Xavier Pic

Marc Antonini

Image coding algorithm for DNA data storage combining JPEG and autoencoders

Proposed workflow

Based on the work of Melpomeni et al. [START_REF] Dimopoulou | A biologically constrained encoding solution for long-term storage of images onto synthetic DNA[END_REF] Block DCT based encoding into a DNA sequence DNA: sequence of nucleotides A, T, C and G : AACTCAGCATGCAGGG....

JPEG thumbnails

Critical information

Thumbnail can be encoded seperately

Results

The compression in terms of performance is lower than the jpegdna one Achieving lower bit-rates is harder because of the size of the compressed representation of the tumbnail Better performance on noisy channels More resistant to noise on the critical data managed by the autoencoder More adapted to a noisy channel like synthetic DNA data storage 1.5% subsitution rate corresponds to the Nanopore sequencing method's substitution rate

Formatted

  oligos synthesized into DNA molecules DNA molecules sequenced to retrieve the oligos Decoding the oligos to retreive the original image

Figure 1 .

 1 Figure 1. JPEG proedure and the thumbnail

Figure 2 .

 2 Figure 2. Original and Reconstructed image at 37.2 dB, compression rate: 33 bits/nt


Quentin Guimard
email: quentin.guimard@univ-cotedazur.fr

Effects of Emotions on Head Motion Predictability in 360°V ideos

Keywords: Human-centered computing → Virtual reality, User models, • Mathematics of computing → Equational models 360°videos, emotions, head motion, predictability

While 360°videos watched in a VR headset are gaining in popularity, it is necessary to lower the required bandwidth to stream these immersive videos and obtain a satisfying quality of experience. Doing so requires predicting the user's head motion in advance, which has been tackled by a number of recent prediction methods considering the video content and the user's past motion. However, human motion is a complex process that can depend on many more parameters, including the type of attentional phase the user is currently in, and their emotions, which can be difficult to capture. This is the first article to investigate the effects of user emotions on the predictability of head motion, in connection with video-centric parameters. We formulate and verify hypotheses, and construct a structural equation model of emotion, motion and predictability. We show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. We also show that the prediction error is lower for higher arousal, but that spatial information moderates the effect of arousal on predictability. This work opens the path to better capture important factors in human motion, to help improve the training process of head motion predictors.

INTRODUCTION

Virtual Reality (VR) headsets allow users to experience 360°videos while being entirely immersed in a virtual environment, be it shot from the real-world or computer-generated. With the increasing affordability of VR headsets and popularity of 360°videos, the demand for streaming 360°videos is also growing. However, the necessary bandwidth to stream these videos with a quality high enough to provide real world-like experience can be up to two orders of magnitude that of a regular video [START_REF] Park | Rate-utility optimized streaming of volumetric media for augmented reality[END_REF]. To decrease the required bandwidth, a well-investigated approach is to send every 360°video segment with spatially heterogeneous quality, so as to focus the bandwidth budget to maximize the visual quality in the field of view (FoV), while decreasing it outside (e.g., with tile-based approaches). Doing so in a networking scenario requires predicting in advance what the user motion is going to be, so that the server can decide which sectors to send in higher or lower quality.

That is why a number of works have developed head motion prediction methods in the last few years, often based on deep neural networks [START_REF] Chao | Transformer-based Long-Term Viewport Prediction in 360°Video: Scanpath is All You Need[END_REF][START_REF] Chen | Sparkle: User-Aware Viewport Prediction in 360-Degree Video Streaming[END_REF][START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF]. For stored video content, major methods [START_REF] Chao | Transformer-based Long-Term Viewport Prediction in 360°Video: Scanpath is All You Need[END_REF][START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF][START_REF] Xu | Gaze Prediction in Dynamic 360°Immersive Videos[END_REF] consider predicting the user's head motion for any new user and any new video, therefore relying only on the knowledge of the past user head position and the past and future video content. However, human motion is a complex process which can depend on many more parameters, including the type of attentional phase the user is currently in [START_REF] Almquist | The prefetch aggressiveness tradeoff in 360°video streaming[END_REF][START_REF] Vincent Sitzmann | Saliency in VR: How Do People Explore Virtual Environments[END_REF] and their emotions [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF][START_REF] Tang | Influence of Emotions on Eye Behavior in Omnidirectional Content[END_REF][START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF]. These parameters may be difficult or impossible to fully infer from the sole video content and past motion, therefore introducing uncertainty in the prediction task impacting the prediction error.

To the best of our knowledge, this is the first article to investigate the following research question: What are user-centric parameters (emotion and motion) and video-centric parameters impacting the head motion predictability in immersive 360°videos, and what are the relationships? This is an important question to understand how well can the human motion be captured, and how to improve prediction approaches, by augmenting the videos to be labelled/experienced wisely, or by changing the architectures or training losses of the deep models.

We make the following contributions: • We propose a subset of user-centric and video-centric measures to investigate the connection between these and head motion predictability. We consider two datasets where the user movements and subjective emotions are made available, one of which we have collected. The considered measures are valence and arousal graded by every user on every video, head motion speed, spatial information (SI) and temporal information (TI), shown to provide important insights into this type of emotion-video feature-predictability relationships.

• We formulate three hypotheses that we verify, and model the data with a directed graph of causal relationships formalized in a structural equation model (SEM). We show that the prediction error is generally lower (higher predictability) for users having provided higher arousal ratings. We also show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. Finally, we exhibit an interaction effect between SI and arousal, SI moderating the effect of arousal on the prediction error.

Section 2 positions our approach with respect to existing works. Sec. 3 defines the head motion prediction problem and presents the prediction method we consider. Sec. 4 describes both datasets considered, made of 360°videos with user emotions and motion traces. Sec. 5 states the hypotheses based on previous works, the validity of which we analyze from the data, and Sec. 6 expresses how the effects of user emotions and motion on predictability are modeled in a SEM, allowing us to quantify the effect sizes of mediation and interactions. Sec. 8 discusses limitations and perspectives.

RELATED WORK

Several approaches have recently investigated head motion prediction, most being based on deep recurrent neural networks fed with both the series of past coordinates and the video content [START_REF] Chao | Transformer-based Long-Term Viewport Prediction in 360°Video: Scanpath is All You Need[END_REF][START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF][START_REF] Wu | PAAS: a preference-aware deep reinforcement learning approach for 360°video streaming[END_REF][START_REF] Xu | Gaze Prediction in Dynamic 360°Immersive Videos[END_REF]. In this article, we consider the prediction methods presented by Romero et al. [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF] to benchmark our approach. Considering the analysis of how the video content or user emotions impact the head motion predictability, to the best of our knowledge only Romero et al. [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF] analyzed the prediction performance disaggregated over video categories. However, none of the above works has looked at motion predictability based on felt emotions, nor did they formalize the relationship between predictability and video features.

The spectrum of human emotions is generally described with two main components of the circumplex model of emotions [START_REF] Russell | A circumplex model of affect[END_REF]: valence, denoting the pleasantness or unpleasantness of emotions (from positive or happiness to negative or sadness/fear), and arousal, representing the intensity of the felt emotion. In the two-dimensional space where valence can be represented on the x-axis and arousal on the y-axis, any point symbolizes an emotion that is a combination of a certain amount of valence and arousal. Circumplex models have been used most commonly to test stimuli of emotion words, emotional facial expressions, and affective states. Immersive environments experienced in a VR headset have been shown to provide more intense emotions than planar presentations of omnidirectional content [START_REF] María Baños | Presence and Emotions in Virtual Environments: The Influence of Stereoscopy[END_REF][START_REF] Felnhofer | Is Virtual Reality Emotionally Arousing? Investigating Five Emotion Inducing Virtual Park Scenarios[END_REF][START_REF] Pallavicini | Gaming in Virtual Reality: What Changes in Terms of Usability, Emotional Response and Sense of Presence Compared to Non-Immersive Video Games?[END_REF]. Understanding the relationships between the components of emotion and the components of the immersive experience (such as presence and immersion), has attracted interest [START_REF] Barreda-Ángeles | An "Empathy Machine" or a "Just-for-the-Fun-of-It" Machine? Effects of Immersion in Nonfiction 360-Video Stories on Empathy and Enjoyment. Cyberpsychology[END_REF][START_REF] Jicol | Effects of Emotion and Agency on Presence in Virtual Reality[END_REF]. For example, Jicol et al. [START_REF] Jicol | Effects of Emotion and Agency on Presence in Virtual Reality[END_REF] have recently investigated how emotions and afforded agency combine and interact to create the feeling of presence. In particular, they resort to a SEM approach to quantify the size of direct and indirect effects. To investigate the emotional process elicited by 360°videos, a first database [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF] made of 73 omnidirectional videos with collected valence and arousal was made publicly available, but without head motion. A more recent dataset was presented by Xue et al. [START_REF] Tong Xue | CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos[END_REF], making both subjective ratings (both after-viewing and continuous inside the video) and head and gaze movements available. The connections between user motion and emotion in virtual environments have been investigated by many [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF][START_REF] Tang | Influence of Emotions on Eye Behavior in Omnidirectional Content[END_REF][START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF]. For example, Xue et al. [START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF] show that the yaw standard deviation (connected to mean speed) negatively correlates with arousal, while Li et al. [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF] show that it positively correlates with valence.

