dataesr/scientific-paragraphs-categorization huggingface dataset:
The simplest form of a QEP is a tree with Data Contributors at the leaves (one per edgelet contributing to the query with its data) sending the requested data to a Snapshot Builder operator, the role of which is to collect a representative snapshot of cardinality C (e. g. , select tuples from C=2000 patients over 65 years old, as considered on Fig. 1 ). The Snapshot Builder then sends the representative snapshot to a Computer operator which, in turn, computes the final result and delivers it to the Querier. The Data Processors (here, the Snapshot Builder and the Computer) can be decomposed into sub-operators assigned to different edgelets for privacy or performance concerns. This can help minimizing the amount of data exposed at each edgelet by horizontally partitioning the dataset. This can also preclude the concomitant exposure, in the same edgelet, of data items that become sensitive when combined (e.
Resiliency strategies. During the execution of a QEP, each edgelet acting as a Data Processor is a potential Single Point of Failure, with presumed failures encompassing temporary disconnection and excessive message delay caused by the OppNet. Fault tolerance is essential to make the approach applicable. We then explored in  [2]  two resiliency strategies, namely Backup and Overcollection, which differ on complexity and performance, on the accuracy of the final result and on the way privacy is enforced, making each strategy best adapted to different contexts. In this demonstration, we focus on the Overcollection strategy which is best adapted to the DomYcile use-case where simplicity and performance take precedence and where approximate results are acceptable (e. g. , statistics, machine learning processes).
Overcollection sketched. Broadly speaking, the Overcollection principle works as follows. Let us consider a Computer operator that executes a distributive operator (e. g. , a Sum) over a sample dataset. Instead of executing this Computer on a single edgelet, we distribute its execution over n+m edgelets where each one processes a partition of the original dataset, with n the minimum number of partitions to be collected and m the overcollection parameter. The validity of the query is trivially preserved as long as less than m partitions are lost. This strategy requires that the commutativity rules between operators of a same QEP allow all distributive operators to be pushed down to the sub-QEPs and all non-distributive operators to be pushed up to the root. In practice, many data-intensive queries of interest satisfy this requirement.
III. DEMONSTRATION Demonstration hardware platform. It includes a set of personal secure boxes, as deployed and delivered to each patient in the DomYcile project (see Fig. 2 ). Medical and social workers interact with the patient folder hosted in the box through a smartphone App. Secure boxes incorporate two STM32F417 microcontrollers (MCUs). The first MCU is dedicated to communications with the outside while the second manages the recorded data. This second MCU is connected to a μ-SD card hosting the patient's raw data, and to a tamper-proof TPM (Trusted Platform Module). This TPM secures the cryptographic keys and guarantees, during the secure boot, that the embedded code has not been tampered with. Hence, these secure boxes act as TEE enabled devices and play the role of edgelets. (2) an Edgelet manager that orchestrates executions and communications between edgelets; (3) an OppNet modeler that models the massive distribution of edgelets over the city of Versailles (chief town of the Yvelines district).
This image offers a direct measurement of the intervortex spacing d as the distance between two consecutive lines. The distance between vertical lines is reduced when angular velocity increases as seen in Figure  2B. This visually demonstrates the expected consequence of Feynman's rule. The movies that we acquired from these patterns demonstrate a long-duration stability (see Movie S1). We have measured that the particles have an average upward velocity of vsettling = 20 ± 20 µm/s that would correspond to particles with a diameter lower than 1 µm if one assumes the viscous Stokes drag with the normal fluid as unique restoring force. As we acquired more than 500 GB of images (~50000 frames), to analyze systematically this dataset, we have developed a technique using a complex Gabor wavelet detailed in the supplementary material.
Gabor wavelet analysis To get reliable and trustworthy results, we have conducted many experimental runs. Precisely, we have acquired 500 GB of images which corresponds to about 50000 frames. To analyze this amount of data, we developed an algorithm based on the 2D Gabor wavelet, using Python3 and the OpenCV library. The algorithm was implemented in an embarrassingly parallel manner. The Gabor wavelet is a 2D Gaussian modulated by a complex exponential. We used a simplified form represented on Figure  S2  and defined as followed: 𝐺(𝑥, 𝑦) = 𝑒 "(& & '( & ) *+ & 𝑒 , *-&. This expression unveils 3 length scales ordered increasingly: σ defining the Gaussian width, λ defining the probed wavelength, and implicitly L the size of kernel. We have chosen the following relations to link them: P 𝜎 = 𝜆 2 ⁄ 𝐿 = 1 + 2 × 𝑐𝑒𝑖𝑙(4𝜎) This definition allows us to consider that G(x,y) has only one parameter λ.
