dataesr/scientific-paragraphs-categorization huggingface dataset:
Unsafe Ads, Advertisers and Users Exposure to Them The first row of Table  1  shows for the case of FB: (i) total number of users, (ii) total number of ads, (iii) total number of advertisers, (iv) the percentage of unsafe ads (PU_Ads), (v) the percentage of highly risky ads (PHR_Ads), (vi) the percentage of malvertisers (P_malvertisers), (vii) the percentage of highly risky malvertisers (PHR_malvertisers), (viii) the percentage of users exposed to unsafe ads (PUEU) ads, and (ix) the percentage of users exposed to highly risky ads (PUEHR). Table  1. This table shows in the first row for Facebook (FDVT dataset) and in the second row for regular Websites (websites dataset): (i) total number of users, (ii) total number of ads in the dataset, (iii) total number of advertisers, (iv) percentage unsafe ads (PU_Ads), (v) percentage highly risky ads (PHR_Ads), (vi) percentage of malvertisers (P_malvertisers), (vii) percentage of highly risky malvertisers (PHR_malvertisers), (viii) percentage of users exposed to unsafe ads (PUEU) ads, and (ix) percentage of users exposed to highly risky ads (PUEHR).
The results reveal that, in fact, the number of ads associated to malvertisers is quite small, since they only represent 0. 17% of the ad impressions in our dataset. This number decreases even more to only 0. 064% if we just focus on the highly risky ads. Similarly, malvertisers represent less than 1% of all the advertisers. If we look at these numbers in isolation, we could conclude that the impact of malvertising is actually negligible in FB. However, despite the small portion of unsafe ads, 1/3 of the FDVT users have been exposed to them, and almost 18% of the users have received ads coming from a malvertiser classified as highly risky. In particular, 12% of the FDVT users have been exposed to ads linked to websites labelled as Malicious Sites, and 7% of the users exposed to websites labelled as Phishing.
The divergence between the low portion of unsafe ads and the large percentage of users impacted is actually plausible, because a low portion of unsafe ads still represents a large overall number in FB. Just in our FDVT dataset, we have detected more than 800 (0. 68%) potential malvertisers that delivered more than 9 K potentially unsafe ads. These numbers are coherent to explain that almost 1000 FDVT users received some of those unsafe ads.
com/y4mkzdkf). The table presents the results for all users, men and women, separately. Men and women results will be used in Section 4. 4. 1. Table  2. This table shows the average Click Through Ratio (CTR) and its associated confidence interval at the 95% level using the Wilson Score Interval method. The CTR is reported in percentage. The table includes the results for level All, Safe, Unsafe, and Highly Risky ads retrieved from the FDVT dataset for the following demographic groups: all users, men, and women. The CTR for safe ads has been computed using more than 5 M ads. This leads to a very narrow confidence interval (CI) around the average CTR value equal to 0. 4%. Contrary, due to the much mall smaller number of unsafe ads (and associated clicks), the CI for unsafe and highly risky ads is larger.
Therefore, the average CTR reported in the table for unsafe (0. 49%) and highly risky (0. 47%) may change if we use a larger dataset for unsafe ads. However, the comparison of the safe ads CI Vs. unsafe ads CI, does not provide any evidence that unsafe ads are actually reaching lower CTR values than safe ads. Even, if we would consider as valid the lowest value in the CTR CI of unsafe ads, i. e. , 0. 365, the CTR difference would be less than 10%. Therefore, our results suggest that malvertisers are savvy users that are able to achieve similar CTR values when compared to professional advertisers.
All In the case of highly risky ads the confidence interval the size of the data sample avoids extracting a statistically significant conclusion. The average CTR in our dataset associated to landing pages classified as Malicious Sites and Phishing websites is equal to 0. 51% an 0. 48%, respectively (although CTR average values of unsafe and highly risky ads are not statistically significant, we will use them in Section 5. 1 to perform a ballpark estimation of the overall impact of malvertising in Facebook). In a nutshell, our results suggest that potential malvertisers are savvy users that carefully design the ad campaigns (e. g. , target group, ad creativity, etc. ) to achieve similar CTR values than professional advertisers.
