Natural versus artificial scene classification by ordering discrete fourier power spectra

Holistic representations of natural scenes is an effective and powerful source of information for semantic classification and analysis of arbitrary images. Recently, the frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. In this paper, we present a new approach to naturalness classification of scenes using frequency domain. The proposed method is based on the ordering of the Discrete Fourier Power Spectra. Features extracted from this ordering are shown sufficient to build a robust holistic representation for Natural vs. Artificial scene classification. Experiments show that the proposed frequency domain method matches the accuracy of other state-of-the-art solutions. © 2008 Springer Berlin Heidelberg.

Continuous F0 in the source-excitation generation for HMM-based TTS: Do we need voiced/unvoiced classification?

Most HMM-based TTS systems use a hard voiced/unvoiced classification to produce a discontinuous F0 signal which is used for the generation of the source-excitation. When a mixed source excitation is used, this decision can be based on two different sources of information: the state-specific MSD-prior of the F0 models, and/or the frame-specific features generated by the aperiodicity model. This paper examines the meaning of these variables in the synthesis process, their interaction, and how they affect the perceived quality of the generated speech The results of several perceptual experiments show that when using mixed excitation, subjects consistently prefer samples with very few or no false unvoiced errors, whereas a reduction in the rate of false voiced errors does not produce any perceptual improvement. This suggests that rather than using any form of hard voiced/unvoiced classification, e.g., the MSD-prior, it is better for synthesis to use a continuous F0 signal and rely on the frame-level soft voiced/unvoiced decision of the aperiodicity model. © 2011 IEEE.

RNAi, DRD1, and histone methylation actively target developmentally important Non-CG DNA methylation in Arabidopsis

Cytosine DNA methylation protects eukaryotic genomes by silencing transposons and harmful DNAs, but also regulates gene expression during normal development. Loss of CG methylation in the Arabidopsis thaliana met1 and ddm1 mutants causes varied and stochastic developmental defects that are often inherited independently of the original met1 or ddm1 mutation. Loss of non-CG methylation in plants with combined mutations in the DRM and CMT3 genes also causes a suite of developmental defects. We show here that the pleiotropic developmental defects of drm1 drm2 cmt3 triple mutant plants are fully recessive, and unlike phenotypes caused by met1 and ddm1, are not inherited independently of the drm and cmt3 mutations. Developmental phenotypes are also reversed when drm1 drm2 cmt3 plants are transformed with DRM2 or CMT3, implying that non-CG DNA methylation is efficiently re-established by sequence-specific signals. We provide evidence that these signals include RNA silencing though the 24-nucleotide short interfering RNA (siRNA) pathway as well as histone H3K9 methylation, both of which converge on the putative chromatin-remodeling protein DRD1. These signals act in at least three partially intersecting pathways that control the locus-specific patterning of non-CG methylation by the DRM2 and CMT3 methyltransferases. Our results suggest that non-CG DNA methylation that is inherited via a network of persistent targeting signals has been co-opted to regulate developmentally important genes. © 2006 Chan et al.

