dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and sample collection Between June and September 2014, a free HIV viral load (VL) test was consecutively proposed for the purpose of the study to all perinatally HIV-1-infected children (aged B 10 years) and adolescents (aged between 10 and 19 years) who were on ART for at least 12 months when attending private or public HIV healthcare services for their routine follow-up visit. HIV care centres were in the urban region of Lome, the capital city, and in three semi-rural areas: the Maritime, Plateaux and Kara regions. Demographic information was collected using a standardized questionnaire by the interviewer during sampling, and information on ART history was collected retrospectively on site from medical records. Whole blood was drawn by venepuncture on EDTA tubes (Becton Dickinson, Franklin Lakes, NJ, USA) and sent within four hours to the national reference laboratory (Laboratoire de Biologie Moleculaire et d'Immunologie, Facultedes Sciences de la Sante, Universitede Lome (BIOLIM/FSS-UL)) in Lomeand its satellite in Kara, especially for samples collected in the Kara sanitary region.
cpr. stanford. edu/cpr. cgi). We used the ANRS interpretation algorithm, version 24 (www. hivfrenchresistance. org/2014/Algo-2014. pdf), to identify relevant drug resistance mutations (DRMs) and to predict drug resistance or possible resistance to antiretroviral drugs. We constructed phylogenetic trees with maximum likelihood methods implemented in PhyML to identify HIV-1 subtypes and circulating recombinant forms and to evaluate eventual epidemiologic links between samples  [20]. The newly reported protease and reverse transcriptase sequences are available in GenBank [accession numbers: KT592383 to KT592507].
CD4 counts were available at sampling for 163 (57. 6%), and median CD4 counts were 610 cells/mm 3 (IQR: 347 to 977). VL was not done in routine follow-up and data on adherence were only occasionally noted in patient records. Only 10 (3. 5%) patients had been exposed to PMTCT, for 165 (58. 3%) no exposure was reported and information was lacking in the patient records for the remaining. For 159 (56. 2%), breastfeeding at a young age was reported, but information on the ART status of the mothers was absent from patient records. Retrospective data collection on ART history showed that a change in ART regimen occurred for 135 (47. 7%) patients, mainly related to a change in the national guidelines (replacement of stavudine in 2010 by AZT) or replacement of NVP by EFV related to age (Table  1 ).
Acknowledgements Funding We thank the  Ministry of Health in Togo  for providing authorizations to implement this survey. Our sincere thanks to  Mr. Ketou Gnagna  at BIOLIM/UK in Kara,  Mr. Douffan Messan  from  CNR 0 Centre National de Reference in Lomeand Mrs. Rachidatou Balogoun  and MawugbeDogbeda at BIOLIM/FSS/ UL. We thank the executive directors of  Espoir Vie Togo , Lome; ACS0  Action Contre le SIDA Lome, AMC0Aide Medicale et ChariteKpalimeand Association Espoir pour Demain in Kara. This study was funded by the  Institut de Recherche pour le Developpement and French cooperation in Togo. SM and EK are doctoral fellows from the  French foreign office.
Model and methods PILOT is our numerical tool based on Monte-Carlo split-step propagation techniques: simulation of optical propagation through discrete turbulent random phase screens and Fresnel propagation. This code is routinely used for endoatmospheric applications  [23] , astronomy  [24] , and ground-space telecommunications modelling  [25]. and the turbulence outer-scale L0. The integrated strength along the line of sight of these wavefront distortions is quantified by the Fried parameter r0, always expressed in plane wave in this article. Even if turbulence induces locally phase only perturbations, diffraction effects during the beam propagation progressively transform phase effects in amplitude perturbations (so-called scintillation). The scintillation pattern produced by a layer at distance L is granular with a characteristic size √ , where λ is the wavelength.
