Study of territorial behaviour and stress for the improvement of European wild rabbit restocking programs

The European rabbit (Oryctolagus cuniculus L.) is a keystone species that is native to the Iberian Peninsula and whose populations have undergone a dramatic decline in abundance in their natural ranges throughout the second half of the 20th century. The economic and ecological importance of the wild rabbit has now led to the need to perform management actions aimed at boosting the remaining populations and establishing new ones, particularly in Mediterranean areas. Restocking actions are one of the handiest management tools, and scientific research whose objective is to define and solve the major methodological problems is abundant in literature. However, the majority of research has focused on factors which are extrinsic to wild rabbits, such as predation, diseases and habitat, while neglecting other factors that are intrinsic to wild rabbit biology. The principal aim of this PhD thesis is to improve the efficiency of wild rabbit restocking programs by taking into account intrinsic aspects of rabbit biology such as social behaviour and stress. In order to do so, I have performed five experiments whose intention has been to test Fecal Near Infrared Reflectance Spectroscopy and indigestible faecal markers as cheap, easy, user-friendly and non-invasive tools by which territorial marking by wild rabbits can be studied; I have also measured restocked wild rabbit populations’ responsiveness to acute stressors in a non-invasive manner as a predictor of population growth during the breeding season; I have additionally studied the density-dependence phenomenon inside wild rabbit restocking plots, with the aim of providing a better management of these plots and improving their efficiency as extensive breeding sites; and finally, I have performed a supplementary experiment with wild-type rats with the intention of attaining a better understanding of the relationship between physiological stress and territorial behaviour. The first four chapters are framed..

Approximate inference on graphical models: message-passing, loop-corrected methods and applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Protection of data privacy based on artificial intelligence in Cyber-Physical Systems

With the rapid evolution of cyber attack techniques, the security and privacy of Cyber-Physical Systems (CPSs) have become key challenges. CPS environments have several properties that make them unique in efforts to appropriately secure them when compared with the processes, techniques and processes that have evolved for traditional IT networks and platforms. CPS ecosystems are comprised of heterogeneous systems, each with long lifespans. They use multitudes of operating systems and communication protocols and are often designed without security as a consideration. From a privacy perspective, there are also additional challenges. It is hard to capture and filter the heterogeneous data sources of CPSs, especially power systems, as their data should include network traffic and the sensing data of sensors. Protecting such data during the stages of collection, analysis and publication still open the possibility of new cyber threats disrupting the operational loops of power systems. Moreover, while protecting the original data of CPSs, identifying cyberattacks requires intrusion detection that produces high false alarm rates. This thesis significantly contributes to the protection of heterogeneous data sources, along with the high performance of discovering cyber-attacks in CPSs, especially smart power networks (i.e., power systems and their networks). For achieving high data privacy, innovative privacy-preserving techniques based on Artificial Intelligence (AI) are proposed to protect the original and sensitive data generated by CPSs and their networks. For cyber-attack discovery, meanwhile applying privacy-preserving techniques, new anomaly detection algorithms are developed to ensure high performances in terms of data utility and accuracy detection. The first main contribution of this dissertation is the development of a privacy preservation intrusion detection methodology that uses the correlation coefficient, independent component analysis, and Expectation Maximisation (EM) clustering algorithms to select significant data portions and discover cyber attacks against power networks. Before and after applying this technique, machine learning algorithms are used to assess their capabilities to classify normal and suspicious vectors. The second core contribution of this work is the design of a new privacy-preserving anomaly detection technique protecting the confidential information of CPSs and discovering malicious observations. Firstly, a data pre-processing technique filters and transforms data into a new format that accomplishes the aim of preserving privacy. Secondly, an anomaly detection technique using a Gaussian mixture model which fits selected features, and a Kalman filter technique that accurately computes the posterior probabilities of legitimate and anomalous events are employed. The third significant contribution of this thesis is developing a novel privacy-preserving framework for achieving the privacy and security criteria of smart power networks. In the first module, a two-level privacy module is developed, including an enhanced proof of work technique-based blockchain for accomplishing data integrity and a variational autoencoder approach for changing the data to an encoded data format to prevent inference attacks. In the second module, a long short-term memory deep learning algorithm is employed in anomaly detection to train and validate the outputs from the two-level privacy modules

Pain Management

Pain Management - Current Issues and Opinions is written by international experts who cover a number of topics about current pain management problems, and gives the reader a glimpse into the future of pain treatment. Several chapters report original research, while others summarize clinical information with specific treatment options. The international mix of authors reflects the "casting of a broad net" to recruit authors on the cutting edge of their area of interest. Pain Management - Current Issues and Opinions is a must read for the up-to-date pain clinician

