K N Sartelet
email: sartelet@cerea.enpc.fr

H Hayami

B Sportisse

MICS Asia Phase II -Sensitivity to the aerosol module

Keywords: aerosol, variability, chemistry transport model, size distribution, coagulation, condensation, cloud chemistry, heterogeneous reactions, sea-salt emissions

In the framework of the model inter-comparison study -Asia Phase II (MICS2), where eight models are compared over East Asia, this paper studies the influence of different parameterizations used in the aerosol module on the aerosol concentrations of sulfate and nitrate in PM 10 .

An intracomparison of aerosol concentrations is done for March 2001 using different configurations of the aerosol module of one of the model used for the intercomparison. Single modifications of a reference setup for model configurations are performed and compared to a reference case. These modifications concern the size distribution, i.e. the number of sections, and physical processes, i.e. coagulation, condensation/evaporation, cloud chemistry, heterogeneous reactions and sea-salt emissions.

Comparing monthly averaged concentrations at different stations, the importance of each parameterization is first assessed. It is found that sulfate concentrations are little sensitive to sea-salt emissions and to whether condensation is computed dynamically or by assuming thermodynamic equilibrium. Nitrate concentrations are little sensitive to cloud chemistry. However, a very high sensitivity to heterogeneous reactions is observed.

Thereafter, the variability of the aerosol concentrations to the use of different chemistry transport models (CTMs) and the variability to the use of different parameterizations in the aerosol module are compared. For sulfate, the variability to the use of different parameterizations in the aerosol module is lower than the variability to the use of different CTMs. However, for nitrate, for monthly averaged concentrations averaged over four stations, these two variabilities have the same order of magnitude.

Introduction

The model inter-comparison study -Asia Phase II (MICS2), which follows the MICS Phase I [START_REF] Carmichael | The mics-asia study: model intercomparison of long-range transport and sulfur deposition in east asia[END_REF], aims at comparing transport and deposition of sulfur, nitrogen compounds, ozone and aerosols in East Asia. Eight models (M 1 , ..., M 8 ) are compared for the following four periods: March 2001, July 2001, December 2001and March 2002. The eight models employed for the intercomparison use the same emission data, meteorological data, boundary conditions. Note that meteorological data that are different from the standard MICS meteorological file have sometimes been used, and that the size of the domain of study may differ from domain to domain. Apart from input data and the choice of the computational domain, the variability among the models is related to numerical schemes and parameterizations used for transport, diffusion, deposition, scavenging and chemical mechanisms. Moreover, the eight models use different parameterizations in the aerosol module. This paper aims investigating to which extent the use of different parameterizations in the aerosol module impact the aerosol concentrations of sulfate and nitrate. An intracomparison of aerosol concentrations is done in March 2001 using different configurations of the aerosol module of one of the model used for the intercomparison. The intracomparison is done using the model M 8 , i.e. Polair3D [START_REF] Boutahar | Development and validation of a fully modular platform for numerical modelling of air pollution: Polair3D[END_REF], [START_REF] Fahey | Formulation, development and preliminary validation of the SIze REsolved Aerosol Model, SIREAM[END_REF] , [START_REF] Tombette | Aerosol modelling at regional scale: a sensitivity study with the Polyphemus platform[END_REF], [START_REF] Sartelet | Application of the 3d ctm polair to air quality over greater tokyo[END_REF]), that has been designed for such multiconfiguration works (see for instance [START_REF] Mallet | Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling[END_REF]). Informations about the domain of study and input data may be found in [START_REF] Carmichael | MICS-Asia Phase II the model intercomparison study for asia phase ii, methodology and overview of findings[END_REF]. [START_REF] Hayami | Model intercomparison study of chemical transport models for east asia (mics-asia): Particulate sulfate, nitrate and ammonium[END_REF] have focused on the intercomparison for aerosols, more especially on inorganic components: sulfate, ammonium and nitrate in PM 10 . They show that models' predictions tend to be closer to observations for sulfate than for nitrate. Sulfate tends to be slightly underestimated in March 2001. [START_REF] Hayami | Model intercomparison study of chemical transport models for east asia (mics-asia): Particulate sulfate, nitrate and ammonium[END_REF] found that the amount of sulfate is lower for the model M 8 than for M 3 , M 5 and M 7 , although these four models have comparable amount of total sulfur. This can probably be explained by the omission of aqueous chemistry in M 8 . Although large discrepancies exist between models, especially for nitrate concentrations, total nitrate is consistently underestimated by most models. The nitrate concentrations predicted by M 8 are comparable to other models, but the total nitrate (nitrate + HNO 3 ) is slightly lower, which can be due to the omission of heterogeneous reactions. In M 8 , most of the total nitrate is in the particulate phase. This article is organised as follows. First, the different aerosol modules used in the different models that participated to the MICS study are presented with an accent on the aerosol module used in Polair3D. Then, the sensitivity to the aerosol module is studied and compared to the sensitivity to the chemistry transport model for monthly averaged concentrations at EANET (Acid Deposition Monitoring Network in East Asia) stations and at Fukue, a remote site between Japan and China.

The aerosol modules

The different physical processes

In the aerosol module of eulerian models, the size distribution is often modeled using a sectional approach (e.g. [START_REF] Pilinis | Madm -a new multicomponent aerosol dynamic model[END_REF], [START_REF] Jacobson | Development and application of a new air pollution modeling system -part iii . aerosol-phase simulations[END_REF]) or a modal approach (e.g. [START_REF] Whitby | Modal aerosol dynamics modeling[END_REF], [START_REF] Sartelet | Development and preliminary validation of a modal aerosol model for tropospheric chemistry: Mam[END_REF]). In the sectional approach, the number of sections varies from study to study, usually from 2 to 15. In the modal approach, typically from 2 to 4 modes are used.

The aerosol composition and distribution are influenced by different physical processes such as condensation/ evaporation, which is thought as one of the major processes influencing aerosol composition, heterogeneous reactions on the surface of particles, coagulation, nucleation, cloud chemistry. Three approaches may be used to model condensation/ evaporation processes [START_REF] Capaldo | A computationally efficient hybrid approach for dynamic gas/aerosol transfer in air quality models[END_REF], Debry and Sportisse (2006))

• a dynamic approach, i.e. the mass transfer between gas and aerosol phases is explicitly taken into account, • a full-equilibrium approach, i.e. the dynamic modeling is replaced with an assumption of thermodynamic equilibrium between the gas and aerosol phases, • an hybrid approach, i.e. full equilibrium is assumed for fine aerosols while the dynamic approach is used for coarse aerosols.

The aerosol modules in MICS models

The main differences of the different aerosol modules used in the MICS models can be classified as differences in the size distribution, i.e. modal versus size resolved, the number of sections or modes, and differences in physical processes, i.e. coagulation, condensation/evaporation, cloud chemistry, heterogeneous re-actions. Although input data, such as boundary conditions and emissions, are common to all models in the MICS study, sea-salt emissions are not given as input data but they are parameterized in some models. These differences are summarized in Table 1.

Polair3D

The sensitivity study is carried out with the model M 8 , i.e. Polair3D [START_REF] Boutahar | Development and validation of a fully modular platform for numerical modelling of air pollution: Polair3D[END_REF]). The aerosol module of Polair3D is fully described in Debry et al. (2006). Aerosols are composed of black carbon, dust, five inorganics (sulfate, nitrate, ammonium, sodium, chloride) and eight organics [START_REF] Schell | Modeling the formation of secondary organic aerosol within a comprehensive air quality model system[END_REF]).

The number of sections used in the modeling is fixed by the user. The sections' diameters are log-distributed in the diameter size-range considered, 0.01µm to 10µm in this study. The bounds of the sections are 0.01, 0.02, 0.04, 0.08, 0.16, 0.32, 0.63, 1.26, 2.51, 5.01, 10.00µm. Brownian coagulation is modeled as described in [START_REF] Debry | Solving aerosol coagulation with size-binning methods[END_REF], and condensation/evaporation as described in Debry and Sportisse (2006) with a moving sectional scheme. The three approaches, the full equilibrium approach, the dynamic approach or the hybrid approach, may be used. For cloud chemistry, the variable size resolved module (VSRM) of [START_REF] Fahey | Size-resolved aqueous-phase chemistry in a threedimensional chemical transport model[END_REF] can be switched on or off. Heterogeneous reactions on the surface of particles are modeled following Jacob ( 2000)

HO 2 -→ 0 •5 H 2 O 2 (R 1) NO 2 -→ 0 •5 HONO + 0 •5 HNO 3 (R 2) NO 3 -→ HNO 3 (R 3) N 2 O 5 -→ 2 HNO 3 ( R
4) They are considered irreversible processes with a first order reaction rate.