Comparing Malvertising in Facebook vs. Web Sites We explore whether regular websites expose users to a larger portion of unsafe ads and malvertisers as compared to FB, and even more important whether the portion of users exposed to unsafe ads is similar in regular websites and FB. To this end, we are using two datasets that include information from ads delivered to real users in FB and the WEB, respectively. As we already mentioned, the WEB dataset just includes tens of thousands of ads and few hundred users. Hence, the results of our analysis cannot be generalized to the whole WEB. Still, this experiment is a good proof of concept to infer whether the malvertising phenomenon is homogeneous across different online services or not. In summary, the concrete reported results are only valid for the datasets used in this work, but the outcome will be a signal of whether malvertising should be studied individually in each large online service.
5× if we only consider highly risky ads. Therefore, the results reveal that, in the analyzed datasets, the probability of finding unsafe ads is higher in a regular website (according to our WEB dataset) than in FB, but the actual number of users exposed to malvertising is higher in FB. Overall, this result suggests that the exposure of users to malvertising differs between online services. This, in turn, recommends individually studying malvertising in large online systems to understand its actual impact.
Gender Table  3  shows for women and men: (i) the number of users, (ii) the number of ads, (iii) the percentage of unsafe ads, (iv) the percentage of highly risky ads, (v) the percentage of users exposed to unsafe ads, and (vi) the percentage of users exposed to highly risky ads. Similarly, Table  2  shows for women and men the average CTR and its confidence interval at the 95% level for all ads, safe ads, unsafe ads, and highly risky ads. As we already mentioned in Section 4. 2. 2, the number of unsafe and highly risky ads is very small when compared to safe ads. That is why the CIs that are associated to unsafe and highly risky ads are considerably larger when compared to the all and safe ads groups. Table  3. This table shows for men and women in the FDVT dataset: (i) the number of users, (ii) the number of ads, (iii) the percentage of unsafe ads (PU_Ads), (iv) the percentage of highly risky ads (PHR_Ads), (v) the percentage of users exposed to unsafe ads (PUEU), and (vi) the percentage of users exposed to highly risky ads (PUEHR).
Estimating Overall Impact of Malvertising in Facebook The results reported in this paper reveal an important security problem. In order to illustrate the potential dimension of this problem, let us perform a ballpark estimation and extrapolate the results obtained in Section 4. 2 to the whole FB ecosystem. Facebook reports that more than 7 M advertisers use their advertising platform, where they can reach more than 2. 4 B users  [11]. In addition, although FB does not publicly release information about overall ad impressions, a report from 2012 informed that FB delivered more than 7 B ad impressions daily. Note that it is very likely this number is much higher nowadays. If we use these numbers along with the results obtained from the FDVT dataset analysis, we can perform a ballpark estimation in order to evaluate the overall impact of malvertising in FB.
Our estimation reveals that there could be more than 47 K (0. 68%) malvertisers in Facebook delivering almost 12 M (0. 17%) of ad impressions every day (which is a lower bound because we are using as reference data from 2012 reporting 7 B daily impressions). More than 777 M users (32. 19%) could have been impacted by the malvertising activity within the 18-month period considered in our analysis. If we just focus on the highly risky group our estimation reduces the previous numbers to 22,400 malvertisers (0. 32%), 4. 5 M daily unsafe ad impressions (0. 064%), and more than 428 M users exposed to malvertising (17. 73%). Even more, if we consider the CTR reported for unsafe ads (0. 49%), 3. 7 M users may have landed in websites managed by malvertisers. Similarly, almost 2 M users (CTR = 0. 47%) may have landed in websites managed by Highly Risky malvertisers.
To this end, the browser extension will query the referred McAfee service and obtain the reputation field label. If the label is slightly or highly risky, we consider the ad as potentially unsafe. Note, we could be more or less conservative in our decision to classify an ad as unsafe. For instance, we could only select as unsafe ads the ones labeled as highly risky. Even more, our solution can be easily adapted to use (or combine) other domain classification services different than McAfee. Step 4-Blocking the ad: in the case, the step 3 returns that an ad is unsafe (note this information will be ready before the ad is visible in the screen), our extension will modify the HTML code hiding the iframe (i. e. , div section of the HTML code) that embeds the unsafe ads. Eventually, if the user scrolls down, she will just watch all of the other loaded posts and ads, but will not notice that the extension has blocked an unsafe ad.
