data reduction pipeline for the Arcminute Microkelvin Imager  (AMI Consortium: Zwart et al. , 2008). The first step in the source extraction process involves determination of the noise level. At each pixel position in the image the noise is taken as the rms inside a square centred on the pixel whose width is set to some multiple of the synthesised beam and, in order to avoid the noise estimate from being significantly affected by source emission, points are clipped iteratively until convergence at ±3 σ is reached.
ACKNOWLEDGEMENTS We thank the referee for suggestions that have helped to improve the accuracy and presentation of this work. This work was supported by resources provided by the  Pawsey Supercomputing Centre  with funding from the  Australian Government and the Government of Western Australia. NS is the recipient of an  ARC Future Fellowship. The LOFAR team acknowledges  Hanno Spreeuw  as the original developer of PySE. The  IFCA team acknowledge Spanish MINECO  Projects  AYA2012-39475-C02-01  and Consolider-Ingenio 2010  CSD2010-00064. RPB has received funding from the  European Union Seventh Framework Programme  under grant agreement  PIIF-GA-2012-332393. CF acknowledges financial support the ' Agence Nationale de la Recherche ' through grant  ANR-09-JCJC-0001-01  and  ANR-14-CE23-0004-01.
HG acknowledges financial support from the  UnivEarthS Labex program of Sorbonne Paris Cité  ( ANR-10-LABX-0023  and  ANR-11-IDEX-0005-02 ). PH has been supported by the  Australian Research Council  through the  Super Science Fellowship  grant  FS100100033. SM and MP acknowledge funding from Contracts  I/038/080/0  and  I/029/12/0  from  Agenzia Spaziale Italiana. JGN acknowledges financial support from the  Spanish MINECO  for a 'Ramon y Cajal' fellowship, cofunded by the  European Social Fund. LR acknowledges support from the  US National Science Foundation  under grant  AST12-11595  to the  University of Minnesota. ES acknowledges support, at the date of this work, from the  NASA   Astrophysics Data Analysis Program  grant  NNX12AE18G. Parts of this research were supported by the  Australian Research Council Centre of Excellence for Allsky Astrophysics (CAASTRO) , under grant number  CE110001020.
Common Airbus practice for designing and validating fault tolerant architectures was fully described in previous contributions  (Goupil, 2011). On most civil/military FBW A/C, the usual strategy for sensor FDD consists in point consolidation of like signals. Majority voting mechanisms were comparably patented by Boeing and Airbus, which rely on multiple sensing channels (triplex or more) and assume that a faulty situation will result in inconsistent signals. Therefore, each parameter is measured by independent sensors, allowing a reference consolidated signal to be computed by the management system, which also monitors the set of sensors simultaneously. Hence, the FDD architecture involves two layers. The first one relies on hardware redundancy using several sensors of the same type, and is also applied to the computing devices: three different ADIRU (air data and inertial reference unit) computers manage all the signals issued from anemometric and inertial sensors (Fig.
The PSO particles are associated with vectors of R 2n gathering the kernel parameters for the n explanatory variables. The coupling of a PSO algorithm with the OLS constructive technique  (Seren et al. , 2011)  allows structural and parametric optimizations to be performed jointly for different types of regressors with local basis. The performance of this approach is strongly dependent on the type of PSO algorithm, e. g. ,  Chen et al. (2009)  use a basic and very simple version of PSO. After a thorough literature analysis, the most promising techniques have been selected and implemented in the part of the KOALA code (PSO internal library) used to optimize the regressors' positioning (see the work of  Seren et al. (2011) ) and the references therein for more details). As a result, the final performance of KOALA results from two complementary aspects: applying efficient OLS-based forward selection and separable nonlinear least squares optimization to powerful modeling (LLM) and, on the other hand, implementing a new PSO algorithm which outperforms the standard ones.
Some details and a limited version of the software are also available at w3. onera. fr/smac/?q=koala for a peculiar application in the framework of rational modeling. To give a rough idea, the use of KOALA results in a model comprising only five radial units in the benchmark case available at that address, whereas a more standard algorithm  (Chen et al. , 2009)  requires not less than 15 RBF units to achieve the same level of approximation.
