dataesr/scientific-paragraphs-categorization huggingface dataset:
IX. CONCLUSION We presented information leakage attacks through a Data and through an Interest in NDN. Particularly, we investigated such attacks through an Interest and showed two possible methods to perform the attack. Moreover, we introduced a steganography-embedded Interest name to leak information as a more sophisticated attack to exploit an Interest name than simply adding leaked information into a name. Then, as a countermeasure against the attack, we proposed a name filter using search engine information and using an isolation forest to classify a name in the Interest as legitimate or not. In order to build the filter using an isolation forest, we collected URLs from a data repository provided by Common Crawl. Our experiments show that (1) the path part in the URL-based NDN name is useful for malware to create malicious names leaking information and hide the activities; (2) it is difficult to completely prevent information leakage even in NDN; and (3) the filters can choke information leakage throughput dramatically and malware needs to send 137 times more Interest packets to leak information than when filters are not used.
04 0. 06 0. 08 0. 10 0. 12 com net org info jp fr uk de 0. 02 0. 00 aA bB cC dD eE fF gG hH iI jJ kK lL mM nN oO pP qQ rR sS tT uU vV wW xX yY zZ (a) Protector's name dataset. AVG F(Path, ) 0. 04 0. 06 0. 08 0. 10 0. 12 com net org info jp fr uk de 0. 02 0. 00 aA bB cC dD eE fF gG hH iI jJ kK lL mM nN oO pP qQ rR sS tT uU vV wW xX yY zZ (b) Attacker's name dataset. FIGURE 8: Average frequencies of letters in the path.
the other printable characters in the path. AVG F(Path, c) 0. 2 0. 3 0. 4 0. 5 0. 6 com net org info jp fr uk de 0. 1 0. 0 1 2 3 4 5 6 7 8 9 ~! @ # $ % & * ( ) - _ = + ; : ' ,. / ? [ ] (b) Attacker's name dataset. AVG F(Query, ) 0. 00 0. 02 0. 04 0. 06 0. 08 0. 10 0. 12 bB FIGURE 9: Average frequencies of aA cC dD eE fF gG hH iI jJ kK lL mM nN oO pP qQ rR sS tT uU vV wW xX yY zZ com net org info jp fr uk de (a) Protector's name dataset. AVG F(Query, ) 0. 04 0. 06 0. 08 0. 10 0. 12 com net org info jp fr uk de 0. 02 0. 00 aA bB cC dD eE fF gG hH iI jJ kK lL mM nN oO pP qQ rR sS tT uU vV wW xX yY zZ (b) Attacker's name dataset.
The tree topology was well supported by three complementary methods (Bayesian, maximum likelihood, and neighboring-joining algorithms) and highlighted Medtr8g085720 (MtBG1), Medtr3g083580 (MtBG2), Medtr4g132280 (MtBG3), Medtr3g065460 (MtBG5), and Medtr1g031900 (MtBG40) as GPI-anchored proteins most closely related to the Arabidopsis PD genes PdBG1 (AT3G13560), PdBG2 (AT2G01630), and PdBG3 (AT1G66250; Figures  1H  and S1A )  [38]. To determine which of these genes are regulated during the establishment of the N-fixing symbiosis, we searched for those expressed in available rhizobial root hair and/or laser-dissected nodule zones (STAR Methods). qRT-PCR analyses then confirmed the upregulation of Medtr3g083580 (MtBG2) and Medtr3g065460 (MtBG5) gene expression in M. truncatula roots after inoculation with rhizobia (Figures  1I  and S1C ), in line with their expression levels in both infectome and nodule transcriptome databases.
ACKNOWLEDGMENTS We thank  D. Barker ,  J. Fournier , and  P. Gamas  for critical reading of the manuscript;  A. Kelner  and M. -F. Jardinaud for valuable advices for statistical analyses using the R software; the TRI-FRAIB miscroscopy platform and  A. Lepage  for technical assistance with the Technovit embedding;  Yk€ a Helariutta (Sainsbury Laboratory, Cambridge, UK)  for kindly providing the cals3m construct;  J. Fournier  for providing the Sm 2011-mCherry strain; and  C. Rosenberg  for providing the pCambia goldengate vectors. R. G. -P. was supported by  Leeds University 110  scholarship, M. B. was supported by the  French Laboratoire d'Excellence (Labex)   TULIP post-doctoral grant  '' Young Scientists for the Future '' and by an  AgreenSkills  fellowship (no. 2013-609398 ). C. R. is a postdoctoral fellow from the  ANR NODCCAAT  ( ANR-15-CE20-0012-01 ) and  INRA Agreenskills  (no.
