L Joly-Pottuz

A Bogner

A Lasalle

A Malchere

G Thollet

S Deville

Improvements for imaging ceramics sintering in situ in ESEM

Keywords: ESEM, high-temperature, in-situ, sintering, ceramics

Sintering of green samples of alumina produced by ice-templating was followed in-situ in an environmental scanning electron microscope (ESEM) up to temperatures as high as 1375°C. These alumina samples with well-defined architectures are of great interest in the field of materials science due to their high specific strength (especially in compression), low density and adaptable porosity. For the present study, they also have the advantage to exhibit an important topography, inducing interesting contrast when imaged in ESEM. Improvements of the imaging conditions in ESEM were essential to really follow the sintering process involving formation of necks between grains or shift of the center of grains. This paper describes the improvements made and the results observed on the sintering process of alumina green samples processed by ice-templating.

Introduction

Using ice as a templating agent is a novel processing strategy to synthesize porous and highly textured ceramics [START_REF] Deville | Freezing as a path to build complex composites[END_REF])- [START_REF] Munch | Tough, Bio-Inspired Hybrid Materials[END_REF]. Studies are conducted to better understand how ceramics slurries behave during the ice-templating process. This approach is a way to improve the final structure via the control of the green samples structure [START_REF] Zou | Effect of Suspension State on the Pore Structure of Freeze-Cast Ceramics[END_REF])- [START_REF] Deville | Influence of Particle Size on Ice Nucleation and Growth During the Ice-Templating Process[END_REF], but the control of the sintering process is also essential as it influences the density of ceramic walls, the porosity and so the properties of the final material which determine its potential for future applications. The sintering mechanisms can be investigated through the observation of sintered ceramics at different steps of the sintering process. Another and more direct way is by following the structure changes of the sample during heating. Environmental scanning electron microscope (ESEM) allows such dynamic study during heating: the sintering of ceramics can be performed in-situ inside the ESEM. Indeed, this approach allows heating the sample at high temperatures, thus studying the sintering process of the powder with a resolution good enough to follow the grain behavior (shift, coalescence, etc). Furthermore, alumina green samples synthesized by ice-templating present a complex structure composed of lamellae. To follow the evolution of the whole sample, the evolution of both lamellae and grains inside the lamellae must be studied. By using several magnifications inside the ESEM, both evolutions can be followed. With TEM technique, even if the resolution would be better to follow the grains, the sample could not be bulk: the sample preparation would be very delicate since green samples are too brittle to obtain a thin foil. Moreover, the heating stage should allow a homogeneous heating of the whole sample.

High temperature experiments inside an ESEM are not straightforward. Few studies in the literature report experiments of heating inside a scanning electron microscope. Srinivasan et al. studied the evolution of the structure and the morphology of iron powder in an ESEM up to 1123 °C [START_REF] Srinivasan | Dynamic study of changes in structure and morphology during the heating and sintering of iron powder[END_REF]. Sintering of copper particles with an original system which allows to apply force on particles during sintering was performed [START_REF] Smith | Use of a micromanipulator at high temperature in an environmental scanning electron microscope to apply force during the sintering of copper particles[END_REF]. Concerning ceramic materials, the behavior of refractory castables under high temperature was followed inside in ESEM up to 1300°C [START_REF] Marzagui | Characterisation of microstructural evolutions in refractory castables by in situ high temperature ESEM[END_REF]. Subramaniam et al. studied the sintering process of BaTiO3 at magnifications 5k and 10k with a heating up to 1300°C (the biggest spot size was used to keep an important signal level, but it induced a lost of resolution) [START_REF] Subramaniam | In situ high temperature environmental scanning electron microscopic investigations of sintering behavior in barium titanate[END_REF]. Nevertheless, no study at higher temperatures and magnification was reported. Above a temperature of 1300°C, thermal emission becomes a serious problem. Imaging becomes very difficult because contrast and signal to noise ratio of images is degraded. In this work, the imaging conditions were optimized to perform the sintering of alumina green samples produced by ice-templating and to follow the evolution of the samples in-situ during heating up to 1375°C and at magnification as high as 40k. Several experimental conditions were studied (material of the crucible used, nature and pressure of the chamber environment, beam spot size, sample and thermal shield biases) in order to obtain images on which grain sizes, grain shifts and densification of the samples could be followed. Images were recorded at several magnifications in order to follow both the general evolution of the sample (evolution of the lamellae) and the evolution at the grain scale. After optimization of the imaging conditions, the shrinkage obtained by heating the sample in different atmospheres was studied in order to determine if the environment used could have an influence on the structure obtained and to correlate in-situ experiments and classical ex-situ sintering process.

Materials and methods

Preliminary observations were made on samples of ZnO powder -its sintering is indeed easier to observe as it occurs at relatively low temperature -and on TM-DAR green samples synthesized by slip-casting. Then green samples of two α-alumina powders: TM-DAR (Taimei Chemicals, Tokyo, Japan) and Ceralox SPA 0.5 (Sasol North America Inc., Tucson, AZ) produced by ice-templating were observed and heated. The mean grain size of the TM-DAR powder is 100-200 nm, resulting in a sintering temperature around 1250°C. The mean grain size of the Ceralox powder is 400 nm with a sintering temperature of 1450°C. Samples produced by ice-templating present a lamellar structure (figure 1) which induces interesting topographic contrast. Green samples subject to "classical" sintering in air (performed ex-situ in an oven) were also observed inside the microscope. The aim was to compare these structures with the ones of samples sintered inside the microscope. The ex-situ heating cycle was as follows: first, a binder burnout in two steps : heating at 50°C/h from 20°C to 490°C, isotherm at 490°C for 1h, heating at 30°C/h from 490°C to 550°C, isotherm at 550°C for 30 min and then the sintering step heating at 300°C/h from 550°C to 1425°C, and isotherm for 1h at 1425°C for samples prepared with Ceralox powder and at 1250°C for TM-DAR.

In-situ experiments were performed in an FEI XL 30 FEG ESEM equipped with a heating stage allowing heating the sample up to 1500°C. A scheme of the device is presented in figure 2. A gaseous secondary electrons detector (GSED) was used to collect the signal. A thermal shield was placed between the heating stage and the GSED to prevent damage of the detector from infrared radiations and also to ensure a better regulation of the temperature of the sample. This shield can be positively biased (from 0 up to 300V). The crucible containing the sample can be biased from -46 to 46 V. These biases can be changed during the experiments to optimize the contrast and brightness of images.

Imaging was optimized in the following conditions: high voltage at 30 kV, spot size 4 at low temperature and 5 at higher temperature, gas pressure around 2-3 Torr and working distance of 20 mm. The working distance used in this work was the minimal distance possible with our system to avoid a contact between the heating stage and the thermal shield (see figure 2). The gas pressure was also optimized in situ at this working distance in order to have a good compromise between enough SE signal amplification and acceptable signal to noise ratio.

A thin foil of the green sample was cut with a razor blade. The thinnest foil is chosen in order to insure a good thermal conductivity between the crucible and the sample. We can assume that the surface of the sample is at the same temperature than the crucible. Thus the surface represents the whole structure evolution.

First, a crucible made of MgO and covered with a Pt layer, available with the heating stage, was used. Then, to enhance the signal and contrast of the images, a new homemade crucible made of platinum (90% Pt-10% Rh) was used.

The measurement of the temperature was calibrated for the use of the commercial MgO crucible. Thus, it was necessary to calibrate the system with the Pt one to be sure to have a good measurement of the temperature. For this experiment, a thermocouple was directly placed in the crucible wall (in a small hole) and the temperature was been followed during a heating experiment. A calibration curve was plotted and shown that the temperature is very close to the setting temperature in the range 200-1400°C.

Three different gases were tested: water, N2 (Linde Gas 5.0) and dry air (Alphagaz 1).

The ex-situ heating cycle duration is more than 24 hours due to the low heating rates chosen. The in-situ heating cycle was chosen so that the duration of one experiment does not exceed 10 hours for sake of security. It is started by heating at 50°C/min from 20°C to 490°C. Then the heating rate was decreased at 5°C/min from 490°C to 550°C to follow the binder and dispersant burnout. After this step, a relatively low heating rate of 20°C/min was chosen in order to be able to follow the evolution of the sample. Then, if required, XRD analyses of sintered samples were performed ex-situ with a Bruker diffractometer (Cu anticathode, λcu 1.54060 Å).

Results and discussion

Optimization of the experimental conditions

As mentioned previously, several parameters can affect imaging conditions: bias of sample and thermal shield, spot size of the beam, nature of crucible used, nature and pressure of gas used in the chamber, working distance, etc.

Both the sample and the shield can be biased: a positive bias of the shield improves the signal by attracting electrons in the direction of the GSED detector; a negative bias of the sample also improves the signal since electron emission from the sample is easier. Thus, at low temperature, a negative bias of the sample and a positive bias of the shield were used. But at high temperature, thermionic emission becomes too significant, and a positive bias of the sample can be necessary to restrain the thermionic emission. Unfortunately, secondary electrons present the same energy range than thermionic electrons and are also retained by the sample. In that case, only backscattered electrons can be collected by the GSED and images obtained have a zcontrast and not a topographic one.

