P Quillery
email: quillery@lmt.ens-cachan.fr

B Durand

O Hubert

H Zhao
email: zhao@lmt.ens-cachan.fr

Lmt

Experimental characterization of the multiaxial dynamic behavior of a nickel-titanium shape memory alloy

Keywords: Multi-axial testing, dynamic testing, shape memory alloys (SMA)

The multiaxial behavior of a nickel-titanium shape memory alloy (SMA) is experimentally studied. Indeed the solid-solid phase transformation that occurs with stress induces a complex behavior, depending on the loading parameters such as the deformation rate or the multiaxial character of the loading. The solid-solid transformation can be initiated by stresses or thermal loading. In this work, the pseudo-elastic behavior is measured for an equi-biaxial compression loading situation thanks to a home made impact testing set-up and Hopkinson split bars.

Measurements are conducted by the use of high speed infrared and optical cameras and a Hopkinson bar set-up. The strain field is estimated by digital image correlation and stresses in the central area of specimen are estimated from boundary conditions through informations given by strain gauges placed on the bars. The strain appears homogeneous in the biaxial loading region of interest and a significant rise in temperature is observed.

Introduction

The measurement of mechanical behavior of materials submitted to biaxial loading is necessary for the identification or validation of models. The bi-axial tests are relatively well controlled and very often used in quasi-static conditions. However, dynamic bi-axial tests remain infrequent and are often difficult to carry out and analyze.

The setting up of biaxial tests is justified by the complexity of some behaviors. For instance, in the case of a simple Von-Mises threshold combined with kinematic hardening, the yield surface is not symmetrical in the principal stress plane. To ensure a perfect identification of yield limit, controlled multiaxial tests are thus necessary. These tests are even more relevant in the case of anisotropic materials. Shape memory alloys exhibit a very complex and non-symmetrical yield surface, as shown by Lavernhe-Taillard [START_REF] Taillard | Multiaxial Shape Memory Effect and Superelasticity[END_REF]. In this former study, the transformation surface of a CuZnAl SMA has been identified. Non proportional effects (strain depends on stress path) have been clearly highlighted.

For many materials, the mechanical behavior also depends on the strain rate. Visco-plastic materials, architectured foams or shape memory alloys can be given as classical examples.

Stiebler and al. [START_REF] Stiebler | Description of the flow behaviour of a high strength austenitic steel under biaxial loading by a constitutive equation[END_REF] and Kraiem [START_REF] Kraiem | Caractérisation et modélisation du comportement mécanique d'une mousse fragile[END_REF] has shown in their studies the strain rate dependency of the yield surface evolution. Modifications being not homothetic, multiaxial characterization at different strain rates are very relevant for identification or validation of models. In this way, many studies are conducted to carry out multiaxial dynamic tests. Because of the necessity of a perfect time synchronization between several short loading waves, this kind of test remains complex to manage. The most explored way consists in separating a unique incident wave in different waves intended for different axes. The time shifting between the generated loading waves is therefore reduced.

Multi-axial dynamic set-ups

Exploratory tests in biaxial dynamics began with simple stress states. The combination of an axial tensile or compressive stress and a shear stress are then the first tests that conducted to biaxial loadings. These tests are often associated with classical Hopkinson bars. Although the impact is easy to generate, the measurement is difficult because the combination of both solicitations is hard to dissociate at the output.

Shear and normal stresses

The set-up presented in the article written by Lewis et al. [START_REF] Lewis | The dynamic fracture and prefracture response of compact bone by split Hopkinson bar methods[END_REF] describes a system of compression and torsion loadings. The torsional loading is applied by a torsionally prestressed bar and locked by a brake. When the system is triggered, the brake is released and the preloaded bar solicits the torsional sample. To ensure a good synchronization, the brake is released by a detonation, and the detonation is controlled by the compression bar. Knowing the velocities of waves, which are not different in case of compression or torsion, and the path followed by of each of them, Lewis's device enables for a synchronization up to ±10µs.

Stiebler et al. [START_REF] Stiebler | Description of the flow behaviour of a high strength austenitic steel under biaxial loading by a constitutive equation[END_REF] present in their study a combined torsion-tension system. The loading is carried out by a previously constrained bar and by a pressure brake applied at the bar end, near the sample. When the brake is suddenly released, the energy stored in the bar, composed on the one hand by a torsion energy, and by a tension energy on the other hand is then released. The inertia of the bars makes it possible to load the sample.