2010-267196 ). P. K. was supported by a  BBSRC DTP  ( BB/M011151/1 ). Y. B. -A. lab work was supported by research grants from the  Royal Society ,  EPSRC  ( EF/M027740/1 ), and the  Leverhulme Trust  grant  RPG-2016-136 , and F. d. C. -N. lab work was supported by  ANR COME-IN  (no. ANR-14-CE35-0007-01 ) and TULIP (no. ANR-10-LABX-41 ) grants.
This work N/A Deposited Data Raw and analyzed data This work Medeley database; https://doi. org/ 10. 17632/5hzcxdb4dp. 1 Software and Algorithms Leica, ImageJ [57] http://imagej. nih. gov/ij/ Volocity version 6. 0. 1 software PerkinElmer License n 147. 99. 102. 74 Figtree http://tree. bio. ed. ac. uk/ N/A software/figtree/ TreeGraph2 [58] http://treegraph. bioinfweb. info/ R software http://r-project. org N/A.
Relative humidity (hur_avg) should be computed with respect to liquid and ice, according to each model's microphysics scheme. We recommend that the Bolton formulation for equivalent potential temperature (thetae_avg) be used  (Bolton, 1980, his Eq. 43). Table  5  indicates the list of two-dimensional variables (functions of x, y, and t) to output as hourly averages. The italicized variables are non-standard outputs; all others are standard CMIP6 output. The starred variables are outputs for   7  indicates the list of three-dimensional variables to output, as instantaneous 6-hourly snapshots. It is optional to upload these variables to the shared data server (we suggest uploading the last 25 days of 3-D output), but the 3-D variables must be saved and stored locally by each modeling group. The italicized variables are non-standard outputs; all others are standard CMIP6 output.
Acknowledgements. RCEMIP arose from discussion at the  Model Hierarchies Workshop , sponsored by the  Working Group on Climate Modeling of the World Climate Research Programme  and held in Princeton, NJ, in November 2016. The authors thank  Nadir Jeevanjee ,  Timothy Cronin ,  Kerry Emanuel ,  George Bryan , and  Travis O'Brien  for helpful feedback and discussion, as well as  Isaac Held ,  Levi Silvers , and two anonymous reviewers for constructive reviews that improved the design and presentation of RCEMIP. The SAM cloud-resolving model, from which results were shown in Sect. 5, is maintained and provided by  Marat Khairoutdinov. Edited by: Chiel van Heerwaarden Reviewed by:  Levi Silvers ,  Isaac Held , and two anonymous referees.
Code and data availability. Scripts to calculate the analytic sounding described in Sect. 3. 2. 3 and the diagnostics described in Sect. 4. 2 will be available on the RCEMIP website at http://myweb. fsu. edu/awing/rcemip. html. The model output from RCEMIP will be made publicly available through the WDCC/CERA archive at DKRZ, accessible at https://cera-www. dkrz. de/WDCC/ ui/cerasearch/.
The September 2017 solar flares and the subsequent geomagnetic storms driven by the coronal mass ejections were recognized as the ones of the most powerful space weather events during the current solar cycle. The occurrence of the most powerful solar flares and magnetic storms during the declining phase of a solar cycle (including the current 24 th cycle) is a well-known phenomenon. The purpose of this study is to better characterize these events by applying the generalized characteristic function approach for combined analysis of geomagnetic activity indices, total electron content data and secondary cosmic ray data from the muon hodoscope that contained Forbush decreases resulting from solar plasma impacts. The main advantage of this approach is the possibility of identification of low-amplitude specific features in the analyzed data sets, using data from several environmental sources.
Here, we are interested in rapid variations of the field, linked to the Sun-Earth interactions. They are described by the socalled geomagnetic activity indices. Among them, two are widely used to characterize the geomagnetic conditions, Kp and Dst indices. The Kp, a geomagnetic three-hour-range index, is derived from the standardized K index of 13 magnetic observatories and is intended to measure the solar particle radiation by its magnetic effects. The Dst index is derived from data provided by 4 near-equatorial geomagnetic observatories and is designed to measure the intensity of the globally symmetrical equatorial electrojet (the "ring current"). Dst is then a measure of geomagnetic activity and is used to assess the severity of magnetic storms. For more details, the reader can access the International Service of Geomagnetic Indices (http://isgi.
