dataesr/scientific-paragraphs-categorization huggingface dataset:
Selected data We used ESO VLT/ESPRESSO spectra of the K2. 5V star HD 40307 (V = 7. 1). The star has a mean radial velocity of ≃31. 3 km s -1 and at least four discovered exoplanets with periods from 4. 3 to 197 days  (Díaz et al. 2016). The target star HD 40307 was chosen for two main reasons: first, detailed measurements of radial velocities of this target with ESPRESSO were obtained as part of an asteroseismological campaign on the star 3. The results of the full campaign are presented by  Pepe et al. (2021). Second, the ESO archive makes available all 1150 spectra of this star recorded over 5 nights. Here, we made use of the set of 200 consecutive exposures taken during one single night from 24 to 25 December 2018. The exposure time was set to 30 s for all data. The reason for the choice of such short and frequent observations by the proposing team was the attempted detection of stellar activity, namely p-mode oscillations.
For the present study, this exceptional dataset of a very large number of exposures in a single night is ideal. First, the number of exposures is high enough to perform statistics on the RV measurements. Second, the total duration corresponds to ≃240 m s -1 Doppler-shift variation of telluric lines with respect to stellar lines, which is a sufficiently large value to detect potential effects of the telluric correction on the absolute value of RV. Finally, the total duration of 4. 3 h for the 200 selected exposures is too short to correspond to a detectable RV variation for the exoplanet given its period of 4. 31 days, especially for the sequence of that particular night, which corresponds to a maximum-velocity region (data points around phase 130 in Fig. 25  of  Pepe et al. 2021)  and to an RV variation of less than a few tenths of a m s -1.
Because one criterion with which to verify the validity of our telluric correction is to check that the retrieved RV is constant over the period of observations, this is an important aspect. We used the fully reduced spectra from the ESPRESSO pipeline. For each spectral order, the data are given separately for each of the two parts of the image slicer and we used these distinct spectra as if they were individual orders (e. g. , orders 154-155 in the following sections correspond to a unique spectral order and the two images of the slicer). For RV measurements, all available spectra are shifted to the barycentric frame. For the purpose of telluric correction, we also used recorded spectra with a wavelength scale in the laboratory frame (see Sect. 4).
A small difference can also be seen between the two determinations. The γ band provides a more noisy determination but at the exact expected level (X=1 for the first exposure, which happens if the fitted column is exactly the reference value for the O 2 column of this exposure). The B band provides a less noisy determination, but with a small relative difference of about 3%. The reason for this small discrepancy is unclear. It is not likely to be a cross-section error in the HITRAN database. It might be due to small differences between the modeled and actual atmospheric profiles, or, more likely, to a lack of precision in the modeled PSF, in particular departures from the Gaussian approximation. In view of these results, we decided to use the B band result for the absolute value of X during the subsequent telluricline removal, because the B band lines are stronger, and for the temporal evolution we chose to use quite simply the airmass as the multiplicative factor for the O 2 vertical column.
The CCF of one line of the BM with one observed line is done by computing the signal contained in a boxcar centered on the BM line, and displaced by a variable RV (by steps of 492 m s -1 , the mean size of one spectel) around the observed minimum. Following the recommendation of  Lafarga et al. (2020) , we chose to use a boxcar width of approximately one spectel of the observed spectrum. As a result, the CCF is very similar to the observed line, because both the boxcar width and the step of the RV grid are of the size of one spectel. This method also has the advantage of providing independent points of the CCF. Indeed, the Gaussian fit algorithm used in Igor language (but this is the same for most coding languages used to code the Numerical Recipe library,  Press et al. 1992 ) assumes that all points to be fitted are independent, and the error bars returned by the fit routine rely on this hypothesis.
