Bruno Machado Carneiro
email: bruno.machadocarneiro@ensea.fr

Michele Linardi

Julien Longhi
email: julien.longhi@cyu.fr

Studying Socially Unacceptable Discourse Classification (SUD) through different eyes: "Are we on the same page ? "

Keywords: SUD Classification, Machine Learning, Deep Learning, Transfer Learning, Annotation Guidelines

We study Socially Unacceptable Discourse (SUD) characterization and detection in online text. We first build and present a novel corpus that contains a large variety of manually annotated texts from different online sources used so far in state-of-the-art Machine learning (ML) SUD detection solutions. This global context allows us to test the generalization ability of SUD classifiers that acquire knowledge around the same SUD categories, but from different contexts. From this perspective, we can analyze how (possibly) different annotation modalities influence SUD learning by discussing open challenges and open research directions. We also provide several data insights which can support domain experts in the annotation task.

Introduction

During these last two decades, the massive popularisation of social media has been changing the way people communicate, interact and collect worldwide news. The dissemination speed rate and the possibility to quickly reach a large audience are some clear advantages of modern social network platforms. By contrast, the potential anonymity and sense of impunity can bring out the worst in people and made them sharing ideas that would not be socially acceptable otherwise. Socially Unacceptable Discourse [START_REF] Sulc | No room for hate: What research about hate speech taught us about collaboration?[END_REF]) (SUD) typically occur in various form; The use of offensive and abusive language represent a common form of SUD, but it is also important to note that controversial narratives are not necessarily bad or immoral, but they closely relate to radicalization and ideologies. Clear contexts in the recent history are the Covid-19 crisis and the the Russian invasion of Ukraine. During these periods, we have witnessed several cases of public debate radicalization, especially favored by the circulation of distorted information [START_REF] De Giorgio | Willingness to receive covid-19 booster vaccine: Associations between green-pass, social media information, anti-vax beliefs, and emotional balance[END_REF] that jeopardizes the knowledge acquisition of complex systems and environments. Another particular trait of SUD is the presence of distinctive grammatical characteristics. To model these features, we require identifying several grammatical substructures such as residual representations, use of pronouns, and future tense [START_REF] Ascone | The expression of threat in jihadist propaganda[END_REF][START_REF] De Maiti | Grammatical footprint of socially unacceptable facebook comments[END_REF]. We note that, in publicly annotated corpus used so far by the Machine Learning community, no standard or common guidelines for SUD annotation exist [START_REF] Fišer | Legal framework, dataset and annotation schema for socially unacceptable online discourse practices in Slovene[END_REF] despite the adoption of the same terminology and/or tags. It derives that different SUD definitions may potentially share overlapping characteristics, or on the other hand a single category may cover text instances with divergent features depending on the context. Furthermore, annotators bias can also play a decisive role as reported by previous works [START_REF] Badjatiya | Stereotypical bias removal for hate speech detection task using knowledge-based generalizations[END_REF]Yuan et al., 2022a;[START_REF] Davidson | Racial bias in hate speech and abusive language detection datasets[END_REF]. In this scenario, it is reasonable to expect a poor generalization capability of ML SUD classifiers trained in a specific context (Yuan and Rizoiu, 2022). To that extent, we study and evaluate the capability of current state-of-the-art Deep Learning models to characterize SUD on different grounds. Other works have recently considered the zero-shot learning problem in hate speech detection, where transfer learning is tested and measured on binary (hate/no hate) [START_REF] Toraman | Largescale hate speech detection with cross-domain transfer[END_REF] and on multi-class (Yuan and Rizoiu, 2022) classification. In this context, we sketch and propose a different approach that first aims to test transfer learning at a class level rather than a dataset level. This approach permits us to provide more interpretable insights on the SUD semantic and to test the transfer over different annotation guidelines on the same speech categories.

Socially Unacceptable Discourse Corpora

We report the corpora we consider in our study in table 1. We use data from various sources recently adopted to assess the performance of state-of-the-art ML solutions for automatic SUD detection (e.g., hate speech detection, sentiment, toxicity, radicalization, and ideology analysis). We selected 13 publicly available datasets containing 470, 768 samples distributed over 12 classes. We generate a unique English text corpus by concatenating all the 13 datasets, denoting it with the label G SU D . Note that the datasets we concatenate in G SU D share multiple overlapping SUD labels, which identify the same SUD category. We consider the presence of bias and ambiguities as physiological, and identifying and analyse the concerned instances is under the lens of our research.

In figure 1(a), we report the instances distribution over SUD classes. Note that the neither class subsumes all texts that do not fall in any SUD categorizations proposed by the annotators. As expected, SUD classes have a sensitive lower support compared to the neither class denoting the typical class imbalance setting of the SUD detection problem.

Datasets

Here, we provide the details of each dataset we join in G SU D .

Davidson [START_REF] Davidson | Automated hate speech detection and the problem of offensive language[END_REF] contains around 25,000 tweets labelled as being hateful, offensive or neither of those randomly sampled from a set of 85.4 million tweets produced by 33,458 different users. Each sample was labelled by at least three different annotators.

Founta [START_REF] Founta | Large scale crowdsourcing and characterization of twitter abusive behavior[END_REF] contains about 100,000 tweets, labeled with four categories: abusive, hateful, normal, and spam. In this dataset, a variable number of users (between five and ten) have annotated each sample.

Fox [START_REF] Gao | Detecting online hate speech using context aware models[END_REF] contains 1528 comments posted on ten different popular threads on the Fox News website. In these data, two native English speakers have produced labels to differentiate hateful from normal content following the same annotation guidelines.

Gab [START_REF] Qian | A benchmark dataset for learning to intervene in online hate speech[END_REF] contains 34, 000 samples extracted form Gab, a social media, where users commonly share farright ideologies [START_REF] Jasser | Welcome to #gabfam: Far-right virtual community on gab[END_REF], annotated in the Amazon Mechanical Turk1 platform, where at least 3 annotators provided a label for each sample.