No work has so far investigated and formalized the effect of emotions and video features on head motion predictability, that is on the performance of prediction methods. To do so, we consider the most recent predictors introduced by Romero et al. [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF], as motivated below, and formulate working hypotheses from initial results obtained in the works mentioned above [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF][START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF].

HEAD MOTION PREDICTION

We first define the problem of head motion prediction in Sec. 3.1, then describe the chosen method and the motivation behind this choice in Sec. 3.2.

Problem definition

The problem we consider is formally described as follows. We consider that a given 360°video v of duration T seconds is being watched by a user u. The head trajectory of the user is denoted P u,v 0:T , with P storing the head coordinates on the unit sphere (as, e.g., Euler angles, Cartesian coordinates or quaternions).

At any time t in [0,T ], we want to predict the future trajectory P u,v t :t +H over a prediction horizon H , assuming only P u,v 0:t and the video content of v are known. That is, we do not assume any knowledge of traces other than u on this video v.

Prediction method

Amongst the existing methods tackling the above prediction problem [START_REF] Chao | Transformer-based Long-Term Viewport Prediction in 360°Video: Scanpath is All You Need[END_REF][START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF][START_REF] Wu | PAAS: a preference-aware deep reinforcement learning approach for 360°video streaming[END_REF][START_REF] Xu | Gaze Prediction in Dynamic 360°Immersive Videos[END_REF], we choose two main methods presented by Romero et al. [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF], named Deep-position-only and TRACK. We make this choice because (i) these approaches are representative of other prior approaches relying on sequence-to-sequence architectures, (ii) they are recent, and (iii) the models and entire framework are made publicly available [START_REF] Fabián Romero-Rondón | A unified evaluation framework for head motion prediction methods in 360[END_REF].

To conduct our study, we consider both models trained on two different head motion datasets from David et al. [START_REF] Erwan | A dataset of head and eye movements for 360[END_REF] and Xu et al. [START_REF] Xu | Gaze Prediction in Dynamic 360°Immersive Videos[END_REF], and selected the trained models that obtained the best results when testing (without re-training or fine-tuning) on our data described in Sec. 4. All the trained models were similar in performance on our data, but the models trained on the dataset by Xu et al. [START_REF] Xu | Gaze Prediction in Dynamic 360°Immersive Videos[END_REF], the largest dataset, performed slightly better. Then, we inspected the mutual effects, such as those shown in Fig. 4, when the prediction error is obtained with Deep-position-only and TRACK. As results were qualitatively similar, for the rest of the paper, we have chosen to only present results obtained with TRACK.

TRACK is a sequence-to-sequence deep model using separate long short-term memory (LSTM) units to encode both the past positions and the visual saliency. The same kind of LSTM units, combined with fully connected layers are then used to decode the future positions based on the embeddings given by the encoder. The visual saliency is made up of 384x216 saliency maps extracted from the video frames by PanoSalNet. TRACK was fully re-implemented using PyTorch and trained on multiple head motion datasets as provided in the repository [START_REF] Fabián Romero-Rondón | A unified evaluation framework for head motion prediction methods in 360[END_REF].

DATASETS AND MEASURES

In this section, we present the datasets considered for our data analysis, and our choice of user-centric and video-centric measures. The effect of these measures on motion predictability in investigated next in Sec. 5 and 6.

Datasets

We consider the only two datasets available where both user movements and emotions have been collected from immersive viewing of 360°videos.

The first dataset we consider is CEAP360-VR [START_REF] Tong Xue | CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos[END_REF]. This dataset is made from user experiments with 32 participants each watching 8 videos in a VR headset equipped with an eye-tracker, recording head and eye movements. After each video, the users grade their emotions valence and arousal. Additionally, emotional ratings are continuously annotated by the users thanks to a controller in their hand, along with physiological measurements with a wristband. We do not use this latter data in this article.

Our second source of data comes from PEM360 [START_REF] Guimard | PEM360: A dataset of 360°videos with continuous Physiological measurements, subjective Emotional ratings and Motion traces[END_REF], our own dataset collected from user experiments. In conditions similar as above, 7 videos were experienced in an eye-tracker equipped VR headset by 31 participants, who rated their valence and arousal perceptions after every video clip. Physiological measurements were also collected but not used in this article. This dataset is now publicly available on public GitLab repository 1 .

In both datasets, users were asked to rate each video using the self-assessment manikin (SAM) [START_REF] Bradley | Measuring emotion: The selfassessment manikin and the semantic differential[END_REF], giving individual ratings of valence and arousal.

Figure 1: Self-Assessment Manikin (SAM) scale for rating of valence (top row) and arousal (bottom row). Taken from [START_REF] Bradley | Measuring emotion: The selfassessment manikin and the semantic differential[END_REF].

The videos shown to users in both datasets come from the same 360°video database [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF], with average ratings of valence and arousal from 95 participants. We report in Table 1 the details of every video from both datasets. The left-most column "ID" refers to the video ID the author used in their dataset. The right-most column "Database ID" indicates the original ID in the 360°video database [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF]. Ratings of valence and arousal given in this table are the original average ratings of the database. In each of the datasets, the videos were trimmed, so the videos are not exactly the same as in the database. The start offset of each video as well as the duration of the trimmed clip are specified in the table.

We should note that videos 32 and V6 are different versions of the same video, which makes a total of 14 distinct videos experienced in VR by 63 different users.

Measures

4.2.1 Outcome measure. The objective of this analysis is to evaluate the influence of various factors on the prediction error of head movements. We define the prediction error as the average displacement error (ADE) between the predicted head positions and the actual future head positions over a prediction horizon H . We set H = 5 seconds, the standard prediction horizon in recent deep prediction methods [START_REF] Chao | Transformer-based Long-Term Viewport Prediction in 360°Video: Scanpath is All You Need[END_REF][START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF], which covers both user inertia and content saliency [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF].

We define the displacement error between two head positions (x 1 , y 1 , z 1 ) and (x 2 , y 2 , z 2 ) as the great circle distance between these two points. Since (x, y, z) are the Cartesian coordinates of a point on the unit sphere, we can easily compute the great circle distance ∆σ from the Euclidean distance d between these two positions as ∆σ = 2 • arcsin d 2 .

4.2.2 User-centric measures. We consider two types of usercentric measures: those related to emotions, and those related to motion. The measures of emotions are considered as the subjective ratings of valence and arousal made by each user after experiencing each 360°video, as detailed above.

The user motion can be characterized by various metrics, such as mean values of the head or gaze yaw and pitch angles, or the standard deviations of these positional components [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF][START_REF] Tong Xue | CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos[END_REF]. Here, we choose to combine these elements and consider the angular speed of the head movements. Specifically, to compute head speed, we first convert the head coordinates collected from the VR headset into Cartesian coordinates, where each recorded head position at time t is a point on the unit sphere of coordinates (x t , y t , z t ).

The head motion data in CEAP360-VR is originally in the format (ψ t , θ t , ϕ t ), where ψ is the yaw, θ is the pitch, and ϕ is the roll. These coordinates were first transformed to have ψ ∈ [0, 2π [ where 0 is the left edge of the equirectangular frame, and θ ∈ [0, π [ where 0 is the top edge of the equirectangular frame. Cartesian (x t , y t , z t ) coordinates are then obtained as projections of these angles using this set of equations:

       x t = cosψ t • sin θ t y t = sinψ t • sin θ t z t = cos θ t
We define the instantaneous head speed at time t as the total angular speed, noted ω t , computed from the great-circle distance between two consecutive positions divided by the sampling rate of the recordings. The average head speed is then taken as the mean of all instantaneous head speeds for a given user on a given video.

Video-centric measures.