The parameterizations of sea-salt emissions and vertical diffusion are preprocessed. Sea-salt emissions are computed with the parameterization of [START_REF] Monahan | A model of marine aerosol generation via whitecaps and wave disruption[END_REF] generalized to varying relative humidity following [START_REF] Zhang | Size distribution of sea-salt emissions as a function of relative humidity[END_REF]. Vertical diffusion is computed with the parameterization of [START_REF] Troen | A simple model of the atmospheric boundary layer: sensitivity to surface evaporation[END_REF].

The sensitivity study

In this sensitivity study, only the concentrations of sulfate and nitrate are studied. The sensitivity tests concern the size distribution, i.e. the number of 1 Comparison of the different aerosol modules used in the different MICS models.

[1]: [START_REF] Meng | Atmospheric gas-aerosol equilibrium, iv: thermodynamics of carbonates[END_REF] Berge (1993), [11]: [START_REF] Mozurkewich | The dissociation constant of ammonium nitrate and its dependence on temperature, relative humidity and particle size[END_REF] sections, and physical processes, i.e. coagulation, condensation/evaporation, cloud chemistry, heterogeneous reactions and sea-salt emissions. Sensitivity to nucleation is not tested because nucleation would involve particles of size between 0.001µm to 0.01µm, a size range smaller than the one considered in this study (0.01µm to 10µm). The sensitivity to whether condensation/evaporation is treated dynamically or assuming thermodynamic equilibrium is also studied. However, the sensitivity to the thermodynamic model is not tested, although it may not be negligible as shown in [START_REF] Zhang | Simulation of aerosol dynamic: A comparative review of algorithms used in air quality models[END_REF]. Heterogeneous reactions as described in [START_REF] Jacob | Heterogeneous chemistry and tropospheric ozone[END_REF] are coded in Polair3D. Other models used in MICS may however take into account different heterogeneous reactions, or similar ones but with a different reaction rate.

In the default run R 1 , ten sections are used, condensation/evaporation pro- cesses are modeled with the full equilibrium approach, coagulation is taken into account, cloud chemistry, sea-salt emissions and heterogeneous reactions are ignored. In each of the runs R 2 to R 7 , one option differs from the run R 1 . For example, in the run R 2 , only three sections are used. A summary of the different runs is presented in Table 2. In the run R 3 , the hybrid approach is used with a cutoff diameter of 0.6µm: the finest 6 sections are computed with the full equilibrium approach, while the dynamic approach is used for the coarsest 4 sections. To show that the aerosol concentrations are not only influenced by the parameterizations in the aerosol module, a run is done with a different parametrization of the vertical diffusion. The sensitivity study of [START_REF] Mallet | Uncertainty in a chemistry-transport model due to physical parameterizations and numerical approximations: An ensemble approach applied to ozone modeling[END_REF] shows a great sensitivity to the parametrization of the vertical distribution for ozone. Therefore, the parameterization of Louis is used instead of the parametrization of Troen-Mahrt in the run R 8 .

R 1 R 2 R 3 R 4 R 5 R 6 R
The sensitivity of sulfate and nitrate concentrations to the different options is now assessed by comparing monthly averaged concentrations.

Monthly averaged concentrations

Monthly averaged concentrations of sulfate and nitrate were measured at some of the EANET (Acid Deposition Monitoring Network in East Asia) stations [START_REF] Hayami | Model intercomparison study of chemical transport models for east asia (mics-asia): Particulate sulfate, nitrate and ammonium[END_REF]. The sensitivity to the aerosol module is assessed by comparing the different runs at 23 EANET stations and at a remote site between Japan and China, Fukue, a station operated by CRIEPI [START_REF] Hayami | Model intercomparison study of chemical transport models for east asia (mics-asia): Particulate sulfate, nitrate and ammonium[END_REF]. The location of the stations is given in Figure 1. Measurements for aerosols, that are in the computational domain of the eight models, are available at only three of the EANET stations (Terelj -Mongolia, Kanghwa and Imsil -Republic of Korea) and at Fukue.

Sensitivity to the aerosol module

To quantify the sensitivity of the different aerosol options, the normalized mean absolute error (NMAE) between R 1 and R i (i = 2, ..., 8) and the normalized mean bias (NMB) are computed for sulfate and nitrate, as follows [START_REF] Yu | New unbiased symmetric metrics for evaluation of air quality models[END_REF])

NMAE = N k=1 |C i,k -C 1,k | N k=1 C 1,k . 100% NMB = N k=1 (C i,k -C 1,k ) N k=1 C 1,k . 100% (1)
where N is the number of stations (23 EANET stations + Fukue), C i,k (i = 1, ..., 8, k = 1, ..., N) represents the concentration of sulfate or nitrate at the station k for the run R i . For each run R i , the higher the NMAE and NMB are, the more sensitive the results are to the option i. A summary can be found in Table 3.

As shown in Table 3, sulfate concentrations are little sensitive to options 3 and 7, ie. to the hybrid option and to sea-salt emissions, with a NMAE under 2%. Sulfate is little sensitive to sea-salt emissions, because sea-salt sulfate is not considered in the sea-salt emissions here. Sea-salt emissions are assumed to be made exclusively of chloride and sodium.

Higher sensitivity of sulfate is observed for options 2 and 4, i.e. for the size distribution and for coagulation with a NMAE of 6% and 11% respectively. Sulfate concentrations are equally sensitive to options 5 and 6, i.e. to heterogeneous reactions and cloud chemistry, with a NMAE around 15%-17%.

Although the formation of sulfate is often thought to be dominated by aque- ous production (e.g. [START_REF] Seinfeld | Atmospheric chemistry and Physics[END_REF]), the sensitivity of sulfate to cloud chemistry is only 17% here. However, this sensitivity varies strongly with the location of stations. Figure 2 shows the total amount of precipitations for March 2001. Precipitations are low over Mongolia and central China, but high over south China and Japan. Accordingly, the sensitivity of sulfate to cloud chemistry is only 1% at Terelj in Mongolia (see Figure 1), whereas it is as high as 71% and 62% in Xiaoping and Hedo. Note that higher sensitivity to cloud chemistry would have been observed in July, when precipations are higher than in March. NMAE tends to be higher for nitrate than for sulfate concentrations, except for the option 6, i.e. for cloud chemistry. Nitrate is more sensitive to the options of the aerosol module than sulfate, explaining the larger discrepancies observed between the models M 1 , ...,M 8 for nitrate than for sulfate. The effects of the size distribution, the hybrid scheme and sea-salt emissions are stronger on nitrate than sulfate because they alter thermodynamic equilibrium. Sulfate is less affected because of its low volatility.

For nitrate, the highest sensitivity is observed for option 5, i.e. heterogeneous reactions, with a NMAE as high as 99%. Only cloud chemistry (option 6) does not significantly modify nitrate concentrations. The effects of the size distribution, the hybrid scheme, coagulation and sea-salt emissions (options 2, 3, 4 and 7) are of similar strength in terms of NMAE with a NMAE around 10%-15%.

The positive NMB of the run R 6 for sulfate illustrates that the concentration of sulfate increases due to cloud chemistry as expected by the oxidation of dissolved SO 2 into sulfate.

Nitrate concentrations increase by heterogeneous reactions as shown by the high positive NMB of 99% for the run R 5 , whereas sulfate concentrations decrease although the NMB -14% is not as high in absolute value as for nitrate. As deduced from the set of reactions R 1, R 2, R 3 and R 4, heterogeneous reactions lead to higher HNO 3 and H 2 O 2 concentrations and to lower HO 2 concentrations. Higher H 2 O 2 concentrations in R 5 would increase sulfate concentrations in the real atmosphere. However, this does not occur in the model runs because R 5 excludes aqueous chemistry. A more detailed comparison of the runs R 1 and R 5 shows that monthly averaged OH and O 3 concentrations are also lower when heterogeneous reactions are taken into account. Because of lower OH concentrations, oxidation of SO 2 by OH is lower resulting in lower sulfate concentrations. Lower sulfate may in turn lead to higher nitrate, which is then required to neutralize ammonia. Higher nitrate concentrations may also be a consequence of the higher HNO 3 concentrations that condense on particles to form ammonium-nitrate.

