36,190 research outputs found
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
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