domain: sdv phys scco spi

text:
Moretto

B Lefèvre

A Thévenon

P Moretto

The effect of similar speed's walking and functional classification of foot contact on variability of the vertical ground reaction force

Keywords: Variabilité, Nombre de Froude, Pied amortisseur, Pied propulseur, Forces de réaction au sol Variability, Froud's number, Propulsor foot, Loading foot, Ground reaction force

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

Introduction

Pour le clinicien, la lecture des enregistrements des paramètres cinétiques permet très souvent l'identification des causes de troubles moteurs affectant la marche du patient. Mais de nombreuses études rapportent des coefficients de variation (CoV) élevés [START_REF] Giakas | Comparison of gait patterns between healthy and scoliotic patients using time and frequency domain analysis of groundreactionforces[END_REF][START_REF] Masani | Variability of ground reaction forces during treadmill walking[END_REF][START_REF] Nilsson | Ground reaction force at different speeds of human walking and running[END_REF][START_REF] White | The variability of force platform data in normal and cerebral palsy gait[END_REF] des paramètres de la marche qui peuvent dans certains cas masquer le facteur pathologique derrière une apparente variété des comportements [START_REF] Borzelli | Inter-and intra-individual variability of ground reaction forces during sit-to-stand with principal component analysis[END_REF][START_REF] Grabiner | Age-related changes in spatial and temporal gait variables[END_REF][START_REF] Kelly | The kinematic patterns of toe-walkers[END_REF][START_REF] Mcpoil | The variability of the centre of pressure pattern[END_REF]. Cette grande variabilité peut être attribuée à l'hétérogénéité de l'échantillon (âge, sexe, forme du pied, pathologie etc.). Néanmoins, les paramètres cinétiques dépendent directement de la vitesse de déplacement du sujet et ce facteur peut être contrôlé de façon simple. De nombreuses études sont réalisées à vitesse spontanée (V Spon ) faute de temps, ou pour des raisons écologiques largement argumentées. Toutefois la V Spon fluctue en fonction de nombreux facteurs comme le genre [START_REF] Finley | Locomotive characteristics of urban pedestrians[END_REF][START_REF] Molen | Fundamental characteristics of human gait in relation to sex and location[END_REF], l'âge [START_REF] Sengler | Coût énergétique de la marche du sujet âgé sain[END_REF], la masse [START_REF] Herfenroeder | Predicting maximum oxygen up-take in adolescents[END_REF], la longueur des segments inférieurs du sujet [START_REF] Philip | Biomechanical considerations for economical walking and running[END_REF], l'environnement [START_REF] Yamasaki | Human walking: With reference to step length, cadence, speed and energy expenditure[END_REF], la maturation du système musculaire et articulaire [2], la condition physique du sujet [START_REF] Cunninggham | Determinants of self selected walking pace accross ages 19 to 66[END_REF][START_REF] Hoshikawa | Review of our researches 1970-1973[END_REF] ou encore selon les biorythmes [START_REF] Sato | Gait patterns of Japanese pedestrians[END_REF]. L'utilisation de la V Spon lors de test de marche représente donc une source de variabilité des données enregistrées.

Des recherches montrent que l'utilisation de vitesses similaires (V sim = N Fr .(gl) 0,5 ) déterminée à partir d'une fraction du nombre de Froude (N Fr ), de la hauteur trochanter-sol (l) du sujet et de l'accélération gravitationnelle (g) [1,[START_REF] Cavanagh | Stride length in distance running: velocity, body dimensions, and added mass effects[END_REF][START_REF] Donelan | The effect of reduced gravity on the kinematics of human walking: a test of the dynamic similarity hypothesis for locomotion[END_REF][START_REF] Kram | Effect of reduced gravity on the preferred walk-run transition speed[END_REF][START_REF] Minetti | Pygmy locomotion[END_REF][START_REF] Minetti | Invariant aspects of human locomotion in different gravitational environments[END_REF], permet de réduire la variabilité interindividuelle. Pour établir des conditions dynamiques similaires lors de la marche, Moretto et al. [START_REF] Moretto | 9th biennal conference of the canadian society for biomechanics, Burnaby, British Columbia , Canada. The use of Froude number to normalize human gait[END_REF] proposent de définir des vitesses de marche à partir de fractions du Nfr et de la longueur segmentaire des sujets. Ils concluent que l'utilisation de fractions de Nfr permet de placer les sujets dans des conditions dynamiques similaires et de mettre en évidence de meilleures similitudes cinématiques et cinétiques entre des sujets hétérogènes [START_REF] Borzelli | Inter-and intra-individual variability of ground reaction forces during sit-to-stand with principal component analysis[END_REF][START_REF] Molen | Fundamental characteristics of human gait in relation to sex and location[END_REF]. Ces meilleures similitudes s'accompagnent d'une réduction de la variabilité interindividuelle [START_REF] Bisiaux | Détermination d'un seuil de pression plantaire attendu : Utilisation de l'approche a-dimensionnelle pour réduire la variabilité des pressions plantaires[END_REF] et représentent une première partie de l'approche méthodologique proposée pour cette étude.

La grande variabilité des paramètres cinétiques résulte certainement aussi du classement des appuis au sol selon la latéralité et non la fonctionnalité des pieds [START_REF] Berges | The problem of pathologic laterality[END_REF][START_REF] Fecteau | Influence de la latéralité et du sexe sur la bascule des épaules et la tendance varisante des pieds[END_REF][START_REF] Gagey | Pied pilier et pied moteur[END_REF][START_REF] Guillodo | Latéralité podale et détente verticale chez le footballeur de haut niveau[END_REF][START_REF] Hecaen | Les gauchers, prévalence manuelle et dominance cérébrale[END_REF][START_REF] Viel | Analyse tridimensionnelle de la marche et de l'appui du pied au sol[END_REF]. Pour cette étude, nous retenons la définition de Viel et al. [START_REF] Viel | Analyse tridimensionnelle de la marche et de l'appui du pied au sol[END_REF] qui différencient la fonction des pieds selon des indices précis apparaissant sur le signal de la force de réaction normale : F(z). L'un des appuis est qualifié d'amortisseur car la F(z) présente un décrochement initial qui signe l'amortissement de l'onde de choc tandis que l'autre est qualifié de propulseur car l'amortissement est absent et que le second pic apparaissant lors de la phase de propulsion est plus élevé. Un classement fonctionnel des appuis plantaires devrait permettre de regrouper les signaux (F(z)) par fonction et de diminuer la variabilité interindividuelle.

L'objet de cette étude est de vérifier que la détermination de vitesses similaires et le classement fonctionnel des appuis plantaires permettent de diminuer significativement la variabilité intersujet de la force de réaction normale (F(z)).

Matériel et méthode

Population

Sept sujets volontaires de sexe masculin, valides et en bonne condition physique ont participé volontairement à l'étude (âge : 21,71 ans ± 1,7 ; taille : 1,75 m ± 0,07 ; masse : 74,85 kg ± 6,91 ; hauteur trochanter-sol : 0,90 m ± 0,03).

Protocole de l'étude

La vitesse spontanée (Vspon i ) des sujets est d'abord identifiée lors de déplacements dans un couloir de 15 m. Pour l'enregistrement des données, les tests sont tous réalisés à vitesses imposées. Cette précaution expérimentale permet de placer les sujets dans les mêmes conditions de contraintes spatio-temporelles et de limiter au facteur « vitesse » les effets sur la variable dépendante (F(z)). Les Vspon i permettent de calculer une fraction moyenne du nombre de Froude (

Nf ¯r = 1 N ͚ Vspon i ͌g.l i
; avec l i longueur du membre inférieur de chacun des sept sujets et N le nombre de sujets). Les vitesses similaires sont calculées à partir de la moyenne des fractions du nombre de Froude (V sim = N Fr ¯.͌g.l i ). Ainsi, les vitesses spontanées et similaires sont identiques en moyenne

͑ V ¯Spon i = V ¯Sim ͒.
Les sujets réalisent deux tests de marche à vitesses imposées Vspon i et V Sim .