Spot size of the beam chosen for the observation also affects imaging. To improve the resolution, the spot size must be reduced. But by reducing the spot size, the signal is also reduced. In fact, the spot size must be chosen large enough to have signal but small enough to keep a sufficient resolution.

The first experiments were performed with the MgO crucible and revealed that the nature of the crucible can also affect imaging. By using this crucible, a quick loss of signal of the images from 900-1000°C was observed. Even with contrast enhancement, it was not possible to follow grain rearrangement or grain coalescence during sintering. This loss of signal can be explained by the thermionic emission from the sample holder and the sample, blurring the image. Thermionic electrons emitted have the same energy than secondary electrons and thus both types of electrons are collected (figure 3).

The thermionic emission can be described by the Richardson-Dushman law where J is the emission current density (A/m 2 ), Φ is the work function of the metal and A is the Richardson constant:

𝐽 = 𝐴𝑇 2 exp - (1)
From this equation, it appears that one way to limit the thermionic emission is to use a material with a high work function for the crucible. Platinum is the best compromise with high work function and melting temperature. Polycrystalline platinum has a work function equal to 5.64 eV (Weast,1981). Lim et al. determined the work function of MgO single crystal at several orientations (111), ( 200) and ( 220) to be respectively equal to 4.22 eV, 4.94 eV and 5.07 eV [START_REF] Lim | Work function of MgO single crystals from ion-induced secondary electron emission coefficient[END_REF]. For a polycrystalline MgO sample, the work function can be estimated as an average of these three values i.e. 4.7 eV. By changing the MgO crucible into a Pt one, we can expect a limitation of the thermionic emission (figure 4). This is confirmed by a calculation of the thermionic emission from MgO and Pt, which shows that thermionic emission from Pt will become important only from 1200°C, instead of 1000°C in the case of the MgO crucible. These results are in good agreement with the observations performed in the microscope during in-situ sintering. With the MgO crucible, signal is lost at 1050°C. With the Pt crucible, images obtained at 1300°C still exhibit a good signal allowing the observation of changes of the sample. It is important to notice that the bottom of the MgO crucible was covered with a Pt layer. But this layer was too thin and not homogeneous, thus not preventing thermionic emission.

A comparison of images obtained at a temperature of 900°C with the two kinds of crucibles confirms that better images are obtained with the Pt crucible (see figure 5).

In the case of the MgO crucible (figure 5a), even after contrast enhancement of the image, it was not possible to observe changes of the grain. In the case of the Pt crucible, good quality images were obtained and it was possible to observe the grains (figure 5b). Furthermore, the grain sizes of the powder observed with the Pt crucible (Al2O3 green samples obtained by slip-casting) was smaller than the one of the powder observed with the MgO crucible (ZnO powder). These examples clearly prove the interest of using a Pt crucible to improve imaging at high temperature.

Another parameter checked in order to improve imaging is the nature of the gas used in the chamber during the experiment. We investigated three gases: water -the most common gas used in ESEM-, N2 and dry air in order to be closer to the ex-situ sintering conditions. Figure 6 presents images obtained with the three gases at several temperatures. During the experiments, bias of both the sample and the thermal shield was changed to optimize imaging. With the three gases, a loss of signal is observed when heating the sample. However, water is the only gas which allows observing the sample at 1300°C. In the case of N2, it is difficult to obtain good quality images at 1250°C (see figure 6f). With dry air, at 1050°C the signal becomes weak (see figure 6h). At 1200°C, the sample was positively biased in order to keep the thermionic electrons away from the detector (otherwise the detector was saturated). But a lot of secondary electrons with the same energy as the thermionic ones were not collected either. Thus the image was obtained by collecting backscattered electrons and corresponds to a chemical contrast and not a topographic one. With this kind of images, it is not possible to follow the evolution of the grains. Results obtained with water were not surprising and several studies have already been described in the literature to compare the performances of gases. Fletcher et al. studied the amplification signal of water compared to nitrogen and other gases (Fletcher, 1997). They conclude that water presents the best signal amplification due to the ionization properties of the gas. In the case of dry air, electric discharges can occur during observation. They occur at high temperature and must be avoided since they can damage the microscope device. Results obtained in the present study clearly indicate that water is the most efficient gas to reach good and stable imaging conditions.

3.2-Study of densification

During in-situ sintering, images at several magnifications were recorded. Images at low magnification of one platelet (see figure 7) were used to follow the densification of the green samples during heating. For the experiments performed in water, the evolution was followed from ambient temperature up to 1375°C. In the case of dry air, the evolution was followed up to 1050°C, then the sample was heated up to 1300°C or 1450°C without imaging, and images were recorded during cooling of the sample (at 400°C after heating at 1300°C and at ambient temperature after heating at 1450°C).

Results obtained on the shrinkage for both environments are summarized in tables 1 and 2.

In the case of green samples sintered in a water environment (table 1), a dilatation of the sample due to increasing temperature was observed first until 1000°C, followed by a shrinkage. To evaluate the total shrinkage, measurements were performed on images at ambient temperature before and after heating: it was estimated at 7-8%. This low value can be explained by the short holding time at 1284°C during the experiment. Experiment at higher temperature shown a small evolution of the shrinkage up to 9%. In dry air, as for the water environment, a dilatation of the sample was observed first, followed by a shrinkage. After sintering at 1300°C, the total shrinkage observed at 400°C was equal to 7%. Considering that the shrinkage would be a little bit more pronounced at ambient temperature, we can conclude that the same shrinkage was obtained with water and dry air. After heating at 1450°C, a shrinkage of 11% was observed.

After the sintering process of the Ceralox powder under a water environment, a glassy surface was observed (figure 8a). It seemed that a compound was covering the surface since it was not possible to distinguish grains anymore. Our first assumption was the formation of boehmite due to the reaction between water and alumina. But the same experiment in a N2 environment led to the same surface aspect of the sample (figure 8b). X-Ray diffraction of the two samples confirmed that the surface was not covered with boehmite (figure 9) as characteristic peaks of boehmite (at 15°, 28°, 38°) were not observed (JCPDS 01-074-1895). Peaks observed were characteristic of α-alumina, except one peak at 21°, corresponding to AlPO4 (JCPDS 011-0500) and only observed in the case of Ceralox powder.

From these results, it seems that Ceralox powder with a sintering temperature higher than the TM-DAR powder reacts with a compound inside the chamber of the microscope at high temperature. At this temperature, the TM-DAR powder is already sintered and thus less reactive: it does not react with this compound. Subramaniam et al. found the presence of Al(H2PO4)3 in the heating jacket of their heating stage [START_REF] Subramaniam | In situ high temperature environmental scanning electron microscopic investigations of sintering behavior in barium titanate[END_REF] which led to the release of phosphate inside the chamber during sintering. In our study, a piece of the heating jacket of the heating stage used in this study was analyzed by XRD. This analysis confirmed the presence of AlPO4 and the assumption of a reaction between the powder and a compound present in the microscope chamber. The heating jacket of our heating stage has to be changed in order to perform heating experiment at very high temperatures. In our case, as the TM-DAR powder does not react with the phosphate compound, it means that the release of phosphate occurred above the TM-DAR sintering temperature (i.e. above 1250°C). This temperature does not match with Subramaniam's experiments. It is important to notice that the heating stages of new generation ESEM seem to present new jackets of stable alumina, avoiding such contamination problems.

The dynamic observation of the Ceralox powder sintering was not possible because of the formation of the phosphate compound which completely covered the sintered materials. But this did not occur in the case of the TM-DAR powder where the following of the sintering process was possible. With the water environment, the formation of necks was observed between grains (figure 10a). In this case, the sintering occurred with diffusion of atoms from the surface. This led to formation of necks between grains but the centers of grains do not move. No densification was observed.

The maximum shrinkage was equal to 7%. With the dry air environment, the diffusion of atoms occurred from the grain volume or from grain boundaries. This mechanism led to a densification of the ceramics (figure 10b). These results are in good agreement with the ones obtained in (Chinelatto, 2009). Our in-situ sintering experiments confirm the sintering mechanisms already observed on α-alumina.

However, it was not always straightforward to correlate sintering experiments performed in-situ inside the microscope and sintering experiments performed ex-situ in a classical oven used to prepare ceramics materials. Indeed, the heating cycle was not exactly the same as the one used for "classical" sintering for experiments inside the microscope. The reason is that the corresponding thermal cycle is too long to ensure security of such experiment in an ESEM (present experimental conditions are at the limit of the temperature range for the device). Final shrinkage obtained with "classical" sintering was equal to 18%. Inside the microscope the shrinkage observed was between 7 and 11%. This discrepancy can be explained by the difference of heating cycles and the duration of heat treatments of both experiments. Nevertheless, sintering process was observed inside the ESEM: it is the only technique allowing a direct observation of the evolution of the sample during heat treatment, with the resolution of a SEM.

