dataesr/scientific-paragraphs-categorization huggingface dataset:
Material and methods The HARMONIC project is organised around four scientific work packages (WP) to build the structure and instruments for the medical and scientific communities to be able to evaluate the potentially detrimental effects of contemporary medical exposure to IR in children, with the potential for advanced patient-specific dose reconstructions and biological investigations (see Fig. 1 ). An initial phase of the project was devoted to setting up the legal and ethical framework for the implementation of activities in 8 countries, including both retrospective and prospective inclusion and setting up a biobank. Information letters and informed consent forms were designed in each specific language for parents/guardians of paediatric patients and for assent to consent to participate.
HARMONIC (WP2 ) is establishing the first European registry of paediatric cancer patients treated with modern radiotherapy techniques, in the context of rapid technological developments. It is supported by several medical organizations: the EPTN 2  , PTCOG 3  , PROS 4  and SIOP  5. Liaising with these organisations facilitates direct communication with health care providers with the objective to engage with them in further development of the project and analyses of the data to provide insights into late health effects, as well as associated patient/parent-reported outcomes (HRQoL, fatigue, education). The registry includes patients treated with external beam radiotherapy (EBRT) from 2000 onwards (providing that treatment data are available in DICOM 6  format) at age <22 years. Data on exposures, outcomes and potential confounding factors are collected retrospectively based on medical records, or prospectively when direct contact with the study participants is required for additional exams and questionnaires.
Passive, long-term follow-up is performed by linkage with external, national/regional morbidity and mortality registries as well as health care databases (in countries where it is feasible). To complement follow-up data collected at the treatment centre, passive follow-up is essential to allow for a (very) long period of follow-up (i. e. decades) which is necessary to investigate late outcomes such as SPC, cardio and neurovascular diseases. It also allows for minimizing biased assessment of outcomes, i. e. to compensate for potential differential follow-up strategies and methods at the treatment centres depending on health status, treatment-related factors and outcomes (and also possibly on socioeconomic and other potential patient-specific factors). Enrolment which successfully started in 2021, aims at including about 2500 patients to estimate the impact of radiation factors (i.
Briefly, the appropriate PDFs, representing the relative likelihood of use of technical parameters will be estimated from data obtained from similar patients, procedure type, X-ray system and/or time period, using the available set of collected data in combination with expert judgement, historical data collected in the archives of the cardiology departments, and analysis of the literature. From these, the imputation of missing data will be performed to produce dose distributions characterising the uncertainty in the individual estimates. For each calculation of a cohort dose distribution, values of parameters will be selected from the appropriate PDFs, while maintaining proper correlations between parameters (for instance, the same X-ray system model will be used for reconstructing the dose for all patients treated in the same hospital during the same period).
Preliminary results The HARMONIC project was launched in June 2019 with initial activities devoted to setting up its legal, administrative, and operational framework with particular attention paid to data protection and ethical issues associated with the vulnerable population of interest (paediatric patients). In the first years of the project, the main activities focused on: • setting up the structure of the databases for inclusion of patients in both cohorts and starting data collection and patient inclusion • developing simulation models for dose reconstruction and implementing measurement sessions for validation of these models • pilot testing methodology and framework for analysis of biomarkers in both blood and saliva.
Radiotherapy The WP2 partners identified the parameters to be collected at standardised points in time (baseline and followup). The study protocol was established to capture the required procedures. The consortium developed, tested, and validated the database, electronic Case Report Forms (eCRFs) for each task and the platforms used to collect, store and process clinical and physical data. Standard Operating Procedures (SOPs) have been developed and validated by the investigators to facilitate patient inclusion and standardise data collection and processing. For each task, partners agreed on a list of critical anatomical structures of interest. A delineation atlas of neurovascular structures was validated and has already been published  [43]. A contouring guideline of the hypothalamus, pituitary, and thyroid and each of their substructures was defined.
