Publications

by Keyword: Networks


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Ruzzene, G., Omelchenko, I., Schöl, E., Zakharova, A., Andrzejak, R. G. , (2019). Controlling chimera states via minimal coupling modification Chaos: An Interdisciplinary Journal of Nonlinear Science 29, (5), 051103

We propose a method to control chimera states in a ring-shaped network of nonlocally coupled phase oscillators. This method acts exclusively on the network’s connectivity. Using the idea of a pacemaker oscillator, we investigate which is the minimal action needed to control chimeras. We implement the pacemaker choosing one oscillator and making its links unidirectional. Our results show that a pacemaker induces chimeras for parameters and initial conditions for which they do not form spontaneously. Furthermore, the pacemaker attracts the incoherent part of the chimera state, thus controlling its position. Beyond that, we find that these control effects can be achieved with modifications of the network’s connectivity that are less invasive than a pacemaker, namely, the minimal action of just modifying the strength of one connection allows one to control chimeras.

Keywords: Complex networks, Oscillators, Spatiotemporal phenomena


Páez-Avilés, C., van Rijnsoever, F. J., Juanola-Feliu, E., Samitier, J., (2018). Multi-disciplinarity breeds diversity: the influence of innovation project characteristics on diversity creation in nanotechnology Journal of Technology Transfer 43, (2), 458-481

Nanotechnology is an emerging and promising field of research. Creating sufficient technological diversity among its alternatives is important for the long-term success of nanotechnologies, as well as for other emerging technologies. Diversity prevents early lock-in, facilitates recombinant innovation, increases resilience, and allows market growth. Creation of new technological alternatives usually takes place in innovation projects in which public and private partners often collaborate. Currently, there is little empirical evidence about which characteristics of innovation projects influence diversity. In this paper we study the influence of characteristics of EU-funded nanotechnology projects on the creation of technological diversity. In addition to actor diversity and the network of the project, we also include novel variables that have a plausible influence on diversity creation: the degree of multi-disciplinarity of the project and the size of the joint knowledge base of project partners. We apply topic modelling (Latent Dirichlet allocation) as a novel method to categorize technological alternatives. Using an ordinal logistic regression model, our results show that the largest contribution to diversity comes from the multi-disciplinary nature of a project. The joint knowledge base of project partners and the geographical distance between them were positively associated with technological diversity creation. In contrast, the number and diversity of actors and the degree of clustering showed a negative association with technological diversity creation. These results extend current micro-level explanations of how the diversity of an emerging technology is created. The contribution of this study could also be helpful for policy makers to influence the level of diversity in a technological field, and hence to contribute to survival of emerging technologies.

Keywords: Innovation projects, Multi-disciplinarity, Nanotechnology, Social networks, Technological diversity, Topic models


Andrzejak, R. G. , Ruzzene, G., Malvestio, I., Schindler, K., Schöl, E., Zakharova, A., (2018). Mean field phase synchronization between chimera states Chaos: An Interdisciplinary Journal of Nonlinear Science 28, (9), 091101

We study two-layer networks of identical phase oscillators. Each individual layer is a ring network for which a non-local intra-layer coupling leads to the formation of a chimera state. The number of oscillators and their natural frequencies is in general different across the layers. We couple the phases of individual oscillators in one layer to the phase of the mean field of the other layer. This coupling from the mean field to individual oscillators is done in both directions. For a sufficient strength of this interlayer coupling, the phases of the mean fields lock across the two layers. In contrast, both layers continue to exhibit chimera states with no locking between the phases of individual oscillators across layers, and the two mean field amplitudes remain uncorrelated. Hence, the networks’ mean fields show phase synchronization which is analogous to the one between low-dimensional chaotic oscillators. The required coupling strength to achieve this mean field phase synchronization increases with the mismatches in the network sizes and the oscillators’ natural frequencies.

Keywords: Chaos, Complex networks, Oscillators, Synchronisation


Arsiwalla, X. D., Pacheco, D., Principe, A., Rocamora, R., Verschure, P., (2018). A temporal estimate of integrated information for intracranial functional connectivity Artificial Neural Networks and Machine Learning (Lecture Notes in Computer Science) 27th International Conference on Artificial Neural Networks (ICANN 2018) , Springer, Cham (Rhodes, Greece) 11140, 403-412

A major challenge in computational and systems neuroscience concerns the quantification of information processing at various scales of the brain’s anatomy. In particular, using human intracranial recordings, the question we ask in this paper is: How can we estimate the informational complexity of the brain given the complex temporal nature of its dynamics? To address this we work with a recent formulation of network integrated information that is based on the Kullback-Leibler divergence between the multivariate distribution on the set of network states versus the corresponding factorized distribution over its parts. In this work, we extend this formulation for temporal networks and then apply it to human brain data obtained from intracranial recordings in epilepsy patients. Our findings show that compared to random re-wirings of the data, functional connectivity networks, constructed from human brain data, score consistently higher in the above measure of integrated information. This work suggests that temporal integrated information may indeed be a good starting point as a future measure of cognitive complexity.

