Annual Report of the Board of Public Education, First School District of Pennsylvania Comprising the City of Philadelphia

Title Varies: 1818-69, Annual Report of the Controllers of the Public Schools

Philadelphia Southern Steamship manufacturers and mercantile register.

Mode of access: Internet.

Handbook of the Central High School of Philadelphia

Mode of access: Internet.

Report of the proceedings at the ... general meeting of the advisory board of the Commercial Museum, Philadelphia

Title varies

Annual report of the superintendent of public schools of the city of Philadelphia.

Latest issue consulted: 1936/1937.

Annual report of the School Controller of the School District of Philadelphia.

Latest issue consulted: 9th (1920).

Romancing the Dane : reviving (and revising) Ophelia in contemporary YA fiction

Shakespeare’s Ophelia has been circulated in recent times as a figure of the adolescent woman at risk. Mary Pipher’s best-selling and influential Reviving Ophelia (1994) argued that the “story of Ophelia […] shows the destructive forces that affect young women” (20). Without undermining Pipher’s project, this paper reads two contemporary YA romance novels—Lisa Fiedler’s Dating Hamlet (2002) and Lisa Klein’s Ophelia (2006)—in order to demonstrate that not only can Ophelia be appropriated as a figure of empowerment for young women today, but that such appropriations are, seemingly ironically, most powerfully rendered within the genre of romance; a genre long-maligned by feminists as recuperative of patriarchy.--------- These two novels stage interventions both into narratives of female adolescence as a time of being ‘at risk’ or ‘under threat’, and also into narratives of canonical literary patriarchy. Rather than a suicidal Ophelia, subject to the whims of men, these authors imagine Ophelias who take charge of their own destiny; who dictate their own romance and agency; who refuse to be subject to or subjected by, those scripts of cultural authority and heteronormative romance so often perceived as antithetical to female agency. In doing so, they force us to revise our own notions of the romance genre and the functions of canonical literary tradition in contemporary YA culture.

Extending Alexander’s ecological dominance-social competition (EDSC) evolutional model

Alexander’s Ecological Dominance and Social Competition (EDSC) model currently provides the most comprehensive overview of human traits in the development of a theory of human evolution and sociality (Alexander, 1990; Finn, Geary & Ward, 2005; Irons, 2005). His model provides a basis for explaining the evolution of human socio-cognitive abilities. Our paper examines the extension of Alexander’s model to incorporate the human trait of information behavior in synergy with ecological dominance and social competition as a human socio-cognitive competence. This paper discusses the various interdisciplinary perspectives exploring how evolution has shaped information behavior and why information behavior is emerging as an important human socio-cognitive competence. This paper outlines these issues, including the extension of Spink and Currier’s (2006a,b) evolution of information behavior model towards a more integrated understanding of how information behaviors have evolved (Spink & Cole, 2006).

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.

Librarian 2.0 : it’s all in the attitude!

In 2006 Stephen Abram stated that we must “become librarian 2.0 now”. But what is librarian 2.0? This pa- per will present the results of a project that identified the skills, knowledge and attributes required by the successful librarian in the web 2.0 world (and be- yond!). Eighty-one Australian librarians participated in a series of 14 focus groups. Eight themes emerged: technology, communication, team work, user focus, business savvy, evidence based practice, learning, and personal traits.

Experimental validation of an unpowered unmanned aerial system : application to forced landing scenarios

The ability to perform autonomous emergency (forced) landings is one of the key technology enablers identified for UAS. This paper presents the flight test results of forced landings involving a UAS, in a controlled environment, and which was conducted to ascertain the performances of previously developed (and published) path planning and guidance algorithms. These novel 3-D nonlinear algorithms have been designed to control the vehicle in both the lateral and longitudinal planes of motion. These algorithms have hitherto been verified in simulation. A modified Boomerang 60 RC aircraft is used as the flight test platform, with associated onboard and ground support equipment sourced Off-the-Shelf or developed in-house at the Australian Research Centre for Aerospace Automation(ARCAA). HITL simulations were conducted prior to the flight tests and displayed good landing performance, however, due to certain identified interfacing errors, the flight results differed from that obtained in simulation. This paper details the lessons learnt and presents a plausible solution for the way forward.