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Artificial Intelligence (AI) has emerged in popularity recently, recording great progress in various industries. However, the environmental impact of AI is a growing concern, in terms of the energy consumption and carbon footprint of Machine Learning (ML) and Deep Learning (DL) models, making essential investigate Green AI, an attempt to reduce the climate impact of AI systems. This paper presents an assessment of different programming languages and Feature Selection (FS) methods to improve computation performance of AI focusing on Network Intrusion Detection (NID) and cyber-attack classification tasks. Experiments were conducted using five ML models - Random Forest, XGBoost, LightGBM, Multi-Layer Perceptron, and Long Short-Term Memory - implemented in four programming languages - Python, Java, R, and Rust - along with three FS methods - Information Gain, Recursive Feature Elimination, and Chi-Square. The obtained results demonstrated that FS plays an important role enhancing the computational efficiency of AI models without compromising detection accuracy, highlighting languages like Python and R, that benefit from a rich AI libraries environment. These conclusions can be useful to design efficient and sustainable AI systems that still provide a good generalization and a reliable detection., Comment: 16 pages, 9 tables, FPS 2024 conference

When a dynamical system is subject to a periodic perturbation, the averaging method can be applied to obtain an autonomous leading order `guiding system', placing the time dependence at higher orders. Recent research focused on investigating invariant structures in non-autonomous differential systems arising from hyperbolic structures in the guiding system, such as periodic orbits and invariant tori. The effect that bifurcations in the guiding system have on the original non-autonomous one has also been recently explored. This paper extends the study by providing a broader description of the dynamics that can emerge from non-hyperbolic structures of the guiding system. Specifically, we prove here that $K$-universal bifurcations in the guiding system persist in the original non-autonomous one, while non-versal bifurcations, such as the transcritical and pitchfork, do not, being instead perturbed into stable bifurcation families. We illustrate the results on examples of a fold, a transcritical, a pitchfork, and a saddle-focus. By applying these results to the physical scenario of systems with time-varying parameters, we show that the average parameter value becomes a bifurcation parameter of the averaged system.

Non-unitary $3 \times 3$ mixing matrices are present in many extensions of the Standard Model. One of the most simple extensions is the addition of $n_R$ right-handed neutrinos without imposing Lepton Number Conservation ($n_R$$\nu$SM). In the region of the parameter space where the usual seesaw approximation is valid, deviations from $3 \times 3$ unitarity of the leptonic mixing matrix are negligible, while one has three light neutrinos and two or three heavy neutrinos with masses close to the GUT scale. This gap between the mass scales occurs while having order 1 Yukawa couplings, providing a natural explanation for the smallness of light neutrino masses - hence the name seesaw. Other regions of the parameter space, with lighter heavy neutrinos - with an eV or keV mass, for instance - and bigger deviations from unitarity of the leptonic mixing matrix, are still allowed by experiment. In fact, whenever heavy neutrino masses are many orders of magnitude below the GUT scale there are interesting phenomenological implications at low energies. To analyse such regions with precision, one needs an exact parameterisation, since approximations that were valid in the usual seesaw case now fail. This thesis will consist of a study and classification of regions of the 3$\nu$SM parameter space in the light of a newly-developed exact parameterisation. Furthermore, it will also include a study of models with vector-like quarks, another simple extension of the SM, that contains a $3 \times 3$ non-unitary quark mixing matrix. Studying the most interesting regions of the parameter space of vector-like quarks models also benefits from the use of the aforementioned exact parameterisation. These regions are phenomenologically rich and may explain certain open questions like the CKM unitarity problem/Cabibbo angle anomaly. A chapter discussing the experimental implications of these models is also included., Comment: PhD Thesis, 186 pages, Abstract in English and Portuguese

Floquet's Theorem is a celebrated result in the theory of ordinary differential equations. Essentially, the theorem states that, when studying a linear differential system with $T$-periodic coefficients, we can apply a, possibly complex, $T$-periodic change of variables that transforms it into a linear system with constant coefficients. In this paper, we explore further the question of the nature of this change of variables. We state necessary and sufficient conditions for it to be real and $T$-periodic. Failing those conditions, we prove that we can still find a real change of variables that is "partially" $T$-periodic and "partially" $T$-antiperiodic. We also present applications of this new form of Floquet's Theorem to the study of the behavior of solutions of nonlinear differential systems near periodic orbits.

Important information about the dynamical structure of a differential system can be revealed by looking into its invariant compact manifolds, such as equilibria, periodic orbits, and invariant tori. This knowledge is significantly increased if asymptotic properties of the trajectories nearby such invariant manifolds can be determined. In this paper, we present a result providing sufficient conditions for the existence of invariant tori in perturbative differential systems. The regularity, convergence, and stability of such tori as well as the dynamics defined on them are also investigated. The conditions are given in terms of their so-called higher order averaged equations. This result is an extension to a wider class of differential systems of theorems due to Krylov, Bogoliubov, Mitropolsky, and Hale.