Coagulation is more efficient for small particles, limiting their concentrations. Figure 3 shows the size distribution of sulfate and nitrate monthly averaged concentrations, averaged over all stations. When coagulation is not taken into account, the distribution is centered at smaller diameters than when coagulation is taken into account.

For both sulfate and nitrate, a high sensitivity to the parameterization of the vertical diffusion (run R 8 ) is observed. For sulfate, this sensitivity is much higher than the sensitivity to any of the options tested in the aerosol module. For nitrate, this sensitivity is very high as well, although not as high as the sensitivity to heterogeneous reactions.

Comparison to the sensitivity to the chemistry transport model

To compare the sensitivity to the aerosol module to the sensitivity to the chemistry transport model, the NMAE is computed for stations where measurements are available (Terelj, Kanghwa, Imsil, Fukue) as follows

NMAE = M k=1 |C i,k -O k | M k=1 O k . 100% (2)
where M = 4 is the number of stations where measurements are available, O k (k = 1, ..., N) is the concentration of sulfate or nitrate at station k. Table 4 shows the mean NMAE for runs R 1 to R 7 as well as the smallest (Min) and largest (Max) value of NMAE obtained for these runs. These values Min, Max and mean of NMAE are computed not only for runs R 1 to R 7 but also for models M 1 to M 8 , in order to compare the sensitivity of the aerosol module (runs R 1 to R 7 ) to the sensitivity of the chemistry transport model (models M 1 to M 8 ). For sulfate, all eight models are included when computing the mean NMAE. For nitrate, the model M 3 is not included because it did not provide results. Furthermore, because the NMAE observed with the model M 4 is one order of magnitude higher than other models for nitrate, results with and without taking M 4 into account are presented.

Values of NMAE are higher for nitrate than sulfate, confirming again the higher sensitivity of nitrate.

For sulfate, variability is larger for models M 1 to M 8 than for runs R 1 to R 7 . The NMAE varies by only 2% for runs R 1 to R 7 whereas it varies by as much as 8% for models M 1 to M 8 . For nitrate, the NMAE varies by 65% for runs R 1 to R 7 whereas it varies by as much as 175% for models M 1 to M 8 . However, without taking into account the model M 4 for nitrate, the NMAE varies by 56% for models M 1 to M 8 , that is the variations are of the same order of magnitude as those of runs R 1 to R 7 . In other words, for nitrate, the variability within the aerosol module (R 1 to R 7 ) is as large as the variability between models (M 1 to M 8 ).

Apart from the uncertainty in aerosol parameterizations, the variability among models M 1 to M 8 is linked to numerical schemes and parameterizations used for transport, diffusion, deposition, scavenging, chemical mechanism, choice of computational domain and input data. Although input data are the same for all models for emission and boundary conditions, meteorological data that are different from the standard MICS meteorological file have sometimes been used. For example, a difference in the relative humidity field used by models as high as 20% has been observed [START_REF] Hozumi | Mics-asia: Comparison study on meteorological fields on the mics-asia[END_REF]. Such variability in relative humidity contributes to variability in aerosol concentrations.

Variability linked to numerical schemes and parameterizations outside the aerosol module is important especially for sulfate for which the variability to parameterizations in the aerosol module is not so high. For example, the sensitivity to the parameterization of the vertical diffusion has shown to be higher than the sensitivity to any options of the aerosol module for sulfate.

Because only uncertainties in aerosol parameterizations are considered for the runs R 1 to R 7 , uncertainties observed for models M 1 to M 8 should be larger. This is true for sulfate. However, for nitrate, if the results of the model M 4 are not considered, the NMAE varies as much for models M 1 to M 8 as for runs R 1 to R 7 . This strong variability of nitrate to the aerosol module stresses the difficulties in modeling nitrate concentrations accurately.

Conclusion

For March 2001, the sensitivity to the aerosol module of the model M 8 , Po-lair3D, is assessed and compared to the sensitivity to the chemistry transport model. Because in the MICS comparison, all models are assumed to use the same input data, the variability between the different models is due to differences in physical parameterizations, differences in numerical schemes and differences in the chemical mechanism. Concerning the sensitivity to the aerosol module, only the sensitivity to the size distribution (number of sections) and the sensitivity to physical parameterizations (coagulation, whether condensation is solved assuming full equilibrium or using an hybrid scheme, cloud chemistry, heterogeneous reactions, and sea-salt emissions) are considered. However, there is also a sensitivity to numerical algorithms for simulation of condensation/evaporation for example as shown by [START_REF] Zhang | Development and application of the model of aerosol dynamics, reaction, ionization, and dissolution (madrid)[END_REF].

To assess the sensitivity to the aerosol module and to the chemistry transport model, monthly averaged concentrations over 24 stations are computed. Sulfate concentrations are little sensitive to the hybrid scheme and to sea-salt emissions, with a normalized mean absolute error (NMAE) and a normalized mean bias under 2%. Nitrate concentrations are little sensitive to cloud chemistry. For sulfate, the sensitivity to cloud chemistry is important although not dominant with a NMAE of about 15%. For sulfate and especially for nitrate, high sensitivity to heterogeneous reactions is observed. For nitrate the sensitivity to heterogeneous reactions is particularly high, with a NMAE as high as 99% for monthly averaged concentrations.

To compare the sensitivity to the chemistry transport model to the sensitivity to the aerosol module, the minimum, the mean and the maximum of the NMAE are computed for R i , i.e. for runs associated to sensitivity tests on the aerosol module, and for M i , i.e. for models participating in the MICS comparison. The values of NMAE are higher for nitrate than sulfate, confirming the higher sensitivity of nitrate. For monthly averaged concentrations at all EANET stations for nitrate, the variations of the NMAE for R i are of the same order as the variations for M i , suggesting a very high sensitivity to the aerosol module. For sulfate, the variations of the NMAE for R i are much smaller than the variations of the NMAE for M i (2% against 8%), suggesting that the sensitivity to the aerosol module is less strong than the sensitivity to the chemistry transport model. However, if R 8 , i.e. the sensitivity to the parameterization of the vertical diffusion, is added in the computation of the variations of the NMAE for R i , then the variations of the NMAE for R i are almost of the same order as the variations for M i (6% against 8%). The high variability observed for nitrate stresses the difficulties in modeling nitrate concentrations accurately. Because nitrate concentrations are highly sensitive to heterogeneous reactions, better knowledge of reaction probabilities for example, some of which may be expressed as a function of aerosol composition, temperature and relative humidity (e.g. [START_REF] Evans | Impact of new laboratory studies of n2o5 hydroly-sis on global model budgets of tropospheric nitrogen oxides, ozone, and oh[END_REF] for R 4), may help to improve the models accuracy.

  , [2]: Saxena et al. (1986), [3]: Heikes and Thompson (1983), [4]: Walcek and Taylor (1986), [5]: Chameides and Davis (1982), [6]: Hov and B.A. (1994), [7]: Nenes et al. (1998), [8]: Tang et al. (2004), [9]: Riemer et al. (2003), [10]:

Fig. 1 .

 1 Fig. 1. Location of the stations used in the sensitivity study

Fig. 2 .

 2 Fig. 2. Precipitations in mm/month for March 2001.

Fig. 3 .

 3 Fig. 3. Size distribution of sulfate and nitrate monthly averaged concentrations averaged over all stations.

  Cond./evap. Equil. Equil. Hybrid Equil. Equil. Equil. Equil. Summary of the different options used in the runs performed for the sensitivity study (these configurations refer to model M 8 ).

								7
	Sections	10	3	10	10	10	10	10
	Coagulation	Yes	Yes	Yes No	No	No	No	Yes
	Table 2

  NMAE and NMB between R 1 and R i (i = 2, ..., 8), for monthly averaged concentrations at 23 EANET stations and at Fukue.