Another study [START_REF] Gary | Essai de cisaillement plan appliqué à des tôles minces[END_REF] presents a complex loading in dynamics. Pure shear loading is performed by using a set-up especially designed for this application and adapted to a classical Hopkinson bar system. The parts of plates between the two jaws are solicited in pure shear.

The set-up presented in the Rittel's study [START_REF] Rittel | A shear-compression specimen for large strain testing[END_REF] uses a unique impact for compression and shear loading. Adapted to a conventional uni-axial Hopkinson split bar system, two pieces with inclined planes enable for a combined shear-compression loading. Shear and compression loadings are proportional and varying the angle of planes changes the proportionality ratio. The two loadings can be dissociated by this method.

In the publication of Hou and al. [START_REF] Hou | Impact behavior of honeycombs under combined shear-compression, Part analysis[END_REF] or Tounsia and al. [START_REF] Tounsi | Dynamic behaviour of honeycombs under mixed shear-compression loading : Experiments and analysis of 24 ème Congrès Français de Mécanique Brest, 26 au 30 Août 2019 combined effects of loading angle and cells in-plane orientation[END_REF], the exploration of a combined loading in shear and compression is also presented. The inclined plane assembly is, this time, confined and the translation of these parts relatively to the bars is therefore not allowed.

Confined testing

Grolleau [START_REF] Grolleau | Biaxial Testing of Sheet Materials at High Strain Rates Using Viscoelastic Bars[END_REF] explored the path of a complex solicitation by putting a membrane submitted to a dynamic pressure. The latter, fixed a the output bar, undergoes isostatic compression applied via the input bar and a fluid cavity. The analysis by strain gauges makes it possible to determine the pressure applied to the membrane.

The researches of Albertini [START_REF] Albertini | Advances in the Hopkinson bar testing of irradiated/non-irradiated nuclear materials and large specimens[END_REF] and Bailly [START_REF] Bailly | Dynamic behavior of an aggregate material at simultaneous high pressure and strain rate : SHPB triaxial tests[END_REF] explored the confinement. By confining the sample in the transverse direction with a rigid system, the loading along the longitudinal axis induces, by Poisson effect, a compression in the transverse direction. Containment can be achieved by a pressurized fluid cavity system or by rigid mechanical parts. Here again, it is difficult to estimate the stress amplitude. By knowing the Poisson's ratio, we can calculate the respective loads.

Multi-impacts

In the 1980s, Albertini [START_REF] Albertini | Dynamic uniaxial and biaxial stress-strain relationships for austenitic stainless steels[END_REF] proposed a biaxial tension system with preloaded cables and explosive charge released brakes. The use of explosive charges allows a good synchronization because the triggering is brief. The ratio between both loading directions can be chosen.

Hummeltenberg [START_REF] Hummeltenberg | Entwurf und Aufbau eines zweiaxialen Split-Hopkinson-Bars[END_REF] presented a biaxial compression test with two separated impactors. Synchronization is difficult to achieve despite the use of electronic triggers .

In the next section a system, allowing to apply a multiaxial loading from a single impactor thanks to an innovating mechanical set-up, is detailed

A new experimental set-up for multi-axial dynamic testing

In the range of dynamic testing, we focused on split Hopkinson bar systems (figure 1), which allow strain rates from 10/s to 100/s [START_REF] Hopkinson | A method of measuring the pressure produced in the detonation of high explosives or by the impact of bullets[END_REF] to be reached. An home-made set-up has been developed and adapted to such a single split system in order to perform equi-biaxial compression.

The material constitutive of the bars is known and used in the range of elasticity, so that the system is considered as the test body of measurement chain. Signals from gauges can be separated into three parts in case of classical testing (and into four parts in our case) : (i) the incident Gauges are glued on the input and on the two output bars to measure strains thanks to usual Wheatstone bridge facilities ( recording frequency is 500 KHz). The knowledge of the measurement chain parameters is useful for strain, force and velocity calculations. The gain of each gauge is therefore properly measured thanks to a force sensor during preliminary static tests.