Grimminger [START_REF] Grimminger | Hate towards the political opponent: A Twitter corpus study of the 2020 US elections on the basis of offensive speech and stance detection[END_REF] contains 3, 000 tweets on 2020 presidential election topic in the United States. The samples were labelled between hate speech or not by three undergraduate students, who dis-cussed the annotation guidelines during the labelling process. HASOC2019 [START_REF] Modha | Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages[END_REF]) and HASOC2020 [START_REF] Mandl | Overview of the hasoc track at fire 2020: Hate speech and offensive content identification in indoeuropean languages[END_REF] are datasets proposed in the Indo-European Languages (HASOC) challenge, which contain 12, 000 English text samples extracted from Twiter and Facebook labeled between hateful, offensive, profane or neither of those. Hateval [START_REF] Basile | SemEval-2019 task 5: Multilingual detection of hate speech against immigrants and women in Twitter[END_REF] gathers around 13, 000 tweets containing hateful and normal speech. The hateful content originates from accounts of potential victims of misogynism and racism. Jigsaw2 (van Aken et al., 2018) is a dataset provided in the Toxic Comment Classification Challenge. It contains about 220,000 samples extracted from Wikipedia talk pages differentiated into seven classes: toxic, severe toxic, obscene, threat, insult, identity hate, and neither of the previous. Olid [START_REF] Zampieri | Predicting the type and target of offensive posts in social media[END_REF] contains 14, 000 tweets annotated using the Figure Eight Data Labelling platform3 . In this context, tweet selection is executed by keyword filtering and human annotation. Reddit [START_REF] Qian | A benchmark dataset for learning to intervene in online hate speech[END_REF] has 22,000 samples extracted from Reddit, labeled for hate speech detection by Amazon Mechanical Turk users. Before the labeling task, the text got selected according to a list of toxic subjects on the Reddit platform. Stormfront (de Gibert et al., 2018) contains 10,500 samples taken from a white supremacy forum called Stormfront and divided into four classes: hate, no hate, related, and skip. The related class contains statements that can not be considered hateful without considering their context. Text belonging to the skip class does not contain enough information to determine if it can be classified as hateful. Trac [START_REF] Kumar | Aggression-annotated Corpus of Hindi-English Code-mixed Data[END_REF] dataset gathers 15, 000 Facebook posts and comments classified into aggressive and non-aggressive language.

SUD Deep Learning Models

In this section, we introduce and describe the state-of-theart Deep Learning models adopted for the SUD detection task in previous works. In Table 1, we show the best performer in each corpus. Here, we report the Macro F1 score, which is the recommended averaging method for F1 score when dealing with class imbalance. It is calculated by averaging the sum of the F1 score of each class. Recall that the F1 score reports the harmonic mean of precision and recall of a classification model. For a particular input class, we compute the precision (P) and recall (R) of a SUD classifier as follows: P = T P T P +F P , and R = T P T P +F N , where TP denotes the number of correctly classified instances of the input class (true positive), FP denotes the number of occurrences that are wrongly assigned with the input class label (false positive), and FN represents the number the input class samples that are erroneously classified (false negative). Hence we have that F 1 = 2 × P ×R P +R . From Table 1, we observe that BERT (Bidirectional Encoder Representations from Transformers [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]) is the best performer model in the majority of the datasets. BERT adopts a Deep Learning (DL) architecture released by the Google AI Language team in early 2019, which is pre-trained by masked language model (MLM) and next sentence prediction (NSP) tasks over a large corpus of English data containing more than 3B words [START_REF] Devlin | Bert: Pre-training of deep bidirectional transformers for language understanding[END_REF]. MLM consists of training the model to predict masked tokens in the corpus sentences, whereas the NSP training aims to predict if two sentences form a sequence in the original text. XLM-RoBERTa [START_REF] Conneau | Unsupervised cross-lingual representation learning at scale[END_REF]) is a multilingual variant of the original BERT model. BERT has clearly shown its superiority over other types of DL models previously adopted in SUD classification, such as Convolutional Neural Networks (CNN) [START_REF] Qian | A benchmark dataset for learning to intervene in online hate speech[END_REF] and Long-short term memory networks LSTM [START_REF] Wang | Ynu wb at hasoc 2019: Ordered neurons lstm with attention for identifying hate speech and offensive language[END_REF]. The attention mechanism used by BERT represents a robust solution that can better learn long-range token dependencies, avoiding the limitation of LSTM networks, which assumes that each token depends only on previous ones. By contrast, BERT learns relationships considering all the tokens in a sentence simultaneously. In this work, we evaluate the SUD classification performance of BERT in the heterogenous corpus we construct. In the next section, we present all the research questions we address, discussing the results we obtain.

Experiments

Multiclass SUD Classification

To conduct our experimental evaluation, we use the BERT BASE (Devlin et Considering that G SU D contains highly unbalanced SUD classes, we repeat classification tasks after training our model on a balanced dataset. To that extent, we have performed random oversampling of minority classes as suggested by several works (Yuan and Rizoiu, 2022;[START_REF] Swamy | Studying generalisability across abusive language detection datasets[END_REF][START_REF] Macavaney | Hate speech detection: Challenges and solutions[END_REF]. Furthermore, given the dominance of the neither class, we also consider a setting with under-sampled non-SUD text (neither class). Here, we have selected 10% of the non-SUD samples in a stratified way, maintaining the same proportion of the neither class samples in every dataset. We note that undersampling the neither class has a sensitive effect on the model prediction capability as the Macro F1 score increases. On the other end, reducing the neutral class causes an increment of model errors for the neither class (majority class) as we observe a significant reduction of the Weighted and Micro F1 scores. It follows that coping with such an imbalance between non-SUD and SUD samples represents a concrete challenge (typically occurring in a real-world scenario), which is amplified in the extended corpus under consideration. We also notice that producing a balanced class scenario by performing random oversampling does not provide any significant benefit. This suggests that class imbalance is only a joint cause of the model discrimination capability.

To better understand how the adopted model discriminates SUD classes, we visualize the generated text representa- tion (output of BERT output pooled layer). To reduce the dimensionality of the latent space, we apply t-distributed Stochastic Neighbor Embedding (t-SNE). Figure 2 shows the plot computed over the testing set, with a model trained on the complete corpus G SU D . In Table 3 we report the Macro F1 score of SUD classification in G SU D for each dataset and each class. Note that each line in this table corresponds to a different model, trained only on the specified dataset, while the first line is the result obtained using the model trained on G SU D .

