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We propose a novel multi-pose loss function to train a neural network for 6D pose estimation, using synthetic data and evaluating it on real images. Our loss is inspired by the VSD (Visible Surface Discrepancy) metric and relies on a differentiable renderer and CAD models. This novel multi-pose approach produces multiple weighted pose estimates to avoid getting stuck in local minima. Our method re

Climate change adaptation is rising on the agenda of cities. However, critics have argued that urban adaptation efforts largely focus on preserving economic growth while overlooking the root causes of unequal vulnerability to climate impacts, giving rise to climate injustices. In response, literature on transformational adaptation has politicized these issues but it has remained largely conceptual

This interview focuses on a spectrum of urgent challenges facing marginalized human and other-than-human communities, including the intersecting crises of global anthropogenic climate disruption and state and institutional racist violence. We discuss and consider the opportunities, limits, and contradictions of pursuing transformative, intersectional political change and scholarship through effort

For linear time-invariant systems with uncertain parameters belonging to a finite set, we present a purely eterministic approach to multiple-model estimation and propose an algorithm based on the minimax criterion using constrained quadratic programming. The estimator tends to learn the dynamics of the system, and once the uncertain parameters have been sufficiently estimated, the estimator behave

The increasing number and complexity of urban risk and disasters have a significant bearing on the emotional and mental wellbeing of those who are exposed and hamper their responses. Nevertheless, current discourses and approaches to increase resilience tend to focus on broader socio-economic, physical and environmental systems. This reflects a failure by the academic and practitioner communities

Non-technical summary Mainstreaming climate objectives into sectoral work and policies is widely advocated as the way forward for sustainable public-private action. However, current knowledge on effective climate mainstreaming has rarely translated into policy outcomes and radical, transformational change. This 'implementation gap' relates to the limitations of current approaches, which do not ade

We present a constructive approach to bounded l2-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of H-infinit

What do your thoughts and intentions have to do with the climate crisis we are living in? Quite a lot, if we believe emerging science. Let’s look at how they influence not only our personal, but also our collective story, and why they are key for creating a more sustainable future.

Our stories and paradigms matter for sustainability. To change paradigms, we need to change our stories. And vice versa. To change our stories, we need to change paradigms. If we want to change our story, and move toward a more sustainable lifestyle, we also need to challenge the social paradigm we are living in.

An inspiring conversation between Sister Jayanti, European Director of Brahma Kumaris, and Professor Christine Wamsler, Director of the Contemplative Sustainable Futures Program.

We present a new, scalable alternative to the structured singular value, which we call ν, provide a convex upper bound, study their properties and compare them to ℓ1 robust control. The analysis relies on a novel result on the relationship between robust control of dynamical systems and non-negative constant matrices.

This thesis concerns the evaluation of cost functionals on H2 when designing optimal controllers using finite Youla parameterizations and convex optimization. We propose to compute inner products of stable, strictly proper systems via solving Sylvester equations. The properties of different state space realizations of Laguerre filters, when performing Ritz expansions of the optimal controller are