14 citations · 59 across the 35 of their papers we have counts for
44 papers
A Linear Generative Framework for Structure-Function Coupling in the Human Brain
Sam Frank Kelemen, Joaquín Gõni, Sérgio Pequito +1
Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the ph…
Shifts in Brain Dynamics and Drivers of Consciousness State Transitions
Joseph Bodenheimer, Paul Bogdan, Sérgio Pequito +1
Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underl…
Robust Multivariate Detection and Estimation with Fault Frequency Content Information
Jingwei Dong, Kaikai Pan, Sergio Pequito +1
This paper studies the problem of fault detection and estimation (FDE) for linear time-invariant (LTI) systems with a particular focus on frequency content information of faults, p…
Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation
Guilherme Ramos, A. Pedro Aguiar, Soummya Kar +1
Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed esti…
Fractional cyber-neural systems -- a brief survey
Emily Reed, Sarthak Chatterjee, Guilherme Ramos +2
Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to s…
Minimum jointly structural input and output selection for strongly connected networks
Guilherme Ramos, A. Pedro Aguiar, Sérgio Pequito
In this paper, given a linear time-invariant strongly connected network, we study the problem of determining the minimum number of state variables that need to be simultaneously ac…
Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs
Emily A. Reed, Guilherme Ramos, Paul Bogdan +1
In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and s…
A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks
Emily A. Reed, Guilherme Ramos, Paul Bogdan +1
Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learni…
Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation
Sarthak Chatterjee, Andrea Alessandretti, A. Pedro Aguiar +1
Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatia…
On Learning Discrete-Time Fractional-Order Dynamical Systems
Sarthak Chatterjee, Sérgio Pequito
Discrete-time fractional-order dynamical systems (DT-FODS) have found innumerable applications in the context of modeling spatiotemporal behaviors associated with long-term memory.…
Structural Systems Theory: an overview of the last 15 years
Guilherme Ramos, A. Pedro Aguiar, Sergio Pequito
In this paper, we provide an overview of the research conducted in the context of structural systems since the latest survey by Dion et al. in 2003. We systematically consider all…
Equilibrium Propagation for Complete Directed Neural Networks
Matilde Tristany Farinha, Sérgio Pequito, Pedro A. Santos +1
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful le…
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Orlando Romero, Subhro Das, Pin-Yu Chen +1
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorit…
Fractional-Order Model Predictive Control for Neurophysiological Cyber-Physical Systems: A Case Study using Transcranial Magnetic Stimulation
Orlando Romero, Sarthak Chatterjee, Sérgio Pequito
Fractional-order dynamical systems are used to describe processes that exhibit temporal long-term memory and power-law dependence of trajectories. There has been evidence that comp…
Finite-dimensional control of linear discrete-time fractional-order systems
Andrea Alessandretti, Sergio Pequito, George J. Pappas +1
This paper addresses the design of finite-dimensional feedback control laws for linear discrete-time fractional-order systems with additive state disturbance. A set of sufficient c…
A Separation Principle for Discrete-Time Fractional-Order Dynamical Systems and its Implications to Closed-loop Neurotechnology
Sarthak Chatterjee, Orlando Romero, Sérgio Pequito
Closed-loop neurotechnology requires the capability to predict the state evolution and its regulation under (possibly) partial measurements. There is evidence that neurophysiologic…
Analysis of a Generalized Expectation-Maximization Algorithm for Gaussian Mixture Models: A Control Systems Perspective
Sarthak Chatterjee, Orlando Romero, Sérgio Pequito
The Expectation-Maximization (EM) algorithm is one of the most popular methods used to solve the problem of parametric distribution-based clustering in unsupervised learning. In th…
Network Design for Controllability Metrics
Cassiano O. Becker, Sérgio Pequito, George J. Pappas +1
In this paper, we consider the problem of tuning the edge weights of a networked system described by linear time-invariant dynamics. We assume that the topology of the underlying n…
Learning Latent Fractional dynamics with Unknown Unknowns
Gaurav Gupta, Sergio Pequito, Paul Bogdan
Despite significant effort in understanding complex systems (CS), we lack a theory for modeling, inference, analysis and efficient control of time-varying complex networks (TVCNs)…
Convergence of the Expectation-Maximization Algorithm Through Discrete-Time Lyapunov Stability Theory
Orlando Romero, Sarthak Chatterjee, Sérgio Pequito
In this paper, we propose a dynamical systems perspective of the Expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM algorithm as a nonlinear state-space…
Dealing with State Estimation in Fractional-Order Systems under Artifacts
Sarthak Chatterjee, Sérgio Pequito
Fractional-order dynamical systems are used to describe processes that exhibit long-term memory with power-law dependence. Notable examples include complex neurophysiological signa…
Resilient Structural Stabilizability of Undirected Networks
Jingqi Li, Ximing Chen, Sérgio Pequito +2
In this paper, we consider the structural stabilizability problem of undirected networks. More specifically, we are tasked to infer the stabilizability of an undirected network fro…
Structural Target Controllability of Undirected Networks
Jingqi Li, Ximing Chen, Sérgio Pequito +2
In this paper, we study the target controllability problem of networked dynamical systems, in which we are tasked to steer a subset of network states towards a desired objective. M…
Approximate Submodular Functions and Performance Guarantees
Gaurav Gupta, Sergio Pequito, Paul Bogdan
We consider the problem of maximizing non-negative non-decreasing set functions. Although most of the recent work focus on exploiting submodularity, it turns out that several objec…