Two-scale homogenization of a stationary mean-field game

In this paper, we characterize the asymptotic behavior of a first-order stationary mean-field game (MFG) with a logarithm coupling, a quadratic Hamiltonian, and a periodically oscillating potential. This study falls into the realm of the homogenization theory, and our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems, which encode the so-called macroscopic or effective behavior of the original oscillating MFG. Moreover, we prove existence and uniqueness of the solution to these limit problems.Comment: 36 page

Activation thresholds in epidemic spreading with motile infectious agents on scale-free networks

We investigate a fermionic susceptible-infected-susceptible model with mobility of infected individuals on uncorrelated scale-free networks with power-law degree distributions $P (k) \sim k^{-\gamma}$ of exponents $2<\gamma<3$. Two diffusive processes with diffusion rate $D$ of an infected vertex are considered. In the \textit{standard diffusion}, one of the nearest-neighbors is chosen with equal chance while in the \textit{biased diffusion} this choice happens with probability proportional to the neighbor's degree. A non-monotonic dependence of the epidemic threshold on $D$ with an optimum diffusion rate $D_\ast$, for which the epidemic spreading is more efficient, is found for standard diffusion while monotonic decays are observed in the biased case. The epidemic thresholds go to zero as the network size is increased and the form that this happens depends on the diffusion rule and degree exponent. We analytically investigated the dynamics using quenched and heterogeneous mean-field theories. The former presents, in general, a better performance for standard and the latter for biased diffusion models, indicating different activation mechanisms of the epidemic phases that are rationalized in terms of hubs or max $k$-core subgraphs.Comment: 9 pages, 4 figure

Disruption Prediction in Fusion Devices through Feature Extraction and Logistic Regression

This document describes an approach used in the Multi-Machine Disruption Prediction Challenge for Fusion Energy by ITU, a data science competition which ran from September to November 2023, on the online platform Zindi. The competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT - with most of the training data coming from the last two, and the test data coming from the first one. Each device has multiple diagnostics and signals, and it turns out that a critical issue in this competition was to identify which signals, and especially which features from those signals, were most relevant to achieve accurate predictions. The approach described here is based on extracting features from signals, and then applying logistic regression on top of those features. Each signal is treated as a separate predictor and, in the end, a combination of such predictors achieved the first place on the leaderboard

Framework for IoT Service Oriented Systems

The forth industrial revolution is here, and with it Industry 4.0, which translates in many changes to the industry. With the introduction of paradigms like Internet of Things, Cyber Physical Systems or Cloud Computing, the so called Smart Factories are becoming a main part of today’s manufacturing systems. The vf-OS Project, where this thesis falls, intends to be an Open Operating System for Virtual Factories where the overall network of a collaborative manufacturing and logistics environment can be managed and thus enabling humans, applications and devices to communicate and interoperate in an interconnected operative environment. This thesis intends to contribute to the vision that any kind of sensor or actuator plugged to the virtual factory network, becomes promptly accessible in the operative environment and the services that it provides can be accessed and used by any API composing the system. Finally, it also aims to prove that an IoT Service Oriented Sys-tem constituted of open software components can be of great assistance and provide numerous contributions to the emerging Industry 4.0 and consequently to the Factories of the Future. With that aim, this thesis will focus on the development of two out of five inter-connected applications that answer not only to different use case scenarios presented in the vf-OS but also provide solutions to answer a practical agriculture scenario, which uses mainly IoT devices and other cutting-edge technologies like cloud compu-ting and FIWARE

Automatic binary patching for flaws repairing using static rewriting and reverse dataflow analysis

Tese de Mestrado, Segurança Informática, 2022, Universidade de Lisboa, Faculdade de CiênciasThe C programming language is widely used in embedded systems, kernel and hardware programming, making it one of the most commonly used programming languages. However, C lacks of boundary verification of variables, making it one of the most vulnerable languages. Because of this and associated with its high usability, it is also the language with most reported vulnerabilities in the past ten years, being the memory corruption the most common type of vulnerabilities, specifically buffer overflows. These vulnerabilities when exploited can produce critical consequences, being thus extremely important not only to correctly identify these vulnerabilities but also to properly fix them. This work aims to study buffer overflow vulnerabilities in C binary programs by identifying possible malicious inputs that can trigger such vulnerabilities and finding their root cause in order to mitigate the vulnerabilities by rewriting the binary assembly code and thus generating a new binary without the original flaw. The main focus of this thesis is the use of binary patching to automatically fix stack overflow vulnerabilities and validate its effectiveness while ensuring that these do not add new vulnerabilities. Working with the binary code of applications and without accessing their source code is a challenge because any required change to its binary code (i.e, assembly) needs to take into consideration that new instructions must be allocated, and this typically means that existing instructions will need to be moved to create room for new ones and recover the control flow information, otherwise the application would be compromised. The approach we propose to address this problem was successfully implemented in a tool and evaluated with a set of test cases and real applications. The evaluation results showed that the tool was effective in finding vulnerabilities, as well as in patching them