dataesr/scientific-paragraphs-categorization huggingface dataset:
To understand how threat models are constructed, it is useful to recall the interdisciplinary work that, in the last fifteen years, has explored the 'collective' dimension of privacy and the extent to which protecting it requires the interdependence of multiple factors and actors. For instance, Daniel Solove has described the ways in which the contours of online social representation are gradually identified as a result of the informational traces left behind by different interactions, dispersed across a variety of databases and networks  (Solove 2006). These traces are at the core of states' and corporations' attempts to track and profile citizens and users, as well as central to activists' strategies to expose corporate and state malfeasance. Successfully preserving one's privacy in the connected world is thus about managing visibilities  (Flyverbom et al.
As the famous EFF guide 'Surveillance Self-Defense' puts it, Trying to protect all your data from everyone all the time is impractical and exhausting. But, do not fear! Security is a process, and through thoughtful This 'tailored approach' to threat models during security training sessions is also important because developers in the secure messaging field are currently discussing a number of unsolved cryptography problems, such as metadata storage, the vulnerabilities of centralised infrastructures, the usage of telephone numbers as identifiers and so on. In the absence of the 'perfect tool' in all these respects, trainers recommend patchworks of different tools and operational security practices ('physical security') that aim to minimise the drawbacks of existing tools and offer different features, from encryption 'in transit' to encryption 'at rest', metadata obfuscation, and so on.
Instead they suggest to sync your address books so they know exactly who you're talking to even though you trust them to somehow make it into hashes or whatever. That's the issue we are not solving with the apps, we make it worse. We now have centralised servers that become honeypots, and it's not about the data, it's about the metadata (Peter S. , Heml. is). 'Actually, in Google we trust': Questioning the privacy-security dichotomy When we first attempted to interpret our fieldwork, our hypothesis was that very distinct threat models could be associated with distinct types of users, grouped according to their risk status -'high' or 'low'. As already noted, this hypothesis was operationally useful, especially for our joint work with computer scientists more familiar with usability studies.
Moreover, many contributors to both efforts use Internet-Relay-Chat (IRC) on freenode, a major gathering point for open-source developments. The #autocrypt and #deltachat rooms each have 60-70 participants, and in September 2018 Delta Chat had around 5500 posted chat lines. Both efforts also use the github. com site and the 'git' versioning tool to collaborate, and to discuss and review each other's changes. Even if the recent funding for Delta Chat provided by the Open Technology Fund since July 2018 has stabilised the core team working with Delta Chat, many regular and active contributors continue working without any official affiliation. Delta Chat seems to share a tacit agreement about certain modes of collaboration that avoid reproducing what is understood as the 'startup way', which attributes fixed roles to team members and establishes hierarchies within teams.
6 See the protocol history section in the Introduction. 7 ChatSecure, and its core developer, have a similar profile and history. 8 https://github. com/signalapp/libsignal-protocol-c. 9 For example, during our observations of Autocrypt usability tests at 33c3 in December 2016. 10 https://omemo. top. 11 The actual bot is called Jabbergram; similar bots now exist that bridge Telegram to IRC and Matrix. 12 https://matrix. org/blog/2018/04/26/matrix-and-riot-confirmed-as-the-basisfor-frances-secure-instant-messenger-app. 13 The harshest critique of Matrix. org actually comes from within the XMPP community, downplaying Matrix. org's innovative aspect and considering that they 'reinvent the wheel'. 14 https://matrix. org/blog/2020/06/02/introducing-p-2-p-matrix. 15 Autocrypt project background: https://autocrypt.
This offers a different way of classifying the data and 'making sense of it', compared to a table. The latter is, as Jack Goody puts it, a graphically organised dataset with a structure that leaves little room for ambiguity, and as such becomes an instrument of governance  (Goody 1979). Another problem posed by the grid format, that the new version of the SMS sought to address, was the projected equivalence of the different criteria, from the open-source release of the code to its audit and the type of encryption. The 'checklist' and 'points' system created the impression of an artificial equality between these criteria, and therefore led to the idea that one can 'quantify' the security of a given tool by counting points. In fact, the presence of some criteria rather than others, or the degree to which they are implemented, may result in different impacts on security and privacy, in particular for users in high-risk contexts: [If you were] someone who does not know anything about crypto or security, you would look at this scorecard and would say, any two tools that have, say, five out of seven checkmarks are probably about the same [while] if… one of those tools actually had end 2 end encryption and the other did not, even though they both had code audit or the code was available, or things like that, their security obviously isn't the same.