Here, we observe that some class features, i.e., Abusive (top-right), Aggressive (bottom-center) form fairly clear clusters. We can expect this behavior as each one of these class labels solely occurs in a single dataset, as depicted in Table 3. Some other classes, i.e., Hate, Offensive, and Toxic, have more sparse values, which is one reason behind the absolutely low F1 score. Once again, these results get confirmed by the absolute low Macro F1 score both in the global corpus and in each single dataset.

Overall, the results explains the poor generalization capabilities of the studied classification model as this latter attains a low Macro F1 (58%) score on G SU D . In detail, we note that problematic classes are not only those with the lowest number of training samples as one might expect. In fact, a performance drop occur in G SU D classes that share samples from multiple corpus, suggesting the presence of intraclass heterogeneous samples as depicted in Table 3.

In this sense, a clear example concerns the hate class that contains samples from ten different datasets (out of thirteen). We note that shaky classification performance in each dataset of G SU D (see Table 3) depends on divergent annotation criteria on a sensibly general concept, which can relate to different textual elements.

In Table 4, we depict the classification results obtained for each dataset in the global corpus G SU D , and when the model was trained only using a single dataset (Individual). We note that only in two cases the global model performs better than the individual counterpart (for the Fox and Grimminger datasets). We believe that the relatively small support of these two corpora is the reason behind this improvement. Nevertheless, leveraging more knowledge from multiple domains does not constitute an advantage in practice.

Binary SUD Classification

For each of the experiments reported in this section, we have also tested the capability of the model to discriminate SUD and non-SUD text in G SU D irrespective of the specific class. To that extent, we use the same configuration for the classification head, changing the output layer

Further Discussion and Perspective

To closely analyze the state-of-the-art limitation on SUD modeling, in figure 3, we plot the confusion matrix computed on the test set. In this case, we consider a test corpus with undersampled instances of neither class since, for this case, the classification model performs (slightly) in the best manner. Here, we can observe multiple critical cases that concern the labels Identity Hate, Toxic, Obscene and Profane. The classification model assigns a random label to these four classes that have overlapping features with all the others. Concerning classification performance, we note that the F1 score is not significantly dropping for these classes when the model applies to G SU D . It derives that learned features are fairly conserved in the new global context. This observation confirms the results proposed by prior studies [START_REF] Yuan | Separating hate speech and offensive language classes via adversarial debiasing[END_REF][START_REF] Fortuna | Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets[END_REF], which already analyzed the relation among several classes in significantly smaller corpora.

We believe the large-scale scenario we propose motivates the need for a more consistent effort in the ML community equip language models with more discriminant power. This the capability to distinguish the source and the the SUD discourse rather than group), as well as the elements that characterize the kind of narrative of each SUD class.

Conclusion and Future Work

In this work, we present an empirical evaluation of automatic SUD detection using the BERT model, a state-of-theart Deep Learning architecture for SUD classification. To test generalization capability, we consider a large and heterogeneous context in which we obtain results that are not in line with the expected performance of the model trained at the local level, i.e., in every single corpus. In this sense, we argue that to build more general and reliable models, the ML community should consider formal guidelines provided by language experts (mostly neglected so far), which can sensibly reduce local bias annotation policy, context, etc.). In future work, we plan to closely analyze the inter-domain mismatches we observe at the class sample level. Such effort would be beneficial to understand how to improve textual feature learning and to communicate requirements and expectations from the annotation task. We furthermore note that the results and the insights we obtained also have the potential for the research linguists, discourse analysis, or semantics, as they show, from a knowledge base constituted by the main works on SUD corpora, the semantic links, and conceptual relationships, between several labels or tags. In fact, over and above terminology, it is crucial to clearly state and understand the specific features of hate speech, offensive speech, or extremist speech. These initial results are necessary to foster several research discussions in the Horizon Europe ARENAS project into which this work integrates. Specifically, the semantic issues in discourse categorization have an impact not only in terminological and computational terms (for annotating and classifying) but also in legal, political, and sociological terms. The impact of different characterizations is not neutral, there are potential issues of moderation or condemnation [START_REF] Longhi | Using digital humanities and linguistics to help with terrorism investigations[END_REF], and it is necessary to proceed cautiously and rigorously in the delimitation of the chosen descriptors and in the way they are defined and characterized. Finally, the explicability of these categories and the classification provided by Artificial Intelligence is central to future research. Making transparent outcomes will enable us to propose valuable results for all those involved in the hate speech and extremism analysis. In the context of a multidisciplinary project like ARENAS, which brings together scientists with different backgrounds (i.e., linguists, political scientists, etc.) and targets a heterogeneous audience, such as lawyers and journalists, the clarity of descriptors, and their ability to be understood by different stakeholders, is an essential element.

  Figure 1: (a) G SU D Class distribution, (b) Corpus distribution in G SU D

Figure 2 :

 2 Figure 2: Two components t-SNE visualization of samples embedding produced by BERT output pooled layer.

Figure 3 :

 3 Figure 3: Confusion matrix of multi-class SUD classification.

Table 1 :

 1 Best performing SUD classification model on each dataset.

	Dataset	Sample type	# Samples	Topic	Best performing SUD classifier F1 Macro (%)
	Davidson (Grimminger and Klinger, 2021)	Tweets	25,000	Generic	BERT	93
	Founta (Swamy et al., 2019)	Tweets	100,000	Generic	BERT	69.6
	Fox (Yuan and Rizoiu, 2022)	Threads	1,528	Fox News Posts	BERT	65
	Gab (Qian et al., 2019)	Posts	34,000	Generic	CNN	89.6
	Grimminger (Grimminger and Klinger, 2021)	Tweets	3,000	US Presidential Election	BERT	74
	HASOC2019 (Wang et al., 2019)	Facebook, Twitter posts	12,000	Generic	LSTM + Attention	78.8
	HASOC2020 (Roy et al., 2021)	Facebook posts	12,000	Generic	XLM-RoBERTa	90.3
	Hateval (MacAvaney et al., 2019)	Tweets	13,000	Misogynist and Racist content	mSVM/BERT	75.4
	Jigsaw (van Aken et al., 2018)	Wikipedia talk pages	220,000	Generic	Bi-GRU + Attention	78.3
	Olid (Zampieri et al., 2019)	Tweets	14,000	Generic	CNN	80
	Reddit (Yuan and Rizoiu, 2022)	Posts	22000	Toxic subjects	BERT	85
	Stormfront (MacAvaney et al., 2019)	Threads	10,500	White Supremacy Forum	BERT	80.3
	Trac (Aroyehun and Gelbukh, 2018)	Facebook posts	15,000	Generic	LSTM	64
	Figure 1(b) illustrates the ratio of each dataset with respect
	to the global corpus. We observe that Jigsaw and Founta
	contain together more than 60% of the data.