Learning Identifiable Representations: Independent Influences and Multiple Views

Intelligent systems, whether biological or artificial, perceive unstructured information from the world around them: deep neural networks designed for object recognition receive collections of pixels as inputs; living beings capture visual stimuli through photoreceptors that convert incoming light into electrical signals. Sophisticated signal processing is required to extract meaningful features (e.g., the position, dimension, and colour of objects in an image) from these inputs: this motivates the field of representation learning. But what features should be deemed meaningful, and how to learn them? We will approach these questions based on two metaphors. The first one is the cocktail-party problem, where a number of conversations happen in parallel in a room, and the task is to recover (or separate) the voices of the individual speakers from recorded mixtures—also termed blind source separation. The second one is what we call the independent-listeners problem: given two listeners in front of some loudspeakers, the question is whether, when processing what they hear, they will make the same information explicit, identifying similar constitutive elements. The notion of identifiability is crucial when studying these problems, as it specifies suitable technical assumptions under which representations are uniquely determined, up to tolerable ambiguities like latent source reordering. A key result of this theory is that, when the mixing is nonlinear, the model is provably non-identifiable. A first question is, therefore, under what additional assumptions (ideally as mild as possible) the problem becomes identifiable; a second one is, what algorithms can be used to estimate the model. The contributions presented in this thesis address these questions and revolve around two main principles. The first principle is to learn representation where the latent components influence the observations independently. Here the term “independently” is used in a non-statistical sense—which can be loosely thought of as absence of fine-tuning between distinct elements of a generative process. The second principle is that representations can be learned from paired observations or views, where mixtures of the same latent variables are observed, and they (or a subset thereof) are perturbed in one of the views—also termed multi-view setting. I will present work characterizing these two problem settings, studying their identifiability and proposing suitable estimation algorithms. Moreover, I will discuss how the success of popular representation learning methods may be explained in terms of the principles above and describe an application of the second principle to the statistical analysis of group studies in neuroimaging

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Numerical simulation of the flow in model skeletal muscle ventricles

Until recently, the only realistic form of treatment available to patients in end stage heart failure was transplantation. In the last few years, the possibility of diverting skeletal muscle from its normal function to perform a cardiac assist role has emerged as a potential alternative to transplant surgery. The introduction of an Skeletal Muscle Ventricle (SMV) to the circulation is a potential long-term hazard, as the patient's blood comes into contact with the non-endothelialised surfaces of the wall of the new ventricle and the connecting conduits. This may trigger a cascade of events leading to deposition of thrombus, whose formation is dependent on the nature of the blood flow. The potential problem of haemostasis may arise in the apex of the artificial ventricle, where little mixing and large residence times may occur. There is therefore a strong need for carrying out flow analysis studies to address in detail the questions of haemostasis and thrombogenesis and in this context to evaluate possible candidate SMV configurations. Research on the dynamics of the flow inside model SMVs has been carried out on physical and numerical models with the objective of aialysing the effect of the size and shape of the ventricle and inlet/outlet orientation of the duct. Due to the physiological limit on the power available to pump the blood out of the ventricle, the efficiency of these potential assistance devices has to be maximized. It is also necessary to minimize the risks of haemolysis and thrombogenesis, which are both related, in different ways, to the level of shear stress on the wall and within the flow. A common feature of these flows is the formation of vortex rings. Vortices enhance mixing, and this is a useful process to encourage in an SMV, as it could assist in the mixing of the blood components and in the reduction of apical residence time. Being able to predict accurately the dynamics of the vortices is therefore important, as this will affect the prediction of residence times and shear stresses at the wall and within the flow. It is also very important to know whether numerical codes can predict vortex ring dynamics from both qualitative and quantitative points of view. In order to study the dynamics of the formation of these vortices, first, mathematical models were studied. The general purpose CFDS-FLOW3D code was used in all numerical simulations. Initial investigations of this research project concerned a progressive validation of the numerical solution predicted by the code when the domain where the flow is calculated had moving boundaries.Firstly, comparisons were made with the analytical solution for expanding/contracting pipes. An adapted compliant SMV model was then generated with a truncated apex using sinusoidally prescribed motion of the wall. With this model, two vortex rings could be predicted as in the experiments. The spherical-end model also gave good agreement with experimental flow patterns (ludicello et al., 1994). Frequency-dependent studies were carried out over the range of cardiac values using single- and multi-block versions of the code. A further validation exercise involved the use of sigmoidal filling curves in the in vitro models (Shortland et a!., 1994). Experimental data provided by such studies were used to drive the wall motion in the numerical simulations, and parametric studies of several simulation parameters were carried out. Flow field features and trajectories of the vortex paths were compared with the experiments for different filling curves, with reasonable agreement. However, because shear stress discontinuities occurred in the predictions a strict volume-defined analytical model was constructed for wall movement with smooth spatial and temporal behaviour reproducing experimental filling curves. Numerical predictions showed not only an improvement in the qualitative features of the flow compared with the experiments, but also a quantitative improvement in the prediction of the vortex core paths. Also the shear stress discontinuities were no longer evident. In order to be able to estimate residence times, instantaneous streamlines and particle tracks were produced. Analysis of shear stresses in the fluid and generation of particle pathlines for residence calculation in 3-D geometries will be carried out in the next feature for model candidates for the final SMV design. Some of the material published during the course of the project is included in APPENDIX 1. In this thesis, attention is paid to the SMV fluid dynamics. However, SMV behaviour is a coupled fluid-solid problem. Future work will be carried out in the muscle modelling. To this end, a careful review has been carried out, and is included in the thesis. Implications for future work are also discussed

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI