Encapsulated search and constraint programming in Oz

Oz is an attempt to create a high-level concurrent programming language providing the problem solving capabilities of logic programming (i.e., constraints and search). Its computation model can be seen as a rather radical extension of the concurrent constraint model providing for higher-order programming, deep guards, state, and encapsulated search. This paper focuses on the most recent extension, a higher-order combinator providing for encapsulated search. The search combinator spawns a local computation space and resolves remaining choices by returning the alternatives as first-class citizens. The search combinator allows to program different search strategies, including depth-first, indeterministic one solution, demand-driven multiple solution, all solutions, and best solution (branch and bound) search. The paper also discusses the semantics of integer and finite domain constraints in a deep guard computation model

Constructive Completeness for Modal Logic with Transitive Closure

Classical modal logic with transitive closure appears as a subsystem of logics used for program verification. The logic can be axiomatized with a Hilbert system. In this paper we develop a constructive completeness proof for the axiomatization using Coq with Ssreflect. The proof is based on a novel analytic Gentzen system, which yields a certifying decision procedure that for a formula constructs either a derivation or a finite countermodel. Completeness of the axiomatization then follows by translating Gentzen derivations to Hilbert derivations. The main difficulty throughout the development is the treatment of transitive closure

Completeness and Decidability Results for CTL in Coq

We prove completeness and decidability results for the temporal logic CTL in Coq/Ssreflect. Our basic result is a constructive proof that for every formula one can obtain either a finite model satisfying the formula or a proof in a Hilbert system certifying the unsatisfiability of the formula. The proof is based on a history-augmented tableau system obtained as the dual of Brünnler and Lange's cut-free sequent calculus for CTL. We prove the completeness of the tableau system and give a translation of tableau refutations into Hilbert refutations. Decidability of CTL and completeness of the Hilbert system follow as corollaries

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

A multimodal neuroimaging classifier for alcohol dependence

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence

Adolescent women induce lower blood alcohol levels than men in a laboratory alcohol self-administration experiment

Background Adolescence is a critical period for the development of alcohol use disorders; drinking habits are rather unstable and genetic influences, such as male sex and a positive Family History of alcoholism (FH), are often masked by environmental factors such as peer pressure. Methods We investigated how sex and FH modulate alcohol use in a sample of 18-19-year-olds from the Dresden Longitudinal Study on Alcohol use in Young Adults (D-LAYA). Adolescents reported their real-life drinking in a TimeLine Follow-Back (TLFB) interview. They subsequently completed a training and an experimental session of free-access intravenous Alcohol Self-Administration (i.v. ASA) using the computer-assisted alcohol infusion system in order to control for environmental cues as well as for biological differences in alcohol pharmacokinetics. During i.v. ASA, we assessed subjective alcohol effects at eight time points. Results Women reported significantly less real-life drinking than men and achieved significantly lower mean arterial Blood Alcohol Concentrations (aBACs) in the laboratory. At the same time, women reported greater sedation relative to men and rated negative effects as high as did men. A positive FH was associated with lower real-life drinking in men but not in women. In the laboratory, FH was not linked to i.v. ASA. Greater real-life drinking was significantly positively associated with higher mean aBACs in the laboratory, and all i.v. ASA indices were highly correlated across the two sessions. Conclusions We conclude that adolescent women chose lower aBACs because they experienced adverse alcohol effects, namely sedation and negative effects, at lower aBACs than men. A positive FH was not apparent as risk factor for drinking in our young sample. The i.v. ASA method demonstrated good external validity as well as test-retest reliability, the latter indicating that a separate training session is not required when employing the i.v. ASA paradigm

ARES:Adaptive receding-horizon synthesis of optimal plans

We introduce ARES, an efficient approximation algorithm for generating optimal plans (action sequences) that take an initial state of a Markov Decision Process (MDP) to a state whose cost is below a specified (convergence) threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both the receding horizon and the particle swarm. Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state. The level relation on states and the plans constructed by ARES implicitly define a Lyapunov function and an optimal policy, respectively, both of which could be explicitly generated by applying ARES to all states of the MDP, up to some topological equivalence relation. We also assess the effectiveness of ARES by statistically evaluating its rate of success in generating optimal plans. The ARES algorithm resulted from our desire to clarify if flying in V-formation is a flocking policy that optimizes energy conservation, clear view, and velocity alignment. That is, we were interested to see if one could find optimal plans that bring a flock from an arbitrary initial state to a state exhibiting a single connected V-formation. For flocks with 7 birds, ARES is able to generate a plan that leads to a V-formation in 95% of the 8,000 random initial configurations within 63 s, on average. ARES can also be easily customized into a model-predictive controller (MPC) with an adaptive receding horizon and statistical guarantees of convergence. To the best of our knowledge, our adaptive-sizing approach is the first to provide convergence guarantees in receding-horizon techniques

COMT val158met Polymorphism and Neural Pain Processing

A functional polymorphism (val158met) of the gene coding for Catechol-O-methyltransferase (COM) has been demonstrated to be related to processing of emotional stimuli. Also, this polymorphism has been found to be associated with pain regulation in healthy subjects. Therefore, we investigated a possible influence of this polymorphism on pain processing in healthy persons as well as in subjects with markedly reduced pain sensitivity in the context of Borderline Personality Disorder (BPD). Fifty females (25 patients with BPD and 25 healthy control participants) were included in this study. Genotype had a significant – though moderate - effect on pain sensitivity, but only in healthies. The number of val alleles was correlated with the BOLD response in several pain-processing brain regions, including dorsolateral prefrontal cortex, posterior parietal cortex, lateral globus pallidus, anterior and posterior insula. Within the subgroup of healthy participants, the number of val alleles was positively correlated with the BOLD response in posterior parietal, posterior cingulate, and dorsolateral prefrontal cortex. BPD patients revealed a positive correlation between the number of val alleles and BOLD signal in anterior and posterior insula. Thus, our data show that the val158met polymorphism in the COMT gene contributes significantly to inter-individual differences in neural pain processing: in healthy people, this polymorphism was more related to cognitive aspects of pain processing, whereas BPD patients with reduced pain sensitivity showed an association with activity in brain regions related to affective pain processing