E Epifano

D Prieur

P M Martin

C Guéneau

K Dardenne

J Rothe

T Vitova

O Dieste

T Wiss

R J M Konings

D Manara

Melting behavior of uranium-americium mixed oxides under different atmospheres

Keywords: Uranium americium dioxide, melting, transmutation targets, Calphad

In the context of a comprehensive campaign for the characterisation of minor actinide transmutation fuels, the melting behaviour of mixed uranium-americium dioxides has been experimentally studied for the first time by laser heating, for Am concentrations up to 70 mol % under different types of atmosphere. Extensive post-melting material characterisation were then performed by X-ray absorption spectroscopy and electron microscopy. The melting temperatures observed for the various compositions follow a markedly different trend depending on the experimental atmosphere.

Uranium-rich samples melt at temperatures significantly lower (around 2700 K) when they are laserheated in a strongly oxidising atmosphere (compressed air at 0.3 MPa), compared to the melting points (beyond 3000 K) registered for the same compositions in an inert environment (pressurised Ar). This behaviour has been interpreted on the basis of the strong over-oxidation in air of such samples, leading to lower-melting actual compositions. Thus, the melting temperature trend observed in air is characterised, in the purely pseudo-binary dioxide plane, by an apparent maximum melting temperature around 2850 K for 0.3 < x(AmO 2 ) < 0.5. The melting points measured under inert atmosphere uniformly decrease with increasing americium content, displaying an approximately ideal solution behaviour if a melting point around 2400 K is assumed for pure AmO 2 .

In reality, it will be shown that the (U, Am) dioxide system can only be rigorously described in the ternary U-Am-O diagram, due to the aforementioned over-oxidation effect in air and general departures from the oxygen stoichiometry (Oxygen/Metal ratios ≠ 2.0) that have been highlighted by the X-ray Absorption Spectroscopy results. Finally, to better understand the experimental results, thermodynamic computations based on the CALPHAD method will be show.

Introduction

A thorough knowledge of the thermodynamic properties of advanced fuels or Minor Actinide Bearing Blankets (MABB), to which minor actinides (MAs) have been incorporated, is essential in order to put into practice the Partition & Transmutation strategy in fast neutron reactors (FNR).

In a heterogeneous transmutation scenario, (U,Am)O 2 pellets with Am/(Am+U) ratios up to 20 mol. % are placed in the periphery of the reactor core, where temperatures can reach 1470 K [1]. For the homogenous transmutation strategy, lower americium contents are foreseen (maximum 5 mol.%), but the pellets are distributed in the entire reactor core. In this case, during normal operation, the fuel is subject to high temperatures (> 2300 K) and strong thermal gradients.

For both scenarios, the thermodynamic properties of the U-Am-O system need to be known for the entire temperature range. In particular, the high temperature thermophysical properties, as for example the melting temperature and the thermal conductivity, are key parameters in the design of a reactor core, because they determine the safety margins and the behaviour of the fuel in the reactor. These properties are dramatically affected by the oxygen stoichiometry of the fuel, usually indicated as the oxygen/metal ratio (O/M). Indeed, the actinide dioxides do not generally form stoichiometric compounds, but solid solutions with large oxygen non-stoichiometric domain. To further complicate the situation, the temperature gradient in the fuel pellets causes a redistribution of both oxygen and metal atoms inside the pellet, meanwhile the overall composition changes with the burnup. The prediction of the possible composition changes and the effects on the thermophysical properties under specific temperature and oxygen potential conditions is thus crucial for the safety perspectives.

The melting behaviour of UO 2±x has been thoroughly studied in the past decades. Congruent melting occurs for an O/M2.0 (1.98 according to the assessment by Gueneau et al. [START_REF] Guéneau | Thermodynamic modelling of advanced oxide and carbide nuclear fuels: Description of the U-Pu-O-C systems[END_REF]), whereas the melting temperature largely decreases for any departure from the oxygen stoichiometry. On the contrary, the melting behaviour of AmO 2 is not well established. Only one set of measurements is reported in the literature [START_REF] Mchenry | Melting Points of Curium and Americium Oxides[END_REF], in which, moreover, largely scattered values were obtained by varying the heating rate. The highest obtained melting temperature-of about 2385 K-was associated to AmO 2 , but the O/Am ratio was not determined after the melting. However, recent CALPHAD assessments [START_REF] Gotcu-Freis | A thermodynamic study of the Pu-Am-O system[END_REF], [START_REF] Epifano | Insight into the Am-O Phase Equilibria: A Thermodynamic Study Coupling High-Temperature XRD and CALPHAD Modeling[END_REF] showed that americium dioxide does not melt congruently, since a reduction occurs at high temperature, leading to an O/Am ratio below 2.0. For (U, Am)O 2 oxides, very few data are available for the melting temperature and these are limited to low Am/(Am+U) ratios (< 20 mol.%) [START_REF] Prieur | Melting behaviour of americium-doped uranium dioxide[END_REF]. It should also be noticed, that the high temperature study of these compounds is particularly challenging, due to their very high radioactivity and the limited amount of americium available for laboratory investigations. This explains the aforementioned lack of data in the U-Am-O system.

In this paper, the melting behaviour of mixed (U,Am)O 2±x oxides is investigated by a laser-heating technique in a large domain of composition, for 0.075 < Am/(Am+U)<0.7. The results of these novel measurements are discussed in the light of the experimental heating-cooling curves, a comprehensive post-melting material characterisation campaign, based on X-ray absorption spectroscopy (XAS) and electron microscopy, and thermodynamic computations based on the CALPHAD method.

Methods

Material Preparation

U 1-y Am y O 2±x oxides with nominal americium content x = 0.07, 0.10, 0.20, 0.30, 0.50 and 0.70 were synthetized for this study, each one identified herein with the reference indicated in Table 1. The samples were manufactured as form of small pellets (diameter = 5 mm, height between 3 and 5 mm) in the Atalante facility of CEA Marcoule, using the UMACS powder metallurgy process [START_REF] Delahaye | Application of the UMACS process to highly dense U1-xAmxO2±δ MABB fuel fabrication for the DIAMINO irradiation[END_REF]. The conditions of this process (oxygen potentials ≈ -400 kJ/mol -1 at T ≈ 1800 K) have proven to be optimized for manufacturing dense and homogeneous pellets from single oxide precursors, such as UO 2 and AmO 2 . The ones used in this work were previously presented in [START_REF] Lebreton | Recent progress on minor-actinide-bearing oxide fuel fabrication at CEA Marcoule[END_REF].

One pellet from each batch was characterized by XRD, thermal ionization mass spectrometry (TIMS) and XAS. TIMS was used to determine the uranium and americium quantities in the oxides and hence to precisely know the Am/(Am+U) ratio of the samples. XRD was performed on grounded samples using a Bruker D8 Advance diffractometer in θ-θ Bragg Brentano geometry. For all the compositions, the diffraction patterns confirmed the presence of one single fluorite-type phase, indicating that the formation of the solid solution was achieved. The lattice parameters, obtained from the Pawley refinement of the patterns, are reported in Table 1, together with the sample age at the time. The values-once corrected for the time dependent swelling due to the α-decay, are consistent with those of oxides previously manufactured using the UMACS process [START_REF] Lebreton | et caractérisation d'oxydes mixtes d'uranium et d'américium[END_REF]. XAS was performed on a few mg of powder for the compositions investigated in this work, at the Rossendorf beamline of the ESRF. The L 3 edges of U and Am were probed and the spectra were compared to those obtained on reference materials (UO 2 , U 4 O 9 , U 3 O 8 , AmO 2 and an oxalate with Am 3+ ). This allowed determining the average oxidation states of U and Am. Then, from the electroneutrality rule, the O/M ratios were calculated.

The same methodology is applied in this work on the melted samples and it will be more detailed in the following. The results of the XAS study on the as-sintered oxides is instead published in another work [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF].

Reference Nominal Am/(Am+U

Laser Heating

Melting experiments were performed at Joint Research Centre Karlsruhe using a laser-heating technique coupled with fast pyrometry. The experimental set-up and technique were described in detail in previous works [START_REF] Manara | New techniques for high-temperature melting measurements in volatile refractory materials via laser surface heating[END_REF]- [START_REF] Bruycker | On the melting behaviour of uranium/plutonium mixed dioxides with high-Pu content: A laser heating study[END_REF]. The main advantages of the laser-heating technique are the short measurement duration and the quasi-containerless conditions. During the experiment, the investigated pellet is held inside an autoclave, supported in a 3-pin mount by graphite screws, and the laser is focused onto a spot smaller than the sample surface, hence only a limited part is melted.

The rest of the pellet can be considered as a "self-crucible" and no contamination with foreign surrounding materials occurs.

In the current experiments, the sample under investigation is heated by a 4.5 kW Nd:YAG CW Laser (HLD4506, TRUMPF, Schramberg, Germany). Radiation pyrometers measure the sample radiance L ex . This is the electromagnetic radiation power density per unit surface, wavelength and solid angle emitted by the sample at a given temperature. It is linked to the sample surface temperature T through modified Planck function:

Equation 1
L  being the radiative power, ε  the spectral emissivity, c 1 = 2•h•c 0 2 is the first radiation constant and

c 2 = h•c 0 •k B = 14388 µm•K is the second radiation constant, c 0 is the speed of light in vacuum, h is
Planck's constant, and k B Boltzmann's constant. The spectral emissivity takes into account the fact that a real body will radiate, at a given wavelength and temperature, only a fraction equal to   of the power emitted by an ideal blackbody at the same temperature. Therefore,   takes values comprised between 0 and 1, 1 corresponding to the ideal blackbody case, for which Planck's law was derived.