They allow the evaluation of the directionality of epistasis through a test of the expected performance and/or the variance of genetic values. This methodology was applied to two large multi-parental populations, i. e. , the American maize and soybean nested association mapping populations, evaluated for different traits. Results showed significant epistasis, especially for the test of directional epistasis, e. g. , the increase in anthesis to silking interval observed in most maize inbred progenies or the decrease in grain yield observed in several soybean inbred progenies. In general, the effects detected suggested that shuffling allelic assocations of both elite parents had a detrimental effect on the performance of their progeny. This methodology is implemented in the EpiTest R-package and can be applied to any inbred bi-/multi-parental population evaluated for a trait of interest.
, 2020). With decreasing costs of genotyping, inbred bi-parental population datasets, including both genomic and phenotypic information, are becoming routinely available in most crops. In this study, we present a framework to test for the existence of epistasis in inbred bi-parental populations genotyped and evaluated for a trait, both through the expectation (i. e. , directional epistasis) and the variance (i. e. , non-directional epistasis) of genetic values. This framework is implemented in the new R-package "EpiTest" available from the CRAN. Applications are presented to two large NAM populations (the American maize NAM and the soybean NAM) evaluated for agronomy, phenology, morphology and/or quality traits.
This observation is consistent with the larger number of significant directional and global tests compared to the test of epistatic variance for real traits in both NAM datasets. However, some traits such as days to maturity in soybean have a higher number of significant tests for σ 2 S×S than for δ, which demonstrates the benefit of testing both parameters to detect epistasis. Note that, thanks to genomic information, the test procedure can be applied only based on the progeny data, i. e. in absence of any parental phenotypic information. While this may be useful in some specific cases, we recommend experimental designs that include a thorough evaluation of both parents since the parental information is expected to have a strong (positive) impact on the estimation of model parameters. In practice, parents are often evaluated along with their progeny as replicated checks in bi-and multi-parental experimental designs -as in the maize and soybean NAM datasets -which provides favorable conditions for the application of the proposed procedure.
Only simple inbred progenies (e. g. , DH or RIL progeny) need to be genotyped and evaluated for a trait of interest. Such datasets are already available in large numbers and can simply be recycled to investigate the presence of epistasis. Our procedure also opens the way to the systematic investigation of epistasis in plant breeding programs and quantitative genetics studies. We highly recommend applying this test procedure prior to any QTL analysis as it helps determine the need to test for pairwise epistatic QTL effects.
Inference In practice, π can be estimated using: (π) i = 1 M M m=1 A im and ∆ using: (∆) ij = 1 M M m=1 (A im A jm -π i π j ) To ease the comparison of variances, all covariance matrices (∆, ∆ 2 and D) are standardized as in  Vitezica et al. (2017)  so that the mean of diagonal elements equals 1. The inference of fixed and variance parameters is done using restricted maximum likelihood (ReML). The nullity of the parameter (i. e. , directional epistasis test) can be tested using a Wald test for which the statistics has a chi-squared distribution. The other tests involving the variance parameter σ S×S are based on a likelihood ratio test for which the distribution of the statistics is specific mixture of chi-square distributions  (Self and Liang, 1987; Stram and Lee, 1994). All tests were adjusted for multiple testing using a 5% Bonferroni threshold over the number of populations in each dataset.
Datasets Two NAM populations were considered in this study, each including multiple bi-parental populations evaluated for several traits of interest. The soybean NAM is described in  Song et al. (2017). It consists of 39 families of 140 recombinant inbred lines generated by crossing 40 diverse inbred lines to the central inbred line "IA3023". All individuals were genotyped using the "SoyNAM6K BeadChip" for which the reference allele (coded 0) corresponds to the homozygous genotype for "IA3023" alleles and the alternative allele (coded 1) corresponds to the homozygous alleles for the other parent. On average, 3,245 SNPs are polymorphic for a given NAM population. As described in  Diers et al. (2018)  and  Xavier et al. (2018) , the NAM populations were evaluated along with parental lines in 19 trials for grain yield in kg/ha, days to maturity, plant height in centimeter, lodging score from 1 to 5, grain moisture in percentage of humidity, protein/oil/fiber in percentage of the grain content.
