dataesr/scientific-paragraphs-categorization huggingface dataset:
Mono (DS1) Multi (DS1) "Ground truth" Figure  8 : Results of our mono-and multi-scale architectures (both trained on the pre-existing dataset DS1) on the copper golf ball from  [31]. The multiscale architecture yields much sharper results, especially around the holes. The example of Fig. 9 , on the contrary, shows the importance of the presence of metallic objects in the training dataset, independently from the network architecture. It can be observed that the network performs much better when it is trained on our new training dataset, even without considering the multi-scale architecture. Mono (DS1) Mono (DS1+DS2) "Ground truth" Figure  9 : Results of our mono-scale architecture on the copper hexagon from  [31]. Since the new dataset (DS2) contains much more metallic objects than the existing one (DS1), training on our new dataset yields largely improved results.
Acrylic ball Mono (DS1+DS2) Multi (DS1+DS2) "Ground truth" Figure  10 : An image of an acrylic ball from  [31] , illuminated from the right, and results of our monoand multi-scale architectures (both trained on the new dataset DS2) on the acrylic golf ball from  [31]. The reconstruction of translucent objects is improved a lot by using the multi-scale approach. Others common phenomenas which are cast-shadows and inter-reflections are also better handled by our multi-scale architecture, as Fig. 11  shows. Mono (DS1) Multi (DS1+DS2) Multi (DS1+DS2) Figure  11 : Angular error map and predicted normal map for the "reading" and "harvest" objects from  [34]. The concave parts, where cast shadows and interreflections occur, are better handled by our approach. Fig. 12  shows several additional qualitative comparisons of the result obtained with our baseline (monoscale architecture trained on the existing dataset) and with both our building blocks included (multi-scale architecture trained on the new dataset).
Quantitative evaluation on DiLi-GenT [34] Next, we compare in Table  2  our results against the most recent state-of-the-art methods, on the DiLi-GenT benchmark  [34]. Let us however remark that PS-transformer  [12]  takes as inputs no more than 10 images, hence the comparison is biased. Besides, we emphasize that our mono-scale architecture is largely inspired from PS-FCN  [6, 7] , hence Mono (DS1) can be considered as an optimized version of  [6, 7] , where we let the training phase run for much longer. This table shows that the proposed multi-scale architecture provides a significant gain of 4. 6%, in comparison with the mono-scale approach -compare Mono (DS1) and Multi (DS1). And, as soon as our new training dataset is considered, the state-of-the-art is outperformed and we reach an average angular error below 6 • , with a particularly visible improvement on the most difficult "reading" object (Fig.
Quantitative evaluation on DiLiGenT 10 2 [31] We now quantatively evaluate the impact of the multi-scale architecture on the DiLiGenT 10 2 benchmark  [31]. Note that we process images at their full resolution (1024 pixels by 1024), requiring 7 scales in the multi-scale architecture. To this end, we show in Table  3  the difference between the mono-and the multi-scale approaches, when they are both trained on the pre-existing dataset. As can be observed, a significant gain of 9. 3% is observed with the multiscale architecture. The gain is most visible on objects which have a spherical shape and anisotropic material (top right of Tab 3c, see also Fig. 8  for a qualitative result on the Golf -CU object), as well as for the most challenging "acrylic" material, which is translucent. We repeat this experiment in Table  4 , but this time with our networks trained on the new dataset.
Comparing Tables  3  and 4  allows one to quantify the benefits of using our new training dataset: the mono-scale architecture gets improved by 14%, and the multi-scale one by 30%. Comparing Tables 4a and 4b also allows one to quantify the impact of switching to the multi-scale architecture: the results improve by 26%. Particularly large improvements can be observed on the Turbine and Acrylic Gulf objects (see also Figs. 12c  and 10 ). For such objects with non-local light transport (due to interreflections or anisotropic reflectance), the ability of tne multi-scale approach to get access to a global information is indeed of primary importance. Overall, the combination of the new architecture and dataset allows one to reach an average error of 11. 33 • on this benchmark. This is to be compared with the 15.