Conclusions

The improvement of imaging conditions allowed performing in-situ observations at high temperature, above 1375°C, inside an ESEM. The use of a platinum crucible led to an increase of signal and contrast, essential for the present study. Other parameters (bias of sample and thermal shield, spot size of the beam and environment) were also proved to affect imaging. In terms of environment, with N2 or dry air, the image signal was lost at 1200°C and 1050°C respectively. Furthermore, with dry air electric discharges occurred during observation. Water was found to be the most efficient gas, as already observed in other studies in the literature. The water environment allowed imaging up to 1375°C. The shrinkage obtained was a function of the chamber environment used, thus correlation between in-situ experiments and classical sintering were not straightforward. However, the study of the shrinkage obtained by heating the sample in water or dry air was in agreement with others studies of alumina sintering, and allowed the description of the different sintering mechanisms. The results of the present study are promising: heating experiments with a Pt crucible are possible at temperature as high as 1375°C. Moreover, with the use of the heating stage available in the new generation of ESEM, and the higher beam current in these microscopes, we can expect that satisfying experiments would be possible up to 1500°C.

  Fletcher, A.L., Thiel, B.L., Donald, A.M. (1997) Amplification measurements of potential imaging gases in environmental SEM. J. Phys. D 30, 2249-2257. Chinelatto, A.S.A., Tomasi, R. (2009) Influence of processing environment on the microstructural evolution of submicron alumina powder during sintering. Ceramics International 35, 2915-2920.

Fig. 3 .

 3 Fig. 3. Scheme of the thermionic emission. Thermionic electrons have the same energy than secondary electrons.

Fig. 4 .

 4 Fig. 4. Thermionic emission of MgO and Pt calculated with the Richardson-Dushman relation.

Fig. 5 .

 5 Fig. 5. Comparison of images obtained at 900°C with (a) the MgO crucible and (b) the Pt crucible.

Fig. 6 .

 6 Fig. 6. Evolution of images obtained with three different gases at different temperatures: water (a) at 1000°C, (b) at 1200°C and (c) 1300°C; N2 (d) at 1000°C, (e) at 1100°C and (f) 1250°C; dry air (g) at 900°C, (h) at 1050°C and (i) at 1200°C.

Fig. 7 .

 7 Fig. 7. Measurement of shrinkage of samples treated (a) in water, (b) in dry air. Two directions were measured on both samples to evaluate the shrinkage.

Fig. 8 .

 8 Fig.8. Surfaces of Ceralox green samples after sintering process (a)in dry air and (b) in N2. A glassy surface is observed in both cases. Scale bar length: 2 μm.

Fig. 9 .

 9 Fig. 9. DRX analyses of Ceralox green samples sintered in water and in N2 inside the microscope or sintered in an oven, of TM-DAR green sample sintered in water inside the microscope, and of a piece of the heating jacket (α = alpha-alumina; * = AlPO4).

Fig. 10 .

 10 Fig. 10. Structure of TM-DAR after treatment (a) in water and (b) in dry air. Scale bar length: 1 μm.

Acknowledgements:

The authors would like to acknowledge two INSA-Lyon SGM department students C. Alaux and N. Herrbach for their training work, and the CLYM for the access of the FEI XL-30 FEG ESEM. Financial support was provided by the National Research Agency (ANR), project NACRE in the non-thematic BLANC programme, reference BLAN07-2_192446.

Fig. 1. Structure of the alumina green samples synthesized by ice-templating (Deville, 2008).

Fig.2. Scheme of the experimental device used in the ESEM.


Romain Cohendet
email: romain.cohendet@technicolor.com

Karthik Yadati
email: n.k.yadati@tudelft.nl

Ngoc Q K Duong

Claire-Hélène Demarty
email: claire-helene.demarty@technicolor.com

Annotating, Understanding, and Predicting Long-term Video Memorability

Keywords: Video memorability, Long-term memory, Scene understanding, Measurement protocol, Global video features, Attributes

Memorability can be regarded as a useful metric of video importance to help make a choice between competing videos. Research on computational understanding of video memorability is however in its early stages. There is no available dataset for modelling purposes, and the few previous attempts provided protocols to collect video memorability data that would be difficult to generalize. Furthermore, the computational features needed to build a robust memorability predictor remain largely undiscovered. In this article, we propose a new protocol to collect long-term video memorability annotations. We measure the memory performances of 104 participants from weeks to years after memorization to build a dataset of 660 videos for video memorability prediction. This dataset is made available for the research community. We then analyze the collected data in order to better understand video memorability, in particular the effects of response time, duration of memory retention and repetition of visualization on video memorability. We finally investigate the use of various types of audio and visual features and build a computational model for video memorability prediction. We conclude that high level visual semantics help better predict the memorability of videos.

INTRODUCTION

Enhancing the relevance of multimedia occurrences in our everyday life requires new ways to organize -in particular, to retrieve -digital content. Like other metrics of video "importance", such as aesthetics [START_REF] Dhar | High level describable attributes for predicting aesthetics and interestingness[END_REF] or interestingness [START_REF] Demarty | Mediaeval 2016 predicting media interestingness task[END_REF], memorability can be regarded as useful to help make a choice between competing videos. In addition, memorability has the advantage of being clearly definable and objectively measurable (i.e., using a measure that is not influenced by the observer's personal judgment). Image memorability has initially been defined as the probability for an image to be recognized a few minutes after a single view, when presented amidst a stream of images [START_REF] Isola | What makes an image memorable?[END_REF]. This definition has been widely accepted within subsequent work (e.g., [START_REF] Celikkale | Visual attention-driven spatial pooling for image memorability[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF][START_REF] Kim | Relative spatial features for image memorability[END_REF][START_REF] Lahrache | Bag-offeatures for image memorability evaluation[END_REF][START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF]).

The computational understanding of video memorability (VM) follows on from the study of image memorability (IM) prediction which has attracted increasing attention since the seminal work of Isola et al. [START_REF] Isola | What makes an image memorable?[END_REF]. With the recent introduction of deep learning to address the challenge of IM prediction, models also achieved very good results [START_REF] Baveye | Deep Learning for Image Memorability Prediction: the Emotional Bias[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF][START_REF] Squalli-Houssaini | Deep learning for predicting image memorability[END_REF]. As a result of this success, researchers have recently extended this challenge to videos. However, to the best of our knowledge, only two available studies focus on VM prediction [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF][START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF].

Several problems could explain this scarcity of studies on VM. Firstly, there is no publicly available dataset to train and test models. This is probably the most serious obstacle to the rapid expansion of research in VM prediction. Following the foot steps of researchers in IM [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], providing data and ground truth for VM should be one of our first objectives. The second point, closely related to the previous one, is the lack of a common definition for VM. The previous attempts to predict VM [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF][START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF] were based on different measures of memorability. Furthermore, in comparison to images, videos have supplementary dimensions -sound and visual movement -that critically contribute to the semantic and emotional information conveyed and this makes it difficult to come up with a common definition of VM. But the videos used and the way memorability is measured have a critical impact on what we understand by VM. Similarly, the definition of IM [START_REF] Isola | What makes an image memorable?[END_REF] inevitably limited researchers. In particular, previous research on IM focused on the measurement of memory performances only a few minutes after memorization. But these short-term memory performances might be poor predictors of longer term memory performances. For example, the memorability of emotional images would decrease in a non-harmonious way between two measures of memory performance (a few minutes and one day after memorization), inducing in a dataset a change in the ranking of memorability values over time [START_REF] Cohendet | Prédiction computationnelle de la mémorabilité des images: vers une intégration des informations extrinsèques et émotionnelles[END_REF]. To this end, VM data would be expected to benefit from a protocol that would measure lasting long-term memory performance.

Regarding modelling, the previous attempts at predicting VM [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF][START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF] highlighted several features which contribute to the prediction of VM, such as semantic, saliency and color features. But the work is far from complete and our capacity to propose efficient computational models will participate to answer the challenge of VM prediction.

While participating in the expansion of research on VM, the contributions of this paper are threefold:

• We propose a new protocol that measures very long-term memory performances (from weeks to years after memorization) to collect ground truth data of good quality and make the corresponding dataset available for the research community (section 3). • We assess and analyze the collected data and provide useful insights on the understanding of VM (section 4). • We build a computational model based on machine learning techniques which allows to predict a VM score for a given video. For this purpose, we investigate the use of various types of audio and visual features, ranging from low-level characteristics to emotional and (highly) semantic features (section 5).

PREVIOUS WORK

In this section, we review previous work on IM and VM prediction, focusing on annotation protocols and modelling aspects.

