Carlos Pedro Gonçalves | ULHT - Universidade Lusofona de Humanidades e Tecnologias (original) (raw)
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Papers by Carlos Pedro Gonçalves
To address Quantum Artificial Neural Networks as quantum dynamical computing systems, a formaliza... more To address Quantum Artificial Neural Networks as quantum dynamical computing systems, a formalization of quantum artificial neural networks as dynamical systems is developed, expanding the concept of unitary map to the neural computation setting and introducing a quantum computing field theory on the network. The formalism is illustrated in a simulation of a quantum recurrent neural network and the resulting field dynamics is researched upon, showing emergent neural waves with excitation and relaxation cycles at the level of the quantum neural activity field, as well as edge of chaos signatures, with the local neurons operating as far-from-equilibrium open quantum systems, exhibiting entropy fluctuations with complex dynamics including complex quasiperiodic patterns and power law signatures. The implications for quantum computer science, quantum complexity research, quantum technologies and neuroscience are also addressed.
The current work addresses quantum machine learning in the context of Quantum Artificial Neural N... more The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks that interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.
ArXiv, 2018
An artificial agent for financial risk and returns' prediction is built with a modular cognit... more An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a co...
The development of automated gate specification for quantum communications and quantum networked ... more The development of automated gate specification for quantum communications and quantum networked computation opens up the way for malware designed at corrupting the automation software, changing the automated quantum communications protocols and algorithms. We study two types of attacks on automated quantum communications protocols and simulate these attacks on the superdense coding protocol, using remote access to IBM’s Quantum Computers available through IBM Q Experience to simulate these attacks on what would be a low noise quantum communications network. The first type of attack leads to a hackercontrolled bijective transformation of the final measured strings, the second type of attack is a unitary scrambling attack that modifies the automated gate specification to effectively scramble the final measurement, disrupting quantum communications and taking advantage of quantum randomness upon measurement in a way that makes it difficult to distinguish from hardware malfunction or f...
SSRN Electronic Journal, 2019
Quantum neural computation employs quantum computational circuits that consist of chains of condi... more Quantum neural computation employs quantum computational circuits that consist of chains of conditional unitary operators that follow the network's neural links leading to entanglement between the local neuron-level computation and the network. When such quantum computational circuits are applied iteratively, instead of an input and output density, we have a sequence of density operators, with the transition from one density to another resulting from a form of unitary quantum map. It has been shown that these unitary quantum neural maps lead to complex emergent dynamics at the level of relevant quantum averages. The present work expands the research on quantum neural maps combining it with quantum stochastic processes theory, introducing the concept of a quantum stochastic neural map, resulting from a coupling to an external noise source. The unitary and stochastic maps are implemented for a quantum recurrent neural network, showing evidence of complex emergent dynamics, including, in the case of the stochastic map, fractal attractors that leave a signature at the level of the energy versus mutual information plots, as well as local (neuron-level) entropy resilient dynamics, where each neuron, as an open computing unit, exhibits dissipation but does not reach full entanglement-related decoherence.
Cyberspace [Working Title], 2019
SSRN Electronic Journal, 2018
The current work addresses a virtual environment with self-replicating agents whose decisions are... more The current work addresses a virtual environment with self-replicating agents whose decisions are based on a form of "somatic computation" (soma - body) in which basic emotional responses, taken in parallelism to actual living organisms, are introduced as a way to provide the agents with greater reflexive abilities. The work provides a contribution to the field of Artificial Intelligence (AI) and Artificial Life (ALife) in connection to a neurobiology-based cognitive framework for artificial systems and virtual environments' simulations. The performance of the agents capable of emotional responses is compared with that of self-replicating automata, and the implications of research on emotions and AI, in connection to both virtual agents as well as robots, is addressed regarding possible future directions and applications.
SSRN Electronic Journal, 2015
The notion of order, in the matricial sense of the term, is worked upon from a systemic ontologic... more The notion of order, in the matricial sense of the term, is worked upon from a systemic ontological approach focused on abstract entities, therefore, identities, gifted of a nature that is proper to them and that distinguishes them from the concrete entities. Taking, as an exemplary case, the mathematical notion of an n-dimensional Euclidean space, we address the ontology of Euclidean spaces, including the definition of these spaces and a notion of Euclidean topological computation which allows us to address the computational systemic dynamics and hypertextual structure of these spaces. The work concludes with a final reflection on the Euclidean spaces, on ontology of presence and order.