In this study we precisely investigate the September 2017's geomagnetic storms linked to an important solar activity observed as Martian aurora  (Xu et al. , 2018) , and across the globe on Earth. The September 2017 magnetic storm driven by the CME also was followed by multiple radio blackouts on the daylight side of the Earth (NOAA, 2017); a similar accident occurred after the storm driven by the X9. 0- To take into consideration these geomagnetic storm aspects, the remainder of the paper is organized as follows. Section 2 presents the different datasets used in the current study. In Section 3 some information on the applied method and its advantages is given, mainly to show the possibilities to apply it for rapid variations as geomagnetic storms are. Section 4 discusses a specific case study, focused on the September 2017 magnetic storm, and finally Section 5 draws some conclusions and maps out future directions.
The hodoscope provides recording of the muon flux intensity from different azimuthal directions (0°-360°) and zenith angles (0°-80°) with a high degree of spatial and angular accuracies (1 cm and 1°, respectively)  (Barbashina et al. , 2008). It enables registration of each muon along with its track reconstruction, as well as every 1-minute construction of two-dimensional angular matrix displaying muon flux from the observed hemisphere. For the current study, we reassembled the data matrices from 1-minute to 1-hour by a simple averaging. Variations of the muon flux in the near-Earth space, which are the secondary cosmic ray data, are associated with the physical processes taking place at the time of anomalous space weather events. During the quiet periods, the muon flux intensity at a ground level is stable and relatively high.
Before major geomagnetic storms driven by CME, a strong and rapid decrease can be seen in the muon intensity time series. The effect, called the Forbush decrease  (Cane, 2000) , occurs due to the screening of the cosmic ray flux by the plasma cloud heading towards the Earth's magnetosphere. The muon data from URAGAN hodoscope has been successfully used for studying the CMEs  (Astapov et al. , 2017)  and corresponding Forbush decreases. We also used hourly sampled Total electron content (TEC) data which is available at the IZMIRAN ionosphere weather database portal (http://www. izmiran. ru/ionosphere/weather/storm/index. shtml). In the GNSS signal processing practice, TEC represents the total number of electrons between transmitter and receiver. TEC data is widely used to characterize conditions of the ionosphere and to estimate the ionosphere impact on a radio wave signal  (Garner et al.
, 2008). The more electrons in the path of the signal, the more it is affected. As TEC responds to varying solar electromagnetic and geomagnetic activity (de  Haro et al. , 2002; Zolotukhina et al. , 2017) , it can be definitively used in a comprehensive analysis of the space weather phenomena. In addition, we studied Dst index time series over the considered period. As seen from Figs. 1c  and 1d , the TEC time series appears to be almost a negative reflection of the Dst index, which indicates their similar response to the equatorial ring current. Also for the demonstration of the onset and evolution of the geomagnetic storm, we used common space weather data from the OMNIWeb database (http://omniweb. gsfc. nasa. gov/), such as the interplanetary magnetic field (IMF), the solar wind speed, density and dynamic pressure for the case study period of September 6-11, 2017.
The IMF and solar wind data are 5min-averaged. These data are plotted in Fig. 2. Unfortunately, the data are missing for September 9-10 and also there are small gaps of unknown origin in the data time series at the beginning of September 7. The gap in the solar wind data from 3:40 to 5:20 UT is most likely caused by some failure of the particle analyzer. As the maximal Kp index values during the storm were 8+ according to the GFZ Potsdam data (http://www. gfzpotsdam. de/sektion/erdmagnetfeld/daten-produkte-dienste/kp-index/), the storm was classified as 'severe' (G4 rate) according to the NOAA space weather scale (http://www. swpc. noaa. gov/noaa-scales-explanation). The first local extremum of Kp reached 8-at the end of September 7; after that Kp decreased to its minimal value 0o on September 9 between 18:00 and 21:00.
Method The proposed technique aims to offer a combined analysis of various space weather related data, including their morphology and time-dependent features. For the needs to better interpret the observations, it is important to identify the particular features of a physical signal, sometimes difficult to reveal due to their low amplitude. In this case, the generalized characteristic function approach  (Troyan and Kiselev, 2010 ) can be used. This approach is widely implemented in exploration geophysics. For example, it is used for complex interpretation of geoelectric and geomagnetic survey data in the ore deposit search. Here, we applied this new approach in the space weather data analysis. The generalized characteristic function is dimensionless and thus it enables comparison of data of different origin, dimensions and scales (ranges of variability) in a single scale of magnitudes.
