dataesr/scientific-paragraphs-categorization huggingface dataset:
PCR reactions were prepared in triplicate with 10 μl of 2× evagreen ddPCR supermix (Bio-Rad), 1 μl of assay mastermix (consisting of 16. 2 μl H 2 O, 14. 4 μl of 25 μM forward primer, and 14. 4 μl of 25 μM reverse primer) and 0. 5 μl of whole genome amplified DNA in a total volume of 20 μl. PCR reactions were partitioned using a QX200 droplet generator according to the manufacturer's instructions. Samples were run on a PCR machine using the following parameters: 95 °C for 10 min, followed by 29 cycles of 94 °C for 30 s and 55 °C for 60 s, finally samples were held at 98 °C for 10 min and then kept at 4 °C until the next step. Plates were read on a Bio-Rad QX100 droplet reader and droplets positive for the assay target were quantified using QuantaSoft software (Bio-Rad). Open Access This article is licensed under a Creative Commons Attribution 4.
Acknowledgements F. M. is supported by  AIL  ( Associazione Italiana Contro le Leucemie-Linfomi e Mieloma ONLUS ), by  SIES  ( Società Italiana di Ematologia Sperimentale ), and by the  Memorial Sloan Kettering Cancer Center NCI   Core Grant  ( P30 CA 008748 ). N. B. is funded by  AIRC  ( Associazione Italiana per la Ricerca sul Cancro)  through a MFAG (no. 17658 ) and by the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 817997 ). This work was supported by  Department of Veterans Affairs Merit Review  Award  I01BX001584-01  (N. C. M. ),  NIH  grants  P01-155258  (N. C. M. , H. A. L. , M. F. , P. J. C. and K. C. A. ) and  5P50CA100707-13  (N. C. M. , H. A. L. and K. C. A).
Word Embeddings (WE) are getting increasingly popular and widely applied in many Natural Language Processing (NLP) applications due to their effectiveness in capturing semantic properties of words; Machine Translation (MT), Information Retrieval (IR) and Information Extraction (IE) are among such areas. In this paper, we propose an open source ArbEngVec which provides several Arabic-English cross-lingual word embedding models. To train our bilingual models, we use a large dataset with more than 93 million pairs of Arabic-English parallel sentences. In addition, we perform both extrinsic and intrinsic evaluations for the different word embedding model variants. The extrinsic evaluation assesses the performance of models on the cross-language Semantic Textual Similarity (STS), while the intrinsic evaluation is based on the Word Translation (WT) task.
Corpus Used The main objective of this work is to provide an efficient Arabic-English cross-lingual word embedding models across different text domains. Indeed, we used a large dataset of parallel Arabic-English sentences mainly extracted from the Open Parallel Corpus Project 2  (OPUS)  (Tiedemann, 2012). OPUS contains 90 languages, and more than 2. 7 billion parallel sentences. This corpus consists of data from multiple domains and sources including: MultiUN Corpus (Daniel Tapias, 2010), OpenSubtitles  (Creutz, 2018 ), Tanzil (ZarrabiZadeh, 2007) , News-Commentary, United Nations (UN)  (Ziemski et al. , 2016) , Wikipedia, TED 2013 3  , GNOME 4  , Tatoeba 5  , Global Voices 6  , KDE4 7  and Ubuntu 8  corpus. To train our models, we extract more than 93. 9 million parallel sentences of Arabic-English from whole collection, this alignment contains more than 800 million Arabic tokens and 1 billion for English.
Intrinsic Evaluation In this step, we basically focused on word translation following (Stephan Gouws, 2015) evaluation procedure, so we generated a 1000 tuples starting with choosing random 1000 words from the model vocabulary. Then, we find their k-closest (k most similar) cross-lingual words based on the cosine similarity in our six ArbEngVec models. In fact, we have used five different values of k to generate 9 https://colab. research. google. com/ 10 All scripts used for training our models in Colab are available on : https://colab. research. google. com/drive/ 1Qe-jCcJ9Ofp07Xw8 IIpr8IeUFbCUy9XQ3 the 1-closest, 2-closest, 3-closest, 5-closest and 10-closest words. For example, Table  4  shows the 5-closest words of and weapons in our random Skip-Gram model. Afterwards, we calculate the accuracy of each range, which has been calculated by giving a value 1 to each word couple that represents a translation, we make sure that the word provided by our model is a translation with comparing it to Google Translate API's bag of words, if this comparison comes negative we compare manually, if also manual comparison comes negative we give negative score 0.
