Polytechnic University of Catalonia

About: Polytechnic University of Catalonia is a education organization based out in Barcelona, Spain. It is known for research contribution in the topics: Finite element method & Population. The organization has 16006 authors who have published 45325 publications receiving 949306 citations. The organization is also known as: UPC - BarcelonaTECH & Technical University of Catalonia.

Liquid-level measurement system based on a remote grounded capacitive sensor

Abstract: This paper describes the design and implementation of a liquid-level measurement system based on a remote grounded capacitive sensor. The electrodes of the capacitive sensor are built with affordable materials: a rod of stainless steel and a PTFE-insulated wire. The interface circuit relies on a simple relaxation oscillator and a microcontroller. A cable with active shielding interconnects the sensor to the interface circuit. The stability of the active-shielding circuit is analysed by taking into account the parasitic components of both the interconnecting cable and the sensor. The system has been experimentally tested by measuring the level of tap water in a grounded metallic container. Over a level range of 70 cm, the system has a non-linearity error smaller than 0.35 mm and a resolution better than 0.10 mm for a measuring time of 20 ms.

Boosting Domain Adaptation by Discovering Latent Domains

Abstract: Current Domain Adaptation (DA) methods based on deep architectures assume that the source samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases exploiting single-source DA methods for learning target classifiers may lead to sub-optimal, if not poor, results. In addition, in many applications it is difficult to manually provide the domain labels for all source data points, i.e. latent domains should be automatically discovered. This paper introduces a novel Convolutional Neural Network (CNN) architecture which (i) automatically discovers latent domains in visual datasets and (ii) exploits this information to learn robust target classifiers. Our approach is based on the introduction of two main components, which can be embedded into any existing CNN architecture: (i) a side branch that automatically computes the assignment of a source sample to a latent domain and (ii) novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We test our approach on publicly-available datasets, showing that it outperforms state-of-the-art multi-source DA methods by a large margin.

Multifunctional Coatings and Nanotopographies: Toward Cell Instructive and Antibacterial Implants

Abstract: In biomaterials science, it is nowadays well accepted that improving the biointegration of dental and orthopedic implants with surrounding tissues is a major goal. However, implant surfaces that support osteointegration may also favor colonization of bacterial cells. Infection of biomaterials and subsequent biofilm formation can have devastating effects and reduce patient quality of life, representing an emerging concern in healthcare. Conversely, efforts toward inhibiting bacterial colonization may impair biomaterial–tissue integration. Therefore, to improve the long-term success of medical implants, biomaterial surfaces should ideally discourage the attachment of bacteria without affecting eukaryotic cell functions. However, most current strategies seldom investigate a combined goal. This work reviews recent strategies of surface modification to simultaneously address implant biointegration while mitigating bacterial infections. To this end, two emerging solutions are considered, multifunctional chemical coatings and nanotopographical features.

Evolutionary dynamics of the cryptocurrency market.

Abstract: The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1469 cryptocurrencies introduced between April 2013 and May 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.