The fact that there is no measurable trend over the time series is a serious indication that the telluric system has indeed been well removed from the telluric-corrected spectra. Unfortunately, the time series that we analyzed covers only 240 m s -1 , while the BERV could change by ≃30 km s -1 , corresponding to a maximum drift of 8 m s -1. The campaign of HD40307 observations covered only a limited excursion of BERV and we cannot significantly improve on this with this campaign. However,  Allart et al. (2022)   tellurics are smaller than ≃0. 2 m s -1 over a large BERV excursion (±28 km s -1 , their Fig. 12 ) in the case of their correction technique. As we perform an accurate computation of the atmospheric transmission (by taking the whole p-T vertical profile into account instead of a monolayer) and are using the same HITRAN molecular database, we may not expect our atmospheric correction scheme to produce more drift.
Conclusions We used an exceptional series of consecutive short exposures of the ESPRESSO spectrograph for a K2. 5V type star to test and estimate the potential increase in precision of the RV measurement brought by telluric-line removal when this correction is based on a publicly available, location-, and time-matched atmospheric transmission spectrum and a simple and conventional fitting code to be performed locally. We used theoretical transmissions of H 2 O and O 2 downloaded from the TAPAS online facility and adjusted them to each exposure before dividing each recorded spectrum by this adjusted transmission. TAPAS conducts a high-quality computation of the telluric absorption based on the vertical T-P profile provided by the ECMWF meteorological field, the HITRAN spectroscopic data base and the LBLRTM code.
Acknowledgements. We thank our referee for his/her very constructive comments and for having pointed out the instrumental reason of increased jitter for the blue detector data. We deeply thank  Piercarlo Bonifacio  for providing his personal ATLAS-SYNTHE environment and helping us to compute an appropriate stellar synthetic spectrum, used for comparison tests (template matching) and stellar line identification. We acknowledge a very useful support from  Burkhard Wolff  from  ESO  who helped us with  ESPRESSO instrumental details  and the binary mask. A. I. acknowledges the support of a  Vernadski Scholarship  for PhD students, sponsored by the  French Government  and the  Ministry of Science and Higher Education of the Russian Federation  under the grant  075-15-2020-780  ( N13. 1902. 21. 0039 ).
We wonder-how many SoSyM readers have tried using OpenAI's ChatGPT to formulate prompts to explore the interesting results that may be produced? It is really fascinating to consider the results when the query is defined appropriately. Of course, we abstained from using ChatGPT to write any part of this editorial, but if we actually would generate portions of our text, we wonder how difficult it would be for readers to identify the generated parts. ChatGPT is rather chatty, but even the produced text can be adapted by asking it for a specific answering style. The options and future extensions seem to be endless. In the context of this editorial, we do not consider legal or ethical issues of ChatGPT-these have to be sorted out eventually. We also do not discuss the problems of having ChatGPT text, or text generated by other Large Language Models (LLMs), in scientific papers.
Rather, in this editorial, we focus more positively and ask the main question: How will ChatGPT be able to help us in a development process, especially in the tasks of developing or using models for analysis, production or understanding of software and systems? ChatGPT can provide useful pieces of programs that are sometimes correct and perform the task that the user asked to be implemented. ChatGPT also knows several textual modeling languages. This even seems to hold for DSLs. The example of GraphGPT suggests that a language designer can tell ChatGPT what the desired modeling language looks like and it will produce pieces within that language. GraphGPT uses the trick of asking for a JSON-encoding of the graph that is rendered into a diagram. The options seem endless.
and we do not have the prophetic ability to predict precisely how generative AI will change the business and activities of modeling in the future. But where will the changes occur? We see several options: (1) Modeling as we know may go completely out of business, because a helpful improved version of ChatGPT will directly map the informal description of a system's requirements into executable code. In this case, enduser programming will have arrived and will be in place in many simpler cases for user-defined languages for everyday use (e. g. customized languages for controlling our lighting or heating systems at home). (2) Another possibility is that models as an intermediate step between informal requirements and the executable solution will still exist, but the transformation between requirements to the models are done by ChatGPT.