  al., 2019; Yuan and Rizoiu, 2022)

			F1 Score (%)
	Training set	Macro Weighted Micro
	G SU D	53.9	86.8	87.1
	G SU D Balanced	51.3	85	84.5
	G SU D with Neither Undersampled	58.5	73.7	73.9
	G SU D balanced with Neither Undersampled	56.8	72.5	72.1
	G SU D (Binary classification)	88.5	91.3	91.2
	G SU D balanced (Binary classification)	89.7	89.7	89.7
	Table 2: Comparison between all experiments
	model pre-trained by WordPiece tokenizer algorithm. For
	the sake of reproducibility, we provide the code and the data
	used in the experiments along with the relative instructions
	in an online repository (Machado Carneiro et al., 2023).
	To perform SUD classification, we connect BERT pooled
	output layers to a Multi-Layer Perceptron (MLP) archi-
	tecture that contains 12 output neurons (one per class).
	We have fine-tuned the MLP layer of proposed model on
	the G SU D corpus using a 80%/10%/10% splitting ra-
	tio for training, validation, and testing respectively. We
	have adopted a stratified sampling technique to keep the
	same class distribution throughout the three splits. Hyper-
	parameters have been tuned by performing several com-
	plete training rounds, picking the setting with the best vali-
	dation performance.
	Note that the Weighted F1 weighs the global F1 average
	according each class support, whereas the Micro F1 score
	computes a global F1 making no distinction across classes.

The research questions we want to address are the following: Which are the state-of-the-art model generalization capability in a global context? What are the main challenges that hamper the SUD modelling effectiveness? Table

2

contains the results, where we report Macro, Weighted and Micro F1 score of the SUD classification.

Table 3 :

 3 Macro F1 Score of SUD classification per class and dataset.

https://www.mturk.com/

https://www.kaggle.com/c/ jigsaw-toxic-comment-classification-challenge

https://f8federal.com/

The work presented in this paper is part of the ARENAS project. This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No:101094731.


Christine Mrad
email: christine.mrad@univ-lorraine.fr

Jean-Christophe Perrin

Assma El Kaddouri

Kévin Mozet

Clémence Marty

Fabrice Micoud

Laouès Guendouz

Jérôme Dillet

Olivier Lottin

Christine Mrad Phd

J-C Perrin

In-situ NMR & MRI characterization of proton exchange membranes for fuel cells

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

Introduction

References [1] : Klein, M., Perrin, J. C., Leclerc, S., Guendouz, L., Dillet, J., & Lottin, O. (2013). Spatially and temporally resolved measurement of water distribution in Nafion using NMR imaging. ECS Transactions, 58(1), 283.

Proton Exchange Membrane Nafion (N1110):

❑ The RH (Relative Humidity) steps and sequences applied to PEM Nafion :

Experimental setup

Objectives

This work aim to develop a NMR methodology and adapted experimental set up to investigate the evolution of the PEM properties (water content and distribution, thickness, water flow…) under controlled humidified air for PEM with and without catalyst layer.

Conclusion

▪ A NMR experimental methodology was developed to allow the precise measurement of the NMR membrane's properties variation with RH sequences.

▪ The measurement of water flow through membrane in the case of N1110 with and without catalyst layer did not show significant differences → no resistance induced by membrane/catalyst interface or difference lower than error bars ?

Results

solid protons PEMFC efficiency depends on the hydration state of the membrane which permits the proton transportation from the anode to the cathode.

Comparison of N1110 with & without catalyst layer:

Nuclear Magnetic Resonance & Magnetic

Resonance Imaging: non invasive and efficient to measure water properties inside the proton exchange membrane (PEM) Step sequence_1

Step sequence_2

Step sequence_3

Step sequence_4

Step sequence_5

➢ PEM is submitted to the same RH values on both sides → RH1 = RH2 ➢ Each RH step is maintained for an optimized time (3h) to ensure that stable state is reached.

➢

  CL induces an additional resistance at the interface → ≠ water flow ? ➢ No significant change or the change induced is lower than the error bars (Std =3.64x10 -4 mol/m²s)

Acknowledgement

This work has received funding from the European Union's EIT Raw Materials project nº 19247, ALPE: "Advanced Low-Platinum hierarchical Electrocatalysts for low-T fuel cells".


Giovanni Fusco
email: giovanni.fusco@univ-cotedazur.fr

Pierre Merlin

Some unexpected trajectories of urban morphology in France. S A J _2023_1-2_Part_2 SOME UNEXPECTED TRAJECTORIES OF URBAN MORPHOLOGY IN FRANCE

Some unexpected trajectories of urban morphology in

France.

Using urban morphologists against Urban Morphology.

The genesis of "Morphologie Urbaine et Parcellaire" is fundamental to understand the scope and, ultimately, the outcome of the book. In what follows, I'll try to remain as factual as possible. My eventual interpretations will always be highlighted as such. Insight of the genesis of the book is indeed given directly from its authors and the workflow of the related conference was double-checked with Ivor Samuels, who took part in it.