The mass of HD 219077 b is close to the deuterium-burning limit and its nature is uncertain because of the imprecise mass of the host star. Using additional RV measurements, we refine the orbital parameters of HIP 70849 b and find a mass in the planetary range. By combining RV data with absolute and relative astrometry, we significantly improve the characterization of HD 211847 B and properly determine its mass, which appears to be in the low-mass star range. This work illustrates how Gaia and HIPPARCOS allow for the orbital properties and masses of long-period RV companions to be further constrained.
2016 ). In the 2000s, the combination of RV data and absolute astrometry, thanks to the fine guidance sensor on board the Hubble Space Telescope, also allowed for the inclination of a few stellar systems to be constrained and a few candidate planets to be confirmed  (Benedict et al. 2002 (Benedict et al. , 2006)) , while others were finally identified as brown dwarfs or low-mass stars  (Bean et al. 2007; Benedict et al. 2010). Today, the position and proper motion measurements obtained with the telescopes HIPPARCOS  (Perryman et al. 1997; van Leeuwen 2007)  and Gaia (Gaia Collaboration 2020) allow us to combine the RV data and more precise absolute astrometry for a large number of systems. Since the publication of the first Gaia data release (DR1), a few studies have proven the efficiency of combining RV data with absolute and/or relative astrometry to improve the constraints on the orbital parameters and mass of a companion  (Grandjean et al.
Star Date OBS Filter DIT(s)×Nframe (a) ∆ PA (°) (a) Seeing ( ′′ ) (b) Airmass (b) τ 0 (ms) (a) Notes. ( a) DIT is the detector integration time per frame. ∆ PA is the amplitude of the parallactic rotation. τ 0 corresponds to the coherence time. ( b) Values extracted from the updated DIMM information, averaged over the sequence. In this paper, we focus on seven long-period single companions detected by the RV method, and combine the available RV data with HIPPARCOS and Gaia early data release 3 (EDR3) absolute astrometry and, when available, relative astrometry, to improve the orbital parameters and determine the true mass of these companions. In Sect. 2, we describe our target selection method and present the RV, HCI, and astrometric data used in our study. Section 3 presents the method used to perform the orbital fitting and, there, we provide the new orbital parameters and mass found for each target.
Target selection We first selected the planetary systems in the exoplanet. eu catalog  (Schneider et al. 2011)  for which a single companion has been reported with a semi-major axis greater than 5 au using the RV method. Twenty-five companions were found with such criteria. For nine of them  (HD 13724 B, HD 25015 b, HD 181234 b, and HD 219828 B Feng et al. 2022; HD 92987 B Venner et al. 2021; HIP 36985 B Biller et al. 2022; and HD 98649 b, HD 196067 b, and HD 221420 B Li et al. 2021) , the orbital parameters and the true mass have already been properly determined in previous studies. We, therefore, do not consider them in the present study. We first discarded the HD 95872 system because no HIPPARCOS data were available. We then discarded four systems for which the available RV time series did not cover both extrema of the RV variations and the orbital period could not be properly determined  (HD 26161, HD 120066, HD 150706, and HD 213472).
RV data The RV data used in this study were obtained with different spectrographs between 1997 and 2021. The HARPS  (Mayor et al. 2003)  data were taken from the ESO archives; the ELODIE  (Baranne et al. 1996)  and SOPHIE  (Perruchot et al. 2008)  data were retrieved from the OHP archives; and the CORALIE  (Queloz et al. 1999) , the HIRES  (Vogt et al. 1994 ), the UVES  (Dekker et al. 2000) , the AAT  (Diego et al. 1990 ), the CES  (Enard 1982)  long camera (LC), and the CES very long camera (VLC) data were taken from the literature. As instrument upgrades can lead to new RV offsets, the same instrument before and after a major upgrade is considered as two different instruments. Consequently, HARPS data obtained before and after the optical fiber upgrade in 2015  (Lo Curto et al. 2015)  are referred to as H03 and H15, respectively.
