Description:
Clustering is an important problem in data mining. It can be formulated as a nonsmooth, nonconvex optimization problem. For the most global optimization techniques this problem is challenging even in medium size data sets. In this paper, we propose an approach that allows one to apply local methods of smooth optimization to solve the clustering problems. We apply an incremental approach to generate starting points for cluster centers which enables us to deal with nonconvexity of the problem. The hyperbolic smoothing technique is applied to handle nonsmoothness of the clustering problems and to make it possible application of smooth optimization algorithms to solve them. Results of numerical experiments with eleven real-world data sets and the comparison with state-of-the-art incremental clustering algorithms demonstrate that the smooth optimization algorithms in combination with the incremental approach are powerful alternative to existing clustering algorithms.
Description:
Several primal and dual quantitative characterizations of regularity properties of collections of sets in normed linear spaces are discussed. Relationships between regularity properties of collections of sets and those of set-valued mappings are provided.
Description:
Several primal and dual quantitative characterizations of regularity properties of collections of sets in normed linear spaces are discussed. Relationships between regularity properties of collections of sets and those of set-valued mappings are provided.
Description:
This narrative chapter unravels the story of the research journey undertaken over the last 16 years as part of a partnership between the Alpine School/School for Student Leadership and staff from Monash University’s Gippsland campus, which later became part of Federation University, Australia. It has been written in a narrative style to provide a conceptual context for discussing some of the major research insights from this partnership, including the significance of a ‘Landscape of Transformation’ as an overarching framework that has emerged from this longitudinal study. An outline of the research directions, and the associated teams who have been involved, is clarified. Also presented is a full listing of publications and reports associated with the research, with a focus on some of the key findings which are covered in more detail in later chapters.
Description:
Understanding the way how genes interact is one of the fundamental questions in systems biology. The modeling of gene regulations currently assumes that genes interact either instantaneously or with a certain amount of time delay. In this paper, we propose an information theory based novel two-phase gene regulatory network (GRN) inference algorithm using the Bayesian network formalism that can model both instantaneous and single-step time-delayed interactions between genes simultaneously. We show the effectiveness of our approach by applying it to the analysis of synthetic data as well as the Saccharomyces cerevisiae gene expression data.
Description:
Understanding gene interactions is a fundamental question in systems biology. Currently, modeling of gene regulations assumes that genes interact either instantaneously or with time delay. In this paper, we introduce a framework based on the Bayesian Network (BN) formalism that can represent both instantaneous and time-delayed interactions between genes simultaneously. Also, a novel scoring metric having firm mathematical underpinnings is then proposed that, unlike other recent methods, can score both interactions concurrently and takes into account the biological fact that multiple regulators may regulate a gene jointly, rather than in an isolated pair-wise manner. Further, a gene regulatory network inference method employing evolutionary search that makes use of the framework and the scoring metric is also presented. Experiments carried out using synthetic data as well as the well known Saccharomyces cerevisiae gene expression data show the effectiveness of our approach.
Description:
Water safety literature has many epidemiological studies, and for high-income countries at least, the demographics and risk factors for drowning are well established. In spite of the introduction of a multitude of measures designed to reduce drowning, a review of current literature shows that to date, evaluations of the effectiveness of water safety interventions are scarce. Worldwide, key stakeholders have implemented various intervention programmes but few have been evaluated. Those which have, typically examine recall of messages rather than changes in skills, knowledge, attitudes and behaviours. At the same time, it should be realised that the impact of interventions on the drowning rate is particularly difficult to measure at least in high-income countries, where drowning is a relatively rare event. Also it seems unrealistic to expect that changes to rates become apparent after a single intervention. To date, home swimming pool fencing appears to be one of the only areas where evidence is available that demonstrates a reduction in drowning figures.
Description:
Introducing a number of innovative and powerful coding tools, the High Efficiency Video Coding (HEVC) standard promises double compression efficiency, compared to its predecessor H.264, with similar perceptual quality. The increased computational time complexity is an important issue for the video coding research community as well. An attempt to reduce this complexity of HEVC is adopted in this paper, by efficient selection of appropriate block-partitioning modes based on motion features and the saliency applied to the difference between successive image blocks. As this difference gives us the explicit visible motion and salient information, we develop a cost function by combining the motion features and image difference salient feature. The combined features are then converted into area of interest (AOI) based binary pattern for the current block. This pattern is then compared with a previously defined codebook of binary pattern templates for a subset of mode selection. Motion estimation (ME) and motion compensation (MC) are performed only on the selected subset of modes, without exhaustive exploration of all modes available in HEVC. The experimental results reveal a reduction of 42% encoding time complexity of HEVC encoder with similar subjective and objective image quality.
Description:
Introducing a number of innovative and powerful coding tools, the High Efficiency Video Coding (HEVC) standard promises double compression efficiency, compared to its predecessor H.264, with similar perceptual quality. The increased computational time complexity is an important issue for the video coding research community as well. An attempt to reduce this complexity of HEVC is adopted in this paper, by efficient selection of appropriate block-partitioning modes based on motion features and the saliency applied to the difference between successive image blocks. As this difference gives us the explicit visible motion and salient information, we develop a cost function by combining the motion features and image difference salient feature. The combined features are then converted into area of interest (AOI) based binary pattern for the current block. This pattern is then compared with a previously defined codebook of binary pattern templates for a subset of mode selection. Motion estimation (ME) and motion compensation (MC) are performed only on the selected subset of modes, without exhaustive exploration of all modes available in HEVC. The experimental results reveal a reduction of 42% encoding time complexity of HEVC encoder with similar subjective and objective image quality.