# ATHOS: a dATabase of 48 3D Human virtual characters with non-emOtional facial expresSion for virtual reality

Alice Cartaud, Yann Coello

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## To cite this version:

Alice Cartaud, Yann Coello. ATHOS: a dATabase of 48 3D Human virtual characters with non-emOtional facial expresSion for virtual reality. 2023. hal-04137382

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Distributed under a Creative Commons Attribution 4.0 International License
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ATHOS: a dATabase of 48 3D Human virtual characters with non-emOtional facial expresSsion for virtual reality

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Alice Cartaud & Yann Coello

Department of Psychology, Research Unit on Cognitive and Affective Sciences, University of

Lille, Lille, France

Running head: Set of neutral virtual characters

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*Corresponding author:

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yann.coello@univ-lille.fr (YC)

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Abstract

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# Introduction

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Immersive Virtual Reality (IVR) is being increasingly used in everyday life, whether for entertainments, distance learning, advertising, consulting or business activities [1]. The expansion of its use is also visible in the field of research with an increasing number of IVR-based studies in the Human and Social Sciences and Information and Communication Technologies and Sciences. Virtual environments offer the possibility of getting closer to ecologic and realistic situations while allowing a strong experimental control [2,3]. Those conditions, often difficult to combine, are essential to improve acceptance of digital devices and replicability of experimental research, which makes virtual reality a precious tool in the domain of perceptual, behavioral, cognitive, affective and social studies.

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In order to study social interactions and their effects on cognitive processes, many researches have used naturalistic stimuli such as virtual characters or avatars [2]. Their presence in a virtual environment is sufficient to develop a feeling of social presence [2], and they are usually used to investigate social cognition and social skills. Virtual characters are for instance of great interest in the study the effects of social constraint on the adjustment of interpersonal distances [4] and the data are consistent with those obtained in real social situations [5]. They are also increasingly used in the context of pathological populations for diagnosis and for developing new technological-based therapeutic tools. For instance, virtual characters were used for training social skills in patients with autistic spectrum disorder [6] or in exposure therapy associated with social anxiety treatments [7].

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Despite human-like stimuli are increasingly used in various scientific and applied domains, there is still a limited access to computer-generated virtual characters, in particular those that offer realistic design and are freely available online. We present here an open access database of 48 male and female Caucasian virtual characters with non-emotional facial expression, which was developed at the University of Lille, on the basis of a survey performed

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online by a number of spontaneous volunteers. The ATHOS database provides for each of the
virtual characters an evaluation in terms of valence, reliability, sympathy and sociability. The
process for designing and animating the virtual characters is detailed below. The ATHOS
database also provides the raw data and a statistical analysis of the reliability of the inter-raters'
evaluation as well as a cluster analysis of the stimuli (S1).

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## Method

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### Participants

The evaluation of the virtual characters was performed online by self-volunteer participants using the LimeSurvey website. The link to the survey was shared on social networks by students in Psychology from the University of Lille (France). From the set of 189 of self-declared participants, only 46 of them completed a minimum of 75% of the survey items and were retained for the data analysis. Among the participants, twenty-seven were females, heighten were males and fourteen did not report their gender. Mean age was 22.11 years (18-36, S.D.= 3.44) and mean study level was 1.80 year after the bachelor's degree (S.D.= 1.56).

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### Stimuli

Forty-eight Caucasian virtual characters (24 females) with a non-emotional facial expression were created from the Beta version of Adobe Fuse CC, using either the Female or Male "Fit A" model. The proportion of the different body parts was at the medium level according to the scale of the software. All virtual characters had a casual-to-classic dress code including no make-up or jewelry. In order to limit any clipping effect resulting from animation, all virtual characters wore pants. Once created, the virtual characters were exported in OBJ file

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format and their textures were also saved in PNG file format. The OBJ version of the virtual characters was uploaded to www.mixamo.com in order to be manually rigged. This procedure enabled to associate different animations with each virtual character. Finally, they were exported in FBX file format. The FBX version of each virtual character as well as their textures in PNG and a full-body pictures are available at the following address https://osf.io/sp938/?view_only=7a5c397f51864d88a7f71af2c18bf478. The file also contains the data related to the individual evaluation.