Phase and scintillation effects of course limit the performance of optical time/frequency comparisons. For down links, considering the beam divergence of the laser coming from the satellite and the small size of the ground receiver aperture, one can assume plane wave propagation. The up link channel is slightly more complex since it requires accounting for Gaussian beam propagation through the turbulent atmosphere. In this case, turbulence still induces phase and scintillation perturbations, as well as beam wander effects. However, we make use of the reciprocity principle to compute the up link performance from plane wave downward propagation coupled to a Gaussian reception mode. We thus compute two downward plane wave propagations along two axis separated by the point ahead angle. We run the wave-optic propagation code PILOT at the working wavelength of λ = 1064 nm.
Performance evaluation for time/frequency transfer We consider a satellite in highly elliptic Earth orbit (inspired by the STE-QUEST proposal [4]), at perigee (z = 3200 km) since this corresponds to the worst case in terms of pointing ahead angle (paa = 63 µrad), and to the largest velocity of the satellite (v = 9 km/s). To evaluate the effect of the pointing ahead, we compare in the next subsection an on-axis two-way link (with point ahead angle artificially set to zero) to a two-way link (with pointing ahead angle paa = 63 µrad). We model a temporal set of 8000 turbulent electromagnetic fields using the propagation code PILOT. The duration of the series is 8 s with 1 ms sampling steps.
These user profiles gather preliminary information about the users collected by the adversary. While the original version of SimAttack  [9]  was designed for a specific target, this paper presents a generalization of this attack for unlinkability and indistinguishability solutions. We exhaustively evaluated our new SimAttack on three popular solutions: Tor-based, TrackMeNot, and GooPIR. Our experiments used real world Web search datasets involving up to 15,000 users. We show that SimAttack scales particularly well with respect to the number of users considered in the system. More precisely, compared to the previous machine learning attacks, SimAttack divides by 158 and 100 the execution time considering respectively 1,000 users protected by an unlinkability solution and 100 users protected by TrackMeNot.
Experimental set-up In this section, we provide the experimental set-up of our evaluation: the datasets, an overview of the considered indistinguishability solutions (i. e. , TrackMeNot and GooPIR), and both the evaluation metrics and the concurrent approaches we use to assess the performance of SimAttack. All our experiments were conducted on a commodity desktop workstation with a 2. 2 GHz quad core processor with 8 GB of memory.
Web search dataset To We then focus on the most active users as they are the most exposed to an adversary. We create 5 datasets containing different number of active users (from 100 to 1,000 users): AOL100, AOL200, AOL300, AOL500, AOL1000. To do that, we order the 18,164 users according to the number of queries they issued and then select the top 1,000 users to create the dataset AOL1000. We then generate the 4 other datasets as a subset of AOL1000. To retain similar statistical properties and ensure that users issued a significant number of queries, we generate these 4 datasets by choosing randomly (according to the distribution of the number of queries per user) the desired number of users (e. g. , 100 users for AOL100). the number of queries issued by user in these 5 datasets. These CDF show that users part of these datasets issued at least 500 queries.
We also note that each of these 5 datasets follows approximately the same distribution. In addition, to assess private Web search solutions with a larger number of users, we create 3 extra datasets containing the top 5,000, 10,000 and 15,000 users: AOL5000, AOL10000 and AOL15000. Figure  1  shows that these 3 datasets do not follow the previous distribution of queries per user due to the lack of high active users in the AOL dataset. However, results obtained with these datasets give a lower bound as having more queries in the user profile would likely increases the efficiency of the attack. Finally, we pre-process and filter the queries of users to remove the irrelevant keywords. To achieve that, we leverage the Stanford CoreNLP library  [27]. Using the tokenizer, we split queries in string vectors and then remove stop words (i.
e. , articles and short function words) and irrelevant keywords. Irrelevant keywords are identified with the Named Entity Tagger and the Part-Of-Speech Tagger. The former enables the recognition of names or numerical and temporal entities while the latter recognizes the function of the word. As consequence, numbers, dates or pronouns are removed. Lastly, we stem each keyword by eliminating or replacing the suffix using Porter algorithm  [28]. As mentioned in Section 3, we considered that the adversary has already built a user profile for each user. Consequently, we split each dataset in two parts: a training set used to build the user profiles, and a testing set used to assess the robustness of the considered privacypreserving mechanism. We used two third of user queries to create the training set and the remaining third of queries to create the testing set.