As for user motion, several characterizations of 360°video content is possible. For example, Almquist et al. [START_REF] Almquist | The prefetch aggressiveness tradeoff in 360°video streaming[END_REF] propose a taxonomy in four categories depending on the location of the regions of interest. Romero et al. [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF] inspire on this taxonomy and categorize videos based on the entropy of the head location heat maps. David et al. [START_REF] Erwan | A dataset of head and eye movements for 360[END_REF] and Xue et al. [START_REF] Tong Xue | CEAP-360VR: A Continuous Physiological and Behavioral Emotion Annotation Dataset for 360 VR Videos[END_REF] consider spatial information and temporal information to characterize the 360°v ideos. In the preliminary study presented in this article, we consider legacy spatial and temporal information, and show their relevance characterizing the effects of emotions on motion predictability.

Spatial information and temporal information are scene-specific metrics defined in ITU-T Recommendation P.910 [START_REF]Subjective video quality assessment methods for multimedia applications[END_REF]. According to the ITU-T recommendation, SI and TI are "critical parameters" playing "a crucial role in determining the amount of video compression that is possible".

Spatial information (SI) or spatial perceptual information is "a measure that generally indicates the amount of spatial detail in a picture. (...) It is usually higher for more spatially complex scenes." "The SI is based on the Sobel filter. Each video frame (luminance plane) is first filtered with the Sobel filter. The standard deviation over the pixels in each Sobel-filtered frame is then computed", resulting in the SI for a single frame. We consider SI v , the average SI for all the frames of video v.

Temporal information (TI) or temporal perceptual information is "a measure that generally indicates the amount of temporal changes of a video sequence. (...) It is usually higher for high motion sequences." "TI is based upon the motion difference feature, that is the difference between the pixel values (of the luminance plane) at the same location in space but at successive frames." The standard deviation over the pixels of all the differences between successive frames is then computed to give the TI for two consecutive frames. We consider TI v , the average TI for all the frames of video v. "More motion in adjacent frames will result in higher values of TI."

HYPOTHESIS TESTING

Based on previous works [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF][START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF], we make the following a priori hypotheses:

H1 Prediction error is lower for higher user arousal. H2 Prediction error is higher for higher user valence. H3 Head speed mediates the effect of valence on error.

To analyze the validity of the above hypotheses, we first binarize some variables and perform analysis of variance (ANOVA) testing, shown in Table 2. The analysis of linear correlations on continuous data is incorporated into the structural equation modeling in Sec. [START_REF] Chen | Sparkle: User-Aware Viewport Prediction in 360-Degree Video Streaming[END_REF] and Valence bin . For SI and TI of every video v, binarization thresholds are chosen so that approximately half of the videos are in each partition: SI bin = -1 (resp. 1) for SI v ≤ 45 (resp. > 45), and TI bin = 0 (resp. 1) for TI v ≤ 3 (resp. > 3). In Fig. 4, SI bin is denoted "Low SI" or "High SI" with the same threshold. For Arousal and Valence of every user-video pair (u, v), Arousal bin = 0 (resp. = 1) for Arousal u,v ≤ 5 (resp. > 5), and the same to obtain Valence bin . In Fig. 2, Arousal bin (resp. Valence bin ) is also referred to as "LA" for low Arousal (resp. "LV" for low Valence) and "HA" for high Arousal (resp. "HV" for high Valence), with the same thresholds as defined above.

We first observe from Table 2 that SI bin significantly impacts all variables (Prediction error, Head speed, Valence and Arousal), while TI bin does not significantly impact Valence.

The relations mentioned in H1 and H2 are significant. Fig. 2left and 2-center show the direction of the association with 95% confidence intervals. We can therefore accept H1 and H2. To investigate the sizes of the significant effects, Fig. 3 shows scatter plots of error as a function of Arousal and Head speed, as well as how Head speed varies with graded Arousal and Valence. We first observe that there is a strong correlation between Prediction error and Head speed. We also observe that, as hinted in preliminary results from Li et al. [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF] and Xue et al. [START_REF] Tong Xue | Investigating the Relationship between Momentary Emotion Self-reports and Head and Eye Movements in HMD-based 360°VR Video Watching[END_REF], Prediction error tends to decrease with Arousal. While Head speed does not seem to significantly vary with Arousal, as confirmed by the ANOVA result in Table 2, the scatter plot of Head Speed versus Valence shows that the significant association between both, shown by the corresponding ANOVA result in Table 2, is an increasing function. This is in line with H3, which will be validated in the next section.

As Fig. 3-top-right shows the strong association of Prediction error with Head speed and Table 2 shows significant associations of Prediction error, Head speed, Arousal and Valence with SI bin , we analyze whether some variables interact with Arousal and Valence in their effect on Head speed and Prediction error. Fig. 4 shows that there is a possible interaction between the video feature SI bin and Arousal, and SI bin and Valence, in their effect on Prediction error and Head speed. This can be seen in the different slopes of linear model fitting the cloud of points, for each set of (u, v) points, for all users u ∈ U and videos v such that SI bin (v) = -1, or 1.

Also, it is interesting to observe in Fig. 2-right that Prediction error does not decrease in the same way with increased Arousal, depending on whether Valence is graded high or low. Indeed, Prediction error decreases more when Arousal increases when Valence is high. We may assume that the user tends to move more when they enjoy the video, and higher Arousal means more involvement/attentional capture, and hence synchronization between motion and the content's salient regions, facilitating the prediction. This corresponds partly to H3 and is investigated in the next section.

MODELLING THE EFFECT OF EMOTIONS AND VIDEO CHARACTERISTICS ON MOTION PREDICTABILITY

We now construct a structural equation model of the data. SEM is established as a methodological approach to represent how different variables affect each other [START_REF]Structural equation modeling: Concepts, issues, and applications[END_REF]. It allows to build a network of causal relations, and to investigate direct and indirect effects with mediating variables and external moderators interacting on the effect. A SEM therefore gathers significant linear relations, enabling to both incorporate the correlation coefficient and measure the size of the effect. We construct a SEM based on accepted H1 and H2, and incorporating the possible interaction of SI bin with Arousal and

Valence. An interaction effect is modeled as the product of two variables, one of which is binary. Owing to the above analysis of Fig. 4, we define interaction variables Arousal × SI bin and Valence × SI bin . We then consider possible causal relationships from Arousal, Valence, Arousal × SI bin and Valence × SI bin to both Head speed and Prediction error, as well as relationship from Head speed to Prediction error. We use the Python toolkit Semopy [START_REF] Igolkina | semopy: A Python Package for Structural Equation Modeling[END_REF][START_REF] Meshcheryakov | semopy 2: A Structural Equation Modeling Package with Random Effects in Python[END_REF], using the Wishart log-likelihood objective function. The resulting SEM is shown in Fig. 5, where only edges with regression coefficients significantly different from 0 have been kept (with p ≤ 0.01). Every edge is tagged with the unstandardized coefficient of the linear relationship between both participating variables, and with the corresponding standardized coefficient in parenthesis. The unstandardized coefficient is impacted by the difference in the relative scale of the variables, while the standardized coefficient is independent of the scale and represents by how many standard deviations the end variable varies when the regressor variable increases by one standard deviation. First, the model shows that the major impact of Valence on Prediction error is mediated by Head speed. Indeed, the standardized indirect effect of Valence on Prediction error is 0.19 × 0.85 = 0.16, while the direct effect is only -0.07. H3 is therefore validated. This suggests that users rating the video with higher Valence tend to move more. This connects with the results obtained by Li et al. [START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF]. Second, the model confirms that the prediction error varies inversely with Arousal, with a standardized linear coefficient of -0.11. Third, the interaction effect is significant. Indeed, for a high SI video with SI bin = 1, the total effect of Arousal on Prediction error is -0.11 + 0.17 × 1 = 0.06. In this case, the effect of Arousal on Prediction error is low and not significantly negative. However, for a low SI video with SI bin = -1, the total effect of Arousal on Prediction error is -0.11 + 0.17 × -1 = -0.28. SI bin is therefore a strong moderator of the effect of Arousal on Prediction error. To interpret this result, one may investigate how SI bin connects with video categories, such as those proposed by Almquist et al. [START_REF] Almquist | The prefetch aggressiveness tradeoff in 360°video streaming[END_REF]. We may think that a high SI bin describes videos with numerous salient areas in frames, hence yielding more exploratory head movements difficult to predict, even though the person rates their arousal/involvement in the video as high. Verifying such an interpretation is left for future works, as described in Sec. 8. Finally, this last result means that the video feature SI is a strong confounding factor which must be taken into consideration when one chooses 360°videos to investigate the impact of user emotion on motion prediction.

DISCUSSION

The results presented above open the path to promising directions to understanding the human motion process in immersive environments, as detailed in Sec. 8. Let us mention here some limits and perspectives of the presented data analysis.