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## Evaluation procedure

The evaluation of the virtual characters was performed online using the LimeSurvey website (version 2.63.1) hosted by the University of Lille' servers. The first page of the website provided the instructions concerning the evaluation task. On the second page, participants provided personal information concerning their gender, age and level of education. Then, on the following pages, successive pictures of one virtual character (randomly selected) was displayed above four successive evaluation scales. For each visual character, the evaluation was made by positioning a cursor on a continuous line according to four criteria: valence, reliability, sympathy and sociability. Each criterion was evaluated according to two opposite levels: negative/positive for valence, not reliable/reliable for reliability, unsympathetic/sympathetic for sympathy and not sociable/sociable for sociability. To avoid random evaluation for some of the criteria, participants were instructed to respond to each criterion only if they can provide an evaluation. The completion of the survey took about 25 minutes.
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# Data analysis

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## Inter-rater reliability

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## Cluster analysis

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The degree of agreement among participants (raters) for each evaluation was carried out using Intraclass Correlation Coefficients (ICCs) with the iccNA function (irrNA package, version 0.1.4, Brueckl & Heuer, 2018) of R and R Studio software (version 3.5.1 and 1.1.463 respectively). This function adopts the ICC conventions of Shrout & Fleiss (1979) and allows ICCs computation with missing data by approximating the missing raters' individual effects. ICC estimates and their 95% confident intervals calculation were based on a mean-rating (k = 46 raters), the absolute-agreement, and a 2-way random effects models. The ICC estimate was significant if p-values <.05. Conventionally, estimates less than 0.5, between 0.51 and 0.75, between 0.76 and 0.9, and greater than 0.9 respectively indicated poor, moderate, good and excellent reliability.
131 were computed using respectively `fviz_cluster` and `fviz_silhouette` functions (`factoextra` package, version 1.0.5).

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## 134 Results and Discussion

135 Considering the different criteria, the evaluation score was on average 5.03 (SD = 2.09) for valence, 4.86 (SD = 2.16) for reliability, 5.15 (SD = 2.23) for sympathy and 5.15 (SD = 2.24) for sociability. S1 Table summarizes the mean, standard deviation and the number of missing ratings for the 4 evaluation criteria and for each virtual character as well as the gender of each virtual character. The number of responses for the criteria considered together was thus 95.21% (4.79% of missing data).

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142 Inter-rater reliability

143 Intraclass Correlation Coefficients estimates of each evaluation criterion are reported in Table 1. The corrected mean ratings for each criterion are available in S1 Table (ICC Corrected Mean). The inter-rater reliability analysis for each evaluation criterion revealed a good degree of agreement among the raters (k = 46 participants) with a correlation estimate varying between 0.85 and 0.89. Thus, participants were consistent in their judgement throughout the evaluation of the different virtual characters and they were also consistent with each other.

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<table><caption>150 Table 1: Inter-rater analysis</caption><thead><tr><td>Criterion</td><td>Estimate</td><td>p value</td><td>95 % CI</td></tr></thead><tbody><tr><td>Valence</td><td>0.88</td><td>&lt;0.001</td><td>[0.82 - 0.92]</td></tr><tr><td>Reliability</td><td>0.85</td><td>&lt;0.001</td><td>[0.78 - 0.9]</td></tr><tr><td>Sympathy</td><td>0.89</td><td>&lt;0.001</td><td>[0.85 - 0.93]</td></tr><tr><td>Sociability</td><td>0.87</td><td>&lt;0.001</td><td>[0.81 - 0.92]</td></tr></tbody></table>
ICC estimate of the Absolute-Agreement, 95% Confidence Interval and associated p-value per evaluation criterion.

