Architecture for a free subjectivity : Deleuze and Guattari at the Horizon of the Real

Architecture for a Free Subjectivity reformulates the French philosopher Gilles Deleuze's model of subjectivity for architecture, by surveying the prolific effects of architectural encounter, and the spaces that figure in them. For Deleuze and his Lacanian collaborator Félix Guattari, subjectivity does not refer to a person, but to the potential for and event of matter becoming subject, and the myriad ways for this to take place. By extension, this book theorizes architecture as a self-actuating or creative agency for the liberation of purely "impersonal effects." Imagine a chemical reaction, a riot in the banlieues, indeed a walk through a city. Simone Brott declares that the architectural object does not merely take part in the production of subjectivity, but that it constitutes its own.

Fixed-wing attitude estimation using temporal tracking of the horizon and optical flow

In this paper, a method has been developed for estimating pitch angle, roll angle and aircraft body rates based on horizon detection and temporal tracking using a forward-looking camera, without assistance from other sensors. Using an image processing front-end, we select several lines in an image that may or may not correspond to the true horizon. The optical flow at each candidate line is calculated, which may be used to measure the body rates of the aircraft. Using an Extended Kalman Filter (EKF), the aircraft state is propagated using a motion model and a candidate horizon line is associated using a statistical test based on the optical flow measurements and the location of the horizon. Once associated, the selected horizon line, along with the associated optical flow, is used as a measurement to the EKF. To test the accuracy of the algorithm, two flights were conducted, one using a highly dynamic Uninhabited Airborne Vehicle (UAV) in clear flight conditions and the other in a human-piloted Cessna 172 in conditions where the horizon was partially obscured by terrain, haze and smoke. The UAV flight resulted in pitch and roll error standard deviations of 0.42◦ and 0.71◦ respectively when compared with a truth attitude source. The Cessna flight resulted in pitch and roll error standard deviations of 1.79◦ and 1.75◦ respectively. The benefits of selecting and tracking the horizon using a motion model and optical flow rather than naively relying on the image processing front-end is also demonstrated.

Adding a receding horizon to Locally Weighted Regression for learning robot control

There have been notable advances in learning to control complex robotic systems using methods such as Locally Weighted Regression (LWR). In this paper we explore some potential limits of LWR for robotic applications, particularly investigating its application to systems with a long horizon of temporal dependence. We define the horizon of temporal dependence as the delay from a control input to a desired change in output. LWR alone cannot be used in a temporally dependent system to find meaningful control values from only the current state variables and output, as the relationship between the input and the current state is under-constrained. By introducing a receding horizon of the future output states of the system, we show that sufficient constraint is applied to learn good solutions through LWR. The new method, Receding Horizon Locally Weighted Regression (RH-LWR), is demonstrated through one-shot learning on a real Series Elastic Actuator controlling a pendulum.

Infinite-horizon policy-gradient estimation

Gradient-based approaches to direct policy search in reinforcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-function methods. In this paper we introduce GPOMDP, a simulation-based algorithm for generating a biased estimate of the gradient of the average reward in Partially Observable Markov Decision Processes (POMDPs) controlled by parameterized stochastic policies. A similar algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are that it requires storage of only twice the number of policy parameters, uses one free parameter β ∈ [0,1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowledge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter β is related to the mixing time of the controlled POMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control spaces, multiple-agents, higher-order derivatives, and a version for training stochastic policies with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient estimates generated by GPOMDP can be used in both a traditional stochastic gradient algorithm and a conjugate-gradient procedure to find local optima of the average reward. ©2001 AI Access Foundation and Morgan Kaufmann Publishers. All rights reserved.

An infinite-horizon robust filter for uncertain hidden Markov Models with conditional relative entropy constraints

We consider a robust filtering problem for uncertain discrete-time, homogeneous, first-order, finite-state hidden Markov models (HMMs). The class of uncertain HMMs considered is described by a conditional relative entropy constraint on measures perturbed from a nominal regular conditional probability distribution given the previous posterior state distribution and the latest measurement. Under this class of perturbations, a robust infinite horizon filtering problem is first formulated as a constrained optimization problem before being transformed via variational results into an unconstrained optimization problem; the latter can be elegantly solved using a risk-sensitive information-state based filtering.

Learning multidimensional joint control of a robot using receding horizon locally weighted regression

In this paper we explore the ability of a recent model-based learning technique Receding Horizon Locally Weighted Regression (RH-LWR) useful for learning temporally dependent systems. In particular this paper investigates the application of RH-LWR to learn control of Multiple-input Multiple-output robot systems. RH-LWR is demonstrated through learning joint velocity and position control of a three Degree of Freedom (DoF) rigid body robot.

The maturity model concept as framework for assessing the capability of higher education institutions to address student engagement, success and retention : new horizon or false dawn?

While the engagement, success and retention of first year students are ongoing issues in higher education, they are currently of considerable and increasing importance as the pressures on teaching and learning from the new standards framework and performance funding intensifies. This Nuts & Bolts presentation introduces the concept of a maturity model and its application to the assessment of the capability of higher education institutions to address student engagement, success and retention. Participants will be provided with (a) a concise description of the concept and features of a maturity model; and (b) the opportunity to explore the potential application of maturity models (i) to the management of student engagement and retention programs and strategies within an institution and (ii) to the improvement of these features by benchmarking across the sector.

