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Author |
Lei Kang; Pau Riba; Yaxing Wang; Marçal Rusiñol; Alicia Fornes; Mauricio Villegas |
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Title |
GANwriting: Content-Conditioned Generation of Styled Handwritten Word Images |
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Conference Article |
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Year |
2020 |
Publication |
16th European Conference on Computer Vision |
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Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words. On the contrary, when writing by hand, a great variability is observed across different writers, and even when analyzing words scribbled by the same individual, involuntary variations are conspicuous. In this work, we take a step closer to producing realistic and varied artificially rendered handwritten words. We propose a novel method that is able to produce credible handwritten word images by conditioning the generative process with both calligraphic style features and textual content. Our generator is guided by three complementary learning objectives: to produce realistic images, to imitate a certain handwriting style and to convey a specific textual content. Our model is unconstrained to any predefined vocabulary, being able to render whatever input word. Given a sample writer, it is also able to mimic its calligraphic features in a few-shot setup. We significantly advance over prior art and demonstrate with qualitative, quantitative and human-based evaluations the realistic aspect of our synthetically produced images. |
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Virtual; August 2020 |
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ECCV |
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DAG; 600.140; 600.121; 600.129 |
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no |
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Call Number |
Admin @ si @ KPW2020 |
Serial |
3426 |
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Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
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Title |
Activities of Daily Living Monitoring via a Wearable Camera: Toward Real-World Applications |
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Journal Article |
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Year |
2020 |
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IEEE Access |
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ACCESS |
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8 |
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Pages |
77344 - 77363 |
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Abstract |
Activity recognition from wearable photo-cameras is crucial for lifestyle characterization and health monitoring. However, to enable its wide-spreading use in real-world applications, a high level of generalization needs to be ensured on unseen users. Currently, state-of-the-art methods have been tested only on relatively small datasets consisting of data collected by a few users that are partially seen during training. In this paper, we built a new egocentric dataset acquired by 15 people through a wearable photo-camera and used it to test the generalization capabilities of several state-of-the-art methods for egocentric activity recognition on unseen users and daily image sequences. In addition, we propose several variants to state-of-the-art deep learning architectures, and we show that it is possible to achieve 79.87% accuracy on users unseen during training. Furthermore, to show that the proposed dataset and approach can be useful in real-world applications, where data can be acquired by different wearable cameras and labeled data are scarcely available, we employed a domain adaptation strategy on two egocentric activity recognition benchmark datasets. These experiments show that the model learned with our dataset, can easily be transferred to other domains with a very small amount of labeled data. Taken together, those results show that activity recognition from wearable photo-cameras is mature enough to be tested in real-world applications. |
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MILAB; no proj |
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Admin @ si @ CRD2020 |
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3436 |
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Author |
M. Li; Xialei Liu; Joost Van de Weijer; Bogdan Raducanu |
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Title |
Learning to Rank for Active Learning: A Listwise Approach |
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Conference Article |
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Year |
2020 |
Publication |
25th International Conference on Pattern Recognition |
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5587-5594 |
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Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks. |
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Virtual; January 2021 |
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ICPR |
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LAMP; 600.120 |
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no |
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Call Number |
Admin @ si @ LLW2020a |
Serial |
3511 |
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Author |
Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Esmitt Ramirez; Carles Sanchez |
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Title |
Intraoperative Extraction of Airways Anatomy in VideoBronchoscopy |
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Journal Article |
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Year |
2020 |
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IEEE Access |
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ACCESS |
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Volume |
8 |
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Pages |
159696 - 159704 |
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Abstract |
A main bottleneck in bronchoscopic biopsy sampling is to efficiently reach the lesion navigating across bronchial levels. Any guidance system should be able to localize the scope position during the intervention with minimal costs and alteration of clinical protocols. With the final goal of an affordable image-based guidance, this work presents a novel strategy to extract and codify the anatomical structure of bronchi, as well as, the scope navigation path from videobronchoscopy. Experiments using interventional data show that our method accurately identifies the bronchial structure. Meanwhile, experiments using simulated data verify that the extracted navigation path matches the 3D route. |
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IAM; 600.139; 600.145 |
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no |
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Call Number |
Admin @ si @ GEB2020 |
Serial |
3467 |
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Author |
Yi Xiao; Felipe Codevilla; Akhil Gurram; Onay Urfalioglu; Antonio Lopez |
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Title |
Multimodal end-to-end autonomous driving |
Type |
Journal Article |
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Year |
2020 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
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Pages |
1-11 |
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Abstract |
A crucial component of an autonomous vehicle (AV) is the artificial intelligence (AI) is able to drive towards a desired destination. Today, there are different paradigms addressing the development of AI drivers. On the one hand, we find modular pipelines, which divide the driving task into sub-tasks such as perception and maneuver planning and control. On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals. The later are relatively less studied, but are gaining popularity since they are less demanding in terms of sensor data annotation. This paper focuses on end-to-end autonomous driving. So far, most proposals relying on this paradigm assume RGB images as input sensor data. However, AVs will not be equipped only with cameras, but also with active sensors providing accurate depth information (e.g., LiDARs). Accordingly, this paper analyses whether combining RGB and depth modalities, i.e. using RGBD data, produces better end-to-end AI drivers than relying on a single modality. We consider multimodality based on early, mid and late fusion schemes, both in multisensory and single-sensor (monocular depth estimation) settings. Using the CARLA simulator and conditional imitation learning (CIL), we show how, indeed, early fusion multimodality outperforms single-modality. |
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ADAS |
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no |
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Call Number |
Admin @ si @ XCG2020 |
Serial |
3490 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix Martinez |
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Title |
Explainable Early Stopping for Action Unit Recognition |
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Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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Pages |
693-699 |
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Abstract |
A common technique to avoid overfitting when training deep neural networks (DNN) is to monitor the performance in a dedicated validation data partition and to stop
training as soon as it saturates. This only focuses on what the model does, while completely ignoring what happens inside it.