## Cluster Analysis

As illustrated in Fig 1, an elbow appeared at k = 3 suggesting that the dataset can be organized in three clusters, corresponding to low, medium and high evaluation scores.

<figure><img src="image_3.png" /></figure>

**Fig 1. Elbow Plot.** Total of within cluster sum of squares as a function of number of clusters computed from Elbow method.

The cluster assignment following the k-means clustering for each virtual character is reported in Table S1 and illustrated in Fig 2. The center of each cluster for each evaluation criterion and the within cluster sum of squares are reported on Table 2. Cluster 1 gathers 13 virtual characters (8 males, 5 females) with higher evaluation scores for every criterion, Cluster 2 gathers 23 virtual characters (12 males, 11 females) with medium evaluation scores for every criterion and Cluster 3 gathers 12 virtual characters (4 males, 8 females) with lower evaluation scores for every criterion. Thus, even though every virtual character was non-emotional, their
facial characteristics varied, as a consequence so did their evaluation, resulting in the emergence of three groups of virtual characters. Indeed, the evaluation of an emotionally neutral face can vary depending on the potential structural resemblance of the face with an emotional expression [9], as it has also been shown in the evaluation of dominance and submissiveness dimensions [10].

<table><caption>**Table 2: Cluster analysis**</caption><thead><tr><th>Cluster</th><th>Size</th><th>Valence</th><th>Reliability</th><th>Sympathy</th><th>Sociability</th><th>Sum of Squares</th></tr></thead><tbody><tr><td>Cluster 1</td><td>13</td><td>6.1</td><td>5.84</td><td>6.28</td><td>6.23</td><td>11.02</td></tr><tr><td>Cluster 2</td><td>23</td><td>4.9</td><td>4.75</td><td>5.03</td><td>5.05</td><td>10.91</td></tr><tr><td>Cluster 3</td><td>12</td><td>4.05</td><td>3.93</td><td>4.03</td><td>4.11</td><td>6.61</td></tr></tbody></table>

Clusters' size and center for each evaluation criterion, and within cluster sum of squares

<figure><img src="image_4.png" /><figcaption>**Fig 2. Cluster Plot.** Two-dimensional cluster plot of the virtual characters and their associated clusters (high scores: squares, medium scores: circles, low scores: triangles). The (x, y) axes represent the first two components, computed directly from the plot function, are derived from</figcaption></figure>
182 the evaluations of the virtual characters and explain 97.4% of their variability. The larger
183 square, circle and triangle represent the center of each cluster. The distance between each virtual
184 character and its cluster's center represents the distance between them scaled on the two
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187 The goodness of the assignment of each virtual character to its cluster is illustrated by
188 the Silhouette analysis. Visual analysis of Fig 3 highlighted the reliability of the assignment of
189 each virtual character in its respective cluster, although some of them (width close to 0) could
190 have been assigned to a neighbor cluster. For example, virtual character F01 was assigned to
191 cluster 2 with a score close to 0, indicating that it could have also been assigned to cluster 1,
192 the closer cluster neighbor. These variations can be explained by the non-emotional expression
193 of the virtual characters. Indeed, although the cluster analysis suggested 3 clusters, the center
194 of each cluster varied only little according to the others, due to the small variations of the
195 evaluation scores between the virtual characters. Therefore, one can select virtual characters
196 from this database based on the degree of differences between each other in terms of scores,
197 but also based on their proximity.

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<figure><img src="image_5.png" /><figcaption>**Fig 3. Silhouette Plot.** Ordered silhouette width for each virtual character in their cluster. The red Dashed line represents the average silhouette width (0.44).</figcaption></figure>

