Log Skeletons: A Classification Approach to Process Discovery

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.Comment: 16 pages with 9 figures, followed by an appendix of 14 pages with 17 figure

Compactly Supported Wavelets Derived From Legendre Polynomials: Spherical Harmonic Wavelets

A new family of wavelets is introduced, which is associated with Legendre polynomials. These wavelets, termed spherical harmonic or Legendre wavelets, possess compact support. The method for the wavelet construction is derived from the association of ordinary second order differential equations with multiresolution filters. The low-pass filter associated with Legendre multiresolution analysis is a linear phase finite impulse response filter (FIR).Comment: 6 pages, 6 figures, 1 table In: Computational Methods in Circuits and Systems Applications, WSEAS press, pp.211-215, 2003. ISBN: 960-8052-88-

Electronic properties of curved graphene sheets

A model is proposed to study the electronic structure of slightly curved graphene sheets with an arbitrary number of pentagon-heptagon pairs and Stone-Wales defects based on a cosmological analogy. The disorder induced by curvature produces characteristic patterns in the local density of states that can be observed in scanning tunnel and transmission electron microscopy.Comment: Corrected versio

Renal sympathetic denervation in resistant hypertension

Resistant hypertension remains a major clinical problem despite the available multidrug therapy. Over the next decades, its incidence will likely increase given that it is strongly associated with older age and obesity. Resistant hypertension patients have an increased cardiovascular risk, thus effective antihypertensive treatment will provide substantial health benefits. The crosstalk between sympathetic nervous system and kidneys plays a crucial role in hypertension. It influences several pathophysiological mechanisms such as the central sympathetic tone, the sodium balance and the systemic neurohumoral activation. In fact, studies using several animal models demonstrated that the renal denervation prevented and attenuated hypertension in multiple species. Large reductions in blood pressure were also observed in malignant hypertension patients submitted to sympathectomy surgeries. However, these approaches had an unacceptably high rates of periprocedural complications and disabling adverse events. Recently, an innovative non-pharmacological therapy that modulates sympathetic activation has been successfully developed. Renal sympathetic percutaneous denervation is an endovascular procedure that uses radiofrequency energy to destroy the autonomic renal nerves running inside the adventitia of renal arteries. This method represents a promising new approach to the strategy of inhibiting the sympathetic nervous system. The aim of this review is to examine the background knowledge that resulted in the development of this hypertension treatment and to critically appraise the available clinical evidenc

Quantum computing with incoherent resources and quantum jumps

Spontaneous emission and the inelastic scattering of photons are two natural processes usually associated with decoherence and the reduction in the capacity to process quantum information. Here we show that when suitably detected, these photons are sufficient to build all the fundamental blocks needed to perform quantum computation in the emitting qubits while protecting them from deleterious dissipative effects. We exemplify by showing how to teleport an unknown quantum state and how to efficiently prepare graph states for the implementation of measurement-based quantum computation.Comment: 5 pages, 5 figure