On Controllability of Delayed Boolean Control Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is devoted to studying the trajectory and state controllability of Boolean control networks (BCNs) with time delay. In contrast to BCNs without time delay, the dynamics of delayed BCNs are determined by a sequence of initial states, named here trajectories. Trajectory controllability means that there exists a control signal steering a system from an initial trajectory to a desired trajectory, while state controllability means that there exists a control signal steering an initial state to a given state. Here, both trajectory controllability and state controllability will be studied. It should be noted that in this paper, trajectory controllability does not mean tracking or following a given trajectory. In fact it means to control BCNs to a destination trajectory of length $\mu$ at the $k$-th step. Using the semi-tensor product of matrices, the delayed BCNs are first converted into an equivalent algebraic description, and then some necessary and sufficient conditions are derived for the trajectory controllability of delayed BCNs. We further present a bijection between the state of BCNs and the trajectory of length $\mu$, which is then used to derive some necessary and sufficient conditions for the state controllability of delayed BCNs. Both the problems of controlling an initial state sequence to a desired state and a desired trajectory are first investigated. We also consider the issues of avoiding some specific states which may cause diseases or lead to dangerous situations. Numerical examples are given to illustrate our theoretical results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it