Protocole du test de marche

Les tests de marche permettant l'acquisition des données sont effectués sur un chemin de marche balisé de repères au sol espacés de 1 m et intégrant deux plateformes de force de type Logabex, (Société Giat-Industrie, Toulouse, France) distantes de 5 mm l'une de l'autre. Les systèmes d'acquisition sont calibrés avant chaque test. La fréquence d'acquisition des plateformes de force est de 200 Hz. Les vitesses de marche sont imposées grâce à des signaux sonores émis à intervalles réguliers signalant au sujet qu'il doit être à hauteur des marques disposées au sol. L'organisation en amplitude et en fréquence des pas est laissée libre au sujet qui dispose de 20 minutes de familiarisation à la vitesse imposée [START_REF] Charteris | The process of habituation to treadmill walking[END_REF][START_REF] Wall | A kinematics study of long-term habitude to treadmill walking[END_REF]. Pour les enregistrements, les marques au sol sont retirées. Pour chaque sujet, trois enregistrements sont retenus et seul celui dont la vitesse horizontale mesurée au Cg est la plus proche de la vitesse imposée est conservé.

Paramètres retenus

L'analyse cinétique est limitée aux forces de réactions normales F(z). Les signaux enregistrés sont rapportés au pourcentage du temps de contact pour permettre de calculer la F(z) moyenne dans les deux conditions de marche (V Spon et V Sim ). Dans un premier temps, la F(z) moyenne du groupe est calculée à partir de deux appuis consécutifs des sept sujets. Les déviations standards puis les coefficients de variation (CoV) sont calculés pour chaque pourcentage du temps de contact.

Dans un deuxième temps, les enregistrements des F(z) obtenus à vitesses similaires sont classés en fonction de critères décrivant un pied amortisseur et propulseur. Les moyennes, déviations standards et CoV sont calculés comme précédemment pour chaque pourcentage du temps de contact.

Les CoV des F(z) des pieds amortisseurs et propulseurs sont comparés à ceux de la F(z) moyenne du groupe évoluant à V Sim . Les coefficients de variation de points précis comme les pics de mise en charge : F( Z1 ) et de propulsion : F( Z2 ) sont également retenus.

Statistiques

Cent vingt-deux points sont obtenus pour décrire la F(z) après normalisation de 0 à 100 % du temps d'appui. La moyenne, la déviation standard (DS) et le coefficient de variation (CoV) de la F(z) du groupe sont calculés à chaque pourcentage, à partir des données des sept sujets.

Un premier test « t » permet de comparer les 122 CoV à V Sim aux 122 CoV à V Spon .

Un second test « t » permet de comparer les 122 CoV à V Sim (regroupant les deux appuis gauche et droit) aux 122 CoV des pieds amortisseurs et propulseurs obtenus à V Sim .

Le seuil de signification est fixé à p < 0,05 pour toutes les analyses.

Résultats

La Fig. 1 présente les surfaces définies par la déviation standard des forces normales des sept sujets lors du contact du pied au sol dans les deux conditions de marche (V Sim et V Spon ). Le coefficient de variation moyen de la force normale F(z) est de 12,67 % à V Spon et diminue significativement à 8,25 % à V Sim (p < 0,01). À V Spon , le CoV des pics F( Z1 ) et F( Z2 ) atteignent respectivement 9,23 et de 8,12 %. Ces mêmes CoV diminuent significativement à V Sim pour atteindre 9,08 % F( Z1 ) et 7,26 % F( Z2 ). Ces premiers résultats indiquent que la variabilité du groupe est plus élevée à V Spon .

Les Figs. 2 et 3 comparent la déviation standard des forces normales des sept sujets marchant à V Sim (les deux pieds confondus) à la déviation standard des F(z) des pieds amortisseurs (Fig. 2.) puis propulseurs (Fig. 3.). Le CoV moyen de la F(z) des pieds amortisseurs est de 6,28 ; et de 7,05 % pour les pieds propulseurs. Le CoV des pics F( Z1 ) et F( Z2 ) atteint respectivement 8,22 et 6,11 % pour les pieds amortisseurs (Fig. 2). Ces mêmes CoV sont de 7,37 % (F Z1 ) et 6,54 % (F Z2 ) pour les pieds identifiés comme propulseurs (Fig. 3.). Ces autres résultats indiquent que le classement fonctionnel des pieds associé à l'utilisation de la V Sim permet encore de réduire la variabilité du groupe.

Discussion

La variabilité des paramètres cinétiques a déjà fait l'objet de nombreuses études visant à caractériser le patron de marche des sujets selon leur genre [START_REF] Crowe | Characterisation of gait of young adult females by means of body center of oscillations derived from ground reaction forces[END_REF] et leur tranche d'âge [START_REF] Watelain | Gait pattern classification of healhy elderly men based on biomechanical data[END_REF]. L'étude de la variabilité des paramètres locomoteurs s'avère précieuse dans le champ médical car elle peut permettre d'identifier des changements annonciateurs du risque de chute [START_REF] Hausdorff | Gait variability and fall risk in community-living older adults: a 1-year prospective study[END_REF] ou l'évaluation de l'efficacité de traitements (médicamenteux et/ou chirurgicaux) chez des patients présentant des troubles moteurs [START_REF] Giakas | Comparison of gait patterns between healthy and scoliotic patients using time and frequency domain analysis of groundreactionforces[END_REF][START_REF] White | The variability of force platform data in normal and cerebral palsy gait[END_REF]. Mais ces études sont confrontées à une trop grande variabilité des données de références qui masque le facteur pathologique et rendent certains paramètres inutilisables à des fins de suivis cliniques [START_REF] Kelly | The kinematic patterns of toe-walkers[END_REF][START_REF] Mcpoil | The variability of the centre of pressure pattern[END_REF]. Notre démarche s'inscrit dans ce cadre et propose une alternative. Nous montrons qu'il est possible de réduire la variabilité interindividuelle d'un groupe de sujets en leur imposant de se déplacer à vitesses similaires ; et qu'un classement postacquisitions des données cinétiques selon la fonction du pied permet encore de réduire significativement la variabilité de l'échantillon.