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To our knowledge, ATHOS is the first database of non-emotional virtual characters, freely available online and providing details about design and evaluation. In some previous studies using immersive virtual environment, either little information was provided regarding the design of the virtual characters [5,11,12], or virtual characters were taken from websites proposing only a few visual attributes often with poor design styles and brief descriptions [13]. In this context, ATHOS database may represent a valuable tool in VR research as well as applied domains as it is freely available, and includes validated evaluation of realistic visual characters. Indeed, the feeling of social presence in immersive environments seems to depend on the realism of the virtual stimuli [2]. As evidence, Iachini and colleagues [14] revealed an increase of comfort distance in social interactions when using virtual robots instead of virtual characters. In the same vein, Fini and colleagues [15] showed that human virtual characters can
be used as allocentric frame of reference but not virtual wooden dummies. Furthermore, ATHOS database, combined with head-mounted display, may offer new research avenues in studies looking for an easy to carry installation. The ATHOS database thus offers a number of advantages for virtual reality providing more immersive stimuli and more social presence than simply using videos displayed on a computer screen [16] or bulky virtual reality equipment [12,17–19].

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## 222 Conclusion

In conclusion, ATHOS database represents the first validated freely available database of male and female Caucasian virtual characters for researches and applications using immersive virtual reality. Furthermore, virtual characters in ATHOS database are compatible with open access software such as Unity or Mixamo, making thus possible their implementation in a virtual environment with specific gait or animation usually also freely available (https://unity.com/). Thus, the ATHOS database paves the way for future research programs in immersive virtual environments, encouraging the development of innovative scenario with improved social presence and enhance potentiality for outcome replicability.

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## 232 Funding Sources

Funding: This work was funded by the French National Research Agency (ANR-11-EQPX-0023) and also supported by the European funds through the program FEDER SCV-IrDIVE.