Finally, to simulate users using TMN, we need to add fake queries to the datasets created in Section 5. 1. To do that, we create our own implementation of TMN to generated the fake queries. We thus collected RSS feeds from the TMN default setting during one month and half (from August 28th, 2014 to October 9th, 2014), and we generate fake queries from the 13,878 news titles that we extracted. Additionally, we need to specify the number of fake queries that we want to generate. To do so, we consider that users used their computers 8 hours a day and have set up 60 queries per hour. Consequently, we generate 14,880 fake queries per users (i. e. , 60 queries × 8 hours × 31 days). We call TMN100 this new dataset that contains the queries of AOL100 plus 1,488,000 fake queries. To ensure that the generated fake queries (built from RSS feeds captured in 2014) are using similar terms that users cared to look for in 2006, we compute the overlap between the words used in fake queries and the words used in the whole AOL dataset.
We found out that 85. 6 % of words used in fake queries are also contained in the AOL dataset (6,918 words out 8,082). Furthermore, we also generate fake queries for the adversary (i. e. , profile of fake queries P FQ defined in Section 4. 3). To do that, we generate the same number of fake queries for the adversary as for users (i. e. , 14,880 fake queries).
GooPIR GooPIR (Google Private Information Retrieval) is a Java program to query Google in a privacy-preserving way. This protection mechanism can also be used with other search engine but only Google is supported by the application. GooPIR obfuscates user queries by adding extra fake queries separated by the logical OR operation. GooPIR uses a dictionary to generate these fake queries. It can exploit any type of dictionary -in the current implementation news articles from WikiNews are used but GooPIR's authors mentioned that query logs can also be used. By default, GooPIR creates three fake queries but users can manually set up this number. To generate k fake queries, GooPIR selects for each keyword of the initial query, k words using the dictionary. All these k selected words have a similar usage frequency than the keyword in the initial query.
Consequently, if the initial query is composed of n keywords, GooPIR selects k ×n words and then creates k fake queries of n words (i. e. , fake queries and user's queries have the same number of keywords). Query answers returned by Google contain results related to the initial query but also to the fake ones. As a consequence, GooPIR implements a filtering phase that tries to remove irrelevant results introduced by fake queries. This algorithm tests for each result if its title or its description contains keywords of the initial queries. If so, the result is displayed, it is discarded otherwise. In our experiments, to implement the behavior of GooPIR, we created the dictionary from the AOL dataset by extracting all keywords and their usage frequency from the 20 million AOL Web search queries.
Impact of smoothing factor α As described in Section 4. 1, the smoothing parameter α of SimAttack influences the similarity distance between a query and the user profile. To measure the impact of this parameter, we report on Fig. 2  the F-Measure returned by SimAttack for varying values of α on three different datasets: AOL100, AOL200, AOL300. We show that α has a limited impact on the performance of SimAttack to break the anonymity of users' queries for all datasets. For instance, for AOL100, the F-Measure only varies from Fig. 2  The smoothing factor α has not a significant impact on the F-Measure of SimAttack 36. 9 to 39. 6 %. As the best F-Measure is observed for α at 0. 5, we fixed the parameter α at 0. 5 in the remaining evaluations.
e. , p = 10 versus p = 1). In addition, if the adversary considers the 10 most probable users (i. e. , p = 10) while the systems gather 100 users, the recall only reaches 52. 8 %. This results is counter-intuitive, as the 10 most probable users represent 10 % of the dataset, this number is expected to be close to 100 % (as there is a high probability that an initial user is among the 10 most probable users). One possible explanation is that a large proportion of non-retrieved queries (74. 8 % if we consider 100 users) was not retrieved because they do not contain keywords that has been already used in the previous queries of users. Therefore, these queries cannot be retrieved as their similarity with the user profile equals 0.