Keywords: Brain networks, Complexity measures, Computational neuroscience, Functional connectivity


Puigbò, J. Y., Arsiwalla, X. D., Verschure, P., (2018). Challenges of machine learning for living machines Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018 (Lecture Notes in Computer Science) , Springer International Publishing (Paris, France) 10928, 382-386

Machine Learning algorithms (and in particular Reinforcement Learning (RL)) have proved very successful in recent years. These have managed to achieve super-human performance in many different tasks, from video-games to board-games and complex cognitive tasks such as path-planning or Theory of Mind (ToM) on artificial agents. Nonetheless, this super-human performance is also super-artificial. Despite some metrics are better than what a human can achieve (i.e. cumulative reward), in less common metrics (i.e. time to learning asymptote) the performance is significantly worse. Moreover, the means by which those are achieved fail to extend our understanding of the human or mammal brain. Moreover, most approaches used are based on black-box optimization, making any comparison beyond performance (e.g. at the architectural level) difficult. In this position paper, we review the origins of reinforcement learning and propose its extension with models of learning derived from fear and avoidance behaviors. We argue that avoidance-based mechanisms are required when training on embodied, situated systems to ensure fast and safe convergence and potentially overcome some of the current limitations of the RL paradigm.

Keywords: Avoidance, Neural networks, Reinforcement learning


Vouloutsi, Vasiliki, Halloy, José, Mura, Anna, Mangan, Michael, Lepora, Nathan, Prescott, T. J., Verschure, P., (2018). Biomimetic and Biohybrid Systems 7th International Conference, Living Machines 2018, Paris, France, July 17–20, 2018, Proceedings , Springer International Publishing (Lausanne, Switzerland) 10928, 1-551

This book constitutes the proceedings of the 7th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2018, held in Paris, France, in July 2018. The 40 full and 18 short papers presented in this volume were carefully reviewed and selected from 60 submissions. The theme of the conference targeted at the intersection of research on novel life-like technologies inspired by the scientific investigation of biological systems, biomimetics, and research that seeks to interface biological and artificial systems to create biohybrid systems.

Keywords: Artificial neural network, Bio-actuators, Bio-robotics, Biohybrid systems, Biomimetics, Bipedal robots, Earthoworm-like robots, Robotics, Decision-making, Tactile sensing, Soft robots, Locomotion, Insects, Sensors, Actuators, Robots, Artificial intelligence, Neural networks, Motion planning, Learning algorithms


Venkova, Tatiana, Juárez, Antonio, Espinosa, Manuel, (2017). Editorial: Modulating prokaryotic lifestyle by DNA-binding proteins: Learning from (apparently) simple systems Frontiers in Molecular Biosciences , 3, Article 86

Within the research in Molecular Biology, one important field along the years has been the analyses on how prokaryotes regulate the expression of their genes and what the consequences of these activities are. Prokaryotes have attracted the interests of researchers not only because the processes taking place in their world are important to cells, but also because many of the effects often can be readily measured, both at the single cell level and in large populations. Contributing to the interest of the present topic is the fact that modulation of gene activity involves the sensing of intra- and inter-cellular conditions, DNA binding and DNA dynamics, and interaction with the replication/transcription machinery of the cell. All of these processes are fundamental to the operation of a biological entity and they condition its lifestyle. Further, the discoveries achieved in the bacterial world have been of ample use in eukaryotes. In addition to the fundamental interest of understanding modulation of prokaryotic lifestyle by DNA-binding proteins, there is an added interest from the healthcare point of view. As it is well-known the antibiotic-resistance strains of pathogenic bacteria are a major world problem, so that there is an urgent need of innovative approaches to tackle it. Human and animal infectious diseases impose staggering costs worldwide in terms of loss of human life and livestock, diminished productivity, and the heavy economic burden of disease. The global dimension of international trade, personal travel, and population migration expands at an ever-accelerating rate. This increasing mobility results in broader and quicker dissemination of bacterial pathogens and in rapid spread of antibiotic resistance. The majority of the newly acquired resistances are horizontally spread among bacteria of the same or different species by processes of lateral (horizontal) gene transfer, so that discovery of new antibiotics is not the definitive solution to fighting infectious diseases. There is an absolute need of finding novel alternatives to the “classical” approach to treat infections by bacterial pathogens, and these new ways must include the exploration and introduction of novel antibacterials, the development of alternative strategies, and the finding of novel bacterial targets. However, all these approaches will result in a stalemate if we, researchers, are not able to achieve a better understanding of the mechanistic processes underlying bacterial gene expression. It is, then, imperative to continue gaining insight into the basic mechanisms by which bacterial cells regulate the expression of their genes. That is why our Research Topic hosted by Frontiers in Molecular Biosciences was timely, and the output of it offers novel and up-to-date points of view to the “simple” bacterial world.

Keywords: DNA-protein interactions, Gene regulation in Prokaryotes, Replication control, Regulation of Bacterial Gene Expression, Global Regulatory Networks


Aviles, A. I., Alsaleh, S. M., Hahn, J. K., Casals, A., (2017). Towards retrieving force feedback in robotic-assisted surgery: A supervised neuro-recurrent-vision approach IEEE Transactions on Haptics , 10, (3), 431-443

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.

