On the suitability of Prototype Selection methods for kNN classification with distributed data

In the current Information Age, data production and processing demands are ever increasing. This has motivated the appearance of large-scale distributed information. This phenomenon also applies to Pattern Recognition so that classic and common algorithms, such as the k-Nearest Neighbour, are unable to be used. To improve the efficiency of this classifier, Prototype Selection (PS) strategies can be used. Nevertheless, current PS algorithms were not designed to deal with distributed data, and their performance is therefore unknown under these conditions. This work is devoted to carrying out an experimental study on a simulated framework in which PS strategies can be compared under classical conditions as well as those expected in distributed scenarios. Our results report a general behaviour that is degraded as conditions approach to more realistic scenarios. However, our experiments also show that some methods are able to achieve a fairly similar performance to that of the non-distributed scenario. Thus, although there is a clear need for developing specific PS methodologies and algorithms for tackling these situations, those that reported a higher robustness against such conditions may be good candidates from which to start.

Applying logic forms and statistical methods to CL-SR performance

This paper describes a CL-SR system that employs two different techniques: the first one is based on NLP rules that consist on applying logic forms to the topic processing while the second one basically consists on applying the IR-n statistical search engine to the spoken document collection. The application of logic forms to the topics allows to increase the weight of topic terms according to a set of syntactic rules. Thus, the weights of the topic terms are used by IR-n system in the information retrieval process.

Computational approaches to subjectivity and sentiment analysis: Present and envisaged methods and applications

Recent years have witnessed a surge of interest in computational methods for affect, ranging from opinion mining, to subjectivity detection, to sentiment and emotion analysis. This article presents a brief overview of the latest trends in the field and describes the manner in which the articles contained in the special issue contribute to the advancement of the area. Finally, we comment on the current challenges and envisaged developments of the subjectivity and sentiment analysis fields, as well as their application to other Natural Language Processing tasks and related domains.

Functionalization of carbon nanotubes using aminobenzene acids and electrochemical methods. Electroactivity for the oxygen reduction reaction

Functionalized carbon nanotubes (CNTs) using three aminobenzene acids with different functional groups (carboxylic, sulphonic, phosphonic) in para position have been synthesized through potentiodynamic treatment in acid media under oxidative conditions. A noticeable increase in the capacitance for the functionalized carbon nanotubes mainly due to redox processes points out the formation of an electroactive polymer thin film on the CNTs surface along with covalently bonded functionalities. The CNTs functionalized using aminobenzoic acid rendered the highest capacitance values and surface nitrogen content, while the presence of sulfur and/or phosphorus groups in the aminobenzene structure yielded a lower functionalization degree. The oxygen reduction reaction (ORR) activity of the functionalized samples was similar to that of the parent CNTs, independently of the functional group present in the aminobenzene acid. Interestingly, a heat treatment in N2 atmosphere with a very low O2 concentration (3125 ppm) at 800 °C of the CNTs functionalized with aminobenzoic acid produced a material with high amounts of surface oxygen and nitrogen groups (12 and 4% at., respectively), that seem to modulate the electron-donor properties of the resulting material. The onset potential and limiting current for ORR was enhanced for this material. These are promising results that validates the use of electrochemistry for the synthesis of novel N-doped electrocatalysts for ORR in combination with adequate heat treatments.

Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research

The microfoundations research agenda presents an expanded theoretical perspective because it considers individuals, their characteristics, and their interactions as relevant variables to help us understand firm-level strategic issues. However, microfoundations empirical research faces unique challenges because processes take place at different levels of analysis and these multilevel processes must be considered simultaneously. We describe multilevel modeling and mixed methods as methodological approaches whose use will allow for theoretical advancements. We describe key issues regarding the use of these two types of methods and, more importantly, discuss pressing substantive questions and topics that can be addressed with each of these methodological approaches with the goal of making theoretical advancements regarding the microfoundations research agenda and strategic management studies in general.

