Multi-view Superpixel Stereo in Urban Environments

Urban environments possess many regularities which can be efficiently exploited for 3D dense reconstruction from multiple widely separated views. We present an approach utilizing properties of piecewise planarity and restricted number of plane orientations to suppress reconstruction and matching ambiguities causing failures of standard dense stereo methods. We formulate the problem of the 3D reconstruction in MRF framework built on an image pre-segmented into superpixels. Using this representation, we propose novel photometric and superpixel boundary consistency terms explicitly derived from superpixels and show that they overcome many difficulties of standard pixel-based formulations and handle favorably problematic scenarios containing many repetitive structures and no or low textured regions. We demonstrate our approach on several wide-baseline scenes demonstrating superior performance compared to previously proposed methods.     

The <Emphasis Type="Italic">L</Emphasis><Superscript>2</Superscript>-Optimality of the IIPG Method for Odd Degrees of Polynomial Approximation in 1D

The paper deals with a numerical analysis of the incomplete interior penalty Galerkin (IIPG) method applied to one dimensional Poisson problem. Based on a particular choice of the interior penalty parameter σ (order of O(h ⁻¹)), we derive the optimal error estimate in the L ²-norm for odd degrees of polynomial approximation for locally quasi-uniform meshes. Moreover, we show that only the mentioned choice of the penalty parameter leads to optimal orders of convergence. Finally, presented numerical experiments verify the theoretical results.     

Adsorption–desorption noise in plasmonic chemical/biological sensors for multiple analyte environment

We investigated intrinsic noise in plasmonic sensors caused by adsorption and desorption of gaseous analytes on the sensor surface. We analyzed a general situation when there is a larger number of different analyte species. We applied our model to calculate various analyte mixtures, including some environmental pollutants, toxic and dangerous substances. The spectral density of mean square refractive index fluctuations follows a dependence similar to that of generation-recombination noise in photodetectors, flat at lower frequencies and sharply decreasing at higher. Some of the calculated noise levels are well within the detection range of conventional surface plasmon resonance sensors. An AD noise peak is observed in temperature dependence of mean square refractive index fluctuations, thus sensor operating temperature may be optimized to obtain larger signal to noise ratio. A significant property of AD noise is its rise with the decreasing plasmon sensor area, which means that it will be even more pronounced in modern nanoplasmonic devices. Our consideration is valid both for conventional surface plasmon resonance devices and for general nanoplasmonic devices.     

Complexity classes for self-assembling flexible tiles

We present a theoretical model for self-assembling DNA tiles with flexible branches. We encode an instance of a “problem” as a pot of such tiles for which a “solution” is an assembled complete complex without any free sticky ends. Using the number of tiles in an assembled complex as a measure of complexity we show how NTIME classes (such as NP and NEXP) can be represented with corresponding classes of the model.     

Discovering implicit intention-level knowledge from natural-language texts

In this paper, we propose a new approach to automatic discovery of implicit rhetorical information from texts based on evolutionary computation methods. In order to guide the search for rhetorical connections from natural-language texts, the model uses previously obtained training information which involves semantic and structural criteria. The main features of the model and new designed operators and evaluation functions are discussed, and the different experiments assessing the robustness and accuracy of the approach are described. Experimental results show the promise of evolutionary methods for rhetorical role discovery.     

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.