F imput (t) = S B E( i (t) + r (t)) and V input (t) = C 0 ( i (t) -r (t)) (1)
F outputext (t) = S B E text (t) and V outputext (t) = C 0 text (t) (2)
F output int (t) = S B E t int (t) and V output int (t) = C 0 t int (t) (3)
S B , E and C 0 are the bar cross-sectional area, Young modulus, and longitudinal elastic wave celerity respectively. i is the incident deformation, r the reflected deformation, t int the input transmitted deformation and text the output transmitted deformation.

The specimen is designed to concentrate stresses in a region of interest (ROI). This ROI is a 3 mm diameter circle with a 0.5 mm thickness, the external part of the specimen is 2 mm thick.

The maximum overall dimension (8 mm × 8 mm) is fixed by the Hopkinson bar system and the biaxial set-up. The cruciform geometry is shown in figure (4a). The specimen has been designed after a finite element pre-simulation using a non-linear behavior close to the expected behavior. The designed shape avoids buckling and optimizes the stress concentration. Buckling is more restrictive in statics than in dynamics because inertia increases the buckling limit. The parametric finite element analysis is thus realized in quasi-static condition and verified in dynamic condition. From the measured forces we can calculate the stresses in the ROI by finite element modeling following equation [START_REF] Lewis | The dynamic fracture and prefracture response of compact bone by split Hopkinson bar methods[END_REF].

   σ xx σ yy    =    m xx m xy m yx m yy       F x F y    (4)
The experiment is multi-instrumented to access on many parameters : the bi-axial specimen is tracked by a high speed numerical camera to accurately calculate the strain field by digital image correlation (DIC). The recording is realized by a SA5 fast-camera at 50000Hz and on a 512px × 271px area. DIC principle consists in calculating the difference between two images, a reference one and a distorted one, assuming the conservation of the brightness level in spite of a possible heterogeneous lightening. Several high-power lights have been arranged in such a way that a homogeneous luminosity during the test can be considered. An image is seen numerically as a matrix function characterizing the gray level of each pixel. If we call x the position, f a function representing the reference image and g a function representing the deformed image, the determination of the displacement field u is obtained by minimizing the residue formed by the square of the left-hand term in equation [START_REF] Gary | Essai de cisaillement plan appliqué à des tôles minces[END_REF].

f (x) -g(x + u(x)) = 0 (5)
Correlation code Correli-RT3 has been used considering the displacement field as continuous.

This code allows the identification of the displacement on each node of a finite element mesh composed of 3-nodes triangular elements. The obtained displacement is regularized by an elastic solution [START_REF] Hild | On the Use of Digital Image Correlation for the Analysis of the Dynamic Behavior of Materials[END_REF].

To complete these measurements, an Infra-Red camera is placed in front of the other side of the sample which has been painted in black in order to assume the ROI emissivity as a constant. The measurement of thermal field is crucial especially for SMA because the austenite to martensite transformation is accompanied by a strong heat release due to latent heat effect (transient temperature increases of about 40 • C for the studied material). The way this transformation takes place will give us information about the transformation rate that is a crucial point of the behavior. A calibration of the infrared sensor with a black body enables the link between the digital level measured by the camera and the temperature to be obtained. Figure (4b) shows the full range calibration and the restricted area chosen to increase the recording frequency to 15000

Hz. This observation area, of about 64 × 8 pixels, enables to track the specimen during the test and to compensate the rigid body displacement. Only a small part of the ROI is observed by the infrared camera at 15000 Hz, this part appears homogeneous in temperature, so that a uniform heating is considered in the whole ROI.

Experimental results

From informations given by the three gauges, figure 5a shows the calculated strains as a function of time.

After time shifting to virtually move the signals from the strain gauges to the multi-axial setup interfaces, and after Hopkinson formulae application, we can calculate the forces and the velocities at the interfaces (figures 5b and 5c). We have then to check the equilibrium of the input bar force and of the total output bar force [START_REF] Zhao | On the use of SHPB techniques to determine the dynamic behavior of materials in the range of small strains[END_REF]. Figure 5c confirms that the balance is preserved. A steady state is also reached because all velocities are equal. However we observe that the force in the internal output bar is a few lower than the force in the external one. This difference can be explained by the friction at the sliding interfaces of the biaxial set up, higher along the x (transverse) direction than along the y (longitudinal)