ABSTRACT The present article synthesizes a general approach to the development of risk governance... more ABSTRACT The present article synthesizes a general approach to the development of risk governance decision support systems, based upon the interdisciplinary dialogue between risk science and the complexity sciences. A conceptual review of risk science and the three main schools of the complexity sciences (the Santa Fe School, the Stuttgart School and the Brussels-Austin School) is provided and addressed with regards to the new challenges faced by organizations in their need for adaptation to interconnected risk situations and the dynamics of risk in networks.
Algorithmic Finance, 2012
SSRN Electronic Journal, 2009
Page 1. Electronic copy available at: http://ssrn.com/abstract=1438013 ... Maria Odete Madeira In... more Page 1. Electronic copy available at: http://ssrn.com/abstract=1438013 ... Maria Odete Madeira Interdisciplinary researcher in philosophy of science, systems science, complexity sci-ences, neurocognition, semiotics, ontology and cosmology mosmg.research@gmail.com (primary ...
SSRN Electronic Journal, 2009
Page 1. Electronic copy available at: http://ssrn.com/abstract=1396841 A Systems Theoretical Form... more Page 1. Electronic copy available at: http://ssrn.com/abstract=1396841 A Systems Theoretical Formal Logic for Category Theory by Carlos Pedro Gonçalves Mathematics researcher at UNIDE-ISCTE cpdsg.research@gmail.com (primary); cpdsg@iscte.pt ...
SSRN Electronic Journal, 2010
Abstract: A model is proposed of a population of competing companies that are in a coevolutionary... more Abstract: A model is proposed of a population of competing companies that are in a coevolutionary race and whose evolutionary performance is evaluated by a financial market, composed of value investors and of a breed of arbitrageurs that perform bargain arbitrage, ...
To address Quantum Artificial Neural Networks as quantum dynamical computing systems, a formaliza... more To address Quantum Artificial Neural Networks as quantum dynamical computing systems, a formalization of quantum artificial neural networks as dynamical systems is developed, expanding the concept of unitary map to the neural computation setting and introducing a quantum computing field theory on the network. The formalism is illustrated in a simulation of a quantum recurrent neural network and the resulting field dynamics is researched upon, showing emergent neural waves with excitation and relaxation cycles at the level of the quantum neural activity field, as well as edge of chaos signatures, with the local neurons operating as far-from-equilibrium open quantum systems, exhibiting entropy fluctuations with complex dynamics including complex quasiperiodic patterns and power law signatures. The implications for quantum computer science, quantum complexity research, quantum technologies and neuroscience are also addressed.
The current work addresses quantum machine learning in the context of Quantum Artificial Neural N... more The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks' processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to solve computing problems. The results are extended to general architectures including recurrent networks that interact with an environment, coupling with it in the neural links' activation order, and self-organizing in a dynamical regime that intermixes patterns of dynamical stochasticity and persistent quasiperiodic dynamics, making emerge a form of noise resilient dynamical record.
ArXiv, 2018
An artificial agent for financial risk and returns' prediction is built with a modular cognit... more An artificial agent for financial risk and returns' prediction is built with a modular cognitive system comprised of interconnected recurrent neural networks, such that the agent learns to predict the financial returns, and learns to predict the squared deviation around these predicted returns. These two expectations are used to build a volatility-sensitive interval prediction for financial returns, which is evaluated on three major financial indices and shown to be able to predict financial returns with higher than 80% success rate in interval prediction in both training and testing, raising into question the Efficient Market Hypothesis. The agent is introduced as an example of a class of artificial intelligent systems that are equipped with a Modular Networked Learning cognitive system, defined as an integrated networked system of machine learning modules, where each module constitutes a functional unit that is trained for a given specific task that solves a subproblem of a co...
The development of automated gate specification for quantum communications and quantum networked ... more The development of automated gate specification for quantum communications and quantum networked computation opens up the way for malware designed at corrupting the automation software, changing the automated quantum communications protocols and algorithms. We study two types of attacks on automated quantum communications protocols and simulate these attacks on the superdense coding protocol, using remote access to IBM’s Quantum Computers available through IBM Q Experience to simulate these attacks on what would be a low noise quantum communications network. The first type of attack leads to a hackercontrolled bijective transformation of the final measured strings, the second type of attack is a unitary scrambling attack that modifies the automated gate specification to effectively scramble the final measurement, disrupting quantum communications and taking advantage of quantum randomness upon measurement in a way that makes it difficult to distinguish from hardware malfunction or f...