In France, like in other countries, and maybe more than in other countries, the praxis of urban planning from the 1950s and up to the 1980s had been strongly rooted in the functionalist approach. Modernist forms were embraced since the 1950s (the first "cité radieuse" by Le Corbusier was built in Marseille in 1952, the second in Rezé in 1955), and later shaped many large housing projects. Growing criticism arose in the 1970s-1980s, among which one from the emerging field of urban morphology. For one, the work by Castex et al. (1977) is exemplary in its critique of the progressive dissolution of the perimeter block through the history of modernism. A French specificity, at least within western countries, is that the top-down governance of the French administration had had an important role in favouring both functionalism and the new modernist forms, both seen as a way to accelerate the modernization of the country after WWII.2 However, the very top of the governmental decision-making process was being affected by these criticisms, as witnessed by the 1973 circular by minister O. Guichard, putting an end to the development of large functionalist public housing projects.

In the mid-1980s, the French Ministry of Urbanism, Housing and Transportation was finally considering the emerging approaches of urban morphology. In 1985, M. Roullier, in charge of research and innovation at the Ministry, looked for academics to produce a report on urban morphology, its conceptual basis, its methods and its pertinence for the urban planning praxis. Was the Ministry interested in understanding the role that urban morphology could play in improving its approaches to urban planning? Or was it disturbed by the fact that the success of urban morphology would necessitate a complete renewal of its policies, praxis and, probably, even internal culture? What we know is that the Ministry did not ask a critical assessment of the new research field from the leading groups of French urban morphologists (those accounted for by Darin, who were in full activity in the mid-1980s). On the contrary, Roullier solicited the research unit Theory of Urban Mutations in Developed Countries at University Paris VIII, led by Pierre Merlin. Merlin was the leading figure of urban planning in French academia at the time and had strong connections with the French central administration. He had just become the president of the French National Council for Higher Education and Research (CNESER), a position that he would hold until 2003. He also participated to the creation of the French Association for the Promotion of Teaching and Research in Urbanism and Planning (APERAU), of which he will be president between 1992 and 2000. He had previously contributed to several public policies of the French government in urban and regional planning and was recognized in academia for his highly praised development of the theory of general cost of transportation. In this endeavour for the Ministry, Merlin associated his colleague Françoise Choay, a leading theorist of urbanism in France, author of the remarkable "Urbanisme: utopies et réalités"3 and promoter in France of a new reading of the urban and architectural theory of Leon Battista Alberti. 4 Despite their intellectual stature, neither Merlin nor Choay were specialists in urban morphology or active in the urban morphological debate.

They thus invited eleven leading scholars and practitioners who were differently related to the emerging field of urban morphology, to present their viewpoints at an international conference at the prestigious site of the Royal Saltworks of Arc-et-Senans (October 28 th -29 th 1985). Special attention was given to three countries where urban morphology seemed particularly important: Italy, Britain and the US. Four invited experts were from Italy: Vittorio Gregotti (architect at IUAV Venice), Bernardo Secchi (urban planner at IUAV Venice), Sergio Crotti and Ernesto D'Alfonso (both architects at Milano Politecnico). Three were from Britain: Bill Hillier (architect at the Barlett School, UCL), Ivor Samuels (architect at the Joint Center for Urban Design, Oxford Polytechnic) and Micha Bandini (architect at the London Architectural Association). Three were from North America: Stanford Anderson (architect at MIT), George Baird (architect at the University of Toronto) and John Whiteman (philosopher, architect and urban planner at Harvard). One was from Switzerland, the architect and urban semiologist Albert Lévy (University of Geneva). None of the French urban morphologists was invited to the conference, and the point of view of Merlin and Choay was considered as a last contribution representing the French community. These twelve contributions made up the theoretical part of the conference, dealing with goals, concepts, theories and methods of urban morphology. The second part of the event was dedicated to more specific advancements in the role of plot-patterns in urban morphology. Mainly carried out by young researchers from the same institutions, these contributions were published in the second tome of the aforementioned book. Research on the role of the plot system in urban morphology was apparently the main reason for the conference. Even in this respect, it is from my point of view surprising that French urban morphologists were not invited to the conference, knowing the leading role that the French school (and more specifically the Versailles school) had had in first highlighting the role of the plot system in morphological processes. 5 However, what interests us most here is the work carried out with the experts, which was published in the first tome of the book and was used to produce the official report demanded by the Ministry. The experts received individually a questionnaire in preparation of the event, asking them to define a certain number of terms (morphology, typology, urban design, urban structure, etc.) and identify recognized leading figures and seminal works in the field. They were also invited to write a personal contribution on the vast subject-matter of the new emerging approach of urban morphology in their countries, which was the object of their oral presentation at the conference.

Urban morphology is characterized by different theoretical and methodological views on the way to study its very object of research (the form of the physical city and its transformation processes over time). This is still true today and was even more the case in the mid-80s, when many scholars and practitioners could declare an interest in urban morphology without inscribing their work in any common scientific or professional institution. Despite two decades of efforts by ISUF (established in 1994) to create a common language or, at least, a common arena of discussion on urban morphology, Gauthier and Gilliland (2006) could thus observe at the beginning of the 2000s how the wide variety of disciplinary, linguistic and cultural backgrounds of urban morphologists was an inevitable source of misunderstanding in the definition of common concepts, methods and aims for the emerging interdisciplinary field. The first tome of "Morphologie Urbaine and Parcellaire" is thus an extremely rich and interesting text, confronting diverse and sometimes divergent positions on the very concepts and motivations of the morphological approaches (the plural is mandatory), on their role in architecture and planning, and even on a renewed relationship between architecture, urban design and urban planning.