Measurement of memorability

Long-term memory has been studied for over a century in psychology, from multiple perspectives, since the seminal experimental studies of Ebbinghaus [START_REF] Ebbinghaus | Memory: A contribution to experimental psychology[END_REF] until the more recent neuro-imaging studies [START_REF] Robert | Prefrontal cortex and longterm memory encoding: an integrative review of findings from neuropsychology and neuroimaging[END_REF]. This work provided researchers, interested in computational understanding of IM and VM, with several memory tests (see [START_REF] Richardson-Klavehn | Measures of memory[END_REF] for an extensive overview), such as the recognition test [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF][START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] or the textual questions-based recall survey [START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF]. It also demonstrated that humans have an extensive long-term visual memory, which enables them to recall a great amount of images [START_REF] Standing | Learning 10000 pictures[END_REF] and image details [START_REF] Brady | Visual long-term memory has a massive storage capacity for object details[END_REF], as well as of videos [START_REF] Furman | They saw a movie: long-term memory for an extended audiovisual narrative[END_REF]. This is one of the observations at the origin of the work on memorability in computer science [START_REF] Isola | What makes an image memorable?[END_REF]. Several factors have also been highlighted for their critical influence on long-term memory, such as emotion [START_REF] Kensinger | Memory and emotion[END_REF], semantics [START_REF] Ross | Semantic memory[END_REF], several demographic factors [START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF], memory re-evocation [START_REF] Nadel | Memory consolidation, retrograde amnesia and the hippocampal complex[END_REF], or passage of time [START_REF] James | Memory-a century of consolidation[END_REF]. These different factors are all important to better understand memorability, and are found valuable computational features for IM and VM prediction. Focusing on the work in computer vision, most studies on IM prediction made use of one of the two available large datasets designed specifically to meet this challenge [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF]. To address more specific problems, several other datasets have also been publicly released concerning memorability of face photographs [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF], visualization pictures [START_REF] Borkin | What makes a visualization memorable[END_REF], emotional images [START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF] and scene categories [START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF]. To build the most used datasets presented in [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], the authors, possibly constrained by the difficulty in conducting long crowdsourcing studies, measured memory performance a few minutes after memorization to obtain their memorability annotations. This could be a problem if we conceive that memorability reflects a lasting memory performance [START_REF] Cohendet | Prédiction computationnelle de la mémorabilité des images: vers une intégration des informations extrinsèques et émotionnelles[END_REF]. Indeed, it has been shown that long-term memories continue to change long after their memorization through an ongoing process called consolidation, which lasts weeks to years [START_REF] James | Memory-a century of consolidation[END_REF]. Because several factors influence the consolidation process (e.g., emotions, sleep, re-evocation), which does not equally affect all memories, the order of memorability ratings measured for content, and especially videos, is susceptible to change over time [START_REF] Cohendet | Prédiction computationnelle de la mémorabilité des images: vers une intégration des informations extrinsèques et émotionnelles[END_REF]. A protocol to collect memorability annotations would benefit from the capacity to capture long-lasting memory performances, averaged to obtain what we will later refer to as "long-term memorability".

To our knowledge, the first attempt at measuring VM [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF] partially adapted the protocol proposed to measure IM [START_REF] Isola | What makes an image memorable?[END_REF] to videos. The resulting protocol is however much heavier than the memory game protocol [START_REF] Isola | What makes an image memorable?[END_REF]. They followed a classical recognition process, which consists of two steps: a free viewing task, followed two days later by a recall task. The task duration, for each of the 20 participants, was about 24 hours, spread over 10 sessions (five free viewing tasks and five recall tasks) of about two hours each. The authors used the same proportion of fillers (i.e., non repeated videos) in the free viewing and recall tasks (i.e., 4/5 of fillers and 1/5 of repeated videos named targets) to guarantee that viewers were unaware of targets. If it was mandatory in [START_REF] Isola | What makes an image memorable?[END_REF] for which encoding and recall tasks were interlaced, there is a way here to alleviate the task without impacting its quality; indeed, reducing the number of fillers in the free viewing task would have very little impact on the memorability scores (often authors even use only material interrogated later in the learning/free viewing task). Furthermore, the long time span of the experiment makes the generalization of this protocol difficult, in particular if one targets the construction of an extensive dataset. Moreover, authors measured memory after two days, but, as aforementioned, it is known that the consolidation process lasts weeks to years: it would be interesting to collect even longer term memory performances.

In another earlier approach for VM measurement, the participants performed a crowdsourcing experiment that consisted of a free viewing task during which they saw a series of videos, followed by a recall task in which they had to answer textual questions (such as: "Did you see a man juggling?", "Did you see a car on road?") [START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF]. The major drawback of the study comes from the use of questions instead of a classic visual recognition task. Indeed, the memorability scores computed for the videos may reflect not only the differences in memory performances but also the differences between the questions in terms of difficulty. The authors tested the complexity of the chosen textual questions using the Flesch-Kincaid Grade Level readability metric, which is designed to quantify how difficult to understand an English text is [START_REF] Peter | Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel[END_REF]. However, this measure might not be sufficient to represent the questions in all their complexities, such as the ease for a person to draw images from the words, to connect the words to the associated concepts in memory, to associate the words to the corresponding scene, etc. Moreover, there is an unequal distribution of the reading comprehension of English among Amazon Mechanical Turk workers, who are not necessary native English speakers. This problem of a potential unequal complexity of the question becomes even more important in view of the use by authors of the response time to calculate memorability scores, which might also critically depend on the complexity of the questions. One will note that this potential bias could have also affected the measure of inter-human consistency. Furthermore, the questions were handcrafted, and the choices for types of questions and videos were very limited (e.g., the question "Did you see a car on road?" implies that in a whole experimental session only one car on a road should appear to connect the memory performance to one particular video). The authors also manually ensured that no textual questions nor videos in a session were similar in content. This makes it difficult to generalize this protocol to the construction of an extensive dataset.

Memorability modelling

Previous attempts at predicting image and video memorability highlighted quite a few features that correlate with memorability.

The pioneering work of Isola et al. focused primarily on building computational models to predict IM from low-level visual features [START_REF] Isola | What makes an image memorable?[END_REF]. From their work, it appeared that the degree of an image's memorability can be predicted to a certain extent. Several characteristics have also been found to be relevant for predicting memorability in subsequent work, for example saliency [START_REF] Mancas | Memorability of natural scenes: The role of attention[END_REF], interestingness and aesthetics [START_REF] Isola | What makes a photograph memorable[END_REF], or emotions [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF]. The best results were finally obtained by using fine-tuned deep features, which outperformed all other features in [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], reaching a rank correlation of .64 which is near human consistency (.68) when measured for the ground truth collected in the study. This result was later confirmed by other researchers [START_REF] Baveye | Deep Learning for Image Memorability Prediction: the Emotional Bias[END_REF][START_REF] Squalli-Houssaini | Deep learning for predicting image memorability[END_REF].

Regarding VM prediction, Han et al. proposed a method which combines the power of audio-visual and fMRI-derived features [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF]. They preliminary built a computational model learned with fMRI features, which supposedly conveys the brain activity of memorizing videos. This enabled them to finally predict VM without the use of fMRI scans in a second step. However, the method would be difficult to generalize. Shekhar et al. conducted a performance analysis of several computationally extracted features before building their memorability predictor [START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF]. The analysis encompassed C3D deep learning features, semantic features obtained from some video captioning process, saliency features, dense trajectories, and color features. They found that the most predictive feature combination used captioning features, dense trajectories, saliency and color features. The features that performed the best when used alone were image captioning features, i.e., those conveying more semantics. Due to the particularity of the dataset of Shekhar et al.

-that is, their aforementioned use of questions to measure memorability -, it would be interesting to confirm if this combination works equally well on another dataset, and in particular if image captioning features also perform the best.

MEMORABILITY DATASET CONSTRUCTION 3.1 Video collection

We want our protocol to measure memory performance after a significant retention period. This can be achieved either with a longitudinal study, or by measuring a memory created prior to the experiment. We chose the latter because it enabled us to immediately measure very long-term memory. Thus, the main characteristic of the proposed protocol, in contrast with previous work, is the absence of learning (often, free viewing) task. It is replaced with a questionnaire designed to collect information about the participants' prior memory. In order to repeat measures of memory performances for different persons on the same material, to obtain average performance, we worked with movies famous enough to have been seen by several of our participants.

We first established a list of 100 occidental movies, taking care of mixing popularity and genres. We then manually selected seven videos of 10 seconds from each movie. To maintain a high intravideo semantic cohesion, we did not make cuts that would impair the understanding of the scene, nor did we aggregate shots that belong to different scenes. Indeed, since the semantics is linked to the memorability of images [START_REF] Isola | What makes a photograph memorable[END_REF], we can expect it is linked to the memorability of videos too.

We also gave preference to the videos we called "neutral", by contrast to the "typical" ones. According to our definition, a neutral video is a part of a movie which contains no element that would enable someone to easily guess this video belongs to a particular movie. The list of undesirable elements includes but is not limited to: recognizable famous actors, typical music, style, etc. Typical videos are simply defined as non-neutral videos. In most movies, just a few or no 10-second neutral videos exist. That explains why we obtained only 127 neutral videos for 573 typical ones (while we expected two neutral and five typical videos per movie, i.e., 200 neutral and 500 typical videos in total).

Annotation protocol

The protocol is composed of two tasks. Firstly, participants had to fill in a questionnaire intended to collect data about whether they have seen the 100 selected movies. Secondly, participants performed a recognition task on videos selected based on their responses to the questionnaire.

104 participants (22 -58 years of age; age average = 37.1; stdev = 10.4; 26% females; mostly educated persons -engineers or researchers mainly), participated in the experiment on a voluntary basis. The experiment was taking place in a well-controlled environment: a room insulated from noise and equipped with subdued lights. The videos, of HD or DVD quality, were displayed on a 60 inch monitor. The participants were seated at a distance of about 220 centimeters from the screen (three times the screen height). Having provided basic demographics, participants answered the questionnaire. For each of the 100 movies, they were asked whether they remembered watching fully the movie. In case of a positive answer, three additional questions followed: 1/ their confidence of watching the movie (not confident / slightly confident / 50% confident / considerably confident / 100% confident), 2/ the last time they saw the movie (less than month / 1 year / 5 years / 10 years / more than 10 years), and 3/ the number of times they saw the movie (once / 2-4 times / 5-9 times / 10-19 times / more than 20 times). In case of a negative answer, only one question was asked, related to their confidence of not having seen the movie. The questionnaire required about 20 minutes to complete.

