Mike R. True

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BACKGROUND Previous studies that have examined genetic influences on suicidal behaviour were confounded by genetic vulnerability for psychiatric risk factors. The present study examines genetic influences on suicidality (i.e. suicidal ideation and/or suicide attempt) after controlling for the inheritance of psychiatric disorders. METHODS(More)
We present an efficient, event-driven simulation framework for large-scale networks of excitable hybrid automata (EHA), a particular kind of hybrid automata that we use to model excitable cells. A key aspect of EHA is that they possess protected modes of operation in which they are non-responsive to external inputs. In such modes, our approach takes(More)
We propose hybrid automata (HA) as a unifying framework for computational models of excitable cells. HA, which combine discrete transition graphs with continuous dynamics, can be naturally used to obtain a piecewise, possibly linear, approximation of a nonlinear excitable-cell model. We first show how HA can be used to efficiently capture the(More)
We introduce cycle-linear hybrid automata (CLHA) and show how they can be used to efficiently model dynamical systems that exhibit nonlinear, pseudo-periodic behavior. CLHA are based on the observation that such systems cycle through a fixed set of operating modes, although the dynamics and duration of each cycle may depend on certain computational aspects(More)
We present a new approach to runtime verification that utilizes classical statistical techniques such as Monte Carlo simulation, hypothesis testing, and confidence interval estimation. Our algorithm, MCM, uses sampling-policy automata to vary its sampling rate dynamically as a function of the current confidence it has in the correctness of the deployed(More)
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