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294 Supporting information

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S1 Table: Overview Table. Mean, standard deviation, number of missing evaluations, and corrected mean computed from the inter-rater reliability analysis for each virtual character as a function of the four evaluation criteria: valence (val), reliability (rel), sympathy (sym) and sociability (soc). For each virtual character, their gender and parent cluster are also provided
<table><thead><tr><th rowspan="2">Virtual Character</th><th rowspan="2">Gender</th><th colspan="4">Mean</th><th colspan="4">Standard Deviation</th><th colspan="4">Number of Missing evaluation</th><th colspan="4">ICC Corrected Mean</th><th rowspan="2">Cluster</th></tr><tr><th>Val</th><th>Rel</th><th>Sym</th><th>Soc</th><th>Val</th><th>Rel</th><th>Sym</th><th>Soc</th><th>Val</th><th>Rel</th><th>Sym</th><th>Soc</th><th>Val</th><th>Rel</th><th>Sym</th><th>Soc</th></tr></thead><tbody><tr><td>F01</td><td>F</td><td>5.38</td><td>5.39</td><td>5.65</td><td>5.51</td><td>1.98</td><td>1.96</td><td>1.93</td><td>2.31</td><td>1</td><td>2</td><td>2</td><td>1</td><td>5.4</td><td>5.35</td><td>5.67</td><td>5.52</td><td>2</td></tr><tr><td>F02</td><td>F</td><td>4.89</td><td>5</td><td>4.83</td><td>5.58</td><td>2.27</td><td>2.6</td><td>2.54</td><td>2.42</td><td>2</td><td>2</td><td>2</td><td>0</td><td>4.85</td><td>5.04</td><td>4.85</td><td>5.58</td><td>2</td></tr><tr><td>F03</td><td>F</td><td>4.16</td><td>4.35</td><td>4.5</td><td>4.1</td><td>2.06</td><td>2.25</td><td>2.45</td><td>2.23</td><td>1</td><td>1</td><td>2</td><td>1</td><td>4.14</td><td>4.38</td><td>4.48</td><td>4.1</td><td>3</td></tr><tr><td>F04</td><td>F</td><td>4.86</td><td>5.16</td><td>5.15</td><td>4.99</td><td>2.38</td><td>2.21</td><td>2.25</td><td>2.17</td><td>1</td><td>3</td><td>4</td><td>1</td><td>4.88</td><td>5.13</td><td>5.14</td><td>5</td><td>2</td></tr><tr><td>F05</td><td>F</td><td>4.05</td><td>3.92</td><td>3.82</td><td>4.51</td><td>2.28</td><td>2.3</td><td>2.23</td><td>2.29</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4.09</td><td>3.97</td><td>3.79</td><td>4.48</td><td>3</td></tr><tr><td>F06</td><td>F</td><td>5.52</td><td>5.69</td><td>5.64</td><td>5.89</td><td>1.67</td><td>1.62</td><td>1.71</td><td>1.82</td><td>0</td><td>1</td><td>0</td><td>0</td><td>5.53</td><td>5.63</td><td>5.63</td><td>5.88</td><td>1</td></tr><tr><td>F07</td><td>F</td><td>4.67</td><td>4.86</td><td>4.76</td><td>4.62</td><td>1.98</td><td>2.22</td><td>2.46</td><td>2.13</td><td>3</td><td>0</td><td>2</td><td>0</td><td>4.71</td><td>4.9</td><td>4.76</td><td>4.62</td><td>2</td></tr><tr><td>F08</td><td>F</td><td>4.67</td><td>4.85</td><td>5.17</td><td>5.12</td><td>2.24</td><td>2.19</td><td>2.35</td><td>2.19</td><td>3</td><td>4</td><td>3</td><td>4</td><td>4.7</td><td>4.8</td><td>5.13</td><td>5.01</td><td>2</td></tr><tr><td>F09</td><td>F</td><td>4.94</td><td>4.68</td><td>4.47</td><td>4.84</td><td>2.15</td><td>2.17</td><td>2.25</td><td>1.91</td><td>4</td><td>5</td><td>3</td><td>4</td><td>4.91</td><td>4.59</td><td