SSRN Electronic Journal, 2019
Quantum neural computation employs quantum computational circuits that consist of chains of condi... more Quantum neural computation employs quantum computational circuits that consist of chains of conditional unitary operators that follow the network's neural links leading to entanglement between the local neuron-level computation and the network. When such quantum computational circuits are applied iteratively, instead of an input and output density, we have a sequence of density operators, with the transition from one density to another resulting from a form of unitary quantum map. It has been shown that these unitary quantum neural maps lead to complex emergent dynamics at the level of relevant quantum averages. The present work expands the research on quantum neural maps combining it with quantum stochastic processes theory, introducing the concept of a quantum stochastic neural map, resulting from a coupling to an external noise source. The unitary and stochastic maps are implemented for a quantum recurrent neural network, showing evidence of complex emergent dynamics, including, in the case of the stochastic map, fractal attractors that leave a signature at the level of the energy versus mutual information plots, as well as local (neuron-level) entropy resilient dynamics, where each neuron, as an open computing unit, exhibits dissipation but does not reach full entanglement-related decoherence.
Cyberspace [Working Title], 2019
SSRN Electronic Journal, 2018
The current work addresses a virtual environment with self-replicating agents whose decisions are... more The current work addresses a virtual environment with self-replicating agents whose decisions are based on a form of "somatic computation" (soma - body) in which basic emotional responses, taken in parallelism to actual living organisms, are introduced as a way to provide the agents with greater reflexive abilities. The work provides a contribution to the field of Artificial Intelligence (AI) and Artificial Life (ALife) in connection to a neurobiology-based cognitive framework for artificial systems and virtual environments' simulations. The performance of the agents capable of emotional responses is compared with that of self-replicating automata, and the implications of research on emotions and AI, in connection to both virtual agents as well as robots, is addressed regarding possible future directions and applications.
SSRN Electronic Journal, 2015
The notion of order, in the matricial sense of the term, is worked upon from a systemic ontologic... more The notion of order, in the matricial sense of the term, is worked upon from a systemic ontological approach focused on abstract entities, therefore, identities, gifted of a nature that is proper to them and that distinguishes them from the concrete entities. Taking, as an exemplary case, the mathematical notion of an n-dimensional Euclidean space, we address the ontology of Euclidean spaces, including the definition of these spaces and a notion of Euclidean topological computation which allows us to address the computational systemic dynamics and hypertextual structure of these spaces. The work concludes with a final reflection on the Euclidean spaces, on ontology of presence and order.
ABSTRACT The present article synthesizes a general approach to the development of risk governance... more ABSTRACT The present article synthesizes a general approach to the development of risk governance decision support systems, based upon the interdisciplinary dialogue between risk science and the complexity sciences. A conceptual review of risk science and the three main schools of the complexity sciences (the Santa Fe School, the Stuttgart School and the Brussels-Austin School) is provided and addressed with regards to the new challenges faced by organizations in their need for adaptation to interconnected risk situations and the dynamics of risk in networks.
Algorithmic Finance, 2012
SSRN Electronic Journal, 2009
Page 1. Electronic copy available at: http://ssrn.com/abstract=1438013 ... Maria Odete Madeira In... more Page 1. Electronic copy available at: http://ssrn.com/abstract=1438013 ... Maria Odete Madeira Interdisciplinary researcher in philosophy of science, systems science, complexity sci-ences, neurocognition, semiotics, ontology and cosmology mosmg.research@gmail.com (primary ...
SSRN Electronic Journal, 2009
Page 1. Electronic copy available at: http://ssrn.com/abstract=1396841 A Systems Theoretical Form... more Page 1. Electronic copy available at: http://ssrn.com/abstract=1396841 A Systems Theoretical Formal Logic for Category Theory by Carlos Pedro Gonçalves Mathematics researcher at UNIDE-ISCTE cpdsg.research@gmail.com (primary); cpdsg@iscte.pt ...
SSRN Electronic Journal, 2010
Abstract: A model is proposed of a population of competing companies that are in a coevolutionary... more Abstract: A model is proposed of a population of competing companies that are in a coevolutionary race and whose evolutionary performance is evaluated by a financial market, composed of value investors and of a breed of arbitrageurs that perform bargain arbitrage, ...