The transformation between the models and the executable solution are then either automated by a standard code generator or ChatGPT. Less bold alternatives could be that ChatGPT is leveraged partially as a kind of recommender or modeling assistant, but still lets the modeler be responsible and the final owner of the produced models. It may also be that this is only an intermediate step to more automated alternatives. The modeler's expertise could be reduced to fine tuning of requirements in such a way that the LLM in use will produce desired, executable results, but is not able to understand, review and assess the generated models. Using models as intermediate steps may have some advantages over the direct mapping of requirements to code. First, humans may look at generated models and use them as a review point to better ensure the software's quality.
Second, the results may become improved, when ChatGPT does not have to make the big step from requirements to implementation, but may go in several small and smooth transformations, using the idea of platform-independent, platform-dependent and finally computational models. These intermediate models may still be models of the standard languages, like UML or SysML or appropriate DSLs, allowing the already existing analysis techniques to be used in between. In critical domains, it may be the case that generated models are then used for certification, verification and other kinds of quality assurance techniques. It may be beneficial to have ChatGPT generate recommendations and explanations, or even verifiable proofs, on why the code fits to a model. In less critical domains, we may still use all of these intermediate steps and only ask ChatGPT for explanations of the tracing between models and code.
ChatGPT also can be applied for identifying potential test cases or test data sets that can then be used as parts of traditional test-driven development. However, it is currently unclear whether or how intensively ChatGPT could be able to produce reliable oracles for for black-box tests of code. As ChatGPT improves at explaining its results, we might also be tempted to ask it to explain pieces of the code or models to us. This would offer the possibility of using Chat-GPT as a kind of replacement for the traditional forms of documentation. There are other forms of usages; for example, using Chat-GPT to produce the correct configuration for a complex software stack or to produce GUI elements or web pages for visual representation of collected data and information. In personal discussions, opinions have been expressed that it should be an algorithm that performs the modeling, because humans are too expensive.
Of course, if automation from generative AI is possible and the results can be truly trusted, then we should consider it. Software engineers always seek higher degrees of automation and the use of generative AI is a viable possibility. We should not forget that ChatGPT actually draws its knowledge from already existing information found in many sources (e. g. , scientific papers, Stack Overflow, arxiv, and archived GitHub projects). Thus, innovations in generative AI presumably do not come from fresh insights, but from re-combinations of existing solutions and knowledge (and sometimes only our expectations of a presupposed solution, which only had to be found). In the case of models, we continue to be constrained by a problem that has existed for many years-we do not have large and open libraries of models, in the same magnitude as open source code already exists.
INTRODUCTION Genome-wide association studies (GWASes) aim at identifying genotype/phenotype associations -the genomic variants that are statistically associated with a certain observable trait, such as a disease -in a cohort of individuals. In a GWAS, genomes of the case population -a group of individuals who express the trait of interest -are compared to a control population comprised of individuals not expressing this trait. Promises are enormous, from early prediction and prevention of diseases to personalized medicine, and are of primary interest to multiple stakeholders, including governments, research, and private companies. To improve the confidence and precision of results, biocenters join forces and perform federated GWASes  [34, 64, 67]. In particular, the possibility to share genomic data among several independent institutions, possibly located in different countries, leads to much more accurate, precise, and hence much more expressive statistical results than would be obtained by just considering the individually possessed datasets  [19, 68].
These works are also limited to a static setting, where the genomic dataset remains constant with exactly one GWAS release over the data. Recently, DyPS  [64]  considered a scenario where the results of a single-GWAS gets updated as more genomes become available or when genomes are removed (a requirement imposed by privacy regulations, such as GDPR  [62] ). However, in real-life settings, GWASes might also consider overlapping sets of individuals and genomic variations, some of which might also be re-used in other studies for economic reasons  [21, 46, 61, 82]. Indeed, it is rather likely that federations will run different GWASes simultaneously (e. g. , one on diabetes and a second studying lung cancer) that possibly share data  [27, 63]. In this work, we show that an adversary might be able to base an attack on the observation of the results of a single multi-trait study or even combining results from multiple GWASes of a federation.
