ISEP – LEMA – Artigos
Permanent URI for this collection
Browse
Recent Submissions
- Lambert W functions in the analysis of nonlinear dynamics and bifurcations of a 2D γ-Ricker Population ModelPublication . Rocha, J. Leonel; Taha, Abdel-Kaddous; Abreu, StellaThe aim of this paper is to study the use of Lambert W functions in the analysis of nonlinear dynamics and bifurcations of a new two-dimensional 𝛾-Ricker population model. Through the use of such transcendental functions, it is possible to study the fixed points and the respective eigenvalues of this exponential diffeomorphism as analytical expressions. Consequently, the maximum number of fixed points is proved, depending on whether the Allee effect parameter 𝛾 is even or odd. In addition, the analysis of the bifurcation structure of this 𝛾-Ricker diffeomorphism, also taking into account the parity of the Allee effect parameter, demonstrates the results established using the Lambert W functions. Numerical studies are included to illustrate the theoretical results.
- Idempotent-generated semigroups and pseudovarietiesPublication . Almeida, Jorge; Moura, AnaThe operator which constructs the pseudovariety generated by the idempotent-generated semigroups of a given pseudovariety is investigated. Several relevant examples of pseudovarieties generated by their idempotent- generated elements are given as well as some properties of this operator. Particular attention is paid to the pseudovarieties in {J, R, L, DA} concerning this operator and their generator ranks and idempotent-generator ranks.
- Representations of the free profinite object over DAPublication . Moura, AnaIn this paper, we extend to DA some techniques developed by Almeida and Weil, and Almeida and Zeitoun for the pseudovariety R to obtain representations of the implicit operations on DA: by labeled trees of finite height, by quasi-ternary labeled trees, and by labeled linear orderings. We prove that two implicit operations are equal over DA if and only if they have the same representation, for any of the three representations. We end the paper by relating these representations.
- The word problem for omega-terms over DAPublication . Moura, AnaIn this paper, we solve the word problem for ω-terms over DA. We extend to DA the ideas used by Almeida and Zeitoun to solve the analogous problem for the pseudovariety R applying also a representation by automata of implicit operations on DA, which was recently obtained by the author. Considering certain types of factors of an implicit operation on DA, we can prove that a pseudoword on DA is an ω-term if and only if the associated minimal DA- automaton is finite. Finally, we complete the result by effectively computing in polynomial time the minimal DA-automaton associated to an ω-term.
- E-local pseudovarietiesPublication . Moura, AnaGeneralizingapropertyofthepseudovarietyofallaperiodicsemi- groups observed by Tilson, we call E-local a pseudovariety V which satisfies the following property: for a finite semigroup, the subsemigroup generated by its idempotents belongs to V if and only if so do the subsemigroups generated by the idempotents in each of its regular D-classes. In this paper, we present sev- eral sufficient or necessary conditions for a pseudovariety to be E-local or for a pseudoidentity to define an E-local pseudovariety. We also determine several examples of the smallest E-local pseudovariety containing a given pseudovariety.
- Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulusPublication . Cruz, Manuel; Santos, Jorge M.; Cruz, NunoIn the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.
- High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer LearningPublication . Kandaswamy, C.; Silva, L. M.; Alexandre, L. A.; Santos, Jorge M.High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.
- Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulusPublication . Cruz, Manuel; Santos, Jorge M.; Cruz, NunoIn the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.
- Classification Performance of Multilayer Perceptrons with Different Risk FunctionalsPublication . Silva, Luís; Santos, Jorge; Marques de Sá, JoaquimIn the present paper we assess the performance of information-theoretic inspired risks functionals in multilayer perceptrons with reference to the two most popular ones, Mean Square Error and Cross-Entropy. The information-theoretic inspired risks, recently proposed, are: HS and HR2 are, respectively, the Shannon and quadratic Rényi entropies of the error; ZED is a risk reflecting the error density at zero errors; EXP is a generalized exponential risk, able to mimic a wide variety of risk functionals, including the information-thoeretic ones. The experiments were carried out with multilayer perceptrons on 35 public real-world datasets. All experiments were performed according to the same protocol. The statistical tests applied to the experimental results showed that the ubiquitous mean square error was the less interesting risk functional to be used by multilayer perceptrons. Namely, mean square error never achieved a significantly better classification performance than competing risks. Cross-entropy and EXP were the risks found by several tests to be significantly better than their competitors. Counts of significantly better and worse risks have also shown the usefulness of HS and HR2 for some datasets.
- Fuzzy modelling of prescribed burning effects on soil physical propertiesPublication . Carvalho, João P.; Meira Castro, Ana C.This paper presents the preliminary work of an approach where Fuzzy Boolean Nets (FBN) are being used to extract qualitative knowledge regarding the effect of prescribed fire burning on soil chemical physical properties. FBN were chosen due to the scarcity on available quantitative data.