Results and discussion The proposed AEKF and AMI approaches were evaluated with flight data issued from both simulations and real tests performed on an A340/600 A/C. Both types of data correspond to a realistic flight path profile, comprising climb, cruise and descent flight conditions with heading changes (see the subplots of Fig. 7 ). To evaluate the estimators, the simulated data present some additional pros with respect to the real flight test since the reference values of α, V are perfectly known (no noise, no biases) and since several disturbances can be added to the flight scenario (cf. Fig. 7 ): (i) a vertical wind gust for t ∈ [500, 600], (ii) turbulence for t ∈ [800, 1000], (iii) a large deflection of airbrakes for t ∈ [2460, 2840] (which are not modeled in M NL ). Strong colored noises were also added to all the measurements to check the process insensitivity to noisy signals (Fig.
Back Close Full BIOMES_data) synthesized palaeovegetation records from many sites to provide global datasets for the LGM and mid-Holocene. Data syntheses are valuable in allowing researchers to see the global picture from scattered, individual records, and to enable model-data comparisons. The data can be viewed through the prism of a global, physically based model that allows the point-wise data to be joined together in a coherent way. There are continuous, multi-millennial palaeoenvironmental records that stretch much further back in time than the LGM but they have not previously been brought together in a global synthesis. These records can provide a global picture of transient change in the biosphere and the climate system. Here we have synthesized and biomized  (Prentice et al. , 1996)  a number of these records, providing a new dataset of land biosphere change that covers the last glacial-interglacial cycle.
6b  and c ). The absolute magnitude of glacial-interglacial variation in HadCM3 is closer to that in the reconstructions, whereas FAMOUS variation is nearly twice the magnitude. However, the temporal variation in FAMOUS has some features that are more similar to the data compilation, such as lighter values in MIS 4 that are similar to the LGM values (Fig. 6b  and c ). The 2009;  Oliver et al. , 2010) , which is not captured here. This may be related to longerterm carbon reservoirs changes that may be linked to changes in ocean ventilation and/or productivity  (Wang et al. , 2001; Hoogakker et al. , 2006; Rickaby et al. , 2007) , not represented in our modelling approach. While the distribution of δ 13 C in oceans is affected by several factors such as reorganisation of water masses (especially in the North Atlantic), ocean productivity and export  (Brovkin et al.
Conclusions We changes. Modelled ocean δ 13 C changes derived with FAMOUS are larger because of larger simulated changes in terrestrial carbon storage. The differences in terrestrial carbon storage between the models in turn derive from differences in the variability of ice-sheet prescription (Fig. 3 ) and regional climate biases, where HadCM3 is generally wetter and slightly warmer in the glacial than FAMOUS, which means productivity and extent of warm temperate and boreal forests does not decrease as it does into the glacial in FAMOUS. Existing data coverage is still low, and so there are still large areas of uncertainty in our knowledge of the palaeo-Earth system. Better spatial and temporal coverage for all parts of the globe, especially lowland areas, is required, and for this we need data from new sites incorporated into global datasets that are easily accessible by the scientific community.
Recent estimates using eddy covariance flux data estimate global NPP as ∼ 62 Pg C yr -1 (assuming 50 % carbon use efficiency to convert from GPP to NPP; Beer et al. , 2010). Some other model estimates for the PI are also lower (e. g. Prentice et al. , 2011: 59. 2 Pg C yr -1 ). As mentioned in Sect. 3. 3. 1, BIOME4 is driven solely by an observational climate dataset due to the anomaly approach used to reduce the impact of climate model biases (see methods Sect. 2. 1. 3). Therefore, any overestimate in NPP is not a result of the climate model but possibly due to biases in the vegetation model, and/or biases in the observational climatology used to drive the model, and low spatial resolution used. For example, the lower resolution topography does not Discussion Paper | Discussion Paper | Discussion Paper |.
Acknowledgement. W. A. Watts  is thanked for his contribution to counting the Monticchio pollen record and  A. Matsikaris  for discussion of the FAMOUS results. The authors would like to acknowledge funding from the  NERC QUEST programme  (grant  NE/D001803/1 ) and  NCAS-Climate. J. S. Singarayer and P. J. Valdes acknowledge BBC  for funding initial climate simulations.