Nos résultats montrent que la détermination de vitesses similaires permet de réduire le CoV moyen de la F(z) de 5 % et d'atteindre des valeurs de 8,25 % en moyenne sur l'ensemble des mesures effectuées lors du contact du pied au sol. De nombreuses études portant sur les paramètres cinétiques enregistrent des CoV élevés mais ne font pas la distinction entre le pied amortisseur et propulseur et proposent au mieux une simple distinction entre l'appui droit et gauche [START_REF] Kirkpatrick | Is the objective assessment of cerebral palsy gait reproducible?[END_REF][START_REF] Masani | Variability of ground reaction forces during treadmill walking[END_REF][START_REF] Nilsson | Ground reaction force at different speeds of human walking and running[END_REF]. Viel et al. [START_REF] Viel | Analyse tridimensionnelle de la marche et de l'appui du pied au sol[END_REF] identifient deux fonctions différentes pour les pieds à partir d'une analyse descriptive des signaux de force normale de réaction au sol (F(z)). Dans notre étude, le classement des données selon la définition de Viel et al. [START_REF] Viel | Analyse tridimensionnelle de la marche et de l'appui du pied au sol[END_REF] permet de réduire encore le CoV de la F(z) d'un groupe de sept sujets. Aux pics (F(z 1 ) et F(z 2 )), ce classement permet d'atteindre les CoV les plus bas : 7,37 % pour la F(z 1 ) des pieds propulseurs et 6,54 % pour la F(z 2 ) des pieds amortisseurs. La littérature fait état de CoV toujours plus élevés. Certains enregistrements de la F(z 1 ) présente des CoV de 19,43 % obligeant les cliniciens à répéter de nombreuses fois les tests de marche afin d'obtenir un CoV moyen plus acceptable : 12,5 % [START_REF] Kirkpatrick | Is the objective assessment of cerebral palsy gait reproducible?[END_REF]. Une étude menée avec une population de 18 enfants marchant à vitesse spontanée (3-4 ans) présente un CoV de 13,6 % pour la F(z 1 ) alors que les auteurs n'ont étudié que le côté droit [START_REF] Greer | Dynamics of children's gait[END_REF]. Selon White et al. [START_REF] White | The variability of force platform data in normal and cerebral palsy gait[END_REF], seule la Fz 1 d'une population témoin valide présente une assez faible variabilité pour servir à identifier le patron pathologique d'enfants infirmes moteur cérébraux. Cette étude différencie l'appui droit (D) ou gauche (G) dans la présentation de ses résultats. Le CoV du pic F(z 1 ) des 15 jeunes sujets sains (6-11 ans) est de 9,82 % à gauche et de 10,03 % à droite ; alors que le CoV du pic F(z 2 ) atteint 7,05 % à gauche et 8,06 % à droite. Comparés à la littérature, il apparaît que la méthodologie de notre étude permet de diminuer les variations de la F(z) liées d'une part aux vitesses de marche et d'autre part aux critères fonctionnels que la force de réaction normale (F(z)) semble respecter. D'autres études rapportent des valeurs de coefficients de variation observées d'un pas à l'autre. Il s'agit de coefficients de variation intra-individuels habituellement utilisés pour quantifier la reproductibilité d'un comportement ou tout problème pouvant le perturber (Équilibre etc.). Winter [START_REF] Winter | Kinematic and kinetic patterns in human gait: variability and compensating effects[END_REF] propose une étude de reproductibilité s'appuyant sur un sujet unique effectuant plusieurs tests de marche à vitesse spontanée. Lors de plusieurs tests consécutifs, le CoV de Fz 1 des appuis droits de ce sujet atteint 7 %. Avec un CoV de Fz 1 égal à 7,37 %, notre étude rapporte des variations interindividuelles d'un groupe de sept sujets très proches de la variabilité intra-individuelle propre à un seul sujet. Ainsi, il semble que la méthodologie proposée pour notre étude permette de définir les caractéristiques cinétiques d'un sujet à partir de celles d'un groupe et que la tolérance (variabilité) des paramètres ainsi définis n'excède pas celle observée d'un pas à l'autre chez le sujet valide.

Les résultats de cette étude permettent de proposer une méthodologie utile à la détermination de variables de références débarrassée d'une variabilité gênante dans le cadre clinique. La détermination de vitesses similaires permet d'évincer la variabilité liée à l'utilisation de la vitesse spontanée et aux différences anthropométriques des sujets. Les connaissances sur le comportement asymétrique de la dynamique du pied permettent d'affiner cette approche et de diminuer la variabilité des paramètres cinétiques liée à des différences comportementales et/ou fonctionnelles. Le cumul de ces deux approches permet de réduire la variabilité des données cinétiques d'un échantillon à la variabilité habituellement propre au sujet sain.

Références

Fig. 1 .

 1 Fig. 1. Représentation de la déviation standard de la force normale lors de tests de marche à vitesse spontanée (V spon ) en blanc puis à vitesse similaire (V sim ) en noir.

Fig. 2 .

 2 Fig. 2. Représentation de la déviation standard de la force normale F(z) d'un groupe de sept sujets marchant à vitesse similaire (V sim ). La déviation standard calculée à partir des enregistrements des deux pieds figure en blanc, celle calculée à partir des pieds amortisseurs figure en gris.

[ 1 ]

 1 Alexander RMCN, Jayes AS. A Dynamic Similarity Hypothesis for the Gaits of quadrupedal Mammals. J Zoology (Lond) 1983;210:135-52. [2] Beck RJ, Andriacchi TP, Kuo KN, Fermier RW, Galante JO. Changes in the gait patterns of growing children. J Bone Joint Surg 1981;63A: 1452-7.

Fig. 3 .

 3 Fig. 3. Représentation de la déviation standard de la force normale F(z) d'un groupe de sept sujets marchant à vitesse similaire (V sim ). La déviation standard calculée à partir des enregistrements des deux pieds figure en blanc, celle calculée à partir des pieds propulseurs figure en gris.


Hakim Benkirane
email: hakim.benkirane@centralesupelec.fr

Maria Vakalopoulou
email: maria.vakalopoulou@centralesupelec.fr

Stergios

Ingrid-Judith Garberis
email: ingrid-judith.garberis@gustaveroussy.fr

Stefan Michiels
email: stefan.michiels@gustaveroussy.fr

S Christodoulidis

I.-J Garberis

Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis

Keywords: Histopathology, Hypergraphs, Survival Analysis, Representation Learning, Interpretability

The emergence of deep learning in the medical field has popularized the development of models to predict survival outcomes from histopathology images in precision oncology. Graph-based formalism has opened interesting perspectives for generating informative representations, as they can be context-aware and model local and global topological structures in the tumor's microenvironment. However, the critical issue in using graph representations lies in their generalizability. They can suffer from overfitting due to their large sizes or high discrepancies between nodes due to random sampling from WSI. In addition, standard graph formulations are limited to pairwise interactions, which can sometimes fail to represent the reality observed in histopathology and hinder the interpretability of those interactions. In this work, we present Hyper-AdaC, an adaptive clustering-based hypergraph representation to model highorder correlations among different regions of the WSIs while being compact enough to help graph neural networks generalize in the case of survival prediction. We evaluate our approach on 5 different public available cancer datasets. Our method outperforms most state-of-the-art graph-based methods for survival prediction with WSIs, creating a more efficient and robust alternative to other graph representations. Moreover, due to our formulation, attention maps are depicted at different resolutions depending on the tissue characteristics of each WSI. The code is available at: https://github. com/HakimBenkirane/Hyper-adaC.

Introduction

Computational Pathology has rapidly developed over the past decade due to the development of whole-slide image (WSI) scanners that digitize histopathology, immunohistochemistry, or cytology slides into highresolution images [START_REF]Pathologist-level interpretable whole-slide cancer diagnosis with deep learning[END_REF][START_REF]Fast scannet: Fast and dense analysis of multi-gigapixel wholeslide images for cancer metastasis detection[END_REF]. Indeed, their use for cancer diagnosis and prognosis has increased, relying on massive progress in gigapixel image analysis with statistical learning.

In this perspective, WSIs have been used for numerous prediction tasks, one of the most challenging being survival prediction (Zhu et al., 2016(Zhu et al., , 2017)), which models the survival function until the occurrence of a particular event (e.g., death, relapse). For this purpose, multiple approaches were adopted in the literature to deal with the challenge of processing large images to obtain survival models.