We apply new empirical methods from chaos theory, aimed at dealing with stochastic chaos, employi... more We apply new empirical methods from chaos theory, aimed at dealing with stochastic chaos, employing adaptive topological artificial intelligence and topological data analysis to sunspots' data for attractor reconstruction analysis and dynamical process decomposition. Applying these methods to sunspots' data we uncover not one but two low-dimensional chaotic attractors, a first dominant attractor that is linked to a strongly persistent process with self-organized criticality and multifractal signatures, and a second chaotic attractor that exhibits intermittent turbulence and anti-persistent multifractal signatures, also present is a third process with an autoregressive moving average structure and an independent and identically distributed (IID) noise component. In this way, the main emergent dynamics associated with the sunspots' data are researched in detail down to the IID noise component.
A rogue wave epidemiological pattern has been identified as a predominant pattern in COVID-19 epi... more A rogue wave epidemiological pattern has been identified as a predominant pattern in COVID-19 epidemiological series with emergent chaotic attractors for different world regions. In the present work, we study People Republic of China’s daily new number of confirmed cases of COVID-19 from 2020-01-03 to 2023-10-27, which provides for a long series with a rogue wave pattern, and apply to this series multifractal analysis, smart topological data analysis and chaos theory, finding that the rogue wave pattern is linked to chaos-induced multifractal self-organized criticality, the source of the rogue wave is shown to be associated with an emergent three-dimensional chaotic attractor with a three winged structure, explaining the rogue wave dynamics, the reconstructed attractor is shown to have exploitable topological information that can be used by adaptive A.I. systems to predict the daily number of confirmed cases of COVID-19 with a high level of performance, the predictability is shown to decrease in a several days ahead prediction and to be linked to the attractor’s Lyapunov time, k-nearest neighbors’ graph analysis, persistent homology and ordinal partition graph analyses are also applied and researched in their relation to the predictability of the target series. The implications of the results for epidemiology, risk science, complexity research and healthcare planning are discussed.
Smart topological data analysis (STDA), previously employed to research the epidemiological dynam... more Smart topological data analysis (STDA), previously employed to research the epidemiological dynamics associated with SARS-CoV-2, is applied to stock market sessions' trading amplitudes for financial indexes and individual stocks, combining chaos theory, topological data analysis and machine learning. The methods employed uncover evidence of chaos-induced self-organized criticality in the daily trading amplitudes, used as a trading session's volatility measure. The topological structure of the reconstructed attractors is researched upon, allowing us to characterize the dynamics of the underlying chaotic attractors and to link the main chaotic features to the markers of self-organized criticality, the implications of the research for finance, risk science and the complexity research are also addressed.
A stochastic chaos model of adaptive financial speculative dynamics is introduced and shown to ca... more A stochastic chaos model of adaptive financial speculative dynamics is introduced and shown to capture several key features of actual financial turbulence, including power law scaling in the squared logarithmic returns' distribution, 1/f spectral signatures and multifractal scaling, the model is expanded to a multiple asset artificial financial market, leading to a coupled stochastic chaos model of financial speculative dynamics, showing evidence of macroscopic financial turbulence, with excess kurtosis, power law signatures, multifractal scaling at the mean field level as well as a relation between dynamical synchronization and financial volatility dynamics. The implications for financial theory and applications of coupled stochastic chaos models to model complex financial coevolutionary dynamics are addressed.
Quantum neural computation employs quantum computational circuits that consist of chains of condi... more Quantum neural computation employs quantum computational circuits that consist of chains of conditional unitary operators that follow the network's neural links leading to entanglement between the local neuron-level computation and the network. When such quantum computational circuits are applied iteratively, instead of an input and output density, we have a sequence of density operators, with the transition from one density to another resulting from a form of unitary quantum map. It has been shown that these unitary quantum neural maps lead to complex emergent dynamics at the level of relevant quantum averages. The present work expands the research on quantum neural maps combining it with quantum stochastic processes theory, introducing the concept of a quantum stochastic neural map, resulting from a coupling to an external noise source. The unitary and stochastic maps are implemented for a quantum recurrent neural network, showing evidence of complex emergent dynamics, including, in the case of the stochastic map, fractal attractors that leave a signature at the level of the energy versus mutual information plots, as well as local (neuron-level) entropy resilient dynamics, where each neuron, as an open computing unit, exhibits dissipation but does not reach full entanglement-related decoherence.