direction. So we have to considere a friction ratio determined thank's to previous experiments carried out using isotropic aluminum cruciform specimen. The friction coefficient has been roughly estimated to 0.03, which is consistent with smooth lubricated steel surfaces [START_REF] Durand | Biaxial Compression Tests on Hopkinson Bars[END_REF]. At the beginning, the strain along y can be related to a Poisson effect. By this calculations and a Von-Mises criterion assumption (equations ( 6) and ( 7)), we can plot the equivalent behavior for biaxial condition of the tested specimen (figure 7). At the end, the strain has reached a deformation of about 3.5%. The infrared observations allow the temperature rise to be calculated during the test. Figure 6a shows a brutal increase during the first 1ms. showing that temperature is increasing at the very beginning of the test. However, it must be noticed that the temperature increases along a time range (about 6ms) much longer than the duration of mechanical loading (lower than 1ms).

σ V M = 1 2 (σ xx -σ yy ) 2 + σ 2 xx + σ 2 yy (6)
All these observations will allow us to to improve the modeling of these materials, using for example the proposition of [START_REF] Maynadier | Thermo-mechanical description of phase transformation in Ni-Ti Shape Memory Alloy[END_REF].

Conclusion

This study presents results obtained from a new set-up for dynamic multiaxial tests. Improvements that can be expected are a better synchronization of the longitudinal and transverse loadings on the one hand and a better control of the loading ratio by a better knowledge of friction coefficient on the other hand. Multi-instrumentation allows accurate measurement of strain and temperature fields. This information is necessary for the characterization of the thermomechanical behavior of complex materials such as shape memory alloys.

F 1 -F 2 -F 3 -

 123 Classical uni-axial split Hopkinson bar set-up strain wave that is imposed in the input bar by projectile impact, (ii) the reflected strain wave which is the response of the specimen into the input bar, (iii) the transmitted strain wave which is the response of the specimen into the output bar, (figure2). Obtaining these three signals that propagate in the bars from strain gauges makes it possible to determine the forces and the velocities at the interfaces between the bars and the sample, and thus to determine its stress and strain state during the test. Typical strain waves measured on a Hopkinson set-up The biaxial compression set-up (figure 3) fits with a classical Hopkinson bar bench. The forces in the sample along both longitudinal and transverse directions are measured by using the gauges glued on the coaxial output bars. The set-up includes 45 • angle pieces that transmit the incident wave to the sample along the transverse direction. The transverse force in the sample is generated by the external bar inertia and the longitudinal force by the internal bar inertia. The Hopkinson formulae [14] enable the determination of the forces at the interfaces between the bars and sketched in figure 3. Knowing the friction coefficient in the mechanism, it is possible to evaluate the longitudinal and transversal forces at the boundary of the sample. Stress state in the area of interest is calculated from these forces using an elastic finite element calcu-Schematic of the set-up lation.

F 4 -

 4 Reference image and area for DIC (b) Observation zone for a 15000 Hz infrared recording (64 px × 8 px) Optical and thermal image settings

F 5 -F 6 -

 56 Quantities calculated from gauge measurements -0.001 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0Strains calculated by DIC in the ROI Measurements from IR and fast cameras

  Average stress components σ xx and σ yy are calculated from the transverse and longitudinal forces F x and F y thanks to equation (4) (Parameters of matrix m ij are : m xx = m yy = 0.2941 mm -2 and m xy = m yx = -0.0794 mm -2 ). Average strain components xx , yy and xy are calculated in the ROI from DIC measurements. xy component is negligible. xx and yy are plotted as function of time in figure 6b. Strains do not seem perfectly synchronized. The lower magnitude of yy and xx are in accordance with the lower stress level along y axis (figure 8a, NiTi1). This difference is explained by unwanted clearances which delay the loading arrival.