Cardiology WP3 brings together the existing French and UK cohorts of paediatric patients with congenital and acquired heart defects treated with CFP and enlarges the binational cohort to include patients from Belgium, Germany, Italy, Norway, and Spain. In France and the UK, cohorts were built on a common collaborative protocol, specifically designed to provide a further understanding of the potential cancer risk associated with paediatric CFP  [22, 23, 44, 45]. The protocol was adapted to ensure the consistency of the data collected in all participating countries  [24]. The structure of the common database was agreed upon and data collection started in all countries but Spain. About half of the target population has been already included (Tab. 2).
Dosimetry for cardiology The estimation of absorbed dose to individual organs is an essential component of HARMONIC. Given the large inter-procedure variability in radiation exposures during CFP  [26] , patient-and procedure-specific dose estimates are essential. P KA also known as Dose Area Product (DAP) has been recorded for the majority of procedures in the hospital Radiology Information System (RIS) for the last 20 years or more; while the main technical parameters necessary to accurately estimate organ doses for each procedure (beam angles, tube potential, added filtration, field size) are usually available in the Radiation Dose Structured Reports (RDSR) for recent years only. A complete solution for dose reconstruction using the most recent data source available was established. It includes a data collection tool, and a fast dose reconstruction software tool.
The reconstruction tool relies on hundreds of thousands of pre-calculated dose conversion coefficients relating the P KA to organ doses and provides rapid dose estimates for 16 organs of interest for epidemiology studies and radiation protection. The coefficients were produced using MC simulations implementing several realistic anthropomorphic phantoms and extended using advanced extrapolation methods. Seven series of phantom measurements were performed for validating the simulation framework using infant (newborn), 1-and 5-year child phantoms in a paediatric cardiologic clinic (see Fig. 2 ). DICOMInspector, the software dedicated to extracting -DICOM meta-data from the Picture Archiving and Communication System (PACS), which stores the RDSRs and other less detailed technical procedure reports, has been developed and is being tested in some participating hospitals.
Currently, the use is planned in Belgium, France, Italy, Spain, and Norway. A dataset of about 1000 patients with 17 different congenital heart disease diagnoses (ICD-10) is being analysed in Norway with respect to other radiological examinations made between 2000 and 2020. While data collection is underway, the structure of the dosimetry database is developed as complementary to the epidemiological database (see Fig. 3 ). A Mixed Reality Prototype, based on 3DSlicer and Unity on the Software side and Microsoft Hololense and ARSpectra dedicated medical devices on the hardware side is being developed to contribute to the optimization of CFP through improved visualization.
Such a registry is an essential tool to gain knowledge as childhood malignancies are rare diseases, and the pooling of international data is crucial in order to establish cohort sizes large enough to attain the required statistical power for sound analyses. The potential health impact of IR treatments on endocrine, cardiovascular, and neurovascular systems as well as on HRQoL is investigated in an integrated, holistic approach. The registry data will allow quantification of radiation-related health risks in the short term but also implement tools for conducting future analyses for late effects evaluation. In addition, prospective data collection allows the possibility of collecting biological samples. As findings will be correlated to dose-volume effects of radiation, HARMONIC will provide healthcare providers and policymakers with an improved understanding of a large range of potential adverse effects of use to tailor and improve patient care.
HARMONIC partners seek to develop harmonised methods for data collection which will not only contribute to long-term sustainable patient follow-up but also optimise diagnostic and treatment procedures and benefit European patients. It contributes to improving organ dose reconstruction from interventional cardiology procedures and estimation of patient-specific doses to non-targeted organs in radiotherapy. To do so, HARMONIC integrates the development of novel dosimetric tools providing the medical community with means to investigate the overall radiation burden in young patients. All of these actions are expected to lead to an improved patient outcome and raise awareness among healthcare professionals of out-offield and imaging doses and the need for dose optimisation. The study of predictive biomarkers aims at finding patients with an elevated risk of severe health effects due to genetic background.
