LaMgAl11O19 synthesis using non-hydrolytic sol-gel methods

Polycrystalline LaMgAl₁₁O₁₉ (LMA) was prepared by four different non-hydrolytic sol-gel methods. From stable solutions, four powder precursors containing an amorphous and nanocrystalline phase with specific reactivity were obtained. The particle size, morphology, thermal behaviour, and phase composition of the powder precursors were studied using DLS, TEM, DSC/TG and XRD. Bulk ceramic samples containing LMA were prepared at 1200?°C for 16?h and examined in terms of phase purity and microstructure using XRD, SEM, and TEM. Raman spectroscopy of pure LMA was used to study the structure in detail. A mechanism of LMA formation and a relation between powder precursor properties and final phase composition is proposed. These findings may be useful for designing modern technologies for fabrication of LMA for optical or protective coating applications.     

Learning Fast Emulators of Binary Decision Processes

Computation time is an important performance characteristic of computer vision algorithms. The paper shows how existing (slow) binary decision algorithms can be approximated by a (fast) trained WaldBoost classifier. WaldBoost learning minimises the decision time of the classifier while guaranteeing predefined precision. We show that the WaldBoost algorithm together with bootstrapping is able to efficiently handle an effectively unlimited number of training examples provided by the implementation of the approximated algorithm. Two interest point detectors, the Hessian-Laplace and the Kadir-Brady saliency detectors, are emulated to demonstrate the approach. Experiments show that while the repeatability and matching scores are similar for the original and emulated algorithms, a 9-fold speed-up for the Hessian-Laplace detector and a 142-fold speed-up for the Kadir-Brady detector is achieved. For the Hessian-Laplace detector, the achieved speed is similar to SURF, a popular and very fast handcrafted modification of Hessian-Laplace; the WaldBoost emulator approximates the output of the Hessian-Laplace detector more precisely.     

Context-sensitive refinements for stochastic optimisation algorithms in inductive logic programming

We describe a new approach to the application of stochastic search in Inductive Logic Programming (ILP). Unlike traditional approaches we do not focus directly on evolving logical concepts but our refinement-based approach uses the stochastic optimization process to iteratively adapt the initial working concept. Utilization of context-sensitive concept refinements (adaptations) helps the search operations to produce mostly syntactically correct concepts. It also enables using available background knowledge both for efficiently restricting the search space and for directing the search. Thereby, the search is more flexible, less problem-specific and the framework can be easily used with any stochastic search algorithm within ILP domain. Experimental results on several data sets verify the usefulness of this approach.     

Reduced Aggregate Scattering Operators for Path Tracing

Aggregate scattering operators (ASOs) describe the overall scattering behavior of an asset (i.e., an object or volume, or collection thereof) accounting for all orders of its internal scattering. We propose a practical way to precompute and compactly store ASOs and demonstrate their ability to accelerate path tracing. Our approach is modular avoiding costly and inflexible scene‐dependent precomputation. This is achieved by decoupling light transport within and outside of each asset, and precomputing on a per‐asset level. We store the internal transport in a reduced‐dimensional subspace tailored to the structure of the asset geometry, its scattering behavior, and typical illumination conditions, allowing the ASOs to maintain good accuracy with modest memory requirements. The precomputed ASO can be reused across all instances of the asset and across multiple scenes. We augment ASOs with functionality enabling multi‐bounce importance sampling, fast short‐circuiting of complex light paths, and compact caching, while retaining rapid progressive preview rendering. We demonstrate the benefits of our ASOs by efficiently path tracing scenes containing many instances of objects with complex inter‐reflections or multiple scattering.     

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper.     

HamleDT: Harmonized multi-language dependency treebank

We present HamleDT—a HArmonized Multi-LanguagE Dependency Treebank. HamleDT is a compilation of existing dependency treebanks (or dependency conversions of other treebanks), transformed so that they all conform to the same annotation style. In the present article, we provide a thorough investigation and discussion of a number of phenomena that are comparable across languages, though their annotation in treebanks often differs. We claim that transformation procedures can be designed to automatically identify most such phenomena and convert them to a unified annotation style. This unification is beneficial both to comparative corpus linguistics and to machine learning of syntactic parsing.     

Disjoint essential sets of implicates of a CQ Horn function

In this paper we study a class of CQ Horn functions introduced in Boros et al. (Ann Math Artif Intell 57(3–4):249–291, 2010). We prove that given a CQ Horn function f, the maximal number of pairwise disjoint essential sets of implicates of f equals the minimum number of clauses in a CNF representing f. In other words, we prove that the maximum number of pairwise disjoint essential sets of implicates of f constitutes a tight lower bound on the size (the number of clauses) of any CNF representation of f.     

Segmentation of CT arteriography based on combination of segmentation methods

This paper describes a solution of CT arteriography vessel segmentation using a combination of methods for segmentation of image data. Computer tomography (CT) is one of the most useful approaches for investigating the arterial system and its pathologies. There are three ways for segmenting the vessels from the CT image — manually, fully automatically and with a user interaction. We considered the (semi)automatic approaches, because the manual way is too time-consuming and human-dependent. We used a combination of three segmentation techniques: thresholding, edge detection and region growing. Results of this method were consulted then with a medicine expert. None of these techniques can be used separately, but a combination of them brought very good results, which can be used in medicine.     

Portal‐Masked Environment Map Sampling

We present a technique to efficiently importance sample distant, all‐frequency illumination in indoor scenes. Standard environment sampling is inefficient in such cases since the distant lighting is typically only visible through small openings (e.g. windows). This visibility is often addressed by manually placing a portal around each window to direct samples towards the openings; however, uniformly sampling the portal (its area or solid angle) disregards the possibly high frequency environment map. We propose a new portal importance sampling technique which takes into account both the environment map and its visibility through the portal, drawing samples proportional to the product of the two. To make this practical, we propose a novel, portal‐rectified reparametrization of the environment map with the key property that the visible region induced by a rectangular portal projects to an axis‐aligned rectangle. This allows us to sample according to the desired product distribution at an arbitrary shading location using a single (precomputed) summed‐area table per portal. Our technique is unbiased, relevant to many renderers, and can also be applied to rectangular light sources with directional emission profiles, enabling efficient rendering of non‐diffuse light sources with soft shadows.     

Open source content contributors’ response to free-riding: The effect of personality and context

We address concerns about the sustainability of the open source content model by examining the effect of external appropriation, whereby the product of open source contributors’ efforts is monetized by a party that did not contribute to the project, on intended effort withdrawal (reduction in contribution level). We examine both the personality of contributors and their contextual motivations to contribute, using a scenario-based survey of Wikipedia contributors. The findings suggest that perceived justice of the open source license terms, and intrinsic motivations are both negatively related with effort withdrawal intentions. Moreover, we find that the effect of the fairness personality trait on effort withdrawal is stronger for individuals who are low in perceived justice and weaker for individuals high in justice. The findings of factors predicting effort withdrawal contribute to the open source literature, which tends to focus on contribution and motivations, but not on what impacts changes in individual contribution levels.