, 2014). The model in Eq. (  5 ) was applied separately to all maize and soybean NAM populations, where parent A refers to the central parent and parent B refers to the alternative parent. For all datasets, a single reference phenotype was considered for each individual after adjusting for experimental design effects. In both the maize and the soybean experiments, parental lines were used as checks and thus had a much larger number of observations, leading to a better precision associated with the resulting reference phenotype. The diagonal elements of D from Eq. (  5 ) were calculated accordingly and are summarized in Table  2.
Data availability statement The test procedure is implemented in a new R-package "EpiTest", which is based on the mixed model inference R-package "MM4LMM"  (Laporte et al. , 2022) , both available from the CRAN. The American maize NAM dataset is available at: https://www. panzea. org/. The soybean NAM dataset is available at https://www. soybase. org/SoyNAM/.
On Oct 29/30, 2020 UTC, HST observed a transit of planet c, with 5 orbits near transit and 4 orbits after the gap. On Jan 28/29, 2021 UTC, it observed a transit of planet b with the same configuration. On Mar 19, 2021, it attempted to observe six consecutive orbits bracketing a second transit of b, but STIS entered safe mode before the observations were to begin and all data were lost. On the following day, it successfully observed a 3-orbit baseline. For our analysis of these data, we rely on stistools 1. 3. 0, a Python package provided by Space Telescope Science Institute that contains several relevant functions for data reduction. We start with the tag files, lists of photons that encode the time of arrival and position on the detector. First, we use inttag to turn the photon lists into raw images by accumulating the photons into 315 second subexposures (303 s for the first orbit).
The first orbit of every visit contains 5 subexposures, while subsequent visits contain 8. The photon wavelengths are Doppler corrected prior to accumulation to account for HST's orbit around the Earth. Second, we use calstis to perform standard data reduction tasks, including subtracting the dark image, flat fielding, rejecting cosmic rays, and wavelength calibration. Wavelength calibration is performed using the wavelength calibration files taken during occultation, which contain lamp lines but no astrophysical signal. These files allow wavecal, a component of calstis, to assign a wavelength to every pixel. The last step is spectral extraction. x1d attempts to locate the spectrum in the spatial direction but often fails because it excludes the region around the Lyα line to remove geocoronal emission-a process which also removes almost all stellar flux.
Keck/NIRSPEC Our NIRSPEC/Keck data (program C261) were taken on Dec 30, 2020 (transit of planet c) and Jan 7, 2021 (transit of planet b). All observations were in Y band in the high resolution mode. On Dec 30, the transit was in progress when our observations started. We collected 3. 4 h of in-transit observations and 5 hours of post-transit baseline. On Jan 7, the transit started toward the end of our observations. We collected 5. 4 h of pre-transit and 2. 6 h of in-transit observations. On both nights, we used the 12 x 0. 432 slit, giving the spectrograph a resolution of 25,000. The sky was clear, and the seeing (1-1. 5 ) was poor but typical of this time of year. In addition, the telescope suffered from wind shake on Dec 30, further broadening the line profile in the spatial direction. We achieved a typical SNR of 400 per spectral pixel in 60second exposures, all taken in the ABBA nod pattern to eliminate background.
Although these observations are not simultaneous with the Lyα and helium mass loss observations, they are within 6 months, while the P cyc /P rot vs. P rot relationship derived by  Suárez Mascareño et al. (2016)  implies a stellar cycle period of 5. 5 years (with ∼ 40% uncertainty). To analyze XMM-Newton data, we download the raw Observation Data File (ODF) and use the Science Analysis System (SAS) 1  provided by the XMM-Newton team to reduce it. We run xmmextractor, thereby going from ODF to spectra with default settings and no human intervention. For the Optical Monitor, SAS produces the light curve of the star in the UVW2 and UVM2 filters, the two mid ultraviolet filters we selected. For each of the two Reflection Grating Spectrometers (RGSs), SAS produces two spectra (first and second order) and other data products, which we do not use because RGS has only a tenth of the throughput of the EPIC detectors in addition to substantial background; no stellar signal is visible in the data.