78 • achieved by CNN-PS  [11]  (Table  4c ), which was the best performing method so far  [31]. By comparing our results with all available state-of-the-art methods  [5, 6, 11, 30, 33, 35, 36, 39, 40, 41] , we found out that the proposed method is the best performer on 73% of the objects of this benchmark, as indicated in Table  5. Table  5 : Mean angular error achieved by the best performer among  [5, 6, 11, 30, 33, 35, 36, 39, 40, 41]  and us, on the 100 objects of  [31]. Green cases indicate when the proposed architecture, combined with the new dataset, gives the best results.
Limitations Even if the combination of our multi-scale and our new training dataset improves the results on non-Lambertian materials, some shortcomings remain. For example, we notice that the normals at the border of some translucent objects are incorrectly predicted (Fig. 13 ). As shown in Fig. 14 , in this example the the opposite side of the incoming light is the most shiny part of the image. Although our multi-scale approach better handles such anisotropic than the mono-scale one or existing methods such as CNN-PS, it shows its limitations when the anisotropy is this much important.
CONCLUSION In this paper, we have proposed a novel deep normal estimation framework for the calibrated photometric stereo problem. It builds upon a multi-scale architecture which is independent from the resolution of the images, as well as a new comprehensive learning dataset. We have shown on publicly available benchmarks that the combination of these two features yields state-of-the-art results, with performances particularly improved on challenging anisotropic materials. In the future, we plan to extend our approach to handle observation maps  [11]  as well, which have recently been shown to benefit from physical interpretability  [13].
This paper describes the ON-TRAC consortium speech translation systems developed for IWSLT 2023 evaluation campaign. Overall, we participated in three speech translation tracks featured in the low-resource and dialect speech translation shared tasks, namely; i) spoken Tamasheq to written French, ii) spoken Pashto to written French, and iii) spoken Tunisian to written English. All our primary submissions are based on the end-to-end speech-to-text neural architecture using a pre-trained SAMU-XLSR model as a speech encoder and an mbart model as a decoder. The SAMU-XLSR model is built from the XLS-R 128 in order to generate language agnostic sentence-level embeddings. This building is driven by the LaBSE model trained on a multilingual text dataset. This architecture allows us to improve the input speech representations and achieve significant improvements compared to conventional endto-end speech translation systems.
Introduction IWSLT is a unique opportunity that allows each year the assessment of progress made in the area of Spoken Language Translation (SLT). This assessment is made possible throughout the organisation of an evaluation campaign including various shared tasks that address specific scientific challenges of the SLT domain. In addition to the well-established shared tasks, IWSLT organisers introduce new tasks to address the many challenges settings related to SLT area like data scarcity, multilingualism, time and computation constraints, etc. In this context, the IWSLT 2023 proposes two interesting shared tasks: low-resource and dialect speech translation (ST). The former aims to assess the exploitability of current translation systems in data scarcity settings. The latter focuses on the assessment of the systems' capabilities in noisy settings: different dialects are mixed in a single dataset of spontaneous speech.
For the low-resource task, several language pairs were proposed this year. In this paper, we focus on Tamasheq-French, Tunisian Arabic-English and Pashto-French. This paper reports the ON-TRAC consortium submissions for the aforementioned tasks. The ON-TRAC Consortium is composed of researchers from three academic laboratories, LIUM (Le Mans University -France), LIA (Avignon University -France), MIT (Cambridge -USA) together with three industrial partners: Airbus France, ELYA-DATA and Systran. Our systems for the dialect task focus on both cascaded and end-to-end approaches for ST. For the low-resource task, we focus on the leveraging of models based on self-supervised learning (SSL), and on the training of ST models with joint automatic speech recognition (ASR), machine translation (MT) and ST losses.
This paper is organized as follows. Section 2 presents the related work. Section 3 is dedicated to detail our primary systems encoder-decoder approach. The experiments with the Tunisian Arabic-English dataset for low-resource and dialect ST tasks are presented in Section 4. Results for the Tamasheq-French and Pashto-French tracks are presented in Section 5 and 6 respectively. Section 7 concludes the paper and discusses future work.