Extrinsic Evaluation Extrinsic evaluating means surveilling the model performance under real-world Natural Language Processing tasks use. Our choice fell on Semantic Sentences Similarity (STS) task. To estimate the semantic similarity between the Arabic-English sentences, we have used the WE-based approach proposed by  Nagoudi et al. (2017b)  jointly with our ArbEngVec models. In fact, we have had STS2017-Eval 11 datasets drawn from the shared taskSemEval-2017 Task1: STS Crosslingual Arabic-English  (Cer et al. , 2017). The sentence pairs of STS2017-Eval have been manually labelled by five annotators, and the similarity score is the average of the annotators judgments. Afterwards, in order to evaluate the performance of each model, we calculate Pearson correlation between our assigned semantic similarity scores and human judgement.
Models Visualization As part of the discussion, we have chosen to illustrate our models using pyplot scatters with Maaten and Hinton (2008) t-SNE algorithm. We provide these visualizations by choosing 20 arbitrary 11 http://alt. qcri. org/semeval2017/task1/index. php?id=dataand-toolsb words from our vocabulary, run 4-closest similarity to each word and finally project all of them on the 2-dimensional plot. Starting with parallel mode models, charts show that distance between Arabic markers are distant from others of English comparing to those of the same language. Same thing can be said on the situation that concerns word by word method CBOW variant with less distant languages but still marker bags most often do not include translation pairs. Eventually, random variant charts make it clear that close markers include translation pairs alongside mono and cross-lingual similarities, six model charts are in figure  2.
Conclusion In this paper, we have presented the open source project named ArbEngVec. This project provides several Arabic-English cross-lingual word embedding models. The embedding models are learned through a large dataset of parallel Arabic-English sentences. Additionally, we evaluated the Ar-bEngVec models via extrinsic and intrinsic evaluations. In the extrinsic evaluation, we used the cross-language semantic similarity task to test the capability of our models to capture the semantic and syntactic properties of words in two different languages. While in the intrinsic evaluations, we employed the embedding vectors to evaluate the word translation task. As future work, we are going to use these models with those of other classical NLP techniques, including word sense disambiguation, named entity recognition to make more improvement in the Arabic-English cross-language semantic similarity and plagiarism detection.
OBSERVATIONS AND DATA REDUCTION We obtained 1. 3 millimeter observations by combining ALMA 12-m array extended (C40-9) and more compact (C40-6) configurations, resulting in baselines ranging from 19 meters to up to 13. 9 kilometers and a total of 40-46 antennas. The combined observations are sensitive to spatial scales of up 2. 0, with a spatial resolution of ∼20 mas. The long baseline observations were acquired between Sept. and Nov. 2017 (Cycle 4) in four different blocks of ∼40 min each. Precipitable water vapor ranged between 0. 7 and 1. 8 mm. Observations of a phase calibrator (J1826-2924) were alternated with the science to calibrate the time-dependent variation of the complex gains. The cycling time for phase calibration was set to 8 minutes and 54 seconds for the compact and extended configurations, respectively.
The ALMA correlator was configured in Frequency Division Mode (FDM). Two spectral windows with 1. 875 GHz bandwidth were set up for detecting the dust continuum, centred at 232. 0 GHz and 218. 0 GHz, respectively. All data were calibrated by the ALMA staff using the ALMA Pipeline version 40896 in the CASA package  (Mc-Mullin et al. 2007) , including offline Water Vapor Radiometer (WVR) calibration, system temperature correction, as well as bandpass, phase, and amplitude calibrations. The short baseline and long baseline datasets were calibrated in-dependently. Self-calibration of the data was performed to improve coherence (a single iteration of phase-only), after which they were combined using the CASA task CONCAT. A positional offset of  (-20,-40)  mas between the short and long baseline resulting images was measured and corrected prior to combining the datasets.