Based on the answers to the questionnaire, an algorithm automatically selected 80 targets (i.e., videos from seen movies) and 40 fillers (i.e., videos from never seen movies) among the movies associated with the highest degree of certitude, with a maximum of two videos from the same movie. The fillers enable to quantify how much lucky confusions account for the correct recognitions. During the video selection, the current number of annotations was also taken into account to equally balance the annotations among all the videos. Given such 120 videos selected, participants performed a recognition task where they saw the videos separated by an inter-stimuli interval of 2 seconds. They had to press the space bar when they recognized a video in particular, and not when they were guessing that a particular video came from a movie they had seen (which was possible only for the typical videos).

Memorability score calculation

After collecting the data, we kept only the 660 videos that had been seen at least 4 times as targets (from the initial set of 700 videos). On average, each video of our dataset had been viewed as a target by 10.7 participants; which corresponds to the mean number of observations that enter into the calculation of a memorability score. We then assigned a memorability score to each video, defined as the correct recognition rate of the video when viewed as target. The average percentage of correct detections for all participants was 46.71% (stdev = 14.65%), and the average false alarm rate (i.e., the percentage of answers on fillers) was 4.16% (stdev = 5.27%). Figure 2(a) provides a distribution of the videos according to their degree of memorability. The dataset is publicly released here 1 . In addition to the videos and corresponding ground truth, we also provide the features used in section 5.

STUDY OF THE MEMORABILITY ANNOTATIONS

In this section, we conduct an analysis of the ground truth data collected through the protocol described above. We firstly perform a human consistency analysis. Then we compare neutral videos with typical videos. We finally study which factors affect the memorability among response time, duration of memory retention and repetition of visualization. In what follows, error bars in the graphs correspond to standard error of the mean, µ to the mean, Mdn to the median and N to the number of observations in the statistics.

Consistency analysis

We follow the method proposed in [START_REF] Isola | What makes a photograph memorable[END_REF] to measure human consistency when assessing memorability of videos. We randomly split our 104 participants into two independent groups of equal size, and calculate how well VM scores from the first group of participants match with VM scores from the second group. Averaging over 25 random half-split trials, an average Spearman's rank correlation (i.e., a global human consistency) of 0.57 is observed between these pairs of scores. We reproduced this calculation to obtain human consistency as a function of the number of annotations per video, presented in Figure 1(b). This graph is to be compared with the histogram presented in Figure 1(a), which shows that the number of videos for each number of annotations was unequal. According to the graph, we achieved a consistency of .70 from about 18 annotations, which is consistent with the previous finding [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF]. This number also corresponds to the maximal consistency obtained when collecting IM scores [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], but for a much bigger number of annotations (80) per image. It must be noted that the protocols are different between the IM experiments conducted in [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] and ours or the work in [START_REF] Han | Learning computational models of video memorability from fMRI brain imaging[END_REF]. We conducted a measure of long-term memory performance after at least two days of memorization, whereas in [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] it is measured after a dozen of seconds to a few minutes. In addition, VM annotations were collected through in-lab experiments, and IM annotations through crowdsourcing experiments. However, it would be interesting in the future to confirm if an important difference exists between images and videos regarding the number of annotations necessary to achieve a high human consistency. Apart from the conclusions we could draw about the universality of the intrinsic memorability of videos compared to images, this would mean that the magnitude of the work to carry out to build an extensive database for VM prediction is substantially smaller than one could expect from work on IM prediction.

Neutral and typical videos

In our experiment, participants were given clear instructions that they had to really recognize any video they were presented as already seen, and not only guess that a video was coming from a movie whose title was proposed in the questionnaire. However, a number of participants reported difficulty in making the distinction between guessing and recognizing: sometimes, because they knew that a video belonged to a movie they had seen, they had the false feeling of recognizing it. In this section, we perform an analysis to compare neutral videos, which contain no element that would enable participants to easily guess that a video belongs to a particular movie, and typical videos, that could be more or less easily related to the movie they belong to.

A Wilcoxon rank-sum test indicated that the memorability was lower for neutral (Mdn = .20, µ = .24) than for typical (Mdn = .53, µ = .53) videos, with Z = 10.22, p < .00001. This result does not necessarily mean that participants tended to guess when it was possible to do so -rather than simply recognize -videos selected from movies they have seen. Indeed, neutral videos contain far less contextual elements useful for recognition than typical ones, making the latter -if more easily "guessable" -at least more easily recognizable. However, it should draw our attention to the possibility that our measure is not purely objective, but -to some extent -context dependent. This would constitute the main weakness of our protocol, that one should counteract by adapted measures of control.

We can also note that the average false alarm rate (that is, the percentage of wrongly recognized filler videos) was low for neutral videos (.05%) as well as for typical videos (.03%). Specifically, we expect lucky confusions to account for little of correct detections on average for the two sorts of videos. shows that the response time to do a correct detection decreases when the memorability of the video increases. We also observed a Pearson's correlation of -0.36 (p < .0001) between the response time on the targets and their memorability scores. These two results indicate that participants tended to answer quicker when the videos were more memorable, even though the participants did not receive any instruction to do so. This suggests that people tend to naturally answer rapidly after having recognized the video. This also suggests either that the most memorable videos are also the most accessible in memory, and/or that the most memorable videos contain more early recognizable elements than the less memorable ones. In [START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF], the response time of the participants was taken to be the measure of VM. The authors chose this measure to avoid a long gap between viewing and recall stage. Our results validate -to some extent -their modus operandi: the fact that the response time decreases linearly when the memorability increases suggest that the response time is a good indicator of the memorability of the videos (at least, in a recognition task).

User context and memorability

To provide us with insights on which context-related factors collected through our questionnaire were linked to memorability, we processed to a logistic regression, using demographics and answers to the questionnaire as regressors, and the detection of a target video (with two possible discrete outcomes, detected or not) as observations to fit. Regarding the participants' nationality, we grouped them into occidental (69 pers) and non-occidental (35 pers) categories, motivated by our use of occidental movies, which could have more meaning for occidental than for non-occidental people. We also tested age and gender to reveal a potential bias in our movies' choice, that may be more memorable for people of a certain age and gender. The model, in case of a single observation n, can be written as:

y n =        1 if β 0 + K k =1 x n,k β k + ϵ n > 0 0 else (1)
where y n denotes the dependent variable which can take two values, 1 for recognition or 0 for omission of the n-th target observation, x n,k our k-th feature value (last view, number of views, nationality, age, gender), β k are the coefficients to be estimated, and ϵ n indicates the error term. According to the results of the logistic regression, the retention duration (last view) is highly negatively correlated with the probability to recognize a video (β = -.37, p < .0001). Figure 3 (a) shows that this decrease in memory for videos over time is continuous. This result indicates that long-term memory of videos continues to be altered over time for years. In the context or experiment, it implies that, to provide an accurate representation of an average long-term memory performance, a memorability score should correspond to a memory measure carried out as late as possible after the memorization.

The results of the logistic regression also show that the number of views is highly correlated with the probability to recognize a video (β = .44, p < .0001). As expected, the more a movie was seen, the better the videos were memorized. Figure 3 (b) shows that this continues to be true even with more than 9 viewings (however, the number of observations -12 -was very low for videos which belong to movies with 10 or more views). One should note that the repetition of a viewing could not be the (only) factor involved in the above phenomenon; in particular, viewing again a movie may be the sign of a special interest which would explained a better memorization (e.g., via a greater attentional and emotional investment). The fact remains that repetition is an important factor to ask people when measuring their prior memory. Furthermore, a protocol used to build an extensive database for VM prediction should, in case of multiple measures of memory (e.g., after the memorization and then after a longer delay), avoid measuring twice the same items, because this repetition could artificially increase the performance measured for the last items.

We observed no significant effects of the demographic factors (nationality, age and gender). This suggests that the videos were equally susceptible to be recognized by the different participants (or, that the relations between these factors and the observations are too complex to have been captured by the model).

MEMORABILITY PREDICTION

Until now, we have presented the video collection and the annotation protocol together with some insights on human VM. In this section, we move towards building a machine learning model that can learn and then predict the VM score of a video from its audiovisual features. The main goal of modelling is to understand if VM is predictable, and if yes identify which features: generic, perceptual, or semantic, are suitable for such prediction. We pose the problem as a standard regression problem and Figure 4 illustrates different steps in our method. In the following sub-sections, we explain our choice of features and models to address the problem in hand. To build our predictive model, we split the dataset, at the level of movies, into training (70%), validation (15%) and test (15%) data, which translates into 70 movies in the training set and 15 movies each in the validation and test sets. We chose to split our dataset at the level of movies, instead of the videos, in order to avoid videos from the same movie being present in the training as well as the evaluation (validation+test) set. To ensure that such random split did not lead to any mismatch, we computed the average number of annotations per video in each of them. We observe that the numbers of annotations are balanced: each video in the test set has around 10 annotations while there are 9 annotations for each one in the train set on an average.