Since pyrometers in the present work were always set up near normal with respect to the sample surface, the angle dependence of ε λ was not considered, and 'emissivity' will always refer to normal spectral emissivity (NSE). The NSE must be determined in order to convert, through equation 1 and a pyrometer calibration procedure, L ex into absolute temperature T. The specimen temperature is detected using a fast pyrometer calibrated against standard lamps up to 2500 K at λ = 655 nm. An additional, 256-channel radiance spectro-pyrometer operating between 515 nm and 980 nm was employed for the study of the sample's NSE (  ). Determination of the NSE is possible by doing a nonlinear fit of the thermal emission spectrum with Equation 1 [START_REF] Dewitt | Thermal Radiative Properties of Materials[END_REF], T and   being the only two free parameters. This approach has been demonstrated to be acceptably accurate in refractory materials [START_REF] Herzfeld | Temperature: Its Measurement and Control in Science and Industry[END_REF], like those usually present in a NPPs, for which the NSE can be assumed to be wavelengthindependent (grey body hypothesis) on a broad spectral range. For the present oxides, the constant value 0.825 was established for the NSE at the pyrometers' wavelengths, in agreement with previous studies and recent theoretical assessments [START_REF] Lu | Electronic, mechanical, and thermodynamic properties of americium dioxide[END_REF].

Once the temperature of the laser-heated sample is correctly measured as a function of time, thermal analysis can be performed on the resulting temperature-time curve (thermogram).

Inflections or thermal arrests in the thermograms give information related to phase transitions (solidus, liquidus and isothermal phase transformations). An additional technique called reflected light signal (RLS) analysis [START_REF] Manara | New techniques for high-temperature melting measurements in volatile refractory materials via laser surface heating[END_REF] is used to confirm phase transitions. It is conducted by using the second channel of the pyrometer tuned to a low-power (1W) Ar + laser (λ = 488 nm). This channel detects the laser beam originating from the Ar + cavity and reflected by the sample surface. A constant RLS signal indicates a solid surface while random oscillations appear after melting due to surface tension-induced vibrations on the sample liquid surface.

Taking into account the instrumental uncertainties linked to the pyrometers' calibration procedures, the uncertainties in the NSE and the experimental data repeatability, the transition temperatures measured in this work are affected by a total relative uncertainty of approximately ±1%, for one standard deviation.

Electron Microscopy

The SEM observations were performed on a Philips XL40 SEM (Philips, Amsterdam, Netherlands), which has been adapted for the examination of highly active or irradiated nuclear materials: the high voltage unit, column, chamber and turbomolecular pump are mounted inside a glove-box in order to keep the contaminated pieces in a confined space. The primary vacuum system, water cooling circuit and acquisition electronic are placed outside, preventing those parts from contamination by the active samples. The microscope is equipped with Secondary electrons detector (SE) which gives a morphology-related signal and Backscattered electrons detector (BSE) which provides images with Zrelated contrast; the beam was always operated at 20 kV.

XAS

Small amounts (1-2 mg) of powder were scratched from the melted part of the samples and these were mixed with polyethylene and pressed into pellets. These samples were put in polyethylene sample holders and confined in a double sealed vinyl bag, for measurement at the INE beamline [START_REF] Rothe | The INE-Beamline for actinide science at ANKA[END_REF] of the KIT Synchrotron light source (Germany), under dedicated operating conditions (2.5 GeV, 120-150 mA). Spectra were collected at the U-L 3 and Am-L 3 edges (17166, 18510 eV), at room temperatures. Spectra were acquired both in transmission and fluorescence modes, using argonfilled ionization chambers and a Canberra LEGe five pixel fluorescence detector. A Ge(422) double crystal monochromator coupled with collimating and focusing Rh-coated mirrors were used for energy selection. The calibration was performed using metallic foils whose K edges are close to the edges of interest: yttrium (17038 eV), zirconium (17998 eV), and molybdenum (20000 eV). The references were measured at the same time of the samples, in transmission mode.

The X-ray fluorescence emission was used to determine the Am/(Am+U) ratio in the melted samples.

The fluorescence intensity I f is proportional to:

Where I 0 is the is the X-ray intensity incident on the sample and µ(E) is the absorption coefficient. For incident X-ray with energy E sufficiently far from the absorption edge, µ(E) is a smooth function of the energy and it can be expressed as:

Equation 2
where Z and A are respectively the atomic and mass numbers and ρ is the element density. In this case, an excitation energy of E = 18900 eV was used. This energy was chosen because it is beyond the U-L 3 (∼ 17166 eV) and Am-L 3 (∼ 18510 eV) edges, but below the L 2 edges. Two characteristic fluorescent peaks are observed at about 13614 eV and 14620 eV. The first peak corresponds to the fluoresce X-rays emitted by uranium during the L α1 (M 5 → L 3 ) and L α2 (M 4 → L 3 ) electronic transitions.

The second peak is due to the emissions from americium, for the same electronic transitions L α1 and L α2 . Therefore, the ratio between the mass of uranium and americium in the samples was obtained as:

Equation 3
The XANES data were analysed using the Athena software [START_REF] Ravel | HEPHAESTUS: data analysis for X-ray absorption spectroscopy using IFEFFIT[END_REF]. The spectra were normalized using a linear function for pre-edge and post-edge signals. The first zero crossings of the first and second derivatives were used to determine the white line (WL) and the inflection point (E 0 ), respectively. The average oxidation states of the cations were determined fitting the XANES data by linear combination of reference spectra. UO 2 , U 3 O 8 and the as-sintered Am30 sample were used for the U-L 3 edge, whereas AmO 2 and Am 3+ -oxalate were used for the Am-L 3 .

Thermodynamic modelling and computations

Thermodynamic modelling and computations were performed according to the Calphad method. The Am-U-O model from [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF] was here adopted as starting point. In [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF], in analogy with all the other actinide oxide systems [START_REF] Guéneau | Thermodynamic modelling of advanced oxide and carbide nuclear fuels: Description of the U-Pu-O-C systems[END_REF], a partially ionic two sublattice liquid model [START_REF] Lukas | Computational Thermodynamics The Calphad Method[END_REF] was adopted for the U-Am-O system, with the form:

(Am +3 , U +4 ) P (O -2 , Va, O) Q
This kind of model allows describing the composition variation from a metallic liquid, here (U, Am) to an oxide liquid (U, Am, O). To handle the metallic liquid, where only cations are present, hypothetical charged vacancies (Va) are included in the second sublattice. To extend to oxide systems, anions and neutral species are introduced. To ensure the electroneutrality, the number of sites on the sublattices, P and Q, vary with the composition and they are equal to the average charge on the opposite sublattice:

where y i and ν i denote the site fraction and the charge of the constituent i, respectively (A for anion and C for cation).

In [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF], the Gibbs energy of the liquid phase was not optimised for the ternary system and its description results purely from the extraction of the binary systems [START_REF] Guéneau | Thermodynamic modelling of advanced oxide and carbide nuclear fuels: Description of the U-Pu-O-C systems[END_REF], [START_REF] Epifano | Insight into the Am-O Phase Equilibria: A Thermodynamic Study Coupling High-Temperature XRD and CALPHAD Modeling[END_REF], [START_REF] Kurata | Thermodynamic database on U-Pu-Zr-Np-Am-Fe alloy system II -Evaluation of Np, Am, and Fe containing systems[END_REF]. Thus, in this work, the ternary U-Am-O liquid phase has been assessed fitting the new experimental data and the resulting model is presented in section 4. The other phases of interest for this work, the U 1-y Am y O 2±x solid solution and the gas phase, were not modified and we refer to [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF] for the description of their thermodynamic models.

Experimental results

Melting temperatures

The melting-T measurements were performed on the Am7, Am10, Am20, Am30 and Am50, both under argon and under air; for the Am70 sample, because of the limited quantity of material available, only measurements under air were performed. To prevent an excessive vaporization of the oxides, the autoclave was pressurized at 0.3 MPa.

A typical result of a melting measurement is reported in Figure 1. The laser-power profile is represented in black. One measurement was constituted by a pre-heating at about 1500 K, followed by four consecutive step-type laser pulses. These were programmed with different duration (100-200 ms) and intensity (for a maximum of 720 W), in order to exclude kinetic effects in the measured melting temperature. The red curve in Figure 1 is the thermogram (temperature vs time), acquired with the 655-nm pyrometer. The melting temperature is determined in two ways: first, from the cooling part of the thermogram, by locating the main thermal arrests; second, with the "reflected light signal" (RLS) method, in which abrupt changes in the reflectivity of the sample indicate the change from the solid to the liquid phase. However, for solid solution such as U 1-y Am y O 2±x , a noncongruent melting is expected and hence two thermal arrests should be observed during the cooling, corresponding to the liquidus and the solidus temperatures, respectively. In our measurements, only one thermal arrest was always clearly visible and, as shown in previous studies on similar materials [START_REF] Bruycker | On the melting behaviour of uranium/plutonium mixed dioxides with high-Pu content: A laser heating study[END_REF], [START_REF] Böhler | Recent advances in the study of the UO2-PuO2 phase diagram at high temperatures[END_REF], this is associated to the solidus temperature T m . Thus, in the following, we will always refer to the solidus temperature, except otherwise specified.

At the end of the measurement (four consecutive laser pulses), the sample was left to cool down to room temperature and checked by visual inspection. The autoclave windows were cleaned (vaporization of the sample) and the gas replenished. Then, the measurement was repeated several times in order to improve the result statistical analysis. For the (U,Am)O 2 oxides with 0.07 ≤ Am/(Am+U) ≤ 0.48, several consecutive measurements of the melting temperature were performed, both under argon and air. For some of the compositions, more than one pellet (from the same batch) were used for the experiments (depending on the material availability). A general overview of all the obtained results is presented in Figure 2.

Error bars were omitted for visual clarity, but the uncertainty of each measurement was determined according to the error propagation law, taking into account the standard uncertainty associated to the pyrometer calibration (±10 K at 3000 K), the sample emissivity (±21 K at 3050 K) and the accuracy in detecting the onset of vibrations in the RLS signal. The estimated cumulative uncertainty is ±2% of the reported temperature. Figure 2 shows that the (U,Am)O 2±x oxides exhibit different behaviours under argon (left) and under air (right). For all the compositions, the measurements under argon were very reproducible. The obtained T m values generally decrease with the americium content of the sample. A more complicated behaviour was observed under air. For the samples with 0.07 ≤ Am/(Am+U) ≤ 0.2, the measured T m decreased during the first laser shots and then it stabilized to 300-400 K lower values.

For Am/(Am+U) = 0.20, three pellets were available for the experiment and this evolution was repeatable for all the samples. For the Am30 and Am50 samples, no clear evolution of the solidus temperature was observed during the attempts in air. The obtained values were close to those measured in argon, but slightly lower in any case.

