The restriction of the Ising model to a layer

We discuss the status of recent Gibbsian descriptions of the restriction (projection) of the Ising phases to a layer. We concentrate on the projection of the two-dimensional low temperature Ising phases for which we prove a variational principle.Comment: 38 page

Bilateral testicular self-castration due to cannabis abuse: a case report

<p>Abstract</p> <p>Introduction</p> <p>The self-mutilating patient is an unusual psychiatric presentation in the emergency room. Nonetheless, serious underlying psychiatric pathology and drug abuse are important background risk factors. A careful stepwise approach in the emergency room is essential, although the prognosis, follow-up, and eventual rehabilitation can be problematic.</p> <p>We present a unique and original case of bilateral self-castration caused by cannabis abuse.</p> <p>Case Presentation</p> <p>We report a case of a 40-year-old Berber man, who was presented to our emergency room with externalization of both testes using his long fingernails, associated with hemodynamic shock. After stabilization of his state, our patient was admitted to the operating room where hemostasis was achieved.</p> <p>Conclusion</p> <p>The clinical characteristics of self-mutilation are manifold and there is a lack of agreement about its etiology. The complex behavior associated with drug abuse may be one cause of self-mutilation. Dysfunction of the inhibitory brain circuitry caused by substance abuse could explain why this cannabis-addicted patient lost control and self-mutilated. To the best of our knowledge, this is the first case report which presents an association between self-castration and cannabis abuse.</p

Stochastic Stability of Weakly Coupled Lattice Maps

We consider a stochastic perturbation of weakly coupled expanding circle maps. We construct the dynamics and its natural invariant measure via a polymer expansion and show the stochastic stability of the system. PACS: 05.20.-y, 05.70.Ln, AMS: 82C05, 82C22 1 Introduction Coupled map lattices are similar to cellular automata except that the single component state space is continuous. So instead of having e.g. f0; 1g-valued components in binary cellular automata we will use the circle S 1 as single site state space for the coupled map lattices in this paper. This is also one of the reasons why it is sometimes said that coupled map lattices are intermediate between cellular automata and systems of coupled partial differential EC grant CHRX-CT93-0411. y Onderzoeksleider NFWO. Email: [email protected] z Aspirant NFWO. Email: [email protected] equations. We refer to [13] for a description of the range of motivations and applications in the study of co..

On the Thermodynamic Limit for a One-Dimensional Sandpile Process

Considering the standard abelian sandpile model in one dimension, we construct an infinite volume Markov process corresponding to its thermodynamic (infinite volume) limit. The main difficulty we overcome is the strong non-locality of the dynamics by which, depending on the sand configuration, changes in the height variables far away can still influence the origin in a single updating. However, using similar ideas as in recent extensions of the standard Gibbs formalism for lattice spin systems, we can identify a set of `good&apos; configurations on which the dynamics is effectively local. Finally, we prove that every configuration converges in a finite time to the unique invariant measure. Up to this time, the expected height increases linearly in time. AMS classification: 60K35, 82C22 Key-words : Sandpile model, non-Feller process, thermodynamic limit, interacting particle systems. Onderzoeksleider FWO, Flanders. Email: [email protected] y Post-doctoraal onderzoeker F..

Hypervolume-based multi-objective reinforcement learning

Indicator-based evolutionary algorithms are amongst the best performing methods for solving multi-objective optimization (MOO) problems. In reinforcement learning (RL), introducing a quality indicator in an algorithm’s decision logic was not attempted before. In this paper, we propose a novel on-line multi-objective reinforcement learning (MORL) algorithm that uses the hypervolume indicator as an action selection strategy. We call this algorithm the hypervolume-based MORL algorithm or HB-MORL and conduct an empirical study of the performance of the algorithm using multiple quality assessment metrics from multi-objective optimization. We compare the hypervolume-based learning algorithm on different environments to two multi-objective algorithms that rely on scalarization techniques, such as the linear scalarization and the weighted Chebyshev function. We conclude that HB-MORL significantly outperforms the linear scalarization method and performs similarly to the Chebyshev algorithm without requiring any user-specified emphasis on particular objectives

Scalarized multi-objective reinforcement learning: Novel design techniques (abstract)

In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand. We propose a non-linear scalarization function, called the Chebyshev scalarization function in multi-objective reinforcement learning. We show that the Chebyshev scalarization method overcomes the flaws of the linear scalarization function and is able to discover all Pareto optimal solutions in non-convex environments

Scalarized multi-objective reinforcement learning : novel design techniques

In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand, as is often the case. We propose a non-linear scalarization function, called the Chebyshev scalarization function, as a basis for action selection strategies in multi-objective reinforcement learning. The Chebyshev scalarization method overcomes the flaws of the linear scalarization function as it can (i) discover Pareto optimal solutions regardless of the shape of the front, i.e. convex as well as non-convex , (ii) obtain a better spread amongst the set of Pareto optimal solutions and (iii) is not particularly dependent on the actual weights used