Feature extraction

The task of remembering a specific video has a high cognitive complexity in general, suggesting that it requires a semantic understanding of the content and/or some other perceptual factors such as the emotion conveyed by the video. Many users who participated in our experiments indicated that it is a difficult task. While trying to build a machine learning model for such a task, we explore different kinds of features that can be extracted from the audiovisual signal. We investigate a variety of generic state-of-the-art features ( [START_REF] Tran | Learning Spatiotemporal Features with 3D Convolutional Networks[END_REF], [START_REF] Jort | Audio Set: An ontology and human-labeled dataset for audio events[END_REF]) and compare them with other semantic ( [START_REF] Karpathy | Deep Visual-Semantic Alignments for Generating Image Descriptions[END_REF]) and perceptual (emotion) features ([5]).

Spatio-temporal visual features (C3D

). These features are extracted from the C3D model, a 3-dimensional convolutional network proposed for generic video analysis [START_REF] Tran | Learning Spatiotemporal Features with 3D Convolutional Networks[END_REF]. The main motivation to use C3D is that it encodes both the spatial and temporal information in the video. The model has been proposed for video analysis and is not an extension of a model for image analysis, unlike other state-of-the-art models like VGG16 [START_REF] Liu | Very deep convolutional neural network based image classification using small training sample size[END_REF]. We use the publicly available model trained on the Sports-1M dataset [START_REF] Tran | Learning Spatiotemporal Features with 3D Convolutional Networks[END_REF] and extract the output of the fully connected layer -fc6 of the network with a dimensionality of 4096. We additionally explore the use of principal component analysis (PCA) (named C3D (PCA) in Table 1) for the dimension reduction, as the original dimensionality is very high when compared to other features.

Audio features (AudioSet).

Using a recently released Au-dioSet [START_REF] Jort | Audio Set: An ontology and human-labeled dataset for audio events[END_REF] model, which was trained on a large dataset for event detection, we extract 128-dimensional embeddings for each audio track associated with a video in our dataset. We use these embeddings for training the regression models. The motivation to use these features is that they are state-of-the-art in the audio event detection research and events could play a major role in how people remember sequences in movies. Additionally, we wanted to investigate how the audio channel contributes to building a model for VM prediction.

Emotion related features (SentiBank and Affect).

As research in psychology showed that emotion and memory are correlated [START_REF] Cahill | A Novel Demonstration of Enhanced Memory Associated with Emotional Arousal[END_REF], we investigate the use of emotion-related feature in our prediction system. For modelling emotion from the visual content, we resort to a visual sentiment concept detector: SentiBank [START_REF] Borth | SentiBank: Large-scale Ontology and Classifiers for Detecting Sentiment and Emotions in Visual Content[END_REF]. Sen-tiBank is a set of 1200 trained visual concept detectors providing a mid-level representation of sentiment from visual content. We use the binary code for concept detection, from images, provided by the authors. The SentiBank concept detector provides two pieces of information: concepts with probabilities and features. Concepts are adjective-noun pairs and the probability represents how likely each concept is depicted visually in an image. Examples of some concepts in the SentiBank ontology are: young driver, scary face, terrible pain, etc.

We sample one frame for every second of the video in our dataset, resulting in 10 frames per video. We run the SentiBank concept detector on each of these 10 frames and rank the concepts based on the probability of their occurrence in the frame and take the top-50 concepts. We extract 300-dimensional word embeddings (Word2Vec [START_REF] Mikolov | Efficient Estimation of Word Representations in Vector Space[END_REF]), for each of the 50 concepts and take an average to obtain a single vector per frame. We repeat this process for all the 10 frames and take the average of all the vectors to obtain a single feature vector for each video. Sentibank detectors also provide a 4096-dimensional feature for each frame and we take the average across all the frames to obtain one 4096-dimensional feature vector for each video. In the end, we use a 300-dimensional concept vector and a 4096-dimensional feature vector.

In addition to SentiBank concepts, we investigate other ways to capture emotional content in a video. Following a circumplex model of affect (the experience of emotion) [START_REF] Russell | A Circumplex Model of Affect[END_REF], we define arousal as the dimension of affect that measures the excitement in the video, while valence measures whether the video invokes positive or negative emotion. We resort to an audio-visual analysis of the video to obtain its arousal and valence scores using the method described in [START_REF] Hanjalic | Affective video content representation and modeling[END_REF]. For each frame in the video, we compute the arousal and valence scores using the method proposed in [START_REF] Hanjalic | Affective video content representation and modeling[END_REF]. In order to keep a fixed dimensionality of the feature vector, we take the first 200 frames in the video because of the varying frame rates across the videos. We concatenate the arousal and valence scores for the first 200 frames in each video resulting in a 400-dimensional feature vector (200 for arousal and 200 for valence) for a video.

Visual semantic features (Image captions).

Visual semantics are known to play an important role in image memorability ( [START_REF] Isola | What Makes a Photograph Memorable?[END_REF], [START_REF] Squalli-Houssaini | Deep learning for predicting image memorability[END_REF]). We utilize the state-of-the-art research in image captioning to capture such high-level semantics of the video [START_REF] Karpathy | Deep Visual-Semantic Alignments for Generating Image Descriptions[END_REF]. We sample one frame for every second of the video in our dataset, resulting in 10 frames per video. For each of these 10 frames, we run the caption detector (code provided by the authors) and obtain a caption for the frame. For each non-functional word in the caption, we extract 300-dimensional word embeddings (Word2Vec [START_REF] Mikolov | Efficient Estimation of Word Representations in Vector Space[END_REF]) and take an average across all the words to obtain a single vector per frame. We repeat this process for all the 10 frames and take the average of all the vectors to obtain a single 300-dimensional feature vector for each video.

Modelling and evaluation metric

We use the features discussed in Section 5.1 to train a Support Vector Regression (SVR) model for the VM score prediction. The choice of SVR is guided by the nature of the problem as well as by the small size of the dataset. We have chosen to go with the same regressor for all the features because our focus is mainly on identifying which features are more important for VM prediction. This way we ensure that the difference in performances is because of the features themselves. Note that, in addition to the variety of features explained in Section 5.1, we also explore a combination of all the features by concatenating them into a single feature vector. While performing such a concatenation, we use the low-dimension version of the features for C3D and SentiBank features, obtained after applying a dimension reduction method (PCA) to the original set. In the experiments where we use PCA for C3D and SentiBank features, we retain 95% of variance in the data while reducing the feature dimensions. Note that we investigated other models like LSTM, to model the sequential nature of videos. The results were not very encouraging and this could be because of the relatively smaller size of our dataset.

We use the grid search strategy to obtain the best hyper-parameters for SVR in term of the Mean Squared Error (MSE) between the predicted scores and the ground-truth on the validation set. The choice of hyper-parameters in the grid search are: kernel = {linear, RBF}, C = {0.1, 1, 10, 100, 1000} and γ = {0.01, 0.1, 1, 10, 100}.

The prediction performance is evaluated by the Spearman correlation (SpCorr ), which measures the rank correlation between the predicted memorability scores and the ground-truth scores. This metric is chosen as [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF] we focus more on the relative memorability between videos rather than on their absolute memorability scores, and (2) it gives an indication of how close the predicted memorability scores are to the human consistency when annotating memorability scores.

Memorability prediction results

In this section, we will discuss how the models trained on different features perform while predicting memorability scores of new videos. Table 1 reports the prediction results obtained by the SVR model when trained on different features, for the validation and test sets. Two sets of results are reported: SpCorr (≥ 4 annotations) and SpCorr (≥ 8 annotations). The former corresponds to the prediction capability of the model when trained on videos with at least 4 annotations, and the latter corresponds to the results when trained on videos with at least 8 annotations where the ground-truth is better annotated.

As it can be seen, visual semantic features derived from image captioning clearly offer better prediction results compared to all other considered features. This is not surprising as they capture visual attributes along the scene, which are known to play an important role in human memory. This is also inline with a previous study in IM [START_REF] Squalli-Houssaini | Deep learning for predicting image memorability[END_REF] where image captioning-based features helped better predict IM than the conventional CNN features. When predicting VM on the test set, C3D features showed to be quite effective to the task as they encode the visual spatio-temporal information of the video. On the contrary, audio information captured by AudioSet features does not seem to be enough for VM prediction. One of our initial hypotheses was that emotion would play an important role in VM, supported by literature from psychology [START_REF] Cahill | A Novel Demonstration of Enhanced Memory Associated with Emotional Arousal[END_REF]. However, when observing the results in Table 1, both Affect [START_REF] Hanjalic | Affective video content representation and modeling[END_REF] and SentiBank [START_REF] Borth | SentiBank: Large-scale Ontology and Classifiers for Detecting Sentiment and Emotions in Visual Content[END_REF] performed quite poorly compared to the image captioning features. Another observation from Table 1 is that the combination of all the features (last row in the table) does not appear in the top-3 best performing features. One of the reasons for this could be that there is a lot of redundance when combining all the features into a single feature vector, or it could be that the size of the combined feature vector is too big when compared to the size of the dataset. Thus, in future work we could look at selectively combining the features to investigate if that improves the performance.