One of the most popular methods, Multiple Instance Learning (MIL), performs weakly-supervised learning on WSIs by extracting small image patches as independent instances and aggregating them in bags of unordered instances [START_REF] Pj Sudharshan | Multiple instance learning for histopathological breast cancer image classification[END_REF]. However, even if this approach has performed well for some tasks like cancer grading (Zhou et al., 2019) and subtyping (Anand et al., 2020), its adaptation to survival prediction is not straightforward as it should rely on local, as well as global-level characteristics of the WSI. Standard MIL approaches only consider bags of instances as independent and thus do not incorporate context information, failing to learn general associations in the tumor or its environment to assess patient mortality risk [START_REF] Saltz | Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images[END_REF]. To alleviate this issue, graph representations have known a growing interest as they can embed global interactions between patches in a network that allows communication between them (Adnan et al., 2020;Li et al., 2018;Chen et al., 2021). However, ex-isting works on this subject either consider huge graphs that can hinder Graph Neural Networks' (GNNs) generalizability [START_REF] Yehudai | From local structures to size generalization in graph neural networks[END_REF] or involve sampling, which covers only part of the WSI and neglects lots of pathological tissues. Moreover, graph representations being limited to pairwise associations can sometimes fail to model local structures when there are significant discrepancies between instances (Garg et al., 2020).

In this work, we propose a novel hypergraph representation (Hyper-AdaC) based on adaptive clustering [START_REF] Müllner | Modern hierarchical, agglomerative clustering algorithms[END_REF] for accurate survival prediction. The contribution of this model to the representation of gigapixel WSIs is threefold. First, we deal with the limitations of the graph size by using hierarchical clustering based on both morphological similarity and spatial proximity to summarize WSIs information efficiently. This method is easy to adapt and does not rely on constraining hypotheses, like the number of clusters to consider. This method can also be seen as a way to efficiently bypass the limitations of random patch sampling as it filters the most relevant patches from the WSI, resulting in less loss of information. Secondly, we overcome the constraints induced by the local structures thanks to our hypergraph representations of those clustered instances depending on morphological and spatial features. Finally, our method succeeds at generating high-resolution attention maps that adapt to the morphology of the tissue thanks to agglomerative clustering, providing more insights into specific elements of the WSI like immunological response and directly linking it to survival. To benchmark the performance of our method, we quantitatively evaluate it on 5 different cancer datasets from The Cancer Genome Atlas (TCGA) and compare it to several state-ofthe-art methods for survival outcome prediction, illustrating its better performance.

Related Work

Several methods have been developed for survival analysis in computational pathology, mainly using MIL approaches [START_REF] Mobadersany | Predicting cancer outcomes from histology and genomics using convolutional networks[END_REF][START_REF] Lu | Data-efficient and weakly supervised computational pathology on whole-slide images[END_REF][START_REF] Yao | Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks[END_REF]. Those methods rely on sampling a limited number of patches to deal with the large size of WSIs, suffering from coverage and generalization issues, as shown in multiple studies (Ciga et al., 2021;Di et al., 2022). To overcome those limitations, multiple approaches have been proposed, in which patches are grouped using clustering algorithms such as K-Means algorithm before sampling (Zhu et al., 2017;[START_REF] Yao | Whole slide images based cancer survival prediction using attention guided deep multiple instance learning networks[END_REF] to identify morphological phenotypes in WSIs and reduce the dimensionality.

Recently, different studies (Chen et al., 2021;[START_REF] Shao | Transmil: Transformer based correlated multiple instance learning for whole slide image classification[END_REF] started taking an interest in correlations between small instances of gigapixel images, which is neglected by the initial hypothesis of the MIL approach (Carbonneau et al., 2018). Following this idea, graph-based representations have become an excellent alternative for robust context-aware representations (Li et al., 2018;[START_REF] Zheng | A deep learning based graphtransformer for whole slide image classification[END_REF]. To alleviate the issue of limited sampling, (Chen et al., 2021) proposed a way to model interactions between features of adjacent patches using a k-nearest neighbors (k-nn) graph. As classical graph representations can only model pairwise interactions between image patches, new methods are considering broader representations by trying to lift the i.i.d. hypothesis from standard MIL [START_REF] Shao | Transmil: Transformer based correlated multiple instance learning for whole slide image classification[END_REF], or by switching to hypergraph representations (Di et al., 2020(Di et al., , 2022)). Contrary to these methods, Hyper-AdaC relies on hypergraphs to capture interesting spatial and morphological features from WSIs, harvesting informative global and local WSI dependencies for survival models.

Method

Within the scope of this study, we design, implement and evaluate a hypergraph-based survival network for survival outcome prediction. For 1 ≤ i ≤ N , let us denote by W i , the WSI of a patient, T i its event time, and C i its censoring status. The goal of this study is to build and train a survival neural network S and to determine a function ϕ that maps the WSI into a hypergraph representation, such that S(ϕ(W i ), Θ) = r i , with Θ a set of trainable parameters and r i the hazard rate of the time-to-event outcome of interest.

Hypergraph Construction

We denote by G i a hypergraph representation of W i such that ϕ(W i ) = G i . Before the construction of the hypergraph, we first performed automatic tissue and background separation using [START_REF] Lu | Data-efficient and weakly supervised computational pathology on whole-slide images[END_REF]. We then extract non-overlapping 256 × 256 patches at 20× magnification that are fed to a ResNet-18 trained using the same contrastive learning strategy (SimCLR) as in (Ciga et al., 2022) that represents a 1024-dimensional feature vector h ∈ R 1024 each patch. The set of (h j ) 1≤j≤np associated to a W i with n p patches will be stacked into a feature matrix X i ∈ R np×1024 . Each patch x j is characterized by its ResNet-18 feature representation h j that embeds the morphological properties of the patch and a set of coordinates g j = (g x,j , g y,j ) that represents the spatial position of the center of the patch. Since the hypergraph should not be too large for the generalizability of the GNN [START_REF] Yehudai | From local structures to size generalization in graph neural networks[END_REF], we perform a first step of Adaptive Agglomerative Clustering on the different patches. For that, we compute two similarity matrices tial proximity metric. Following the ideas presented in [START_REF] Lu | Slidegraph+: Whole slide image level graphs to predict her2 status in breast cancer[END_REF], we use the kernel κ(x i , x j ) = κ h (h i , h j )κ g (g i , g j ) as a similarity kernel for agglomerative clustering. This kernel will be computed for each pair of patches from the same WSI. All patches for which similarity will be greater than a threshold δ will be considered to belong to the same cluster C k and merged hierarchically into a single patch representation p k = ( hk , gk ) where hk = 1

K h ∈ R np×np and K g ∈ R np×np such that K h = (κ h (x i , x j )) 1≤i,j≤np and K g = (κ g (x i , x j )) 1≤i,j≤np where κ h (x i , x j ) = e -λ h ||h i -h j || 2 is a morphological similarity metric and κ g (x i , x j ) = e -λg||g i -g j || 2 is a spa-
|C k | j∈C k h j and gk = 1 |C k | j∈C k g j .
Now that we have a reduced set of points P i , a hypergraph denoted by G i =< V i , E i , X i > is constructed. For a single WSI, we consider each clustered patch as a vertex of the hypergraph such that

V i = [p j ] j∈P i . Each hyperedge is associated to the neighbourhood of each node V i . This neighborhood is defined as γ(p j ) = {p k ∈ P i ; κ h (p k , p j ) ≥ δ h },
where δ h is a threshold value to fine-tune. Those hyperedges are indicated by an incidence matrix

H ∈ R |P i |×|E i | such that, h(k, j) = 1 if p j ∈ γ(p k ) 0 else (1)
The interesting aspect about a hypergraph compared to a regular graph is that the neighborhood of each node is depicted as a single hyperedge. This allows us to train our model with fewer parameters and thus decrease the time complexity of the convolution. In addition to this, it creates a community effect that gives more importance to bigger hyperedges, which will represent denser regions of our WSI.