		Sulfate	Nitrate
	NMAE NMB NMAE NMB
	R 2	6%	5%	15%	15%
	R 3	2%	2%	13%	-12%
	R 4	11%	-10%	15%	-15%
	R 5	15%	-14%	99%	99%
	R 6	17%	17%	2%	-1%
	R 7	3%	3%	10%	10%
	R 8	34%	34%	71%	71%
	Table 3

  Min, mean and Max of NMAE associated to runs R 1 to R 7 and to models M 1 to M 8 for monthly averaged concentrations.

			Sulfate			Nitrate
		Min Mean Max Min Mean Max
	R 1 -R 7	12%	13%	14% 20%	34%	85%
	M 1 -M 8	10%	14%	18% 13%	57%	188%
	M 1 -M 8 (no M 4 )	-	-	-	13%	35%	69%
	Table 4

Acknowledgements

The authors are very thankfull to C. Bennet, G.R. Carmichael, M. Engardt, C. Fung, Z. Han, M. Kajino, U. Park, T. Sakurai, N. Thongboonchoo, H. Ueda for providing the data for the different models, the EANET monitoring data and for very valuable discussions. We would also like to thank T. Holloway for providing boundary counditions, and A. Kannari for emissions.


Jean-Baptiste Louvet
email: jeanbaptiste.louvet@insa-rouen.fr

Cédric Fleury
email: cedric.fleury@lri.fr

Combining Bimanual Interaction and Teleportation for 3D Manipulation on Multi-Touch Wall-sized Displays

Keywords: multi-touch interaction, 3D manipulation, wall-sized display, virtual reality Concepts:, Human-centered computing → Virtual reality, Touch screens, Usability testing

Figure 1: 6 degrees of freedom manipulation of a 3D object on a multi-touch wall-sized display combining bimanual interaction and teleportation. The user is performing a xy translation (main picture), z translation (a), roll rotation (b), and pitch & yaw rotation (c).

Introduction

Even if touch has been reserved for small devices such as smartphones and tablets in its early years, more and more large screens, such as computer screens, televisions, whiteboards, and wall-sized displays, are now equipped with multi-touch capabilities. Large multi-touch screens will become widespread in a near future in various locations such as public places, meeting rooms, classrooms. For example, we can already see such large multi-touch displays in shopping malls or airports to enable visitors to browse information. At the same time, Microsoft is currently launching its new Surface Hub1 which is designed for meeting rooms.

Large screen displays are powerful tools to visualize the increasing amount of data from science, industry, business, and society. They are also appropriate to support collaboration among small groups of users. However, these benefits should not be limited to 2D content.

Large multi-touch devices are a relevant solution to interact with 3D virtual environments. While the large display surface increases immersion in virtual environments, multi-touch input is a simple and efficient way to perform 3D interaction. Touch-based interaction can be easy to use and to learn for none-expert users since most of the people are familiar with touch devices. It also does not require additional external equipment since everything is embedded in the display device, which makes it particularly suitable for public areas, classrooms, and meeting rooms.

Most of the current touch-based 3D interaction techniques are designed either for small screens or consider that users stay static in front of the screen. Consequently, these techniques are not well adapted to the situation where users are immersed in a 3D virtual environment and freely move in front of a large vertical screen. In particular, finger combinations could be hard to achieve if users have to perform actions at the top or the bottom of the screen. In this paper, we propose In(SITE), a bimanual interaction technique designed to perform 6-DOF manipulation of 3D virtual objects using multi-touch input on a wall-sized display. This technique takes advantage of the large interaction space available on the display, in addition to the fact that users can easily move along the screen and perform actions with their two hands. On large wall-sized displays, it can be uncomfortable and inefficient to have to drag objects all along the screen when performing large translations. Consequently, we also propose to combine the In(SITE) technique with object teleportation features in order to overcome this drawback.

Two controlled experiments were performed to assess the usability of the In(SITE) technique in comparison with a standard 3D interaction technique which is used as a baseline for interaction in virtual reality. The first experiment evaluates the two techniques for 3D manipulation tasks which require only translations, while the second experiment focuses mainly on rotations. The first experiment also compares both techniques with and without teleportation features to determine the benefits of teleportation for each technique.

After reviewing the related work (section 2), we present the In(SITE) technique (section 3). Section 4 describes the usability testing of the In(SITE) technique through two controlled experiments. Then, we discuss the implications for touch-based 3D interaction on wall-sized displays (section 5). Finally, we conclude and propose a set of future research directions for 3D interaction on multi-touch wall-sized displays (section 6).

Related Work

The increasing accessibility to multi-touch technology has drawn the attention of researchers on the capabilities of touch-based interaction for 3D manipulation. On a state of the art about 3D interaction, [START_REF] Jankowski | Advances in Interaction with 3D Environments[END_REF] point out that "3D transformation widgets need to be reinvented to adapt to the tactile paradigm", as users of touchscreens do not have access to an unobstructed view of the screen, an accurate pointing, and a direct access to buttons. The emergence of large wall-sized displays with multi-touch capabilities also introduces new constraints which should be taken into account when designing 3D touch-based interaction techniques.

3D Touch-based Interaction

Multiple approaches using touch input for 3D interaction have been explored. Cohé and Hachet [2012] conducted an experiment to understand how non-technical users a-priori interact with multi-touch screens to manipulate 3D objects. The experiment was conducted by showing participants a video of a cube transformation (rotation, scaling, translation) and then asking them to draw a gesture that best matches this transformation on a static image displayed on a touchscreen. The results of the study made them define a taxonomy based on the analysis of the users' gestures. It contains different strategies applied by participants to perform the actions for rotation, scaling, and translation. This study gave us some guidelines on how to design the interaction technique presented in this paper. Some techniques try to mimic direct manipulation of an object in 3D such as the screen-space technique presented by [START_REF] Reisman | A Screen-space Formulation for 2D and 3D Direct Manipulation[END_REF]. This technique was designed to ensure that the screen-space projection of the object-space point "touched" by users always remains under their fingertip. It uses a constraint solver to minimize a function that measures the error between the points in the object local-space and the points in screen-space and update the object position and rotation according to the result. Even if these manipulation techniques seem more intuitive, the users can have troubles to perform specific manipulation (especially for rotation) because it is not always easy to predict how the manipulated object will react and to determine what is the correct action to perform. In a similar way, sticky fingers presented by [START_REF] Hancock | Tools: Full 6DOF Force-based Interaction for Multitouch Tables[END_REF] is a force-based interaction technique designed to maintain the feeling of physical interaction with a virtual object. It is a transposition of the 2D multi-touch interaction paradigm (rotate, spin, scale) to 3D interaction. [START_REF] Jackson | Nailing Down Multi-Touch: Anchored Above the Surface Interaction for 3D Modeling and Navigation[END_REF] proposes to extend touch-based interface techniques by using hand gestures above the surface. Some other techniques use multiple simultaneous touch inputs from several fingers or/and from both hands to control each one of the 6 DOF. In particular, [START_REF] Hancock | Shallow-depth 3D Interaction: Design and Evaluation of One-, Two-and Three-touch Techniques[END_REF] have tested different 3D manipulation techniques in "shallow-depth" (i.e., in limited depth) involving 1, 2, or 3 touch points, making possible to control 5 or 6 DOF. The user study carried out shows that user preference and performance are higher with techniques involving multiple touch points which enable users to separate DOF. To facilitate the control of the depth, the multi-touch viewport presented by Martinet et al. [2010a] divides the screen in four viewports, each one corresponding to a different viewpoint on the 3D scene. Interacting in the first viewport allows a 2 DOF translation of the object, while interacting at the same time in a second viewport makes it possible to control the third DOF of translation of the object. Martinet et al. [2010a] also proposed the Z-technique in order to only use a single view of the scene for 3D positioning. When a first finger touches the screen, a raycast is done from the camera center and passing by the touch position and the first object intersected can then be translated in the plane parallel to the camera plane passing through the object center. The depth of the manipulated object is controlled in an indirect way using a second finger. Its up and down movements on the screen are mapped to forward and backward movements of the object relatively to the user position. A docking task experiment did not show significant results on performance but the Z-technique was preferred by a majority of participants in comparison to the multi-touch viewport technique. Dividing the screen into four viewports does not seem a solution suitable for large wall-sized displays. In addition, Martinet et al. [2010b] combined the Z-technique to the screen-space technique [START_REF] Reisman | A Screen-space Formulation for 2D and 3D Direct Manipulation[END_REF]] into a technique called DS3 and compared it to the sticky fingers [START_REF] Hancock | Tools: Full 6DOF Force-based Interaction for Multitouch Tables[END_REF]] and screen-space technique. The results of this study show that separating the control of translation and rotation, as it is done with DS3, makes the interaction significantly faster.

