Orientador: Itala Maria Loffredo D'Ottaviano / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Filosofia e Ciencias Humanas / Made available in DSpace on 2018-08-03T19:40:31Z (GMT). No. of bitstreams: 1 Hifume_Carlos_M.pdf: 426389 bytes, checksum: 487e63a8f673121cd57152f9b71a1d61 (MD5) Previous issue date: 2003 / Resumo: Alfred Tarski, ao apresentar sua concepção semântica de verdade - uma definição para linguagens formalizadas -, buscou capturar as intenções presentes na concepção clássica de verdade de Aristóteles - esta, uma concepção de verdade correspondencial. De forma semelhante, Mikenberg, da Costa & Chuaqui apresentam uma concepção formal de verdade pragmática - a quase-verdade -, baseados nas intenções presentes nas teorias de verdade de pragmatistas tais como C.S. Peirce, William James e John Dewey. A concepção de quase-verdade vale-se de um tipo de estrutura matemática denominada estrutura parcial. Apresentamos o sistema lógico modal QT - um tipo de lógica de Ja'skowski - , associado à lógica QV (S5Q=). QT constitui uma formalização adequada à noção de quase-verdade. Definimos uma semântica de modelos de Kripke para QT. São apresentados e demonstrados os principais metateoremas do sistema associado QT/QV. Definimos a noção de conseqüência sintática pragmática, introduzimos os conectivos pragmáticos e demonstramos que QT constitui uma lógica paraconsistente. Demonstramos a corretude e completude de QT, simplificando a demonstração valendo-nos dos metateoremas de QT/QV apresentados anteriormente / Abstract: Alfred Tarki, in presenting his semantical conception of truth - a definition for formalized languages -, captured Aristotle¿s classical conception of truth - this one, a correspondencial truth conception. Mikenberg, da Costa and Chuaqui, introduced a formal conception of pragmatic truth - da Costa¿s quasi-truth -, based on the theories of truth of some pragmatists, such as C.S. Peirce, William James and John Dewey. The conception of quasi-truth is based on a kind of mathematical structure named, by da Costa, partial structure. We present the modal logical system QT - a kind of Ja'skowski¿s discussive logic -, associated to the logic QV (S5Q=). The logic QT constitutes an adequate formalization for the notion of quasi-truth. We introduce a Kripke model semantics for QT. We prove some metatheorems relative to the associated systems QT and QV. We define the notion of pragmatic sintactical consequence, present the pragmatic connectives and prove that QT is a paraconsistent logic. By using the mentioned metatheorems, we obtain a simplified proof of soundness and completeness of QT / Mestrado / Filosofia / Mestre em Filosofia

Passive network reconstruction is the process of learning a structured (networked) representation of a dynamic system through the use of known information about the structure of the system as well as data collected by observing the inputs into a system along with the resultant outputs. This work demonstrates an improvement on an existing network reconstruction algorithm so that the algorithm is capable of consistently and perfectly reconstructing a network when system inputs and outputs are measured without error. This work then extends the improved network reconstruction algorithm so that it functions even in the presence of noise as well as the situation where inputs into the system are unknown. Furthermore, this work demonstrates the capability of the new extended algorithms by reconstructing financial networks from stock market data, and then performing an analysis to understand the vulnerabilities of the reconstructed network to destabilization through localized attacks. The creation of these improved and extended algorithms has opened many theoretical questions, paving the way for future research into network reconstruction.

The signal structure is a partial structure representation for dynamic systems. It characterizes the causal relationship between manifest variables and is depicted in a weighted graph, where the weights are dynamic operators. Earlier work has defined signal structure for linear time-invariant systems through dynamical structure function. This thesis focuses on the search for the signal structure of nonlinear systems and proves that the signal structure reduces to the linear definition when the systems are linear. Specifically, this work: (1) Defines the complete computational structure for nonlinear systems. (2) Provides a process to find the complete computational structure given a state space model. (3) Defines the signal structure for dynamic systems in general. (4) Provides a process to find the signal structure for a class of dynamic systems from their complete computational structure.

Dynamical structure functions were developed as a partial structure representation of linear time-invariant systems to be used in the reconstruction of biological networks. Dynamical structure functions contain more information about structure than a system's transfer function, while requiring less a priori information for reconstruction than the complete computational structure associated with the state space realization. Early sufficient conditions for network reconstruction with dynamical structure functions severely restricted the possible applications of the reconstruction process to networks where each input independently controls a measured state. The first contribution of this thesis is to extend the previously established sufficient conditions to incorporate both necessary and sufficient conditions for reconstruction. These new conditions allow for the reconstruction of a larger number of networks, even networks where independent control of measured states is not possible. The second contribution of this thesis is to extend the robust reconstruction algorithm to all reconstructible networks. This extension is important because it allows for the reconstruction of networks from real data, where noise is present in the measurements of the system. The third contribution of this thesis is a Matlab toolbox that implements the robust reconstruction algorithm discussed above. The Matlab toolbox takes in input-output data from simulations or real-life perturbation experiments and returns the proposed Boolean structure of the network. The final contribution of this thesis is to increase the applicability of dynamical structure functions to more than just biological networks by applying our reconstruction method to wireless communication networks. The reconstruction of wireless networks produces a dynamic interference map that can be used to improve network performance or interpret changes of link rates in terms of changes in network structure, enabling novel anomaly detection and security schemes.