We review the theory and phenomenology of isosinglet vector-like quarks (VLQs). In recent years, interest in VLQs has been increasing, due to their contributions to new physics effects that can be tested in experiments at LHC and High-Luminosity LHC. The similarities of models with isosinglet VLQs and the seesaw framework in the leptonic sector are pointed out. The existence of VLQs leads to flavour-changing neutral currents at tree level and deviations from unitarity of the CKM matrix, introducing rich phenomenological implications. These new effects are naturally suppressed by the masses of the new quarks, that are constrained to be above the electroweak scale. In addition, striking new effects can be achieved with the inclusion of an extra complex scalar singlet. Such a minimal extension of the SM can give rise to new sources of CP violation with profound theoretical implications, allowing for a solution to the strong CP problem and a possible explanation for the baryon asymmetry of the Universe. We list and explain strong motivations to consider this class of models. We also briefly review how models with VLQs can be matched to the SM effective field theory (SMEFT). A detailed analysis of flavour observables that can be affected by the presence of VLQs is presented. Current bounds from collider searches of VLQs are summarized. We point out that the discovery of VLQs can be within the reach of present or future colliders being planned., Comment: 121 pages, 7 figures, 7 tables. Work intended as a review. Comments and references added; typos corrected. New appendix on UV completions; section 2.3 expanded. Matches version published in Physics Reports

Hilbert's 16th Problem, about the maximum number of limit cycles of planar polynomial vector fields of a given degree $m$, has been one of the most important driving forces for new developments in the qualitative theory of vector fields. Increasing the dimension, one cannot expect the existence of a finite upper bound for the number of limit cycles of, for instance, $3$D polynomial vector fields of a given degree $m$. Here, as an extension of such a problem in the $3$D space, we investigate the number of isolated invariant tori in $3$D polynomial vector fields. In this context, given a natural number $m$, we denote by $N(m)$ the upper bound for the number of isolated invariant tori of $3$D polynomial vector fields of degree $m$. Based on a recently developed averaging method for detecting invariant tori, our first main result provides a mechanism for constructing $3$D differential vector fields with a number $H$ of normally hyperbolic invariant tori from a given planar differential vector field with $H$ hyperbolic limit cycles. The strength of our mechanism in studying the number $N(m)$ lies in the fact that the constructed $3$D differential vector field is polynomial provided that the given planar differential vector field is polynomial. Accordingly, our second main result establishes a lower bound for $N(m)$ in terms of lower bounds for the number of hyperbolic limit cycles of planar polynomial vector fields of degree $[m/2]-1$. Based on this last result, we apply a methodology due to Christopher & Lloyd to show that $N(m)$ grows as fast as $m^3/128$. Finally, the above-mentioned problem is also formulated for higher dimensional polynomial vector fields.

Microservices architecture has been widely adopted to develop software systems, but some of its trade-offs are often ignored. In particular, the introduction of eventual consistency has a huge impact on the complexity of the application business logic design. Recent proposals to use transactional causal consistency in serverless computing seems promising, because it reduces the number of possible concurrent execution anomalies that can occur due to the lack of isolation. We propose an extension of the aggregate concept, the basic building block of microservices design, that is transactional causal consistent compliant. A simulator for the enriched aggregates is developed to allow the experimentation of this approach with a business logic rich system. The experiment results shown a reduction of the implementation complexity. The simulator is a publicly available reusable artifact that can be used in other experiments., Comment: 11 pages, 6 figures

Determining the existence of compact invariant manifolds is a central quest in the qualitative theory of differential equations. Singularities, periodic solutions, and invariant tori are examples of such invariant manifolds. A classical and useful result from the averaging theory relates the existence of isolated periodic solutions of non-autonomous periodic differential equations, given in a specific standard form, with the existence of simple singularities of the so-called guiding system, which is an autonomous differential equation given in terms of the first non-vanishing higher order averaged function. In this paper, we provide an analogous result for the existence of invariant tori. Namely, we show that a non-autonomous periodic differential equation, given in the standard form, has a normally hyperbolic invariant torus in the extended phase space provided that the guiding system has a hyperbolic limit cycle. We apply this result to show the existence of normally hyperbolic invariant tori in a family of jerk differential equations.

The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

This work analyses a class of extensions of the Standard Model through the lens of a novel exact parameterisation. This parameterisation is specially suitable to the analysis of extensions of the Standard Model with non-unitary mixing matrices, like models with vector-like fermions or right-handed neutrinos. The usefulness of this parameterisation is motivated with two example models: A) Standard Model with the addition of $n_u$ up and $n_d$ down singlet vector-like quarks and B) Standard Model with the addition of $n_R$ right-handed neutrinos., Comment: Proceedings of the Corfu Summer Institute 2021 "Workshop on the Standard Model and Beyond" August 29 - September 8, 2021, 9 pages. Typo in the 1st version: In equation 24 $Z$ should be $\overline{K}$. Versions 3 and 4: Clarified notation in the neutrino section

