Statistical Mechanics Approach to Inverse Problems on Networks

Statistical Mechanics has gained a central role in modern Inference and Computer Science. Many optimization and inference problems can be cast in a Statistical Mechanics framework, and various concepts and methods developed in this area of Physics can be very helpful not only in the theoretical analysis, but also constitute valuable tools for solving single instance cases of hard inference and computational tasks. In this work, I address various inverse problems on networks, from models of epidemic spreading to learning in neural networks, and apply a variety of methods which have been developed in the context of Disordered Systems, namely Replica and Cavity methods from the theoretical side, and their algorithmic incarnation, Belief Propagation, to solve hard inverse problems which can be formulated in a Bayesian framework

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Performance Evaluation of Class A LoRa Communications

Recently, Low Power Wide Area Networks (LPWANs) have attracted a great interest due to the need of connecting more and more devices to the so-called Internet of Things (IoT). This thesis explores LoRa’s suitability and performance within this paradigm, through a theoretical approach as well as through practical data acquired in multiple field campaigns. First, a performance evaluation model of LoRa class A devices is proposed. The model is meant to characterize the performance of LoRa’s Uplink communications where both physical layer (PHY) and medium access control (MAC) are taken into account. By admitting a uniform spatial distribution of the devices, the performance characterization of the PHY-layer is studied through the derivation of the probability of successfully decoding multiple frames that were transmitted with the same spreading factor and at the same time. The MAC performance is evaluated by admitting that the inter-arrival time of the frames generated by each LoRa device is exponentially distributed. A typical LoRaWAN operating scenario is considered, where the transmissions of LoRa Class A devices suffer path-loss, shadowing and Rayleigh fading. Numerical results obtained with the modeling methodology are compared with simulation results, and the validation of the proposed model is discussed for different levels of traffic load and PHY-layer conditions. Due to the possibility of capturing multiple frames simultaneously, the maximum achievable performance of the PHY/MAC LoRa scheme according to the signal-to-interference-plus-noise ratio (SINR) is considered. The contribution of this model is primarily focused on studying the average number of successfully received LoRa frames, which establishes a performance upper bound due to the optimal capture condition considered in the PHY-layer. In the second stage of this work a practical LoRa point-to-point network was deployed to characterize LoRa’s performance in a practical way. Performance was assessed through data collected in the course of several experiments, positioning the transmitter in diverse locations and environments. This work reports statistics of the received packets and different metrics gathered from the physical-layer