We note there are previous works in the literature that already proposed to hide ads as an ad-blocking alternative  [20]. There are already efficient solutions integrated in major browsers, such as Google Safe Browsing (https://safebrowsing. google. com/) or Microsoft SmartScreen (http://tiny. cc/6xm0jz), which warn users when they try to access a website these systems consider unsafe. In addition, personal and enterprise firewalls usually operate with databases that include blacklists of unsafe domains. This allows the network administrator configuring the firewall to block any outgoing connection that tries to reach potentially unsafe domains (e. g. , Palo Alto Networks (https://tinyurl. com/y6xrefms), Cisco (https://tinyurl. com/y9ll9w7t), etc. ). Our solution does not aim to compete with any of these solutions, but just complement them in the particular case of FB.
Later, they analyze the ads delivered to the referred software, intermediary third-parties and landing pages. This methodology is valid for analyzing how many apps or websites are exposed to potentially unsafe ads but cannot quantify how many users are actually exposed to malvertising. Hence, overall, the proposed methodologies fail to measure the actual number/portion of users exposed to malvertising. Our work is the first one that analyzes ads delivered to real users, which allows quantifying the actual exposure of users to malvertising; (iii) Our dataset (+5 M ads) exceeds in several orders of magnitude existing datasets used to study malvertising (tens of thousands of ads); and, (iv) some of the existing works in the literature propose solutions to mitigate the malvertising problem. However, these solutions propose to remove unsafe ads from the inventory in the supplier side (e.
Figure3. This figure is a bar plot that shows the McAfee domain topics that are related to more than 10% of the ad impressions in our dataset for: Safe ads, Slightly Risky ads and Very Risky ads. The x axis shows the topics and the y axis the presence of each topic in percentage. The sum of percentages in a specific group of ads could go beyond 100% because McAfee may label a domain using up to three topic labels, thus an ad impression may be linked to up to three topics.
Funding: The research leading to these results has received funding from: the  European Union 's  Horizon 2020 innovation action programme  under grant agreement No  786741  ( SMOOTH  project) and the gran agreement No  871370  ( PIMCITY  project); the  Ministerio de Economía, Industria y Competitividad, Spain , and the  European Social Fund(EU) , under the  Ramón y Cajal  programme (grant  RyC-2015-17732 ); the  Ministerio de Educación, Cultura y Deporte, Spain , through the  FPU programme ( Grant  FPU16/05852 ); the  Ministerio de Ciencia e Innovación  under the project  ACHILLES  (Grant  PID2019-104207RB-I00 ); the  Community of Madrid  synergic project  EMPATIA-CM  (Grant  Y2018/TCS-5046 ); and the  Fundación BBVA  under the project  AERIS.
unsa f e U tot , where U unsa f e refers to the number of users that have received unsafe ads and U tot to the total number of users in our dataset. Exposed to Unsafe Ads (PUEU): this metric measures the volume of users that have received unsafe ads. It is computed, as follows PUEU = U -Percentage of Users Exposed to Highly Risky Ads (PUEHR): this metric measures the volume of users that have received highly risky ads. It is computed as follows PUEHR = U risky.
Analysis methods To investigate analytical uncertainties, two independent approaches to the abundance analyses were followed. Spite and Barbuy performed a χ 2 fit of the observed spectrum to a synthetic spectrum computed with the spectrum synthesis code Turbospectrum  (Alvarez & Plez 1998; Plez 2012). Peterson ran the spectrum synthesis code SYNTHE  (Kurucz 1993 (Kurucz , 2004) )  over the region from 1850 Å to 6000 Å, deriving abundances visually by altering the elemental abundances adopted for SYNTHE until the calculated strengths of the relevant lines of an element matched those in the observed spectrum. The SYNTHE input line list is a more recent version of the one employed by  Peterson et al. (2017) , in which log gf-values are originally adopted from the Kurucz web site and continually updated to best match high-quality spectra for stars spanning a range of line strengths, as illustrated below.
