Functional check test for high-speed milling centres of up to five axes

This paper offers a system for checking three or five axes high-speed machining centres by milling a sample sphere pattern. Using the right choice of toolpath, it is possible to check and compare the performance which the centre is capable of following. The main features under study here are ease of use and the great amount of information that can be drawn regarding high-speed machine performances, including the geometrical precision of the machine and tool setup for the chosen machining conditions.     

Switching Futile para‐Quinone to Efficient Reactive Oxygen Species Generator: Ubiquitin‐Specific Protease‐2 Inhibition,Electrocatalysis, and Quantification

Understanding the correlation between structural features of small‐molecule drugs and their mode of action is a fascinating topic and crucial for the drug‐discovery process. However, in many cases, knowledge of the exact parameters that dictate the mode of action is still lacking. Following a large screening for ubiquitin specific protease 2 (USP2) inhibition, an effective para‐quinone‐based inhibitor with an unclear mode of action was identified. To gain a deeper understanding of the mechanism of inhibition, a set of para‐quinones were prepared and studied for USP2 inhibition, electrocatalysis, and reactive oxygen species (ROS) quantification. The excellent correlation obtained from the above‐mentioned studies disclosed a distinct pattern of “N?C=O?N” in the bicyclic para‐quinones to be a crucial factor for ROS generation, and demonstrated that minor changes in such a skeleton drastically altered the ROS‐generating ability. The knowledge acquired herein would serve as an important guideline for future medicinal chemistry optimization of related structures to select the preferred mode of action.     

Using obstacle analysis to identify contingency requirements on an unpiloted aerial vehicle

This paper describes the use of Obstacle Analysis to identify anomaly handling requirements for a safety-critical, autonomous system. The software requirements for the system evolved during operations due to an on-going effort to increase the autonomous system’s robustness. The resulting increase in autonomy also increased system complexity. This investigation used Obstacle Analysis to identify and to reason incrementally about new requirements for handling failures and other anomalous events. Results reported in the paper show that Obstacle Analysis complemented standard safety-analysis techniques in identifying undesirable behaviors and ways to resolve them. The step-by-step use of Obstacle Analysis identified potential side effects and missing monitoring and control requirements. Adding an Availability Indicator and feature-interaction patterns proved useful for the analysis of obstacle resolutions. The paper discusses the consequences of these results in terms of the adoption of Obstacle Analysis to analyze anomaly handling requirements in evolving systems.     

E-empowerment: Empowerment by the Internet

This article focuses on the concept of empowerment and the ways in which the Internet is being utilized as an empowering tool. This analysis ranges from the personal to the global levels and the consequences of that empowerment are also discussed. We propose a four-level model that serves to explain what we term E-empowerment and the effects that can be observed at each of the four levels, ranging from (1) the personal; (2) the interpersonal; (3) group; and (4) citizenship. The potential for future development of E-empowerment is also discussed.     

Paper craft models from meshes

This paper introduces an algorithm for segmenting a mesh into developable approximations. The algorithm can be used in various applications in CAD and computer graphics. This paper focuses on paper crafting and demonstrates that the algorithm generates approximations that are developable, easy to cut, and can be glued together. It is also shown that the error between the given model and the paper model is small.     

Anytime similarity measures for faster alignment

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.     

When standard RANSAC is not enough: cross-media visual matching with hypothesis relevancy

The same scene can be depicted by multiple visual media. For example, the same event can be captured by a comic image or a movie frame; the same object can be represented by a photograph or by a 3D computer graphics model. In order to extract the visual analogies that are at the heart of cross-media analysis, spatial matching is required. This matching is commonly achieved by extracting key points and scoring multiple, randomly generated mapping hypotheses. The more consensus a hypothesis can draw, the higher its score. In this paper, we go beyond the conventional set-size measure for the quality of a match and present a more general hypothesis score that attempts to reflect how likely is each hypothesized transformation to be the correct one for the matching task at hand. This is achieved by considering additional, contextual cues for the relevance of a hypothesized transformation. This context changes from one matching task to another and reflects different properties of the match, beyond the size of a consensus set. We demonstrate that by learning how to correctly score each hypothesis based on these features we are able to deal much more robustly with the challenges required to allow cross-media analysis, leading to correct matches where conventional methods fail.