Construction of the Graph

Neural Network

Network's Architecture: The GNN we propose (Fig. 1) consists of a series of hypergraph convolutions and attentions as defined in (Bai et al., 2021), with each layer using a multi-layer perceptron to generate embedding of nodes based on the features of the node itself and its neighbors. Each layer consists of batch normalization and dropout layers to avoid instability during training. We also use the idea introduced in [START_REF] Lu | Slidegraph+: Whole slide image level graphs to predict her2 status in breast cancer[END_REF] of accumulating the feature representations of the convolution layers in the GNN. Those node-level representations are then pooled to generate a graph-level representation. This representation is then fed to a survival network composed of multi-layer perceptrons that predict the hazard rate used for survival outcome prediction.

Network's Loss Function: The entire network is trained using the Cox proportional hazard loss introduced in (Ching et al., 2018), it uses the partial log-likelihood as the cost function, defined as follows:

pl(Θ) = 1 |{i : C i = 1}| i:C i =1 [S(ϕ(W i ), Θ)) -log T i ≥T j exp(S(ϕ(W j ), Θ))] (2)
where ϕ is a neural network modelling the hazard ratio and Θ are the network's parameters. The cost function to train the model is therefore defined by:

L(Θ) = pl(Θ) + λ||Θ|| 2 2 (3)

Experimental Setup

Dataset

For this study, we performed extensive experiments using five different cohorts from The Cancer Genome Atlas (TCGA) detailed in Table 1. We chose those five datasets based on size and censoring rate. On average, each WSI contains approximately 12691 patches at 20× magnification that are then reduced by hierarchical clustering to around 3147 points.

Implementation Details

The architecture of the GNN is constructed using three hypergraph convolution layers of 256 neurons each followed by a three layers survival network of respectively 256, 128, and 64 neurons with ReLU activation that outputs the hazard ratio using a sigmoid activation function in the output layer. The entire architecture is built using fully-connected blocks. For each layer, we use a batch normalization layer to address the problem of internal covariate shift. Also, to avoid overfitting problems, we use dropout with a rate of 0.2. For the graph construction, we select a similarity threshold of 80% with λ h = 3λ g to give more importance to morphological features during the clustering. This choice of hyperparameters has been validated with the experiments presented in Appendix A. To train Hyper-AdaC, we used Adam optimization with a learning rate of 10 -3 with an exponential scheduler, a weight decay of 10 -5 , and 20 epochs. All models were trained using an Nvidia Tesla V100S with 32 GB of memory.

Evaluation

To evaluate Hyper-AdaC, we perform 5fold cross-validation for each cancer type. We compute the concordance index (Cindex) [START_REF] Uno | On the c-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data[END_REF]) across all the validation folds to measure the predictive performance of the method. We also compare our proposed method to multiple other stateof-the-art methods for the same task. For all our experiments and for a fair comparison, we used the same survival loss function, the exact SimCLR feature embeddings, and training hyperparameters for all methods. The basis of comparison we consider is the following: • knn-hypergraph (Di et al., 2020): knearest neighbors hypergraph construction using sampling of patches. We use the same pipeline as Hyper-AdaC.

Results & Discussion

When comparing our approach to other methods, we note that Hyper-AdaC outperforms most of these in terms of C-index (Table 2 and Figure 2). In general, our approach outperforms by at least 1.6% the overall C-index on all datasets and, more specifically, in most of the individual datasets (except for BLCA and GBMLGG).

When comparing with the results of Deep-GraphSurv, we can immediately identify the limitations of sampling patches from WSIs, as this method is the weakest in these comparisons. It only covers around 20% of the WSI and fails to train GNNs due to significant discrepancies between sampled patches. We also witness a clear improvement by adding context information, as almost all the graph representations outperform the multiple-instance learning method DeepAt-tnMISL.

Except for the superiority in performance, our method reports better robustness, high- representations and fewer parameters. Finally, when we compare the adaptive clustering to k-means through C.DeepGraphSurv, we observe that the adaptive property of the hierarchical clustering compared to K-means provides us with more information as it sums up quite well the discrepancies in the tissue without having to include the number of clusters as it can be adapted one slide to another, depending on the depicting texture.

Our experiments indicate lower performances on BLCA and GBMLGG datasets.

To analyze this point more, we performed some additional experiments detailed in Appendix B. In fact, for the BLCA the number of elements conserved after the agglomerative clustering is still too high, resulting in a bigger graph and, therefore weaker performances. This reasoning can be inverted for GBMLGG for which agglomerative clustering conserves only very little information, meaning that the morphological structure of this particular cancer is more homogeneous than others, and we lose a lot of information as this clustering disregards local variability. To alleviate these issues, datasetspecific hyperparameter tuning can be performed (while we originally preferred common hyperparameters for all datasets to enhance the generalizability of our model). In practice, we add more constraints on the graph construction for BLCA dataset by setting the similarity threshold δ to 85% and relax them on the GBMLGG dataset where δ was set to 70%. We also set λ h = 2λ g for the GBMLGG dataset to focus less on morphological properties since the tissue is generally highly homogeneous and the clustering will be more uniform across the WSI. By doing this, we can witness a spike in performance as the C-index for our method in the BLCA dataset gets to 0.619 ± 0.037 and to 0.812 ± 0.025 for the GBMLGG dataset, similar to the state-of-the-art results.

Examples of WSIs annotated by a pathologist and the corresponding model attention heatmaps are presented in Figure 3. We can observe that our model succeeds in discriminating zones based on their morphological and spatial features. Agglomerative clustering, by being able to adapt the number of clusters to the WSI, enables us to output attention maps that adapt well to the morphology of the slide, focusing on more relevant information and thus providing more precise information on important local regions. Moreover, the tumoral zone indicated by the pathologist in the first column of Figure 3 matches the regions where attention is at its highest. In addition, the model focused on dense inflammatory cell regions for patients with low predicted risk, which are signs of good immunity response. The multiple purple dots highlight those inflammatory cell regions in high-attention regions for the two low-risk patients (third column of Figure 3 showing a zoom of the attended patches). For high-risk patients, regions of tumor cells contain more attention due to their density. This is where the hypergraph construction presents its advantage: it creates a community behavior with hyperedges. It can assess the density of small regions through their weights. Thanks to message passing between hyperedges, areas with more significant communities have a more decisive influence on survival prediction.

Conclusion

Computational Pathology has made tremendous progress when dealing with WSI global representations. However, many approaches still suffer from generalizability problems and do not properly model the whole tumor's microenvironment. In this work, we have introduced a compact hypergraph representation, Hyper-AdaC, that solves the size issue of graphs for GNNs without losing important and patient-specific information from wholeslide images. We showed through our experimentation that Hyper-AdaC creates an efficient and robust representation for training GNNs and allows broader associations between patches. In the future, we aim to explore the efficiency of this representation in the promising context of multi-modal learning for survival outcome prediction, combining WSIs with multi-omics and clinical data.

Table 3: Average number of nodes after the hierarchical clustering step for each dataset.