However, Merlin and Choay went beyond a simple anthology and proposed a quantitative and qualitative synthesis of the viewpoints, which was the very demand of the French Ministry. Their protocol was neither Delphi (which could have been allowed by their preestablished questionnaire) nor focus group, for which the meeting in Arc-et-Senans could have offered an excellent opportunity. Actually, both Delphi and focus group aim at helping participants identify common points and possibly converge towards a consensus, although retaining the different opinions that resist this attempt of convergence. Experts were neither confronted with their fellows' answers to the questionnaire, nor asked to participate in a common discussion to elaborate consensual proposals. The organizer's goal was not to arrive to any sort of consensus, be it full of nuances and exceptions and hard to obtain. They limited themselves to take stock of the existing divergences among the experts (including themselves in the observed panel) and come to the following conclusions, which are first stated in the introduction of the book, later developed in its first chapter, and constitute the core of the report produced for the French Ministry:

The morphological approach has no serious scientific bases (Introduction, p. 7). There is no agreement among international experts of this approach on common concepts, common historical roots and founding authors or texts. Therefore, we cannot be surprised by its lack of theoretical content (ibid p.7). The success of the urban morphology fashion in some professional or academic circles is proportional to its conceptual emptiness. 6 Even methodologically, the conclusion is harsh: Urban morphology has not been able to develop any specific methodology, disseminated in the praxis and recognized by all (ibid, p. 61). As a consequence, the impact of urban morphology both in higher education and training and in institutions is insignificant, with the possible exception of Italy. 7 Merlin being the only author of these texts, we can infer that these conclusions are more Merlin's than Choay's. Inasmuch they were integrated in the report for the Ministry, their logical consequence in terms of policy is clear: there is no need to modify the functionalist approach to planning by integrating the new insight of urban morphology. We are thus not surprised that no sign of renewal in urban planning policies and practices was observed at the French Ministry of Urbanism, Housing and Transportation in the following years.

In my opinion, leaving deliberately aside the community of French urban morphologists, two leading figures of French urban planning had organized a relevant event, bringing together internationally renowned urban morphologists, but had used this event against the emergence of urban morphology in France. Morphological research remained a niche of some schools of architecture and never played a significant role in the French urban planning institutes. Of course, this didn't prevent French urban morphologist from carrying on their research agenda and even their cultural battle, with some success in professional praxis. And it did not even make the French Ministry of Urbanism, Housing and Transportation completely impervious to any interest in urban morphology, as witnessed by Levy and Spigai's report for the Ministry on the quality of urban form in contemporary French urbanization. 8 But urban morphology underperformed in both its research and policy potential, beyond heritage conservation. The Ministry of Urbanism and Transportation (which had between them incorporated the responsibility of the Environment) and French research agencies renewed their interest in urban form at the end of the 1990s and at the beginning of the 2000s, within the new agenda of sustainable urban development. We can only regret that early debates on urban densification to contain urban sprawl, just like the latest policy of net zero net land take to protect natural and agricultural land, have made so little consideration of urban morphological insight on these very issues.

In the most recent years, local planning agencies have shown a sincere and pragmatic interest in urban morphology, as witnessed by the growing number of morphological atlases of French cities: sampling urban fragments as in Marseille (AGAM 2005), or covering the whole urban area as in Paris/Ile-de-France region (IAU-Idf 2016) and Lille (ADULM 2016). The beautiful cycle of seminars "Morphogenèse et dynamiques urbaines" (Franceschelli et al. 2012) has also been organized by several academic institutions (EHESS, ENSAD, FMSH) in partnership with PUCA (Plan Urbanisme Construction Architecture), a governmental agency particularly linked to the Ministry of Urbanism.

However, this movement has not completely rehabilitated urban morphology in ministerial and academic circles of urban planning. Merlin's book continues to exert a distant, but never extinguished negative influence on urban morphology. New research projects in urban morphology have always to first overcome that peremptory judgement formulated in the mid-1980s and show that urban morphology has indeed taken stock of those early hesitations and is now capable of a more theoretical and methodological coherence.

Urban Morphology where you wouldn't expect it: the contribution of theoretical and quantitative geographers.

The renewed interest in urban form at the end of the 1990s also motivated different disciplinary traditions to study urban morphological issues. Urban geography has always been a founding discipline of urban morphology, as witnessed by the works of German urban geographers in the inter-war period and by the historico-geographical approach to urban morphology developed at the University of Birmingham after M.R.G. Conzen's seminal work. This is true in France as well. Darin (1998) thus cites the early contribution by Marcel Poëte on the forms of French cities, but also the more recent work by Marcel Roncayolo (1996) on the genesis of urban forms in Marseille. However, all these works belong to the specific tradition of cultural and historical urban geography.

During the 1970s, with a delay of 10-20 years in respect to English-speaking countries, a new research tradition emerged in French geography: theoretical and quantitative geography. Its origins date back to the new geography movement, which can be also linked to Walter Isard's regional science in American economics during the 1950s. Works like "Locational analysis in human geography"9 or "Models in geography"10 began to have an audience in France, passing through young colleagues who had started their academic career in Canada. Urban geography was also concerned by the new theoretical and quantitative approach. The new domain of urban spatial analysis was established. However, the kind of problems treated by French quantitative urban geographers, were mainly urban regional systems, urban factorial analysis within the city, city/transportation interaction seen through the lenses of land use and mobility flows, and urban locational analysis at different scales. Urban form was not the focus of quantitative urban geographers. France academia lacked the equivalent of the Centre for the Land Use and Build Form Studies at Cambridge University,11 which was seminal in introducing quantitative approaches in the study of urban form.

Several factors contributed to a partial change in the research landscape during the 1990s and 2000s. First of all, the already mentioned renewed interest in urban form within policies of sustainable urban development. Secondly, the diffusion of geographical information systems, both within academia and local urban planning departments, as a support for urban spatial analysis. Thirdly, the new availability of intra-urban data in the context of technological advancements and the open data movement: more precise remote sensing data (allowing meaningful intra-urban analysis at metric scale) and urban vector data (first from national and/or local agencies, later from collaborative platforms like OSM) were now available. Since 2006, the French National Geographic Institute IGN has opened its Géoportail initiative, a web-based platform giving access to the BD Topo, a nation-wide vector description of metric precision, including 2.5D buildings and streets, BD Ortho, a nation-wide orthorectified high-resolution raster image, and the numeric version of the national Cadastre. Finally, the lowering cost of computing power has allowed the development of faster and more sophisticated algorithms in urban spatial analysis.