First, obtaining results on more than 14 videos will be important for generalization, and to avoid possible spurious correlation between arousal and valence ratings that may impact our findings. Second, the main outcome variable considered here being the prediction error, the results may depend on the type of predictor considered. While, for the reasons described in Sec. 3.2, we verified that the results were similar between both methods taken from [START_REF] Fabián Romero-Rondón | TRACK: A New Method from a Re-examination of Deep Architectures for Head Motion Prediction in 360-degree Videos[END_REF], other families of approaches might lead to different effects of emotion on predictability. Third, we have considered head motion in this work, but it would be important to identify how the effects of emotions differ when predicting eye motion. More generally, while we have focused on only three types of user-centric measures (arousal, valence and head motion speed) and two types of video-centric measures (SI and TI), it will be most interesting to generalize this approach to more user-centric measures such as electrodermal activity and gaze, and video-centric measures such as video categories (focus or exploration [START_REF] Almquist | The prefetch aggressiveness tradeoff in 360°video streaming[END_REF], fear or happiness [START_REF] Jicol | Effects of Emotion and Agency on Presence in Virtual Reality[END_REF], possibly relating SI and TI to these).

CONCLUSION AND FUTURE WORK

In this article, we have presented a first investigation into the effect of emotion on head motion predictability. We considered two datasets totalling 14 videos and 63 users, providing head motion traces and arousal and valence subjective ratings. Through hypothesis testing and structural equation modelling, we have shown that the predictability of head motion increases with arousal but decreases with valence, that the effect of valence on predictability is mediated by head speed, and that video SI interacts in the effect of arousal on predictability, a high SI moderating the effect.

This work opens the way to better understand factors impacting the human motion and their effect on the performance of head motion predictors, and how such knowledge can be leveraged to improve prediction. This can be done by augmenting the datasets with videos with specific emotional and visual features where head motion prediction is harder, or by designing ancillary training losses where a deep neural model would have to learn how to predict the user emotional state from the video content and the user's past motion. An important outcome of this work is also to estimate the motion predictability from user emotional state. Such an estimation of the confidence of head motion prediction can readily be leveraged in the optimization of a 360°streaming system, even more so if the user emotional state is estimated with lightweight non-invasive device such as finger straps to measure electrodermal activity. This is the subject of our very next work.

Figure 2 :

 2 Figure 2: Prediction error against binarized Arousal bin (left) and Valence bin (center). Right: Difference in variation of Prediction error against Arousal bin depending on Valence bin .

Figure 3 :

 3 Figure 3: Scatter plots of Prediction error against Arousal and Head speed (top row), and Head speed against Arousal and Valence (bottom row). Straight lines are linear regression models fitted on the data. Shaded areas represent 95% confidence intervals.

Figure 4 :

 4 Figure 4: Scatter plots of Prediction error and Head speed against Arousal and Valence, disaggregated over SI bin . Straight lines are linear regression models fitted on the data. Shaded areas represent 95% confidence intervals.

Figure 5 :

 5 Figure 5: Structural equation model (SEM) describing the direct and indirect effects of user Valence and Arousal onto Prediction error, mediated by Head speed and moderated by video measure SI bin .

Table 1 :

 1 Details of selected videos. The ID refers to the original database[START_REF] Li | A Public Database of Immersive VR Videos with Corresponding Ratings of Arousal, Valence, and Correlations between Head Movements and Self Report Measures[END_REF].

	1 https://gitlab.com/PEM360/PEM360

Table 2 :

 2 . The binarization is performed on SI, TI, Arousal and Valence (denoting continuous variables) to obtain SI bin , TI bin , Arousal bin SI bin TI bin Arousal bin Valence bin F-scores of one-way ANOVA. The significance of group difference is denoted with * for p < 10 -2 and ** for p < 10 -3 .

	Prediction error 70.89** 79.09**	15.15**	7.67*
	Head speed	17.77** 19.94**	2.76	15.78**
	Arousal	51.90** 55.50**	(1253**)	0.37
	Valence	30.60**	2.69	0.42	(1266**)

ACKNOWLEDGMENTS

This work has been partly supported by the French government, through the UCA JEDI and EUR DS4H Investments in the Future projects ANR-15-IDEX-0001 and ANR-17-EURE-0004. This work was partly supported by EU Horizon 2020 project AI4Media, under contract no. 951911 (https://ai4media.eu/).


domain: sde math

text:
B Renard

D Mcinerney

S Westra

M Leonard

D Kavetski

M Thyer

J.-P Vidal

Hydrologic Extremes at the Global Scale 100-year Analysis and 180-year Reconstruction

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               E[P(s, t)] = c P (s) E[Q(s, t)] = c Q (s) constant (intercept)
Legend: varies in space and time ; varies in space

               E[P(s, t)] = c P (s) + λ P (s)τ (t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (
               E[P(s, t)] = c P (s) + λ P (s)τ (t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t)
               E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t
E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t) Q-specific
               E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t)
Legend: varies in space and time ; varies in space ; varies in time

All covariates are considered unknown and are estimated → Hidden Climate Indices (HCI)

Renard et al. HEGS EGU May 2022 5 / 9                E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t)
Legend: varies in space and time ; varies in space ; varies in time

All covariates are considered unknown and are estimated → Hidden Climate Indices (HCI)

τ (t) ∼ AR(1) + trend.                E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t)
Legend: varies in space and time ; varies in space ; varies in time

All covariates are considered unknown and are estimated → Hidden Climate Indices (HCI) τ (t) ∼ AR(1) + trend. Same for δ(t) and ω(t)

               E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t)
Legend: varies in space and time ; varies in space ; varies in time

All covariates are considered unknown and are estimated → Hidden Climate Indices (HCI) τ (t) ∼ AR(1) + trend. Same for δ(t) and ω(t) λ(s) ∼ Spatial Gaussian Process. Same for others

               E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) + more components... E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t

Model

After suitable data transformation...

               E[P(s, t)] = c P (s) + λ P (s)τ (t) + π(s)δ(t) + more components... E[Q(s, t)] = c Q (s) + λ Q (s)τ (t) + θ(s)ω(t

Model

Beta distribution reparameterized in terms of mean µ and precision γ

             P(s, t) ∼ Beta (µ P (s, t), γ P (s)) ; Q(s, t) ∼ Beta (µ Q (s, t), γ Q (s)) logit (µ P (s, t)) = λ P,0 (s) + K k=1 λ P,k (s)τ k (t) + K k=1 π k (s)δ k (t) logit (µ Q (s, t)) = λ Q,0 (s) + K k=1 λ Q,k (s)τ k (t) + K k=1 θ k (s)ω k (t)
P-specific HCIs with large trends

Introduction"

  The frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which observational data are sufficient [. . . ]" "The frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which observational data are sufficient [. . . ]" "Confidence about peak flow trends over past decades on the global scale is low [. . . ]" "The frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which observational data are sufficient [. . . ]" "Confidence about peak flow trends over past decades on the global scale is low [. . . ]" "The frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which observational data are sufficient [. . . ]" "Confidence about peak flow trends over past decades on the global scale is low [. . . ]" "changes may be more complex than simple trends" Objectives Better understand the temporal variability of heavy precipitation (P) and flood (Q) at the global scale by means of an innovative probabilistic model 100-year analysis Identify common (P+Q) vs. specific (P-only or Q-only) signals behind global extremes Look for trends and low-frequency variability in those signals 180-year reconstruction Using 20CRv3, reconstruct probabilities of extreme P/Q since 1836 Q: a selection from GSIM Do et al. (2018); Gudmundsson et al. (2018) Extract seasonal maxima at each site (SON, DJF, MAM, JJA) The rectangle dilemma... In this work, we'll use all data available during 1916-2015

→Renard→

  Using 20CRv3, reconstruction of HCIs from 1836 → Hydro-extreme probability maps from 1836 Using 20CRv3, reconstruction of HCIs from 1836 → Hydro-extreme probability maps from 1836 Non-exceedance probability (⇔ return period) of the largest event of the season Example: Maximum streamflow in December-January-February for 2 Australian stations

  varies in space and time

	Renard et al.	HEGS	EGU May 2022	5 / 9

  t)

	Legend: varies in space and time ; varies in space ; varies in time
	Renard et al.	HEGS	EGU May 2022	5 / 9

  )

		P-specific covariate and its effects
	       E[P(s, t)] = c P (s) + λ P (s)τ (t)	+	π(s)δ(t)
	      
	Legend: varies in space and time ; varies in space ; varies in time
	Renard et al.	HEGS		EGU May 2022	5 / 9

  covariate and its effects

	Legend: varies in space and time ; varies in space ; varies in time
	Renard et al.	HEGS	EGU May 2022	5 / 9

P: a selection from Hadex 2+3 Donat et al. (2013); Dunn et al. (2020)

