Liberate your avatar; the revolution will be social networked

This paper brings together the practice-based creative research of artists Charlotte Gould and Paul Sermon, culminating in a collaborative interactive installation that investigates new forms of social and political narrative in multi-user virtual environments. The authors' artistic projects deal with the ironies and stereotypes that are found within Second Life in particular. Paul Sermon’s current creative practice looks specifically at the concepts of presence and performance within Second Life and 'first life', and attempts to bridge these two spaces through mixed reality techniques and interfaces. Charlotte Gould’s Ludic Second Life Narrative radically questions the way that users embody themselves in on-line virtual environments and identifies a counter-aesthetic that challenges the conventions of digital realism and consumerism. These research activities and outcomes come together within a collaborative site-specific public installation entitled Urban Intersections for ISEA09, focusing on contested virtual spaces that mirror the social and political history of Belfast. The authors' current collaborative practice critically investigates social, cultural and creative interactions in Second Life. Through these practice-based experiments the authors' argue that an enhanced social and cultural discourse within multi-user virtual environments will inevitably lead to growth, cohesion and public empowerment, and like all social networking platforms, contribute to greater social and political change in first life

Slow‐Wave Structures Utilizing Superconducting Thin‐Film Transmission Lines

Slow‐wave propagation of electromagnetic waves in transmission lines formed of thin‐film superconductors has been studied theoretically and experimentally. Previous theoretical analyses have been extended to include nonlocal theories. Strong dependence of phase velocity is found on film thickness and interfilm spacing when these become less than a few penetration depths. Velocity is also modified by coherence length, mean free path, nature of reflection of electrons at the film surfaces, and by temperature and magnetic field. Experimental measurements were made to verify the dependence on thickness, spacing, and temperature by means of a resonance technique. Agreement with theory was excellent in the case of temperature. Data taken for varying thickness and spacing verified the general trend of theoretical predictions. They indicate a nonlocal behavior with some specular reflection, but scatter of the data taken for different films prevents precise comparison of theory and experiment. Estimates of bulk penetration depths were made for indium, λ_In = 648±130 Å. For tantalum a rough estimate could be made of λTa = 580 Å. Data were consistent with the estimate of coherence length for indium of ξ_0 ≈ 3000 Å. Velocity was found to be independent of frequency in the range 50–500 MHz, while losses increased as the square. Pulse measurements indicated that delays of several microseconds and storage of several thousand pulses on a single line are feasible

Development, construction and testing of an ultrahigh vacuum dc sputtering system Final report

Design and performance of ultrahigh vacuum system for direct current sputtering electrode

A second derivative SQP method: theoretical issues

Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding their global solutions may be computationally nonviable. This paper presents a second-derivative SQP method based on quadratic subproblems that are either convex, and thus may be solved efficiently, or need not be solved globally. Additionally, an explicit descent-constraint is imposed on certain QP subproblems, which “guides” the iterates through areas in which nonconvexity is a concern. Global convergence of the resulting algorithm is established

A second derivative SQP method: local convergence

In [19], we gave global convergence results for a second-derivative SQP method for minimizing the exact ℓ1-merit function for a fixed value of the penalty parameter. To establish this result, we used the properties of the so-called Cauchy step, which was itself computed from the so-called predictor step. In addition, we allowed for the computation of a variety of (optional) SQP steps that were intended to improve the efficiency of the algorithm. \ud \ud Although we established global convergence of the algorithm, we did not discuss certain aspects that are critical when developing software capable of solving general optimization problems. In particular, we must have strategies for updating the penalty parameter and better techniques for defining the positive-definite matrix Bk used in computing the predictor step. In this paper we address both of these issues. We consider two techniques for defining the positive-definite matrix Bk—a simple diagonal approximation and a more sophisticated limited-memory BFGS update. We also analyze a strategy for updating the penalty paramter based on approximately minimizing the ℓ1-penalty function over a sequence of increasing values of the penalty parameter.\ud \ud Algorithms based on exact penalty functions have certain desirable properties. To be practical, however, these algorithms must be guaranteed to avoid the so-called Maratos effect. We show that a nonmonotone varient of our algorithm avoids this phenomenon and, therefore, results in asymptotically superlinear local convergence; this is verified by preliminary numerical results on the Hock and Shittkowski test set

A second-derivative trust-region SQP method with a "trust-region-free" predictor step

In (NAR 08/18 and 08/21, Oxford University Computing Laboratory, 2008) we introduced a second-derivative SQP method (S2QP) for solving nonlinear nonconvex optimization problems. We proved that the method is globally convergent and locally superlinearly convergent under standard assumptions. A critical component of the algorithm is the so-called predictor step, which is computed from a strictly convex quadratic program with a trust-region constraint. This step is essential for proving global convergence, but its propensity to identify the optimal active set is Paramount for recovering fast local convergence. Thus the global and local efficiency of the method is intimately coupled with the quality of the predictor step.\ud \ud In this paper we study the effects of removing the trust-region constraint from the computation of the predictor step; this is reasonable since the resulting problem is still strictly convex and thus well-defined. Although this is an interesting theoretical question, our motivation is based on practicality. Our preliminary numerical experience with S2QP indicates that the trust-region constraint occasionally degrades the quality of the predictor step and diminishes its ability to correctly identify the optimal active set. Moreover, removal of the trust-region constraint allows for re-use of the predictor step over a sequence of failed iterations thus reducing computation. We show that the modified algorithm remains globally convergent and preserves local superlinear convergence provided a nonmonotone strategy is incorporated