Direct imaging data In three cases, HCI data are available in the SPHERE archive and can provide relative astrometry. The three targets were observed in angular (and spectral) differential imaging (A(S)DI,  Marois et al. 2006 ) using the telescope in pupil tracking mode. The standard observing mode of SPHERE was used, with IRDIS  (Dohlen et al. 2008)  dual band images at H2 and H3 (K1 and K2, respectively) and IFS  (Claudi et al. 2008 ) data covering the YJ (YJH, respectively) bands. The observing log is given in Table  1. Whenever possible, the robust PACO A(S)DI algorithm  (Flasseur et al. 2020a,b)  was used. The processing step takes advantage of the developments made to the COBREX data center pipeline (the prereduction improvement as well as the improvement of the detection capability of PACO).
If a dataset did not sufficiently cover the field-of-view rotation to apply ADI-based algorithms, the SPECAL  (Galicher et al. 2018)  No-ADI algorithm was used. In those three systems, only one companion was detected (HD 211847 B). The detected companion is characterized in Table  2. No detection above 5σ was found around HD 219077. Six sources were detected around HIP 70849 but, given their position in a color-magnitude diagram and their separations, they are likely background sources.
Orbit fitting Orbits were fitted using a custom Markov chain Monte Carlo (MCMC) tool, based on the emcee 3. 0 python package  (Foreman-Mackey et al. 2013). It uses a mixture of move functions (such as the differential evolution move function) to alleviate potential multimodality issues. The HIPPARCOS/Gaia data processing uses the HTOF package  (Brandt et al. 2021c ) and borrows large sections of the orvara code  (Brandt et al. 2021d)  for the likelihood computation. The HTOF package  (Brandt et al. 2021c ) was used to fit the intermediate astrometric data (IAD) from HIPPARCOS, based on the 1997  (Esa 1997 ) and 2007 (van Leeuwen 2007)  reductions and from Gaia, thanks to the Gaia Observation Forecast Tool (GOST) which allowed us to obtain the estimated Gaia observations and scan angles for each target, in order to reproduce proper motion and position of each observation.
Using the HIPPARCOS and Gaia positions and the temporal baseline, the algorithm derived a tangential proper motion value that allowed us to better constrain the orbital fit when combined with RV data. We considered ten free parameters for each system: the semimajor axis (a), the eccentricity, the orbital inclination (i), the host star mass, the companion mass, the longitude of ascending node (Ω), the argument of periastron (ω), the phase, a stellar jitter, and the distance of the system. In addition, to combine data from different instruments, we added an instrumental offset for each instrument as a free parameter of the model (see above). Finally, we considered uniform priors for all fitting parameters, except for the host star mass and the distance of the system for which we considered Gaussian priors.
Epsilon Indi A Epsilon Indi is a triple system with a 0. 76 ± 0. 04 M ⊙ , K2V star (Epsilon Indi A) and a binary composed of a 75. 0 ± 0. 8 M Jup , T1. 5 brown dwarf (Epsilon Indi B) and a 70. 1 ± 0. 7 M Jup , T6 brown dwarf (Epsilon Indi C) separated by about 2. 6 au  (Dieterich et al. 2018). The projected separation between the binary brown dwarfs and the star is about 1460 au. Combining RV data and absolute astrometry based on HIPPARCOS and the Gaia data release 2 (DR2) measurements,  Feng et al. (2019)  reported a giant planet with a semi-major axis of 11. 55 +0. 98  -0. 86 au, a mass of 3. 25 +0. 39  -0. 65 M Jup , an inclination of 64. 25 +13. 80 -6. 09 °, and an eccentricity of 0. 26 +0. 07 -0. 03. Yet, the Gaia EDR3 proper motion and position measurements are significantly more precise compared to the Gaia DR2 measurement and they significantly improve the characterization of Epsilon Indi Ab.
We used 4278 RV measurements obtained with the HARPS spectrograph between 2003 and 2016 1 , 163 RV measurements obtained with the UVES spectrograph between 1996 and 2017, 72 RV measurements obtained with the LC spectrograph between 1992 and 1997, and 53 RV measurements obtained with the VLC spectrograph between 2000 and 2006. We also combined these RV data with absolute astrometry based on HIPPARCOS and the Gaia EDR3 measurements (Fig. 1 ). We found significantly different orbital parameters with a semimajor axis of 8. 8 +0. 2 -0. 1 au, a mass of 3. 0 ± 0. 1 M Jup , an inclination of 91 +4 -5 °, and an eccentricity of 0. 48 ± 0. 01. The posteriors obtained for each free parameter are reported in Table  3. It is important to note that if we consider only the 539 HARPS RV data with a signal-to-noise ratio greater than 110 and thus remove the high cadence observations made in August 2011, we find similar solutions.