Here, we present the data from an in-depth analysis of the HSP70 family in the non-model banana during osmotic stress  [1]. First, a manual curation of HSP70 sequences from the banana genome was performed and updated on the Musa hub http://banana-genome. cirad. fr/. These curated protein sequences were then introduced into our in-house Mascot database for an in-depth look at the HSP70 protein profiles in banana meristem cultures and roots during osmotic stress. A 2D-DIGE LC MS/MS approach was chosen to identify and quantify the different paralogs and allelic variants in the HSP70 spots.
Subject area Biology More specific subject area Plant proteomics, HSP70 family Type of data Tables, text files, figures How data was acquired 2D-DIGE and mass spectrometry (Q Exactive Orbitrap) Data format Analyzed Experimental factors Meristem cultures and plant roots were exposed to osmotic stress Experimental features Meristem and root proteomes were separated using 2D-DIGE and proteins belonging to the HSP70 family were identified using LC MS/MS Data source location Leuven, Belgium Data accessibility Within this article.
Value of the data Data provides an overview of the identification and quantification of the HSP70 family in a nonmodel plant with special attention to osmotic stress. Manual curation of the banana HSP70 sequences was performed as correct structural annotation of a sequenced genome is essential prior to proteomic analysis. The proteomics approach using 2DE and LC MS/MS was a deciding factor in the successful identification and quantification of the HSP70 paralogs and allelic variants.
Analysis of the Musa HSP70 family Musa HSP70 nucleotide and protein sequences were obtained from GreenPhyl and the Banana Genome Hub  [2] [3] [4]. Since many HSP70 genes were incorrectly predicted, all HSP70 predicted from the Acuminata A genome sequences were manually curated as well as the cytoplasmic and luminal HSP70 sequences predicted from the Balbisiana B genome. The manually curated B genome cytoplasmic and luminal protein sequences can be found in Supplementary file 1. The manually curated A genome protein sequences are available at the Banana Genome Hub. Cytoplasmic HSP70 from rice and Arabidopsis thaliana were retrieved from Greenphyl with the accessions as described by Jung et al. [5]. Alignments of protein sequences were created using the ClustalX 2. 1 software  [6]. An alignment of the main cytoplasmic HSP70 isoforms identified later in this manuscript can be found in Supplementary file 2.
Proteomics Meristem and root proteins were extracted and analyzed using the phenol extraction/ammonium acetate precipitation protocol reported by Carpentier et al. [10]. 50 mg of proteins was labeled with Cy2, Cy3 and Cy5 (GE Healthcare) for a total of 150 mg protein per gel, separated on gel and scanned according to Carpentier et al. [11]. Data were analyzed using the DeCyder software version 7. 0 (GE Healthcare). Statistical analysis of the standardized abundance of spots was performed in DeCyder. Statistical analysis of the raw spot intensities was performed using ANOVA in STATISTICA software 10 on the log of the peak height of the internal standard samples exported from DeCyder. Dynamic abundance profiles of the root HSP70 spots 1, 2, 3, 4, 5 and 6 after 0, 1, 4 and 14 days of stress (n¼ 3) can be found in Fig.
The Q Exactive was operated in positive ion mode with a nanospray voltage of 1. 5 kV and a source temperature of 250 1C. ProteoMAss LTQ/FT-Hybrid ESI Pos. Mode CalMix (MSCAL5-1EA SUPELCO, Sigma-Aldrich) was used as an external calibrant and the lock mass 445. 12003 as an internal calibrant. The instrument was operated in data-dependent acquisition (DDA) mode with a survey MS scan at a resolution of 70,000 (fwhm at m/z 200) for the mass range of m/z 350-1800 for precursor ions, followed by MS/MS scans of the top 10 most intense peaks with þ2, þ3 and þ4 charged ions above a threshold ion count of 16,000 at 35,000 resolution using normalized collision energy (NCE) of 28 eV with an isolation window of 3. 0 m/z and dynamic exclusion of 10 s. All data were acquired with Xcalibur 2. 2 software (Thermo Scientific).