, 2021; Baevski et al. , 2019 Baevski et al. , , 2020)) , but recent speech benchmarks  (Evain et al. , 2021b,a; Yang et al. , 2021)  cover tasks such as ST, spoken language understanding, emotion recognition from speech and more. 3 Primary systems encoder-decoder architecture 3. 1 SAMU-XLS-R (SAMU-XLS-R) SAMU-XLS-R is a multilingual multimodal semantic speech representation learning framework where the speech transformer encoder XLS-R  (Babu et al. , 2021a ) is fine-tuned using semantic supervision from the pre-trained multilingual semantic text encoder LaBSE  (Feng et al. , 2022). The training and modeling details can be found in the original paper  (Khurana et al. , 2022). In this work, we use the same training framework but train the model using transcribed speech collected from approximately 100 spoken languages from several datasets such as CommonVoice-v10  (Ardila et al.
Data The training and development data conditions are identical to IWSLT 2022 edition. It consisted of two types of datasets: (1) 383h of manually transcribed conversational speech and (2) 160h, subpart of it, augmented with their English translations to form a three-way parallel corpus (audio, transcript, translation). This dataset is made available by LDC under reference LDC2022E01. The goal of this track is to train speech translation systems under two training conditions: constrained, in which only the provided dataset resources are allowed, and unconstrained where participants may use any public or private resources.
Data This dataset, recently introduced in Boito et al. (  2022 ), contains 14 h of speech in the Tamasheq language for the training split which corresponds to 4,444 utterances translated to French. The development set contains 581 utterances (a little bit less than 2 h of speech), the 2022 test set contains 804 utterances (approximatively 2 h of speech). The 2023 test set contains 374 utterances (approximatively 1 h of speech). Additional audio data was also made available through the Niger-Mali audio collection: 224 h in Tamasheq and 417 h in geographically close languages (French from Niger, Fulfulde, Hausa, and Zarma). 3  For all this data, the speech style is radio broadcasting, and the dataset presents no transcription.
Models For the Tamasheq to French task, we performed several experiments. First of all, we did the same experiment that was done for Pashto-French and Tunisian-English tasks. We used the end-to-end translation model presented in section 3. 2, directly trained on the Tamasheq→French task. Directly means that we used SAMU-XLS-R-xx (xx corresponds to the number of languages in the training set, equals to 53, 60 and 100) to initialise the encoder and performed the training of the encoderdecoder model using the Tamasheq→French training set. We used the CoVoST-2 (Wang et al. , 2020) X →EN speech-translation dataset in which we translated the EN text into French (using Mbart Many-to-Many). Additionally, we exploited the Europarl benchmark, which comprises 72 translation tasks (denoted as X →Y), with the source language set (X ) consisting of nine languages: FR, DE, ES, IT, PL, PT, RO, NL, and EN.
The target language set (Y) is equivalent to the source language set. For the specific training data distribution of each of the 72 translation tasks, refer to  (Iranzo-Sánchez et al. , 2019). We trained a translation model using CoVost-2 X→FR,EN and Europarl X→FR, namely models samu60l[cv2_xx→(en,fr)+europarl_xx→fr] and samu100l[cv2_xx→(en,fr)+europarl_xx→fr]). We also translated the French translation of the Tamasheq speech into Spanish, Portuguese and English (still using MBart Many to Many). Using the pre-trained models, we trained a translation model from Tamasheq to French, Spanish, English and Portugese. We added the 2022 test set inside the training corpus for the Primary model. Moreover, we used the last checkpoint of the SAMU-XLS-R training (100 languages) and pushed further the training using the LaBSE embeddings of the translations of the Tamasheq into French, Spanish, English and Portuguese.
Data The Pashto-French dataset used in our experiments was provided by ELDA. This dataset is available in the ELRA catalog, TRAD Pashto Broadcast News Speech Corpus (ELRA catalogue, 2016b) concern audio files and TRAD Pashto-French Parallel corpus of transcribed Broadcast News Speech -Training data (ELRA catalogue, 2016a) are their transcriptions. This dataset is a collection of about 108 hours of Broadcast News with transcriptions in Pashto and translations in French text. Dataset is build from collected recordings from 5 sources: Ashna TV, Azadi Radio, Deewa Radio, Mashaal Radio and Shamshad TV. Training data contains 99h of speech in Pashto, which corresponds to 29,447 utterances translated into French. We participated for Pashto to French task for both types of submissions: constrained and unconstrained conditions.
