An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Performance analysis of a metro WDM ring networks with variable-length packets under symmetric and asymmetric traffic

In this paper, we discuss and evaluate two proposed metro wavelength division multiplexing (WDM) ring network architectures for variable-length packet traffic in storage area networks (SANs) settings. The paper begins with a brief review of the relevant architectures and protocols in the literature. Subsequently, the network architectures along with their medium access control (MAC) protocols are described. Performance of the two network architectures is studied by means of computer simulation in terms of their queuing delay, node throughput and proportion of packets dropped. The network performance is evaluated under symmetric and asymmetric traffic scenarios with Poisson and self-similar traffic. (C) 2011 Elsevier Inc. All rights reserved.

Testing quantum contextuality of continuous-variable states

We investigate the violation of noncontextuality by a class of continuous-variable states, including variations of entangled coherent states and a two-mode continuous superposition of coherent states. We generalize the Kochen-Specker (KS) inequality discussed by Cabello [A. Cabello, Phys. Rev. Lett. 101, 210401 (2008)] by using effective bidimensional observables implemented through physical operations acting on continuous-variable states, in a way similar to an approach to the falsification of Bell-Clauser-Horne-Shimony-Holt inequalities put forward recently. We test for state-independent violation of KS inequalities under variable degrees of state entanglement and mixedness. We then demonstrate theoretically the violation of a KS inequality for any two-mode state by using pseudospin observables and a generalized quasiprobability function.

Tripartite nonlocality and continuous-variable entanglement in thermal states of trapped ions

We study a system of three trapped ions in an anisotropic bidimensional trap. By focusing on the transverse modes of the ions, we show that the mutual ion-ion Coulomb interactions set entanglement of a genuine tripartite nature, to some extent persistent to the thermal nature of the vibronic modes. We tackle this issue by addressing a nonlocality test in the phase space of the ionic system and quantifying the genuine residual tripartite entanglement in the continuous variable state of the transverse modes.

Stable breeding despite variable feeding in two sympatric auk species

Capsule Despite substantial inter-annual and inter-specific variance in the composition of chick diet, the breeding success of Guillemots (Common Murres) Uria aalge and Razorbills Alca torda remained constant from 2008 to 2010.
Aims To examine inter-specific and inter-annual differences in breeding success, chick provisioning behaviour and predation between two sympatric auk species.
Methods Focal observations of breeding auks at Rathlin Island, Northern Ireland, during 2008, 2009 and 2010 recorded reproductive success, reasons for breeding failure, prey composition and quality and chick provisioning rates.
Results Breeding success of both species was stable over the three years, despite significant variance in the composition and quality of the diet provided to chicks. Razorbills experienced greater rates of failure than Guillemots owing to chick loss and had lower overall breeding success.
Conclusion Guillemot and Razorbill breeding success was independent of the composition and quality of prey items delivered to chicks. Inter-specific differences in reproductive success may have been attributed to greater rates of predation at Razorbill rather than Guillemot nests.

The impact of biomarkers in multivariate algorithms for bladder cancer diagnosis in patients with hematuria

BACKGROUND: We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P