Kinematic Analysis and Dimensional Synthesis of Exechon Parallel Kinematic Machine for Large Volume Machining

A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

Parallels between machine and brain decoding

We report some existing work, inspired by analogies between human thought and machine computation, showing that the informational state of a digital computer can be decoded in a similar way to brain decoding. We then discuss some proposed work that would leverage this analogy to shed light on the amount of information that may be missed by the technical limitations of current neuroimaging technologies. © 2012 Springer-Verlag.

Starbog: The Garden Virtuoso and the Turing Machine

This article will discuss a recent ensemble composition entitled Starbog which was toured and broadcast in Britain in 2006 . The composition of Starbog focused on developing working methods which combined computer-based techniques (using OpenMusic) with more subconscious means of generating musical ideas. The challenge in achieving this was as much aesthetic/philosophical as it was technical and the present article is intending as a ‘sounding’ which focuses on the influence OpenMusic has had on the composer’s music, rather than documenting the nature of the often simple application of algorithms.

Taxonomic and functional diversity of farmland bird communities across Europe:Effects of biogeography and agricultural intensification

In eight European study sites (in Spain, Ireland, Netherlands, Germany, Poland, Estonia and Sweden), abundance of breeding farmland bird territories was obtained from 500 × 500 m survey plots (30 per area, N = 240) using the mapping method. Two analyses were performed: (I) a Canonical Correspondence Analysis of species abundance in relation to geographical location and variables measuring agricultural intensification at field and farm level to identify significant intensification variables and to estimate the fractions of total variance in bird abundance explained by geography and agricultural intensification; (II) several taxonomic and functional community indices were built and analysed using GLM in relation to the intensification variables found significant in the CCA. The geographical location of study sites alone explains nearly one fifth (19. 5%) of total variation in species abundance. The fraction of variance explained by agricultural intensification alone is much smaller (4. 3%), although significant. The intersection explains nearly two fifths (37. 8%) of variance in species abundance. Community indices are negatively affected by correlates of intensification like farm size and yield, whereas correlates of habitat availability and quality have positive effects on taxonomic and functional diversity of assemblages. Most of the purely geographical variation in farmland bird assemblage composition is associated to Mediterranean steppe species, reflecting the bio-geographical singularity of that assemblage and reinforcing the need to preserve this community. Taxonomic and functional diversity of farmland bird communities are negatively affected by agricultural intensification and positively affected by increasing farmland habitat availability and quality. © 2011 Springer Science+Business Media B.V.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

Why are you tall while others are short? Agricultural production and other proximate determinants of global heights

We analyze the proximate determinants of the biological standard of living from a global perspective, namely high-quality nutrition and the disease environment during the nineteenth and twentieth centuries. Until the mid-twentieth century, the local availability of cattle, meat, and milk per capita and the local disease environment mainly determined the stature of the population – and, by implication, how long they lived and how healthy they were. During the late twentieth century, the trade of agricultural products and health-promoting technologies increased in relative importance; hence, the local availabilities became less decisive in explaining height differences.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.