dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Figure  6a3 , this strategy identified well the regions with higher densities that were observable by the eye in the pointillistic image (Figure  6a1 ). Indeed, this double-detection strategy was also able to detect sub-synaptic domains (nanodomains) of GABA A receptors in synapses. Super-resolution microscopy revealed that neurotransmitter receptors, including the GABA A receptors  (26) (27) (28)  , can form sub-synaptic domains in synapses whose existence can tune the efficacy of synaptic transmission (reviewed in Yang and Specht  (29)  , Maynard et al. (30)  ). We analyzed a STORM dataset of the α1-subunit of GABA A receptor, which displayed a non-homogeneous clustered distribution in synapses  (26)  , and a mixed clustered and non-clustered distribution outside synapses. Figures  6b1  and b2  show respectively the pointillistic and rendered images of an ROI on a dendrite of a hippocampal neuron.
The existing software relies on the calculation of the mean density outside clusters (SR-Tesseler  (18)  ) or on the simulation of random distributions (ClusterViSu  (19)  ) to set this value. We preferred to introduce a userdefined threshold that considers the labeling method, which heavily affects the expected density of detections. This threshold was then calculated considering the size of the labeled molecule, the expected number of fluorophores and detections per molecule, and the maximum possible distance between the fluorophore and the labeled molecule. An important difference with respect to previously proposed algorithms, Diinamic-V introduced a second density-based selection, using a grid defined by the expected resolution of the data. Our analysis thus pondered the result of Voronoi tessellation with the localization precision.
AILA is general in support of any risk assessment and, for the sake of demonstration, is applied to assess the privacy risk induced over physical persons by three real-world manufacturers from the automotive domain, namely Toyota, Mercedes and Tesla. AILA is also validated against a risk assessment methodology by ENISA, thereby confirming effectiveness and efficiency of the new methodology (which is dramatically more automated than ENISA's). AILA combines and consolidates together several techniques in an unprecedented fashion, including Natural Language Processing by summarisation and entity recognition, dataset labelling by appeal to the ToS;DR service, and fully-supervised Machine Learning and regression analysis. Finally, to contribute to open knowledge, the general, executable components of AILA, the AILA Entity Extractor and the AILA Classifier are released open source along with the privacy-specific components, the AILA Privacy Dataset and the AILA Privacy Model.
RQ2 Can the methodology mentioned in the previous question also assist the human analyst through the assignment of a likelihood to each given threat for any of the assets extracted from the given policy? This is most challenging. It would be extremely helpful for the analyst if a computer-supported methodology could provide a likelihood indication on the extent to which each asset may be affected by any of the given threats. RQ3 Can the methodology mentioned in the previous questions be integrated with or automated through a tool, namely a software application, if this exists, supporting the overall risk assessment process? As we shall see below, also this question is currently open. A few tools exist, and noteworthy is the one that we choose as our main software application due to its maturity and European Commission endorsement, PILAR  [2].
1) ANSWERING THE RESEARCH QUESTIONS The first challenge that the AILA Methodology takes up, following RQ1, is the automated recognition of the relevant assets from the policy by means of Natural Language Processing (NLP) techniques: The software component that we contribute to address this challenge is the AILA Entity Extractor, which is released open source  [4]. The biggest challenge for our methodology, following RQ2, is the automated classification of (each statement of) the policy. This is inherently prone to errors  [5]  but AILA takes an intelligent approach, namely it prescribes the use of a Convolutional Neural Network to train a model in a semi or fully supervised fashion, depending on the available dataset. Next, AILA adopts the trained model to classify the target policies and, following the identification of the relevant assets, facilitate the analyst's task of assigning likelihood values to threats, thereby thwarting subjectivity and distraction.
The AILA Classifier is the software component that addresses this second challenge, and is released open source  [4]. AILA is general and applicable to different inputs concerning relevant properties such as privacy, cybersecurity and safety. This article demonstrates it over privacy policies, also to overcome the limitations of previous work whose authors state that ''we do not have any publicly available large dataset in the legal domain that has explicitly tagged privacy policies''  [6]. By contrast, we obtain that dataset from outputs of the ''Terms of Service; Didn't Read''  [7]  (ToS;DR), which labels a sentence as fair when it will ''respect your rights and will not abuse your data''  [7]. Therefore, a fairness label signifies the extent to which a policy statement respects natural persons' privacy.