Due to our selection criteria, the GBMLGG dataset had a significantly lower number of nodes (as the ratio is also lower, it may indicate higher homogeneity among tissues), which may explain the lower performance with respect to the other cancer types. In this study, we selected the same hyperparameters for all the cancer types to prove the generalizability of our method, outperforming the other state-of-theart methods. Some specific hyperparameters tuning for the GBMLGG and BLCA may resolve this issue. phological features can hinder the accuracy of our survival predictions, as sometimes the homogeneity of specific tissues can make the filtering biased and overlook chunks of WSIs that may hold vital information.

Figure 1 :

 1 Figure 1: Overview of the Hyper-AdaC pipeline. We first perform a feature extraction step using SimCLR trained on TCGA images. Then the features are processed into a clustering step that performs agglomerative clustering based on a similarity metric κ. The clustered features serve as nodes to construct a hypergraph fed to a Graph Neural Network (GNN) optimized by hazard-based loss function. The GNN is composed of multiple hypergraph convolutions and attention modules, followed by an FC-block (Fully-Connected block) and a global pooling layer.

Figure 2 :

 2 Figure 2: Survival prediction performances across all datasets. They are computed by taking the C-indices on all folds of the evaluation, for all datasets.

Figure 5 :

 5 Figure 5: Ablation study for λ h λg used in the hierarchical clustering step. We evaluate for each hyperparameter the 5-fold cross-validated C-index on the overall 5 TCGA datasets.

Table 1 :

 1 A detailed description of the cohorts used for the study. The table includes the different cancer types, as well as the number of patients and WSIs per type.

	Cancer Type	# of Patients # of WSIs
	Bladder Urothelial Carcinoma (BLCA)	437	457
	Breast Invasive Carcinoma (BRCA)	1022	1133
	Glioblastoma & Lower Grade Glioma (GBMLGG)	1011	1704
	Lung Adenocarcinoma (LUAD)	515	541
	Ulterine Corpus Endometrial Carcinoma (UCEC)	538	566

Table 2 :

 2 Survival prediction of state-of-the-art methods using the concordance index (Cindex) on 5 TCGA cohorts: Bladder Urothelial Carcinoma (BLCA), Breast Invasive Carcinoma (BRCA), Glioblastoma & Lower Grade Glioma (GBMLGG), Lung Adenocarcinoma (LUAD) and Ulterine Corpus Endometrial Carcinoma (UCEC).

Acknowledgments

The project is supported by the Public Health graduate school of Paris-Saclay University and by the Prism project, funded by the Agence Nationale de la Recherche under grant number ANR-18-IBHU-0002. The Data has been made available by the TCGA research network.

Appendix A. Patch Clustering

We compute the average number of elements remaining after the hierarchical clustering step for each dataset separately, the results along with the ratio between initial and filtered patches are represented in Table 3. We observe that, in general, approximately 14% of the WSI is used (see 3), and as shown in Figure 3, those elements are well spread across the WSI. However, we can see that both BLCA and GBMLGG datasets behave differently from the others. For BLCA, the ratio of remaining elements over the total number of patches is higher than all the other datasets, whereas for GBMLGG it is the opposite. Our method does not perform well for those particular test cases.

Appendix B. Ablation Studies

We perform an ablation study on the different graph hyperparameters to justify our construction choices. In Figure 4, we can see the effect of the similarity threshold δ h on the survival performances. The stricter the constraint, the better the performance, indicating that larger graphs fail at learning generalizable properties. This idea is also supported by the standard deviation across the 5-folds that decreases, suggesting that the model is less robust with larger graphs. A similarity threshold of 80% achieves the peak performance; past that point, the performances start to decrease again because we tend to oversimplify the WSI and start neglecting information.

Figure 5 highlights the relationship between morphological features and geographical properties with respect to the survival prediction performance. We see that, in general, focusing on morphological properties is more beneficial to the performances than spatial properties as they hold more information about the structure of the tissue (including, to a certain extent, spatial information because similar patches tend to be close). However, focusing too much on mor-

Appendix C. About Hypergraphs

A Hypergraph is a generalization of the graph structure that extends the interaction between instances to a higher level. To describe this complex relationship where an edge can connect to more than two nodes, we define a hypergraph G = (V, E) as a hypergraph with M vertices and N hyperedges. The hypergraph can then be generated using an incidence matrix H ∈ R N ×M . For each vertex i, the vertex degree is defined as D ii = e∈E H ie and the hyperedge degree will be B ee = i∈V H ie .

C.1. Hypergraph Convolution

This hypergraph can be associated to a feature matrix X ∈ R N ×F where F is the feature dimension of one node. In the context of our study, this node feature will represent the aggregated Resnet-18 features of one cluster. A step of this convolution is defined in (Bai et al., 2021) as follows:

X (l+1) = σ(D -1 2 HWB -1 H T D -1 2 X (l) P) (4) where W is the weight matrix, σ a nonlinear transformation and P is the weight matrix between layer l and l+1.

C.2. Hypergraph Attention

To build the attention visualization, we use an attention mechanism for hypergraphs described in Bai et al. (2021) as:

exp(σ(sim(x i P, x j P)))

k∈N i exp(σ(sim(x i P, x k P)))

(5)

where the similarity function computes similarity between two vertices as follows:

where a is a weight vector and [.||.] denotes concatenation.


barrier and immune function

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.

• The profile of HM bacterial SynComs reflected the coexistence of specific bacterial profiles that comprise the HM microbiota, leading to its role in driving gut homeostasis

• The functional properties of the HM bacterial community relied on a combination of strain-specific features rather than on the taxonomic composition itself

 Conclusion

Questions & Answers


A post-normal approach

p. 18 https://shorturl.at/dijs9

Toward transformative sciences

"a specific type of science that does not only observe and describe societal transformation processes, but rather initiates and catalyses them. Transformative science aims to improve our understanding of transformation processes and to simultaneously increase societal capacity to reflect on them", • http://www.commod.org

Co-designing… = modeling what ?

• socio-environmental systems & their dynamics

○ "where we live, what we do in/with our environment, how it changes and may adapt"

• science-policy-society co-design procedures

○ "what-when-how we should think and decide together"

• values, principles, social justice orientations

○ "what makes us acting, how should we share"

• strategies and action plans, feasible and efficient

○ " what-when-where we should act/launch/make together"

• co-monitoring, evaluation and adaptive steering protocols

○

  Schneidewind U., M. Singer-Brodowski, K. Augenstein, F. Stelzer, 2016, Pledge for a Transformative Science: A Conceptual Framework. Wuppertal Papers No. 191. Wuppertal Institut, p. 6. + Action-Research, Intervention Research cf. FutureEarth France hub meeting june 28-29, 2023 → " Sustainability sciences and (in)actions: rethinking our practices" Impact pathway assessment • Change posture ○ Take the transformation due to knowledge as the topic ○ Or the knowledge on the transformation challenges • Reckon extended roles of stakeholders in / by research ○ To be specified. To be agreed. See below. ○ Accept the transaction cost • Accept to lose full (some) control Co-designing = participatory modeling Participatory modeling as co-construction of models with non-scientists for the needs of: • knowledge structuring and discovery • social transformation Our challenge → which meta-models / methods ? Voinov A., Jenni K., Gray S., Kolagani N., Glynn P., Bommel P., Prell C., Zellner M., Paolisso M., Jordan R., Sterling E., Schmitt Olabisi L., Giabbanelli P.J., Sun Z., Le Page C., Elsawah S., BenDor T., Kubacek K., Laursen B., Jetter A., Basco-Carrera L., Singer A., Young L., Brunacini J., Smajgl A.. 2018. Tools and methods in participatory modeling: Selecting the right tool for the job -Environmental Modelling and Software, 109 : p. 232-255. DOI: 10.1016/j.envsoft.2018.08.028 Building on Companion Modeling • Participatory modeling & simulation coupled • A different "posture" which legitimates all points of view, even when they are contradictory, and reliaise science, society & policy • Developed and structured since 1993 by a group of (French) researchers F. Bousquet, M. Etienne, O. Barreteau, C Le Page, M. Antona (CIRAD, INRA, Cemagref)