Thank you! Renard & Thyer (2019). Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes. Water Resources Research. Renard et al. (2021). A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research. https://globxblog.inrae.fr/ https://github.com/STooDs-tools This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 835496

→ Reliability: good (cross-validation); Sharpness: poor (P) to good (Q)


Arnaud Can
email: arnaud.can@univ-eiffel.fr

Judicaël Picaut
email: judicaël.picaut@univ-eiffel.fr

Jeremy Ardouin

Pierre Crepeaux

Erwan Bocher

David Ecotiere

Mathieu Lagrange

Catherine Lavandier

Vivien Mallet

Christophe Mietlicki

Jérémy Ardouin

Pierre Crépeaux

David Ecotière

Marc Paboeuf

CENSE Project: general overview

Keywords: Dynamic noise maps, sensor networks, data assimilation, sound recognition, soundscape

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

Introduction

The reduction of the noise exposition represents both societal and environmental concerns, in particular for cities that are subjected to a multitude of noise sources and that count de facto numerous exposed people. In this context, noise mapping is acknowledged as a relevant tool to diagnose urban sound environments, to propose action plans to reduce noise annoyance, as well as to communicate with city dwellers. Nowadays, noise maps are essentially elaborated by means of numerical simulations, with high spatial precision, from a census of noise sources (mainly road, railway and air traffic, and the biggest industries), followed by a sound propagation modelling [START_REF] Kephalopoulos | Advances in the Development of Common Noise Assessment Methods in Europe: The CNOSSOS-EU Framework for Strategic Environmental Noise Mapping[END_REF]. However, this method has some well-known limitations especially concerning the inaccuracy of input data, the simplified emission and propagation modelling [START_REF] Van Renterghem | Meteorological Influence on Sound Propagation between Adjacent City Canyons: A Real-Life Experiment[END_REF][3][4] [START_REF] Prezelj | Traffic noise modelling and measurement: Inter-laboratory comparison[END_REF], and, lastly, the inadequacy of classical output noise indicators to describe the perceived sound environments [START_REF] Brown | A review of progress in soundscapes and an approach tosoundscape planning[END_REF][7] [START_REF] Lavandier | The contribution of sound sourcecharacteristics in the assessment of urban soundscapes[END_REF]. In parallel, noise observatories have been deployed in some cities, which give locally access to the time variations of the real sound levels, but entail high operational costs that forbid their dense deployment, limiting the number of observation points to few units [START_REF] Can | Measurement network for urban noise assessment: comparison of mobile measurement and spatial interpolation approaches[END_REF]. Given the recent developments in noise measurement technologies and computational methods, it is now possible to combine these two approaches in order to benefit from the advantages of each method [10][11]. This would be a significant advance in the development of predictive noise models, and would open many opportunities for assessment and improvement of urban soundscapes. The CENSE project, funded by the French Research National Agency from 2017 to 2021, aimed at improving the characterization of urban sound environments, by combining in situ observations and numerical noise predictions. The project relied on a dense network of low-cost sensors deployed in an experimental site in the city of Lorient (France), and data assimilation techniques between simulated and measured data. Beyond the production of physical indicators, the project was also positioned on the characterization of sound environments based on sound recognition and perceptual assessments. The project involved ten partners, namely University Gustave Eiffel, Wi6labs, Ville de Lorient, Lab-STICC (CNRS), Cerema, LS2N (CNRS), ETIS (Cergy Paris Université), INRIA, BruitParif and Bouygues E&S. The combination of modelling and measurements within a common modelling framework has been investigated in few recent projects, among which:

• The IDEA project (Intelligent Distributed Environmental Assessment), which hosted the development of local, intelligent measurement networks for noise and UFP (ultra-fine particulate matter) [12][13]. The collected data were used to feeding models for short-term prediction, by updating noise maps through parameter model tuning [START_REF] Wei | Dynamic noise mapping: A map-based interpolation between noise measurements with high temporal resolution[END_REF]; • The DYNAMAP project, which aimed at developing a dynamic noise mapping system able to detect and represent in real time the acoustic impact of road infrastructure, based on customized low-cost sensors and a software tool implemented on a general purpose GIS platform [14][15][16]; • The SONyC project, which aims to create technological solutions for: (1) the systematic, constant monitoring of noise pollution at city scale; (2) the accurate description of acoustic environments in terms of its composing sources; (3) broadening citizen participation in noise reporting and mitigation; and (4) enabling city agencies to take effective, information-driven action for noise mitigation [START_REF] Mydlarz | The life of a New York City noise sensor network[END_REF][18] [START_REF] Bello | SONYC: A System for Monitoring, Analyzing, and Mitigating Urban Noise Pollution[END_REF]. The CENSE projects distinguished from these previous works by the proposed integrated approach, mainly based on the use of assimilation methods, novel sensor network technologies, consideration of uncertainties on noise prediction from input data, and sound recognition and perceptual assessments, all in order to predict more accurate and relevant noise assessment in urban areas. The project, which is drawing to a close, has produced advances on noise modelling [START_REF] Aumond | Improvement of city noise map production processes and sensitivity analysis to noise models inputs[END_REF], low-cost sensor network technologies [START_REF] Ardouin | A high density network of low cost acoustic sensors based on wired and airborne transmission of spectral data[END_REF], data assimilation techniques applied to sound levels prediction [START_REF] Lesieur | Faster and more accurate noise mapping combining meta-modeling and data assimilation[END_REF], urban sound recognition, and perception [START_REF] Lavandier | Urban sensor network for characterizing the sound environment in Lorient (France) through an automatic assessment of traffic, voice and bird presence ratios[END_REF], each topic corresponding to a specific Work Package of the project. This first presentation, of a series of five, will give a general overview of the project comprehensive approach and framework, and its operational outputs. The project advances are described from sections 2 to 5, then more in-depth in articles [START_REF] Aumond | Improvement of city noise map production processes and sensitivity analysis to noise models inputs[END_REF][21] [22][23]. Section 6 discusses the advances of the project as well as the encountered difficulties, so as to draw the further research and development avenues for the years to come as regarding the characterization and monitoring of urban sound environments.

2 Improvement of city noise map production processes and sensitivity analysis to noise models inputs

Improvement of city noise map production processes

Collecting input data for noise mapping is often a difficult task, and obtaining good quality for this data is certainly even more difficult. A specific process for noise mapping was developed under the CENSE project, based on the coupling between an open source noise mapping software and an open source spatial database that can provide most of the input data for noise mapping. All noise mapping achieved under the CENSE project relied on the NoiseModelling software, which has been improved and subject to a new release during the project [START_REF] Aumond | Overview of the NoiseModelling open-source software version 3 and its applications[END_REF]. It is a free and open-source software dedicated to environmental noise maps on large-scale outdoor spaces. It can be used as a Java library or be controlled through a web interface [START_REF] Bocher | NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps[END_REF]. The CNOSSOS-EU model is implemented for the estimation of road traffic emissions, as well as for the calculation of its attenuation along propagation paths [START_REF] Kephalopoulos | Advances in the Development of Common Noise Assessment Methods in Europe: The CNOSSOS-EU Framework for Strategic Environmental Noise Mapping[END_REF]. NoiseModelling allows information to be stored at three levels: the noise sources and their sound levels, the geometry of the propagation paths and finally the transfer matrix for each of the source/receiver pairs. This choice was made because the computation time of such a software is essentially concentrated into the pathfinding algorithm. A groovy script manages interactions between NoiseModelling libraries and a spatial database (PostGIS or H2GIS) for getting most input parameters. Simulation can be performed using a configuration file containing the values on the input parameters, and results are stored in dedicated compressed folders. All the framework is open-source and available on GitHub to ensure the research is reproducible and adaptable to other case studies. Most input data of NoiseModelling came from OpenStreetMap (OSM) data, and are requested, processed and formatted by means of the opensource geospatial toolbox GeoClimate. Details on input data can be found in [START_REF] Aumond | Improvement of city noise map production processes and sensitivity analysis to noise models inputs[END_REF]. OSM-based noise predictions were compared to a noise map of a part of Paris, calculated with reference input data. Noise maps show a moderate agreement, with nearly 40% of the receivers with a deviation lower than 2.5%. Discrepancies might be attributed to road pavement classifications [START_REF] Aumond | Improvement of city noise map production processes and sensitivity analysis to noise models inputs[END_REF].

