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Abstract
This letter presents experimental results of a refractive index sensor using a bent optical fiber taper. The approach of this sensor is based on an in-line Michelson interferometer implemented with a single mode tapered fiber with a cleaved tip end and changing tilt angle, enabling to tune its refractive index sensitivity. Several radii of curvature are tested and their refractive index sensitivities are analyzed for a refractive index range between 1.333 and 1.405. A clear enhancement of the sensor response is achieved at specific taper bending radii. A substantial improvement in the refractive index sensitivity, at values very close to distilled water, is obtained with a radius of curvature of 11 mm. A significant enhancement of the sensor response is also achieved for a refractive index close to 1.40 with a radius of curvature of 16.5 mm. (c) 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:921-924, 2015

Abstract
The ultimate goal of feature selection is to select the smallest subset of features that yields minimum generalization error from an original set of features. This effectively reduces the feature space, and thus the complexity of classifiers. Though several algorithms have been proposed, no single one outperforms all the other in all scenarios, and the problem is still an actively researched field. This paper proposes a new hybrid parallel approach to perform feature selection. The idea is to use a filter metric to reduce feature space, and then use an innovative wrapper method to search extensively for the best solution. The proposed strategy is implemented on a shared memory parallel environment to speedup the process. We evaluated its parallel performance using up to 32 cores and our results show 30 times gain in speed. To test the performance of feature selection we used five datasets from the well known NIPS challenge and were able to obtain an average score of 95.90% for all solutions.

Abstract
Nature and biological inspired search and optimization methods are simple and powerful tools that can be used to design classical industrial controllers. In this paper a particle swarm optimization (PSO) algorithm based technique is deployed to design proportional integrative and derivative controllers to fulfill minimum robustness constraints. PID robustness design using maximum sensitivity and complementary sensitivity values is re-addressed and formulated within a constrained PSO. Results are presented and analyzed regarding the control objective of load disturbance rejection and compared with other techniques.

Abstract
Determining the frequency of small subgraphs is an important graph mining primitive. One major class of algorithms for this task is based upon the enumeration of all sets of k connected nodes. These are known as network-centric algorithms. FAst Subgraph Enumeration (FaSE) is a exact algorithm for subgraph counting that contrasted with its past approaches by performing the isomorphism tests while doing the enumeration, encapsulating the topological information in a g-trie and thus largely reducing the number of required isomorphism tests. Our goal with this paper is to expand this approach by providing an approximate algorithm, which we called Rand-FaSE. It uses an unbiased sampling estimator for the number of subgraphs of each type, allowing an user to trade some accuracy for even faster execution times. We tested our algorithm on a set of representative complex networks, comparing it with the exact alternative, FaSE. We also do an extensive analysis by studying its accuracy and speed gains against previous sampling approaches. With all of this, we believe FaSE and Rand-FaSE pave the way for faster network-centric census algorithms.

Abstract