ALMA continuum imaging Image reconstruction was initially performed using the CLEAN algorithm (CASA version 5. 2, task tclean). As the source is relatively bright, we super-resolve the continuum data using our Maximum Entropy Method (MEM) package UVMEM, which is part of the family of algorithms based on maximum-entropy regularization. Here we used the publicly-available GPU adaptation GPUVMEM 1   (Cárcamo et al. 2018) , and regularized by minimizing the Laplacian of the model image with an objective function. The MEM reconstruction provides the highest resolution without compromising on sensitivity. We adopted the MEM image for our analysis. The final image has a peak of 67 mJy beam -1 and an rms (1σ) of 14. 7µJy beam -1 for a synthesized beam of 27×20 mas. The total flux density over the whole image is 178±18 mJy (10% flux calibration uncertainty).
Hydrodynamical simulations Our 2D hydrodynamical simulation of the gas and dust in the HD 169142 disk has been carried out with the code Dusty FARGO-ADSG. It is an extended version of the publicly available code FARGO-ADSG, which solves the gas hydrodynamics equations on a polar mesh  (Masset 2000; Baruteau & Masset 2008a,b) , and which models dust as Lagrangian test particles 2   (Baruteau & Zhu 2016; Fuente et al. 2017). The simulation has been designed to test whether the three bright rings (B2, B3, B4) unveiled by our ALMA observations can be explained by a single forming planet. In our simulation the star's mass is assumed to be 2 M. We perform a set of simulations sampling the main parameter space {planet mass, disk scale-height, disk alpha viscosity}, although no exhaustive exploration is intended as finding a perfect fit is not in the scope of this work.
The α turbulent viscosity is set to 10 -5. This low viscosity is motivated by the sharpness of the observed rings. Accretion onto the planet is not included. The code solves the equations of motion for 200,000 dust particles with radii between 10 µm and 3 mm. Dust particles feel the gravity of the star, the planet, the disk gas (since gas self-gravity is included) and gas drag. Dust turbulent diffusion is also included as stochastic kicks on the particles position vector  (Charnoz et al. 2011). However, dust selfgravity, dust drag, growth and fragmentation are not taken into account. Dust particles (assumed to be compact spheres of 2 g cm - 3  internal density) are inserted at the beginning of the simulation with a number density profile decreasing as r -2 between 59 and 90 AU. This corresponds to an initial dust surface density decreasing as r -4.
Radiative transfer calculation We compute the continuum emission from our dust simulations using the public radiative transfer (RT) code RADMC3D (version 0. 41). Twenty logarithmically-spaced bins are used to allocate the 200,000 dust particles from the simulation. The dust sizes (s) follow a power-law distribution n(s) ∝ s -3. 5 , with minimum and maximum sizes of 1 µm and 1mm, respectively. Assuming a gas-to-dust ratio of 33 yields a total dust mass in the outer region of ∼100M ⊕. Each 2D distribution of dust particles is turned into a surface density and expanded in the vertical direction assuming hydrostatic equilibrium. The dust's scale height h i,d is sizedependent and follows h i,d = h × D z D z + St i , (1) where h is the initial gas pressure scale-height, St i is the averaged Stokes number for the i th dust size bin, and D z is a turbulent diffusion coefficient in the vertical direction which depends on the level of turbulent activity across the vertical extent of the disk  (Yang et al.
We thank  Ed Fomalont ,  Anya Yermakova  and  Philipp Weber  for useful discussions, as well as our anonymous referee for their constructive comments. Financial support was provided by the government of  Chile grants Millennium Scientific Initiative   RC130007 ,  CONICYT-Gemini 32130007 , and  CONICYT-FONDECYT  grant numbers  1171624 ,  1171246  and  1191934. S. P acknowledges support from the  Joint Committee of ESO  and the  Government of Chile. The data analysis and some of the simulations were carried out in the Brelka cluster, hosted at  DAS/U. de Chile (Fondequip EQM140101 ). Numerical simulations were carried out on the  CalMip machine of the Centre Interuniversitaire de Calcul de Toulouse , which is gratefully acknowledged. This work was performed in part at the  Aspen Center for Physics , which is supported by  National Science Foundation  grant  PHY-1607611.
