Feature Model-Guided Online Reinforcement Learning for Self-Adaptive Services

International audienceA self-adaptive service can maintain its QoS requirements in the presence of dynamic environment changes. To develop a self-adaptive service, service engineers have to create self-adaptation logic encoding when the service should execute which adaptation actions. However, developing self-adaptation logic may be difficult due to design time uncertainty ; e.g., anticipating all potential environment changes at design time is in most cases infeasible. Online reinforcement learning addresses design time uncertainty by learning suitable adaptation actions through interactions with the environment at runtime. To learn more about its environment, reinforcement learning has to select actions that were not selected before, which is known as exploration. How exploration happens has an impact on the performance of the learning process. We focus on two problems related to how a service's adaptation actions are explored: (1) Existing solutions randomly explore adaptation actions and thus may exhibit slow learning if there are many possible adaptation actions to choose from. (2) Existing solutions are unaware of service evolution, and thus may explore new adaptation actions introduced during such evolution rather late. We propose novel exploration strategies that use feature models (from software product line engineering) to guide exploration in the presence of many adaptation actions and in the presence of service evolution. Experimental results for a self-adaptive cloud management service indicate an average speed-up of the learning process of 58.8% in the presence of many adaptation actions, and of 61.3% in the presence of service evolution. The improved learning performance in turn led to an average QoS improvement of 7.8% and 23.7% respectively

Dust observations with antenna measurements and its prospects for observations with Parker Solar Probe and Solar Orbiter

The electric and magnetic field instrument suite FIELDS on board the NASA Parker Solar Probe and the radio and plasma waves instrument RPW on the ESA Solar Orbiter mission that explore the inner heliosphere are sensitive to signals generated by dust impacts. Dust impacts have been observed using electric field antennas on spacecraft since the 1980s and the method was recently used with a number of space missions to derive dust fluxes. Here, we consider the details of dust impacts, subsequent development of the impact generated plasma and how it produces the measured signals. We describe empirical approaches to characterise the signals and compare these in a qualitative discussion of laboratory simulations to predict signal shapes for spacecraft measurements in the inner solar system. While the amount of charge production from a dust impact will be higher near the Sun than observed in the interplanetary medium before, the amplitude of pulses is determined by the recovery behaviour that is different near the Sun since it varies with the plasma environment

Nearly conformal gauge theories in finite volume

We report new results on nearly conformal gauge theories with fermions in the fundamental representation of the SU(3) color gauge group as the number of fermion flavors is varied in the Nf = 4-16 range. To unambiguously identify the chirally broken phase below the conformal window we apply a comprehensive lattice tool set in finite volumes which includes the test of Goldstone pion dynamics, the spectrum of the fermion Dirac operator, and eigenvalue distributions of random matrix theory. We also discuss the theory inside the conformal window and present our first results on the running of the renormalized gauge coupling and the renormalization group beta function. The importance of understanding finite volume zero momentum gauge field dynamics inside the conformal window is illustrated. Staggered lattice fermions are used throughout the calculations.Comment: 9 pages and 7 figure

Gauge-Independent Analysis of K_L --> e \mu in Left-Right Models

The lepton-flavour-violating decay K_L --> e \mu is studied in detail within the context of SU(2)_R x SU(2)_L x U(1)_(B-L) models, which include heavy Majorana neutrinos. Particular attention is paid to the gauge independence of this decay process to one loop. In analogy with earlier studies on the K^0\bar{K}^0 mixing, it is explicitly shown how restoration of gauge invariance occurs in the decay amplitude containing the box diagrams, when the relevant Higgs-dependent self-energy and vertex graphs are taken into account in the on-shell skeleton renormalization scheme. Based on the analytic expressions so derived, we find that the branching ratio B(K_L --> e \mu) can be considerably enhanced due to the presence of left- and right-handed currents in the loop, and can reach values close to or even larger than the present experimental limit 3.3 x 10^{-11} in the manifest left-right symmetric model. Constraints on the parameter space of typical left-right models are derived from the possible decay K_L --> e \mu and a global analysis of other low-energy data.Comment: 44 pages, LaTeX, six encapsulated figures include

Revisiting the Dexamethasone Suppression Test in unipolar major depression: an exploratory study

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Prediction and prevention of suicide in patients with unipolar depression and anxiety

Epidemiological data suggest that between 59 and 87% of suicide victims suffered from major depression while up to 15% of these patients will eventually commit suicide. Male gender, previous suicide attempt(s), comorbid mental disorders, adverse life-situations, acute psycho-social stressors etc. also constitute robust risk factors. Anxiety and minor depression present with a low to moderate increase in suicide risk but anxiety-depression comorbidity increases this risk dramatically Contrary to the traditional psychoanalytic approach which considers suicide as a retrospective murder or an aggression turned in-wards, more recent studies suggest that the motivations to commit suicide may vary and are often too obscure. Neurobiological data suggest that low brain serotonin activity might play a key role along with the tryptophan hydroxylase gene. Social factors include social support networks, religion etc. It is proven that most suicide victims had asked for professional help just before committing suicide, however they were either not diagnosed (particularly males) or the treatment they received was inappropriate or inadequate. The conclusion is that promoting suicide prevention requires the improving of training and skills of both psychiatrists and many non-psychiatrists and especially GPs in recognizing and treating depression and anxiety. A shift of focus of attention is required in primary care to detect potentially suicidal patients presenting with psychological problems. The proper use of antidepressants, after a careful diagnostic evaluation, is important and recent studies suggest that successful acute and long-term antidepressant pharmacotherapy reduces suicide morbidity and mortality