The first group of French quantitative urban geographers developing a research agenda on urban form has been the Théma research unit in Besançon. Pierre Frankhauser wrote a seminal book on fractal analysis of urban space, 12 which was published the very same year as Batty and Longley's at UCL. Fractal analysis of urban forms mainly addresses the question of the distribution in space, and through different scales of observation, of builtup elements and voids within the city. It is a very specific approach to urban morphology, in some respects just as innovative in the urban morphological agenda, as Bill Hillier's space syntax. The former focuses on the full-void spatial distribution of build-up forms, the latter on the topological properties of networks of axial lines within the voids. Both innovate in studying urban form through geographic scales within a unique geocomputational approach. Intense collaboration has later been established between French geographers at Théma and the Centre for Operation Research and Econometrics (CORE) at the Catholic University of Louvain-la-Neuve, Belgium, around Isabelle Thomas. Fractal analysis of built-up forms has been used to study Brussels and its periphery 13 and later to differentiate and characterize whole cities and neighbourhoods in Wallonia 14 and in Europe. 15 Fractal analysis has also been extended to the street network in the case of Antwerp. 16 Tannier (2023, in press) gives a general overview of fractal analysis in urban geography, synthesising research at Théma and CORE. Another work giving an overview on urban planning challenges for fractal analysis is Dupuy (2017), integrating contributions beyond Théma and CORE. In most cases, however, geographers working on fractal analysis of urban form have failed to connect their new insight into urban form with the existing corpus of knowledge produced by urban morphology. Spatial analysis of urban form at CORE has not been limited to fractal analysis, as witnessed by Caruso et al. (2017). Within Théma, other geographers worked on the link between urban morphology and mobility behaviours in urban space. 17 Cybergeo: European Journal of Geography, 248 (2003).

approach to the morphological process in the urban morphology and planning literature. 19 He later worked at the development of a new spatial analysis protocol to study topological neighbourhoods of buildings within the city, which was applied to the cities of Strasbourg and Pau, 20 introducing a new network dimension in urban morphology, beyond street networks. His team also worked at a morphogenetic model of urban sprawl, which was never applied to a precise case study. [START_REF] Moreno | Un automate cellulaire pour expérimenter les effets de la proximité dans le processus d'étalement urbain : le modèle Raumulus[END_REF] Research at LIVE has later been developed towards the more classical issue of the link between mobility and urban form, [START_REF] Piombini | Usage de la morphométrie dans la révélation des préférences de mobilité. Application aux cheminements piétons[END_REF] less focused on the study of urban forms and their evolution over time.

The fourth group of French (or French-speaking concerning CORE) quantitative geographers having invested in urban morphological issues is ESPACE, and more specifically its unit at Côte d'Azur University in Nice, around Giovanni Fusco. This group followed a reversed trajectory compared to the one in Strasbourg. First interested in the interaction between urban form and urban mobility, [START_REF] Fusco | Beyond the Built-up Form / Mobility Relationship: Spatial Affordance and Lifestyles[END_REF] it later focused increasingly on urban morphology issues. Fractal analysis played a minor role for morphological research at ESPACE, and was mainly used to characterise retail fabrics in the city, within the protocol of Retail Fabric Assessment. [START_REF] Araldi | Retail Fabric Assessment: describing retail patterns within urban space[END_REF] Building typology was also addressed by quantitative geographers at ESPACE, with computer-aided protocols capable of processing data of a whole metropolitan area [START_REF] Perez | The Character of the Japanese Urbanization: Overview of Osaka-Kobe's Cityscapes[END_REF] or even for the whole of France. [START_REF] Araldi | Building types in France. Clustering building morphometrics using national spatial data[END_REF] Above all, geographers at ESPACE developed the AI-based data-driven protocol Multiple Fabric Assessment (MFA) to identify and characterize urban fabric types within large metropolitan areas. 27 Following the Italian tradition of urban morphology, the basic unit of analysis for MFA is the streetsegment, and its goal is typifying the organization of plots and buildings in a proximity band around it. The latter is an operationalization of the "banda di pertinenza"28 and the analysis of its skeletal streetscape29 allows the consideration of the pedestrian view of the urban fabric. MFA was used to study urban forms on the French Riviera,30 Marseille,31 Osaka, [START_REF] Perez | The Character of the Japanese Urbanization: Overview of Osaka-Kobe's Cityscapes[END_REF] Bruxelles, 33 Izmir (forthcoming) and in a comparative analysis of Lyon, Marseille, Lille and the French Riviera. [START_REF] Fusco | Formes résilientes, urbanisme et métropole. Learning from Nice[END_REF] Geographers at ESPACE are presently working on a morphological atlas of French cities, using the MFA protocol. Other research subjects at ESPACE are the forms of self-organized urbanisation, [START_REF] Venerandi | Exploring similarities between informal and medieval settlements: A methodology and an application[END_REF] morphological resilience, [START_REF] Fusco | Formes résilientes, urbanisme et métropole. Learning from Nice[END_REF] and the morphological process. [START_REF] Prouin | Morphogenesis of urban peripheries in the 20th century: examples from the French Riviera[END_REF] Many of these works have been presented within the ISUF conferences, which resulted in a more regular participation of French quantitative urban geographers to international urban morphological research.

Other quantitative geocomputational contributions to the analysis of urban form came from research units outside of, but close to, theoretical and quantitative geography. This is the case for the works of Olivier Bonin and Jean-Paul Hubert at LVMT, Gustave Eiffel University (formerly IFSTTAR) in Paris. Baro et al. (2016) proposes thus a new approach to urban morphological analysis using raster socio-economic and building data. The application of their protocol produces morphological regions for French cities. Bonin also worked with Pierre Frankhauser to a new urban model, Franctalopolis, integrating a fractal approach to urban planning.

Interdisciplinary research between physicists and geographers has also contributed to the agenda of computational urban morphology. The research group around Marc Barthélémy at the Center of Social Analysis and Mathematics at EHESS in Paris has worked more specifically on the analysis and on the morphogenesis of urban street networks. 38 The same can be said for the Morphocity research group between MSC and LAVUE research units in Paris, federating physicists like Stéphane Duady and architects/morphologists like Philippe Bonnin. Lagesse et al. (2016) is an example of the contribution of this research group to the quantitative analysis of urban street networks, applied to the city of Paris.

Quantitative computer-aided approaches to urban form have also been developed in engineering, architecture, urban geography and planning research on energy consumption (consumption model related to different urban forms and building types), urban climates (urban micro-climate, urban heat island) and urban pollution (dispersion of air pollutants, noise), but these applied domains of urban morphology will not be considered in this account.

