876 research outputs found
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[News Clip: Erskine Caldwell]
No full textVideo footage from the WBAP-TV station in Fort Worth, Texas to accompany a news story about author Erskine Caldwell coming to Dallas to promote the film adaptation of one of his books
Deep optimisation: learning and searching in deep representations of combinatorial optimisation problems
No full textEvolutionary algorithms are a class of optimisation techniques used to solve problems by emulating evolutionary processes (variation and selection) to search the solution space. In this thesis, we focus on the evolutionary process of Evolutionary Transitions in Individuality (ETI). In this case, evolutionary processes are scaled up via a multi-scale process whereby individuality (and hence variation and selection) is continually revised by forming associations between formerly independent entities. This thesis develops a novel Model-Building Optimisation Algorithm (MBOA) called Deep Optimisation (DO) that exploits deep learning methods to enable multi-scale optimisation. DO uses an autoencoder model to induce a multi-level representation of solutions. Variation and selection are then performed within the induced representations, allowing search to continue in a new and reorganised space. By using a class of configurable problems, we find and understand more precisely the distinct problem characteristics that DO can solve that other MBOAs cannot. Specifically, we observe a polynomial vs exponential scaling distinction where DO is the only algorithm to show polynomial scaling for all problems. We also demonstrate that some problem characteristics need a deep network in DO. Further, for the first time, we show that overlap differentiates the performance between current MBOAs that are considered state-of-the-art. DO is then applied to different optimisation problem domains to demonstrate its potential for exploiting unknown problem structure and overcoming infeasible solution spaces. Here, DO shows impressive performance and does so without using a domain-specific operator. This thesis provides a connection between deep learning models and MBOAs, showing results that outperform existing algorithms can be achieved by utilising the tools available in deep learning. This suggests numerous avenues for further investigation, transferring deep learning methods into the domain of MBOAs
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Card of J. A. Caldwell, Esq., of Lincoln ::Lincolnton, June 1st, 1855. To the voters of the 7th Congressional District.
No full textCard of J.A. Caldwell of Lincoln, in an address dated June 1, 1855. The author presented a pro-slavery, anti-Know-Nothing position to the voters in the 7th Congressional District
Correspondence | Letter from unknown author to Ed Caldwell, February 1875
No full textLetter from unknown author [one of Mary Darthula Greer and John Henry Caldwell’s children, probably John M.] to Ed G. Caldwell, February 2, 1875.https://digitalcommons.jsu.edu/lib_ac_caldwell/1089/thumbnail.jp
Erskine Caldwell News Conference, March 05, 1968
No full textAuthor Erskine Caldwell gives an ask and answer session with Journalists and Journalism students
Essay | Clasroom lecture on the Bible, 1848-1857
No full textClassroom lecture on the Bible, author unknown [possibly Mary Darthula Greer Caldwell][1848-1857].https://digitalcommons.jsu.edu/lib_ac_caldwell/1238/thumbnail.jp
Delaney, Caldwell
No full textThomas Caldwell Delaney (1918-2007), noted author, one-time director of the Museum of Mobile, dean of the University Military School, and headmaster of Julius T. Wright School for girls, signs a copy of his book Deep South inside the Haunted Bookshop for Ann Schoffner
Deep Optimisation: Multi-scale Evolution by Inducing and Searching in Deep Representations
No full textThe ability of evolutionary processes to innovate and scale up over long periods of time, observed in nature, remains a central mystery in evolutionary biology, and a challenge for algorithm designers to emulate and explain in evolutionary computation (EC). The Major Transitions in Evolution is a compelling theory that explains evolvability through a multi-scale process whereby individuality (and hence selection and variation) is continually revised by the formation of associations between formerly independent entities, a process still not fully explored in EC. Deep Optimisation (DO) is a new type of model-building optimization algorithm (MBOA) that exploits deep learning methods to enable multi-scale optimization. DO uses an autoencoder model to induce a multi-level representation of solutions, capturing the relationships between the lower-level units that contribute to the quality of a solution. Variation and selection are then performed within the induced representations, causing model-informed changes to multiple solution variables simultaneously. Here, we first show that DO has impressive performance compared with other leading MBOAs (and other rival methods) on multiple knapsack problems, a standard combinatorial optimization problem of general interest. Going deeper, we then carry out a detailed investigation to understand the differences between DO and other MBOAs, identifying key problem characteristics where other MBOAs are afflicted by exponential running times, and DO is not. This study serves to concretize our understanding of the Major Transitions theory, and why that leads to evolvability, and also provides a strong motivation for further investigation of deep learning methods in optimization.</p
Rural and regional Victorian women more likely to experience violence
No full textWomen in rural and regional Victoria are up to three times more likely to experience family violence than women in Metropolitan areas, according to research by Deakin University. The study shows a woman\u27s postcode also appears to play a role in determining her chances of receiving justice. The lead author of the study, interviewed by Alison Caldwell, believes the findings apply nationwide
Social Enterprise and Higher Education in a Globalized World
No full textSocial enterprise has become a global phenomenon, changing the lives of millions of people and addressing social issues that have previously been outside of the reach of governments or the private sector. Many higher education institutions have engaged with social enterprise in a variety of ways, including providing facilities to external social enterprises, supporting and advising student and faculty social enterprises, providing placements and internships for students in social enterprise organisations and embedding social enterprise directly into the curriculum. This chapter reviews the current relationship between higher education and the social enterprise phenomenon. While there is a growing body of research on the concept of social enterprise itself, there is a paucity of research on the pedagogical aspects of teaching and embedding social enterprise into the curriculum. From related literatures on curriculum design it is clear that a flexible, holistic approach is needed to embed experiential learning about social enterprise to produce learning environments that foster high levels of student engagement and enhanced employability
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