These will help define more effective radioprotection as well as more effective treatment strategies to reduce the risk of radiation-induced late toxicities and cancer. HARMONIC registries and databases are meant to be maintained for long-term follow-up of these two paediatric populations and will expand (pending availability of funding) to include more patients from European countries with the potential to coordinate actions with registries from outside Europe. HARMONIC contributes to providing tools for an improved understanding of sequela to be considered and integrated in to patients' care and follow-up. Translation of the research results into improved practical measures for the effective protection of patients will lead to a more robust system of radiation protection in the medical sector.
Introduction As more and more data is collected on individuals and data science techniques become more powerful, threats to privacy have multiplied and serious concerns have emerged  [31, 6, 21, 13, 23, 27, 32, 37, 41, 38]. Against this background, differential privacy (DP)  [19]  has become the gold standard in privacy protection. By introducing randomness calibrated to the sensitivity of a query  [18] , it enables the inference of global statistics on a dataset while bounding each sample's influence and ensuring that the presence or absence of an individual in the dataset cannot be deduced from the result. In the last decade, research results have brought nice building blocks and composition theorems  [18, 25, 15, 14, 1]. They paved the way for many applications in data analysis, from basic statistics to advanced artificial intelligence algorithms.
Given a real random variable X of probability distribution P X , the cumulative distribution function (CDF) of X (or P X ), noted F X (or F P X ) is classically defined as F X (t) = P X (X ≤ t), ∀t ∈ R. Its pseudo-inverse, the quantile function, F -1 X (or F -1 P X ) is defined as F -1 X (p) = inf {t ∈ R|F X (t) ≥ p} , ∀p ∈ [0, 1] , with the convention inf ∅ = +∞. The quantity F -1 X (p) is the quantile of order p of the distribution of X. Furthermore, when given a dataset (a collection of real numbers), the empirical quantile of order p of this dataset is the quantile of order p of its empirical distribution. When privacy is not an issue, it is well known that the empirical quantiles of a dataset of i. i. d. random variables are good estimators of the quantiles of the underlying distribution  [40].
There has been a lot of recent research on how to privately estimate multiple empirical quantiles from a dataset with a privacy overhead as small as possible. This article builds on this framework by considering private estimates of the empirical quantiles as estimators of the quantiles of the true distribution. Among several ways to privately estimate empirical quantiles, a naive one is to add Laplace noise to non-private quantile estimates  [18]. It is straightforward and easy to compute, but the amount of added noise is based on a pessimistic scenario that cannot materialize simultaneously for all quantiles. To reduce the variance of the estimates, a variant that uses so-called smoothed sensitivity instead of the worst case scenario was introduced  [33]. It has the drawback, however, of using approximate differential privacy  [16]  instead of pure differential privacy, which allows some catastrophic failures with small probability.
The current state of the art for single quantile estimation uses a fine-tuned version of the standard exponential mechanism for differential privacy  [30]  that is called ExponentialQuantile  [35]. It is cheap to compute and the recent theory of Inverse Sensitivity  [5]  shows that a conceptually simple smoothing operation allows levels of privacy that are quadratically better than those guaranteed for the smoothed sensitivity approaches while sticking to pure differential privacy, achieving near-instance optimality (i. e. with a risk comparable to the local minimax risk when the set of hypotheses is the set of neighboring datasets). As a result, it has been adopted by the main DP-ready software libraries  [3, 24]  used in production. All the algorithms previously mentioned are designed to estimate a single quantile from a dataset.
Composition theorems make them usable for multiquantile estimation by evaluating each quantile independently, but with an increased privacy noise. Recent work by  [22]  has presented a new algorithm, JointExp, that was the first to exploit the non-decreasing constraint of quantiles at the core of its sampling procedure and became the empirical state of the art for a small period. Even more recent work  [26]  cleverly exploits the structure of quantiles by computing them recursively / hierarchically on disjoint subsets of the dataset, which reduces the privacy overhead in composition. It is the current empirical state of the art for estimating many quantiles.