>4.51</td><td>4.87</td><td>2</td></tr><tr><td>F10</td><td>F</td><td>5.68</td><td>5.35</td><td>5.72</td><td>5.75</td><td>1.85</td><td>2.14</td><td>1.91</td><td>2.15</td><td>0</td><td>0</td><td>0</td><td>0</td><td>5.68</td><td>5.39</td><td>5.71</td><td>5.75</td><td>1</td></tr><tr><td>F11</td><td>F</td><td>3.8</td><td>3.34</td><td>3.35</td><td>3.6</td><td>2.04</td><td>1.64</td><td>1.58</td><td>1.73</td><td>2</td><td>5</td><td>4</td><td>1</td><td>3.81</td><td>3.26</td><td>3.39</td><td>3.6</td><td>3</td></tr><tr><td>F12</td><td>F</td><td>3.82</td><td>3.82</td><td>3.74</td><td>3.89</td><td>2</td><td>1.85</td><td>1.65</td><td>1.87</td><td>5</td><td>4</td><td>3</td><td>4</td><td>3.83</td><td>3.77</td><td>3.71</td><td>3.8</td><td>3</td></tr><tr><td>F13</td><td>F</td><td>4.54</td><td>4.5</td><td>4.71</td><td>4.72</td><td>2.23</td><td>2.44</td><td>2.34</td><td>2.42</td><td>3</td><td>6</td><td>5</td><td>2</td><td>4.6</td><td>4.44</td><td>4.79</td><td>4.75</td><td>2</td></tr><tr><td>F14</td><td>F</td><td>3.62</td><td>3.29</td><td>3.19</td><td>3.64</td><td>1.93</td><td>1.86</td><td>1.83</td><td>2.26</td><td>4</td><td>7</td><td>6</td><td>5</td><td>3.65</td><td>3.33</td><td>3.17</td><td>3.65</td><td>3</td></tr><tr><td>F15</td><td>F</td><td>3.68</td><td>3.7</td><td>3.59</td><td>4.35</td><td>2.13</td><td>2.32</td><td>2.33</td><td>2.28</td><td>4</td><td>5</td><td>4</td><td>5</td><td>3.66</td><td>3.64</td><td>3.63</td><td>4.29</td><td>3</td></tr><tr><td>F16</td><td>F</td><td>4.26</td><td>4.72</td><td>4.3</td><td>4.81</td><td>2.22</td><td>2.13</td><td>2.28</td><td>2.06</td><td>3</td><td>4</td><td>4</td><td>3</td><td>4.31</td><td>4.8</td><td>4.29</td><td>4.82</td><td>2</td></tr><tr><td>F17</td><td>F</td><td>6.17</td><td>6.06</td><td>6.28</td><td>6.54</td><td>1.98</td><td>1.79</td><td>2.06</td><td>2.35</td><td>0</td><td>1</td><td>0</td><td>0</td><td>6.17</td><td>6.01</td><td>6.27</td><td>6.54</td><td>1</td></tr><tr><td>F18</td><td>F</td><td>4.12</td><td>4.54</td><td>4.18</td><td>3.91</td><td>2.18</td><td>2.23</td><td>1.98</td><td>2.06</td><td>6</td><td>8</td><td>9</td><td>5</td><td>4.19</td><td>4.61</td><td>4.23</td><td>3.95</td><td>3</td></tr><tr><td>F19</td><td>F</td><td>5.24</td><td>5.54</td><td>5.72</td><td>5.28</td><td>1.67</td><td>1.76</td><td>2.21</td><td>1.81</td><td>2</td><td>2</td><td>1</td><td>1</td><td>5.26</td><td>5.6</td><td>5.72</td><td>5.29</td><td>2</td></tr><tr><td>F20</td><td>F</td><td>5.04</td><td>5.01</td><td>5.21</td><td>5.21</td><td>1.88</td><td>1.8</td><td>2.11</td><td>2.33</td><td>1</td><td>2</td><td>1</td><td>1</td><td>5.05</td><td>4.96</td><td>5.18</td><td>5.22</td><td>2</td></tr><tr><td>F21</td><td>F</td><td>5.71</td><td>5.59</td><td>5.84</td><td>5.82</td><td>2.02</td><td>2.18</td><td>2.26</td><td>2.37</td><td>1</td><td>3</td><td>2</td><td>2</td><td>5.73</td><td>5.56</td><td>5.84</td><td>5.82</td><td>1</td></tr><tr><td>F22</td><td>F</td><td>5.71</td><td>5.56</td><td>5.5</td><td>5.62</td><td>1.76</td><td>2.11</td><td>2.05</td><td>2.06</td><td>0</td><td>3</td><td>1</td><td>1</td><td>5.71</td><td>5.51</td><td>5.51</td><td>5