Sensitivity analysis of noise mapping modeling

When producing a noise map, it is essential to be able to identify the most influential input data and parameters in order to prioritize them in the data collection and modelling process. In order to help this prioritization, a methodology for sensitivity analysis built on an open-source modeling framework has been proposed under the CENSE project, relying on the Morris method; see [START_REF] Aumond | Global Sensitivity Analysis for Road Traffic Noise Modelling[END_REF] for details. The total procedure was repeated 50 times for a group of 15 inputs, resulting in 800 simulations. To ensure that the space of exploration did not favor any area, 500 trajectories were drawn and only the fifty trajectories that maximize exploration were retained. Figure 1 shows the chosen parameters and ranges of variation. All ranges of variation and parameters chosen are specific to this study and should be adapted to any other case study. Above all, the aim was to propose a methodology that can be replicated, including long-distance sound propagation for example. The study area retained all along the CENSE project is the city of Lorient. It covers an area of about 2 km 2 , in which 14,343 receivers (around 1772 buildings) were selected to serve as a support for the sensitivity analysis. The influent input parameters are a compilation of data collected from Cerema, IGN (the French National Institute of Geographic and Forest Information) and the Lorient city council. Figure 1 shows an example of results through the median Lden value of the 800 simulations in dB(A) representing 9672 inhabitants. Approximatively 24% of them are exposed to road traffic Lden values above 65 dB(A).

Results are detailed in [START_REF] Aumond | Global Sensitivity Analysis for Road Traffic Noise Modelling[END_REF]. In brief, the sensitivity to the input parameters of the CNOSSOS-EU model highly depends on the location of the receivers. The most influential parameter is whether diffraction over horizontal edges is considered or not, regardless of the indicator observed: average sound level over the area or ratio of the population exposed to more than 65 dB(A). This can be easily explained by the fact that some receivers may not be reached by a propagation path until this parameter is introduced in the calculation.

When model configuration parameters are excluded from the analysis, it can be shown that for most receivers, the most influent parameters are linked with the emission part of the CNOSSOS-EU model, and concern the mean flow rates of the road category the closest to the receiver.

A high density network of low cost acoustic sensors based on wired and airborne transmission of spectral data

The CENSE network, currently deployed in the downtown area of the city of Lorient, is described in this section. More details are given in [START_REF] Ardouin | A high density network of low cost acoustic sensors based on wired and airborne transmission of spectral data[END_REF].

Network

Following a literature review [START_REF] Picaut | Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review[END_REF], the CENSE network considered a hybrid communication scheme where each node of the network is a sensor with acoustic monitoring capability. Those nodes can be leaf nodes. In this case the sensor is energetically autonomous and sends wirelessly the data to the other types of nodes, the gateway nodes. A gateway node is wired both in terms of power and network connectivity. The degree of precision of the measure can vary greatly from device to device. As discussed in [START_REF] Picaut | An open-science crowdsourcing approach for producing community noise maps using smartphones[END_REF], this degree of precision is balanced with the cost of the sensors. While high precision sonometers are of great interest for metrologic use, we believe that low cost sensors based on the MEMS technology has the potential of allowing the use of large scale and dense network of sensors at a reasonable price. Following the constraints of the project, the specifications of the microphone have been defined and more than 20 MEMS microphones have been considered [START_REF] Ardouin | An innovative low cost sensor for urban sound monitoring[END_REF]. The Invensense ICS-43432 MEMS microphone is selected because of its good overall performance and the fact that it offers an I2S fully digital interface, thus reducing the risk of any electrical noise addition to the audio data. The installation of the sensors was planned in two phases on an experimental site in the heart of the city of Lorient in France. The first phase consisted of the installation of 78 gateway nodes, then in a second phase, the installation of 65 nodes. The maintenance of the network is a crucial element of the project. The entire network was developed specifically by the project partners, from scratch, be it for the design of the sensors, the installation of the network, and the implementation of an IT infrastructure for data management. On one hand, it allowed rapid intervention; on the other hand, numerous skills from very different technical areas were required to cope with unforeseen issues. Among the difficulties encountered were: the failure of some sensors after installation (a few units); the multiplicity of risks of measurement stoppage due to malfunctioning of either the sensors themselves, or the Citybox ®, or the controller, or the router; the maintenance of the servers. In spite of these difficulties inherent to the initial choices for the development of the network, the whole network was successfully implemented during the first months of the study, allowing the acquisition of a large amount of data and the experimentation of different sound analysis.

Acoustic sensitivity and robustness analysis

In order to study the acoustic behavior of the developed sensors, several tests were performed in an acoustic test room, which evaluated the linearity, directivity, sensitivity and background noise characteristics. The tests were limited to values measured in one-third octave bands over a frequency range from 20 Hz to 12500 Hz. Concerning the background noise of the sensor, results obtained showed that the CENSE sensor has a threshold of about 22 dB, over all the frequency bands concerned. The frequency sensitivity was very good for the octave bands from 250 Hz to 4000 Hz, with a difference of less than 0.5 dB. For the 125 Hz, 8k Hz and 12.5k Hz octave bands, the differences were a little larger, from 1 to 2 dB. For the 2 lowest octave bands (31.5 Hz and 63 Hz), the differences were much more important (from 4 to 10 dB approximately). Finally, the linearity was tested: from 125 Hz to 12.5 kHz, the slope measured for the sensor was very close to unity, which is the expected theoretical behavior. The conclusion of these tests is that the sensor results appear to be acceptable for the frequency bands between 400 Hz and 5000 Hz. The reliability, accuracy and robustness of the developed acoustic sensors has been qualified under various meteorological conditions in a climate chamber in order to check the potential sensor drift according to both the temperature and relative humidity. Results are detailed in [START_REF] Guillaume | Qualification of innovative low-cost sensors dedicated to environmental noise monitoring[END_REF]. In brief, both temperature and humidity have a weak effect on the acoustic measurements, except for one of the six sensors, and uncertainties for these two environmental parameters fulfill specifications of class I sound level meter. Results demonstrate the satisfying accuracy of the implemented acoustic sensors for the targeted purpose.

Data archiving

Each node produces acoustic data, pressure level statistics, and third octave spectral data, that are sent every 10 seconds. Control information, such as temperature and humidity inside the sensor are sent every 60 seconds. If the sensor is a leaf node, information about battery voltage and connectivity are also sent every 5 minutes. Acoustic pressure statistics are the LAeq and Leq computed every second (520 bytes). Third octave spectra with frequency range 20Hz-12500Hz are computed every 125 ms using a rectangular window Fast Fourier Transform (15 kbytes). Bandwidth testing showed a bandwidth consumption of 1769 bytes per second. Each gateway node is responsible for sending its data through the internet to an archiving architecture using the opensensorhub protocol.

The storage architecture of the CENSE project was designed to maintain high availability. To this end, the data are distributed on 2 distinct sites (see [START_REF] Ardouin | A high density network of low cost acoustic sensors based on wired and airborne transmission of spectral data[END_REF]). The archiving system should therefore be operational even if one site is no longer reachable. Each gateway node collects data through its sensors then aggregates into an embedded OpenSensorHub instance. This aggregated data is transferred to a random server through a secured http request using the open source Elasticsearch interface. The receiving server then duplicates the data to another Elasticsearch server for data safety (https://github.com/elastic/elasticsearch). Archived data can be accessed using low level request protocol using the Elasticsearch engine as it allows database replication for further data processing purposes by serving json files. For example, the acoustic data is pushed to the servers of bruitparif (https://www.bruitparif.fr) that services a range of display tools available at the following url: https://rumeurlorient.bruitparif.fr. Higher level data visualization and monitoring can also be performed using a Kibana interface. Finally, by recording spectral data, the CENSE project aims at allowing the automatic estimate of those quantities using deep learning techniques (see section 5). Coding schemes have been proposed in that respect [31][32]. Figure 3 shows the presence of three sources of interest: traffic, voice and birds.

Faster and more accurate noise mapping combining meta-modeling and data assimilation

The CENSE project relied on a two steps approach to combine measurements and modeling, in order to go towards faster and more accurate noise mapping:

• A meta-model (also sometimes called surrogate model) is built to statistically reproduce the behavior of the reference simulation software, NoiseModelling in this case; • The meta-model is then employed to assimilate acoustic measurements from 16 locations. State estimation, inverse modeling and joint state-parameter estimation are compared and evaluated using cross validation. This section summarizes the results of the CENSE project in terms of meta-modelling and data assimilation. The reader can refer to [START_REF] Lesieur | Faster and more accurate noise mapping combining meta-modeling and data assimilation[END_REF], or to the following papers for more details [START_REF] Lesieur | Meta-modeling for urban noise mapping[END_REF][34] [START_REF] Lesieur | Inverse modeling and joint state-parameter estimation with a noise mapping meta-model[END_REF].