How to avoid the problems of target-setting

Purpose: Advocates and critics of target-setting in the workplace seem unable to reach beyond their own well-entrenched battle lines. While the advocates of goal-directed behaviour point to what they see as demonstrable advantages, the critics of target-setting highlight equally demonstrable disadvantages. Indeed, the academic literature on this topic is currently mired in controversy, with neither side seemingly capable of envisaging a better way forward. This paper seeks to break the current deadlock and move thinking forward in this important aspect of performance measurement and management by outlining a new, more fruitful approach, based on both theory and practical experience. Design/methodology/approach: The topic was approached in three phases: assembling and reading key academic and other literature on the subject of target-setting and goal-directed behaviour, with a view to understanding, in depth, the arguments advanced by the advocates and critics of target-setting; comparing these published arguments with one's own experiential findings, in order to bring the essence of disagreement into much sharper focus; and then bringing to bear the academic and practical experience to identify the essential elements of a new, more fruitful approach offering all the benefits of goal-directed behaviour with none of the typical disadvantages of target-setting. Findings: The research led to three key findings: the advocates of goal-directed behaviour and critics of target-setting each make valid points, as seen from their own current perspectives; the likelihood of these two communities, left to themselves, ever reaching a new synthesis, seems vanishingly small (with leading thinkers in the goal-directed behaviour community already acknowledging this); and, between the three authors, it was discovered that their unusual combination of academic study and practical experience enabled them to see things differently. Hence, they would like to share their new thinking more widely. Research limitations/implications: The authors fully accept that their paper is informed by extensive practical experience and, as yet, there have been no opportunities to test their findings, conclusions and recommendations through rigorous academic research. However, they hope that the paper will move thinking forward in this arena, thereby informing future academic research. Practical implications: The authors hope that the practical implications of the paper will be significant, as it outlines a novel way for organisations to capture the benefits of goal-directed behaviour with none of the disadvantages typically associated with target-setting. Social implications: Given that increased efficiency and effectiveness in the management of organisations would be good for society, the authors think the paper has interesting social implications. Originality/value: Leading thinkers in the field of goal-directed behaviour, such as Locke and Latham, and leading critics of target-setting, such as Ordóñez et al. continue to argue with one another - much like, at the turn of the nineteenth century, proponents of the "wave theory of light" and proponents of the "particle theory of light" were similarly at loggerheads. Just as this furious scientific debate was ultimately resolved by Taylor's experiment, showing that light could behave both as a particle and wave at the same time, the authors believe that the paper demonstrates that goal-directed behaviour and target-setting can successfully co-exist. © Emerald Group Publishing Limited.

Semantic Discriminant mapping for classification and browsing of remote sensing textures and objects

We present a new approach based on Discriminant Analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1. each dimension corresponds to a semantic likelihood, 2. an efficient and simple multiclass classifier is proposed and 3. it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We will show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks. © 2005 IEEE.

Classification and choice in product design

Life is full of difficult choices. Everyone has their own way of dealing with these, some effective, some not. The problem is particularly acute in engineering design because of the vast amount of information designers have to process. This paper deals with a subset of this set of problems: the subset of selecting materials and processes, and their links to the design of products. Even these, though, present many of the generic problems of choice, and the challenges in creating tools to assist the designer in making them. The key elements are those of classification, of indexing, of reaching decisions using incomplete data in many different formats, and of devising effective strategies for selection. This final element - that of selection strategies - poses particular challenges. Product design, as an example, is an intricate blend of the technical and (for want of a better word) the aesthetic. To meet these needs, a tool that allows selection by analysis, by analogy, by association and simply by 'browsing' is necessary. An example of such a tool, its successes and remaining challenges, will be described.

Bayesian classification of acoustical waveforms under environmental variability

We present in this paper a new multivariate probabilistic approach to Acoustic Pulse Recognition (APR) for tangible interface applications. This model uses Principle Component Analysis (PCA) in a probabilistic framework to classify tapping pulses with a high degree of variability. It was found that this model, achieves a higher robustness to pulse variability than simpler template matching methods, specifically when allowed to train on data containing high variability. © 2011 IEEE.

Developing a technology intelligence strategy at kodak european research: Scan & target

OVERVIEW: Kodak European Research (KER) developed a strategy for technology intelligence based on a theoretical model developed by Kerr et al. (2006). KER scouts designed and implemented a four-step approach to identify relevant technologies and research centers across Europe, Africa and the Middle East. The approach provides clear guidance for integrating web searches, scouting trips, networking and interactions with intermediaries. KER's example illustrates how companies can organize themselves to look outside corporate boundaries in search of technologies relevant for their business. The approach may be useful to those in other companies who have been asked to start a technology intelligence activity. © 2010 Industrial Research Institute, Inc.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.