○•

  " what-how can we know about where we are and drive" Using the ENCORE framework : External / Normative / Cognitive / Operational / Relationnal / Equity (Ferrand, Le Bars, 2004) • Toward endo-evaluative participation Source: E. Hassenforder 2015 Ferrand, N., & Daniell, K. A. Comment évaluer la contribution de la modélisation participative au développement durable ? Séminaire DDT. Lille, France. • [2015] Hassenforder, E., Pittock, J., Barreteau, O., Daniell, K.A., Ferrand, N., "MEPPP Framework: A framework for monitoring and evaluating participatory planning processes" Environmental management, 2015, 57 (1), 79-96. DOI 10.1007/s00267-015-0599-5. Lien • [2016] Hassenforder, E., Ducrot, R., Ferrand, N., Barreteau, O., Daniell, K.A., Pittock, J. "Four methodological challenges in the monitoring and evaluation

An iterative and continuous process (Barnaud, Promburom, Trebuil, Bousquet, 2008)

	e.g.
	1994	1998	2002	2004
	Knowledge integration in a first (mainly agronomic) MAS model Linked to a GIS • Coupling modeling, L a b . field interactions & social experiments F i e l d Knowledge acquisition: 1st role-playing of a 2nd MAS model similar to Construction the 1 st RPG for collective simulations to support exchanges: new questions Specific farm modules in the 2nd Insertion of the relevant socio-economic MAS model 2 nd role-playing … on-farm surveys on game to validate the survey on key game to validate the agro-bio-physical processes model (simple version). social & economic new MAS model / soil erosion & farmers' Feed back from players: processes re. perennial & to discuss socio-économic strategies socio-economic crops, credit, off-farm the socio-economic
		components &	jobs	issues
		behaviour

-Barreteau O., Antona M., D'Aquino P., Aubert S., Boissau S., Bousquet F., Daré W., Etienne M., Le Page C., Mathevet R., Trébuil G., Weber J.. 2003. Our companion modelling approach -Journal of Artificial Societies and Social Simulation, 6 (2) p. 34 https://shorturl.at/dijs9 Let's co-create your / our thinking… During 2 mins, with you closest neighbor, please consider what / with whom can be (co-created = co-modeled / explored / assessed) for the needs of socio-environmental transformation → be creative and open-minded ! p. 35 https://shorturl.at/dijs9

http://www.commod.org

Je veux améliorer la qualité de l'eau

CHANGEMENTS

Je veux améliorer la qualité de l'eau

Je veux améliorer la qualité de l'eau

Je veux améliorer la qualité de l'eau


Réjanne Le Bivic

Sébastien Ottavi

Pierre Saulet

Pauline Louis

Arnaud Coutu
email: arnaud.coutu@unilasalle.fr

Designing an interactive environment to share education materials

Keywords: Innovation diffusion, open science, e-Learning, Educational innovation

Designing an interactive environment to share educational materials and resources.

Introduction: teaching geomatics at UniLaSalle -Campus of Beauvais

Geomatics is an umbrella term for all disciplines related to collecting, analyzing and storing geographic data. It gathers Geographic Information Systems (GIS), Remote Sensing, and Photogrammetry. The sensors and platforms are extremely varied, from ground-based GNSS to imaging satellites, or airborne LiDAR systems. The full Data Life Cycle is included, from the acquisition to the processing and communication of the results [START_REF] Levitin | A model of the data (life) cycles with application to quality[END_REF]. At UniLaSalle, the topics studied are mostly centered around Earth Observation, and more precisely Agriculture and Geology.

UniLaSalle Beauvais is an engineering school, teaching to about 3000 students in 5 years. Geomatics is an important part of the curriculum, with on average XX % of the time spent learning GIS, Remote Sensing and Photogrammetry, with an emphasis on the former (REN, 2022). Since the students have different career ambitions in Earth and Life sciences, being able to offer a wide range of real-life examples, especially for lab sessions, is estimated crucial as a way to keep them interested and therefore more engaged. One goal is to bring the students to work autonomously on Labs that they choose from a library. Taking ownership of their learning experience by choosing exercises could increase the efficiency of the course [START_REF] Kolb | Learning styles and learning spaces: Enhancing experiential learning in higher education[END_REF].

Additionally, as teachers we are aware that some knowledge learned during in college years, if not used, will be forgotten. We wanted to provide Alumni with a tool that will allow them to access hands-on knowledge and tutorials even years after graduating, if the need arises for them to use the geomatics tools in their careers.

Teaching requires to produce a non-negligible quantity of materials. While some teachers choose to make those available through their professional or personal webpage, some of these materials are lost. Making all materials available for the teachers of an organization could provide more time for others to improve their own curriculum, without having to reproduce materials that already exist. It is also in line with the Open Access movement claims, which is that everyone would gain from making materials public, by avoiding duplicate work [START_REF] Hylén | Open educational resources: Opportunities and challenges[END_REF].

Creating interactive online course materials was selected as the way to evolve teaching Geomatics at UniLaSalle. The Geomatics Hub was invented as a platform to gather all those materials and bring them beyond the classroom, in the light of the new paradigm of Education 4.0 which emphasizes experimentation as a way to learn, especially by taking advantage of emerging technologies [START_REF] Almeida | The role of serious games, gamification and industry 4.0 tools in the education 4.0 paradigm[END_REF].

Development of the Geomatics Hub

The Geomatics Hub: a repository for course material

The Geomatics hub was developed in 2019. It is freely accessible online at https://hubgeomatique-unilasalle-fr.hub.arcgis.com/. About four teachers are involved and make their materials available in the platform. The main strategy is to provide students, as future Technician and Engineers, real-world examples and problem-based exercises to work on. The design is meant to be very interactive, with the implementation of naturally interactive tools, such a Genially, a platform to create dedicated interactive content.

The original idea was to give more autonomy to students by making them work on a workflow-based approach, where they were provided data, some context, a general workflow and access to a user manual created by the teacher. This allowed the teacher to focus on students with more difficulties. Those labs were then hosted and gathered under one Hub. Student can work in complete autonomy, by choosing in a library of labs with varying levels of difficulty, and also check their skills autonomously using lists of technical skills. GIS was originally the main discipline taught in Geomatics but

Designing an interactive environment to share education materials and resources. Application to the Geomatics Hub at Institut Polytechnique UniLaSalle Beauvais.

3 creating an online Hub was to opportunity to gather together all the disciplines in Geomatics.

The three main disciplines are easily available: GIS, Remote Sensing, and Photogrammetry. For GIS, the main parts are: the lecture slides, about different topics related to GIS, the exercises as Lab Sessions, and an online help on three software: ArcGIS Pro, ArcGIS Online and qGIS. Online skills trees also guide students to different parts of the user manual depending on the skill they want to practice. For the Photogrammetry part, lecture slides are available as well as an online help on the software Agisoft Metashape (proprietary). There is a similar structure for Remote Sensing: lecture slides, labs and online help on ENVI (proprietary). In terms of web hosting, the user manuals are written and hosted on Scenarii, the lecture slides are in Genially, the Labs are on ESRI's servers in the form of Story Maps.

