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[EN] Accessibility to historical documents is mostly limited to scholars. This is due to the language barrier inherent in human language and the linguistic properties of these documents. Given a historical document, modernization aims to generate a new version of it, written in the modern version of the document's language. Its goal is to tackle the language barrier, decreasing the comprehension difficulty and making historical documents accessible to a broader audience. In this work, we proposed a new neural machine translation approach that profits from modern documents to enrich its systems. We tested this approach with both automatic and human evaluation, and conducted a user study. Results showed that modernization is successfully reaching its goal, although it still has room for improvement. ; The authors wish to thank the anonymous reviewers for their careful reading and in-depth criticisms and suggestions. The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valenciana (2014-2020) under project Sistemas de frabricacion inteligentes para la industria 4.0 (grant agreement IDIFEDER/2018/025); from Ministerio de Economia y Competitividad (MINECO) under project MISMIS-FAKEnHATE (grant agreement PGC2018-096212-B-C31); from Fundacion BBVA under project Carabela (grant agreement CARABELA); and from Generalitat Valenciana (GVA) under project DeepPattern (grant agreement PROMETEO/2019/121). We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research, and Andres Trapiello and Ediciones Destino for granting us permission to use their book in our research. Additionally, we would like to thank all the volunteers that took part in the user study, and the scholars from Prolope that took part in the human evaluation. ; Domingo-Ballester, M.; Casacuberta Nolla, F. (2020). Modernizing historical documents: A user Study. Pattern Recognition ...

We discuss sincere voting when voters have cardinal preferences over alternatives. We interpret sincerity as opposed to strategic voting, and thus define sincerity as the optimal behaviour when conditions to vote strategically vanish. When voting mechanisms allow for only one message type we show that this optimal behaviour coincides with an intuitive and common definition of sincerity. For voting mechanisms allowing for multiple message types, such as approval voting (AV), there exists no conclusive definition of sincerity in the literature. We show that for AV, voters' optimal strategy tends to one of the existent definitions of sincerity, consisting in voting for those alternatives that yield more than the average of cardinal utilities. ; Financial support from Ministerio de Ciencia y Tecnología through grants BEC 2005-00836, SEJ2005-01481/ECON and FEDER, Generalitat de Catalunya through grant 2005SGR00454 and Barcelona Economics-XREA is gratefully acknowledged.

This essay deals with the notion and content of freedom of choice proposing a new set up and a new family of measures for this concept which is, indeed, an ethical value of paramount importance in a well ordered and open society. Following some ideas of John StuartMill, we propose that freedom of choice has to be understood not in a single stage of choice, but in the ordered collection of choices that a person can make in her life.We then suggest to represent a life in a tree structure, where each node represents a state of life and the edges between nodes will represent possible decisions in life. In this new framework, we propose a set of axioms that imply the following family of measures of lifetime's freedom of choice: the lifetime's freedom of choice has to be evaluated by a weighted sum of all possible states of life an individual might visit, with weights representing the number of decisions the individual took to reach that state.

A coordinate system parameterizing the interior of organs is/na powerful tool for a systematic localization of injured tissue. If the same/ncoordinate values are assigned to speci c anatomical sites, parameterizations/nensure integration of data across di erent medical image modalities./nHarmonic mappings have been used to produce parametric meshes over/nthe surface of anatomical shapes, given their /nexibility to set values at/nspeci c locations through boundary conditions. However, most of the existing/nimplementations in medical imaging restrict to either anatomical/nsurfaces, or the depth coordinate with boundary conditions is given at/nsites of limited geometric diversity. In this paper we present a method for/nanatomical volumetric parameterization that extends current harmonic/nparameterizations to the interior anatomy using information provided by/nthe volume medial surface. We have applied the methodology to de ne/na common reference system for the liver shape and functional anatomy./nThis reference system sets a solid base for creating anatomical models of/nthe patient's liver, and allows comparing livers from several patients in/na common framework of reference. ; This research has been funded by the Catalan project 2009-TEM-00007, Spanish/nprojects TIN2009-13618, TIN2012-3311, and the European Union FP7 grant agreement/nno. HEAR-EU 304857

Trabecular bone fracture is a traumatic and localized event studied worldwide in order to predict it. During the years, researchers focused over the mechanical characterization of the trabecular tissue to understand its mechanics. Several studies pointed out the very local nature of the trabecular failure, finally identifying the fracture zone with the aim to study it separately. The complexity of the three-dimensional trabecular framework and the local nature of the fracture event do not allow the direct evaluation of a single trabecula's behavior within its natural environment. For this reason, micro-Finite Element Modeling has been seen as the best way to investigate this biomechanical issue. Mechanical strain analysis is adopted in the literature for the identification of micro fracture using criteria based on principal strains. However, it was never verified if the fracture zone is actually the zone where principal strains are concentrated. Here, we show how the maximum strain of the tissue might not be directly correlated to the fracture. In the present work, a previously validated technique was used to identify the fracture zone of 10 trabecular specimen mechanically tested in compression and scanned in micro-CT before and after the mechanical test. Before-compression datasets were used to develop 10 micro-FE models were the same boundary conditions of the mechanical test were reproduced. Our results show how the known linear behavior of the trabecular framework might not be directly related to the development of the fracture suggesting other non-linear phenomenon, like buckling or microdamage, as actual cause of the traumatic event. This result might have several implications both in micro-modeling and in clinical applications for the study of fracture related pathology, like osteoporosis. ; The micro-CT datasets were produced by Laboratorio di Tecnologia Medica, Istituto Ortopedico Rizzoli, Bologna, Italy, with the financial support of the EU project LHDL (IST-2004-026932). This work was partially supported by the Spanish Government (project number RYC—2015-18888) and by Chair QUAES-UPF Computational Technologies for Healthcare.

