Ezequiel Bianco Martinez

Ezequiel Bianco Martinez
University of Aberdeen | ABDN · Institute for Complex Systems and Mathematical Biology (ICSMB)

PhD. in Physics

About

12
Publications
2,420
Reads
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172
Citations
Citations since 2017
2 Research Items
146 Citations
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
Additional affiliations
November 2017 - March 2020
ZorgDomein
Position
  • Analyst
January 2016 - October 2017
IBM
Position
  • Analyst

Publications

Publications (12)
Article
Full-text available
When the state of the whole reaction network can be inferred by just measuring the dynamics of a limited set of nodes the system is said to be fully observable. However, as the number of all possible combinations of measured variables and time derivatives spanning the reconstructed state of the system exponentially increases with its dimension, the...
Article
Full-text available
Information needs to be appropriately encoded to be reliably transmitted over a physical media. Similarly, neurons have their own codes to convey information in the brain. Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance...
Article
Full-text available
In a causal world the direction of the time arrow dictates how past causal events in a variable $X$ produce future effects in $Y$. $X$ is said to cause an effect in $Y$, if the predictability (uncertainty) about the future states of $Y$ increases (decreases) as its own past and the past of $X$ are taken into consideration. Causality is thus intrins...
Data
This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noi...
Article
Full-text available
This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noi...
Article
Full-text available
Observability is a very useful concept for determining whether the dynamics of complicated systems can be correctly reconstructed from a single (univariate or multivariate) time series. When the governing equations of dynamical systems are high-dimensional and/or rational, analytical computations of observability coefficients produce large polynomi...
Article
Full-text available
The inference of an underlying network structure from local observations of a complex system composed of interacting units is usually attempted by using statistical similarity measures, such as Cross-Correlation (CC) and Mutual Information (MI). The possible existence of a direct link between different units is, however, hindered within the time-se...
Article
Full-text available
We present novel results that relate energy and information transfer with sensitivity to initial conditions in chaotic multi-dimensional Hamiltonian systems. We show the relation among Kolmogorov-Sinai entropy, Lyapunov exponents, and upper bounds for the Mutual Information Rate calculated in the Hamiltonian phase space and on bi-dimensional subspa...

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Projects

Project (1)
Project
- determine the minimal set of variables to measure for getting a full observability of the network - instigate the conditions required to get a complete synchronization - Dynamical analysis of (non)-synchronized networks