The service covers the privacy policies of popular services such as Amazon, Facebook and Wikipedia. Assuming that dataset, termed AILA Privacy Dataset, to only suffer negligible bias, AILA trains a model, termed AILA Privacy Model, through fully supervised Machine Learning. Both the AILA Privacy Dataset and the AILA Privacy Model are released open source. AILA also takes the challenge, following RQ3, of enabling the analyst to combine the automatedly assigned likelihood values with those derived from PILAR. We gained by regression analysis an in-depth understanding of PILAR's calculation of risk so as to meaningfully combine it with the AILA Likelihood with the ultimate aim of increasing realism and reliability. AILA's required inputs are one or more policy documents aimed at regulating assets to achieve an overarching property (and the documents are meant to source a risk assessment exercise over the property), a labelled dataset of policies with the same aim and a list of threats to the property.
2) REAL-WORLD DEMONSTRATION This article continues by demonstrating AILA for the privacy property over three real-world privacy policies, using the dataset derived from ToS;DR and considering a list of threats derived from PILAR. Our target case studies come from the automotive industry and are large car manufacturers' privacy policies. Our choices are Toyota and Mercedes, the first two car manufacturers in Interbrand's 2020 Best Global Brands (BGB) Report  [8]  (7th and 8th places, respectively, in the overall classification, which also accounts for other areas than automotive). We also add Tesla as a third case study due to its pioneer role on electric cars. We intentionally choose policies that are not currently available from (ToS;DR) to ensure that the particular sentence structure of the dataset would not influence our case studies.
Furthermore, Ghosh et a. [18]  outlined the need for automation to extract requirements specification from documents in a formal fashion. This has subsequently involved the combination of Natural Language Processing with Machine Learning techniques, proven to be a successful duo to fulfil common requirements for policies analysis, namely text summarisation and classification. In fact, while Sathyendra et al. [19]  described approaches to automatically extract choice instances from privacy policy documents involving pure NLP, Zaeem et al. [20]  advanced a data mining methodology, along with a tool named PrivacyCheck, leveraging both NLP and ML to automatically extract summaries of online privacy policies. Similarly, Tesfay et al. [21]  proposed a method to summarise privacy policies into short and condensed notes following a risk-based approach, under the EU GDPR aspects as assessment criteria.
2) AILA LIKELIHOOD DETERMINATION This step leverages ML for the computer-based assignment of a likelihood value. Of course, having a properly labelled dataset to use to train a model is an essential prerequisite. One of the overarching properties that our example policy aims at is user privacy, which lies among the most discussed ones worldwide at present. Therefore, a dataset that is labelled according to privacy is necessary. We found that this can be formed out from a corpus of privacy policies where each sentence is labelled by the ToS;DR community to signify the sentence's fairness, which the community itself interprets as the extent to which the sentence respects natural persons' privacy. Precisely, each sentence is labelled either with a 0 or with a 1 fairness value. Therefore, a privacy-labelled dataset is reached, which we term the AILA Privacy Dataset, and signifies a tangible and general result that can be used to train ML models in various ways.
IV. AILA STEP 1: ENTITY EXTRACTION This step can be taken using the AILA Entity Extractor (AILAEE), which implements some preprocessing task and then Named Entity Recognition (NER), as shown in Figure  1. Preprocessing influences both the number and the significance of the entities that are extracted later. Named Entity Recognition is now invoked to select the sentences pertaining to the entities produced by the AILA Preprocessor. AILAEE, with its two modules, the AILA Preprocessor and the NER Engine, is available open source  [4]. Table  3  compares the number of extracted entities using various preprocessors, or no preprocessing at all, from the privacy policies of our case studies. It can be seen that the AILA Preprocessor leads to more entities than other existing preprocessors do but, at the same time, that number is much lower than the number originating from no preprocessing.