For identification, all raw data were converted into mgf files using Progenesis v4. 1 (Nonlinear Dynamics, UK). The spectra were searched using Mascot (version 2. 2. 04) against our in-house Musa database (76,220 sequences) containing all the protein sequences of the published A and B genome plus contaminant sequences (trypsin and keratin). Redundancy was eliminated from the database using the program cdhit  [13]. If both A and B isoforms were identical, the B genome isoform was eliminated. The original HSP70 protein sequences were removed and replaced by the manually curated HSP70 sequences. Search parameters were set at: tryptic digestion, one miscleavage allowed, 10 ppm precursor mass tolerance and 0. 02 Da for fragment ion tolerance with a fixed modification of cysteine carbamidomethylation and a variable modification of methionine oxidation.
The advantage of an LC-separation is nicely illustrated by the separation of the peptides FSDSSVQSDIK (encoded by gene GSMUA_Achr7T15160) and YSDASVQSDIK (encoded by gene GSMUA_Achr10T00900). These two isoforms of the peptide have the same monoisotopic mass but have different retention times on the RP column because of their different hydrophobicity (approximately 19 and 17 min) (Fig. 3 ). This would have resulted in a chimeric spectrum using MALDI-TOF/TOF MS but produces separate spectra using LC-MS/MS. An isoform was retained as positively identified in a spot if at least one tryptic specific peptide was found with an ion score higher than the Mascot identification score. Cytoscape v3. 0 software was used to visualize tryptic specific peptides  [14] [15] [16]. Supplementary file 3 contains a list of all identified HSP70 paralogs and allelic variants per spot based on this Mascot analysis.
45 GHz were measured simultaneously with a RUSK MEDAV channel sounder  [23]. For the measurement data, totally 120 time blocks covering a time duration of 32 s and 220 frequency bins covering a bandwidth of 70 MHz were recorded. In the following, we calibrate the MR-FDPF model in two cases. The first case is to calibrate the MR-FDPF model with all the available measurement data, while the second case is to calibrate it with only a part of the available measurement data. Intuitively, the prediction performance of the MR-FDPF model calibrated with all the measurement data should be better than that calibrated with only a part of the measurement data since more measurement data are used.
Calibration with the measurement data from the links between all Txs and all Rxs This calibration is performed with the measurement data from all the 64 links, i. e. , the links between all the 8 Txs and all the 8 Rxs. The measurement data used to perform the calibration are taken only from the center frequency of 2. 45 GHz but are averaged along the time axis, i. e. , averaged over the 120 time blocks. The parameter values of materials obtained from the calibration process are listed in Table  1. Then, these parameter values are configured to run the MR-FDPF simulation at 2. 45 GHz with 0 dBm transmit power. The simulation step is 2 cm. The radio coverage Fig. 2  The measurement scenario map of Tx1 simulated with the MR-FDPF model is shown in Fig. 3  as an example. The obtained offset and the RMSE computed from the 64 links compared to the measurement data are: offset = -60.
Calibration with the measurement data from the links between only Tx1 and all Rxs This calibration is performed with the measurement data from 8 links, i. e. , the links between the Tx1 and all the 8 Rxs. The same as above, the measurement data used to perform the calibration are taken only from the center frequency of 2. 45 GHz but are averaged over the 120 time blocks. The obtained parameter values of materials from the calibration process are listed in Table  2. Similarly, these parameters are also configured to run the MR-FDPF simulations at 2. 45 GHz. Thus, we can compute the offset and the RMSE between the simulation and measurement as follows: • Comparing the two RMSEs computed both from the 64 links in the above two different cases A and B, it is obvious that we obtain a smaller RMSE when all the simulated points are calibrated with the measurement data than only a part of them are calibrated, which is consistent with our intuition.