1 F 7 -

 17 Von-Mises mechanical behavior

F 8 - 2 F 9 -

 829 Axial stress by transversal stress Multiaxiality ratio for the two specimens Several tests were conducted on specimens machined in the same material. We thus tried to evaluate the reproducibility of the tests. Figures8a and 8bshow the axial paths followed during the two performed tests in terms of deformation and stress. Even if the two curves are different and do not correspond to the expected loading path shown in red, the equivalent behavior appears repeatable as shown on figure9, highlighting that a 2nd invariant based criterion may be enough to represent the observed behavior. Sum of strains by sum of stresses


Jean-François Bonastre

Tomi Kinnunen

Anthony Larcher

Junichi Yamagishi

la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Welcome to this special issue on Speaker and Language Characterization which features, among other contributions, some of the most remarkable ideas presented and discussed at Odyssey 2018: the Speaker and Language Recognition Workshop, held in Les Sables d'Olonne, France, in June 2018. This issue perpetuates the series proposed by ISCA Speaker and language Characterization Special Interest Group in coordination with ISCA Speaker Odyssey workshops [START_REF] Campbell | Introduction to the issue[END_REF][START_REF] Berkling | Introduction to the special section on speaker and language recognition[END_REF][START_REF] Lleida | Speaker and language recognition and characterization: Introduction to the csl special issue[END_REF].

Voice is one of the most casual modalities for natural and intuitive interactions between humans as well as between humans and machines. Voice is also a central part of our identity. Voice-based solutions are currently deployed in a growing variety of applications, including person authentication through automatic speaker verification (ASV).

A related technology concerns digital cloning of personal voice characteristics for text-to-speech (TTS) and voice conversion (VC). In the last years, the impressive advancements of the VC/TTS field opened the way for numerous new consumer applications. Especially, VC is offering new solutions for privacy protection. However, VC/TTS also brings the possibility of misuse of the technology in order to spoof ASV systems (for example presentation attacks implemented using voice conversion). As a direct consequence, spoofing countermeasures raises a growing interest during the past years.

Moreover, voice is a central part of our identity and is also bringing other characteristics on the persons than their identity, which could be extracted with or without the consent of the speaker. This brings up the need to tackle in ASV and VC/TTS not only the technical challenges, but specific ethical considerations, as shown, for example, by the recent General Data Protection Regulation (GDPR). Time has passed since the previous Computer Speech and Language (CSL) special issue that focused on speaker and language recognition and summarized contributions originating from the 2016 edition of the Odyssey workshop [START_REF] Lleida | Speaker and language recognition and characterization: Introduction to the csl special issue[END_REF]. This special issue presents the latest progress in speaker and language characterization. But it also extends the topic to voice modeling, conversion, synthesis and ethical aspects, in order to reflect the relations of these themes with speaker and language characterization. As dedicated Editors of this special issue, we wished to propose high quality but also timely information.

To achieve this objective, we accepted a loss in term of coverage and we selected only 8 high quality and (quite) ready for press articles, presented below.

The article entitled Vocoder-Free Text-to-Speech Synthesis Incorporating Generative Adversarial Networks Using Low-/ Multi-Frequency STFT Amplitude Spectra by Saito et al. addresses quality degradation in text-to-speech (TTS) synthesis. The authors devise a vocoderfree approach that predicts high-dimensional amplitude spectrum from linguistic features. As the high-dimensional amplitude spectra has a complicated distribution, the resulting speech has degraded quality. This lead the authors to propose a novel training loss to combine prediction error (square error between the target and predicted amplitude spectra) with an adversarial loss term. The latter enforces the distribution of broad amplitude spectrum of generated speech to match closely the distribution of natural speech. The study represents an interesting example of knowledge transfer from ASV spoofing research to TTS: besides conventional mel-frequency scale (with a low-frequency focus), the authors incorporated inverse melscale (with high-frequency focus), originally used in detecting TTS and voice conversion attacks. Further, the authors used a specific metric, spoofing rate, defined as the percentage of generated spectra classified by the discriminator network mistakenly as human speech, as part of their objective evaluation.

The article entitled Voice Mimicry Attacks Assisted by Automatic Speaker Verification by Vestman et al. addresses a potential security threat caused by the malicious use of automatic speaker verification (ASV) technology. The authors used one ASV system (i-vector) to assist mimicry attack of naive speakers against another ASV system (x-vector). They selected closeby target speakers for naive attackers from a large publicly available database for each of the mimickers. The authors additionally included perceptual experiments and studies on changes in prosody. Although the results from this simulated attack scenario reveal that the malicious use of the technology improves the chances of breaking a speaker verification system, it was not generally not enough to break the attacked system.