V. AILA STEP 2: AILA LIKELIHOOD DETERMINATION The previous step is preparatory to our development of the AILA Classifier (AILAC), as depicted in Figure  2. As noted above (Section III), its core includes a dataset to train a ML model to label sentences with a likelihood value, then the model's practical use over real-world policies. Also, AILAC is available open source  [4]. While all techniques and tools discussed thus far are general, the specific dataset and ML model discussed below are tailored to privacy policies, hence their names. Equivalent versions could be similarly built for other properties. The resulting corpus contains 500 sentences, which are then enriched by using text augmentation and synonyms following a standard practice  [35]. This yields the AILA Privacy Dataset, a corpus of over 100.
B. THE AILA PRIVACY MODEL Convolutional Neural Networks are normally represented by a sequential architecture, which is created by passing a list of layers  [36]. We build the AILA Privacy model by using Keras, a Deep Learning API written in Python, as demonstrated in Figure  3. The model is trained on more than 75. 000 labelled sentences, which are transformed into a 2-D feature matrix for the training step. Precisely, the Relu function is chosen as activation function of the first layer, the Sigmoid function for the second layer and the Adam function for the optimisation. The model gets trained for 15 epochs using the binary crossentropy function as loss function, 0. 0001 as learning rate, and 50 as batch size. The dataset is split into two parts: the first, corresponding to the 75% of the total corpus, is used to train the model; the rest is employed for the testing step.
With this information, the tool is able to automatically determine all the values needed to calculate impact and then risk. It is clear that the automated choice of assets and the determination of likelihood values from given policies explained above facilitate the analyst also through the interaction with PILAR. First of all, the analyst quickly determines the assets to insert in the tool but; most importantly, the analyst mechanically obtains likelihood values that are not only informed by the given policies but also free from subjective influence. The analyst may finally choose to update the original Pilar Likelihood values in the tool, for example, with the AILA ones or with an average of the two. However, PILAR is not open source, hence we have no access to the source code and, especially, to its algorithms.
On one hand, the fact that these values may appear to be high may be due to the stringent criteria that ToS;DR adopts for their labelling, which our ML model inherits through our training dataset. On the other hand, the significance of the very numbers through qualitative risk assessment is limited, while the relative differences are most relevant: Tesla is found to come with the fairest privacy policy. Finally, Table  8 , Table  9  and Table  10  show one example assets per car brand and its respectively associated PILAR class and threats. The asset different yet similar among the brands, pertain to the same PILAR classes, Software. Therefore, it can be noticed that the associated threats and Pilar Likelihood values remain unvaried across the brands. This is the best the tool can do without the analyst's intervention.
AILA's integration with PILAR is conceptually simple now that we understand how the latter calculates impact and risk levels, but cannot be completed because PILAR is not open source. The application of AILA to the automotive field was profitable on all three case studies, Toyota, Mercedes and Tesla, showing how to reduce a few thousand words to only a few dozen entities, hence facilitating asset extraction dramatically. AILA also conveniently automated the assignment of the likelihood values for all assets, offering significant reduction of subjectivity. A limitation of AILA is that it can be used only when a non biassed and labelled dataset is available in order to train the ML model; still, this is an inherent limitation of ML in general. Future work includes deeper semantic analysis of the policies to tune the likelihood values, which are currently assigned per asset, more per asset and per threat.
A. THE AILA PRIVACY DATASETAs an ML model requires solid data for training, we created a dataset starting from a corpus of sentences already labelled by the ToS;DR community in terms of fairness. Such sentences are gathered from the following popular services: Amazon, Apple, Blizzard, CNN, DuckDuckGo, Facebook, Google, Khan Academy, Paypal, Pinterest, Pornhub, Quora, Reddit, Spotify, Startpage, WikiHow, Wikipedia, YouTube.