For the U 0.33 Am 0.67 O 1.90 composition, only a small fragment of pellet was available for the experiment.

and hence only one measurement under air could be performed.

The average values of the melting temperature measurements are represented as a function of the initial Am/(Am+U) ratio in Figure 3. For the measurements under air, only the data obtained after the initial evolution were considered. For comparison, the literature data for UO 2 [START_REF] Manara | Melting of stoichiometric and hyperstoichiometric uranium dioxide[END_REF], AmO 2-x [START_REF] Mchenry | Melting Points of Curium and Americium Oxides[END_REF] and (U, Am)O 2+x oxides [START_REF] Prieur | Melting behaviour of americium-doped uranium dioxide[END_REF] are also reported in Figure 3. The solidus temperatures measured by Prieur et al. [START_REF] Prieur | Melting behaviour of americium-doped uranium dioxide[END_REF] under argon are in good agreement with the values observed in this work in the same atmosphere. A monotonic, almost linear decrease of the melting temperature with the Am/(Am+U) ratio is observed in this case. It is important to remember that the initial O/M ratio of the samples was not equal to 2.0 for all the compositions. Besides, the O/Am ratio corresponding to the melting of AmO 2-x is unknown. Therefore, the UO 2 -AmO 2-x dashed line in Figure 3 was drawn as a guide to the eye only.

The melting temperatures measured under air are generally lower than those obtained under argon.

The variation with the Am/(Am+U) ratio is not monotonic in this case. Close values, around 2700-2760 K, were observed for Am/(Am+U) = 0.07 and 0.10. These values are almost 400 K lower than those measured in argon. For 0.10 < Am/(Am+U) ≤ 0.48, an increase of the melting temperature with the americium content is observed. The highest values are found for the Am30 and Am50 samples.

Interestingly, the values obtained for these two compositions under air and under argon are very close. Finally, a large decrease of the melting temperature was observed for Am/(Am+U) = 0.67 in air.

The difference between the results obtained under argon and air clearly indicates a variation of the composition of the investigated samples. The clear evolution/decrease of the melting temperatures in air can be attributed to the O/M increase and, probably, the formation of hyper-stoichiometric oxides such as M 4 O 9 and M 3 O 8 . Moreover, also changes of the Am/(Am+U) ratios cannot be excluded.

Despite the overpressure imposed during the experiment, a partial vaporization of the sample could not be completely avoided. This was clearly observed during the experiments under air: at the end of each measurement series, the windows of the autoclave were covered in powder. Thus, the vaporization of the solid phase could also lead to variations of the Am/(Am+U) ratio.

In order to obtain reliable phase diagram data, post-melting characterizations were carried out.

Post-melting SEM

SEM microscopy images were obtained on the melted fragments, as those shown in Figure 4 for the Am50 sample melted in argon. The images confirm that only a small fraction of the pellet was melted during the measurement. Indeed, the more internal part in respect to the surface targeted with the laser (region 3) maintained the microstructure usually observed in ceramic actinide dioxides, with well-faceted grains of about 10-20 μm. Moving toward the surface, an "intermediate region" appears (region 2), with round-shaped grains that start to connect each other. This part of the sample was at high temperature and it was about to melt.

Finally, the surface heated by the laser is clearly discernible in the fragment, because the initial microstructure with well-faceted grains is lost. The melted part seems more dense and compact, without grain boundaries. At higher magnification (region 1), columnar dendrites, typical of the solidification process, can be observed.

Figure 4 shows that the melted part is a small fraction of the sample, with a thickness of about 150-200 μm. Although this characteristic of the laser-heating technique is ideal for the study of high temperature property, the drawback is that the material available for the characterization is very limited. For this reason, the melted samples could only be investigated by XAS, since this technique demands only few mg of materials.

Am/M ratio by X-ray fluorescence spectroscopy

All the as-sintered and melted oxides were characterized by XAS, with the exception of the Am70 sample, which could not be retrieved after melting.

As explained in 2.4, for incident X-ray with energy equal to 18.9 keV (above both the U-L3 and Am-L3 edges), the ratios between the intensities of the fluorescence X-rays emitted by americium and uranium will be proportional to the ratio of their masses in the sample (Equation 3). As an example, one of the X-ray fluorescence spectrum acquired on the as-sintered Am20 sample is shown in Figure 5-(a). The two peaks observed at about 13614 eV and 14620 eV corresponds to the characteristic fluoresce X-rays emitted by uranium and americium, respectively. For all the as-sintered samples, the ratio between the intensity of these two fluorescent peaks was computed. For the intensity, both the maxima and the area of the peaks were tested. Besides, two different detectors were simultaneously used for recording the fluorescence emission. For all the cases, the obtained I f (Am)/[I f (Am)+ I f (U)] ratios were very similar. The values are represented as a function of the "real" (previously determined by TIMS) composition of the sample in Figure 5-(b). As expected, a linear trend was obtained:

Equation 4
The a and b parameters were obtained by fitting the points of Figure 5-(b). The fluorescence spectra of the melted oxides are compared to the as-sintered samples in Figure 6.

Small but clear differences in the I f (Am)/I f (U) ratios between the melted and as-sintered samples are observable for the Am20, Am30 and Am50 oxides. A higher Am intensity is observed for all the melted samples, especially for those melted in air. The Am/(Am+U) ratios were determined by For the Am-L 3 edge, the inflection point and white line positions of all the melted samples are identical, within the experimental uncertainty, to the as-manufactured oxides. In these latter, as

shown in [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF] and [START_REF] Lebreton | Peculiar Behavior of (U,Am)O2-δ Compounds for High Americium Contents Evidenced by XRD, XAS, and Raman Spectroscopy[END_REF], americium is purely trivalent. Only for the Am50Air sample (melted in air), a small shift of the inflection point and the white line toward higher energies is observed. This indicates a slight oxidation of americium in this sample.

A more complicated picture is observed for the U-L 3 edge. For the as-sintered samples, the white line energies generally increase with the americium content, indicating a higher average oxidation state of uranium. This is consistent with the previous results reported in the literature [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF], [START_REF] Lebreton | Peculiar Behavior of (U,Am)O2-δ Compounds for High Americium Contents Evidenced by XRD, XAS, and Raman Spectroscopy[END_REF], [START_REF] Prieur | Local Structure and Charge Distribution in Mixed Uranium-Americium Oxides: Effects of Oxygen Potential and Am Content[END_REF]. For the oxides melted under argon, the white lines are very close to the corresponding as-sintered samples, for Am/(Am+U) ≤ 0.20. For Am30Argon, the white line is shifted to higher energy, indicating an increased oxidation. On the contrary, for Am50Argon, the white line is shifted to lower energy, pointing out a reduction. Finally, for the samples melted in air, the white lines are always shifted toward higher values, indicating an increase of the oxidation state of uranium.

The average oxidation state of uranium and americium were obtained by linear combination fit of the XANES spectra, using reference compounds. For the U-L 3 edge, U 4+ O 2 , (U 5+ 2/3 ,U 6+ 1/3 ) 3 O 8 [START_REF] Kvashnina | Chemical State of Complex Uranium Oxides[END_REF] and Am 3+ 0.28 U 4+ 0.40 U 5+ 0.32 O 2.02 [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF] (the Am30 sample) were used as references, whereas Am 4+ O 2 and a Am 3+ -oxalate were adopted for the Am-L 3 edge. The results of the linear combination fits are detailed in the Annex (Table 4) and the obtained O/M ratios (computed using the measured Am/M values) are represented in Figure 9. For the as-sintered samples, the O/M values are identical (within the uncertainty) to those obtained in the previous investigation [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF]. For the samples melted in argon, the oxygen stoichiometry was maintained for the compositions with Am/(Am+U) ≤ 0.20. For Am50Argon, the O/M ratios was lower than the initial ones. For Am30Argon, an oxidation is observed, from O/M = 2.02 to 2.05, which is quite surprising because this result is not consistent with the trend of the other compositions.

Therefore, this result should be considered with caution because probably an alteration of the sample occurred during the storage or the preparation for the XAS measurements. Finally, an increase of the O/M ratio occurred for all the samples melted in air. The oxidation was particularly large for the oxides with low americium content, and especially for the Am10Air sample. In this case,

an O/M = 2.28(2) was obtained and hence hyperstoichiometric oxide as M 4 O 9 and M 3 O 8 were surely formed, even if the identification of the phases could not be performed. The increase of the O/M ratio seems less and less important with the increasing of the Am/M ratio: the oxidation was very limited for the Am50Air sample, whose final O/M was equal to 2.04(2).

Thermodynamic modelling

The U-Am-O Calphad model proposed in [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF] was here modified in order to reproduce the new solidus temperature data. First, the AmO 2 species was added to the partially ionic 2-sublattice model [START_REF] Lukas | Computational Thermodynamics The Calphad Method[END_REF] of the liquid phase, which became:

(Am +3 , U +4 ) P (O -2 , Va, O, AmO 2 ) Q

The choice of the constituents in the liquid sublattices is generally based on the assumption that the compounds showing a high stability in the solid state should be present in the liquid state. For the Am-O system, the highest melting points are exhibited by hexagonal-Am 2 O 3 and AmO 2 [START_REF] Epifano | Insight into the Am-O Phase Equilibria: A Thermodynamic Study Coupling High-Temperature XRD and CALPHAD Modeling[END_REF], [START_REF] Mchenry | Melting Points of Curium and Americium Oxides[END_REF]. The first was already represented in the liquid state by Am 3+ cations and O -2 anions. AmO 2 was here added as a neutral species based on the analogy with the similar Pu-O system modelling [START_REF] Guéneau | Thermodynamic modelling of advanced oxide and carbide nuclear fuels: Description of the U-Pu-O-C systems[END_REF].

Since no data are reported for the enthalpy of melting of AmO 2 , the value of UO 2 was adopted in first approximation (75 kJ/mol) [START_REF] Konings | The Thermodynamic Properties of the f-Elements and their Compounds. Part2. The Lanthanide and Actinide Oxides[END_REF]. Hence, by imposing the melting point of AmO 2 at 2386 K [START_REF] Mchenry | Melting Points of Curium and Americium Oxides[END_REF], an entropy of melting of 24,959 J•mol -1 •K -1 was obtained. The following thermodynamic parameter was hence added to the Gibbs energy function of the liquid phase:

Where GAMO2 is the Gibbs energy function of the solid stoichiometric AmO 2 reported in [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF].