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering. ; This work is partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the European Union Seventh Frame Programme (FP7/2007-2013), Grant agreement 304857, HEAR-EU project.

An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta, that if not treated, tends to grow and may rupture. The most common treatment for AAAs is the endovascular aneurysm repair (EVAR), which requires that patients undergo Computed Tomography Angiography (CTA)-based post-operative lifelong surveillance due to the possible appearance of complications. These complications may again lead to AAA dilation and rupture. However, there is a lack of advanced quantitative image-analysis tools to support the clinicians in the follow-up. Currently, the approach is to evaluate AAA diameter changes along time to infer the progress of the patient and the post-operative risk of AAA rupture. An increased AAA diameter is usually associated with a higher rupture risk, but there are some small AAAs that rupture, whereas other larger aneurysms remain stable. This means that the diameter-based rupture risk assessment is not suitable for all the cases, and there is increasing evidence that the biomechanical behavior of the AAA may provide additional valuable information regarding the progression of the disease and the risk of rupture. Hence, we propose a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis. From these strains, quantitative image-based descriptors are extracted using a principal component analysis of the tensile and compressive strain fields. Evaluated on 22 patients, our approach yields a mean area under the curve of 88.6% when correlating the strain-based quantitative descriptors with the long-term patient prognosis. This suggests that the strain information directly extracted from the CTA images is able to capture the biomechanical behavior of the aneurysm without relying on finite element modeling and simulation. Furthermore, the extracted descriptors set the basis for possible future imaging biomarkers that may be used in clinical practice. Apart from the diameter, these biomarkers may be used to assess patient prognosis and to enable informed decision making after an EVAR intervention, especially in difficult uncertain cases. ; This research was undertaken as part of the MEASURE-Análisis basado en iMagen de la Evolución de Aneurismas de aorta abdominal tratados mediante Reparación Endovascular project funded by the Basque Government. Funding from the Spanish Government (RYC-2015-18888) is also acknowledged.

[EN] We introduce a demonstration of our system, which implements online learning for neural machine translation in a production environment. These techniques allow the system to continuously learn from the corrections provided by the translators. We implemented an end-to-end platform integrating our machine translation servers to one of the most common user interfaces for professional translators: SDL Trados Studio. Our objective was to save post-editing effort as the machine is continuously learning from human choices and adapting the models to a specific domain or user style. ; The research leading to these results has received funding from the Spanish Centre for Technological and Industrial Development (Centro para el Desarrollo Tecnologico Industrial) (CDTI) and ¿ the European Union through Programa Operativo de Crecimiento Inteligente (Project IDI20170964). We gratefully acknowledge the support of NVIDIA Corporation with the donation of a GPU used for part of this research. ; Domingo-Ballester, M.; García-Martínez, M.; Estela, A.; Bié, L.; Helle, A.; Peris, Á.; Casacuberta Nolla, F. (2019). Demonstration of a Neural Machine Translation System with Online Learning for Translators. Association for Computational Linguistics. 70-74. http://hdl.handle.net/10251/180931 ; S ; 70 ; 74

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process. ; This work was partly supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Program (MDM-2015-0502), by the AGAUR grant 2016-PROD-00047, the European Union Seventh Framework Program (FP7/2007-2013), Grant agreement 304857, HEAR-EU project and the QUAES Foundation Chair for Computational Technologies for Healthcare.

Comunicació presentada al MICCAI 2019: Medical Image Computing and Computer Assisted Intervention, celebrat del 13 al 17 d'octubre de 2019 a Shenzhen, Xina. ; Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement. Our strategies are derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allows us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning "easy" examples and move towards "hard", the model can reach a better performance, even with fewer data. The evaluation is performed on the classification of a clinical dataset of about 1000 X-ray images. Our results show that, compared to class-uniform and random strategies, the proposed medical knowledge-based curriculum, performs up to 15% better in terms of accuracy, achieving the performance of experienced trauma surgeons. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 713673 and by the Spanish Ministry of Economy [MDM-2015-0502]. A. Jiménez-Sánchez has received financial support through the "la Caixa" Foundation (ID Q5850017D), fellowship code: LCF/BQ/IN17/11620013. D. Mateus has received funding from Nantes Métropole and the European Regional Development, Pays de la Loire, under the Connect Talent scheme. Authors thank Nvidia for the donation of a GPU.

Comunicació presentada a la 9th international conference on Functional Imaging and Modeling of the Heart (FIMH 2017), celebrada els dies 11 a 13 de juny de 2017 a Toronto, Canadà. ; Many cardiac diseases are associated with changes in ventricular shape. However, in daily practice, the heart is mostly assessed by 2D echocardiography only. While 3D techniques are available, they are rarely used. In this paper we analyze to which extent it is possible to obtain the 3D shape of a left ventricle (LV) using measurements from 2D echocardiography. First, we investigate this using synthetic datasets, and afterwards, we illustrate it in clinical 2D echocardiography measurements with corresponding 3D meshes obtained using 3D echocardiography. We demonstrate that standard measurements taken in 2D allow quantifying only the ellipsoidal shape of the ventricle, and that capturing other shape features require either additional geometrical measurements or clinical information related to shape remodelling. We show that noise in the measurements is the primary cause for poor association between the measurements and the LV shape features and that an estimated 10% level of noise on the 2D measurements limits the recoverability of shape. Finally we show that clinical variables relating to the clinical history can substitute the lack of geometric measurements, thus providing alternatives for shape assessment in daily practice. ; This study was partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014-52923-R; Maria de Maeztu Units of Excellence Programme - MDM-2015-0502), FEDER and the European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion).