2,176 research outputs found
An Interview with Tony David Sampson: Author of Virality: Contagion Theory in the Age of Networks
No full textTony D. Sampson is Reader in Digital Culture and Communication in the School of Arts and Digital Industries (ADI) at the University of East London, where he directs the EmotionUX lab, supervising research on the cognitive, emotional, and affective aspects of user experience. In 2013, he co-founded Club Critical Theory, an organization dedicated to the application of critical theory in everyday life in Southend-on-Sea, Essex. Tony is the author of Virality: Contagion Theory in the Age of Networks and The Assemblage Brain: Sense Making in Neuroculture, both from the University of Minnesota Press. He blogs at viralcontagion.wordpress.com.
The editors of this special NANO issue are delighted to have the opportunity to talk with Tony about how his work touches on issues of imitation and contagion—a loaded term unpacked within his 2012 book
Tony Tulathimutte: 48th Annual ODU Literary Festival
No full textTony Tulathimutte is the author of Private Citizens and Rejection. A graduate of Stanford University and the Iowa Writers’ Workshop, he’s received a Whiting Award and an O. Henry Award, was longlisted for the National Book Award, and has written for The Paris Review, N+1, The New York Times, Playboy, The Nation, and others. He also runs CRIT, a writing class in Brooklyn
Tokyo Burning Interview with Tony Barnstone
No full textInterview with Tony Barnstone about adapting his poetry to music. Tokyo\u27s Burning is a CD that tells history from the inside, telling stories of the Pacific theater of WWII not from the God\u27s eye view but from the points of view of American and Japanese civilians and soldiers who lived and suffered through Pearl Harbor and Iwo Jima, the firebombing of Tokyo and the atom bomb drop on Hiroshima. Songs in the CD are based upon 15 years of research into the Pacific theater of WWII by Tony Barnstone—poet, author, and professor at Whittier College in Los Angeles. Tony worked with oral histories, histories, diaries, letters, and memoirs, and did his own interviews with vets and their families to write a book of poems titled Tongue of War: From Pearl Harbor to Nagasaki (BkMk Press, 2009). Though many of the songs deal with atrocity—sex slavery, torture, internment camps, even cannibalism—the CD itself is meant to take a neutral stance, allowing each character to speak his or her view, without judgment, assuming that the readers will find their own moral paths through these competing voices and viewpoints. As one character says, Seems everyone has a point of view, but no one has perspective. L.A.-based songwriters John Clinebell and Ariana Hall, who work together under the name Genuine Brandish, were commissioned by Tony to work with him to translate his book into 15 songs (with the essential help of producer Andrew Bush). What if history had a human face? What if the people who lived history could speak to it? This CD is an attempt to amplify the smaller voices, the human voices, of those who lived through the war and help them to sing history to us
Tony Ardizzone, 3rd Annual ODU Literary Festival
No full textFrom the training grounds of Chicago and Bowling Green, Tony Ardizzone serves as running guard for the creative writing program at ODU. Author of a novel ( In the Name of the Father ) and a collection of short stories ( Idling ), he is also the editor of Intro, an annual journal of the best writing from college workshops around the country. In a nearly completed accompanying volume to In the Name of the Father, Ardizzone traces the route by which the character Vito Scaparelli reaches Vietnam. Ardizzone has published 15 short stories in distinguished fiction quarterlies. He believes that the writing of fiction is the crafting of interiorized drama
Improving urban planning: the case of New South Wales. by Tony Sorensen
No full texttag=1 data=Improving urban planning: the case of New South Wales. by Tony Sorensen
tag=2 data=Sorensen, Tony
tag=3 data=Policy,
tag=4 data=8
tag=5 data=2
tag=6 data=Winter 1992
tag=7 data=31-36.
tag=8 data=PLANNING
tag=10 data=The NSW Department of Planning is proposing changes to the Environmental Planning and Assessment Act. The author reviews the proposals and highlights the difficulties of making urban planning efficient and equitable.
tag=11 data=1992/4/10
tag=12 data=92/0672
tag=13 data=CABThe NSW Department of Planning is proposing changes to the Environmental Planning and Assessment Act. The author reviews the proposals and highlights the difficulties of making urban planning efficient and equitable
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Control System Design Automation Using Reinforcement Learning
No full textConventional control theory has been used in many application domains with great success in the past decades. However, novel solutions are required to cope with the challenges arising from complex interaction of fast growing cyber and physical systems. Specifically, integration of classical control methods with Cyber-Physical System (CPS) design tools is a non-trivial task since those methods have been developed to be used by human experts and are not intended to be part of an automatic design tool. On the other hand, the control problems in emerging Cyber-Physical Systems, such as intelligent transportation and autonomous driving, cannot be addressed by conventional control methods due to the high level of uncertainty, complex dynamic model requirements and operational and safety constraints.In this dissertation, a holistic CPS design approach is proposed in which the control algorithm is incorporated as a building block in the design tool. The proposed approach facilitates the inclusion of physical variability into the design process and reduces the parameter space to be explored. This has been done by adding constraints imposed by the control algorithm.Furthermore, Reinforcement Learning (RL) as a replacement for convection control solutions are studied in the emerging domain of intelligent transportation systems. Specifically, dynamic tolling assignments and autonomous intersection management are tackled by the state-of-the-art RL methods, namely, Trust Region Policy Optimization and Finite-Difference Gradient Descent. Additionally, Q-learning is used to improve the performance of an embedded controller using a novel formulation in which cyber-system actions, such as changing control sampling time, is combined with the physical action set of the RL agent. Using the proposed approach, it is shown that the power consumption and computational overhead of the embedded control can be improved.Finally, to address the current lack of available physical benchmarks, an open physical environment benchmarking framework is introduced. In the proposed framework, various components of a physical environment are captured in a unified repository to enable researchers to define and share standard benchmarks that can be used to evaluate different reinforcement algorithms. They can also share the realized environments via the cloud to enable other groups perform experiments on the actual physical environments instead of currently available simulation-based environments
Tony Ardizzone, 12th Annual ODU Literary Festival
No full textTony Ardizzone, former Director of Creative Writing at Old Dominion University from 1979-1987, now teaches at Indiana University. He is the author of two novels, In the Name of the Father, 1978, and Heart of the Order, 1986, which was awarded the 1985 Virginia Prize for Fiction and named by The National Sports Review as one of the ten best sports books of 1986. He has also published a collection of short stories, The Evening News, 1986, which won the Flannery O\u27Connor Award. His stories have been cited twice in Best American Short Stories and been given Prairie Schooner\u27s Lawrence Foundation Award and the Black Warrior Review Fiction Prize. He is a member of the Board of Directors of the Associated Writing Programs
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Design Space Exploration in Cyber-Physical Systems
No full textCyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations
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Adaptive Real-Time Systems
No full textModern embedded systems are required to work in ever increasing dynamic environments, where predicting the computational load on those systems is intractable. However, timely responses to events have to be provided within precise timing constraints in order to guarantee a required level of performance. Consequently, embedded systems by their very nature exhibit real-time characteristics which impose an additional set of restrictions than those in a typical general purpose system. In addition to the limitations of having to perform to strict timing constraints, most embedded systems are constrained by size, weight, energy consumption and cost limitations. As a result, efficient resource management is a critical aspect in embedded systems that must be considered at multiple architectural levels. The main objective of this work is to present our work on real-time systems that progress to make the next generation embedded systems more predictable and adaptive to dynamic computational changes. To achieve these goals, this phase of our research has focused on the resource synchronization and adaptive scheduling of real-time embedded applications in uni-processor and multi-core environments. The analysis and experiments show that our resource synchronization protocols outperformed other state-of-the art resource access control protocols used in hierarchical scheduled systems. Implemented in VxWorks and applied to applications used in the aerospace industry response times for hard real-time tasks were improved and deadline misses for hard real-time tasks were substantially reduced
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Resource-Aware Predictive Models in Cyber-Physical Systems
No full textCyber-Physical Systems (CPS) are composed of computing devices interacting with physical systems. Model-based design is a powerful methodology in CPS design in the implementation of control systems. For instance, Model Predictive Control (MPC) is typically implemented in CPS applications, e.g., in path tracking of autonomous vehicles. MPC deploys a model to estimate the behavior of the physical system at future time instants for a specific time horizon. Ordinary Differential Equations (ODE) are the most commonly used models to emulate the behavior of continuous-time (non-)linear dynamical systems. A complex physical model may comprise thousands of ODEs that pose scalability, performance and power consumption challenges. One approach to address these model complexity challenges are frameworks that automate the development of model-to-model transformation. In this dissertation, a state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the physical system. Moreover, we propose a run-time switching algorithm that selects the best model using machine learning. We employed a metric that formulates the trade-off between the error and computational savings due to model reduction. Building statistical models are constrained to having expert knowledge and an actual understanding of the modeled phenomenon or process. Also, statistical models may not produce solutions that are as robust in a real-world context as factors outside the model, like disruptions would not be taken into account. Machine learning models have emerged as a solution to account for the dynamic behavior of the environment and automate intelligence acquisition and refinement. Neural networks are machine learning models, well-known to have the ability to learn linear and nonlinear relations between input and output variables without prior knowledge. However, the ability to efficiently exploit resource-hungry neural networks in embedded resource-bound settings is a major challenge.Here, we proposed Priority Neuron Network (PNN), a resource-aware neural networks model that can be reconfigured into smaller sub-networks at runtime. This approach enables a trade-off between the model's computation time and accuracy based on available resources. The PNN model is memory efficient since it stores only one set of parameters to account for various sub-network sizes. We propose a training algorithm that applies regularization techniques to constrain the activation value of neurons and assigns a priority to each one. We consider the neuron's ordinal number as our priority criteria in that the priority of the neuron is inversely proportional to its ordinal number in the layer. This imposes a relatively sorted order on the activation values. We conduct experiments to employ our PNN as the predictive model in a CPS application. We can see that not only our technique will resolve the memory overhead of DNN architectures but it also reduces the computation overhead for the training process substantially. The training time is a critical matter especially in embedded systems where many NN models are trained on the fly
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