For each of the three EPIC detectors, SAS generates the light curve, the backgroundsubtracted spectrum, the Redistribution Matrix File (RMF), and the Ancillary Response File (ARF). The ARF gives the effective area of the detector as a function of photon energy, while the RMF gives the probability of a photon being detected in each channel as a function of photon energy. In optical astronomy terminology, the ARF gives the throughput multiplied by aperture area, while the RMF gives the wavelength-dependent line spread profile. With XMM, as with ROSAT, the RMF is nearly diagonal for high energies, but is far from diagonal at low energies, where most of the stellar flux resides. In addition, the ARF is highly energy dependent for the two EPIC MOS detectors, although not for the pn-detector. These factors mean it is impossible to simply plot the measurements and see what the X-ray spectrum looks like.
ROSAT To analyze the ROSAT All-Sky Survey (RASS) data for HD 63433, we download the data for the relevant sector 2 and reduce it using HEAsoft 6. 28 3 by following the guide "ROSAT data analysis using xselect and FTOOls" 4. We define the source region to be a circle centered on the source with a radius of 200 arcseconds. Following the advice of  Belloni et al. (1994) , we define two circular background regions on either side of the source along the scan direction, both 800 arcseconds away and with a radius of 200 arcseconds. Using xselect, we extract the source spectrum and the background spectrum from the events list. We download the RMF for the PSPC-C detector 5 and use pcarf (part of the ROSAT subpackage of FTOOLS) to generate the ARF. The image file has negative EXPOSURE, DETC (deadtime correction), and ONTIME header values to indicate that there is no unique value: the image is pieced together from scanning observations, and the effective exposure time is different at each pixel.
Stellar variability in Lyα Since HD 63433 is a young star, it is important to determine whether stellar variability could explain the absorption signal. Only the visit containing the transit of planet c shows significant blue wing variability, which we are ascribing to planetary absorption. Among the other 4 visits, which contain a total of 16 orbits, the blue wing is remarkably stable, with a standard deviation of 1. 8%. The only outlier is the first orbit of the second visit of the successful b observation. However, the first orbit in a visit is expected to be more variable than the others. The science exposure is shorter, giving rise to 30% higher photon noise. It also starts at a later HST orbital phase, violating our logic for looking at orbit-aggregated data points-namely that repeatable systematics that depend on orbital phase will be averaged out.
We derive a 0. 124-2. 48 keV flux of 1. 5-1. 9 ×10 -12 erg/s/cm 2 and a 0. 2-2. 4 keV flux of 1. 25-1. 71 ×10 -12 erg/s/cm 2. This is in line with the flux reported by the Second ROSAT all-sky survey (2RXS) source catalog  (Boller et al. 2016 ) for both a power-law fit (1. 39 ×10 -12 erg/s/cm 2 ) and a blackbody fit (1. 77 ×10 -12 erg/s/cm 2 ). We conclude that the star's X-ray flux appears to have been 33% higher during the ROSAT observation than during the XMM observation, but the two measurements are consistent at the 2σ level. Our measurements of HD 63433 were made differentially with respect to the comparison star HD 64465 (HIP 38677; F5). A second star, HD 63432 (HIP 38231; A2), was used to check the stability of the relative photometry using HD 64465. Details of the robotic telescopes and photometers, observing procedures, and data reduction can be found in  Henry (1999)  and  Fekel & Henry (2005).
2007). Therefore, the APT data suggest that HD 63433 was unusually quiescent during the time of our mass-loss observations, consistent with the marginally lower X-ray flux observed by XMM-Newton in March 2021 compared to ROSAT in 1990. We computed a Lomb-Scargle Periodogram of the data and found a clear, narrow peak corresponding to the rotation period of the star. Both the V and B data indicate P rot = 6. 413 ± 0. 002 d, slightly lower than the 6. 46 ± 0. 01 d reported by  Gaidos et al. (2000)  using the APT data available then. The APT-inferred rotation period is far more precise than the one inferred from TESS and K2 data  (Mann et al. 2020 ) because of the much longer baseline (1200 rotation periods vs. 3-4). The periodogram also shows a broad double peak around 800-1100 d, perhaps indicative of a short stellar cycle.