.61</td><td>1</td></tr><tr><td>F23</td><td>F</td><td>4.47</td><td>4.58</td><td>4.54</td><td>4.96</td><td>1.95</td><td>2.3</td><td>2.09</td><td>1.96</td><td>4</td><td>3</td><td>4</td><td>3</td><td>4.45</td><td>4.66</td><td>4.54</td><td>4.96</td><td>2</td></tr><tr><td>F24</td><td>F</td><td>4.21</td><td>4.33</td><td>4.02</td><td>4.47</td><td>2.01</td><td>2.09</td><td>1.87</td><td>2.26</td><td>4</td><td>3</td><td>4</td><td>4</td><td>4.25</td><td>4.42</td><td>4.05</td><td>4.56</td><td>3</td></tr><tr><td>M01</td><td>M</td><td>6.35</td><td>5.84</td><td>6.52</td><td>6.43</td><td>1.71</td><td>2</td><td>1.69</td><td>1.87</td><td>1</td><td>1</td><td>0</td><td>0</td><td>6.32</td><td>5.88</td><td>6.5</td><td>6.43</td><td>1</td></tr><tr><td>M02</td><td>M</td><td>6.03</td><td>5.7</td><td>6.44</td><td>6.4</td><td>1.88</td><td>1.94</td><td>1.84</td><td>1.93</td><td>2</td><td>3</td><td>1</td><td>1</td><td>6</td><td>5.66</td><td>6.44</td><td>6.43</td><td>1</td></tr></tbody></table>
<table><tr><td>M03</td><td>M</td><td>4.52</td><td>4.1</td><td>4.58</td><td>4.41</td><td>2.23</td><td>2.06</td><td>2.33</td><td>2.25</td><td>4</td><td>2</td><td>1</td><td>0</td><td>4.49</td><td>4.13</td><td>4.55</td><td>4.41</td><td>3</td></tr><tr><td>M04</td><td>M</td><td>4.48</td><td>4.47</td><td>5.13</td><td>4.8</td><td>1.49</td><td>2.19</td><td>1.94</td><td>2.13</td><td>3</td><td>3</td><td>2</td><td>0</td><td>4.49</td><td>4.52</td><td>5.14</td><td>4.79</td><td>2</td></tr><tr><td>M05</td><td>M</td><td>5.53</td><td>4.86</td><td>5.75</td><td>5.1</td><td>2.13</td><td>2.3</td><td>2.31</td><td>2.27</td><td>2</td><td>2</td><td>2</td><td>1</td><td>5.51</td><td>4.91</td><td>5.78</td><td>5.11</td><td>2</td></tr><tr><td>M06</td><td>M</td><td>6.7</td><td>6.17</td><td>6.78</td><td>7</td><td>1.64</td><td>1.94</td><td>1.82</td><td>1.87</td><td>2</td><td>3</td><td>2</td><td>2</td><td>6.73</td><td>6.14</td><td>6.79</td><td>7.04</td><td>1</td></tr><tr><td>M07</td><td>M</td><td>6.98</td><td>6.58</td><td>7.02</td><td>7.12</td><td>1.37</td><td>1.95</td><td>1.6</td><td>1.75</td><td>1</td><td>1</td><td>0</td><td>0</td><td>6.94</td><td>6.52</td><td>7</td><td>7.12</td><td>1</td></tr><tr><td>M08</td><td>M</td><td>4.15</td><td>3.75</td><td>4.04</td><td>4.22</td><td>1.9</td><td>2.03</td><td>2.11</td><td>2.11</td><td>4</td><td>4</td><td>3</td><td>4</td><td>4.14</td><td>3.73</td><td>4.07</td><td>4.2</td><td>3</td></tr><tr><td>M09</td><td>M</td><td>4.63</td><td>4.82</td><td>5.3</td><td>5.15</td><td>2.2</td><td>2</td><td>2.09</td><td>2.11</td><td>1</td><td>3</td><td>1</td><td>2</td><td>4.65</td><td>4.8</td><td>5.31</td><td>5.08</td><td>2</td></tr><tr><td>M10</td><td>M</td><td>6.39</td><td>6</td><td>6.66</td><td>6.52</td><td>1.79</td><td>1.67</td><td>1.49</td><td>1.78</td><td>1</td><td>1</td><td>0</td><td>0</td><td>6.36</td><td>5.94</td><td>6.65</td><td>6.52</td><td>1</td></tr><tr><td>M11</td><td>M</td><td>5.3</td><td>4.39</td><td>5.07</td><td>5.38</td><td>2.1</td><td>2.33</td><td>2.27</td><td>2.38</td><td>2</td><td>2</td><td>1</td><td>1</td><td>5.34</td><td>4.45</td><td>5.07</td><td>5.38</td><td>