We point out that hints of deviations from unitarity in the first row of the CKM matrix may be explained by the presence of a single vector-like top. We study how the stringent experimental constraints arising from CP Violation in the kaon sector and from meson mixing such as $D^0$-$\overline{D}^0$, $K^0$-$\overline{K}^0$ and $B^0_{d,s}$-$\overline{B}^0_{d,s}$ can be satisfied in the proposed framework. In order for the deviations from unitarity to be of the required size while keeping the theory perturbative, the new top quark should have a mass $m_T \lesssim 7$ TeV which could be probed in upcoming experiments at the energy frontier., Comment: 24 pages, 4 figures, 2 tables; comments and references added

This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features. Experiments were held, using the six machine type datasets of the challenge. Overall, competitive results were achieved by the proposed dense and convolutional AE, outperforming the baseline challenge method., Comment: 5 pages, 3 figures, submitted for DCase 2020 challenge

We consider seesaw type-I models including at least one (mostly-)sterile neutrino with mass at the eV scale. Three distinct situations are found, where the presence of light extra neutrinos is naturally justified by an approximately conserved lepton number symmetry. To analyse these scenarios consistently, it is crucial to employ an exact parametrisation of the full mixing matrix. We provide additional exact results, including generalised versions of the seesaw relation and of the Casas-Ibarra parametrisation, valid for every scale of seesaw. We find that the existence of a light sterile neutrino imposes an upper bound on the lightest neutrino mass. We further assess the impact of light sterile states on short- and long-baseline neutrino oscillation experiments, emphasise future detection prospects, and address CP Violation in this framework via the analysis of CP asymmetries and construction of weak basis invariants. The proposed models can accommodate enough active-sterile mixing to play a role in the explanation of short-baseline anomalies., Comment: 40 pages, 3 figures, 4 tables

Segmentation is a key stage in dermoscopic image processing, where the accuracy of the border line that defines skin lesions is of utmost importance for subsequent algorithms (e.g., classification) and computer-aided early diagnosis of serious medical conditions. This paper proposes a novel segmentation method based on Local Binary Patterns (LBP), where LBP and K-Means clustering are combined to achieve a detailed delineation in dermoscopic images. In comparison with usual dermatologist-like segmentation (i.e., the available ground-truth), the proposed method is capable of finding more realistic borders of skin lesions, i.e., with much more detail. The results also exhibit reduced variability amongst different performance measures and they are consistent across different images. The proposed method can be applied for cell-based like segmentation adapted to the lesion border growing specificities. Hence, the method is suitable to follow the growth dynamics associated with the lesion border geometry in skin melanocytic images., Comment: submitted to MIPRO DC 2019

Iterative geostatistical seismic inversion integrates seismic and well data to infer the spatial distribution of subsurface elastic properties. These methods provide limited assessment to the spatial uncertainty of the inverted elastic properties, overlooking alternative sources of uncertainty such as those associated with poor well-log data, upscaling and noise within the seismic data. We express uncertain well-log samples, due to bad logging reads and upscaling, in terms of local probability distribution functions (PDFs). Local PDFs are used as conditioning data to a stochastic sequential simulation algorithm, included as the model perturbation within the inversion. The problem of noisy seismic and narrow exploration of the model parameter space, particularly at early steps of the inversion, is tackled by the introduction of a cap on local correlation coefficients responsible for the generation of the new set of models during the next iteration. A single geostatistical framework is proposed and illustrated with its application to a real case study. When compared against a conventional iterative geostatistical seismic inversion, the integration of additional sources of uncertainty increases the match between real and inverted seismic traces and the variability within the ensemble of models inverted at the last iteration. The selection of the local PDFs plays a central role in the reliability of the inverted elastic models. Avoiding high local correlation coefficients at early stages of the inversion increases convergence in terms of global correlation between synthetic and real seismic reflection data at the end of the inversion., Comment: 13 Figures

Machine Learning techniques have been used to teach computer programs how to play games as complicated as Chess and Go. These were achieved using powerful tools such as Neural Networks and Parallel Computing on Supercomputers. In this paper, we define a model of populational growth and evolution based on the idea of Reinforcement Learning, but using only the 3 sources stated in the title processed on a low-tier laptop. The model correctly predicts the development of a population around food sources and their migration in search of a new one when the known ones become saturated. Additionally, we compared our model to a pure random one and the population number was fitted to a logistic function for two interesting evolutions of the system.

We address the question of deviations from $3\times 3$ unitarity of the leptonic mixing matrix showing that, contrary to conventional wisdom, one may have significant deviations from unitarity in the framework of type I seesaw mechanism. In order for this scenario to be feasible, at least one of the heavy neutrinos must have a mass at the TeV scale, while the other two may have much larger masses. We present specific examples where deviations from $3\times 3$ unitarity are sufficiently small to conform to all the present stringent experimental bounds but are sufficiently large to have the potential for being detectable at the next round of experiments., Comment: 17 pages, 4 figures, 3 tables