Pipeline models For the cascaded approach, i. e. the task of using an ASR model followed by a MT model, we focused on Wav2Vec2. 0  (Baevski et al. , 2020)  as a Speechto-Text system. The architecture used is Wav2Vec2-XLSR-53  (Conneau et al. , 2020) , a large version of Wav2Vec2 pre-trained on the multilingual dataset Common-Voice  (Ardila et al. , 2020b). Once adding a language modeling head on top of the model for fine-tuning on the Pashto dataset, we observed a score of less than 20% of WER and a good modeling of the reference language since the difference of the scores for translating written Pashto to written French when using either the reference or the generated Pashto text, was always less than 0. 5 of BLEU. For the MT system, we tested multiple approaches using auto regressive Sequence-2-Sequence models.
We mainly focused on transformers encoderdecoder systems from small basic transformers (contrastive3 in Table  3 ) to large pre-trained multilingual text-to-text transformers such as T5  (Raffel et al. , 2020)  and mT5 (multilingual T5). For primary cascaded system, models are based on a convolutional model (fconv)  (Gehring et al. , 2017) upgraded (fconv-up). We reduced the depth and the width of both the encoder and decoder to adapt the size of our fconv model to our dataset. Our fconvup model achieves 14. 52 of BLEU on valid set and 15. 56 on the test set, while fconv would give 13 of BLEU. Compared to the cascaded baseline system, based on small basic transformers (contrastive3), fconv-up cascaded system outperforms by 6 BLEU points. Experiments have been carried out in order to extract the encoder of the fine-tuned W2V and use the latent representation of the audio to train an auto-regressive decoder and thus to skip the Speechto-Text part, but without any success.
End-to-end models We used the end-to-end translation model presented in section 3. 2. The model was trained directly on the Pashto to French task (no pre-training of the encoder-decoder model), using SAMU-XLS-R trained on 53 and 100 languages. We used adapters  (Houlsby et al. , 2019)  inside the encoder to keep the semantic information while fine-tuning. Two constrained contrastive end-to-end systems were submitted for this task. Both share the same encoder-decoder architecture using transformers  (Vaswani et al. , 2017). The system encoder is the encoder from a Whisper small (768)  (Radford et al. , 2022)  pre-trained model. The decoder has a dimension of 512 using 8 heads and 6 layers. It is not pretrained. A feed forward network projection layer is used between the encoder and decoder to connect both modules.
Results Results for constrained and unconstrained conditions are presented in Table  3  and Table 4   As for constrained setting, we noted that a pipeline of two E2E ASR and NMT system gives better results compared to using one speech translation E2E system. Although the usage of a LM improves the E2E ST further, we were not able to exceed the pipeline of the two E2E systems (ASR+NMT). When we switch to the unconstrained setting, we see a significant improvement demonstrated by a dramatic increases of the BLEU score with the SAMU-XLS-R system. SAMU-XLS-R obtained a BLEU of 24. 87 on the test set when trained starting from a pretrained encoder with 100 languages (SAMU-XLS-R-100) and a full BLEU point less (23. 87) when we start from a 53 languages encoder (SAMU-XLS-R-53).
Conclusion This paper presents results obtained on three tasks from the IWSLT 2023 Dialectal and Low-resource ST track, namely Tunisian to English, Tamasheq to French and Pashto to French. Given an unconstrained condition, our submission relies heavily on the semantic speech representation learning framework SAMU-XLS-R that greatly improves results compared to the other submitted end-toend ST models by leveraging multilingual data from other languages. These data can thus come from high resource languages and help to alleviate the low-resource setting difficulty. We indeed observe slightly improved results when using a SAMU-XLS-R model trained on more languages (Tamasheq to French : 15. 35 BLEU when using 60 languages, 16. 52 BLEU when using 100 languages). We believe results could be further improved by using the unlabelled data available for the Tunisian to English and the Tamasheq to French tasks, and by investigating other decoders in our encoder-decoder framework.
Acknowledgements This work was partially funded by the following projects:  French Research Agency  ( ANR )  ON-TRAC  project under contract number  ANR-18-CE23-0021 ,  European Commission   SELMA  project under grant number  957017 ,  European Union 's  Horizon 2020 ESPERANTO research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  101007666  and the  DGA   RAPID COMMUTE  project. This work was partially performed using HPC resources from  GENCI-IDRIS , grants  AD011012527. The Pashto-French data was totally provided by ELRA. We acknowledge ELRA catalogue (http://catalog. elra. info) TRAD Pashto-French Parallel corpus of transcribed Broadcast News Speech -Training data, ISLRN:  802-643-297-429-4 , ELRA ID:  ELRA-W0093 ,  TRAD Pashto Broadcast News Speech Corpus ,  ISLRN :  918-508-885-913-7 , ELRA ID:  ELRA-S0381.