Material and methods

For phasing and data availability reasons, the model was built on data produced on the City of Paris, and not on the Lorient measurement network of the CENSE project. The study area is located in the 13th district of Paris, France and covers an area of 3 km2, and is described in details in [START_REF] Aumond | Modeling soundscape pleasantness using perceptive assessments and acoustic measurements along paths in urban context[END_REF]. It consists of an array of 25 microphones that has been deployed across the study area in 2015, from which 16 have been selected for the present study.

Meta-model

Due to the large number of model calls in the optimization algorithm, data assimilation techniques are made practicable only after replacing the noise mapping software with a meta-model, i.e., a statistical emulation of the noise mapping software that can compute similar maps at a much lower computational cost. The metamodel is built in three steps:

• The generation of the training sample. Here, the training sample is made of 2000 simulations run with model input parameters defined a so-called design of experiment; • The dimension reduction, through a PCA, which aims to select a reduced amount of maps that explain most of the variance of the noise maps of the training sample; • The kriging, which interpolates with a statistical method the projected values of the training sample onto the basis vectors of the reduced subspace. Once the meta-model is built, each call with new input parameters takes a very small amount of time (100 ms versus ~ 1 h for the complete model). For more details about the meta-model construction, the reader can refer to [START_REF] Lesieur | Meta-modeling for urban noise mapping[END_REF].

Data assimilation methods

Data assimilation is the process of combining a noise model simulation and field observations, in order to build an improved representation of the real noise levels, at least at simulated points. In our context, the data assimilation makes use of a model or a model simulation (simulated noise map) and field observations at a given time, in order to produce another noise map closer to the real noise map. It can be seen as a correction on the simulated noise map, induced by the additional information brought by the observations. Among all the data assimilation approaches, we compared state estimation, inverse modeling and a joint state-parameter estimation:

• The state estimation applies a direct correction on a simulation map. It assumes the simulated map is an a priori map with some unknown error at every point of the map. The so-called best linear unbiased estimator (BLUE) is computed, which is an improved noise map whose error has minimum variance. • Another approach is called inverse modeling (IM). In this case, we do not correct directly the simulated noise map, but instead the input parameters of the noise model, or in our case, of the metamodel. The method allows to determine better parameters, which with the simulated map gets closer to the observations.

• Joint state-parameter estimation (JSPE) combines both approaches: the parameters are improved and also a correction of the resulting noise map is corrected. This allows for further flexibility and gives more room for improvement. Both methods, IM and JSPE, can work with or without prior knowledge of the input parameters; four optimization algorithms are therefore analyzed. If satisfying results are obtained with optimization algorithms that do not need a priori parameters, then it is possible to expand the inverse modeling data assimilation processes to a wider range of urban areas where the input parameters values are not known.

Results

Methods are compared through a leave-one-out cross validation, which consists in removing the observations of a given microphone from the data assimilation process. Results are detailed in [START_REF] Lesieur | Inverse modeling and joint state-parameter estimation with a noise mapping meta-model[END_REF]. In brief, the inverse modeling shows similar results as the data assimilation method BLUE (Best Linear Unbiased Estimator) only, whereas the JSPE always has a lower RMSE value than the state estimation only. The performance of IM and JSPE without a priori parameters remains very similar to the performance with these parameters. The most accurate noise map is generated computing a JSPE algorithm without a priori knowledge about traffic and weather and shows a reduction of the RMSE from 3.5 dB to 2.6 dB compared to the reference metamodel noise map with 16 microphones over an area of 3 km 2 . This means that the operators who wish to obtain a dynamic noise mapping for a given area can get a satisfying level of accuracy without the need to get real-time traffic and weather data, and extend the availability of dynamic noise mapping to areas where no traffic measurement is available.

Characterizing the sound environment through an automatic assessment of traffic, voice and bird presence ratios

Going further than just the sound levels, the data collected through the urban sensor network allow to perceptually characterize the soundscape as it is defined in the ISO standard as the "acoustic environment as perceived or experienced and/or understood by people, in context" [START_REF]Acoustics -soundscape -part 1: definition and conceptual framework. Standard, International Organization for Standardization[END_REF], as soon as acoustic measurements could correspond to perceptual data. In the continuity of previous works that were interested in the outdoor [38][39] or indoor [START_REF] Dedieu | Pleasantness of Typical Acoustic Environments inside a Living Room in a European Residential Context[END_REF] perceived sound quality, in the CENSE project, we focused on the perceived sound quality and on noise annoyance for pedestrian point of view (soundscape characterization) as well as for resident point of view (at home situation). As these perceptual characteristics depend on perceived sources, we also focused on the possibility to measure automatically the sound source presence ratios on the sensors, in addition to classical level indicators.

Questionnaire campaign

The sound quality and the noise annoyance has been assessed for both the pedestrian and inhabitant points of view thanks to a questionnaire that has been sent by postal mail to about 2000 inhabitants in the city center of Lorient between January and March 2019. It was divided into four parts:

• The first one concerned the sound environment quality: it concerned the Short-Term (ST) assessment on 7 levels semantic differential scales of variables such as pleasantness, eventfulness, etc.; • The second part focused on the perceived time of presence ratio and loudness of 13 sound sources, such as road traffic, rail traffic, calm voices, expressive voices, small birds, gulls, etc.; • The third part concerned the Long-Term (LT) annoyance for both points of view; • A last part was dedicated to collect personal data, such as noise sensitivity, gender, age, socioprofessional category, etc. Outdoor pleasantness and outdoor annoyance models were built (see [START_REF] Lavandier | Urban sensor network for characterizing the sound environment in Lorient (France) through an automatic assessment of traffic, voice and bird presence ratios[END_REF] or [START_REF] Aumond | Links between representation of outdoor soundscape, noise annoyance at home and outdoor acoustic measurement[END_REF] for details). Overall Loudness (rated on the Noisy/Silent scale) appears in all models at the first place showing that this variable is the most influent one. Sound sources such as road and air traffic, expressive voices and birds also appear in the regressions. Long-term inside annoyance models are also built, which make a distinction between calm voices (perceived positively) and expressive voices (perceived negatively). Finally, it is found that Noise Sensitivity and Age increase the adjusted explained variance of the models.

Sound identification

The calculation of the source presence ratios has been automatically implemented on the low-cost sensors, for relevant sources such as Road traffic, 2-wheel motor vehicles, Voices and Birds for examples. The source identification is realized every second for each source of interest on each sensor. Source identification is conducted using a deep convolutional architecture that first extracts time-frequency patterns relevant to the identification of sound sources from each 1s third-octave segment. At inference, presence or absence labels predicted for each sound source are aggregated over time to obtain the time of presence over the first 10 minutes of each hour. The model is trained on a fully synthetic set of 400 sound scenes of 45s each as described in [START_REF] Gontier | Polyphonic training set synthesis improves self-supervised urban sound classification[END_REF]. Synthetic scene generation and automatic annotation processes are further detailed in [START_REF] Gontier | Estimation of the perceived time of presence of sources in urban acoustic environments using deep learning techniques[END_REF]. Results are in accordance with expectations (see [START_REF] Lavandier | Urban sensor network for characterizing the sound environment in Lorient (France) through an automatic assessment of traffic, voice and bird presence ratios[END_REF] for details), highlighting for instance an increase of the time of presence of birds in the morning corresponding to the sunrise birds chorus, an increase of voices on Wednesdays and week-ends, and a cars evolution following the rhythms of the days.

The cartographic web portal

To highlight the results on a map, a cartographic portal has been set up. Publicly available at http://cense.noise-planet.org/, this website allows users to navigate the map (with the classic zoom and address search functions) and to consult the calculated indicators in detail. Figure 4 illustrates some screenshots of layers accessible to the user.

Conclusion

The CENSE project, funded by the French Research National Agency from 2017 to 2021, aimed at improving the characterization of urban sound environments, by combining in situ observations and numerical noise predictions. The project, which is drawing to a close, has produced advances on:

• Enhancing the noise production process and proposing a sensitivity analysis for noise mapping [START_REF] Aumond | Improvement of city noise map production processes and sensitivity analysis to noise models inputs[END_REF];

• Designing and deploying a low-cost sensor network, as well as a data archiving [START_REF] Ardouin | A high density network of low cost acoustic sensors based on wired and airborne transmission of spectral data[END_REF];

• Proposing data assimilation techniques applied to sound levels prediction [START_REF] Lesieur | Faster and more accurate noise mapping combining meta-modeling and data assimilation[END_REF];

• Characterizing urban sound environments through sound recognition and perception advances [START_REF] Lavandier | Urban sensor network for characterizing the sound environment in Lorient (France) through an automatic assessment of traffic, voice and bird presence ratios[END_REF].