Privacy protection We now compare the performance of both SimAttack and the concurrent machine learning attack on unlinkability solutions. Figure  7  measures for both attacks the precision and the recall for different number of users in the system. Results show that both attacks have a similar recall and precision. For instance, for AOL200, SimAttack has a recall of 36. 8 % and a precision of 41. 4 % against 34. 4 and 39. 1 %, respectively, for the machine learning attack. Nevertheless, regardless the dataset, SimAttack is slightly better than the machine learning attack. In average, the F-Measure of SimAttack is 3 % higher than the F-Measure obtained with the concurrent attack.
Privacy protection We now compare the performance of both SimAttack and the concurrent machine learning attack on TrackMeNot using the TMN100 dataset. Firstly for the concurrent machine learning attack, Table  2  lists the precision, the recall, and the F-Measure with two different classifiers. We show that compared to the logistic regression classifier, the SVM classifier provides better performance in term of precision while achieving a slightly lower recall. Interesting enough, the SVM classifier performs better than the classifiers suggested in  [8]. Secondly, as the machine learning attack requires prior knowledge on the RSS feeds, we also consider SimAttack using the same prior knowledge (results depict in Table  1 ). Results show that while the recall provided by SimAttack is lower than the recall provided by the machine learning attack with both classifiers, SimAttack outperforms the machine learning attacks in term of precision (87.
Scalability We finally compare the performance in term of scalability to conduct SimAttack on TMN against the concurrent machine learning approach. More precisely, we measure the execution time of both attacks (using prior knowledge on RSS feeds) on the TMN100 dataset. Results are reported in Fig. 12. We show that SimAttack is faster than the machine learning attack, especially for the logistic regression classifier. The training phase of the machine learning attack is the main reason of this long execution time. Besides, using SimAttack without prior knowledge will sill reduce its execution time as SimAttack using prior knowledge spends additional time to build a profile composed of fake queries.
Impact of the size of the user profiles Similarly to the other versions of SimAttack (i. e. , applied to unlinkability solutions or TrackMeNot), the size of the user profiles impacts the efficiency of SimAttack on GooPIR. To measure this impact, we assess the performance of SimAttack when the user profiles contains only a limited part of the user query history (from 0 to 100 %). Figure  14  depicts for AOL100 dataset, the percentage of Fig. 14  GooPIR ensures a better protection if the adversary has only a smaller and inaccurate user profile identified queries according to the ratio of the query history considered in the user profile for varying number of fake queries generated. Results show that GooPIR ensures a better protection if the adversary has only a smaller and inaccurate user profile.
Without knowledge on RSS feeds We now analyze the robustness of TMN over an unlinkability solution if the adversary does not consider prior knowledge on RSS feeds of users. Figure  15  presents the recall, the precision, and the F-Measure for several values of δ on the TMN100 dataset. Results show that δ significantly impacts the performance of SimAttack: the Fig. 15  The value of δ significantly impacts the performance of SimAttack F-Measure from to 27. 3 and gives it best value δ equals 0. 8. For this an adversary is able to identify 20. 5 % of real queries with a precision of 40. 7 %. Compared to the results obtained with Track-MeNot alone, adding the unlinkability solution decreases the recall and the precision (20. 5 % versus 36. 8 % for the recall, and 40. 7 % versus 62. 4 % for the precision).