To better detect extremely weak lines, this analysis also incorporated a high S/N spectrum of HD 140283 obtained with the spectrograph ESPADONS at the CFHT in Hawaii under program 11AB01 (PI Barbuy). This had a resolving power R = 81 000, and covered the wavelength range 3700-10475 Å with S/N = 800-3400 per pixel. The SYNTHE optical analysis also relied on UVES archival spectra, along with Keck HIRES spectra from the KOA archive. Table  1  of  Peterson & Kurucz (2015)  includes the specifics of the optical spectra adopted for HD 94028, HD 140283, and HD 160617 from KOA. While these supported the Turbospectrum analysis, they are less accurate, being based on spectra of lower S/N and resolution. Consequently the SYNTHE optical values were not included in the final results below.
Comparison of methods and results from each approach Because the same log gf-values were adopted for the UV lines of trans-Fe species, the line-by-line differences in the abundances found by these independent analyses are useful in assessing the uncertainties in the derivation of the abundances of individual elements. In general, these agree to better than 0. 1 dex, when based on largely unblended lines of medium strength in regions where the continuum is well defined. Line-to-line differences reach 0. 2 dex when lines become blended or very weak, and can exceed this both for saturated lines and for lines blueward of 2000Å, where the S/N of the observed spectra decreases and the number of unidentified lines rises rapidly, as illustrated just below. We also investigated potential UV systematics due strictly to the code and the source of models, by incorporating both the line lists and the  Castelli & Kurucz (2003)  models adopted by Peterson into Turbospec runs.
For the latter star, they had only a single UV spectrum centered at 2013Å; for HD 140283, only an E230M spectrum for the critical 2390 -3135Å region. As discussed above, our use of a more extensive Ge i line list with consistent gf values, and of an expanded E230H dataset better able to detect them  (Cowan et al. 2005) , undoubtedly has led to more robust results for this difficult analysis. Differences are also large for arsenic. The Roederer et al. values for [As/Fe] are higher than ours by 0. 4 dex for HD 94028  (Roederer et al. 2016 ) and 0. 5 dex for HD 160617  (Roederer & Lawler 2012). As their adopted temperatures and gravities are similar to ours, we attribute the discrepancy to the choice of As i lines, all of which lie blueward of 2000Å. Notes. For element X, [X/Fe] = log(N X /N Fe ) star -log(N X /N Fe ) Sun We adopted only the lines at 1890.
Acknowledgements. We are indebted to  Don Morton  for the verification of oscillator strengths adopted in Table 3 for this work. We also thank  Jim Lawler  and the anonymous referee for their comments. Support for RCP for this work was provided by  NASA  through grant number  GO-14762  from the  Space Telescope Science Institute , which is operated by  AURA, Inc. , under  NASA  contract  NAS 5-26555. MS is supported by the  "Programme National de Physique Stellaire"  and the  "Programme National de Cosmologie et Galaxies (PNCG)" (CNRS-INSU). BB acknowledges partial financial support from  FAPESP ,  CNPq , and  CAPES -Financial code 001.
The main drawback of Strale is that it does not take interference into consideration. Minstrel-HT  [15]  relies on three parameters: channel width, guard interval, and number of streams to create group rates. Each group contains eight different data rates represented by the MCS index. Minstrel uses probing to determine the best data rate. This process consists of two periods: sampling and non-sampling periods. During the sampling period, Minstrel-HT selects a random data rate from all of the available data rates in each group. If a data rate results in a higher throughput than the previous data rate, then it is used for subsequent MPDU transmissions. Otherwise, Minstrel-HT keeps using the previous data rate. Throughput is calculated based on Frame Loss Rate (FLR) while taking EWMA into account.
Performance Evaluation In this section, we selected representative algorithms from each of the categories in order to evaluate their performance under mobility scenarios. Very few papers have dealt with mobility and interference impact on RAAs, according to our knowledge. The selected algorithms are: Ideal, Strale, MinstrelHT, and MutFed. The selected algorithms cover the different categories, MinstrelHT and Strale are both implicit feedback algorithms, MinstrelHT, which is a widely used algorithm, as it is implemented in the linux kernel  [23] , claims being able to select a sampling rate that results in the highest throughput and highest probability of successful delivery of the frames. The main difference is that Strale can be used as an extension algorithm, it can be placed on top of MinstrelHT to enhance its performance, and it is a promising algorithm that tries to adapt the number of MPDUs in aggregated frames without reducing the transmission rate.