• We extensively evaluate I-GWAS in several scenarios and against Differential Privacy (DP) based schemes for dynamic releases of interdependent GWASes. Our analysis is based on real datasets comprised of 27,895 genomes. Although both DP and I-GWAS successfully protect interdependent GWAS results, we found our solution to exhibit a better utilityprivacy trade-off, in particular as the number of releases increases. While DP inevitably has to stop releasing data once the privacy budget is exhausted, I-GWAS' exhaustive verification method ensures a continuous release of unperturbed GWAS statistics over a variable number of genomic variations. The rest of this paper is organized as follows. Sec. 2 provides background on genomics, GWAS-associated privacy attacks, and existing countermeasures. Section 3 presents our assumptions and our objectives.
In contrast, our paper is the first to evaluate the privacy risks that interdependent GWASes introduce under continuous releases. Solutions to mitigate membership attacks include statistical inference methods, such as the likelihood-ratio test (LR-test) to measure membership inference risk of individuals from published GWAS statistics  [11, 40, 48, 70, 82]  and approaches that perturb a GWAS result to enforce Differential Privacy at the expense of a reduced data utility  [49, 73, 74, 76, 78, 87]. Differential Privacy. DP guarantees that adding or removing a record in a dataset does not substantially modify the distribution of the outcome of a query  [30]. There are several means to achieve DP depending on the type of perturbation added. The most common approach uses the Laplace distribution  [30, 31].
More formally, let D and D ′ be two neighboring datasets that differ by a single element, and let O be the set of all possible outputs of a query. A release R is ϵ-differentially private if: P r [(R(D) ∈ O ] ≤ exp(ϵ ) × P r [R(D ′ ) ∈ S ] ( 2 ) where ϵ is the privacy parameter that determines the level of privacy protection that comes as a random noise added to the outputs in O. When leveraging the Laplace mechanism with l 1 sensitivity level of a function f defined as ∆ f = max D, D ′ || f (D) -f (D ′ )|| (which portraits the largest change in f when a single record is replaced)  [32] , the applied noise is derived from the Laplace distribution with mean 0 and scale ∆ f ϵ. In particular, as the probabilities differ by a factor of ϵ, DP has a privacy and data utility trade-off. Intuitively, a smaller ϵ means stronger privacy with lower accuracy  [31].
In particular, SG applies several genome privacy verifications in combination with a LR-test to determine which SNP statistics of a given dataset can be safely released. As a first step, SG makes sure that only independent SNPs are considered in the LR-test. To do so, SG first (i) checks SNP positions with rare allele frequencies (e. g. , MAF below a given threshold, e. g. , ≤ 0. 05) and preliminary removes them from the original SNP-set of a study; and then (ii) discards SNPs that presents a certain level of pairwise linkage disequilibrium (e. g. , p-value on r 2 below < 10 -5 ). Posteriorly, SG proceeds with the following LR-test analysis. SG's LR-test null hypothesis represents the possibility for an individual of interest to belong to the study population (consisting of N independent individuals) under Hardy-Weinberg equilibrium (HWE)  1.
Besides, DP does not make any assumption on the background knowledge of adversaries and its computational cost is cheap once the privacy budget is calibrated (in case of static setting). However, Liu et al. [56]  have identified that the presence of correlated data within a dataset can be exploited by adversaries to breach DP guarantees. Similarly, Almadhoun et al. [1, 2]  recently showed that this issue also impacts genomic privacy. In particular, they demonstrated that inference attacks might become possible when adversaries leverage dependencies (e. g. , statistically linked genomic variations or kinship) among the genomes in a study. In contrast, SG impedes inference attacks by preventing the release of statistics over SNPs that have a low frequency, are highly associated with another SNP (linkage disequilibrium), or whose release would lead to a high identification power through a LR-test.
In this work, we extend SG because it does not manage a privacy budget, which would eventually be exhausted and prevent releases. However, since we consider a dynamic setting, where data is released (queried) multiple times, and the presence of overlapping studies, which might impact the privacy guarantees of both SG and DP, we identify new privacy conditions that need to be enforced. In particular, managing the privacy budget of DP mechanisms for protecting continuous observation  [13, 30]  and growing databases  [23]  are at an early development stage, and we are unaware of a practical implementation of these concepts. Equivalently, SG does not directly support dynamic releases and overlapping datasets  [64]. I-GWAS builds on SG  [70]  and Zhou et al's. work  [88] , and presents new privacy conditions for the continuous release of overlapping GWASes, whose results are continuously updated as additional genomes become available or are removed.
