Non‐iterative Second‐order Approximation of Signed Distance Functions for Any Isosurface Representation

Signed distance functions (SDF) to explicit or implicit surface representations are intensively used in various computer graphics and visualization algorithms. Among others, they are applied to optimize collision detection, are used to reconstruct data fields or surfaces, and, in particular, are an obligatory ingredient for most level set methods. Level set methods are common in scientific visualization to extract surfaces from scalar or vector fields. Usual approaches for the construction of an SDF to a surface are either based on iterative solutions of a special partial differential equation or on marching algorithms involving a polygonization of the surface. We propose a novel method for a non‐iterative approximation of an SDF and its derivatives in a vicinity of a manifold. We use a second‐order algebraic fitting scheme to ensure high accuracy of the approximation. The manifold is defined (explicitly or implicitly) as an isosurface of a given volumetric scalar field. The field may be given at a set of irregular and unstructured samples. Stability and reliability of the SDF generation is achieved by a proper scaling of weights for the Moving Least Squares approximation, accurate choice of neighbors, and appropriate handling of degenerate cases. We obtain the solution in an explicit form, such that no iterative solving is necessary, which makes our approach fast.     

EXPLORATION OF MASSIVE CRIME DATA SETS THROUGH DATA MINING TECHNIQUES

We incorporate two data mining techniques, clustering and association-rule mining, into a fruitful exploratory tool for the discovery of spatio-temporal patterns in data-rich environments. This tool is an autonomous pattern detector that efficiently and effectively reveals plausible cause–effect associations among many geographical layers. We present two methods for exploratory analysis and detail algorithms to explore massive databases. We illustrate the algorithms with real crime data sets.     

O films with adjustable tilt of optical axis for LCD compensation

Abstract— A method of preparation of positive O films with the tilt angle of the optic axis continuously controlled in the range 0–90° is proposed. It is based on the use of reactive mesogens and alignment materials that provide a wide range of pretilt angles. The method developed allows for further improvement in the viewing‐angle characteristics of LCDs with O compensation films.     

Design of peptidomimetic inhibitors of aspartic protease of HIV-1 containing -Phe Psi Pro- core and displaying favourable ADME-related properties

Aspartic protease (PR) of HIV-1 virus represents a valid therapeutic target for the design of antiviral agents suitable for treatment of AIDS. We have designed peptidomimetic PR inhibitors containing a novel dihydroxyethylenediamine -Phe-Psi[CHOH-CHOH]-Pro- core using molecular modelling approach that predicts the inhibitory potencies (IC(50)(pre)) in terms of computed relative enzyme-inhibitor complexation Gibbs free energies (Delta Delta G(comp)). The modelling approach considers not only the enzyme-inhibitor interactions, but includes also the solvent and entropic effects affecting the enzyme inhibition. The objectives of this study were to optimize the number and type of flanking residues that occupy the S(3), S(2) and S(2'), S(3') positions in the PR binding pocket and to select potent lead candidates, which display also favourable ADME-related properties. The structure-based design was combined with a synthetic strategy used to prepare a training set of 10 analogues sharing the -Phe Psi Pro- core. This strategy couples stereochemical control with full flexibility in the choice of the flanking residues and in vitro activity assays. A QSAR model correlating calculated Delta Delta G(comp) with the measured IC(50)(exp) values for the training set was prepared and confirmed that our computational approach can serve for reliable prediction of PR inhibitory potencies of peptidomimetics. The appropriate choice of the flanking residues allowed us to design virtual lead compounds, such as FP14, FP23 and FP76, with reduced molecular weight, predicted inhibitory potencies in the picomolar range, promising ADME profiles and a potential to escape drug resistance due to favourable interactions with the PR backbone.     

Robot-assisted shopping for the blind: issues in spatial cognition and product selection

Research on spatial cognition and blind navigation suggests that a device aimed at helping blind people to shop independently should provide the shopper with effective interfaces to the locomotor and haptic spaces of the supermarket. In this article, we argue that robots can act as effective interfaces to haptic and locomotor spaces in modern supermarkets. We also present the design and evaluation of three product selection modalities—browsing, typing and speech, which allow the blind shopper to select the desired product from a repository of thousands of products.     

Minimizing nonsubmodular functions with graph cuts - a review

Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov random fields (MRFs). Currently, there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of MRF energies (e.g., submodular functions). In this survey, we review some results that show that graph cuts can be applied to a much larger class of energy functions (in particular, nonsubmodular functions). While these results are well-known in the optimization community, to our knowledge they were not used in the context of computer vision and MRF optimization. We demonstrate the relevance of these results to vision on the problem of binary texture restoration.     

Agent Mining: The Synergy of Agents and Data Mining

Autonomous agents and multiagent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can't be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.     

Guest Editors' Introduction: Agents and Data Mining

On top of two active research streams, agents and data mining, a most recent and exciting trend is their interaction and integration. Agent mining has emerged as a very promising field due to its unique contributions to complementary and innovative methodologies, techniques, and applications for complex problem-solving. This editorial summarizes the structure of this special issue.     

Internal behaviour provided by nonlinear l1-optimal controllers

An l₁-optimal linear time-invariant (LTI) compensator may have an order significantly higher than that of the plant, even when the state is measurable. Recently there has been work exploring the use of nonlinear static feedback controllers which provide near optimal performance. Here we consider a class of nonlinear state feedback controllers and derive superposition-like bounds on both the plant state and the controlled output in the event that the plant initial condition, the disturbance, and the noise are all non-zero.