Abstract

Abstract
Open set recognition is a classification-like task. It is accomplished not only by the identification of observations which belong to targeted classes (i.e., the classes among those represented in the training sample which should be later recognized) but also by the rejection of inputs from other classes in the problem domain. The need for proper handling of elements of classes beyond those of interest is frequently ignored, even in works found in the literature. This leads to the improper development of learning systems, which may obtain misleading results when evaluated in their test beds, consequently failing to keep the performance level while facing some real challenge. The adaptation of a classifier for open set recognition is not always possible: the probabilistic premises most of them are built upon are not valid in a open-set setting. Still, this paper details how this was realized for WiSARD a weightless artificial neural network model. Such achievement was based on an elaborate distance-like computation this model provides and the definition of rejection thresholds during training. The proposed methodology was tested through a collection of experiments, with distinct backgrounds and goals. The results obtained confirm the usefulness of this tool for open set recognition.

Abstract
A microfiber knot resonator integrated in an abrupt taper-based Mach-Zehnder interferometer was used for simultaneous measurement of temperature and refractive index. This compact structure was fabricated using only CO2 laser processing. The transmission spectrum is the combination of the microfiber knot resonator and the Mach-Zehnder interferometer responses. The two different components of the transmission spectrum (the microfiber knot resonator and the MachZehnder interferometer components) present different sensitivities when subjected to physical or chemical parameters. A characterization in temperature, refractive index, and sodium chloride (NaCl) concentration was performed. A simple matrix method was used for simultaneous measurement of temperature and refractive index.

Abstract

Abstract
In this paper, we address the Capacitated Single Allocation ?-Hub Location Problem (CSA?HLP) in which the capacities of the hubs limit the flows in the network and every non-hub node must be allocated to only one hub. The objective is to choose a fixed number of ? nodes to be established as hubs that minimizes the costs of allocating all the non-hub nodes to the chosen hubs. We propose a simple Relaxation Adaptive Memory Programming (RAMP) approach that uses Lagrangean Relaxation with subgradient optimization to explore the dual side, a projection method to project dual solutions into the primal solutions space and an improvement method to guide the search in the primal side. The computational results obtained on a classical set of benchmark problems showed that our algorithm achieved the best results in the literature, demonstrating the advantages of exploring primal-dual relationships.

Abstract