Moreover, a ternary interaction parameter between UO 2 and AmO 2 , was optimized by fitting the experimental solidus temperatures, as shown in Figure 10 (dashed lined). For the calculations, the total pressure was fixed to 0.3 MPa and the solidus temperature was computed as a function of the Am/(Am+U) ratio for U 1-y Am y O 2±x in equilibrium with the gas phase (argon or air). To reproduce the experiments under argon, the total composition of the system was set equal to 1 mole of metal (Am+U), 2 moles of oxygen and 5 moles of argon; to reproduce the experiments in air, 1 mole of metal, 3 moles of oxygen and 4 moles of nitrogen were considered. The choice of the number of gaseous moles (argon, nitrogen and oxygen) is quite arbitrary, because it is rather difficult to assess the actual mole ratio of the condensed and the gaseous species interacting during the laserheating experiments. Nevertheless, the approach here adopted for the calculations is as close as possible to the experimental conditions, since the oxygen stoichiometry of the condensed phase was not fixed in the computation but left free to vary as a result of the interaction with the gas. A very good result, in particular for the solidus temperatures under argon, was obtained for an interaction parameter equal to -680 kJ•mol -1 .

The liquidus temperatures were also calculated with the new model and represented in Figure 10 with solid lines. For the investigated composition range, the computations show that the liquidus is quite close to the solidus, with a maximum difference of about 100 K. This is consistent with the impossibility to observe the liquidus during the experiments, since previous studies have shown that the laser-flash technique can hardly discern the solidus and the liquidus in such cases because of the rapidity during the cooling [START_REF] Bruycker | On the melting behaviour of uranium/plutonium mixed dioxides with high-Pu content: A laser heating study[END_REF], [START_REF] Böhler | Recent advances in the study of the UO2-PuO2 phase diagram at high temperatures[END_REF].

Discussion

The melting behaviour of (U,Am)O 2 oxides is dramatically affected by the oxygen partial pressure of the surrounding environment. Indeed, two different behaviours were observed for the experiments performed in this work under inert (argon) and oxidizing atmosphere (air). In the first case, the measurements were always repeatable, and the resulting melting temperatures decreased with the americium content of the sample, with a regular trend. On the contrary, under air, the measurements were not reproducible for the oxides with Am/(Am+U) ≤ 0.20. In these cases, the observed melting temperature decreased during the first attempts and then it stabilized at about 400 K lower values. Based on this behaviour, it was clear that the composition of the oxide varied during the measurements in air, likely because of an increase of the O/M ratio, whereas a mostly constant composition could be supposed for the measurements in argon. These hypotheses were checked performing post-melting characterizations, by determining the Am/(Am+U) and O/M ratios of the melted samples by X-ray fluorescence spectroscopy and XANES, respectively.

The variations of the Am/(Am+U) ratios were limited, as expected considering the rapidity of the laser-heating technique. However, slightly higher Am/(Am+U) ratios were systematically observed for the melted samples, especially for those measured in air. This indicates that, in the Am/(Am+U) ≤ 0.48 domain, the vapor pressures of the uranium gaseous species are higher than those of americium, leading to a preferential vaporization of uranium, in agreement with the partial vapour pressures recently measured by Epifano et al. [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF].

The changes of the O/M ratios during the laser-heating measurements were instead very important.

The variations were particularly evident for the samples melted in air, for which a systematically increase of the O/M ratio was observed. The O/M changes were less important for the oxides melted in argon. For Am/(Am+U) ≤ 0.30, the initial and final O/M were equal, within the experimental uncertainty. A reduction was observed for the Am50Argon sample, whose initial O/M = 1.96 [START_REF] Guéneau | Thermodynamic modelling of advanced oxide and carbide nuclear fuels: Description of the U-Pu-O-C systems[END_REF] passed to O/M = 1.93(2). This is not surprising, since [START_REF] Epifano | Unique features in the (U,Am)O2 solid solution[END_REF], [START_REF] Lebreton | Peculiar Behavior of (U,Am)O2-δ Compounds for High Americium Contents Evidenced by XRD, XAS, and Raman Spectroscopy[END_REF] showed that the addition of americium in the fluorite structure generally stabilizes the hypostoichiometric domain.

Am50, and this value is then stable till room temperature. Comparing the calculations to the experiments, a very good agreement is observed for Am50Argon, where the final O/M is equal to the composition of the solidus point (at 2848 K), whereas a re-oxidation occurs for Am7Argon during the cooling. Interestingly, for the latter, the O/M = 2 is reached around 2700 K. From these results, one can hence suppose that, in the O/M< 2 domain and for temperatures above 2700-2800 K, the diffusion of the oxygen is fast enough to induce composition variations in the solid phase.

For the experiments in air, the calculations show a continuous increase of the O/M ratio during the entire cooling, leading also to the formation of oxides richer in oxygen for Am7Air. For Am7Air and Am50Air, the O/M ratios determined experimentally intersect the calculated path at an intermediate point between the solidus and the room temperature. In both cases, this point corresponds to a temperature around 2300-2400 K. Thus, the variations of the O/M ratios occur also in the solid phase, likely in the temperature range T > 2300 K.

In conclusion, the comparison between the thermodynamic calculations and the compositions of the melted samples show that the laser-flash technique is fast enough to avoid the complete reoxidation of the samples, but not enough to avoid slight increases of the O/M ratio at high temperature.

Conclusions

In this work, the laser-heating technique was used to investigate the melting behaviour of the (U,Am)O 2±x solid solution over a wide composition domain, for Am/(Am+U) ratios ranging from 7 to 70 mol.%. Two different atmospheres (argon and air), were used for the experiments and this allowed to show the dramatic effects of the oxygen stoichiometry on the melting points. Thanks to accurate post-melting characterizations by XAS technique, the final compositions of the melted samples were determined. For the measurements performed in argon, O/M ratios close to 2.0 (or slightly lower) were generally maintained and, in this case, an almost linear decrease of the melting point was observed with the increase of the americium content, passing from 3072(55) K for Am7 to 2887 K for Am50. However, our study shows that the most dramatic effect on the melting point is related to the oxygen hyperstoichiometry: in the measurements in air, the O/M ratios increased, causing drops of the melting temperature up to almost 400 K. The oxidation and the related decrease in the melting temperature were more dramatic for the oxides richer in uranium, whereas they were hindered in the Am-rich samples by the high content of Am 3+ . This effect is consistent with the previous similar results on (U,Pu)O 2+x oxides [START_REF] Strach | Melting behavior of mixed U-Pu oxides under oxidizing conditions[END_REF], but the resistance against the oxidation is even more evident in the americium-doped oxides. Finally, the new experimental data of the solidus temperatures were used to improve the Calphad modelling of the U-Am-O system, optimizing the Gibbs energy of the liquid phase. For this assessment, an approach as close as possible to the experimental conditions was applied, simulating the interaction between the condensed and the gas phase, letting the O/M ratio free to vary. The obtained model was then used to better understand the laser-heating experiments, by tracing some solidifications paths for selected compositions. The results confirm that under argon, the oxygen to metal ratio is below 2 at the solidus temperature and that the samples are oxidized under air with O/M ratio higher than 2 for uranium rich samples. Thus the compositions determined at room temperature after the melting must be cautiously considered because, despite the rapidity of the experiments, variations of the O/M can occur during the cooling, also after re-solidification.

Figure 1 .

 1 Figure 1. Thermogram (red), laser power profile (black) and reflected light signal (blue) recorded in a laser melting measurement in argon on the Am50 sample.

Figure 2 .

 2 Figure 2. Solidus temperature results obtained in successive measurements in Ar and air.

Figure 3 .

 3 Figure 3. Melting (solidus) temperatures as a function of the initial Am/(Am+U) ratio of the samples. The results of this work are compared to the data reported in the literature for UO 2 , AmO 2-x and (U, Am)O 2 oxides with Am/(Am+U)=0.10, 0.15, 0.20.

Figure 4 .

 4 Figure 4. SEM images acquired on the Am50 sample melted in argon.

Figure 5 .

 5 Figure 5. (a) Fluorescence X-ray spectrum of the Am20 sample for incident X-ray with E = 18.9 keV. (b) Am/(Am+U) ratio obtained by fluorescence spectroscopy for the as-sintered samples.

Figure 7 .

 7 Figure 7. XANES spectra collected at the Am-L 3 edge of Am on (U,Am)O 2 oxides, before and after melting. Bottom right: observed white lines.

Figure 8 .

 8 Figure 8. XANES spectra collected at the U-L 3 edge of Am on (U,Am)O 2 oxides, before and after melting. Bottom right: observed white lines.

Figure 9 .

 9 Figure 9. Composition of the melted samples obtained by XAS investigation.

Figure 10 .

 10 Figure 10. Computed solidus (--) and liquidus (-) temperatures under argon (blue) and air (red), compared to the experimental points of Prieur [6] and this work.

) Measured composition

				XRD data
				a (Å)	Age (days)
	Am7	0.07	U 0.93 Am 0.07 O 2.01	5.467(1)	6
	Am10	0.10	U 0.90 Am 0.10 O 2.01	5.466(1)	5
	Am20	0.20	U 0.81 Am 0.19 O 2.01	5.460(1)	4
	Am30	0.30	U 0.72 Am 0.28 O 2.02	5.455(1)	5
	Am50	0.50	U 0.52 Am 0.48 O 1.97	5.456(1)	4
	Am70	0.70	U 0.33 Am 0.67 O 1.90	5.450(1)	100

Table 1 . Characteristics of the materials used in this work.

