Cephalopods exhibit a wide variety of behaviors such as prey capture, communication, camouflage, and reproduction thanks to a complex central nervous system (CNS) divided into several functional lobes that express a wide range of neuropeptides involved in the modulation of behaviors and physiological mechanisms associated with the main stages of their life cycle. This work focuses on the neuropeptidome expressed during egg-laying through de novo construction of the CNS transcriptome using an RNAseq approach (Illumina sequencing). Then, we completed the in silico analysis of the transcriptome by characterizing and tissue-mapping neuropeptides by mass spectrometry. To identify neuropeptides involved in the egg-laying process, we determined (1) the neuropeptide contents of the neurohemal area, hemolymph (blood), and nerve endings in mature females and (2) the expression levels of these peptides. Among the 38 neuropeptide families identified from 55 transcripts, 30 were described for the first time in Sepia officinalis, 5 were described for the first time in the animal kingdom, and 14 were strongly overexpressed in egg-laying females as compared with mature males. Mass spectrometry screening of hemolymph and nerve ending contents allowed us to clarify the status of many neuropeptides, that is, to determine whether they were neuromodulators or neurohormones.

We give a quadratic algorithm for the following structure identification problem: given a Boolean relation R and a finite set S of Boolean relations, can the relation R be expressed as a conjunctive query over the relations in the set S? Our algorithm is derived by first introducing the concept of a plain basis for a co-clone and then identifying natural plain bases for every co-clone in Post's lattice. In the process, we also give a quadratic algorithm for the problem of finding the smallest co-clone containing a Boolean relation.

Improving exact exponential-time algorithms for NP-complete problems is an expanding research area. Unfortunately, general methods for comparing the complexity of such problems is sorely lacking. In this article we study the complexity of SAT(S) with reductions increasing the amount of variables by a constant (CV-reductions) or a constant factor (LV-reductions). Using clone theory we obtain a partial order ≤ on languages such that SAT(S) is CV-reducible to SAT(S) if S ≤ S. With this ordering we identify the computationally easiest NP-complete SAT(S) problem (SAT({R})), which is strictly easier than 1-in-3-SAT. We determine many other languages in ≤ and bound their complexity in relation to SAT({R}). Using LV-reductions we prove that the exponential-time hypothesis is false if and only if all SAT(S) problems are subexponential. This is extended to cover degree-bounded SAT(S) problems. Hence, using clone theory, we obtain a solid understanding of the complexity of SAT(S) with CV-and LV-reductions.

Conditional preference networks (CP-nets) have recently emerged as a popular language capable of representing ordinal preference relations in a compact and structured manner. In this paper, we investigate the problem of learning CP-nets in the well-known model of exact identification with equivalence and membership queries. The goal is to identify a target preference ordering with a binary-valued CP-net by interacting with the user through a small number of queries. Each example supplied by the user or the learner is a preference statement on a pair of outcomes. In this model, we show that acyclic CP-nets are not learnable with equivalence queries alone, even if the examples are restricted to swaps for which dominance testing takes linear time. By contrast, acyclic CP-nets are what is called attribute-efficiently learnable when both equivalence queries and membership queries are available: we indeed provide a learning algorithm whose query complexity is linear in the description size of the target concept, but only logarithmic in the total number of attributes. Interestingly, similar properties are derived for tree-structured CPnets in the presence of arbitrary examples. Our learning algorithms are shown to be quasi-optimal by deriving lower bounds on the VC-dimension of CP-nets. In a nutshell, our results reveal that active queries are required for efficiently learning CP-nets in large multi-attribute domains.

Abduction is a fundamental form of nonmonotonic reasoning that aims at finding explanations for observed manifestations. This process underlies many applications, from car configuration to medical diagnosis. We study here the computational complexity of deciding whether an explanation exists in the case when the application domain is described by a propositional knowledge base. Building on previous results, we classify the complexity for local restrictions on the knowledge base and under various restrictions on hypotheses and manifestations. In comparison to the many previous studies on the complexity of abduction we are able to give a much more detailed picture for the complexity of the basic problem of deciding the existence of an explanation. It turns out that depending on the restrictions, the problem in this framework is always polynomial-time solvable, NP-complete, coNP-complete, or Σ P 2 -complete. Based on these results, we give an a posteriori justification of what makes propositional abduction hard even for some classes of knowledge bases which allow for efficient satisfiability testing and deduction. This justification is very simple and intuitive, but it reveals that no nontrivial class of abduction problems is tractable. Indeed, tractability essentially requires that the language for knowledge bases is unable to express both causal links and conflicts between hypotheses. This generalizes a similar observation by Bylander et al. for set-covering abduction.

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Abduction is the process of explaining a given query with respect to some background knowledge. For instance, p is an explanation for the query q given the knowledge p → q. This problem is well-known to have many applications, in particular in Artificial Intelligence, and has been widely studied from both an AI and a complexity-theoretic point of view.In this paper we completely classify the complexity of propositional abduction in Schaefer's famous framework. We consider the case where knowledge bases are taken from a class of formulas in generalized conjunctive normal form. This means that the propositional formulas considered are conjunctions of constraints taken from a fixed finite language.We show that according to the properties of this language, deciding whether at least one explanation exists is either polynomial, NP-complete or Σ P 2 -complete. Our results are stated for a query consisting of a single, positive literal and for assumption-based solutions, i.e., the solutions must be formed upon a distinguished subset of the variables that is part of the input. We however show that our results can be interpreted "dually" for negative queries, and thus also for unrestricted (positive or negative) queries.