To improve the understanding of 3D manipulation and the separation of the 6 DOF, Cohé et al. [2011] introduced tBox, a 3D transformation widget designed for touchscreens. This widget is inspired by standard box-shaped 3D transformation widgets operated from mouse and keyboard in desktop 3D applications. The widget is a wireframe box that appears on top of the manipulated object. The rotation is carried out with one finger, rotating the widget (with the object) around one of its primary axes, depending of the direction of the gesture. The translation is carried out with one finger on an edge of the widget, allowing to translate the object in the direction of the selected edge. The scaling is carried out on the direction of the primary axes of the widget, with two fingers selecting two opposed edges of a face and and moving them away from each other.

Touch-based Interaction on Large Displays

Several previous works started to explore touch-based interaction techniques for 3D manipulation in the context of large vertical displays. For example, [START_REF] Yu | FI3D: Direct-Touch Interaction for the Exploration of 3D Scientific Visualization Spaces[END_REF] proposed FI3D, a touch-based interaction technique to navigate in a 3D scene on a large vertical screen. Traditional pan and zoom can be performed with 2-finger interaction with the display, but manipulation of other DOF can be achieved either by starting the drag gesture on the frame of the screen or just by touching the frame of the screen with the second finger. However, this technique is specific to the exploration of a single object in the 3D scene which does not require any object selection. Touching the frame of the screen is also not suitable with wall-sized displays because they are too large. In most recent work, [START_REF] Lopez | Towards An Understanding of Mobile Touch Navigation in a Stereoscopic Viewing Environment for 3D Data Exploration[END_REF] presented a variation of the previous technique in which the FI3D technique is used on a separate tablet to explore data displayed a large stereoscopic display. While this variation enables the user to control data on a larger display and deals with stereoscopic screen, the user has to frequently switch between the tablet view and the stereoscopic view. It also requires that the user hold an additional device, which does not seem appropriate when large displays are used in public places. In a similar way, [START_REF] Coffey | Interactive Slice WIM: Navigating and Interrogating Volume Data Sets Using a Multisurface, Multitouch VR Interface[END_REF] manipulated 3D medical data displayed on a large stereoscopic screen through 2D widget on a tabletop. Consequently, this technique implies indirect manipulation of the data displayed on the stereoscopic screen and also requires an additional device. Finally, [START_REF] Gilliot | Direct and Indirect Multi-touch Interaction on a Wall Display[END_REF] presented a touch-based interaction technique called WallPad to perform direct and indirect manipulation on large wall-sized displays. However, this technique mainly focuses on touch-based interaction with a 2D graphical user interface.

Touch-based Interaction on Stereoscopic Displays

Some other related work focus on 3D touch-based interaction in the context of stereoscopic vision. For example, [START_REF] Benko | Balloon Selection: A Multi-Technique for Accurate Low-Fatigue 3D Selection[END_REF] introduced the Balloon Selection which makes the selection of 3D objects possible in augmented reality settings. This multitouch interaction technique consists in controlling the 2D position in the screen plane with a finger and to adjust the depth with the finger of the second hand using the metaphor of the string of an helium balloon which can be pulled down or released according to the second finger position. This technique only deals with translations and not with rotations. To improve the technique and to perform rotations, [START_REF] Strothoff | Triangle Cursor: Interactions with Objects Above the Tabletop[END_REF] proposed a variation of the Balloon Selection called the Triangle Cursor in which two fingers of the user (same hand) define the base of an isosceles triangle which is perpendicular to the screen. The top of the triangle is the cursor for selection and manipulation. The user can control the height and the orientation of the triangle by moving these two fingers. The user can performed the additional rotation with his second hand. However, only objects which "come out" of the screen can be selected with both of these techniques. Consequently, they cannot be useful in the general context of immersive systems in which the 3D scene stands behind and in front of the display area.

On a more theoretical point of view, [START_REF] Valkov | 2D Touching of 3D Stereoscopic Objects[END_REF] studied touch-based interaction with a 2D screen to interact with 3D stereoscopic objects. They showed that users tend to touch between the projections for the two eyes with an offset towards the projection for the dominant eye. They also provided guidelines on how to design touch-based interaction for systems with 3D stereoscopic vision.

Synthesis

To sum up, many different touch-based interaction techniques have been proposed to perform 6 DOF manipulation of 3D objects. Several exhaustive studies have been achieved to better understand 3D touch-based interaction and provide useful guidelines to design a 3D touch-based interaction technique. In particular, [START_REF] Hancock | Shallow-depth 3D Interaction: Design and Evaluation of One-, Two-and Three-touch Techniques[END_REF] provide as guidelines to separate DOF and enable users to simultaneously control them. Even if some previous works have explored large and/or stereoscopic displays, most of the proposed techniques consider systems in which users stay static in front of the display and none of them deal with large wall-sized display in front of which users can freely move. In particular, touch input techniques which combine several fingers from the same hand are not convenient when users have to perform actions at the top or bottom of a large wall-sized display. Long drags all along the screen must also be avoided. In addition, techniques suitable with large displays often require control devices such a tablet or a tabletop. In the context of wall-sized displays in public area, we want to find solutions which avoid these additional devices and we would rather prefer solutions which only use direct interaction on the display.

In(SITE) Technique

We designed a new interface for interacting with a 3D virtual environment using multi-touch input: In(SITE), Interface for Spatial Interaction in Tactile Environments. We decided to focus on selection, translation and rotation of objects in virtual environments, so scaling is not present in this first version of the interface.

Manipulation Mode

The In(SITE) technique provides a widget, presented in Figure 2, which separates manipulation of the 6-DOF for 3D interaction. When the user touches the screen to select an object, a raycast is performed starting from his head and passing by his finger tip, which makes it possible to select the object under his finger tip according to his point of view on the 3D scene. If it reaches an object of the virtual environment, the widget appears on the screen under the user's finger. This widget allows the user to fully control the 6-DOF of the selected object using multi-touch input. With his primary finger (i.e., the one used for the selection), positioned at the center of the central circle of the widget, the user controls in a direct way the two x and y translations of the object in a plane parallel to the screen (see Figure 1(main picture)). This method allows co-planar dragging and is called ObjectCorrection by Möllers et al. [2012] in the literature. It implies that if the user moves his head, the object will also move to remain under his finger. The central circle of widget is a dead zone to avoid false-positive detections of secondary fingers while dragging, especially when the touch-detection system is not very precise (as it is the case on our wall-sized display).

The z translation is controlled in an indirect way by interacting with the area outside the widget with a secondary finger (see Figure 1(a)). This interaction design has been strongly influenced by the Z-technique [Martinet et al. 2010a]. The main difference is that the gesture of the secondary finger does not have to be vertical but towards the primary finger: when the user's secondary finger gets closer to his primary finger mimicking a large pinch gesture, the object gets closer to him, and when his secondary finger gets away from his primary finger mimicking a large unpinch gesture, the object gets away from him. The direction of the z translation is given by the ray starting from the head of the user and passing through his primary finger. The mapping between the finger displacement and the object displacement is linear.

The upper and lower areas of the widget are used to control the rotation of the object. The lower area allows users to control the roll of the object by doing curve gestures which follow the object rotation (see Figure 1(b)). These curve gestures are suggested to the user by the round shape of the area. The upper area allows users to manipulate the yaw and pitch of the object by doing respectively horizontal and vertical movements (see Figure 1(c)). In the upper area, the yaw and pitch rotations be combined by diagonal movements. The square shape of this area aims to highlight these 2D interaction possibilities. For both rotations, the rotation axis are defined by the gravity center of the manipulated object. 1: Description of the In(SITE) technique using the taxonomy described by Martinet et al. [2010b].

Table 1 presents the different DOF manipulation modes offered by In(SITE) using the taxonomy introduced by Martinet et al. [2010b]. The primary finger is used to control Tx and Ty in a direct way, as the projection on the screen of the object is directly mapped to the position of the finger. The secondary finger can control Tz, Rx and Ry, or Rz. Tz is controlled in an indirect way using the external area of the widget, Rx and Ry are controlled in a direct way using the upper area of the widget, and Rz is controlled in a direct way using the lower area of the widget.