 1

Acknowledgements

We acknowledge the KIT synchrotron light source for provision of the INE-Beamline instrumentation and would like to thank the KIT Institute for Beam Physics and Technology (IBPT) for operation of the storage ring, the Karlsruhe Research Accelerator (KARA). E. Epifano thanks the SETAM project for financial support of the Ph.D and the GENTLE European project (Grant agreelent no: 323304) for founding the internship at the JRC Karlsruhe.

inverting Equation 4 and the results are shown in Figure 6-bottom, right (see also Table 2 in Annex). The obtained Am/(Am+U) ratios of the melted samples are very close to the initial ones. This confirms that the rapidity of the laser-heating technique prevents important chemical composition changes. However, for the nominal compositions Am/(Am+U) = 0.2, 0.3 and 0.5, the results systematically show a slightly higher americium content (2-3 mol.%).

O/M ratio by XANES

The XANES spectra collected on the as-sintered as well as on the melted samples are show in Figure 7 and Figure 8, respectively for the Am-L 3 and U-L 3 edge. The white line positions are compared in the bottom-right of Figure 7 and Figure 8 (see also Table 3 in the Annex).

The effect of the composition on the melting temperature is evident in Figure 11 (see also table in Annex), where the melting temperatures are plotted as a function of the final O/M and Am/M ratios. The blue points in Figure 11 correspond to the measurements in argon. These points are close to the oxygen stoichiometry O/M = 2.00 and they show a regular decreasing trend with the increase of the americium content. The red points, corresponding to the melting temperatures observed in air, are generally lower than those in Argon, but the trend with the Am/(Am+U) ratio is opposite. The cause of this difference can clearly be attributed to the O/M ratios. Indeed, a consistent O/M increase was observed for Am/M ≤ 0.2, whereas it was more limited for Am/M = 0.30 and 0.50, confirming once again that the resistance toward the oxidation improves with the increasing americium content [START_REF] Epifano | Study of the U-Am-O ternary phase diagram[END_REF].

The increase of the O/M ratio has dramatic effects on the melting temperature, causing a 300-400 K decrease. This is clear looking to the Am10air sample: at the highest O/M ratio (2.28(2)) corresponds the lowest melting temperature (2697 ± 35 K).

Considering the composition changing during the laser-flash measurements, one can wonder whether the composition of the melted samples determined at room temperature is representative of the composition at the melting point, or if the O/M ratio evolves also during the cooling, even after re-solidification. To have some insight on this subject, the Calphad model here optimized was used to compute the solidification paths of the two extreme compositions for which the final O/M has been measured: Am7 and Am50. The same computation strategy described in section 4 was here adopted to simulate argon and air atmospheres. The results are shown in Figure 12 All the computed solidus temperatures (intersection between the parts 2 and 3 of the curve) are really close to the experimental values, showing again that a good fit of these data was obtained.

Concerning the composition variation, the calculations show that an oxidation occurs along the cooling paths, as expected. More specifically, according to the model, the O/M ratio is not constant from the solidus point to room temperature: a further oxidation is expected also in the solid phase.

Under argon, the stoichiometry O/M = 2 is reached at ≈2700 K and ≈2400 K, respectively for Am7 and


Y Gründer

Yves Joly

Yvonne Soldo-Olivier

New tool for simulation of surface resonant x-ray diffraction

come

New tool for simulation of surface resonant x-ray diffraction Y. Gründer, Y. Joly, Y. Soldo-Olivier,-for more information contact Y. Joly, Institut Néel, CNRS, Grenoble, France.

yves.joly@neel.cnrs.fr

In-situ studies of the chemical bonding at an electrochemical interface are rather difficult due to the presence of the electrolyte and also because standard characterisation techniques which are mostly UHV based cannot be applied.

Surface x-ray diffraction (SXRD) is widely used to solve the atomic structure at the surface of singlecrystals. One of its advantages is that it can be performed in-situ and even operando. On the other hand, x-ray absorption near edge spectroscopy (XANES) as well as resonant x-ray diffraction (XRD) are highly sensitive to the oxidation states considering the edge energy shift compared to a known reference. Recording spectra across an absorption edge is possible on specific points of the crystal truncation rods (CTR), which are surface sensitive points in reciprocal space. As in 3D anomalous diffraction, spectra exhibit large intensity variations around the edge. The so-called surface resonant x-ray diffraction (SRXRD) is therefore expected to be sensitive to both the surface structure and the electronic structure around the absorbing elements. The existing fitting tool allows the description of the geometry but no one up to now was able to reproduce the spectra experimentally.

We have successfully employed SRXRD to gain site specific information about the charge distribution at a buried interface. Furthermore, the development of a theoretical tool based on an ab-initio approach further elucidated the charge transfer and bond formation at a polarised solidliquid interface [1].

The calculation uses density functional theory to calculate the electronic structure around the resonant atoms. The atomic charges and the bonding with the neighbouring atoms can then be deduced. With this information the modified form factors and thus energy and angular dependent resonant scattering amplitudes around the x-ray absorption edges can be modelled. For SRXRD, all contributions from the bulk and from the surface, which includes the substrate topmost atomic layers and the eventual adsorbed layers, are summed up and finally one obtains the intensity of the diffracted beams. It is the angular dependence of the surface alignment with the incoming x-ray beam, specifically the incoming beam polarisation, which shows that specific chemical bounds are probed. We have demonstrated the validity of our approach studying different systems. As an example, we consider the formation of a bromine ad-layer on copper at an electrochemical interface. We show here the comparison of the simulated spectra with the data recorded at the XMaS beamline, at the Cu K-edge and along two polarisation directions of the incoming light (Fig. 1). The relative shift of these spectra versus references are related to the oxidation state of Cu. Furthermore, most of the features are signature of the bounding with the neighbouring atoms. Because all the Cu atoms from deep in the bulk and at the surface contribute, spectra recorded at different L values in reciprocal space must be analysed. Beyond this peculiar study, this work opens a new root to explore the electronic structure at surfaces and interfaces.

Fig. 1 :

 1 Fig. 1: Spectra at the Cu K-edge in the Br c(2x2) adlayer on Cu(100) system recorded at XMaS (dots) for two polarisation directions with respect to the scattering plane, and compared with the simulations (lines).

[1] Y.Joly et al., J. Chem. Theory Comput. 14, 973 2018).


Behrooz Omidvar-Tehrani

Sihem Amer-Yahia

Eric Simon
email: 2eric.simon@sap.com

Fabian Colque Zegarra

João L D Comba
email: comba@inf.ufrgs.br

Viviane Moreira
email: viviane@inf.ufrgs.br

UserDEV: A Mixed-Initiative System for User Group Analytics

The increasing availability of user data constitutes new opportunities in various applications ranging from behavioral analytics to recommendations. A common way of analyzing user data is through "user group analytics" whose purpose is to breakdown users into groups to gain a more focused understanding of their collective behavior. The process consists of group discovery, group exploration, and group visualization. To date, user group analytics is done using separate tools which makes it fragmented and burdensome for analysts. In this paper, we describe UserDEV, a fullfledged user group analytics pipeline which combines discovery, exploration, and visualization of user groups, in a fully-connected fashion. UserDEV contributes a star-like architecture as well as a common data exchange model to tighten connections between the analytics components. We provide a realistic use case to show how UserDEV helps analysts perform analytical tasks on user groups. While we report a preliminary user study, we also discuss opportunities for an end-to-end evaluation of a group analytics framework.

INTRODUCTION

Today, user data is ubiquitous in various domains, ranging from the social Web to medical records, scientific publications, and retail store receipts. This data is characterized by a combination of demographics (e.g., age, gender) and actions (e.g., rating a movie, publishing a paper, following a medical treatment). Analysts rely on user data to achieve a variety of tasks with the target goal of finding people of interest or analyzing collective behavior. A common way of analyzing user data is through "user group analytics" whose purpose is to breakdown users into groups to gain a more focused understanding of their collective behavior. Group analytics is a shift towards Quantified-Us [START_REF]Forget the quantified-self, we need to build the quantified-us[END_REF] and helps analysts make better and faster decisions [START_REF] Connor C Gramazio | The relation between visualization size, grouping, and user performance[END_REF] with more certainty [START_REF] Doodson | Getting acquainted with groups and individuals: Information seeking, social uncertainty and social network sites[END_REF]. It also addresses peculiarities of user data such as noise and sparsity. A list of group-centric scenarios in various domains is provided in Table 1.

To obtain insights on user groups, analysts need to go through a group analytics pipeline, which consists of three main components: discovery, exploration, and visualization [START_REF] Omidvar | Tutorial on data pipelines for user group analytics[END_REF]. Group discovery takes as input raw user data and finds groups that reflect the behavior of a set of users, e.g., "Asian women who publish regularly in databases" [START_REF] Omidvar-Tehrani | Multi-objective group discovery on the social web[END_REF]. Once groups are discovered, group exploration is used to tackle information overload in the plethora of generated groups by enabling navigation in the group space [START_REF] Omidvar-Tehrani | Interactive user group analysis[END_REF]. To render groups in a human-understandable form, group visualization maps groups to visual variables [START_REF] Amer-Yahia | Exploration of user groups in VEXUS[END_REF][START_REF] Vikram Pandey | The persuasive power of data visualization[END_REF]. The following example describes a realistic scenario of analyzing user groups.

Example. Emma is an organizer of movie critics panels. She aims to gather a diverse set of reviewers at the first screening of Drama and Comedy movies. For this task, she relies on the MovieLens movie rating dataset. 1 She first discovers groups of reviewers with common demographics. In order to pick diverse reviewers for both Drama and Comedy genres, she needs to expand and explore various discovered groups. For a better goal-oriented exploration, she decides to visualize exploration options. Through visual inspection, she handpicks reviewers from groups with different age categories and occupations, and builds a diverse set of reviewers.

