The problem of passing restrictions more general than a binding to a set of constants has been the subject of some other recent research. Ramakrishnan ((Ram88]) introduced Magic Templates to pass restrictions due to the presence of function symbols and the relationships between otherwise free arguments. However, the method generates non-range-restricted rules from range-restricted programs, and...

Hugh Everett III proposed his relative-state formulation of pure wave mechanics as a solution to the quantum measurement problem. He sought to address the theory’s determinate record and probability problems by showing that, while counterintuitive, pure wave mechanics was nevertheless empirically faithful and hence empirical acceptable. We will consider what Everett meant by empirical faithfuln...

In many languages, the stress pattern of a complex word resembles the stress of its morphological base or another related word. I refer to this phenomenon as accentual faithfulness (cf. Alderete 1999). A classic example comes from the English word orìginálity, which resembles the accentual profile of its immediate subconstituent oríginal (cf. monomorphemic words like Mèditerránean). This paper ...

Rat, mouse and human liver microsomes and S9 fractions were analyzed using an optimized method combining ion exchange fractionation of digested peptides, and ultra-high performance liquid chromatography (UHPLC) coupled to high resolution tandem mass spectrometry (HR-MS/MS). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteo...

Most methods for learning causal structures from non-experimental data rely on some assumptions of simplicity, the most famous of which is known as the Faithfulness condition. Without assuming such conditions to begin with, we develop a learning theory for inferring the structure of a causal Bayesian network, and we use the theory to provide a novel justification of a certain assumption of simp...

We consider the problem of learning a Bayesian network or directed acyclic graph (DAG) model from observational data. A number of constraint-based, score-based and hybrid algorithms have been developed for this purpose. For constraint-based methods, statistical consistency guarantees typically rely on the faithfulness assumption, which has been show to be restrictive especially for graphs with ...