The Hub as a pedagogic tool

The use of StoryMaps allows to set levels for the Lab sessions. For the GIS part, all labs sessions are taught as Problem-based-Learning, where a situation is described to the student, who then has to follow steps of the processing tasks to complete the scenario. While in the first labs, the workflow is often given to the student, it is progressively less and less detailed, in order to let the student, improve and decide on their own of which tools to use, with the end goal of writing and developing their own workflows. Added to the list of labs, the students have access to an auto-evaluation of their skills, with a division in small skills that they can improve by completing the corresponding lab.

In Photogrammetry, the students are provided with a generic workflow and a detailed user manual, developed by the teaching team. The student then has to apply the processing to given sets of data, that can be changed year after year depending on the current need, for example when we want to student to work on new data.

The display tools chosen as a support (StoryMaps, ESRI hub, Scenari) are easy to edit if the leading teacher is not in charge of the Hub anymore, as it has been the case in this study.

As for now the platform is in French only. Finally, it is worth mentioning that geomatics also includes data acquisition. That encompasses a wide range of platforms: groundbased, drone, satellite, and of sensors. The data acquisition part is not covered by the materials in the Geomatics Hub yet.

The Geomatics hub as a showcase platform to enhance relations between companies and education

The Geomatics Hub is in close relation with the GéoLab, an innovation center at UniLaSalle Beauvais that provide an access to students to work on innovative numeric tools for 3D modelling and 3D printing. This includes high-quality PCs, software licenses, technical support and more. The Hub is a way to present the current and past student projects. Beyond being a platform for students, it is also the opportunity to build bridges between the academic world and industry. By showcasing the researchers and students work, companies are more willing to offer internships or job offers, or rent the services of the GéoLab for one-time projects, such as fossils and minerals 3D modelling, bio-mimicry research work. It is also a showcase of the education offered at UniLaSalle and bring in new customers for one-time workshops and companies' workshops. By interacting both with students and professionals, the benefits are coming back to students because the teachers are on the front line and can estimate the needs of the companies, by being in direct communication with them (see Figure 1).

Figure 1 -Multi-parts benefits of an Open Educational Resources with an emphasis on the communication between all three actors of learning: students, teachers and the industry

Three years in: evaluating the use of the platform

The main objectives of the Geomatics Hub are (1) to be an efficient tool for current students at UniLaSalle Beauvais, (2) to be useful for former students (3) to be used as a showcasing website for external users, whether they are individuals or companies. Two metrics are here studied to quantify the success of the platform. Firstly, we will evaluate the impact on student through surveys. Secondly, a study of the hub engagement metrics will be used to quantify the impact of the website.

Students survey

A study was conducted in September 2022, and 66 students answered of the 406 students surveyed. The students targeted were those who already studied GIS in their course, so the third, fourth and fifth year in the curriculum, and the students who had recently graduated. 48.5% of the students who replied are in their third year at UniLaSalle. The questions were divided into three sections: The Geomatics Hub in general, the chronology of the Lab Sessions, and the skills tree. The objective of the survey was to target those specific pedagogic tools in order to assess them individually.

To the first question ("To which occasions are you using the Geomatics Hub?"), and besides a use during the class, 67% say they use the Hub when studying for the exam, 17% say they use it to know more about geomatics in general, 56% say they use it for projects outside of the class, and 15% say they are using it when working for a company

Designing an interactive environment to share education materials and resources. Application to the Geomatics Hub at Institut Polytechnique UniLaSalle Beauvais. 5

(either during an apprenticeship, internships, or in their new position for the recently graduated students). When asked which resources the students were looking at the most, the first place comes for the course materials (73%), then the chronology of Exercises in ArcGIS (59%), the user manuals (55%), the library of exercises/examples (41%). About the use of the Hub outside of the classroom, a third of the student said they were using it regularly (33%°), a third replied they were using it outside of the classroom but not regularly (35%) and another third replied that they were not using it outside of the classroom (31%). Three following questions are about the display and ease of use of the platform. In a large majority, students find that the Hub is easy to use (90%), and would recommend it to someone who is a total beginner in GIS (94%). However, those results are to put in perspective with some written comments by students who say that the interface is sometimes too busy and that there are too many steps to get to the course content.

The second part of the survey deals with the chronology of the exercises. Almost all the students say that the chronology is easy to understand and use (98%), and that it allows them to understand better the course in terms of skills to learn, by putting all the exercises in perspective (94%). The third part of the survey is about the skills list. While only half of the students had seen the skills list before (53%), a large majority of them think that it can be useful for them to learn GIS (85%), and that they would use it autonomously, without being expressively told to during a lecture (75%).

This first experience in surveying the students provided insights on the use of the platform but will also be used to improve the next surveys, especially in terms of focusing questions and getting the students more engaged.

Website engagement overview

ArcGIS Hubs show a part of statistics on the website views. In the past year (Nov.2021 to Nov. 2022), over 15,000 visits were recorded, and on average in the past three months the visits are about 1,400 visits / month. There is no information on summer views, which could have been a good indication of views from students vs. Views from external users. Those metrics are limited but give a good quantitative indicator on the website's relevance and use.

Conclusion

After three years of use of the platform, we now feel confident in putting in perspective the use of the Geomatics Hub. Overall, the feedback from students and teachers alike is in the majority positive. The students can work autonomously, during labs the teacher is in a support position. Students can follow their own pace and see the big picture of the skills expected from them at any time of their education. During the lifetime of the platform, teachers in Geomatics have partially changed. The existence of the platform made those changes easier, and lessened the disruption on students. The school's industrial partners also gave positive feedback. ESRI France, the French branch of the leader in GIS and Remote Sensing software, broadcasted the Geomatics Hub in their magazine "SIGMag" in December 2020.

The feedback is for the moment limited and there is no way for website visitors to leave comments that could let the developer team that something is wrong with the content (broken links, outdated data or new processing technologies, general remarks and suggestions). Additionally, including Single Sign-On (SSO) to the website would give more information on the profile of the users.

With the improvement in topics such as small objects photogrammetry, there is a need to update the tutorials. New topics related to Spatial Data Science should be included, like Machine Learning techniques, now broadly used in Spatial Data Science, including Geomatics. A part on data mining and data downloading should also be added, as well as reflecting on how to include the data acquisition part. A translation in English would also be a plus. Following the survey with students, it seems important to modify the architecture of the website to streamline the access to the different parts of the materials.

A focus should also be made on accessibility (option for contrast, image alternative text for example). One main risk taken with the architecture of the hub is that it relies heavily on external storage. The StoryMaps and main interface are hosted on ESRI's servers, the data is hosted internally on the school's own platform, Claroline. The Software manuals are hosted on Scenarii, and the slides are hosted on Genially and also archived on the teacher's own computers. A way to bypass this would be to develop a server internal to the school, like an ESRI ArcGIS Enterprise solution. In terms of pedagogy, more didactic tools should be tested, such as glossaries, quizzes, serious games.

After three years of experimentation, the students and teachers alike are using the Geomatics Hub for its intended purpose with success. The students appreciate the diversity of materials, but it can also be a drawback by making the platform busy and hard to navigate in. The teachers can rely on previously prepared materials, and also have a freedom to edit anything they want. In the future, the objective is to add or complete the contents, update the information on the software and document the process. Adding levels of help for the GIS labs is proven to be efficient so it should be generalized to all three disciplines. The open science approach is a philosophy the team still fully adhere to, and therefore once the Geomatics Hub fully updated it will be more widely advertised at the level of UniLaSalle Group at first (Beauvais -Amiens -Rouen -Rennes) and in the Geomatics community in general.