Slow horizon

“Slow Horizon” is comprised of six lenticular panels hung in an even, horizontal sequence. As the viewer moves in front of the work, each panel alternates subtly between two vertical colour gradients. From left to right, the panels move through yellow, orange, magenta and violet to ‘midnight blue’. Together, the coloured panels comprise an abstract horizon line that references the changing nature of light at sunset. The scale, movement and chromatic qualities of the panels also allude to the formal characteristics of the screen technologies that pervade contemporary visual culture. “Slow Horizon” contributes to studies in the field of contemporary art. It is particularly concerned with the relationships between abstraction, colour, signification and perception. Since early Modernity, debates concerning representation and the formal qualities of the picture plane have been fundamental to art practice and theory. These debates have often dovetailed with questions of art’s capacity to generate shifts in thought and perception. Practitioners such as Ellsworth Kelly, James Turrell and Ed Ruscha have variously used block and blended colour to engage in these formal, symbolic and perceptual potentials of colour. Using a practice-led research methodology, “Slow Horizon” furthers this creative inquiry. By conflating the reductive visual logics of abstraction and minimalism with the iconic, romantic evocations of sunset imagery, it questions not only the contemporary relationship between abstraction and image-making, but also art’s ability to create moments of stillness and contemplation in a context significantly shaped by screen technologies. “Slow Horizon” has been exhibited internationally as part of “Supermassive” at LA Louver Gallery, Venice, California in 2013. The exhibition was reviewed in The Los Angeles Times.

Assessing the damage caused by deepwater horizon : not just another Exxon Valdez

In light of the high stakes of the deepwater horizon civil trial and the important precedent-setting role that the case will have on the assessment of future marine disasters, the methodologies underpinning the calculations of damage on both sides will be subjected to considerable scrutiny. Despite the importance of the case, however, there seems to be a pronounced lack of convergence about it in the academic literature. Contributions from scientific journals frequently make comparisons to the Ixtoc I oil spill off the coast of Mexico in 1979; the legal literature, by stark contrast, seems to be much more focused on the Exxon Valdez spill that occurred off the shores of Alaska in 1989. This paper accordingly calls for a more thorough consideration of other analogs beyond the Exxon Valdez spill—most notably, the Ixtoc I incident—in arriving at an assessment of the damage caused by the Deepwater Horizon disaster.

For farmers, drought not the most dangerous predator on the horizon

Lack of water can be the least of a farmer’s worries in times of drought. Predatory banks can be just as deadly to a farmer’s livelihood. Former Queensland farmer and grazier Lynton Freeman has become increasingly anxious about the financial tightrope being walked by Australian farmers as the record floods of 2011 have given way in only three years to savage drought. He fears for hundreds of family farmers at risk — not from lack of water, but from lack of knowledge in how to manage their businesses through the drought and keep their heads above the financial water that will threaten many before this drought breaks.

Parametrized actor-critic algorithms for finite-horizon MDPs

Due to their non-stationarity, finite-horizon Markov decision processes (FH-MDPs) have one probability transition matrix per stage. Thus the curse of dimensionality affects FH-MDPs more severely than infinite-horizon MDPs. We propose two parametrized 'actor-critic' algorithms to compute optimal policies for FH-MDPs. Both algorithms use the two-timescale stochastic approximation technique, thus simultaneously performing gradient search in the parametrized policy space (the 'actor') on a slower timescale and learning the policy gradient (the 'critic') via a faster recursion. This is in contrast to methods where critic recursions learn the cost-to-go proper. We show w.p 1 convergence to a set with the necessary condition for constrained optima. The proposed parameterization is for FHMDPs with compact action sets, although certain exceptions can be handled. Further, a third algorithm for stochastic control of stopping time processes is presented. We explain why current policy evaluation methods do not work as critic to the proposed actor recursion. Simulation results from flow-control in communication networks attest to the performance advantages of all three algorithms.

A reinforcement learning based algorithm for finite horizon Markov decision processes

We develop a simulation based algorithm for finite horizon Markov decision processes with finite state and finite action space. Illustrative numerical experiments with the proposed algorithm are shown for problems in flow control of communication networks and capacity switching in semiconductor fabrication.

Short-Horizon Asymmetric Mean-Reversion and Overreactions: Evidence from the Nordic Stock Markets

This paper examines the asymmetric behavior of conditional mean and variance. Short-horizon mean-reversion behavior in mean is modeled with an asymmetric nonlinear autoregressive model, and the variance is modeled with an Exponential GARCH in Mean model. The results of the empirical investigation of the Nordic stock markets indicates that negative returns revert faster to positive returns when positive returns generally persist longer. Asymmetry in both mean and variance can be seen on all included markets and are fairly similar. Volatility rises following negative returns more than following positive returns which is an indication of overreactions. Negative returns lead to increased variance and positive returns leads even to decreased variance.