Weaving behavior into feature models for embedded system families

Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.

Origin of double dinitrogen release feature during fast switching between lean and rich cycles for NOx storage reduction catalysts

The performance of NOx storage and reduction over 1.5 wt% Pt/20 wt% KNO3/K2Ti8O17 and 1.5 wt% Pt/K2Ti8O17 catalysts has been investigated using combined fast transient kinetic switching and isotopically labelled (NO)-N-15 at 350 degrees C. The evolution of product N-2 has revealed two significant peaks during 60 s lean/1.3 s rich switches. It also found that the presence of CO2 in the feed affects the release of N-2 in the second peak. Regardless of the presence/absence of water in the feed, only one peak of N-2 was observed in the absence of CO2. Gas-phase NH3 was not observed in any of the experiments. However, in the presence of CO2 the results obtained from in situ DRIFTS-MS analysis showed that isocyanate species are formed and stored during the rich cycles, probably from the reaction between NOx and CO, in which CO was formed via the reverse water-gas shift reaction. 

Whole-mitochondrial genome sequencing in primary open-angle glaucoma using massively parallel sequencing identifies novel and known pathogenic variants

Purpose:The aim of this study was to determine whether mutations in mitochondrial DNA play a role in high-pressure primary open-angle glaucoma (OMIM 137760) by analyzing new data from massively parallel sequencing of mitochondrial DNA.
Methods:Glaucoma patients with high-tension primary open-angle glaucoma and ethnically matched and age-matched control subjects without glaucoma were recruited. The entire human mitochondrial genome was amplified in two overlapping fragments by long-range polymerase chain reaction and used as a template for massively parallel sequencing on an Ion Torrent Personal Genome Machine. All variants were confirmed by conventional Sanger sequencing.
Results:Whole-mitochondrial genome sequencing was performed in 32 patients with primary open-angle glaucoma from India (n = 16) and Ireland (n = 16). In 16 of the 32 patients with primary open-angle glaucoma (50% of cases), there were 22 mitochondrial DNA mutations consisting of 7 novel mutations and 8 previously reported disease-associated sequence variants. Eight of 22 (36.4%) of the mitochondrial DNA mutations were in complex I mitochondrial genes.
Conclusion:Massively parallel sequencing using the Ion Torrent Personal Genome Machine with confirmation by Sanger sequencing detected a pathogenic mitochondrial DNA mutation in 50% of the primary open-angle glaucoma cohort. Our findings support the emerging concept that mitochondrial dysfunction results in the development of glaucoma and, more specifically, that complex I defects play a significant role in primary open-angle glaucoma pathogenesis.

Green Political Economy: beyond orthodox undifferentiated economic growth as a permanent feature of the economy

This chapter outlines the main features of green political economy and the principal ways in which it differs from dominant mainstream or orthodox neoclassical economics. Neoclassical economics is critiqued on the grounds of denying its normative and ideological commitments in its false presentation of itself as ‘objective’ and ‘value neutral’. It is also critiqued for its ecologically irrational commitment to the imperative of orthodox economic growth as a permanent feature of the economy, compromising its ability to offer realistic or normatively compelling guides to how we might make the transition to a sustainable economy. Green political economy is presented as an alternative or heterodox form of economic thinking but one which explicitly expresses its normative/ideological value bases (hence it represents a return to ‘political economy’, the origins of modern economics). Green political economy also challenges the commitment to undifferentiated economic growth as a permanent objective of the human economy. In its place, green political economy promotes ‘economic security’ as a better objective for a sustainable, post-growth economy. The latter includes the transition to a low-carbon energy economy, and is also one which maximises quality of life (as oppose to formal employment, income and wealth), and actively seeks to lower socio-economic inequality. Green political economy views orthodox economic growth as having passed the threshold in most ‘advanced’ capitalist societies beyond which it has undermined quality of life and at best manages rather than reduces socially and ecologically damaging inequalities.

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.