To date, user group analytics is done using separate tools. As these approaches only address isolated parts of the group analytics pipeline, they impede analysts to obtain end-to-end group-centric insights directly from raw user data. In [START_REF] Omidvar-Tehrani | Multi-objective group discovery on the social web[END_REF][START_REF] Agrawal | Automatic subspace clustering of high dimensional data for data mining applications[END_REF][START_REF] Mark Ej Newman | Detecting community structure in networks[END_REF], thousands to millions of user groups are discovered using different methods such as subspace clustering and community detection, but it is not clear how analysts can find an interesting subset of results in this voluminous search space. In [START_REF] Amer-Yahia | Exploration of user groups in VEXUS[END_REF][START_REF] Felix | Texttile: an interactive visualization tool for seamless exploratory analysis of structured data and unstructured text[END_REF], on the other hand, a visual analytics methodology is proposed to explore user groups as first class citizens, but it is not clear how those groups should be discovered first. In such cases, analysts must connect different analytical tools together (e.g., provide the output of a group discovery or group exploration method as the input for a group visualization method) to manually build a pipeline. The drawbacks of fragmented pipelines are mentioned as follows (C1 to C5). C1: Lack of connectivity. When group analytics components are not inherently connected (i.e., siloed components), they are agnostic about the functionality of each other, hence the whole pipeline would not function in an optimized and cooperative way. For instance, an analyst wants to visualize two millions of generated groups using a visualization library such as D32 . As the visualization layer is not aware of the group discovery's output, it aims to visualize all groups at once, which exceeds browser's buffer limit. C2: Switching cost. In user group analytics, it is crucial for analysts to be able to switch between group-view and user-view (i.e., members of groups) easily. For instance, an analyst may need to verify users of a generated group in a visual view. Lack of connectivity between components results in switching costs, i.e., the visual component may need to send a new overhead request to the discovery component and ask for members of a discovered group.

Domain

Group-centric needs Health-care [START_REF] Omidvar-Tehrani | Cohort representation and exploration[END_REF][START_REF] Jiang | Cohort query processing[END_REF] How has the health status of a group of patients with common symptoms evolved? Crowdsourcing [START_REF] Rahman | Optimized group formation for solving collaborative tasks[END_REF] What is the most appropriate group of workers to perform a collaborative task (e.g., sentence translation)? Airline [START_REF] Aksoy | Airline services marketing by domestic and foreign firms: differences from the customersâĂŹ viewpoint[END_REF] Which groups of travelers tend to travel to promoted destinations? Finance [START_REF] Hu | A data mining approach for retailing bank customer attrition analysis[END_REF] What are customer groups in a bank with common trends (e.g., high attrition rate) for product targeting? Sport [START_REF] Machado | Visual soccer match analysis using spatiotemporal positions of players[END_REF] Which group of players has an exceptional performance for improvement planning? Education [START_REF] Palombi | Ontosides: Ontology-based student progress monitoring on the national evaluation system of french medical schools[END_REF][START_REF] Rani | An ontological learning management system[END_REF] How is the overall progress of student groups who are weak in a specific discipline? Fairness [START_REF] Speicher | A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices[END_REF] How much fair different classification algorithms are to sensitive groups (identified by race, gender, and age)? In this paper, we introduce UserDEV, a full-fledged user group analytics pipeline which combines Discovery, Exploration, and Visualization of User groups, in a fully-connected fashion. UserDEV provides a star-like architecture where both components and analysts are connected to a central node (corresponding to C1 and C2). This central node is responsible for handling analyst's input as well as connecting components together, using a common data exchange model as a communication means (corresponding to C3 and C4). The common data exchange model captures properties of user data and user groups and is independent from any particular implementation of group discovery, exploration, and visualization (corresponding to C5).

While there exists several data analytics pipeline approaches ( [START_REF] Zaharia | Apache spark: a unified engine for big data processing[END_REF][START_REF] Dean | Mapreduce: simplified data processing on large clusters[END_REF][START_REF] Rabl | Apache flink in current research[END_REF], to name a few), the literature on discover-explore-visualize pipelines for user groups is sparse, as it is an ongoing research direction. Mixed-initiative systems are proposed in [START_REF] Hu | Dive: A mixed-initiative system supporting integrated data exploration workflows[END_REF][START_REF] Daniel B Perry | Vizdeck: Streamlining exploratory visual analytics of scientific data[END_REF][START_REF] Van Den Elzen | Small multiples, large singles: A new approach for visual data exploration[END_REF][START_REF] Adil Yalçın | Keshif: Rapid and expressive tabular data exploration for novices[END_REF][START_REF] Wongsuphasawat | Voyager: Exploratory analysis via faceted browsing of visualization recommendations[END_REF] to incorporate analyst interactions and enable explorations on raw data sources. However, most visualization insights are in the form of simple histogram and regression recommendations, whereas user groups require more expressivity in visualizations. Picasso [START_REF] Huang | Picasso: exploratory search of connected subgraph substructures in graph databases[END_REF] and C-Explorer [START_REF] Fang | Cexplorer: Browsing communities in large graphs[END_REF] are other instances where user group components are combined to serve one specific aim, i.e., graph and community visual exploration. Lumira [START_REF] Jugel | Faster visual analytics through pixel-perfect aggregation[END_REF] and VAS [START_REF] Park | Visualization-aware sampling for very large databases[END_REF] are visual exploration methods which prune discovered groups based on available pixels to visualize. Ermac [START_REF] Wu | The case for data visualization management systems: vision paper[END_REF] is a vision to connect different components (in our case, group analytics components) together using a common data exchange model, and execute the whole session with a traditional database engine. All aforementioned approaches suffer from some or all of the drawbacks C1 to C5, as they are tailored to specific applications, and a common communication means is not defined between the components. Paper organization. Section 2 defines the user data model. Section 3 describes design considerations. Section 4 provides details on the functionality of UserDEV's pipeline. Section 5 reports a realistic use case of user group analytics in UserDEV. In Section 6, we present a preliminary user study which evaluates the usefulness of our framework, and discuss on-going efforts and challenges in evaluating our framework.

USER DATA MODEL

UserDEV requires a generic data model to support the variety of user data in different domains. We define user data D as a quadruple ⟨U, I, E, X⟩, where U is a set of users, I is a set of items, E is set of events, and X is a similarity matrix. Each user u ∈ U is described as u = {⟨a, v⟩}, where a ∈ A is a demographic attribute (e.g., gender, age, occupation), and v is a value in a's domain, i.e., v ∈ dom(a). For instance u 1 = {⟨gender, female⟩, ⟨age, young⟩, ⟨occupation, student⟩} represents a young female student. Similarly, each item i ∈ I is described using attributes such as movie information or medical treatments. The set E connects users and items, and contains triples e = ⟨u, i, t⟩ which describes that user i performs an action (e.g., rating, watching, voting, consuming) on item i at time t. Last, X captures similarities between user pairs, and contains tuples ⟨u, u ′ ⟩ → [0, 1] which associates a similarity value to the pair of users u and u ′ .

A user group is a triple д = ⟨members, demogs, items⟩ where д.members ⊆ U, and "demogs" and "items" contain expressionbased conditions that those members should satisfy:

• ∀u ∈ д.members, ∀⟨a, v⟩ ∈ д.demogs, ⟨a, v⟩ ∈ u; • ∀u ∈ д.members, ∀i ∈ д.items, ∃⟨u, i, t⟩ ∈ E.
For instance, д 1 = ⟨{u 1 , u 2 , u 3 }, {⟨gender, female⟩}, {Titanic}⟩ represents a user group whose members are all females and watched the movie Titanic. We denote the set of all user groups as G.

DESIGN CONSIDERATIONS

To achieve a fully-connected pipeline for user group analytics, we need to resolve fragmentations between components, so that analysts can obtain end-to-end group-centric insights from raw user data. Akin to any other data pipeline, UserDEV consists of a "logical layer", which describes components and their inputs/outputs, and a "physical layer", where implementations are hosted. In the logical layer, UserDEV adapts a star-like architecture where all communications are orchestrated by a central node (i.e., a visual analytics interface) using a common data exchange model. Figure 1 User Data Index Generation file) or as a data stream. An ETL process (including data cleaning) precedes the data import to prepare data for analysis. Each record in user data describes one user action (e.g., rating a book). We consider the generic schema [user, item, value] for user data. For instance the tuple [Mary, Mr Miracle, 4] means that the user "Mary" rates the book "Mr Miracle" with the score 4 (out of 5). Each user is also associated to a set of demographics.

Pre-processing

The user data is given as input to a group discovery algorithm. VEXUS is independent of this process. For user datasets, different group discovery algorithms such as LCM [START_REF] Hu | A data mining approach for retailing bank customer attrition analysis[END_REF] and ↵-MOMRI [START_REF] Jiang | Cohort query processing[END_REF] can be used. In case of user data streams, STREAMMINING [START_REF] Agrawal | Automatic subspace clustering of high dimensional data for data mining applications[END_REF] and BIRCH [START_REF] Palombi | Ontosides: Ontology-based student progress monitoring on the national evaluation system of french medical schools[END_REF] can be employed. For each group, its members and their common attributes will be returned.

For efficient navigation in the space of groups, we build an inverted index per group in g 2 G that contains all groups in G {g} in decreasing order of their similarity to g. We use the Jaccard distance to compute the similarity between each pair of groups. To reduce both time and space complexity, we only materialize 10% of each inverted index which is shown in [START_REF] Rahman | Optimized group formation for solving collaborative tasks[END_REF] to be adequate to deliver satisfying results. Group Exploration. Fig. 2 shows a screen-shot of group exploration. Inspired from OLAP and in conformance with visual analytics principles [START_REF] Omidvar | Tutorial on data pipelines for user group analytics[END_REF], we consider five visual modules in VEXUS: GROUPVIZ, CONTEXT, STATS, HISTORY and MEMO. In GROUPVIZ, an explorer examines a limited number of groups to obtain one or more groups of interest. She can then ask to navigate to other groups which are similar to what she has already liked (i.e., interactivity). The explorer preference, captured in the form of feedback, is illustrated in CONTEXT. The sequence of selected groups is visualized in HISTORY. The explorer can backtrack to any previous step in HISTORY. The explorer may request to delve into more details and observe group members. In this case, an exhaustive set of statistics will be shown in STATS. At any stage of the process, the explorer can bookmark a group or a user in MEMO. The analysis ends when the explorer is satisfied with her collection UserDEV architecture where the visual interface is considered as the central node. In the following, we explain how common data exchange model and visual-centric analytics ensure that group analytics pipelines are fully connected with reduced switching, learning, and transfer costs.

Common Data Exchange Model

To reduce transfer cost in pipelines (i.e., C3) and ensure genericness (i.e., C5), it is necessary to define a common data exchange model to represent the inputs/outputs of all components. Formalizing such a model for group analytics is challenging, because of the different nature of the components. Group discovery has an optimization nature which admits user data as input and generates a group space as output. Group exploration has a focus on selecting a subset of groups which interests analysts. On the other hand, group visualization admits as input a set of groups, and returns their association to visual variables.