Contributions and organization of the paper In this paper, we study the use of the Inverse Sensitivity mechanism  [5, 4]  in order to estimate multiple empirical quantiles from a dataset, and their statistical properties as estimators of the quantiles of the underlying distribution. In Section 2 we recall the needed background on differential privacy, Inverse Sensitivity, and JointExp. Section 3 is devoted to characterizing the Inverse Sensitivity mechanism for private multiquantile estimation. In particular, our first contribution is to obtain the precise expression of the utility function of the Inverse Sensitivity mechanism for an arbitrary number of empirical quantiles, which was previously known for a single quantile only. In particular, this expression is quite similar to the utility function of JointExp  [22] , which was proposed as an ad-hoc algorithm based on a heuristic.
Background Considering datasets of the form X = (X 1 ,. , X n ) ∈ X n , where X denotes our feature space and n ≥ 1 is the sample size, the Hamming distance d(X, Y) between two datasets X, Y ∈ X n is defined as the minimal number of changes (i. e. , substitutions of entries) required to transform X into a permutation of Y. Hence, d(X, Y) = 0 i. f. f. there exists a permutation σ on {1,. , n} such that ∀i ∈ {1,. , n} , X i = Y σ(i). We say that X and Y are neighbors (noted X ∼ Y) if d(X, Y) = 1, that is if there exist two permutations σ 1 , σ 2 such that ∀i ∈ {1,. , n -1} , X σ 1 (i) = Y σ 2 (i). Note that d(X, Y) is the minimum length of a path on consecutive neighbors linking X to Y.
Differential Privacy Given a privacy budget > 0, a randomized algorithm A : X n → O is called -differentially private ( -DP, see  [16] ) if for all pairs of datasets X, Y ∈ X n and all measurable sets S ⊆ O, X ∼ Y ⇒ P A(X) ∈ S ≤ e × P A(Y ∈ S). Differential privacy offers strong privacy protections by bounding the efficiency of any test trying to distinguish two neighboring databases. A classical way to design -DP algorithms is the Exponential Mechanism  [30]. For a utility function u : X n × O → R that measures the relevance u(X, o) of the output o for the dataset X, the Exponential Mechanism E (α) u defined by u and with parameter α > 0 outputs a random variable on O with a density proportional to e u(X,o)/α with respect to some reference measure on O. For example, when O is discrete, P E (α) u (X) = o = e u(X,o)/α o ∈O e u(X,o )/α ∀o ∈ O.
The inverse sensitivity mechanism When considering some deterministic function Q : X n → O as the target of privatization, a specific choice of utility function in the exponential mechanism, long known as folklore, was proved to have remarkable optimality properties under certain assumptions  [5]. The inverse sensitivity function u IS (X, o) := -inf d(X, Y) s. t. Y ∈ Q -1 (o) is easily seen to have sensitivity ∆u IS = 1. The resulting -DP mechanism E (2/ ) u IS (X) has then a behavior that is quite intuitive: the likelihood of its output o decreases when Q -1 (o) becomes further apart from X in the Hamming distance, which means that the probability of an output decreases the more points have to be modified in the dataset for it to be a viable deterministic output. representing m quantiles.
JointExp meets Inverse Sensitivity At first glance, there is no connection between the theory of the Inverse Sensitivity and Joint-Exp. The first one is born from the need to build a general mechanism that is endowed with optimality properties  [5, 4]  for a broad class of problems, while the second comes from the idea that good empirical quantiles should separate the data points proportionally. In the case of the estimation of a single quantile (i. e. m = 1), it was observed  [5]  that the two algorithms are similar. Our contribution in this section is to prove that, up to minor differences, this remains true with an arbitrary number of quantiles. For this, we provide the precise expression of the inverse sensitivity function for the multiquantile problem. Deriving the expression of the inverse sensitivity for a dataset X and an output candidate q boils down to answering the question: What is the minimal number of points from X that need to be changed in order to obtain a vector that has q as its empirical quantiles? Theorem 1 solves this question for Lebesgue-almost-any q.