In this work, we open the “black-box” of DNN in order to perform early stopping. We propose to use a novel theoretical framework that analyses meso-scale patterns in the topology of the functional graph of a network while it trains. Based on it,
we decide when it transitions from learning towards overfitting in a more explainable way. We exemplify the benefits of this approach on a state-of-the art custom DNN that jointly learns local representations and label structure employing an ensemble of dedicated subnetworks. We show that it is practically equivalent in performance to early stopping with patience, the standard early stopping algorithm in the literature. This proves beneficial for AU recognition performance and provides new insights into how learning of AUs occurs in DNNs. |
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Virtual; November 2020 |
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FGW |
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HUPBA; |
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no |
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Call Number |
Admin @ si @ CME2020 |
Serial |
3514 |
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Author |
Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski |
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Title |
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch |
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Conference Article |
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Year |
2020 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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Aspen; Colorado; USA; March 2020 |
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WACV |
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MSIAU; 600.122; 600.130 |
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no |
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Call Number |
Admin @ si @ RMP2020 |
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3291 |
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Author |
Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images |
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Book Chapter |
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2020 |
Publication |
Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
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World Scientific |
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978-981-120-323-7 |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ FLP2020 |
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3350 |
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Author |
David Berga; Xavier Otazu |
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Title |
Computations of top-down attention by modulating V1 dynamics |
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2020 |
Publication |
Computational and Mathematical Models in Vision |
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St. Pete Beach; Florida; May 2020 |
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MODVIS |
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NEUROBIT |
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no |
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Admin @ si @ BeO2020a |
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3376 |
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Author |
Jun Wan; Guodong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
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Title |
Multi-modal Face Presentation Attach Detection |
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2020 |
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Synthesis Lectures on Computer Vision |
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13 |
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HuPBA |
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no |
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Admin @ si @ WGE2020 |
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3440 |
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Author |
Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan |
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Title |
Statistical Machine Learning for Human Behaviour Analysis |
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2020 |
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Entropy |
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ENTROPY |
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25 |
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5 |
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530 |
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action recognition; emotion recognition; privacy-aware |
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HuPBA; no proj |
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Admin @ si @ MEA2020 |
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3441 |
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Mohamed Ali Souibgui; Y.Kessentini; Alicia Fornes |
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A conditional GAN based approach for distorted camera captured documents recovery |
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2020 |
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4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence |
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Virtual; December 2020 |
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MedPRAI |
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DAG; 600.121 |
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Admin @ si @ SKF2020 |
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3450 |
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Author |
Fernando Vilariño |
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Unveiling the Social Impact of AI |
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2020 |
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Workshop at Digital Living Lab Days Conference |
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September 2020 |
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MV; DAG; 600.121; 600.140;SIAI |
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Admin @ si @ Vil2020 |
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3459 |
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Author |
Debora Gil; Guillermo Torres |
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A multi-shape loss function with adaptive class balancing for the segmentation of lung structures |
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2020 |
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34th International Congress and Exhibition on Computer Assisted Radiology & Surgery |
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Virtual; June 2020 |
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CARS |
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IAM; 600.139; 600.145 |
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Admin @ si @ GiT2020 |
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3472 |
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Author |
Debora Gil; Oriol Ramos Terrades; Raquel Perez |
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Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution |
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Conference Article |
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2020 |
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Women in Geometry and Topology |
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Barcelona; September 2019 |
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Notes |
IAM; DAG; 600.139; 600.145; 600.121 |
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no |
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Admin @ si @ GRP2020 |
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3473 |
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