As our dataset contains videos with different numbers of annotations, we further investigate the effect of the number of annotations on the prediction performance. For this purpose, we train a first SVR model, using image captioning features, on videos with at least 4 annotations and use this model to predict the memorability score for videos in the validation set. We repeat this process for different numbers of annotations per video (from 4 to 15) in the training set. Please note that the validation set in each of the repetitions is fixed and only the training set changes. We provide a demonstration of how SpCorr varies with an increasing number of annotations in the training set in Figure 5. As can be seen, SpCorr first increases up to 5 annotations and then remains more or less constant before decreasing (beyond 10 annotations). In the wake of this observation, we also investigated the performance of all the features when we train the regression model with videos that have at least 8 annotations as reported in the second part of Table 1: SpCorr ( ≥ 8 annotations).

Comparing the two sets of results in Table 1, we observe that globally the models trained on videos with at least 8 annotations perform better than the models trained on videos with at least 4 annotations. These results are comparable to the human consistency analysis shown in Section 4.1. We finally performed an additional 10-fold cross-validation on videos with at least 8 annotations (train+validation set) using the image captioning features.

From this study, we re-confirmed that there is no overfitting issue in our model and we observed the average value of SpCorr across the 10 folds to be 0.33, which is close to the performance on the test set.

CONCLUSIONS

In this paper, we have presented a novel protocol to collect longterm memorability annotations for videos, which enabled us to build a dataset to support research in this subject. We then performed a range of statistical studies on this dataset to understand important factors in the annotation process as well as how they can affect video memorability. The main strength of our protocol is that it enables to measure memory performance weeks to years after memorization without requiring a longitudinal study. It appears from the analysis that memory of videos continues to decrease for years, which justifies such a distant measurement, in contrast to previous work. The counterpart is that we cannot control the memorizing process. In particular, the correctness of the answers to the questionnaire designed to collect information about the participants' prior memory is not absolute. Even with additional information about the movies (e.g. showing an excerpt/trailer), the possibility would remain that a participant falsely answers he/she has already seen/never seen a movie. Our current work focuses on collecting VM annotations at a large scale using crowdsourcing, measuring VM in a more objective manner, by controlling the memorizing step. We proceed of the measurement after different periods of retention (from a few minutes to a few days), which enables us also to study long-term memory, although not as long as in the present experiment. Another limitation of the presented protocol is the limited choice of content. To design our experiment, we needed contents broadly disseminated among the population surveyed. In our current work, we are not limited anymore by this problem, and decided to work on neutral videos.

We finally proposed computational models for VM prediction where we investigated the use of various audio and visual features for the task. For this, we observed that the visual semantic features offer the best prediction result, which re-confirms the correlation between the visual attributes and memorability. Although very promising, these results seem to be limited by the relative small size of the dataset. Again, our current work might enable us to reconfirm these results on a larger dataset and for more general videos.

Figure 1 :

 1 Figure 1: (a) Distribution of the number of videos according to the number of annotations per video. (b) Human consistency (with linear trendline).

Figure 2 :

 2 Figure 2: (a) Distribution of the number of videos depending on memorability score ranges. (b) Mean response time for correct recognitions against memorability scores.

Figure 2 (

 2 Figure 2 (b)shows that the response time to do a correct detection decreases when the memorability of the video increases. We also observed a Pearson's correlation of -0.36 (p < .0001) between the response time on the targets and their memorability scores. These two results indicate that participants tended to answer quicker when the videos were more memorable, even though the participants did not receive any instruction to do so. This suggests that people tend to naturally answer rapidly after having recognized the video. This also suggests either that the most memorable videos are also the most accessible in memory, and/or that the most memorable videos contain more early recognizable elements than the less memorable ones. In[START_REF] Shekhar | Show and Recall: Learning What Makes Videos Memorable[END_REF], the response time of the participants was taken to be the measure of VM. The authors chose this measure to avoid a long gap between viewing and recall stage. Our results validate -to some extent -their modus operandi: the fact that the response time decreases linearly when the memorability

Figure 3 :

 3 Figure 3: Correct recognition rate depending on (a) when occurred the last viewing, and (b) the number of views.

Features:

  C3D, AudioSet, Affect, Sentibank (concepts & features), Image captions. Regression model: Support vector regression (Linear and RBF kernel).

Figure 4 :

 4 Figure 4: Proposed approach for memorability score prediction.

Figure 5 :

 5 Figure 5: Spearman correlation on the validation set with models using image captioning features trained on videos with varying number of annotations.

Table 1 :

 1 Prediction results in terms of Spearman correlation scores on validation and test data for different features with models trained on videos that have at least 4 (columns 4-5) or 8 (columns 6-7) annotations.

	Feature	Feature type	Dimension SpCorr (≥ 4 annotations) SpCorr (≥ 8 annotations)
				validation set	test set	validation set	test set
	C3D	visual spatio-temporal	4096	0.20	0.26	0.31	0.34
	C3D (PCA)	visual spatio-temporal	225	0.24	0.21	0.18	0.17
	AudioSet	audio related	128	0.23	0.22	0.21	0.24
	Affect	affect related	400	0.19	0.17	0.26	0.23
	SentiBank concepts	emotion related	300	0.16	0.13	0.15	0.17
	SentiBank features	emotion related	4096	0.25	0.21	0.27	0.26
	SentiBank features (PCA)	emotion related	225	0.22	0.21	0.22	0.23
	Captions	visual semantics	300	0.29	0.31	0.39	0.38
	Combination (PCA)	combine all features	1578	0.24	0.23	0.29	0.27

https://www.technicolor.com/dream/research-innovation/ movie-memorability-dataset


Sylvain Deville

Eric Maire

Audrey Lasalle

Agnes Bogner

Catherine Gauthier

Jérôme Leloup

Christian Guizard

Influence of particle size on ice nucleation and growth during the freeze-casting process

Keywords: X-ray computed tomography, alumina, nucleation, freeze-casting

The solidification behaviour of suspensions of alumina particles during directional solidification is investigated here by in situ observations using X-ray radiography and tomography. The objective of this study is to assess the influence of particle size on the solidification behaviour of the suspensions during the early stages of solidification. Four powders with particle size in the range 0.2-3.4 μm (median size) were investigated. Solidification is obtained by cooling at a constant rate, starting from room temperature. Attention is specifically paid to the nucleation and growth behaviour of the ice crystals in these suspensions. We propose that the nucleation of ice crystals is controlled by the particle size, the surface of the particles acting as nucleation sites. Smaller particle size leads to a lower degree of supercooling since nucleation and growth can proceed at higher temperature than with larger particles. The initial interface velocity is dependent on the degree of supercooling, and controls the extent of the initial structural gradient in the resulting porous materials.

Introduction

The templating of porous materials using ice, through the process of freeze-casting, have seen a greatly increased attention during the past few years, regarding not only ceramics, but also polymers 1 and metals [START_REF] Yook | Fabrication of porous titanium scaffolds with high compressive strength using camphene-based freeze casting[END_REF] . The process is environmentally-friendly, using water as a removable template, highly versatile, and the resulting structures highly tuneable by various tweaks of the process such as an improved control of the nucleation conditions or the use of additives affecting the morphologies of the growing crystals [START_REF] Munch | Architectural control of freeze-cast ceramics through additives and templating[END_REF] . The typical processing conditions include directional solidification, using a cooling step starting at room temperature. Under these conditions, nucleation and growth in the suspension has to occur before the ice crystals can reach a steady state, progressively growing in the temperature gradient, and yielding homogeneous and directional materials, after sublimation of the ice and sintering of the resulting green body. In all cases, this initial nucleation and growth stage results in the presence of a structural gradient close to the cooling surface, corresponding to the progressive selection of the stable crystals structure [START_REF] Deville | In Situ X-Ray Radiography and Tomography Observations of the Solidification of Aqueous Alumina Particles Suspensions. Part I: Initial Instants[END_REF] . Such gradient has been reported numerous times, such as figure 2 of reference 5 , figure 12 of reference [START_REF] Moritz | Ceramic Bodies with Complex Geometries and Ceramic Shells by Freeze Casting Using Ice as Mold Material[END_REF] , figure 8 of reference [START_REF] Moritz | Ice-mould freeze casting of porous ceramic components[END_REF] , figure 6a-b of reference [START_REF] Sofie | Fabrication of Functionally Graded and Aligned Porosity in Thin Ceramic Substrates With the Novel Freeze-Tape-Casting Process[END_REF] , figure 2 of reference [START_REF] Yumin | Preparation of a Dense/Porous BiLayered Ceramic by Applying an Electric Field During Freeze Casting[END_REF] , and a number of other studies. The presence of this structural gradient is explained by the initial conditions during the freezing stage, and is characterized by two distinct regions. A first region, with no porosity, corresponds to the formation of amorphous ice during the very first stages of solidification [START_REF] Gutierrez | Ice-Templated Materials: Sophisticated Structures Exhibiting Enhanced Functionalities Obtained after Unidirectional Freezing and Ice-Segregation-Induced Self-Assembly[END_REF] , where supercooling effects are probably present. A second region, with a gradient of pore size, is related to the nucleation, growth, and selection of the preferred population of ice crystals [START_REF] Deville | In Situ X-Ray Radiography and Tomography Observations of the Solidification of Aqueous Alumina Particles Suspensions. Part I: Initial Instants[END_REF] . Being related to the initial nucleation and growth properties, the gradient cannot be avoided so far, although it might be controlled to some extent. Applying an electric field will favour the electromigration towards or away from this initial transition [START_REF] Yumin | Preparation of a Dense/Porous BiLayered Ceramic by Applying an Electric Field During Freeze Casting[END_REF] , and can partially affect the extent of this structural gradient. We investigate here the influence of particle size on the characteristics of this initial transition and in particular on the nucleation behaviour in this system. X-Ray radiography and tomography experiments were used here to investigate the initial instants of solidification and the arrangement of the crystals and concentrated particles after complete solidification. The experiments were conducted at the ESRF, on the beamline ID19.