In today's digital world, personal information is both harder to protect than ever before and holds an increasing monetary value to companies in the business of buying online advertisements (Pachilakis, Papadopoulos, Laoutaris, Markatos, & Kourtellis, 2019). Google, Facebook and other companies possess large quantities of user data. But psychological research is also increasingly data rich, with larger sample sizes, data downloaded from social media platforms, and studies including a multitude of variables. In parallel, the credibility revolution (Vazire, 2018) calls for more transparency, including making datasets openly accessible.
on online platforms such as the open science framework  (OSF, osf. io)  and the open science data cloud (intended for large datasets, https://www. opensciencedatacloud. org/). In addition, with the introduction of the General Data Protection Regulation (GDPR, Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), 2016) in the European Union (EU), researchers have to abide by rules on how to handle personal and sensitive information. Given this climate of increased data availability and uncertainty in how data is actually treated "behind the scenes", it is ethically crucial (and, depending on local regulations, often compulsory) to inform potential participants that their anonymous data will be accessible to others before they can proceed the study.
Chances are, they would have thought it too boring for their time. More importantly for the context of this work, truly anonymizing a dataset is more complex than simply removing name, age, and location. We can safely assume that every single one of our participants has some form of online presence. This means that for every person there is a dataset to cross-reference with ours, thereby increasing the potential for re-identification. Just recently, researchers demonstrated how little data is necessary to re-identify up to 99. 98% of all individuals  (Rocher, Hendrickx, & Montjoye, 2019). While this might not necessarily be problematic for typical participants in datasets without any sensitive content, it can take only one or two unusual data points to make a participant stand out. In a typical psychological experiment, the most common combination of features is first year psychology student, 18-25 years of age, and female.
Being a male student aged between 30-40 reduces the list of candidates to just a few participants. To identify the male participants by name, it takes just little additional information: roughly the year in which the study was conducted, and, for example the corresponding cohort's Facebook group, or an automated search on twitter to find profiles that match the description (male, "psychology" mentioned in profile, location corresponding to that of the university…). Again, for most studies, the additional information gained from crossing several datasets is benign. But would all participants who are unaware of the exact hypothesis of the study, and of the way their data could be re-identified still agree to participate? In addition to the recombination of openly accessible data, there is the possibility that data is leaked, servers hacked, or data integrity otherwise compromised without the researcher's (or participant's) consent.
Consequently, a good open access dataset is one where personal data has never been asked in the first place. In all other cases, data needs to be anonymized. However, if personal data is indispensable for a study, participants need to be informed of all potential use cases of their data. Making participant data accessible without restriction implies that the researcher cannot (and should not) control by whom, when and why a given dataset is reused, which makes this restriction rather complicated. We might use a science-minded data repository, but the people downloading the freely accessible data are not necessarily researchers (although see  Databrary, 2012 , for an exception, more can be found at re3data. org). Even if the data can be anonymized, it is not trivial to understand when a complex dataset is actually not at risk of re-identification.
As stated above, it can be surprisingly easy to identify outliers in an anonymous dataset. But given enough knowledge and resources, sometimes even entire anonymized datasets can be de-anonymized with legal means. This was demonstrated by  Narayanan and Shmatikov (2006)  for the famous Netflix dataset, by  de Montjoye, Hidalgo, Verleysen, and Blondel (2013)  for cellphone mobility data such as the publicly available data from sites like Foursquare, and most recently as a general case by  Rocher, Hendrickx, and Montjoye, (2019). But even for datasets containing personal information with the participants' consent, there is a major caveat with its own set of problems: The GDPR grants participants who provide personal data the right to withdraw their consent to use the data. First, it is entirely unrealistic that the researchers can fulfill their responsibility to find all potential copies.
