Meta-level abduction is a method to abduce missing rules in explaining observations. By representing rule structures of a problem in a form of causal networks, meta-level abduction infers missing links and unknown nodes from incomplete networks to complete paths for observations. We examine applicability of meta-level abduction on networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in several biological pathways. Reasoning in networks with inhibition involves nonmonotonic inference, which can be realized by making default assumptions in abduction. We show that meta-level abduction can consistently produce both positive and negative causal relations as well as invented nodes. Case studies of meta-level abduction are presented in p53 signaling networks, in which causal relations are abduced to suppress a tumor with a new protein and to stop DNA synthesis when damage has occurred. Effects of our method are also analyzed through experiments of completing networks randomly generated with both positive and negative links.

Self-mixing effect occurs when a small fraction of a laser beam backscattered from diffusely-reflecting targets reenters the laser active cavity. This generates notably variations of the optical output power (OOP) which can be monitored for sensing achievements as displacement measurements. A phase unwrapping algorithm has been performed for self-mixing sensor based on a laser under moderate feedback enabling the displacement sensor to achieve a resolution of 40 nm. Unfortunately, this algorithm leads to strong measurement errors when the signal is noisy. In order to improve the robustness of this algorithm, we characterize the variations of the OOP by computed Holder exponent using Differential Evolution Algorithms. The Holder exponent enables us to differentiate the shape of the signal in order to recover the target direction of displacement. This algorithm tested on experimental data allows to improve the performances of our sensor in terms of robustness to hostile environment.