PHOTOSIGNAL ENHANCEMENT IN AL-GAAS DIODES DUE TO SURFACE-PLASMONS AND GUIDED-WAVE MODES

Light of wavelength 632.8 nm and p-polarization is incident on a prism-air gap (varied from 0.7 to 7 mum)-Al-GaAs arrangement. Both the photosignal generated by the Schottky diode and the reflectance are measured as a function of the internal angle of incidence in the prism. There is significant, well-defined enhancement of the photosignal, up to a factor of approximately 7.5, associated with two different types of enhanced absorption modes. For air gaps <1.5 mum there is photosignal enhancement due to an enhanced absorption feature (reflectance dip) that occurs at an angle of incidence just above critical angle in the prism; this feature corresponds to the excitation of a surface plasmon polariton at the Al-air interface. For air gaps > 1 mum there are between one and ten photoresponse peaks at input angles less than the critical angle. The corresponding enhanced absorption features are due to leaky guided wave modes set up in the air gap.

Latanoprost for open-angle glaucoma (UKGTS): a randomised, multicentre, placebo-controlled trial

Background
Treatments for open-angle glaucoma aim to prevent vision loss through lowering of intraocular pressure, but to our knowledge no placebo-controlled trials have assessed visual function preservation, and the observation periods of previous (unmasked) trials have typically been at least 5 years. We assessed vision preservation in patients given latanoprost compared with those given placebo.

Methods
In this randomised, triple-masked, placebo-controlled trial, we enrolled patients with newly diagnosed open-angle glaucoma at ten UK centres (tertiary referral centres, teaching hospitals, and district general hospitals). Eligible patients were randomly allocated (1:1) with a website-generated randomisation schedule, stratified by centre and with a permuted block design, to receive either latanoprost 0·005% (intervention group) or placebo (control group) eye drops. Drops were administered from identical bottles, once a day, to both eyes. The primary outcome was time to visual field deterioration within 24 months. Analyses were done in all individuals with follow-up data. The Data and Safety Monitoring Committee (DSMC) recommended stopping the trial on Jan 6, 2011 (last patient visit July, 2011), after an interim analysis, and suggested a change in primary outcome from the difference in proportions of patients with incident progression between groups to time to visual field deterioration within 24 months. This trial is registered, number ISRCTN96423140.

Findings
We enrolled 516 individuals between Dec 1, 2006, and March 16, 2010. Baseline mean intraocular pressure was 19·6 mm Hg (SD 4·6) in 258 patients in the latanoprost group and 20·1 mm Hg (4·8) in 258 controls. At 24 months, mean reduction in intraocular pressure was 3·8 mm Hg (4·0) in 231 patients assessed in the latanoprost group and 0·9 mm Hg (3·8) in 230 patients assessed in the placebo group. Visual field preservation was significantly longer in the latanoprost group than in the placebo group: adjusted hazard ratio (HR) 0·44 (95% CI 0·28–0·69; p=0·0003). We noted 18 serious adverse events, none attributable to the study drug.

Interpretation
This is the first randomised placebo-controlled trial to show preservation of the visual field with an intraocular-pressure-lowering drug in patients with open-angle glaucoma. The study design enabled significant differences in vision to be assessed in a relatively short observation period

Modulation Insensitive PLL for Tracking Antenna Applications

This article shows practical results of a self-tracking receiving antenna array using a new phase locked loop (PLL) tracking configuration. The PLL configuration differs from other architectures, as it has the new feature of being able to directly track phase modulated signals without requiring an additional unmodulated pilot carrier to be present. The PLLs are used within the antenna array to produce a constant phase intermediate frequency (IF) for each antenna element. These IF's can then be combined in phase, regardless of the angle of arrival of the signal, thus utilizing the antennas array factor. The article's main focus is on the phase jitter performance of the modulation insensitive PLL carrier recovery when tracking phase modulated signals of low signal to noise ratio. From this analysis, it is concluded that the new architecture, when optimally designed, can produce phase jitter performance close to that of a conventional tracking PLL.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.