Theorem 1. For any X ∈ X n and q ∈ ([a, b] \ X) m without collision, -u IS (X, q) = 1 2 m+1 i=2 |δ(i, X, q)| + m i=2 1 R + (δ(i, X, q)) + 1 2 |δ closed (1, X, q)| + 1 R + (δ closed (1, X, q)) with δ(i, X, q) = # (X ∩ (q i-1 , q i ]) -( np i -np i-1 ) δ closed (i, X, q) = # (X ∩ [q i-1 , q i ]) -( np i -np i-1 ). We postpone the proof to Appendix C. 1 for brevity. The case when q has collisions or shares some common points with the dataset is more difficult. Luckily, those cases can be neglected when considering the sampling mechanism. Indeed, E (2/ ) u IS (X) has a density that is absolutely continuous w. r. t. Lebesgue measure, the expression of the resulting mechanism can be further simplified (see Corollary 2) by modifying the density on outcomes of null Lebesgue measure. Corollary 2. For any X ∈ X n , E (2/ ) u IS (X) has the same output distribution as E (2/ ) ũIS (X) where ∀X ∈ X n , ∀q ∈ O, -ũ IS (X, q) = 1 2 m+1 i=1 |δ(i, X, q)| + m i=1 1 R + (δ(i, X, q)).
JointExp fails on atomic distributions To the best of our knowledge, no theoretical utility guarantee for JointExp has been derived yet, and the performance of this algorithm has only been demonstrated experimentally. Even if it outperforms by multiple orders of magnitude previous techniques on many real life datasets  [22] , we prove in this section that it can also completely fail on some distributions (see Proposition 3). As illustrated in Section 6, JointExp is indeed observed to be suboptimal on several real world datasets associated to peaked distributions, such as the US Census Bureau "Dividends" and "Earnings" data. In order to understand the origin of this weakness of JointExp, we analyse the density of the distribution of its output. This density is constant on the "blocks" [X i 1 , X i 1 +1 ) ×.
× • • • × [X im , X im+1 ) ∩[a, b] m for each i = (i 1 ,. , i m ) ∈ O where O = {i ∈ {0,. , n} m , i 1 ≤ • • • ≤ i m }. The probability of the output of JointExp being in a given block is proportional to the volume of this block. What can happen in practice is that even though a block is interesting in terms of utility level, its volume can in fact be close to zero if the data points are close. The volume can even be zero in case of equality, hence this block is never selected by the exponential mechanism. This phenomenon occurs particularly often for data drawn from distributions with isolated atoms: asymptotically, the dataset will almost surely contain collisions among the data points as n grows and JointExp will fail on the corresponding quantiles. To formally capture this phenomenon, from now on, X is supposed to be a collection of n i.
e. not on unrealistic corner cases). This behavior is all the more counterintuitive as one would think that on datasets with a lot of collisions, very little noise would be needed to ensure privacy since the points are already indistinguishable. Since m = 1, JointExp coincides with ExponentialQuantile, and when all data points are equal to 0 (i. e. P X = δ 0 ) its output is uniformly distributed in [-1, 1] whatever the sample size n as long as it is even. When considering estimation on real-world distributions, many real-life datasets show accumulation points and can be modeled as continuous distributions with some Diracs at specific points. A famous example is the revenue statistics of the US Census Bureau: many participants in surveys are not qualified to have some category of revenue (too young or not investing in some assets) hence the presence of accumulations at the zero value for these categories.
Introducing the HSJointExp algorithm Since JointExp has a density that is constant on the blocks [X i 1 , X i 1 +1 )×. ×• • •×[X im , X im+1 ) ∩ [a, b] m for i = (i 1 ,. , i m ) ∈ O , it fails when the blocks that have a great utility (i. e. the ones leading to interesting quantile candidates) have a volume that is too small. By adding noise to the data points, we ensure a minimal volume for the blocks, and in particular for the interesting regions, while only shifting the empirical quantiles of the dataset by a small amount. Let w 1 ,. , w n be i. i. d variables, and let X = (X 1 + w 1 ,. , X n + w n ). (5. 1) The Heuristically Smoothed JointExp (HSJointExp) is defined as the algorithm that returns E (2/ ) u JE ( X), the output of the JointExp on the noisy data X. Let us now discuss the choice of the distribution P w of the (w i ) s.