HD 115954 HD 115954 is a 1. 18 ± 0. 06 M ⊙ , G0V star  (Demangeon et al. 2021). Based on four RV measurements obtained with the 1 The 3636 RV data obtained between Julian days 2455790 and 2455805 were obtained to study high-frequency oscillations of the star. These data were measured with high cadence, which led to a significantly lower signal-to-noise ratio compared to the other data. A65, page 3 of 16 A&A 670, A65 (2023)  Notes. The results were obtained by combining RV, absolute astrometry, and, when available, relative astrometry. We provide 68% confidence intervals for each parameter and the median is only given when the probability distribution has a profile close to a Gaussian distribution. 2021 ) reported a giant planet with a semi-major axis of 5. 00 +1. 3  -0. 36 au, a minimum mass of 8.
HD 211847 HD 211847 is a 0. 94 ± 0. 04 M ⊙ , G5V star  (Sahlmann et al. 2011 ). Sahlmann et al. (2011)  reported a brown dwarf candidate orbiting around HD 211847 based on 31 RV measurements obtained with the CORALIE spectrograph between 2002 and 2009. However, only one minimum of the HD 211847 B RV curve was covered by the dataset. Thus, the orbital parameters and minimum mass reported in this study are poorly constrained. Using the Levenberg-Marquardt method, they found ranges corresponding to a 3σ confidence interval for the semi-major axis, the eccentricity, and the minimum mass of 4. 6-42 au, 0. 48-0. 95, and 16. 3-24. 3 M Jup , respectively. Moutou et al. (2017)    significantly different from those reported by  Feng et al. (2022). We found a semi-major axis of 6. 78 ± 0. 08 au and an eccentricity of 0.
HD 222155 HD 222155 is a 1. 13 ± 0. 11 M ⊙ , G2V star  (Boisse et al. 2012). Based on 44 RV measurements obtained with the ELODIE spectrograph between 1997 and 2005 and 67 RV measurements obtained with the SOPHIE spectrograph between 2007 and 2011,  Boisse et al. (2012)  reported a giant planet with a semi-major axis of 5. 1 +0. 6  -0. 7 au, a minimum mass of 1. 90 +0. 67 -0. 53 M Jup , and an eccentricity of 0. 38 +0. 28  -0. 32. We considered 31 additional pieces of SOPHIE RV data obtained between 2011 and 2016. We combined the RV data with the absolute astrometry (Fig. 6 ). We found orbital parameters within the error bars associated with the values found by  Boisse et al. (2012)  with a = 4. 7 ± 0. 1 au and e = 0. 34 ± 0. 09. As the RV baseline is now much larger than the orbital period, A65, page 6 of 16 the orbital parameters are better constrained.
HIP 70849 HIP 70848 is a 0. 63 ± 0. 03 M ⊙ , K7V star  (Ségransan et al. 2011). Ségransan et al. (2011)  reported the first detection of HIP 70849 b based on 18 RV measurements obtained with the HARPS spectrograph between 2006 and 2010. However, only one minimum of the HIP 70849 b RV curve was covered by the dataset. The observations carried out by  Ségransan et al. (2011)  led to poorly constrained orbital parameters and minimum mass. Using a genetic algorithm followed by MCMC simulations, they reported a semi-major axis between 4. 5 and 36 au, a minimum mass between 3 and 15 M Jup , and an eccentricity between 0. 47 and 0. 96 with ranges corresponding to a 3σ confidence interval. We considered 39 additional pieces of HARPS RV data obtained between 2011 and 2021. We combined the RV data with the absolute astrometry (Fig.
7 ). With these additional observations, the dataset then covered two minimum and one maximum of the RV curve of HIP 70849 b and this allowed us to properly constrain the properties of the companion. We found a semi-major axis of 3. 99 +0. 06 -0. 07 au and an eccentricity of 0. 65 +0. 02 -0. 01. Using, in addition, the absolute astrometry, we found an orbital inclination of 96 ± 16°and a true mass of 4. 5 +0. 4 -0. 3 M Jup. The posteriors obtained for each free parameter are reported in Table  3.
