Abstract. We propose a new method, based on Sparse Distributed Memory (Kanerva Networks), for studying dependency relations between different syntactic parameters in the Principles and Parameters model of Syntax. We store data of syntactic parameters of world languages in a Kanerva Network and we check the recoverability of corrupted parameter data from the network. We find that different syntactic parameters have different degrees of recoverability. We identify two different effects: an overall underlying relation between the prevalence of parameters across languages and their degree of recoverability, and a finer effect that makes some parameters more easily recoverable beyond what their prevalence would indicate. We interpret a higher recoverability for a syntactic parameter as an indication of the existence of a dependency relation, through which the given parameter can be determined using the remaining uncorrupted data.

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Purpose To classify tumor imaging voxels at-risk for treatment failure within the heterogeneous cervical cancer using DCE MRI and determine optimal voxel’s DCE threshold values at different treatment time points for early prediction of treatment failure. Material and Method DCE-MRI from 102 patients with Stage IB2–IVB cervical cancer was obtained at 3 different treatment time points: before (MRI 1) and during treatment (MRI 2 at 2–2.5 weeks and MRI 3 at 4–5 weeks). For each tumor voxel, the plateau signal intensity (SI) was derived from its time-SI curve from the DCE MRI. The optimal SI thresholds to classify the at-risk tumor voxels was determined by the maximal area under the curve using ROC analysis when varies SI value from 1.0 to 3.0 and correlates with treatment outcome. Results The optimal SI thresholds for MRI 1, 2 and 3 were 2.2, 2.2 and 2.1 for significant differentiation between local recurrence/control, respectively, and 1.8, 2.1 and 2.2 for death/survival, respectively. Conclusion Optimal SI thresholds are clinically validated to quantify at-risk tumor voxels which vary with time. A single universal threshold (SI = 1.9) was identified for all 3 treatment time points and remain significant for the early prediction of treatment failure.

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