Experimental methods

All the experimental methods and powders characteristics have been described in reference [START_REF] Deville | In Situ X-Ray Radiography and Tomography Observations of the Solidification of Aqueous Alumina Particles Suspensions. Part I: Initial Instants[END_REF] and [START_REF] Deville | Metastable and unstable cellular solidification of colloidal suspensions[END_REF] . All the results presented here have been obtained with a 5°C/min cooling ramp, starting from room temperature.

Results

The solidification interface velocity can be measured from the pictures sequence acquired by radiography. The measurements of interface velocities for the various powders are given in figure 1. The acquisition frequency (3Hz) is still too low to observe precisely the stage where the ice nuclei reach their critical size and keep growing irreversibly. In the time span (0.3 sec.) between the last frame with no crystals and the next one, crystals have already grown a few hundreds of micrometers; there is therefore an important uncertainty on the first point of the plot for each powder. The minimum growth velocity during these first instants can accordingly be estimated to be between 750 microns/s and 1200 microns/s, depending on the powder, and is probably faster than that. It is nevertheless clear that the initial growth velocity is related to the particle size, larger particles leading to greater initial interface velocity. Interestingly, all of the measurements fall on the same interface velocity vs. time trajectory, but with a different initial interface velocity. The arrows in figure 1 indicate the velocity of the interface when it reaches the top of the observation windows. Again, the larger the particle size, the greater the velocity at the end of the observation window and the smaller the structural periodicity in the frozen sample, which can clearly be observed on section 1 of figure 3.

Using the tomography scan performed on the frozen body, the solidified structure corresponding to the initial instants observed in figure 1 can be reconstructed. The corresponding cross-sections in the xz plane (parallel to the growth direction) and xy plane (perpendicular to the growth direction) are shown, respectively, in figure 2 and3. The z location of the xy cross-sections of figure 3 are indicated in figure 2. In all cases, a morphological transition can be observed, with an intermediate stage where two populations of crystals can be observed, as reported previously [START_REF] Deville | In Situ X-Ray Radiography and Tomography Observations of the Solidification of Aqueous Alumina Particles Suspensions. Part I: Initial Instants[END_REF] . The extent of this transition zone is dependent on the initial interface velocity, the higher the initial interface velocity, the larger the transition stage.

Measurements of the temperature close to the nucleation surface were performed at the same time, so that an estimation of the freezing temperature is available. We define here the freezing temperature as the time where the ice nuclei reach their critical size and keep growing irreversibly, so that they can actually be observed. The temperatures were not measured exactly at the surface, but the thermocouple is situated slightly below the nucleation surface. We can reasonably expect a small difference between the measured temperature and the true nucleation temperatures, but this should not affect the relative variations between the different powders, since the measurements were performed at the same location for all experiments. The freezing temperature is strongly dependent on the particle size, as shown in figure 4. Unfortunately, the freezing temperature for the largest particle size was not measured, due to difficulties with the experimental setup. It is nevertheless clear that the larger the particle size, the lower the freezing temperature. As a consequence, the lower the initial freezing temperature, the larger the initial interface velocities.

Discussion

All the experiments were performed by starting from the room temperature, and cooling at a constant rate of 5°C/min, until complete solidification was achieved. Ice crystals nuclei are therefore never present initially in the suspension and nucleation has to occur before growth can proceed and a steady state of growth can be achieved. We propose here that the particle size has a direct influence on the ice crystals nucleation in the suspension. The presence and role of supercooling was already mentioned [START_REF] Gutierrez | Ice-Templated Materials: Sophisticated Structures Exhibiting Enhanced Functionalities Obtained after Unidirectional Freezing and Ice-Segregation-Induced Self-Assembly[END_REF] to explain the structure formation mechanisms when the suspensions are solidified by direct immersion in liquid nitrogen. We propose here to extend this to the current situation where suspensions are solidified at a constant cooling rate and starting at room temperature. The following scenario can be applied to the initial instants of solidification and explain the current experimental observations:

the temperature is progressively lowered from room temperature. When the temperature becomes negative, nucleation of ice crystals can occur.

until nucleation occurs and nuclei reach their critical size, the system enters a supercooled state; the temperature is lower than the equilibrium temperature, so that it is driven far away from equilibrium.

when the ice nuclei reach their critical size and keep growing, the system wants to recover its equilibrium, therefore the greater the degree of supercooling, the higher the initial interface velocity. We propose that in this system, nucleation is governed by the presence of impurities, that is, particles in our case. The lower the particle size, the greater the number of available nucleation sites. When plotting the freezing temperature vs. specific surface area of the powders (figure 4), the relationship between both seems relatively straightforward, although more data points would be desirable.

The influence of particle size can now be understood in a relatively straightforward manner. Large particle will offer less nucleation sites, so that the system will be highly supercooled when nucleation finally occurs. The interface velocity vs. time trajectory should be independent of the particle size: the starting velocity is related to the initial degree of supercooling, and hence dependent on particle size, but the following interface displacement kinetics are controlled by the thermal properties of the system, and therefore should not be related to the particle size. The common trajectory observed in figure 1 for all particle size seems to be in good agreement with these explanations. The diffusivity of the particles is of course directly related to the particle size [START_REF] Peppin | Morphological instability in freezing colloidal suspensions[END_REF] , and this could have a great influence on the interface stability, as reported previously [START_REF] Deville | Metastable and unstable cellular solidification of colloidal suspensions[END_REF] . In the initial instants, the interface velocity is nevertheless too high; particles do not have enough time to rearrange or redistribute by diffusion mechanisms.

This suggests a few possibilities to control the initial morphological transition and the resulting structure. The influence of an electric field, driving the particles away or towards the nucleation surface, has already been illustrated [START_REF] Yumin | Preparation of a Dense/Porous BiLayered Ceramic by Applying an Electric Field During Freeze Casting[END_REF] . Recent and intriguing results of the influence of the electric field on the freezing temperature of supercooled water have also been reported [START_REF] Ehre | Water Freezes Differently on Positively and Negatively Charged Surfaces of Pyroelectric Materials[END_REF] , hinting at a possible influence on the nucleation behaviour, a result that may well be applied to freeze-casting. Another possibility that comes to mind is to start with ice crystals template, an idea already successfully applied [START_REF] Munch | Architectural control of freeze-cast ceramics through additives and templating[END_REF] to the control of the orientation of the ice crystals and structural features of the freeze-casting process. The initial instants were not investigated in this case, although a great deal of information could be retrieved from such experiments. In regards of the present results, we should expect the initial transition zone to be absent.

Conclusions

Based on X-ray radiography and tomography experiments of the solidification of alumina suspensions with various particle sizes, under conditions similar to that used in the freeze-casting process, we can draw the following conclusions:

when cooling starts from room temperature, the suspension enters a supercooled state before nucleation and subsequent crystals growth can occur, driving the system away from equilibrium.

the nucleation stage is controlled by the particle size, the surface of the ceramic particles acting as nucleation sites. The smaller the particles, the larger the surface area and the higher the number of nucleation sites.

larger degree of supercooling will lead to faster interface propagation during the initial instants of solidification, which leads to a larger structural gradient in the resulting materials.

Figure 1 :

 1 Figure 1: Interface velocity during the initial instants, influence of particle size. The arrows indicate the time at which the interface reaches the top of the observation windows (upper limit of figure 2).

Figure 2 :

 2 Figure 2: Initial transition zone, influence of particles size. Scale bars 250 μm. Vertical cross-sections. Sections 1-3 refer to cross-sections in figure 3.

Figure 3 :

 3 Figure 3: Initial transition zone, influence of particles size. Scale bars 150 μm. Horizontal cross-sections. The different sections are defined in figure 2. The cohabitation of r-and z-crystals (see reference 4 for definition) during the initial instants is clearly visible.

Figure 4 :

 4 Figure 4: Freezing temperature vs. specific surface area. The nucleation temperature for the 3.4 μm powder is not available.

Acknowledgements

We acknowledge the European Synchrotron Radiation Facility for the provision of synchrotron radiation beam time and we would like to thank E. Boller and J.-P. Valade for their irreplaceable assistance in using beamline ID19. Financial support was provided by the National Research Agency (ANR), project NACRE in the nonthematic BLANC programme, reference BLAN07-2_192446.