Vicious Circle Principle and Logic Programs with Aggregates

The paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches

Solving Functional Constraints by Variable Substitution

Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ CSP-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other non-functional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints

A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network

Epilepsy is one of the most common neurological disorders that greatly impair patient' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting segments of the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, if not impossible, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The authors are not aware of any report on automated EEG diagnostic system that can accurately distinguish patients' interictal EEG from the EEG of normal people. The research presented in this paper, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. Such a system should also detect seizure activities for further investigation by doctors and potential patient monitoring. To develop such a system, we extract four classes of features from the EEG data and build a Probabilistic Neural Network (PNN) fed with these features. Leave-one-out cross-validation (LOO-CV) on a widely used epileptic-normal data set reflects an impressive 99.5% accuracy of our system on distinguishing normal people's EEG from patient's interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 77.5% accuracy respectively on the data set.Comment: 5 pages, 6 figures, 1 table, submitted to IEEE ICTAI 200

Numerical simulation and experimental study of PbWO4/EPDM and Bi2WO6/EPDM for the shielding of {\gamma}rays

The MCNP5 code was employed to simulate the {\gamma}ray shielding capacity of tungstate composites. The experimental results were applied to verify the applicability of the Monte Carlo program. PbWO4 and Bi2WO6 were prepared and added into ethylene propylene diene monomer (EPDM) to obtain the composites, which were tested in the {\gamma}ray shielding. Both the theoretical simulation and experiments were carefully chosen and well designed. The results of the two methods were found to be highly consistent. In addition, the conditions during the numerical simulation were optimized and double-layer {\gamma}ray shielding systems were studied. It was found that the {\gamma}-ray shielding performance can be influenced not only by the material thickness ratio but also by the arrangement of the composites.Comment: 8 pages,7 figures,Submitted to Chin.Phy.

The potential dual effects of sevoflurane on AKT/GSK3β signaling pathway

Background: Anesthesia with multiple exposures of commonly used inhalation anesthetic sevoflurane induces neuroinflammation and cognitive impairment in young mice, but anesthesia with a single exposure to sevoflurane does not. AKT/glycogen synthase kinase 3β (GSK3β) signaling pathway is involved in neurotoxicity and neurobehavioral deficits. However, whether sevoflurane can induce a dual effect (increase versus decrease) on the activation of AKT/GSK3β signaling pathway remains to be determined. We therefore set out to assess the effects of sevoflurane on AKT/GSK3β signaling pathway in vivo and in vitro. Methods: Six day-old wild-type mice were exposed to 3% sevoflurane two hours daily for one or three days. In the in vitro studies, H4 human neuroglioma cells were treated with 4% sevoflurane for two or six hours. We then determined the effects of different sevoflurane treatments on the levels of phosphorylated (P)-GSK3β(ser9) and P-AKT(ser473) by using Western blot analysis. Results: Here we show that anesthesia with 3% sevoflurane two hours daily for one day increased the levels of P-GSK3β(ser9) and P-AKT(ser473), but the anesthesia with 3% sevoflurane daily for three days decreased them in the mice. The treatment with 4% sevoflurane for two hours increased, but the treatment with 4% sevoflurane for six hours decreased, the levels of P-GSK3β(ser9) and P-AKT(ser473) in the H4 human neuroglioma cells. Conclusions: Anesthetic sevoflurane might induce a dual effect (increase versus decrease) on the activation of the AKT/GSK3β signaling pathway. These studies have established a system to perform further studies to determine the effects of sevoflurane on brain function

Maintaining Arc Consistency with Multiple Residues

International audienceExploiting residual supports (or residues) has proved to be one of the most cost-effective approaches for Maintaining Arc Consistency during search (MAC). While MAC based on optimal AC algorithm may have better theoretical time complexity in some cases, in practice the overhead for maintaining required data structure during search outweighs the benefit, not to mention themore complicated implementation. Implementing MAC with residues, on the other hand, is trivial. In this paper we extend previous work on residues and investigate the use of multiple residues during search. We first give a theoretical analysis of residue-based algorithms that explains their good practical performance. We then propose several heuristics on how to deal with multiple residues. Finally, our empirical study shows that with a proper and limited number of residues, many constraint checks can be saved. When the constraint check is expensive or a problem is hard, the multiple residues approach is competitive in both the number of constraint checks and cpu time