If u[X i ] = v[X i ], and because u ∼ v, at least one of u or v is not defined on X i. Let us assume (without loss of generality) that u is not defined on X i. In that case, u[V ] is not defined on X i either and T u[V ] the same value for x i as T u. Therefore, the execution still have the same values of inst. By recurrence, since the values of inst are the same at each point of the executions, then opt(u[V ]) = opt(u). By assumption, u is minimal for cardinality. Therefore U V , so u v and |u| < |v|, so u is the unique minimum for cardinality. We are now able to compute the length of the compression of data D, given some CO-net H that is uniquely encoding: L(D | H) = o∈D L N (|code(o, H)|) + log 2 |X | |code(o,H)| + x∈code(o,H) log 2 (|X| -1) (2) For each outcome o of the dataset, the terms encode, in order: the length of the minimal code of o, the set of variables that are assigned in this code, and the value for each attribute.
Compression experiment While MDL is typically used for model selection and not compression, we propose to experimentally assess the relevance of CO-nets and separable CO-nets in the context of preference representations by comparing the length of the codes of several datasets with implementations of efficient compression algorithms configured for the highest compression ratio. Source code, data and CO-nets are available online 3. 3 https://github. com/PFGimenez/co-net-ecai23 The datasets are real-world sales history of cars from the Renault car manufacturer: "Small" has 48 variables and is 2. 7MB, "Medium" has 44 variables and is 1. 4MB and "Big" has 87 variables and is 3. 2MB. These files are in a csv format which is a text format. They can be easily compressed, as shown by the space savings in Table  1.
The three algorithms with the highest space saving are PPMd, bzip2, and our encoding based on a CO-net. Separable CO-nets have the lowest space saving of all methods in this Table, due to they very limited expressivity, but they still achieve 90%+ space saving. Table  1  does not include snappy and LZ77 algorithms because of their poor compression efficiency: they obtained about 70% and 85% space saving, respectively. While a direct comparison is unfair because MDL is a theoretical tool that does not need to comply with the technical constraints of file formats and compression speed, we can still conclude that CO-nets can represent the regularities of real-world datasets with an efficiency similar to the most efficient compression algorithms.
g. ,  Fekete et al. , 2016). Therefore, assuming a sufficient acidity of fluids in host-rock to trigger greisenization, it is also worth testing configurations without magmatic fluid production. Thus, we have generated four different numerical models, involving two different types of magmatic-hydrothermal systems: (i) one with a "passive" granitic intrusion without fluid production and where fluid flow is only driven by convective processes, and (ii) another one involving magmatic fluid production during granite crystallization and where overpressure fluid conditions constitute an additional driving force. These models were performed with the finite element code Comsol Multiphysics™. This numerical code has been tested and validated by several benchmarks in cases of fluid flow around hot granite intrusions  (Eldursi et al.
Fluid properties For the fluid phase, we assume a porous medium saturated by a single fluid phase composed of pure water. The fluid density was calculated at each time step as a function of the pressure and the temperature using an interpolated function based on the IAPWS-IFP97 equation of state for pure water defined by the International Association for the Properties of Water and Steam (IAPWS,  Wagner et al. , 2000). From this database, an interpolated function (polynomial with 15 coefficients, as detailed in  Guillou-Frottier et al. , 2020)  was implemented as a parameter in the solver. Based on the analytical approximation of  Kestin et al. (1978)  and  Rabinowicz et al. (1998) , the dynamic viscosity evolution with temperature is given by: (6)   = 2. 414 10 -5. 10 ( 247. 8   -140 ) with T the fluid temperature in K.
Acknowledgments This research took part in the  ERAMIN  project " New Ores " ( ANR-14-EMIN-0001 ) and was financially supported by the  French Geological Survey (BRGM) , the  Region Centre , and the  Labex Voltaire  ( ANR-10-LABX-100-01 ). We are grateful to anonymous reviewers for their constructive remarks, which have greatly improved this manuscript.