Successful functionalization of superporous zeolite templated carbon using aminobenzene acids and electrochemical methods

A novel and selective electrochemical functionalization of a highly reactive superporous zeolite templated carbon (ZTC) with two different aminobenzene acids (2-aminobenzoic and 4-aminobenzoic acid) was achieved. The functionalization was done through potentiodynamic treatment in acid media under oxidative conditions, which were optimized to preserve the unique ZTC structure. Interestingly, it was possible to avoid the electrochemical oxidation of the highly reactive ZTC structure by controlling the potential limit of the potentiodynamic experiment in presence of aminobenzene acids. The electrochemical characterization demonstrated the formation of polymer chains along with covalently bonded functionalities to the ZTC surface. The functionalized ZTCs showed several redox processes, producing a capacitance increase in both basic and acid media. The rate performance showed that the capacitance increase is retained at scan rates as high as 100 mV s−1, indicating that there is a fast charge transfer between the polymer chains formed inside the ZTC porosity or the new surface functionalities and the ZTC itself. The success of the proposed approach was also confirmed by using other characterization techniques, which confirmed the presence of different nitrogen groups in the ZTC surface. This promising method could be used to achieve highly selective functionalization of highly porous carbon materials.

Music and movement: A comparative study between the BAPNE and Suzuki Methods

The aim of this article is to compare the Suzuki and BAPNE methods based on bibliography published for both approaches. In the field of musical and instrumental education and especially for the childhood stage, the correct use of the body and voice are of fundamental importance. These two methods differ from one another; one principally musical and instrumental, which is the Suzuki method, and one non-musical, the BAPNE method, which aims at stimulating attention, concentration, memory and the executing function of the pupil through music and body percussion. Comparing different approaches may provide teachers with a useful insight for facing different issues related to their discipline.

Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

Deformable templates for tracking and analysis of intravascular ultrasound sequences

Deformable Template models are first applied to track the inner wall of coronary arteries in intravascular ultrasound sequences, mainly in the assistance to angioplasty surgery. A circular template is used for initializing an elliptical deformable model to track wall deformation when inflating a balloon placed at the tip of the catheter. We define a new energy function for driving the behavior of the template and we test its robustness both in real and synthetic images. Finally we introduce a framework for learning and recognizing spatio-temporal geometric constraints based on Principal Component Analysis (eigenconstraints).

VdBH 222: a starburst cluster in the inner Milky Way

Context. It has been suggested that the compact open cluster VdBH 222 is a young massive distant object. Aims. We set out to characterise VdBH 222 using a comprehensive set of multi-wavelength observations. Methods. We obtained multi-band optical (UBVR) and near-infrared (JHKS) photometry of the cluster field, as well as multi-object and long-slit optical spectroscopy for a large sample of stars in the field. We applied classical photometric analysis, as well as more sophisticated methods using the CHORIZOS code, to determine the reddening to the cluster. We then plotted dereddened HR diagrams and determined cluster parameters via isochrone fitting. Results. We have identified a large population of luminous supergiants confirmed as cluster members via radial velocity measurements. We find nine red supergiants (plus one other candidate) and two yellow supergiants. We also identify a large population of OB stars. Ten of them are bright enough to be blue supergiants. The cluster lies behind ≈7.5 mag of extinction for the preferred value of RV = 2.9. Isochrone fitting allows for a narrow range of ages between 12 and 16 Ma. The cluster radial velocity is compatible with distances of ~6 and ~10 kpc. The shorter distance is inconsistent with the age range and Galactic structure. The longer distance implies an age ≈ 12 Ma and a location not far from the position where some Galactic models place the far end of the Galactic bar. Conclusions. VdBH 222 is a young massive cluster with a likely mass >20 000 M⊙. Its population of massive evolved stars is comparable to that of large associations, such as Per OB1. Its location in the inner Galaxy, presumably close to the end of the Galactic bar, adds to the increasing evidence for vigorous star formation in the inner regions of the Milky Way.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.