Behavioural skeletons in GCM: autonomic management of grid components

Autonomic management can be used to improve the QoS provided by parallel/distributed applications. We discuss behavioural skeletons introduced in earlier work: rather than relying on programmer ability to design “from scratch” efficient autonomic policies, we encapsulate general autonomic controller features into algorithmic skeletons. Then we leave to the programmer the duty of specifying the parameters needed to specialise the skeletons to the needs of the particular application at hand. This results in the programmer having the ability to fast prototype and tune distributed/parallel applications with non-trivial autonomic management capabilities. We discuss how behavioural skeletons have been implemented in the framework of GCM(the Grid ComponentModel developed within the CoreGRID NoE and currently being implemented within the GridCOMP STREP project). We present results evaluating the overhead introduced by autonomic management activities as well as the overall behaviour of the skeletons. We also present results achieved with a long running application subject to autonomic management and dynamically adapting to changing features of the target architecture.
Overall the results demonstrate both the feasibility of implementing autonomic control via behavioural skeletons and the effectiveness of our sample behavioural skeletons in managing the “functional replication” pattern(s).

Quasiparticle calculations of the electronic properties of ZrO2 and HfO2 polymorphs and their interface with Si

Quasiparticle calculations are performed to investigate the electronic band structures of various polymorphs of Hf and Zr oxides. The corrections with respect to density-functional-theory results are found to depend only weakly on the crystal structure. Based on these bulk calculations as well as those for bulk Si, the effect of quasiparticle corrections is also investigated for the band offsets at the interface between these oxides and Si assuming that the lineup of the potential at the interface is reproduced correctly within density-functional theory. On the one hand, the valence-band offsets are practically unchanged with a correction of a few tenths of electron volts. On the other hand, conduction-band offsets are raised by 1.3-1.5 eV. When applied to existing calculations for the offsets at the density-functional-theory level, our quasiparticle corrections provide results in good agreement with the experiment.

Effect of interleukin-6 polymorphisms on human longevity:a systematic review and meta-analysis

Several studies have assessed changes in frequency of -174 interleukin (IL)-6 single nucleotide polymorphism (SNP) with age. If IL-6 tracks with disability and age-related diseases, then there should be reduction, in the oldest old, of the frequency of homozygous GG subjects, who produce higher IL-6 levels. However, discordant results have been obtained. To explore the relationship between this polymorphism and longevity, we analyzed individual data on long-living subjects and controls from eight case-control studies conducted in Europeans, using meta-analysis. There was no significant difference in the IL-6 genotype between the oldest old and controls (Odds Ratio [OR]=0.96; 95% C.I.: 0.77-1.20; p=0.71), but there was significant between-study heterogeneity (I2=55.5%). In a subgroup analyses when male centenarians from the three Italian studies were included, the frequency of the IL-6 -174 GG genotype was significantly lower than the other genotypes (OR=0.49; 95% C.I.: 0.31-0.80; p=0.004), with no evidence of heterogeneity (I2=0%). Our data supports a negative association between the GG genotype of IL-6 SNP and longevity in Italian centenarians, with males who carry the genotype being two times less likely to reach extreme old age compared with subjects carrying CC or CG genotypes. These findings were not replicated in other European groups suggesting a possible interaction between genetics, sex and environment in reaching longevity.

Modification of the formation of high-Mach number electrostatic shock-like structures by the ion acoustic instability

The formation of unmagnetized electrostatic shock-like structures with a high Mach number is examined with one- and two-dimensional particle-in-cell (PIC) simulations. The structures are generated through the collision of two identical plasma clouds, which consist of equally hot electrons and ions with a mass ratio of 250. The Mach number of the collision speed with respect to the initial ion acoustic speed of the plasma is set to 4.6. This high Mach number delays the formation of such structures by tens of inverse ion plasma frequencies. A pair of stable shock-like structures is observed after this time in the 1D simulation, which gradually evolve into electrostatic shocks. The ion acoustic instability, which can develop in the 2D simulation but not in the 1D one, competes with the nonlinear process that gives rise to these structures. The oblique ion acoustic waves fragment their electric field. The transition layer, across which the bulk of the ions change their speed, widens and their speed change is reduced. Double layer-shock hybrid structures develop.

Outgassing Behavior of C/2012 S1 (ISON) from 2011 September to 2013 June

We report photometric observations for comet C/2012 S1 (ISON) obtained during the time period immediately after discovery (r = 6.28 AU) until it moved into solar conjunction in mid-2013 June using the UH2.2 m, and Gemini North 8 m telescopes on Mauna Kea, the Lowell 1.8 m in Flagstaff, the Calar Alto 1.2 m telescope in Spain, the VYSOS-5 telescopes on Mauna Loa Hawaii and data from the CARA network. Additional pre-discovery data from the Pan STARRS1 survey extends the light curve back to 2011 September 30 (r = 9.4 AU). The images showed a similar tail morphology due to small micron sized particles throughout 2013. Observations at submillimeter wavelengths using the James Clerk Maxwell Telescope on 15 nights between 2013 March 9 (r = 4.52 AU) and June 16 (r = 3.35 AU) were used to search for CO and HCN rotation lines. No gas was detected, with upper limits for CO ranging between 3.5-4.5 × 1027 molecules s-1. Combined with published water production rate estimates we have generated ice sublimation models consistent with the photometric light curve. The inbound light curve is likely controlled by sublimation of CO2. At these distances water is not a strong contributor to the outgassing. We also infer that there was a long slow outburst of activity beginning in late 2011 peaking in mid-2013 January (r ~ 5 AU) at which point the activity decreased again through 2013 June. We suggest that this outburst was driven by CO injecting large water ice grains into the coma. Observations as the comet came out of solar conjunction seem to confirm our models.

Characterisation of deuterium spectra from laser driven multi-species sources by employing differentially filtered image plate detectors in Thomson spectrometers

A novel method for characterising the full spectrum of deuteron ions emitted by laser driven multi-species ion sources is discussed. The procedure is based on using differential filtering over the detector of a Thompson parabola ion spectrometer, which enables discrimination of deuterium ions from heavier ion species with the same charge-to-mass ratio (such as C^{6 +}, O^{8 +}, etc.). Commonly used Fuji Image plates were used as detectors in the spectrometer, whose absolute response to deuterium ions over a wide range of energies was calibrated by using slotted CR-39 nuclear track detectors. A typical deuterium ion spectrum diagnosed in a recent experimental campaign is presented, which was produced from a thin deuterated plastic foil target irradiated by a high power laser.

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

Virtual metrology enabled early stage prediction for enhanced control of multi-stage fabrication processes

Semiconductor fabrication involves several sequential processing steps with the result that critical production variables are often affected by a superposition of affects over multiple steps. In this paper a Virtual Metrology (VM) system for early stage measurement of such variables is presented; the VM system seeks to express the contribution to the output variability that is due to a defined observable part of the production line. The outputs of the processed system may be used for process monitoring and control purposes. A second contribution of this work is the introduction of Elastic Nets, a regularization and variable selection technique for the modelling of highly-correlated datasets, as a technique for the development of VM models. Elastic Nets and the proposed VM system are illustrated using real data from a multi-stage etch process used in the fabrication of disk drive read/write heads. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.