Teleportation

As In(SITE) aims to be used in large displays, we combine teleportation with the In(SITE) technique to improve its effectiveness for translations. Indeed, teleportation avoids that the user drags objects all along the screen when he needs to perform large translations.

To perform teleportation, the first step consists in selecting the object that the user wants to teleport. If he does a short touch (less than 1s) on the object, it is selected. As a feedback of the selection, the object color is changed. If he does a long touch (more than 1s), the interface enters directly in the manipulation mode of the In(SITE) technique, without selection. If the user selects an object by mistake, he can deselect it just by touching it again. In this case, the color feedback is removed and the user can select another object.

Once the user has selected an object, he can teleport it anywhere in the environment by doing a short touch on the destination location (either on the floor or on another object). This instantly moves the object above the destination and makes it fall downwards with an animation. The animation stops when the teleported object collides with another object of the environment. This animation is particularly useful for physically simulated virtual environments because the object can be piled up on other objects. Once the animation is over, the color feedback is still present for a short period of time. During this period, the user can catch the object again by touching it. If the object is caught, the interface enters in manipulation mode.

It is also possible to teleport the object without animation by doing a long touch on the destination. In this case the object is directly teleported at the location designated by the intersection between the environment and the ray starting from the user's head and passing by the finger used for the touch. Once the object has been teleported under the user's finger, the interface enters in manipulation mode. The teleportation is especially useful when the user wants to put the object under some other objects.

Usability Testing

We conducted two experiments to assess the usability of the In(SITE) technique combined with object teleportation in the context of an immersive virtual environment based on a large wallsized display. The goal was to determine if the In(SITE) technique could reach the same level of performance than a standard 3D interaction technique for 6-DOF manipulation tasks of 3D objects in terms of completion time of the task and precision. We also considered precision, ease of use, frustration and fatigue of users as important evaluation criteria. In particular, we wanted to assess the four following hypotheses:

H1 Users can reach the same level of performance with In (SITE) in comparison to standard 3D interaction technique for tasks which require only translations.

H2 still can reach the same level of performance with In(SITE) in comparison to standard 3D interaction technique if the task requires also rotations.

H3 In(SITE) have additional advantages, in particular in terms of precision, ease of use, frustration, and fatigue.

H4

The teleportation can be beneficial to both techniques.

For the standard 3D interaction technique, we decided to use the virtual ray technique (also known as the ray-casting technique [START_REF] Jacoby | Gestural interaction in a virtual environment[END_REF][START_REF] Mine | Virtual Environment Interaction Techniques[END_REF]]) as a baseline for 3D interaction in virtual reality (see Figure 3). Some other techniques have been developed to improve the virtual ray technique such as HOMER [START_REF] Bowman | An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments[END_REF] or Voodoo Dolls [START_REF] Pierce | Voodoo Dolls: Seamless Interaction at Multiple Scales in Virtual Environments[END_REF]], but they are still hard to manage for non-expert users and require additional tracking of the users' hand. Consequently, we have chosen to stick to the more standard version of the virtual ray technique since this version is the most commonly used as a mainstream interaction technique in various virtual reality systems such as CAVE. However, a virtual ray is not very efficient for pitch and yaw rotations because the manipulated object is attached to the ray and cannot easily rotate in that case. After pilot experiments, we decided to add a feature to the virtual ray technique for rotation along a vertical axis in order to be as fair as possible in our comparison between the two techniques. Users can thus easily perform yaw rotations and even pitch rotations by turning objects of 90

• on the roll axis before using this additional feature (see section 4.1.1 for implementation details).

The two experiments followed the same experimental method, so the common parts are described in the following subsection. The first experiment focused on tasks which require only translations, while the second one concentrated on tasks which require rotations.

Experimental Method

Apparatus

The experiments were performed on a 5.90m × 1.96m wall-sized display composed of 75 thin-bezel screens for a total resolution of 14400 × 4800 pixels. Applications ran on a server that distributes the environment to the 10 machines running the wall-sized display.

Each machine has one Intel Xeon CPU at 3.7 GHz and a Nvidia Quadro K5000 graphic card. The virtual environment was designed using the game engine Unity2 .

While this wall-sized display offers a very large surface with multitouch capabilities, it does not support stereoscopy. Even if the ideal case would have been to have stereoscopic vision to support immersion in the virtual environment, the device still provide a interesting level of immersion because it supports other cues of immersion: a wide field of view, a complete visual immersion when users are close to the screens and a good resolution. The user's head was also tracked in front of the screen by a Vicon3 system. Displayed images were deformed according to the user head position to match the viewing frustum defined by the user's head and the four corners of the wall-sized displays. Consequently, motion parallax was respected and participants had a sense of depth when they moved.

For the In(SITE) technique, the touch was detected by a PQLabs4 infrared frame surrounding the wall-sized display. This frame is composed of infrared emitters and sensors which shoot and capture infrared light along the screen on the two directions (up-down and right-left). can thus determine the 2D position of the fingers when they intersect with the infrared light. The main drawback of this system is that fingers can be detected slightly before they touch the screen (around 0.5cm from the screen). It can induce false-positive touch detections when fingers or parts of the hand are close to the screen. This system has an accuracy of 1cm all over the screen surface and can detect up to 32 contact points.

For the virtual ray technique, the participants held a wireless mouse attached to a Vicon target which was the starting point of the virtual ray (see Figure 3). The Vicon tracking system has an accuracy of 1mm, but can suffer from jitter, especially for rotations. To reduce the jitter of the virtual ray, we used a 1e filter [START_REF] Casiez | 1e filter: A simple speed-based low-pass filter for noisy input in interactive systems[END_REF] with the following parameters: for translations, min cut-off = 5 and beta = 0.8; for rotations, min cut-off = 0.5 and beta = 0.5. The virtual ray technique was designed as similar as possible to the In(SITE) technique. The first difference was that participants did not select objects by touching them, but by left-clicking while the virtual ray was pointing at them. The second difference was that participants did not use the In(SITE) widget, but had objects directly attached to the ray when they manipulated them. To perform rotations along the vertical axis, participants used the scroll wheel of the mouse. The right button of the mouse was used to fix the rotation of the selected object, and enabled participants to move the virtual ray without modifying the object rotation.

General Task

We asked participants to perform a docking task using the two interaction techniques. We set up a simple environment that enabled participants to perform the task without any navigation actions: a floor and a 1-meter high, 6-meter wide and 3-meter deep table, positioned just behind the screen. Participants had to put an object present at the left of the table in a target placed on the right of the table as fast as possible. The target, which was slightly bigger than the moved object (set after pilot experiments to 10% for experiment 1 and 20% for experiment 2), was red and turned green when the object was correctly placed inside. In order to validate the completion of the task, the object had to stay in the target for one second, whether it was released or not by the participant. A directional light was placed in the scene, casting shadows vertically on the table and the floor to help participants to perceive the depth in the scene. Physics, including gravity, were disabled during the experiment to avoid bias due to the physics engine.

Participants

adults (5 females and 11 males) with a mean age of 24.4 (SD 5.0) participated in both experiments. There were all right-handed. 13 of them use touch devices every day. They had variable experience with 3D visualization systems. They all performed the second experiment after the first one. Participants were not remunerated for their participation.

Data Collection

For each trial, we collected the task completion time. The completion time measurement started when the participant selects the manipulated object for the first time and stopped when the task was accomplished (i.e., when the object stayed in the target for one second). We also collected the number of overshoots. An overshoot was defined by the fact that the manipulated object reached the targeted position (which means that the target turns green), but could not stay within the target during the mandatory 1 second. This usually happened when participants moved the object too fast and could not adjust precisely enough its final position. After the experiments, participants filled out a subjective questionnaire.