The reader can refer to the corresponding Euronoise papers, or to the literature produced during the project, for in-depth results. The main encountered limitation consisted of the technical difficulties linked to the sensor network maintenance, which has desynchronized part of the perceptual study from the physical data collected, and necessitated that the work on data assimilation be done on another measurement network. However, this in no way calls into question the scientific results of the project. The CENSE network being now operational, it will allow researchers to answer many scientific and technological questions regarding the characterization of the urban sound environment. Finally, the CENSE project opens up many avenues of research, for example on the enrichment of the input data with other sources of traffic and acoustic data, on the production of perceptual noise maps, or on the use of sensor networks for decision making.

Figure 1 -

 1 Figure 1 -Left: Study area of the analysis. 14,343 receivers are represented on the map. The color represents the median Lden value at each receiver over the 800 simulations. Right: Sensitivity analysis parameters, related topic reference codes, ranges of variation and step types (multiplicative * or additive +)

Figure 2 -

 2 Figure 2 -The leaf (left) and gateway (middle) sensors. Right: Deployment in the City of Lorient

Figure 3 -

 3 Figure 3 -Left: The Harmonica profile for a given sensor of the CENSE network for the month of December 2020. Center: Evolution of the LAeq over a week for a given sensor using the Kibana dashboard. Right: Estimation of the time of presence of birds (green) from spectral data using deep learning techniques.

Figure 4 -

 4 Figure 4 -Traffic (left), birds (middle), and expressive voices (right) layers, which represent the presence ratios of different sources.

Acknowledgements

This research was funded by the french National Agency for Research (Agence Nationale de la Recherche) grant number ANR-16-CE22-0012.


Manon Jammes

Romain Contentin

Frédéric Cassé

Bastien Bourdon

Arnaud Bianchi

Fabrice Audigié

Thomas Branly

Emilie Velot

Philippe Galéra

The secretome of equine bone marrow-derived mesenchymal stem cells enhanced regenerative phenotype of equine articular chondrocytes

come

C o l 2 a 1 / C o l 1 a 1 D 0 B M P 2 1 / 2 0 1 / 1 2 1 / 8 1 / 4 e A C D 0 B M P 2 1 / 2 0 1 / 1 2 1 / 8 1 / 4 e A C 1 0 -2 1 0 -1 1 0 0 1 0 1 1 0 2 1 0 3 1 0 4 1 0 5 1 0 6 1 0 7 * Col2a1/Col1a1 * D7 D14 Relative mRNA expression
The secretome of equine bone marrow-derived mesenchymal stem cells enhanced regenerative phenotype of equine articular chondrocytes

Conclusions

Results

Co-culture experiments

Exosomes characterization Conditioned media experiments

mRNA levels of hyaline cartilage and fibrocartilage matrix markers were assessed by RT-qPCR after 7 (D7) and 14 days (D14) of culture. Normalization was performed according to D0 (eAC cultured in monolayer until P3). The significance of the values was tested using a Mann-Withney test (* p<0,05). A chondrogenic control was performed using bone morphogenetic protein 2 (BMP2), and eAC condition refers to mRNA extracted from eAC at P0.

Expression of exosomal specific markers

Protein extraction was performed after purification of exosomes according to the detailed protocol. Then, a western-blot was performed with antibodies against exosome-specific proteins. MSC-CM were able to favor eAC migratory capacities and cartilage matrix synthesis in vitro by increasing types II and I collagens protein amounts. These effects could be mediated by exosomes since we have demonstrated that MSC-CM contain these nanovesicles that can be uptaken by eAC. However further investigations are needed to better define the contribution of exosomes to the therapeutic potential of MSC-CM in OA.

The

These results demonstrate that MSC secretome influences eAC phenotype and could relieve OA symptoms or delay OA outcomes in equine medicine. Consequently, an acellular therapy avoiding disadvantages of direct cell use must be considered, especially because this process could be potentially transposed to human medicine according to the One Health concept.

Equine osteoarthritis (OA) is a sequential disease which leads to cartilage degradation and painful bone frictions. It induces impaired animal well-being, premature cessation of sport activity, and financial losses. Fibrocartilage synthesis occurring during cartilage destruction is a physiological response, allowing bone protection but reducing tissue mechanical resistance. To date, there is a lack of curative therapies. Mesenchymal stem cell (MSC)-based therapies are promising for cartilage repair, but face limitations inherent to cell itself which can be overcome using their secretome through acellular therapy approaches.

To understand the effects of equine bone marrow (BM-)MSC secretome on equine articular chondrocytes (eAC) phenotype, indirect co-culture experiments were first performed. Then, we wanted to recapitulate the effects we observed in co-culture on eAC using the MSC-conditioned medium (CM) to make acellular therapy conceivable. We assessed hyaline cartilage and fibrocartilage markers at the transcription and protein levels, and evaluated eAC migratory capacities, which are of interest during OA therapy to favor the filling of the cartilage defects. To optimize immunomodulation properties of MSC secretome for future experiments, MSC priming relevance with interleukin (IL)1-β was evaluated. Suspected to be the principal vectors of the effects observed, exosomes were isolated through chemical precipitation and then characterized to confirm their nature.

Aurélie Cullier1 , Romain Contentin 1 , Frédéric Cassé 1 , Florence Legendre 1 , Seng Manivong2,3 , Araceli Ac Garcia 3 , Gaëlle Roullin 3 , Florina Moldovan 2 , Xavier Banquy 3 , Lélia Bertoni 4 , Philippe Galéra 1 , Fabrice Audigié 4 , Magali Demoor 1 Introduction

  eAC co-culture with BM-MSC improved eAC phenotype by enhancing Col2a1, Col1a1, Prg4 markers expressions, but Col2a1/Col1a1 remained unchanged. → BM-MSC secretome modulates eAC phenotype nanoparticle size distribution and concentration were determined by NTA (A). Particle morphology was observed by TEM to confirm their EV nature and assess EV integrity (B).

  European projects "EQUISTEM" and "OA-ACTIVE" are funded by the European Union within the framework of the Operational Program ERDF/ESF 2014-2020 3. Uptake of exosomes by eAC CM from BM-MSC contains undamaged exosomes expressing Alix/TSG101/CD81. These extracellular vesicles interact with eAC and can be uptaken by the cells. -CM enhanced eAC migratory capacities 3D medium corresponds to 2D eAC culture medium supplemented with ascorbic acid. Media were tested with ou without the BMP2 chondrogenic molecule. improved mRNA and protein expression of healthy and pathologic cartilage markers by eAC After 7 and 14 days of 2D and 3D culture of eAC with MSC-CM, mRNA expression of hyaline cartilage and fibrocartilage markers was assessed by RT-qPCR. Normalization was realized towards the D0 condition. CM3D-IL1β condition refers to CM from MSC primed with interleukine(IL)-1β (10 ng/mL) during 24h prior to conditioning. After 14 days, eAC cultured with CM supplemented or not with BMP2, in organoïd-like collagen sponges were harvested and protein extraction was performed.MSC-CM enhanced hyaline cartilage and fibrocartilage markers expression of eAC after 14 days and increased their migratory capacities → BM-MSC-CM recapitulates the effect of co-culture on eAC MSC secretome was able to increase collagen protein accumulation especially when primed with the OA-related inflammatory cytokine IL-1β, suggesting that the therapeutic potential of MSC-CM could be enhanced by an optimized cytokine priming.

Manon Jammes 1, † , Romain Contentin 1, † , Frédéric Cassé 1 , Bastien Bourdon 1 , Arnaud Bianchi 2 , Fabrice Audigié 3 , Thomas Branly 1 , Emilie Velot 2 , and Philippe Galéra 1

  Center of Imaging and Research on Locomotor Affections on Equines (CIRALE), Unit Under Contract 957 Equine Biomechanics and Locomotor Disorders (USC 957 BPLC), French National Research Institute for Agriculture Food and Environment (INRAE), Ecole Nationale Vétérinaire d'Alfort, F-94700 Maisons-Alfort, France.† These authors contributed equally to this work.

1 Normandie Univ, UNICAEN, BIOTARGEN, F-14000 Caen, France.

2 University of Lorraine, CNRS, IMoPA, F-54000 Nancy, France. 3

: University of Caen Normandie, UNICAEN, BioTARGen EA7450, 14000 Caen, France

: Sainte-Justine University Hospital Center,

Côte-Sainte-Catherine Rd., Montréal, Canada 3 : Faculty of pharmacy, University of Montréal, 2940 chemin de Polytechnique, Montréal, Canada 4 : Center of Imaging and Research on Locomotor Affections on Equines (CIRALE) USC 957 BPLC INRAE, Ecole Nationale Vétérinaire d'Alfort F-94700 Maisons-Alfort, France

The European project "OA-ACTIVE" is funded by the European Union within the framework of the Operational Programme ERDF/ESF 2014-2020