Keywords: Computer-assisted surgery, Deep networks, Force estimation, Visual deformation


Estrada, L., Torres, A., Sarlabous, L., Jané, R., (2016). Evaluating respiratory muscle activity using a wireless sensor platform Engineering in Medicine and Biology Society (EMBC) 38th Annual International Conference of the IEEE , IEEE (Orlando, USA) , 5769-5772

Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearson's correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.

Keywords: Biomedical monitoring, Electrodes, Medical services, Monitoring, Muscles, Wireless communication, Wireless sensor networks


Estrada, Luis, Torres, Abel, Sarlabous, Leonardo, Fiz, Jose A., Gea, Joaquim, Martinez-Llorens, Juana, Jané, Raimon, (2014). Estimation of bilateral asynchrony between diaphragm mechanomyographic signals in patients with Chronic Obstructive Pulmonary Disease Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3813-3816

The aim of the present study was to measure bilateral asynchrony in patients suffering from Chronic Obstructive Pulmonary Disease (COPD) performing an incremental inspiratory load protocol. Bilateral asynchrony was estimated by the comparison of respiratory movements derived from diaphragm mechanomyographic (MMGdi) signals, acquired by means of capacitive accelerometers placed on left and right sides of the rib cage. Three methods were considered for asynchrony evaluation: Lissajous figure, Hilbert transform and Motto's algorithm. Bilateral asynchrony showed an increase at 20, 40 and 60% (values of normalized inspiratory pressure by their maximum value reached in the last inspiratory load) while the very severe group showed and increase at 20, 40, 80, and 100 % during the protocol. These increments in the phase's shift can be due to an increase of the inspiratory load along the protocol, and also as a consequence of distress and fatigue. In summary, this work evidenced the capability to estimate bilateral asynchrony in COPD patients. These preliminary results also showed that the use of capacitive accelerometers can be a suitable sensor for recording of respiratory movement and evaluation of asynchrony in COPD patients.

Keywords: Accelerometers, Diseases, Estimation, Fatigue, IP networks, Protocols, Transforms


Estrada, L., Torres, A., Sarlabous, L., Fiz, J. A., Jané, R., (2014). Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals Engineering in Medicine and Biology Society (EMBC) 36th Annual International Conference of the IEEE , IEEE (Chicago, USA) , 3204-3207

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RRMmg) was compared with that measured from inspiratory pressure signal (RRP). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RRmmg and RRP measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

Keywords: Accelerometers, Band-pass filters, Biomedical measurement, Empirical mode decomposition, Estimation, IP networks, Muscles


Vinagre, M., Aranda, J., Casals, A., (2014). An interactive robotic system for human assistance in domestic environments Computers Helping People with Special Needs (ed. Miesenberger, K., Fels, D., Archambault, D., Pe, Zagler), Springer International Publishing 8548, 152-155

This work introduces an interactive robotic system for assistance, conceived to tackle some of the challenges that domestic environments impose. The system is organized into a network of heterogeneous components that share both physical and logical functions to perform complex tasks. It consists of several robots for object manipulation, an advanced vision system that supplies in-formation about objects in the scene and human activity, and a spatial augmented reality interface that constitutes a comfortable means for interacting with the system. A first analysis based on users' experiences confirms the importance of having a friendly user interface. The inclusion of context awareness from visual perception enriches this interface allowing the robotic system to become a flexible and proactive assistant.

Keywords: Accessibility, Activity Recognition, Ambient Intelligence, Human-Robot Interaction, Robot Assistance, Augmented reality, Complex networks, Computer vision, User interfaces, Accessibility, Activity recognition, Ambient intelligence, Domestic environments, Heterogeneous component, Interactive robotics, Robot assistance, Spatial augmented realities, Human assistance, Robotics


Gonzalez, H., Acevedo, H., Arizmendi, C., Giraldo, B. F., (2013). Methodology for determine the moment of disconnection of patients of the mechanical ventilation using discrete wavelet transform Complex Medical Engineering (CME) 2013 ICME International Conference , IEEE (Beijing, China) , 483-486

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform


Llorens, Franc, Hummel, Manuela, Pastor, Xavier, Ferrer, Anna, Pluvinet, Raquel, Vivancos, Ana, Castillo, Ester, Iraola, Susana, Mosquera, Ana M., Gonzalez, Eva, Lozano, Juanjo, Ingham, Matthew, Dohm, Juliane C., Noguera, Marc, Kofler, Robert, Antonio del Rio, Jose, Bayes, Monica, Himmelbauer, Heinz, Sumoy, Lauro, (2011). Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis BMC Genomics , 12, 326

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer. Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions. Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Keywords: Gene-expression measurements, Quality-control maqc, Cancer-cell-lines, Real-time pcr, Oligonucleotide microarrays, Phosphorylation dynamics, In-vivo, Networks, Signal, Technologies