Joint transit + RV modeling. We use the exoplanet package (Foreman-Mackey et al. 2021a) to jointly model the measured photometric and radial velocity signals for TOI-4127, with Gaussian priors on the stellar mass and radius imposed by the results of our SpecMatch-Emp and SED analysis, and flat priors on the exoplanet orbital period (P), transit depth (δ), and transit impact parameter (b). We model the orbit using a full Keplerian with uniform priors on exoplanet mass (M p ), orbital period (P), and transit impact parameter (b), and we sample the eccentricity and argument of periastron on the unit disk in e sin we cos w space. We elect not to place informative priors on the eccentricity because previous work has shown that there is no strongly favored eccentricity distribution for the known warm Jupiter population  (Dong et al.
 where we adopt the approximation F(e 2 ) ≈ 1 + 6e 2 + e 4 , dropping higher order terms. Again assuming Q p = 10 6. 5 , we find 780 circ 210 280 t = - + Gyr. Tidal dissipation would need to be Figure  3. NEID and SOPHIE RV data for TOI-4127. We show the NEID data in blue and the SOPHIE data in red, as well as the median orbit fit, as determined in Section 3, as a solid black line. We also highlight the times during which TESS was observing the target in Sectors 40 and 47, with colors matching the light curves in Figure  1. two orders of magnitude more efficient than the value assumed here for the orbit to circularize before TOI-4127 evolves off the main sequence. This is plausible given that Q p is difficult to constrain for any individual system because this parameter depends strongly on the physical parameters of the planet (e.
In-transit TESS data for TOI-4127 b, folded to the fitted orbital period. In the top panel, we plot the Sector 20 and Sector 26 data in green and blue, respectively, and the Sector 53 data in pink, to match the light curves shown in Figure  1. We include both the binned (30 minutes cadence) and unbinned (2 minutes cadence) Sector 53 transit data. We show the best-fit model in black and 3σ uncertainties in gray, and we plot the residuals to this fit in the bottom panel. TOI-4127 b does not presently have the characteristics of a hot Jupiter progenitor in the same class as HD 80606 b and TOI-3362 b. This will remain true if the present eccentric orbit is the result of a one-time excitation event with no avenue for future angular momentum exchange, such as planet-planet scattering  (Rasio & Ford 1996)  in which the second body is ejected from the system.
Constraints on the Presence of an Undetected Perturber We do not detect any additional stellar or planetary companions to TOI-4127, but coupling with an undetected perturber might still be responsible for the high eccentricity of the detected warm Jupiter. We show in Section 2. 2 using NESSI speckle imaging data that TOI-4127 lacks any bright neighbors, but the NESSI data alone do not constrain the presence of low mass companions such as mid-to late-K dwarfs and M dwarfs and substellar objects. We consider the detectability of such an object given the available data here. Figure  8. Eccentricities for all transiting exoplanets with >3σ mass measurements. We show Jupiter-sized exoplanets (R > 8R ⊕ ) in blue and smaller planets in gray. TOI-4127 b (red) has an eccentricity higher than all but five exoplanets in this class.
We draw planetary parameters for TOI-4127 b from the best-fit posterior distribution of the joint transit + RV fit in Section 3. 2 and we randomly draw a companion from our simulated perturber population. Using the NEID and SOPHIE observation times and measurement uncertainties, we calculate the RV signal that would have been observed for this two-planet system. We then fit and subtract the TOI-4127 b signal using the MPFIT IDL package  (Markwardt 2009) , fixing the orbital period and time of conjunction. This leaves us with the residual signal from the perturbing companion. We repeat this process for 1000 perturbers and then determine the detectability of each residual signal by measuring the ratio of the slope of the residual RV signal to the estimated error in the slope (|slope|/e slope ).
As in  Jackson et al. (2021) , we consider the companion to be detectable for each trial in which this metric is greater than 3. 5 over the full RV baseline. To assess sensitivity to perturbers with orbital periods shorter than the full RV baseline, we also calculate the slope ratio for each of the following limited baselines: (1) the full NEID baseline, including all overlapping SOPHIE measurements, (2) the full SOPHIE baseline, including all overlapping NEID measurements, and (3) BJD 2459450 through BJD 2459700 (i. e. , all data excluding the earliest NEID measurements and latest SOPHIE measurements). Perturbing companions may also be detectable with Gaia astrometry. For each of the above trials, we model the transverse motion of the stellar host due to the gravitational pull of the perturber following  Quirrenbach (2010)  and subtract off a linear fit to account for proper motion.