The article entitled Deep Domain Adaptation for Anti-Spoofing in Speaker Verification Systems by Himawan et al. describes domain adaptation techniques of anti-spoofing countermeasure models for ASV. Countermeasure models trained on a database should be usable even on a different database ideally, but, in practice, this is not true due to mismatched acoustic conditions between the two databases. To address this issue, they proposed several interesting networks for supervised and unsupervised settings. For supervised setting, they used a Siamese like network that has two outputs, spoofing/genuine classification and adversarial domain classification. This transforms input spectrum into a new domain-invariant feature that can still be used for spoofing/genuine classification. For unsupervised setting, they further constrained the network so that weights for handing two databases are linearly correlated. The CORAL loss was also added. They show very detailed analysis results of the proposed domain adaptation techniques using the ASVspoof 2015 and AVspoof databases.

The survey article Preserving Privacy in Speaker and Speech Characterisation by Nautsch et al. is a recommended reading for speech, biometrics and applied cryptography researchers and relevant readers who works on speaker and speech privacy since privacy preservation is mandated by recent European and international data protection regulations. It covers a legal perspective on privacy preservation of speech data, the requirements for effective privacy preservation, and cryptography-based solutions that are applicable to speaker characterization and speech characterization, respectively.

In their article End-to-end DNN Based Text-Independent Speaker Recognition for Long and Short Utterances, Rohdin et al. proposed to mimic an i-vector/PLDA system using an end-to-end neural network to address over-fitting problems in usual end-to-end systems. Each part of a classical i-vector/PLDA system, including sufficient statistics computation, i-vector extraction and PLDA scoring is replaced by a neural network. Training this system in an end-to-end manner makes the tasks in training and evaluation the same which is beneficial compared to standard x-vector systems. The article additionally describes the entire training process and details the optimization process required to limit the memory requirement. The proposed solution performs similarly to an x-vector system but without requiring data augmentation.

An adversarial approach is proposed by Chien and Peng in their article Neural Adversarial Learning for Speaker Recognition. Their method can be used in two tasks. First, adversarial training can be used to construct a manifold PLDA that preserves neighbor embedding of i-vectors in a lowdimensional space to benefit speaker recognition. Second, the generative network can be used to tackle the problem of imbalanced and insufficient data in PLDA speaker recognition by generating artificial examples. To train the couple of networks, they propose to perform multi-objective learning for minimax optimization and introduce a regularization of Gaussianity and cosine similarity.

In their article Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition, Novotný et al. report the results of a detailed analysis of speaker verification noise robustness. They study the use of deep neural networks for audio enhancement and analyze the performance of standard i-vector system as well as x-vectors, considered as the current state-of-the art in speaker verification. Experiments cover a large number of standard corpora and datasets derived from standard corpora to cover multiple acoustic domains. This work demonstrates the effectiveness of some methods while alerting on the degradation that denoising may induce in clean conditions. The methods compared include denoising as well as data augmentation, robust features and multi-condition training.

Finally, Monteiro et al. proposed in their article entitled Residual Convolutional Neural Network with Attentive Feature Pooling for Endto-End Language Identification from Short-Duration Speech a solution for end-to-end language identification. They propose to use residual convolutional neural networks due to their property to take into account large contextual segments. They associate this architecture with different attention mechanisms. They demonstrate that their approach improves the average cost of classical methods by 30% to 40% on standard benchmarks.

We express our gratitude to the authors of the submissions to this special issue and present our apologizes to the authors of the submissions postponed due to our strict selection rules. We thank the reviewers for the huge time they invest in reviewing the submissions and particularly for their fruitful comments in order to improve the papers. We wish also to thank Prof.

Introduction to the Special Issue "Speaker and Language Characterization and Recognition: Voice Modeling, Conversion, Synthesis and Ethical Aspects"

  Jean-François Bonastre 1 , Tomi Kinnunen b , Anthony Larcher c , Junichi Yamagishi d

	Introduction to the Special Issue
	"Speaker and Language Characterization and
	Recognition: Voice Modeling, Conversion, Synthesis and
	Ethical Aspects"

a LIA, Avignon University, Avignon, France b University of Eastern Finland, Joensuu, Finland c LIUM, Le Mans Universit, Le Mans, France d National Institute of Informatics, Tokyo, Japan

Roger K. Moore, the Editor-in-Chief, who supported our project, including our "rush" planning and the associated selection rules, and advise us during all the (long and complex) editing process.