2</td></tr><tr><td>M12</td><td>M</td><td>4.6</td><td>4.3</td><td>4.81</td><td>4.68</td><td>1.46</td><td>1.73</td><td>1.9</td><td>1.79</td><td>0</td><td>2</td><td>0</td><td>0</td><td>4.6</td><td>4.35</td><td>4.8</td><td>4.67</td><td>2</td></tr><tr><td>M13</td><td>M</td><td>5.38</td><td>4.84</td><td>5.4</td><td>5.35</td><td>1.81</td><td>2.03</td><td>2.18</td><td>1.94</td><td>2</td><td>1</td><td>1</td><td>1</td><td>5.35</td><td>4.89</td><td>5.41</td><td>5.37</td><td>2</td></tr><tr><td>M14</td><td>M</td><td>4.21</td><td>4.15</td><td>4.66</td><td>3.76</td><td>1.91</td><td>2.01</td><td>2.3</td><td>2.04</td><td>0</td><td>1</td><td>2</td><td>2</td><td>4.22</td><td>4.2</td><td>4.67</td><td>3.77</td><td>3</td></tr><tr><td>M15</td><td>M</td><td>5.06</td><td>4.96</td><td>5.11</td><td>5.16</td><td>2.26</td><td>2.14</td><td>2.38</td><td>2.3</td><td>1</td><td>1</td><td>0</td><td>0</td><td>5.02</td><td>5</td><td>5.1</td><td>5.16</td><td>2</td></tr><tr><td>M16</td><td>M</td><td>4.87</td><td>4.81</td><td>5.05</td><td>4.77</td><td>1.91</td><td>1.81</td><td>1.99</td><td>2.17</td><td>3</td><td>3</td><td>1</td><td>2</td><td>4.83</td><td>4.77</td><td>5.06</td><td>4.8</td><td>2</td></tr><tr><td>M17</td><td>M</td><td>5.74</td><td>5.39</td><td>5.85</td><td>5.9</td><td>1.92</td><td>2.15</td><td>1.99</td><td>1.99</td><td>3</td><td>2</td><td>1</td><td>1</td><td>5.74</td><td>5.45</td><td>5.86</td><td>5.93</td><td>1</td></tr><tr><td>M18</td><td>M</td><td>4.26</td><td>3.88</td><td>4.74</td><td>4.45</td><td>1.65</td><td>1.57</td><td>1.87</td><td>2.03</td><td>3</td><td>2</td><td>2</td><td>3</td><td>4.22</td><td>3.82</td><td>4.65</td><td>4.39</td><td>3</td></tr><tr><td>M19</td><td>M</td><td>4.99</td><td>3.95</td><td>4.57</td><td>5.07</td><td>1.84</td><td>1.95</td><td>2.16</td><td>2.24</td><td>3</td><td>4</td><td>2</td><td>3</td><td>4.89</td><td>3.93</td><td>4.51</td><td>5.01</td><td>2</td></tr><tr><td>M20</td><td>M</td><td>4.64</td><td>4.45</td><td>4.91</td><td>4.6</td><td>1.75</td><td>1.65</td><td>1.92</td><td>1.91</td><td>2</td><td>4</td><td>2</td><td>3</td><td>4.65</td><td>4.39</td><td>4.92</td><td>4.63</td><td>2</td></tr><tr><td>M21</td><td>M</td><td>6.74</td><td>6.18</td><td>7.23</td><td>6.35</td><td>1.71</td><td>2.21</td><td>1.55</td><td>2.08</td><td>1</td><td>0</td><td>0</td><td>0</td><td>6.71</td><td>6.22</td><td>7.22</td><td>6.35</td><td>1</td></tr><tr><td>M22</td><td>M</td><td>4.88</td><td>4.23</td><td>4.75</td><td>4.95</td><td>1.36</td><td>1.77</td><td>1.55</td><td>1.86</td><td>5</td><td>4</td><td>4</td><td>4</td><td>4.92</td><td>4.2</td><td>4.76</td><td>4.99</td><td>2</td></tr><tr><td>M23</td><td>M</td><td>5.44</td><td>4.85</td><td>5.36</td><td>5.4</td><td>2.12</td><td>2.05</td><td>1.87</td><td>2.08</td><td>2</td><td>1</td><td>1</td><td>2</td><td>5.41</td><td>4.9</td><td>5.36</td><td>5.45</td><td>2</td></tr><tr><td>M24</td><td>M</td><td>5.55</td><td>5.82</td><td>6.17</td><td>5.69</td><td>1.64</td><td>1.54</td><td>1.65</td><td>1.82</td><td>2</td><td>3</td><td>1</td><td>1</td><td>5.53</td><td>5.79</td><td>6.18</td><td>5.71</td><td>1</td></tr></table>