Experiment 1: Translations

The first experiment compared the two interaction techniques and assessed the benefits of the teleportation for 3D manipulation tasks which required only translations. Consequently, we set the TECH-NIQUE and the TELEPORTATION as two primary factors. For teleportation, both techniques used the process described in section 3.2. The touch and long touch for In(SITE) were replaced by a click and a long click on the mouse in the case of the virtual ray technique. When the participants used teleportation, they could make the object fall down from the top in the direction of the indicated destination. The object stopped when it reached the table in the particular case of this experiment. The vertical position of the target could thus have an impact on the performance. Indeed, the object stopped at a position close to the target when the target was on the table, while it still stopped on the table even if the target was in the air. Consequently, we decided to set the VERTICAL POSI-TION of the target as a secondary factor to evaluate the influence of the height of the target on the performance.

Task

The manipulation task followed the general task described in section 4.1.2. The manipulated objects were spheres to insure that participants did not need to rotate the objects for putting them in the targets (see Figure 4). We picked 4 random positions for each object, on the left of the table for the manipulated object and on the right for the target, making sure that the objects were at least 50 cm away from each other.

Experimental Design & Procedure

The experiment had a [2 × 2 × 2] within-subject design with the following factors:

• TECHNIQUE, with the two treatments: VRay for the virtual ray technique and Touch for the In(SITE) technique.

• TELEPORTATION, with the two treatments: NoTele when the technique was not combined with teleportation and +Tele when the technique was combined with teleportation.

• VERTICAL POSITION, the two levels: Floor condition for which the position of the target was on the table and Midair condition for which the position of the target was above the table at a random altitude of at least 50 cm (see Figure 4).

Trials were grouped by TECHNIQUE and TELEPORTATION. The order of TECHNIQUE×TELEPORTATION was counterbalanced across participants using a balanced latin square. For each combination of TECHNIQUE×TELEPORTATION, the session began by 4 training trials, during which we explained to the participant how to use the TECHNIQUE with or without the TELEPORTATION. After the training, the participant performed 8 trials: 4 trials for the Midair condition using the 4 different object/target positions and 4 trials for the Floor using the same 4 object/target positions, but with a zero altitude for the target (target laying on the table ). The order of the 8 trials was randomly chosen. Consequently, each one of the 16 participant performed 2 TECHNIQUEs × 2 TELEPORTA-TIONs × 2 VERTICAL POSITIONs × 4 trials = 32 trials (bringing total to 512 trials for the whole experiment). The participants were authorized to take a break whenever they wanted between trials, but we encouraged them to take a break between each block of the experiment corresponding to a particular combination TECH-NIQUE×TELEPORTATION. Sessions lasted from 25 to 40 minutes depending on the participants.

Results

Task Completion Time An analysis of outliers detected two extreme outliers. The global mean for completion time was 14.34s (SD: 11.3) while the two trials corresponding to the two outliers lasted more than 130s. Since we had recorded during the experiment that two participants faced critical problems during these two trials, we chose to remove these two extreme outliers. One participant faced technique issues, while the other did not understand that the depth was wrong and kept trying to adjust the position during a very long time. Figure 5 illustrates the results for each TECH-NIQUE×TELEPORTATION combination and VERTICAL POSITION with regard to task completion time. A repeated measures ANOVA5 on completion time with the model TECHNIQUE×TELEPORTATION×VERTICAL POSITION revealed no significant effect of TECHNIQUE, TELEPORTATION, and VER-TICAL POSITION, but it showed a significant interaction effect of TELEPORTATION×VERTICAL POSITION (F(1, 14.3) = 8.3, p = 0.01). Pairwise comparisons revealed that techniques combined with teleportation (+Tele) were significantly faster in Floor condition (avg. 12.66s) than in Midair condition (avg. 15.12s , p < 0.001). This showed that teleportation was more efficient when the target was laying on another horizontal object (Floor condition). In addition, the overall mean values of completion time for the two TECHNIQUEs were very close to each other (avg. VRay: 13.92s and Touch: 13.83s), which suggests that there is no practical difference between VRay and Touch with respect to the completion time.

Overshoots For the overshoots analysis, we also excluded the two extreme outliers detected during the completion time analysis in order to be consistent.

Figure 6 shows the mean number of overshoots for each TECHNIQUE×TELEPORTATION combination and VERTICAL POSITION. A repeated measures ANOVA on overshoots with the model TECHNIQUE×TELEPORTA-TION×VERTICAL POSITION revealed a significant effect of TECH- Subjective Questionnaire After the experiment, participants had to rate on a 5-point Likert scale if each TECHNIQUE×TELE-PORTATION combination was mentally demanding, physically demanding, frustrating, easy to use, precise (1:not at all/5:very) and they had also to give an overall evaluation (1:bad/5:good). (avg. 4.21 vs. 3.47) according to the overall evaluation. Finally, the analysis did not reveal any significant effects of TECHNIQUE and TELEPORTATION on the frustrating criterion, as well as any significant interaction effects of TECH-NIQUE×TELEPORTATION on all the criteria.

Experiment 2: Rotations

The second experiment compared the In(SITE) technique with the virtual ray technique for 3D manipulation tasks which required rotations. In order to minimize the influence of translation on task performance, the object and the target were placed close to each other with different orientations. Since the translations to perform were really small and the teleportation did not have an effect on rotations, it seemed reasonable to consider that teleportation did not impact the task performance. Consequently, only the VRay and Touch TECHNIQUEs both without teleportation were evaluated in this experiment to shorten the global duration.

Task

The manipulation task followed the general task described in section 4.1.2. The manipulated objects were wedges (triangular prisms with one right angle) to insure that there was only one correct ro- tation to fit the object in the target. Since it was not always easy to determinate the angle values in the 3D virtual environment, the right angle of the wedge was marked on both the object and the target to help participants identifying the rotation to perform (see Figure 8). The manipulated object and the target were placed 1m from each other and the manipulated object was located on the left of the target. We picked random values for the orientations of both the object and the target. The rotation was considered as correct if the dot product between the manipulated object and the target's up, right, and forward were bigger than or equal to 0.98 (set after pilot experiments).

Experimental Design & Procedure

The experiment had a [2 × 2] within-subject design with the following factors:

• TECHNIQUE, with the two treatments: VRay and Touch both without teleportation.

• VERTICAL POSITION, the two levels: Floor and Midair conditions. For the Midair positions, the target height was set at 50 cm above the table. For the Floor positions, the target position was defined with one side of the target laying on the virtual table (see Figure 8).

Trials were grouped by TECHNIQUE and the order of the TECH-NIQUEs were counterbalanced across participants. For each TECH-NIQUE, the session began by 4 training trials. After the training, the participant performed 8 trials: 4 trials for the Floor condition and 4 trials for the Midair condition. The order of the 8 trials was randomly chosen. Consequently, each one of the 16 participant performed 2 TECHNIQUEs × 2 VERTICAL POSITIONs × 4 trials = 16 trials (bringing the total to 256 trials for the whole experiment). The participants were authorized to take a break whenever they wanted between trials, but we encouraged them to take a break between the two blocks of the two TECHNIQUEs. Sessions lasted from 15 to 20 depending on the participants. Participants performed the task significantly faster in Floor condition (avg. 27.4s) than in Midair condition (avg. 43.61s). However, the analysis did not reveal any significant effects of TECHNIQUE or interaction effect of TECHNIQUE×VERTICAL POSITION.

Overshoots Figure 10 shows the mean number of overshoots for each TECHNIQUE and VERTICAL POSITION. A repeated measures ANOVA with the model TECHNIQUE×VERTICAL POSI-TION showed a significant effect of TECHNIQUE (F(1, 15) = 20.25, p < 0.001) on overshoots. Touch (avg. 0.45) led to significantly less overshoots than VRay (avg. 0.84). However, the analysis revealed neither a significant effect of VERTICAL POSITION nor a significant interaction effect of TECHNIQUE×VERTICAL POSITION.

Subjective Questionnaire After the experiment, participants filled out a similar questionnaire to the one of experiment 1, but only for the two TECHNIQUE conditions (Touch and VRay). Pair-Wilcoxon rank sum tests with Bonferroni corrections among TECHNIQUEs did not reveal any significant differences for all the criteria, and for the overall evaluation.