In conclusion, theoretical and quantitative urban geography has increasingly contributed to research in urban morphology in France (and in French-speaking Belgium) in the last two to three decades. These contributions come from a small number of research groups which have often collaborated and worked in interdisciplinary contexts. Methodological innovation has been a main focus of quantitative urban geographers, allowing for innovative applications, which have been used in different research agendas. Globally, they worked at the emergence of the domain of computer-aided urban morphometrics, but in many cases they overlooked the connection with the tradition of urban morphological research and participation to the ISUF network. The geographic scales of their works are extremely varied, and generally wider than architectural and urban design research in urban morphology. Some approaches, like fractal analysis of urban form, are more specifically trans-scalar and applied to a range of geographical scales. A wider and deeper dialogue between quantitative and qualitative approaches to urban morphology, among all the disciplines of urban morphology and first among architecture, geography, and urban planning, could only be beneficial to the advancement of the urban morphological agenda and to its contribution to the understanding and the answers to the current challenges of our urban world. 38 M. Barthelemy, P. Bordin, H. Berestycki, and M. Gribaudi, "Self-organization versus top-down planning in the evolution of a city," Science Reports 3, 2153 (2013) ; (Barthélémy and Flamini 2008)

A

  third group of quantitative urban geographers working on urban form was established around Dominique Badariotti at the LIVE research unit at the University of Strasbourg (including a period of activity at the University of Pau). Badariotti was first interested in possible applications of fractal analysis of urban form in planning, 18 while rooting his 13 M.-L. De Keersmaecker, P. Frankhauser, and I. Thomas, "Using Fractal Dimensions for Characterizing Intra-urban Diversity: The Example of Brussels," Geographical Analysis, 35(4) (2003): 310-328; M.-L. De Keersmaecker, P. Frankhauser, I. Thomas, "Dimensions fractales et réalités périurbaines. L'exemple du Sud de Bruxelles," L'Espace géographique, 33(3) (2004): 219-240; I. Thomas, P. Frankhauser, and M.-L. De Keersmaecker "Fractal dimension versus density of built-up surfaces in the periphery of Brussels," Papers in Regional Science, 86(2) (2007): 287-308. 14 I. Thomas, P. Frankhauser, and C. Biernacki. "The morphology of built-up landscapes in Wallonia (Belgium): A classification using fractal indices," Landscape and Urban Planning, 84(2) (2008): 99-115. 15 I. Thomas, P. Frankhauser, and D. Badariotti. "Comparing the fractality of European urban neighbourhoods: do national contexts matter?," Journal of Geographical Systems, 14(2), (2010): 189-208. 16 I. Thomas, and P. Frankhauser. "Fractal Dimensions of the Built-up Footprint: Buildings versus Roads. Fractal Evidence from Antwerp (Belgium)," Environment and Planning B: Planning and Design, 40(2) (2013): 310-329.

  17

  See : C. Genre-Grandpierre, and J.-C. Foltête. "Morphologie urbaine et mobilité en marche,"

P. Merlin, F. Choay, and E. D'Alfonso, eds., Morphologie urbaine et parcellaire.

volumes (Paris: Presses Universitaires de Vincennes, 1988).

K.Cupers, La banlieue, un projet social. Ambitions d'une politique urbaine, 1945-1975 (Marseille: Parenthèses, 2018).

F.Choay, Urbanisme: utopies et réalités (Paris: Seuil, 1965).

F. Choay, La règle et le modèle. Sur la théorie de l'architecture etde l'urbanisme (Paris: Seuil, 1980).

J.Castex, P. Celeste, and Ph. Panerai. Lecture d'une ville : Versailles (Paris: Moniteur, 1980).

(Chapter 1, p.28)

(ibid, p. 61)

A. Levy, and V. Spigai. La qualité de la forme urbaine. Rapport pour le Ministère de l'Équipement du Logement et des Transport (Paris : Secrétariat Permanent du Plan Urbain, 1992).

(Haggett 1965)

(Chorley and Hagget 1967)

[START_REF] Martin | Urban Spaces and Structures[END_REF]

P. Frankhauser, La fractalité des structures urbaines (Paris: Anthropos, 1994).

D. Badariotti, "Des fractales pour l'urbanisme ?," Cahiers de géographie du Québec 49 (2005): 133-156.

D. Badariotti, "Les processus morphodynamiques des villes, énigmes et traces," in 13èmes journées de Rochebrune, Rencontres interdisciplinaires sur les systèmes complexes naturels et artificiels (Paris : ENST,

2006), 325-336. 20 D. Badariotti, A. Banos, and D. Moreno, "Morphologie urbaine et réseau. Etude des discontinuités et des ruptures induites par le réseau de circulation à l'aide du modèle Remus,"

A. Araldi, and G. Fusco, "Retail Fabric Assessment: describing retail patterns within urban space." Cities, 85, (2019): 51-62.

[START_REF] Caniggia | Lettura dell'edilizia di base[END_REF]

[START_REF] Harvey | Streetscape skeleton measurement and classification[END_REF]

[START_REF] Fusco | The Nine Forms of the French Riviera: Classifying Urban Fabrics from the Pedestrian Perspective[END_REF]

G.Fusco, A. Araldi, and J. Perez. "The City and the Metropolis: Urban Form through Multiple


domain: chim.mate phys spi

text:
est destinée au dépôt

Polarization-dependent angular distribution of the absorption behavior in ytterbium-doped monoclinic LYB and LGB compounds

  Polarization-dependent angular distribution of the absorption behavior in Ytterbium-doped monoclinic LYB and LGB compoundsWendwesen Gebremichael, Yannick Petit, S. Rouzet, Alexandre Fargues, Philippe Veber, Matias Velázquez, Veronique Jubera, Lionel Canioni, InkaManek-HönningerTo cite this version: Wendwesen Gebremichael, Yannick Petit, S. Rouzet, Alexandre Fargues, Philippe Veber, et al.. Polarization-dependent angular distribution of the absorption behavior in Ytterbium-doped monoclinic LYB and LGB compounds. SPIE LASE 2017 -Solid State Lasers XXVI: Technology and Devices, SPIE, Jan 2017, San Francisco, United States. pp.1008212, 10.1117/12.2251903. hal-03898515