We consider the least common atomic concept among all components as the data exchange model, i.e., a subset of users, S ⊆ U. All conversations between the components are reduced to the set S. Discovery communicates its output to exploration and visualization in form of a group set. Also, exploration reports explored options as a group set. Visualization returns a set of users for another round of discovery or exploration. Note that all concepts of "user sets", "groups", "group sets", and even users in their entirety U, are instances of S. In Figure 1, labels on arrows illustrate these concepts. To minimize the transfer cost, we also add some meta data when transferring a set of users from one component to the other, i.e., the distribution of demographics for users inside the subset. This simple yet powerful communication means does not depend on any particular implementation of components in the physical layer, and guarantees genericity of UserDEV. Hence, the components are considered as generic black-boxes whose inputs and outputs are all instances of S.

Visual-centric Analytics

In most data analytics scenarios, analysts make sense of results using visualization [START_REF] Heer | Tutorial on data visualization and social data analysis[END_REF]. In UserDEV, we consider the group visualization component as the central node which connects other components together. This is the one destination where both analysts and user group analytics components meet. UserDEV is the first system which combines the power of a visual interface with discovery and exploration of user groups.

In UserDEV, the visualization layer communicates with the analyst to either output a visualized subset of users as the final result, or get as input a subset of users. The input subset can feed either discovery or exploration components. In the former, discovery generates all interesting groups within the input subset. In the latter, exploration finds groups that are relevant to the input subset. In both cases, the output will be delivered to the visualization layer again, in form of a group set (i.e., an instance of S). The set will then be visualized using visual variables. The system will iterate again based on new feedback on the visualized results.

FRAMEWORK

UserDEV provides a visualization-centric analytics interface which communicates with group discovery and exploration using a common data exchange model S. In this section, we review the functionality of UserDEV components. We also discuss how UserDEV is used as a visual enabler for user group analytics.

Group Discovery

User group discovery refers to a set of approaches which derive value from user data in the form of user groups. Discovery is defined as a function discover(D, ρ) → G which admits as input the user data D and an optimization objective ρ, and returns the set of groups where ρ(G, D) is optimized. In [START_REF] Geng | Interestingness measures for data mining: A survey[END_REF][START_REF] Kirchgessner | Testing interestingness measures in practice: A large-scale analysis of buying patterns[END_REF], several optimization objectives for group discovery are listed, such as frequency, density, coverage, and surprisingness.

To discover user groups in D, different aspects of the data can be used, i.e., demographic attributes A, items I, and similarities X. Attribute-based discovery methods consider users as individual entities and leverage their common demographic attributes to form groups (e.g., discovering the group of young female students) [START_REF] Omidvar-Tehrani | Multi-objective group discovery on the social web[END_REF][START_REF] Das | Mri: Meaningful interpretations of collaborative ratings[END_REF][START_REF] Liu | Finding pareto optimal groups: group-based skyline[END_REF]. Item-based discovery mines groups based on common items between users (e.g., discovering a group of users who watched the movie Titanic) [START_REF] Agrawal | Automatic subspace clustering of high dimensional data for data mining applications[END_REF]. Similarity-based discovery methods leverage connections between pairs of users (e.g., affinity, following, friendship) and divide users into communities with stronger internal connectivity than external connectivity [START_REF] Plantié | Survey on social community detection[END_REF][START_REF] Kim | Community detection in multi-layer graphs: A survey[END_REF][START_REF] Rossetti | Community discovery in dynamic networks: a survey[END_REF][START_REF] Harenberg | Community detection in large-scale networks: a survey and empirical evaluation[END_REF][START_REF] Leskovec | Empirical comparison of algorithms for network community detection[END_REF][START_REF] Gregory | Automatically identifying groups based on content and collective behavioral patterns of group members[END_REF] In UserDEV, the analyst defines the subset of users, the optimization function, and the aspect of data that should be used for discovery. The system then returns the set of groups with optimized values on the optimization objective.

Group Exploration

Exploration refers to a set of approaches which enable interaction with user groups. It is subsumed by HILDA (Human-in-the-Loop Data Analytics) which reflects the involvement of analysts in the data analytics pipeline. Group exploration helps analysts navigate the space of user groups to obtain insights and validate their hypotheses on user data [START_REF] West | Automatic versus human navigation in information networks[END_REF]. In many exploration scenarios, the analyst only has a partial understanding of her needs and seeks to refine them as she extracts more insights from the data. Exploration is defined as a function explore(д) → P(G) which admits as input a group д ∈ G and returns a set of relevant groups to д. Note that P(G) is the power-set of G.

G Explora@on box

Explore groups

Output size 6

B Users

Save users At each exploration iteration, the analyst increases her partial understanding of the analysis task. UserDEV captures this awareness in form of "group example", and returns few other groups similar to the provided example, by aggregating similarities in X [START_REF] Omidvar-Tehrani | Interactive user group analysis[END_REF].

C Items D Demographic Distribu@on

The exploration parameter "output size" (denoted as k) defines how many groups should be returned as exploration options. UserDEV returns by default top-k groups with minimal "overlap" in-between (by minimizing Jaccard similarity between group pairs), to enable analysts investigate different directions. For simplicity, we assume that UserDEV hard-wires an overlap-based exploration [START_REF] Omidvar-Tehrani | Interactive user group analysis[END_REF]. However in practice, other exploration mechanisms can also be employed, e.g., contrast-based exploration [START_REF] Omidvar-Tehrani | Cohort representation and exploration[END_REF] and multi-objective exploration [START_REF] Omidvar-Tehrani | Multi-objective group discovery on the social web[END_REF]. Note that the output of exploration is always an instance of S.

Group Visualization

Visualization is the central node in UserDEV as it orchestrates the components of user group analytics. At the core of visualizing user groups sits a mapping function visualize(G ⊆ G) → V(G) which associates G's characteristics (size, overlapping users, items in common, user similarities) to visual variables, i.e., V(G).

The literature contains very few approaches for visualizing user groups. Traditionally, this is performed directly on raw user data using off-the-shelf visualization products and libraries such as D3, Tableau3 , Spotfire4 , QlikView5 , Gephi6 , and OpenGL7 . Applied directly on raw data, these solutions are mostly static and do not fully support sophisticated views and exploratory analysis on user groups. More advanced techniques employ graph visualization, where nodes are either users or groups, and edges are weighted using similarity values or overlapping users, respectively [START_REF] Di Battista | Graph drawing: algorithms for the visualization of graphs[END_REF][START_REF] Herman | Graph visualization and navigation in information visualization: A survey[END_REF][START_REF] Koutra | Perseus: an interactive large-scale graph mining and visualization tool[END_REF].

Figure 2 illustrates the visual interface of UserDEV applied to MovieLens. The interface has distinct views to display information and statistics of users and groups, and enable access to discovery and exploration. UserDEV uses a coordinated user interface that updates the information displayed after any interaction. Hereafter, we explain different views of this interface, and discuss how the entirety of views addresses concerns of fragmented pipelines.

A "Users similarity" view displays a 2D projection of the similarity values in X using t-SNE projection [START_REF] Van Der Maaten | Visualizing high-dimensional data using t-sne[END_REF], which dictates the position of users in the view. This provides an overall view of the user data. UserDEV computes similarities based on commonalities in E. Two users u and u ′ are maximally similar iff ∀⟨u, i, t⟩ ∈ E, ∃⟨u ′ , i, t⟩ ∈ E. Analysts can color-code users based on an attribute. For instance in Figure 2, users are color-coded based on their dominant genre, i.e., the genre for which they have reviewed movies the most. Analysts have access to a lasso tool which lets them pick a subset of users. B "Users" view lists user's detailed information in form a table whose schema contains demographic attributes A. In case of Movie-Lens, demographic attributes are gender, age, occupation, and total number of reviews. Users can be sorted according to any column in the table, by clicking on the column. Analysts can also perform search on users using any demographic attribute. By default, the view lists all users. When the lasso tool is used in view A, the list will be limited to the users retained by the lasso. The analyst can select one or multiple users in this table and keep them for future investigation (view E), by clicking on the "Save users" button. C "Items" view lists items in form of a table whose schema is item's attributes. The view only shows items associated to users in view B. Hence a lasso in view A restricts the list of items as well. Items can be sorted according to any column in the table, by clicking on the column name. Analysts can also perform search on items using any item attribute. D "Demographics distribution" view displays statistics over the demographic attributes of users. The statistics is only computed for users in view B. Hence a lasso in view A changes the statistics as well. Analysts can interact with the charts in this view by clicking an attribute value, e.g., the "female" bar in the gender bar chart. This will filter users in all other views to ones which satisfy the selected attribute value. A second click on a selected attribute value will undo the operation. E "Save area" view displays the context of a buffer that lists the users saved in view B. Analysts can remove any user from this view or even truncate the buffer to restart the selection process. F "Discovery box" view lets analysts discover groups, by specifying user data, and configuring discovery parameters, i.e., optimization objective, and discovery aspect (demographic attribute, item, similarity, or all of them). User data should be adapted to the model discussed in Section 2. The "Users" parameter lets analysts limit the scope of discovery: the discovery can be done on the entire user data, or the selection shown in view E. It is shown in Figure 2 that the analyst requests an attribute-based discovery on a selection of users in MovieLens by optimizing frequency (a common optimization objective in data mining [START_REF] Srikant | Mining generalized association rules[END_REF]). To avoid information overload, UserDEV does not show all discovered groups G. Instead, it shows a subset only as a result of exploration (i.e. view G). G "Exploration box" view is an area for displaying the results of group exploration. Group exploration functions on the set G. Hence before any exploration, at least one round of discovery (view F) is required. By default, the input group д ∈ G for exploration is the one whose members are shown in view E. The analyst can also click on one of the exploration groups to set it as the input group for the next exploration round. The exploration is fired when the button "Explore groups" is clicked.