Another significant advantage is its flexibility: it is possible to adjust the data set contributions using weight coefficients with the standardized time series. As mentioned earlier, depending on the specialties of some additive components, they can be included into the generalized function with a negative sign, which enables setting several characteristic functions instead of one and further analysis of their mutual behavior. With the application of the generalized characteristic function approach to such particular space weather parameters as geomagnetic Dst index, TEC and cosmic ray secondary data, we highlight the particular advantages of this technique that can be useful in future space weather studies.
Also, as mentioned, the muon flux intensity increase during the decay phase of the storm can be related to the change of conditions in the ionosphere. Fragments of incoherency between the  1 and  2 at different phases of the storm duration indicate the relative contributions from the parameters analyzed and their mutual alternation. Summarizing all the results, the generalized characteristic functions approach is a combined analysis tool for different space weather data sets that can provide mutually supportive information on the onset, evolution and recovery phase of a geomagnetic storm. It is most likely applicable to estimating separate contributions of geomagnetic, ionosphere and cosmic ray origins during evolution of geomagnetic storm. Moreover, this approach provides an opportunity to apply muon and TEC data as a support for the determination of time moments of geomagnetic storm decay.
Acknowledgements The results presented in this paper rely on data collected at the INTERMAGNET magnetic observatories. We express our gratitude to the national institutes that support them and the  INTERMAGNET community  for promoting the high standards of magnetic observatory practice (http://www. intermagnet. org). We also acknowledge URAGAN muon hodoscope data provided by the  National Research Nuclear University MEPhI , and total electron content data provided by IZMIRAN ionosphere weather portal. Facilities of  GC RAS Common Use Center "Analytical Center  of Geomagnetic Data" were used for conducting the research. The research has been conducted in the framework of the  Russian Science Foundation  project No. 17-17-01215.
FUNCTIONAL INSIGHTS ON FXS FROM DROSOPHILA STUDIES The first model of FXS was the mammalian mouse model  (The Dutch-Belgian Fragile X Consortium, 1994; Mientjes et al. , 2006) , which recapitulates some major patients' phenotypes  (Dahlhaus, 2018 and references therein). However, ever since then, also research on Drosophila melanogaster has brought important knowledge on the basic mechanisms underlying FMRP function. The Drosophila homolog of FMR1 was first identified in 2000  (Wan et al. , 2000)  and named dfmr1. Over the years, it has been called by many different names that are listed in the Drosophila database FlyBase (http://flybase. org/reports/FBrf0174476. html). It is now named Fmr1 with a capital F, meaning that it has been identified through the human homolog FMR1. Here, we will call it dFMR1 to distinguish it from the mouse gene (Fmr1).
Learning is defective only in old flies, without the involvement of cell death. Treatment with four different mGluR inhibitors or LiCl exclusively during development rescues the learning, but not the courtship defects. Despite these successful treatments in pre-clinical trials, all clinical trials carried out so far have failed, although others are currently underway (https://clinicaltrials. gov). One reason could be the timing of the treatment or the need to combine different drugs since many other molecular mechanisms have been evoked as causative aspects of this disease. Importantly, in addition to FXS, many other neurodevelopmental disorders are correlated with defects in mGluR signaling. As opposed to glutamate, GABA is the main inhibitory neurotransmitter in the brain. It functions through Gamma-AminoButyric Acid (GABA) A and GABA B receptors (R) that are fast-acting inotropic and metabotropic receptors, respectively.
ACKNOWLEDGMENTS We are very grateful to the  Drosophila Genomics Resource Center  for the dFMR1 cDNA and to FlyBase for always useful informations. We thank  Angela Algeri  and  Sara Castagnola  for their comments on the manuscript,  Arnaud Fernandez  and  Thomas Maurin  for valuable discussions,  Viviana Trezza  for sharing her expertise on the rat model, and  Olivier Pierre  from SPIBOC for imaging. This study was supported by  INSERM ,  CNRS ,  CNRS   LIA  NEOGENEX,  ANR-11-LABX-0028-01 ,  ANR-12-BSV4-0020 ,  ANR-12-SVSE8-0022 ,  ANR-15-CE16-0015 , and  FRM DEQ20140329490  to BB; Monaco Against Autism Foundation to BB and MC. MD is recipient of a  Signalife-LabEx Program international Ph. D. fellowship. This review is dedicated to  Kathy Matthews , co-founder of the  Bloomington Drosophila Stock Center and of FlyBase.