Discussion

On one hand, the two experiments did not reveal any significant effects of TECHNIQUE completion time. This result, in addition to the fact that the overall mean values of completion time were very similar for the two techniques, suggests that users can reach the same level of performance with the In(SITE) technique in comparison with a standard 3D interaction technique even if the manipulation tasks require both translations and rotations. It suggests a validation of H1 and H2.

the other hand, the results of both experiments revealed that the In(SITE) technique led to a significant smaller number of overshoots in comparison to the technique based on a virtual ray. It shows that the In(SITE) technique was more precise for fine adjustments of 3D object position in the immersive context of large vertical displays. This result is consistent with the fact that participants perceived the In(SITE) technique as more precise than the virtual ray technique for manipulation tasks which required only translations. Concerning H3, we can note that the In(SITE) technique was more precise for fine adjustments of 3D object position, which was confirmed by the subjective questionnaire only for translations. For translations, the In(SITE) technique was also perceived easier to use by the participants.

Moreover, the results of the first experiments revealed that teleportation significantly improved the performance of the two techniques in terms of completion time for the Floor condition. This result seems coherent since users can select a destination for teleportation close to the targeted position in this condition. In addition, the subjective questionnaire of the first experiment showed that teleportation significantly reduced the mental and physical loads. For the mental load, we cannot formulate a strong claim, but it might be explained by the fact that the participants only had to focus on the destination with teleportation and not on the full path to reach the target. For the physical load, it was probably due to the gorilla-arm effect [Hincapié-Ramos et al. 2014] which could affect participants if they hold the 3D virtual ray during a long time or drag an object all along the screen with In(SITE). Teleportation avoids to always raise the arm during interaction and reduces the arm fatigue. This is also confirmed by the fact that participants preferred and found easier to use the two techniques when they were combined with teleportation. These results show that H4 is confirmed and that the teleportation has a real benefit on large wall-sized displays.

Finally, the difference on completion time between the Floor and Midair in the second experiment can be explained by the fact that the targeted positions in Floor condition were defined with one side of the object laying on the virtual table. Consequently, the performed rotations were less complicated and the participants achieved more quickly the manipulation task in Floor condition. This paper aims to assess the usability of the In(SITE) technique combined with teleportation for 3D manipulation on multi-touch wall-sized displays. The In(SITE) technique is a bimanual touchbased technique for 3D interaction adapted to the situation where users are immersed in a 3D virtual environment and move in front of a large vertical screen. The usability testing shows that the In(SITE) technique can reach the same level of performance than a standard 3D interaction technique with respect to completion time. In addition, the In(SITE) technique improves precision for fine adjustments of 3D object position. The results also show that combined with teleportation were found easier to use, less tiring and were globally preferred by participants for translation tasks.

This paper is a first study of combining bimanual touch-based interaction with teleportation on large wall-sized display. Since we demonstrated that the In(SITE) technique can be beneficial in such context, we want now to improve it by adding scaling functionalities and to explore different variations of the proposed widget to question the design choices we have done. For example, it will be interesting to study if the choice of rotation axis of the manipulated object (object gravity center or selection point of the user) or if the choice of the transfer function (which is currently linear) used to control the translation along the z-axis as studied by [START_REF] Casiez | The Impact of Control-Display Gain on User Performance in Pointing Tasks[END_REF] have some impacts on performance. In addition, we want to study more in depth the different teleportation strategies such as the animated teleportation (objects falling down when a physical simulation is used) or the direct teleportation (under the user's finger).

The wall-sized display used during the experiments does not support stereoscopy, but we think that the In(SITE) technique could be extended to stereoscopic vision using the guidelines defined by [START_REF] Valkov | 2D Touching of 3D Stereoscopic Objects[END_REF]. Consequently, it would be interesting to study the In(SITE) technique with another system which support stereoscopy and to compare the results. In addition, we want to adapt and study the In(SITE) technique for systems which do not use head-tracking and viewing frustum deformation. These systems are especially relevant to enable multiple users to collaborate together in front of a same wall-sized display. We think that In(SITE) could be easily adapted to this context by using the center of the virtual camera instead of the user's head as the starting of the raycast when performing object selection and manipulation.

Finally, touch-based 3D interaction techniques could be an issue for remote collaboration in a shared virtual environment. Indeed, when a user interacts with 3D objects through his own multi-touch device, it is impossible for the remote users to understand which objects he is manipulating. It is an issue for the communication between the remote users. We need to provide a feedback which links the user's 2D touch inputs to the manipulated 3D objects. A similar solution to the 2D pointer / 3D ray proposed by [START_REF] Duval | An Asymmetric 2D Pointer/3D Ray for 3D Interaction Within Collaborative Virtual Environments[END_REF] could be an interesting way to implement this feedback.

Figure 2 :

 2 Figure 2: In(SITE) manipulation widget.

Figure 3 :

 3 Figure3: For the virtual ray technique, the ray is co-located with the wireless mouse held by the user.

Figure 4 :

 4 Figure 4: Relative positions of the manipulated sphere and its associated target in Floor condition (bottom) and Midair condition (top) for experiment 1.

Figure 5 :

 5 Figure 5: Mean completion time by TECHNIQUE×TELEPORTA-TION and VERTICAL POSITION in experiment 1. Error bars represent 95% confidence intervals.

Figure 6 :

 6 Figure 6: Mean numbers of overshoots by TECHNIQUE×TELE-PORTATION and VERTICAL POSITION in experiment 1. Error bars represent 95% confidence intervals. NIQUE (F(1, 14.96) = 3.63, p = 0.002), but no significant effect of TELEPORTATION and VERTICAL POSITION, as well as no significant interaction effects. The Touch technique (avg. 1.38) led to significantly less overshoots than the VRay technique (avg. 2.13).

  Figure 7 illustrates the results of the subjective questionnaire. To analyze the results of the subjective questionnaire, we used the aligned rank transform (ART) procedure proposed by Wobbrock et al. [2011] with the model TECHNIQUE×TELEPORTATION. It showed a significant effect of TECHNIQUE on the easy (F(1, 45) = 19.21, p < 0.001) and precise (F(1, 45) = 30.49, p < 0.001) criteria. Touch was perceived easier to use (avg. 4.31 vs. 3.61) and more precise (avg. 4.47 vs. 3.5) than VRay. The analysis also showed a significant effect of TELEPORTATION on the demanding (F(1, 45) = 5.36, p = 0.03), physically demanding (F(1, 45) = 7.64, p < 0.001) and easy (F(1, 45) = 18.41, p < 0.001) criteria, as well as on the overall evaluation (F(1, 45) = 14.57, p < 0.001). +Tele was perceived less mentally demanding (avg. 1.91 vs. 2.31), less physically demanding (avg. 2.19 vs. 2.75) and easier to use (avg. 4.28 vs. 3.69) than NoTele. +Tele was also preferred to NoTele

Figure 7 :

 7 Figure7: Boxplots for the answers to the subjective questionnaire of experiment 1 using a 5-point Likert scale. Each boxplot is delimited by the quartile (25% quantile and 75% quantile) of the distribution of the effect over the participants. The median is also represented for each TECHNIQUE×TELEPORTATION combination.bars represent data minimum and maximum.

Figure 8 :

 8 Figure 8: Relative positions of the manipulated wedge and its associated target in Floor condition (top) and Midair condition (bottom) for experiment 2.

Figure 9 :

 9 Figure 9: Mean completion time by TECHNIQUE and VERTICAL POSITION in experiment 2. Error bars represent 95% confidence intervals.

Figure 10 :

 10 Figure 10: Mean numbers of overshoots by TECHNIQUE and VER-TICAL POSITION in exp2. Error bars represent 95% confidence intervals.

Table

			Translation			Rotation
	Mode	Tx	Ty	Tz	Rx	Ry	Rz
	1d(center)
	1d(center) + 1i(out)			i
	2d(center + up)
	2d(center + down)

Table 2

 2

	describes the TECHNIQUE×TELEPORTATION combinations.
		3D Virtual Ray	In(SITE)
	No teleportation	VRay, NoTele	Touch, NoTele
	Teleportation	VRay+Tele	Touch+Tele

Table 2 :

 2 TECHNIQUE×TELEPORTATION combinations in exp1.

https://www.microsoft.com/microsoft-surface-hub/

http://unity3d.com/

http://www.vicon.com/

http://www.multitouch.com/

All analyses except the ART procedure were performed with the SAS JMP statistical platform. The ART procedure[START_REF] Wobbrock | The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only Anova Procedures[END_REF]] was performed with R.

Acknowledgements

This research was supported by the French National Research Agency under grant ANR-10-EQPX-26-01 DIGISCOPE.