2  [DR08] , but there is also a working draft for version 1. 3  [DR15]  which modifies the protocol considerably. While the initial protocol was developed for protecting HTTP connections between a browser and a web server, many current Internet protocols including, e. g. , SMTP or IMAP for transmitting e-mails and LDAP for accessing directories, have been extended to allow for securing the transmission with TLS. Because of its practical importance, the security of SSL/TLS has attracted a lot of attention in the literature, see e. g. [WS96, Pau99, Kra01, CK02, JK02, Bar04, HSD + 05, MSW08, MT10, PRS11, AP12, BFCZ12, FHM + 12, GIJ + 12, JKSS12, ABP + 13, AP13, BFK + 13a, BFS + 13, GKS13, KSS13, KPW13, BMM + 15], in chronological order.
g. ,  [BR93, CK01, CK02] ). Our analysis of the initial part of the handshake can be interpreted as that the handshake without the final message can indeed be seen as a key-exchange protocol. Comparing this definition to previous models, our analysis covers the (game-based) notion of "key revealing" (because the distinguisher obtains all generated keys), but no "adaptive state reveals" defined in some game-based models. 2 eyond that, because of the non-standard use of schemes and primitives, papers that analyze (parts of) TLS 1. 2 must generally choose between analyzing a modified version of the protocol, analyzing the original protocol in idealized models (such as the random oracle model), or using tailor-made computational assumptions. Proving modified protocols was an early approach towards obtaining an intuition of the security of the TLS handshake.
For example,  [JK02]  drop the final message of the handshake, while  [MSW08]  do consider the final messages, but in an unencrypted form. A more extreme approach is taken by Gajek et al. [GMP + 08], who modify several details to achieve security in the UC framework  [Can01]. (As pointed out by Küsters et al. [KT11]  this additionally requires the insertion of session identifiers into the protocol. Küsters et al. also give a case study and describe how one could model TLS more faithfully in the IITM  [KT11]  model. ) However, extending the security proof of a modified protocol to that of the real protocol is usually hard; for SSL/TLS, it is nearly impossible. Recent game-based analyses of TLS made important steps towards assessing the security of the actual protocol. Jager et al. [JKSS12]  treated the security of a specific configuration for the key-exchange step, i.
Recently, [BFK + 13a] broke with symbolic models and gave a meaningful formal statement about miTLS, a standard compliant implementation of TLS. They describe cryptographic idealizations of the cryptographic primitives of TLS which are indistinguishable by typed adversaries. This allows for a type-based and cryptographically faithful verification of miTLS using a dialect of f * [SCF + 11]. [BFK + 13b] builds on [BFK + 13a] and focuses on the details of the TLS handshake using the EasyCrypt tool in addition to f *. As both EasyCrypt and constructive cryptography build on a simple, modular semantic theory, we are planning on investigating the formal relation between constructive cryptography and EasyCrypt's new module system. Previous results in constructive cryptography. Some related schemes have already been analyzed following the constructive cryptography paradigm  [MR11, Mau11].
Furthermore, it appears impossible to perform a reasonably modular analysis for the RSA-PKCS variant of the handshake under the OW-PCA assumption  [JK02] , even in the random oracle model. This is evidence that it is difficult to modularize the proof of the CCCA security of the TLS key extraction mechanism of  [KPW13]  further unless one is willing to make additional assumptions. Building on a recent result by [BFK + 13b], we show that a reasonably weak nonrandomizability assumption that they call NR-PCA is sufficient for a more modular constructive proof. We describe these problems in more detail in Section 3. 3. 3. Although various of the above mentioned obstacles could have been bypassed by modifying the scheme, we kept our analysis close to the realistic TLS protocol. The result is a more complicated analysis; however, the artifices are immanent to the protocol, not to our technique.
Key expansion and generating confirmations. Both client and server compute keys κ C and κ S and "finished" messages ξ C and ξ S via a pseudo-random function PRF keyed with msk. The two finished messages are then encrypted, in a concatenation of all the messages in the protocol, and sent. Since the transmission of ξ C and ξ S in the protocol is protected by Authenticate-then-Encrypt with the same keys as the payload messages, a modularization at the intuitively appealing point after performing the confirmation is impossible. Only after this protocol step can we fully separate different "sessions," as the computation of ξ C and ξ S still requires data which is correlated between different sessions (e. g. , the server certificate). The behavior of this resource is similar to that of MSK with a different key space, but the resource can now be described as the parallel composition of one (single-session) resource KSP, * = =• per client.