Summary and concluding remarks Combining RV measurements from various spectrographs with absolute astrometry based on HIPPARCOS and Gaia EDR3 data and, when available, relative astrometry, we determined the orbital parameters and, in particular, the orbital inclination and the true mass of seven long-period single companions detected by the RV method. Figure  8  summarizes the true mass and the semi-major axis of the companions and compares them with the previous estimations. Clearly, Gaia EDR3 data allow for a better determination of these companions' orbital parameters and mass. All of these companions have true masses of 2 M Jup or more and A65, page 7 of 16 A&A 670, A65 (2023) Fig. 8. Update of the orbital parameters and masses of the seven analyzed systems thanks to the combination of absolute astrometric and RV data and, when available, absolute astrometry data.
obtained one HCI detection with SPHERE of HD 211847 B for a projected separation of 11. 3 au. Using the BT-Settl models (Allard 2014), they fit the HD 211847 B spectrum and found a low stellar mass of 155 ± 9 M Jup assuming an age of 3 Gyr for the host star. Based on the result of Sahlmann et al. (2011), Moutou et al. (2017) estimated the inclination of the companion orbit to be around seven°. Recently, combining the CORALIE RV measurement and the absolute astrometry, Feng et al. (2022) reported HD 211847 B as a brown dwarf with a semi-major axis of 4. 514 +0. 458 -0. 287 au, a mass of 55. 32 +1. 335 -18. 48 M Jup , an inclination of 163. 649 +36. 239 -5. 017 °, and an eccentricity of 0. 419 +0. 035 -0. 064. We combined the RV dataset used by Sahlmann et al. (2011), the relative astrometry observation obtained with SPHERE in June 2015, and the absolute astrometry (Fig.
Acknowledgements. This study was funded by a grant from  PSL/OCAV. This project has also received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (COBREX ; grant agreement n°885593). This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. This publications makes use of the Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis.
DACE is a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , federating the Swiss expertise in Exoplanet research. The DACE platform is available at https://dace. unige. ch. This research has made use of the SIMBAD database and VizieR catalogue access tool, operated at  CDS, Strasbourg, France. Based on data retrieved from the  SOPHIE archive at Observatoire de Haute-Provence (OHP) , available at atlas. obs-hp. fr/sophie. Based on spectral data retrieved from the  ELODIE archive at Observatoire de Haute-Provence (OHP). Based on observations collected at the  European Southern Observatory  under  ESO  programme(s)  183. C-0972 (A),  108. 22KV. 001 , 108. 22KV. 002,072. C-0488 (E),  108. 222V. 001 , 090. C-0421 (A),  085. C-0019 (A),  087. C-0831 (A),  095. C-0551 (A),  0100.
SPECT imaging system This study was performed with the VERITON-CT (Spectrum Dynamics, Caesarea, Israel) hybrid CZT-camera installed at the nuclear medicine department of the Léon Bérard Center (Lyon, France). The system is composed of twelve CZT detector columns regularly spaced around 360 degrees, that swivel to acquire data from the entire field of view of 32 cm in axial direction, as detailed in  [28]. Each detector module consists of 128 x 16 pixel solid state detectors with non-removable tungsten parallel hole collimators. This system detects photons in the energy range 40-200 keV, which limits the use of certain isotopes and/or photopeaks (e. g. 208 keV peak of 177 Lu).
Ph1 Ph2 Ph3 swept through 60 angular projections of 3. 75 seconds each in step-and-shoot mode, giving a total data acquisition duration of 900 seconds. The energy windows used for photopeak and scatter are described in Table  2. Acquisitions were performed for all three phantoms, at 8 timepoints, from 4 to 339 hours after injection, as shown Table  3. Low dose CT acquisitions were performed for attenuation correction. A voltage of 120 kVp and a current/time product of 17 mAs were used to generate the X-ray beam. The CT images had slice thicknesses of 2. 5 mm in the cranio-caudal direction and less than 1 mm in the other two directions.
Conclusion We evaluated the quantitative performance of VERITON for 177 Lu treatments and proposed a set of recommended reconstruction parameters for quantification purposes called RecQuant. We advocate the use of two sets of reconstruction parameters: one for quantification and one for visualisation with the same acquisition parameters. For quantification, CT-based attenuation correction, scatter correction, 12 iterations, 8 subsets and no filter provided the best compromise between ARC and RMS values. For large volumes such as kidneys, ARCs were in the order of 0. 91. In this work, the VERITON 200 series system with SW version 2. 3. 0. 1234 was used. The reconstruction software is still evolving and additional improvements are expected in future versions. While the system is limited to 113 keV detection for 177 Lu, the ARC obtained with RecQuant demonstrates it can be used for 177 Lu dosimetry purpose.