Therefore, I-GWAS determines the minimal number of genomes each GWAS should use for any type of statistics to prevent recovery attacks and performs additional checks to detect vulnerable data to avoid membership attacks. All requests are evaluated in rounds, and I-GWAS certifies that a safe batch of requests (that meets the interdependent GWASes criteria) can be selected. If a safe batch of genomes cannot be found in a round, I-GWAS aborts the round. Eventually, a safe release will take place as new genome requests come over time. Each biocenter must ensure the genome privacy of individuals it has sequenced. For that reason, they encrypt all sequenced genomes and offload their processing into a mutually TEE-enabled server. I-GWAS performs its privacy-protection analysis inside a TEE enclave.
Moreover, we assume that no information leaves the TEE before I-GWAS explicitly releases it and that cryptographic primitives are secure. In addition, TEE's remote attestation  [22, 60]  ensures to biocenters that the piece of software deployed in the TEE is the expected one. The encryption is constructed in such a way that only the TEE can decrypt the data from the biocenter (e. g. , by placing the private key required for encrypting this biocenter's genomes into the sealed memory of the TEE  [22] ). Sealed memory only grants access to attested enclaves to the private data they have sealed. Furthermore, data sealing is also used to increase the scalability of TEE-based solutions by allowing the retrieval of data at later stages for future steps of an algorithm running inside an enclave  [9, 22, 64].
Next, I-GWAS performs its extended LR-test analysis to evaluate the feasibility of membership attacks on the selected genome set merged with potentially overlapping releases. Only SNPs that would not allow such an attack will be considered in the study. (4) Finally, the TEE uses the data (genomes and SNPs) selected in step (3) to compute and release the actual GWAS statistics. The TEE periodically executes this workflow. In summary, this paper contributes precise conditions under which interdependent GWAS preserve the privacy of the individuals who share their genetic data.
Pairwise allele frequencies This analysis produces maps (also presented in Appendix B) that associate tuples (i, j, p, q) to pairwise allele counts in the respective datasets. Here, i, j denote the SNPs and p, q the allele types  [88]. The solution space complexities for |S add | and |S union | are the same as for singlewise allele frequencies. In contrast, |S sub | = 2 L 1 •N 1 +L 2 •N 2 -(L ov l •N ov l ) , because m sub depends on the values in the intersection (i. e. , (i, j) ∈ (N ovl , L ovl )), which are canceled out. Like in the singlewise frequencies case, |D add | and |D sub | are obtained by computing the product of possible values of the SNPs over the released frequencies. Let |D N 1 | = (N 1 +1) ( L 1 -L ov l 2 )+(L1-Lovl )•L ov l +(L 1 -L ov l ) and |D N 2 | = (N 2 + 1) ( L 2 -L ov l 2 )+(L2-Lovl )•L ov l +(L 2 -L ov l ).
Sequential releases of GWASes Let us assume that a first GWAS 1 has released statistics over L 1 SNPs with N 1 genomes, and that a second, GWAS 2 , aims at releasing statistics over L 2 SNPs. We note LtoN sinдl e (L) the minimum number of genomes to use to release the results of a single GWAS on L SNPs (cf. Equation  1 ). Our approach consists in increasing the number N 2 of genomes that GWAS 2 uses so that the safety bounds for interdependent studies are verified. Particularly, we discovered that releasing GWAS 2 with LtoN sinдl e (L 2 ) genomes is not safe with overlapping datasets. We evaluate in Fig. 3  the smallest safe value for N 2 when L 1 = 1,000 and N 1 =LtoN sinдl e (L 1 ), and when L ovl ∈ [0, L 1 ] and N ovl ∈ { N 1 2 , N 1 }. In this figure, the default line represents LtoN sinдl e (L 2 ), i.