(2005b)  used the profile of the observed Mg II h and k lines (2796 and 2804 Å) as their template for the Lyα line shape. Unfortunately, we have no such data for HD 63433. Instead, we start with the reconstructed Lyα profile of HD 165185 from  Wood et al. (2005b) , a star with the same spectral type as HD 63433 and a similar rotation period. We then allow stan to modify the profile as follows: F (λ) = F init (λ) + ∆ 1 (λ) (1) ∆ 1 (λ) = cumsum(∆ 2 (λ)) (2) ∆ 2 (λ) ∼ N (0, 5 × 10 -14 ), (3) where F init is the HD 165185 profile, cumsum is the cumulative sum, and N(0, 5 × 10 -14 ) is a normal distribution with a mean of 0 and standard deviation of 5 × 10 -14. The intrinsic spectrum of HD 63433 is that of HD 165185 plus differences, the differences are in turn the cumulative sum of second differences, and we impose a Gaussian prior on the second differences with a standard deviation of 5 × 10 -14 erg s -1 cm -2 Å-1.
These photons are not energetic enough to ionize hydrogen or ground state helium, and therefore do not create the ions or electrons which, through recombination, create triplet state helium. However, they are energetic enough to destroy triplet ground state helium via ionization. This means that the level population of triplet helium, and the magnitude of the corresponding triplet helium absorption signal during transit, is sensitive to the value of the stellar flux in this wavelength range. HD 63433 is a solar analogue, so we adopt the solar MUV spectrum as measured by the Solar Radiation and Climate Experiment (SORCE) satellite. 7 We verify the applicability of this spectrum by comparing to data from XMM-Newton's Optical Monitor, which observed the star's MUV flux through two filters: UVM2 (λ = 231 ± 48 nm) and UVW2 (λ = 212 ± 50 nm).
3D hydrodynamic models Our 3D models utilize the approach outlined in  Wang & Dai (2018) ;  Wang & Dai (2021) , which combines ray-tracing radiative transfer, real-time non-equilibrium thermochemistry, and hydrodynamics based on the higher-order Godunov method code Athena++  (Stone et al. 2020). We include a stellar wind with a massloss rate of ∼ 8× solar, as calculated in §6. 2. 1, and a roughly solar velocity of 400 km s -1. Our models account for the hydrodynamic and thermochemical interactions between the stellar wind and the planetary outflow. These 3D models capture the anisotropy of the outflow pattern better than the 1D LTE models, while the non-LTE thermochemistry self-consistently predicts the mass loss rate and the line profiles. The model incorporates a total of 26 species and 135 reactions, includ-ing various relevant heating and cooling processes (e.
Magnetic fields In order to evaluate the potential importance of magnetic fields, we first need to estimate the stellar magnetic field strength. Vidotto et al. (2014)  measure the magnetic field strength of 73 stars ranging from 1 Myr to 10 Gyr and found that it is proportional to t -0. 655±0. 045. We use this scaling relation to estimate the magnetic field of the 440 Myr HD 63433. We find that this star is predicted to have a magnetic field strength of approximately 5 times solar, or ∼13 G. This is consistent with  Rosén et al. (2016) , who used polarization data to measure the mean magnetic fields of five young Sun-like stars between 300 and 700 Myr and found B * ≈ 20G (with a range of 10-25 G), with no evidence for any age dependence. We adopt 20 G as the fiducial field strength and assume that, in the absence of a stellar wind, its strength falls off as r -3 (consistent with a dipole).
Fortunately, HD 63433 is a bright nearby star, and its planets are favorable targets for atmospheric characterization by HST and JWST. If planet b does host a hydrogen-dominated atmosphere, it may have detectable absorption from water and other molecules in its infrared transmission spectrum. If b hosts a high molecular weight atmosphere, detecting it in outflow may still be possible by looking at metal FUV lines  (García Muñoz et al. 2021). Software: numpy (van der Walt et al. 2011), scipy  (Virtanen et al. 2020) , matplotlib  (Hunter 2007) , stan  (Stan Development Team 2018) , stistools, SAS, HEAsoft to stellar activity, which is expected for young stars, in this analysis. This is very difficult to do without simultaneous observations at other wavelengths. In LF's analysis, he first considered the calibrated 2-dimensional spectra providing information on the photon arrival time (" tag.