Audit report on the Gilbert/Franklin Township Fire and Emergency Response Agency for the years ended June 30, 2015 and 2014

Audit report on the Gilbert/Franklin Township Fire and Emergency Response Agency for the years ended June 30, 2015 and 2014

Map of valuable property in the 6th ward of the city of Brooklyn belonging to John Dikeman & others to be sold by Franklin & Jenkins on Monday, 29th August, 1836 at 12 o'colck at their sales room, 15 Broad St.

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Citizen journalism and everyday life : a case study of Germany's myHeimat.de

The impact of citizen journalism on the established journalism industry, and its role in the future news media mix, remain key topics in current journalism studies research, not least in the context of the current crisis facing many news organisations around the globe. The centrality of this issue is also reflected in the substantial number of ‘citizen journalism’ monographs and collections published across the last few years (see for example Paterson & Domingo, 2008; Boler, 2008; Allan & Thorsen, 2009; Neuberger, Nuernbergk, & Rischke, 2009; Gordon, 2009; Russell & Echchaibi, 2009; Meikle & Redden, forthcoming). With relatively few notable exceptions, much of the research and wider public discussion surrounding the citizen journalism phenomenon has employed a relatively narrow definition of the term, with many researchers focussing on citizen journalism projects which provide mainly political news and commentary, and on their role in influencing the political process especially in countries like the U.S.

Double jeopardy : comorbid anxiety and depression in later life

Comorbid depression and anxiety in late life present challenges for geriatric mental health care providers. These challenges include identifying the often complex diagnostic presentations both clinically and in a research context. This potent comorbidity can be conceived as double jeopardy in older adults, further diminishing their quality of life. Geriatric health care providers need to understand psychiatric comorbidity of this type for accurate diagnosis and early referral to specialists, and to coordinate interdisciplinary care. Researchers in the field also need to recognize potential multiple impacts of comorbidities with respect to assessment and treatment domains. This article describes the prevalence of late-life depression and anxiety disorders and reviews studies on this comorbidity in older adults. Risk factors and protective factors for anxiety and depression in later life are reviewed, and information is provided about comparative symptoms, the selection of assessment tools, and challenges to the provision of interdisciplinary, evidence-based care.

A stochastic model for natural feature representation

This paper presents a robust stochastic model for the incorporation of natural features within data fusion algorithms. The representation combines Isomap, a non-linear manifold learning algorithm, with Expectation Maximization, a statistical learning scheme. The representation is computed offline and results in a non-linear, non-Gaussian likelihood model relating visual observations such as color and texture to the underlying visual states. The likelihood model can be used online to instantiate likelihoods corresponding to observed visual features in real-time. The likelihoods are expressed as a Gaussian Mixture Model so as to permit convenient integration within existing nonlinear filtering algorithms. The resulting compactness of the representation is especially suitable to decentralized sensor networks. Real visual data consisting of natural imagery acquired from an Unmanned Aerial Vehicle is used to demonstrate the versatility of the feature representation.

Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

Rich probabilistic representations for bearing only decentralised data fusion

The aim of this paper is to demonstrate the validity of using Gaussian mixture models (GMM) for representing probabilistic distributions in a decentralised data fusion (DDF) framework. GMMs are a powerful and compact stochastic representation allowing efficient communication of feature properties in large scale decentralised sensor networks. It will be shown that GMMs provide a basis for analytical solutions to the update and prediction operations for general Bayesian filtering. Furthermore, a variant on the Covariance Intersect algorithm for Gaussian mixtures will be presented ensuring a conservative update for the fusion of correlated information between two nodes in the network. In addition, purely visual sensory data will be used to show that decentralised data fusion and tracking of non-Gaussian states observed by multiple autonomous vehicles is feasible.

Applying incremental EM to Bayesian classifiers in the learning of hyperspectral remote sensing data

In this paper, we apply the incremental EM method to Bayesian Network Classifiers to learn and interpret hyperspectral sensor data in robotic planetary missions. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. Many spacecraft carry spectroscopic equipment as wavelengths outside the visible light in the electromagnetic spectrum give much greater information about an object. The algorithm used is an extension to the standard Expectation Maximisation (EM). The incremental method allows us to learn and interpret the data as they become available. Two Bayesian network classifiers were tested: the Naive Bayes, and the Tree-Augmented-Naive Bayes structures. Our preliminary experiments show that incremental learning with unlabelled data can improve the accuracy of the classifier.