This rationale also applies to the Chi-square test and the corresponding Bayesian contingency table test on the careless response items: is there one condition where more participants answer without paying attention the question content? All materials and analysis scripts are available for inspection and reuse. We can only provide files for the participants in the "open access" consent condition for both studies conducted. To provide as much detail as possible without compromising the consent for those participants who were in the "no sharing" condition, we created detailed metadata with the R codebook package  (Arslan, 2019). All files are available on this project's osf. io page https://osf. io/zyux4/.
Participants Sample size was determined beforehand based on practical implications: 9 students participating in a research seminar collected participant data until each student had obtained at least 20 participants. Of the potential participants, one person refused to take part after having read the consent, and one abandoned the questionnaire before completion. Both were included in the main analysis, for a total N = 193, with 111 participants reporting being women, 81 men, with one missing value. We excluded 1 participant from all further analyses based on the person's score outside 3 absolute deviations around the median, a procedure robust with regards to sample size and the values of the outliers themselves (MAD, Leys, Ley,  Klein, Bernard, & Licata, 2013). In the anchoring paradigm, we excluded additional participants based on the same procedure to avoid confounds due mistakes in the unit (i.
We chose three items of the anchoring paradigm  (Jacowitz & Kahneman, 1995)  as used in  Klein and colleagues (2014)  to assess whether the data sharing policy would in any way impact response behavior in a well-established finding. In this task, participants are asked to estimate a number (in our case: the height of Mt. Everest, the number of inhabitants of Chicago, and the number of babies born each day in the US). Critically, there are two between-participant conditions: one group receives an underestimation, while a second sees an overestimation as reference points. Typically, this leads to group differences in mean estimations of the target number (R. A. Klein et al. , 2014). We chose the specific questions based on the magnitude of their effect size (R. A. Klein et al. , 2014)  and presumed applicability in a Belgian context.
Here, the participants were further divided into two subgroups per consent condition, resulting in four different groups (data shared/low anchor, data shared/high anchor, confidential data/low anchor, confidential data/high anchor). Finally, 7 careless-response items, based on the bogus items in  Meade and Craig (2012)  were mixed into the two questionnaires. 1  The items were translated and adapted to the context in the study. We did not intent to exclude participants based on their incorrect responses to these items. Rather, we included them as a measure of participation quality: would participants read and respond more carefully in one consent condition than in the other? Participant attribution to both consent and anchoring condition was randomized: The questionnaires were assembled including a cover page, with consent condition marked on the last page, and given in random order to the students administering them.
Results There was only one person who declined to participate (consent condition: open access to data). This corresponds to a measurement error of 2%, with an upper limit of a 95% confidence interval of 3. 14%, for an acceptable difference of measurement error of 5% between the conditions). Although there was only one refusal to participate, we still tested as planned for a systematic difference between the two groups' average responses. We used a mixed measures 5 (within: Big Five subscales) by 2 (between: consent condition) ANOVA. For all Bayesian analyses throughout the two studies, we used the priors provided by the R package BayesFactor (Version 0. 9. 12. 4. 2; Morey & Rouder, 2018). We identified a main effect of subscales F(2. 95,559. 7) = 45. 56, MSE = 0. 72, p <. 001, 2 2 ˆ. 16 G η = , BF 10 = 1.
93 × 10 32 , but moderate support for the absence of a main effect of consent condition F(1,190) = 0. 29, MSE = 0. 50, p =. 589, 2 0 ˆ. 00 G η = , BF 10 = 0. 08 (see Figure  1 ). There was strong support for the absence of an interaction consent and Big-Five subscales: F(2. 95,559. 7) = 0. 34, MSE = 0. 72, p =. 790, 2 1 ˆ. 00 G η = , BF 10 = 0. 01. To test whether participant responses were more reliable in one condition than in the other, we compared the Cronbach alphas obtained for each subscale using the method developed and implemented in the R package cocron by  Diedenhofen and Musch (2016). We did not find any difference for 4 out of the 5 scales (Extraversion: χ 2 (1) = 0. 89, p =. 34; Agreeableness: χ 2 (1) = 0. 01, p =. 93; Conscientiousness: χ 2 (1) = 0. 43, p =. 51; Neuroticism: χ 2 (1) = 0.