For each trial, we sample the model at 473 points, corresponding to the number of measurements used in the Gaia Data Release 3 astrometric solution  (Gaia Collaboration et al. 2022)  for TOI-4127 and we calculate the maximum angular distance, Δθ, between model samples. We compare this value to a conservative detection limit of 100 μas  (Perryman et al. 2014) , and we label trials with Δθ above this limit as detectable via Gaia astrometry. We show the sample of potential perturbers in Figure  9. While signals from most of these objects with RV semiamplitudes greater than ∼10 m s -1 would have been detected in the available RV or astrometric data sets, exoplanets with masses K < 10 m s -1 are overwhelmingly not detectable. Therefore, we cannot rule out perturber-coupled eccentricity oscillations as the source of the eccentric orbit of TOI-4127 b and as a mechanism for future high-eccentricity tidal migration.
Summary In this work, we present the discovery of the highly eccentric warm Jupiter TOI-4127 b with TESS, and the confirmation and characterization of the exoplanet signal with the NEID and SOPHIE spectrographs. We jointly fit the transit and RV data to constrain the physical (R R 1. 096 ) parameters of the exoplanet. The high orbital eccentricity makes this rare system a compelling target for dynamical studies. We comment on the potential for dynamical evolution of TOI-4127 b in the context of hot Jupiter formation pathways, and we find that TOI-4127 b may indeed be a hot Jupiter progenitor but only if a perturbing companion is present. While we find no evidence for additional bodies in this system. We show that an undetected perturber could be capable of exciting eccentricity oscillations and high-eccentricity migration.
NEID is funded by  NASA  through  JPL  by contract  1547612  and the  NEID Data Reduction Pipeline  is funded through  JPL  contract  1644767. Funding for this work was partially provided by  Research Support  Agreements  1646897  and  1679618  administered by  JPL. The  Center for Exoplanets and Habitable Worlds  and the  Penn State Extraterrestrial Intelligence Center  are supported by the  Pennsylvania State University  and the  Eberly College of Science. G. S. acknowledges support provided by  NASA  through the  NASA   Hubble Fellowship  grant  HST-HF2-51519. 001- A awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. P. D. acknowledges support by a  51 Pegasi b Postdoctoral Fellowship  from the  Heising-Simons Foundation.
P. C. Z. acknowledges funding from the  French National Research Agency (ANR)  under contract number  ANR-18-CE31-0019  (SPlaSH). D. D. acknowledges support from the  NASA   Exoplanet Research Program  grant  18-2XRP18_2-0136. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France , and  NASA 's Astrophysics Data System Bibliographic Services. Part of this work was performed for the  Jet Propulsion Laboratory, California Institute of Technology , sponsored by the  United States Government  under the Prime Contract  80NM0018D0004  between  Caltech  and  NASA. This work is partly supported by the  French National Research Agency  in the framework of the  Investissements d'Avenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Grenoble-Alpes University  This work has been partly supported by a grant from  Labex OSUG at 2020  ( Investissements d'avenir -ANR10 LABX56 ).
This work was supported by the " Programme National de Planétologie" (PNP) of CNRS/INSU  and the  French Space Agency (CNES). The results reported herein benefited from collaborations and/or information exchange within NASAʼs Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by  NASAʼs Science Mission Directorate  under Agreement No. 80NSSC21K0593  for the program " Alien Earths. " This paper contains data taken with the NEID instrument, which was funded by the  NASA-NSF   Exoplanet Observational Research  ( NN-EXPLORE ) partnership and built by  Pennsylvania State University. NEID is installed on the WIYN telescope, which is operated by the  NSF's National Optical-Infrared Astronomy Research Laboratory , and the NEID archive is operated by the  NASA Exoplanet Science Institute at the California Institute of Technology.