Views A to G constitute the visualization component of UserDEV. A valid question is "how do these views address concerns of fragmented pipelines, collectively?" The views in UserDEV are dependent on each other and consume common resources. Hence they are aware of each other's situation (addressing C1). For instance the buffer in view E makes a dependency connection between visualization of users in view A and both discovery and exploration, in views F and G, respectively. On the other hand, the views enable the analysis of users and groups at the same time, hence reducing the switching cost (i.e., C2). Also all views communicate only using the common data exchange model S, hence reducing the transfer cost (i.e., C3). The coordinated interface of UserDEV facilitates the explainability of results, hence reducing the learning cost. The consequence of each interaction will be immediately displayed on all other views which helps analysts keeps a bird's-eye view on their data (i.e., C4). Also thanks to its simple communication model, UserDEV views are independent from any physical implementation of discovery/exploration algorithms, hence a variety of algorithms can be employed (addressing C5).

USERDEV IN PRACTICE

In this section, we extend the example in Section 1 as a use case and demonstrate how UserDEV helps analysts exploit user groups, in practice. Recall that Emma's goal in our example is to gather a diverse set of reviewers at a first screening of Drama and Comedy movies. We set out to identify a group that contains reviewers with Comedy as their dominant genre, reviewers with Drama as their dominant genre, and a mix of additional reviewers who differ from others either in demographics or in interests. Our use case is defined on the MovieLens 1M dataset where |U| = 6, 040, |I| = 3, 900 (items are movies), and |E | = 1, 000, 209 (events are movie ratings). Group Discovery. Initially, Emma wants to select a subset of users and discover groups in the subset. The traditional way of performing this is to first use Excel or a DBMS to select users, export the subset, re-format it to match the discovery's input, and then run discovery. An alternative way is to use a fully connected pipeline to reduce the burden. In UserDEV, Emma selects relevant users to her search using the lasso tool in view A. She then selects users with either Comedy or Drama dominant genres (green-shaded and red-shaded points, respectively) and saves them. The coordinated interface gets updated immediately to only display the information associated with the current selection. Then she requests to discover frequencyoptimized item-based groups (in view F) using the selection in view E. She is now ready to explore discovered groups. Group Exploration and Visualization. After discovery, Emma needs to explore groups and form her reviewer list. Typically it requires to import discovery results in an off-the-shelf visualization tool and follow several sub-optimal back-and-forth loops. In UserDEV, groups are ready for a visual exploration. Emma looks at views B and C to find an overall understanding of the subset of users and items she is currently looking at. Using view D, she performs selections on the age to only keep 25-34 years old, resulting in 78 reviewers (44 male and 34 female). She also limits the occupation to "academic/educator" resulting in 10 reviewers (6 male, 5 female). These 10 users constitute her input group for exploration. Emma then requests an exploration with k = 6 (following output size recommendations in [START_REF] Miller | Human memory and the storage of information[END_REF]) in view G. She will then examine the exploration options to pick interesting reviewers. For instance, she observes that members of 3 out of 6 groups review many Romance-genre movies alongside Comedy and Drama. Emma hand-picks 5 reviewers among those groups whose age > 34 and whose occupation differs from academic/educator (to corroborate diversity), and saves them. Users in the other groups review many Horror, Thriller, and Sci-Fi movies. She hand-picks 5 reviewers from those groups as well, with demographics of age being within 25-34 and occupation being academic/educator. By saving those 5 reviewers, view E contains 20 reviewers in total, which serve as Emma's reviewer list.

DISCUSSION ON EVALUATION

We described UserDEV, a visualization-centric pipeline for user group discovery and exploration. The principled question is "how such a group analytics pipeline can be evaluated?" In this section, we first present a preliminary user study to evaluate the effectiveness of UserDEV. Then we discuss future directions of more extensive evaluations for user group analytics pipelines.

We performed a preliminary user study on MovieLens with 24 participants. Most participants were male (70.83%) with an average age of 30 years old. Also, 51.7% of participants were experts in information visualization, and others were novice. We used the following physical implementations of group analytics components: LCM frequent item-set mining [START_REF] Uno | Lcm ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets[END_REF] as attribute-based and item-based group discovery (each frequent item-set is a group), IUGA [START_REF] Omidvar-Tehrani | Interactive user group analysis[END_REF] as group exploration, and a web-based GUI implemented in Angular framework as group visualization. Based on the categorization of visualization tasks in [START_REF] Shneiderman | The eyes have it: A task by data type taxonomy for information visualizations[END_REF][START_REF] Gotz | Characterizing users' visual analytic activity for insight provenance[END_REF], we consider six following tasks in increasing order of difficulty: τ 1 : "inspect demographics of users who mostly watch romantic movies, and discover groups accordingly", τ 2 : "filter users in age and save the smaller set", τ 3 : "explore groups with saved users as the input group", τ 4 : "identify two highly-reviewed movies which are watched both by the input group and users in explored groups", τ 5 : "detect differences in reviews among explored groups", τ 6 : "name few genres of users which are different from the ones for the input groups". Note that the first three tasks focus on the common data exchange model, and the rest on the visual-centric analytics.

We ask participants about their level of comfort (from 1 to 5) in performing these tasks where the scale 5 denotes the easiest.

Figure 3 illustrates the results in form of a box-plot, where tasks τ 1 to τ 6 are on the X-axis and their comfort score is on the Y-axis.

We observe that participants are successful in completing tasks τ 1 and τ 2 which relate to filtering user data and discovering groups. An average comfort scale of 4 for τ 1 shows the tight connection between group discovery and visualization. Tasks τ 3 and τ 4 seem to be more challenging as participants require to make group-level interactions while scanning users and movies. An average comfort scale of 3.5 for τ 3 shows the tight connection between group exploration and visualization. The task τ 5 requires visual comparisons among user groups, and our participants succeed to handle that. Concerning the task τ 6 , while most participants were able to complete it, many others complained about its difficulty as it needs the verification of several views. In general, an overall comfort scale of 3.62 shows that participants are at ease in performing user group analytics task with UserDEV.

Our user study is preliminary and requires further investigations on the architecture and common data exchange model. Hence the need for a principled usability evaluation arises, which we consider as a future perspective. Despite the established body of related work for evaluating group discovery, group exploration, and group visualization separately, there is no evaluation methodology for their combination [START_REF] Omidvar | Tutorial on user group analytics: Discovery, exploration and visualization[END_REF][START_REF] Jiang | Evaluating interactive data systems: Workloads, metrics, and guidelines[END_REF]. A valid question is whether we can evaluate UserDEV with a combination of methods proposed to evaluate its components? We briefly mention two opportunities of all-together evaluation of group discovery, group exploration, and group visualization, i.e., isolation and benchmarking. Isolation. The most popular approach is to isolate human-oriented aspects of UserDEV (i.e., exploration and visualization) and evaluate the remaining aspects using traditional discovery-based measures, such as execution time and memory usage. For human-oriented aspects, a user study must be designed using crowdsourcing platforms such as Amazon Mechanical Turk8 and CrowdFlower9 [START_REF] Inasu Chittilappilly | A survey of generalpurpose crowdsourcing techniques[END_REF].

Benchmarking. The quality of UserDEV can be assessed by comparisons against standard tests, i.e., benchmarks. Benchmarks are a common practice in the database community (e.g., LDBC social network benchmark [START_REF] Erling | The ldbc social network benchmark: Interactive workload[END_REF] and REACT data exploration benchmark [START_REF] Milo | Next-step suggestions for modern interactive data analysis platforms[END_REF]). The visualization community still lacks a benchmark and relies on user studies as the only means of evaluation [START_REF] Munzner | A nested model for visualization design and validation[END_REF][START_REF] James | Illuminating the path:[the and development agenda for visual analytics[END_REF]. In [START_REF] Eichmann | Towards a benchmark for interactive data exploration[END_REF][START_REF] Battle | Evaluating visual data analysis systems: A discussion report[END_REF], a vision towards an interactive visual benchmark is proposed. One of our ongoing directions is to build a benchmark which covers the whole pipeline of user group analytics.

In summary, the evaluation of a user group analytics framework need to go far beyond typical user studies and quantitative measures. Domain-specific benchmarks should be designed to capture human factors in group analytics in an objective fashions and accompany subjective user studies.

ACKNOWLEDGMENT

This work is supported by CDP LIFE project under grant C7H-ID16-PR4-LIFELIG.

Fig. 1 .

 1 Fig. 1. VEXUS Architecture.

  cs Interface for Users and Groups

Figure 1 :

 1 Figure 1: UserDEV architecture.

Figure 2 :

 2 Figure 2: UserDEV visual interface displaying the MovieLens dataset.

Figure 3 :

 3 Figure 3: Comfort scales of tasks τ 1 to τ 6 .

Table 1 :

 1 Real-world scenarios centered on user groups.C3: Transfer cost. Inputs and outputs of different group analytics methods do not necessarily match. Hence there should be mediators (adapters) between each pair of components to transfer the output of the precedent component to the input of the ensuing[START_REF] Hu | DIVE: A mixed-initiative system supporting integrated data exploration workflows[END_REF]. This impedance mismatch puts burden on analysts to build a pipeline. C4: Learning cost. Fragmented pipelines lack explainability. As there is no inherent connection between the components, analysts cannot easily learn how an exploration option or a visual view originate from group discovery. Hence there is a learning cost associated to such manually-built pipelines, where analysts require to check data lineage in different components manually.

	C5: Lack of genericity. Even if a pipeline is manually built by
	an analyst to serve her specific group-centric goals, it cannot be
	re-used for other applications, as it lacks genericity. For instance,
	C-Explorer [22] provides a visual exploration layer tailored to
	groups generated by attributed community search, and it needs re-
	configuration for employing another community detection method.

  illustrates

	arty ber ying list rms XUS s in	User Database	User Group Discovery
	for-
	ose and bers data rth, ned the s in ives p of ging for.
	sce-rtial fine loits ons. s it -fly rves ding eam ems ung
	12]. ork tory
	ech-ized and stics one sly. oup iew

https:// grouplens.org/ datasets/ movielens/ 1m/

https://d3js.org

Tableau software suite: https://www.tableau.com/

Tibco Spotfire: https://spotfire.tibco.com

QlikView: https:// www.qlik.com/ us/ products/ qlikview

Gephi: The Open Graph Viz Platform: https://gephi.org

https:// www.opengl.org

https:// www.mturk.com

https:// www.crowdflower.com