Conclusion This paper presents SimAttack, a generic attack that targets popular private Web search solutions. SimAttack leverages a similarity metric to capture the distance between a query and pre-built user profiles gathering preliminary information about the user interests. We exhaustively evaluate SimAttack using a real world Web search dataset. We show that SimAttack succeeds to deanonymize, or retrieve among fake queries a high ratio of initial queries from user. Our analysis shows that neither unlinkability solutions, nor TrackMeNot and GooPIR protects properly the users. Besides, we study the combination of TrackMeNot and GooPIR over an unlinkability solution. The first combination (i. e. , TrackMeNot over an unlinkability solution) gives a satisfactory protection when enough fake queries are periodically sent.
evaluate the robustness of private Web search solutions, we use a real world Web search dataset from AOL Web search logs [26] published in 2006. AOL dataset contains approximately 21 million queries formulated by 650,000 users over three months (March, April and May of 2006). As this dataset contains many inactive users (i. e. , users that issued too few queries), we first filtered the whole dataset to target active users. More precisely, we select users that: (i) sent queries on at least 45 different days (i. e. , half of the dataset period), and (ii) issued queries on a period of at least 61 days (i. e. , two-thirds of the dataset period). Finally, after this filtering phase, our dataset gathers 18,164 users who issued from 62 queries to 3,156 queries over the dataset period.
Illustrations and performances In this section, we apply our method to actual radial velocity data to illustrate its accuracy and limitations. All the algorithms described in this article have been implemented in the DACE platform (http://dace. unige. ch, see  Buchschacher et al. 2015). The DACE platform also implements a Levenberg-Marquardt algorithm that enables us to refine the solution initially found with our methods. We denote by Fourier fit (FF) (see Sect. 3), min/max fit (MMF) (see Appendix C), and Levenberg-Marquardt (LM) the different algorithms we use to fit the data and find the orbital elements. All the following results are obtained using the DACE platform. We selected four representative cases: GJ 3021 (one eccentric planet), HD 156846 (one illsampled, very eccentric planet), HD 192310 (two planets with a low SNR), and HD 147018 (two planets with equal semiamplitudes).
Acknowledgements. We thank the anonymous referee for his/her useful comments. This work has, in part, been carried out within the framework of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation. The authors acknowledge financial support from the  SNSF. This publication makes use of DACE, a Data Analysis Center for Exoplanets, a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , based at the  University of Geneva (CH).
Last but not least, full provenance information can be stored alongside each item of data, thereby enhancing reproducibility and accountability. Any data set in our proposed format is self-describing and can be readily exchanged with others, facilitating collaboration. The utilization of the HDF5 container format grants efficient and parallel I/O operations, integrated compression algorithms and check sums to guard against data corruption. To not reinvent the wheel and to build upon past developments, we use existing standards like QuakeML, StationXML, W3C PROV and HDF5 wherever feasible. Usability and tool support are crucial for any new format to gain acceptance. We developed mature C/Fortran and Python based APIs coupling ASDF to the widely used SPECFEM3D_GLOBE and ObsPy toolkits.
In contrast, ASDF is a well-defined format that can be used to store and exchange full seismological data sets, including all necessary meta information. (ii) It is oftentimes convenient to locally build up a database of pre-processed waveforms. A common example is storage of instrument corrected and bandpass filtered data. If a project continues for some years, it might ultimately no longer be known how exactly data were processed. The makeup of the team may have changed, or perhaps the processing software had a bug that has been fixed in the meantime, and this may or may not have affected the data. Provenance, that is, the tracking and storing of the history of data, solves this particular problem, and ASDF accommodates that. Existing data formats do not (or only in a very limited manner) track the origin of data and what operations were performed on it due to limited and inflexible metadata allowances.
OV E RV I E W O F T H E F O R M AT ASDF, at its most basic level, organizes its data in a hierarchical structure inside a container-in a simplified manner a container can be pictured as a file system within a file. The contents are roughly arranged in four sections, as follows. (i) Details about seismic events of any kind (earthquakes, mine blasts, rock falls, etc. ) are stored in a QuakeML document. (ii) Seismic waveforms are sorted in one group per seismic station together with meta information in the form of a StationXML document. Each waveform is stored as an HDF5 array. (iii) Arbitrary data that cannot be understood as a seismic waveform is stored in the auxiliary data section. (iv) Data history (provenance) is kept as a number of SEIS-PROV documents (an extension to W3C PROV). Existing and established data formats and conventions are utilized wherever possible.