We explored different magnetisations with the midplane β parameter ranging from 10 5 to 10 3 and included dust grains -treated in the fluid approximation-ranging from 100µm to 1 cm in size. We first show that the gaseous rings (associated with zonal flows) are tightly linked to the existence of MHD winds. Secondly, we find that millimetresize dust is highly sedimented, with a typical scale height of 1 AU at R = 100 AU for β = 10 4 , compatible with recent ALMA observations. We also show that these grains concentrate into pressure maxima associated with zonal flows, leading to the formation of dusty rings. Using the radiative transfer code MCFOST, we computed the dust emission and make predictions on the ring-gap contrast and the spectral index that one might observe with interferometers like ALMA.
For that purpose, we performed global axisymmetric simulations of stratified discs with a net vertical magnetic field, including ambipolar diffusion and dust. For that, we used a customised version of the PLUTO code, based on PLUTO v4. 3. To simplify the problem, we considered a portion of the disc spanning from 10 to 200 AU where we could neglect the Hall effect  (Simon et al. 2015). The dust population is approximated as a pressure-less multi-fluid made up of different sized particles, from one hundred micrometres to one centimetre, relevant for radio-observations. The back reaction of the dust on the gas is taken into account, but we neglected coagulation or fragmentation processes. To produce synthetic images of the disc, we used the radiative transfer code MCFOST. As a preliminary step, the radiative transfer calculation was not performed during the hydrodynamic steps, but was based on a fixed snapshot (the hydrodynamic calculation was post-processed with MCFOST).
Numerical code To simulate the dynamics of the gas and dust, we use a modified version of the Godunov-based PLUTO code  (Mignone et al. 2007). This code solves the approximate Riemann problem at each cell interface and is written so that it ensures conservation of mass and angular momentum. Inter-cell fluxes are computed with a HLLD solver for the gas and an HLL solver for the dust. We use a piecewise linear space reconstruction to estimate the Godunov fluxes at cell interfaces, with the monotonised central limiter. Physical quantities are evolved in time with a second order explicit Runge-Kutta scheme. Parabolic terms, including ambipolar diffusion, are also solved explicitly and introduce a strong constraint on the CFL condition. We note that we do not use the FARGO orbital advection scheme, as the CFL is limited by the parabolic terms due to ambipolar diffusion.
Radiative transfer with MCFOST The aim of this Section is to produce synthetic images derived from our simulations in order to predict the observable properties of ambipolar-dominated discs threaded by a net vertical field. For that we use the 3D continuum and line radiative transfer code MCFOST based on the Monte Carlo method  (Pinte et al. 2006). Assuming a given gas and dust density distribution, the code first computes the temperature structure assuming that the dust is in radiative equilibrium with the local radiation field. This is done by propagating photon packets, originally emitted by the central star (assumed to be of solar-type), through a combination of scattering, absorption and re-emission events until they exit the computation grid. During this step, we use 1 280 000 photon packets in total.
2006; Fromang et al. 2006). Another result is that the dust particles are trapped within the pressure maxima and form thin axisymmetric structures with high contrast (for mm to cm particles) and typical separation of 2. 5 to 5 H g at R = 20 AU, wider separation being obtained in stronger field situations. The width of the millimetre dust rings is about 0. 3 -0. 4H for β = 10 3. This is comparable with the width of dust ring structures observed by the DSHARP survey  (Dullemond & Penzlin 2018)  in objects like AS 209 (although the comparison is done at different radii, and we implicitly assume that the ratio width over H does not depend on radius). We estimate that the typical dust mass contained inside a single ring at R 20 AU is about 5 Earth mass. Using the radiative transfer code MCFOST, we were able to produce synthetic images of the disc in the ALMA band (1mm) for β = 10 3.
1a ). Using algorithms and a dashboard, the centre can automatically filter data so that nurses or physicians can focus on patients with early warning signs. Smart Angel™ (Evolucare, France) is a new digital technology for remotely monitoring patients at home using both text messages (self-report questionnaires) and wireless Bluetooth monitors (SpO 2 , HR and MAP). The patient can thus evaluate pain relief on a numerical rating scale (NRS), comfort and adverse events (nausea, vomiting) via self-report questionnaires. The Smart Angel system is initialized at the ambulatory centre before discharge (login) and continued at home by patients using a specific application on a dedicated tablet used to record their self-assessment and start the wireless Bluetooth monitor. Remote data are collected three times a day (Fig.