Isotopic composition of organic carbon Because carbon stable isotope fractionation occurs during photosynthesis, organic matter δ 13 C is markedly different than the carbon pool from which it was formed  (Degens, 1969; Freeman and Hayes, 1992). δ 13 C of organic matter in modern marine surface sediment reflects a variety of factors and ranges between -20 and -22. 5 ‰  (Wang and Druffel, 2001). Using our database, we extracted all the δ 13 C measurements performed on marine sediment organic matter within the uppermost 10 cm of the sediment. We estimated the mean δ 13 C of organic matter in the first 10 cm of the sediment as -22. 2 ‰ with a standard deviation of 2. 3 ‰, consistent with prior literature  (Sundquist and Visser, 2003).
Relative changes in burial rate from sediment core database We created a database including surface-and downcore sediment datasets, retrieved from the National Oceanic and Atmospheric Administration (NOAA) (ftp://ftp. ncdc. noaa. gov/pub/data/paleo/paleocean/sediment_files/, last access: Mai 2015) and PANGAEA (http://www. pangaea. de, last access: Mai 2015) online repositories. All the datasets that contained CaCO 3 concentrations, CaCO 3 accumulation rates, age models and sediment density values were taken into consideration (for a complete detailed description of the procedure and quality control, the reader is referred to  Cartapanis et al. , 2016). Whenever available, 230 Th-normalized sediment accumulation rates were used. Where these measurements were not available, sedimentation rates were determined at the depth of each carbonate measurement from the reported age-depth relationship.
Absolute changes in CaCO 3 burial were obtained by scaling reconstructed relative changes in CaCO 3 MAR to the modern MAR (Figs. 2  and A1 ). Uncertainty in the global burial flux was estimated using a bootstrap technique (Appendix A). The resulting reconstruction is sure to contain biases and will hopefully be improved in future; however, it represents the most comprehensive such undertaking to date and it is not obvious to us how the reconstruction could be improved significantly without a major expansion of the existing dataset. As shown in Fig. 11 , the reconstruction indicates that global mean deep-sea CaCO 3 burial was not significantly different from the present day to MIS5, dropped to the lowest rates during MIS4, and then remained below interglacial rates until the deglaciation. The spatial pattern of change, which displays the well-known deep Pacific-Atlantic antiphasing  (Farrell and Prell, 1989; Hain et al.
1 ). Essentially, without slower burial during the glacial, modern-day burial estimates would require larger carbon input fluxes than supported by observational estimates in order to balance the long-term inventory. Although this consistency does not amount to proof, it provides encouraging support for the emergent picture of a geological carbon cycle that is dynamic on the timescale of glacial cycles. We have made use of an extensive, quality-controlled database for marine sedimentary proxies, to present the first global quantitative reconstruction of carbon and alkalinity burial in deep-sea sediments over the last glacial cycle. Our reconstructions of carbonate and C org burial in the deep ocean provide the first dynamic estimate of these major components of carbon removal over a full glacial cycle.
A1 Uncertainty in the sedimentary records First, there is some uncertainty associated with the quantification of past changes in the CaCO 3 accumulation rate in each downcore record. This can be further subdivided into three uncertainties: a. Measurement of CaCO 3 concentration. This has an analytical error order of 3 %; it is independent. b. Linear sediment accumulation rate. This is potentially large over periods less than age control point spacing but small (a few percent) when integrated over the span of multiple age control points; it is independent. c. Sediment density. Based on the literature, and using our database, we attempted to understand the relationships between depth in the sediment, sediment composition, and sediment density. The density of the sediment is partly dependent on the carbonate content, but carbonate content influence on the density depends also on the density of the other components of the sediment  (Reghellin et al.
Olivier Cartapanis  and  Samuel L. Jaccard  were funded by the  Swiss National Science Foundation  (grants  PP00P2-144811  and  PP00P2_172915 ). Olivier Cartapanis  and  Daniele Bianchi  were funded by the  Canadian Institute for Advanced Research (CIFAR). Eric D. Galbraith  acknowledges financial support from the  Spanish Ministry of Economy and Competitiveness , through the  María de Maeztu Programme for Centres/Units of Excellence in R&D  ( MDM-2015-0552 ). The author would like to thank editor  Luke Skinner , anonymous.