Quantum Ecologies in Cosmological Infrastructures: A Critical Holographers Encounters with the Meta/Physics of Landscape-Laboratories

Quantum Ecologies interrogates the role of physics in the construction of an indifferent and disenchanted universe. It explores conceptual resonances within and between new materialism, Indigenous philosophy of place, science fiction, and art. Quantum Ecologies recognizes that the world is alive and wise and considers relevant modes of responsible address within and as the Earth. Through theoretical and historical analysis, site based research and a/v installation Quantum Ecologies has developed the heuristic of the ‘holographic’ as a way to attend to the multi-temporal, co-present, and multi-scalar pluralities and layers of knowing, agency, and landscape. This feminist, anti-colonial art-science framework for critically engaging (physics) sites and philosophies addresses the scientific cosmology of the West that (inadvertently) legitimates the exploitation, dispossession, and extraction of Earthly beings and bodies. Holography as critical interferometry is applied to experimental sites and assemblages known as ‘landscape-laboratories’ as a mode of both reading and (re)writing them. My field/work has taken place in remote environmentally protected sites that are entangled and instrumentalized as cosmological sensing arrays, experimental nuclear fusion energy, or dark matter particle physics laboratories in Russia, France, the UK, Germany, and Canada. By thinking through the strangeness of these planetary quantum assemblages alongside sciences inheritances and genealogies in magic, alchemy, and mysticism I argue for the necessity of ‘another science’ that is situated, compassionate, and responsible. Quantum Ecologies proposes a plural, poly-perspectival assessment of place, where accounting for the promiscuous more-than of materials, sites, forces, and energies is a necessary and continuous (re)configuring of meta/physics and respectful anti-colonial engagement with Land

Adapted Compressed Sensing: A Game Worth Playing

Despite the universal nature of the compressed sensing mechanism, additional information on the class of sparse signals to acquire allows adjustments that yield substantial improvements. In facts, proper exploitation of these priors allows to significantly increase compression for a given reconstruction quality. Since one of the most promising scopes of application of compressed sensing is that of IoT devices subject to extremely low resource constraint, adaptation is especially interesting when it can cope with hardware-related constraint allowing low complexity implementations. We here review and compare many algorithmic adaptation policies that focus either on the encoding part or on the recovery part of compressed sensing. We also review other more hardware-oriented adaptation techniques that are actually able to make the difference when coming to real-world implementations. In all cases, adaptation proves to be a tool that should be mastered in practical applications to unleash the full potential of compressed sensing

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Optimal Distribution Reconfiguration and Demand Management within Practical Operational Constraints

This dissertation focuses on specific aspects of the technical design and operation of a `smart\u27 distribution system incorporating new technology in the design process. The main purpose of this dissertation is to propose new algorithms in order to achieve a more reliable and economic distribution system. First, a general approach based on Mixed Integer Programming (MIP) is proposed to formulate the reconfiguration problem for a radial/weakly meshed distribution network or restoration following a fault. Two objectives considered in this study are to minimize the active power loss, and to minimize the number of switching operations with respect to operational constraints, such as power balance, line ow limits, voltage limit, and radiality of the network. The latter is the most challenging issue in solving the problem by MIP. A novel approach based on Depth-First Search (DFS) algorithm is implemented to avoid cycles and loops in the system. Due to insufficient measurements and high penetration of controllable loads and renewable resources, reconfiguration with deterministic optimization may not lead to an optimal/feasible result. Therefore, two different methods are proposed to solve the reconfiguration problem in presence of load uncertainty. Second, a new pricing algorithm for residential load participation in demand response program is proposed. The objective is to reduce the cost to the utility company while mitigating the impact on customer satisfaction. This is an iterative approach in which residents and energy supplier exchange information on consumption and price. The prices as well as appliance schedule for the residential customers will be achieved at the point of convergence. As an important contribution of this work, distribution network constraints such as voltage limits, equipment capacity limits, and phase balance constraints are considered in the pricing algorithm. Similar to the locational marginal price (LMP) at the transmission level, different prices for distribution nodes will be obtained. Primary consideration in the proposed approach, and frequently ignored in the literature, is to avoid overly sophisticated decision-making at the customer level. Most customers will have limited capacity or need for elaborate scheduling where actual energy cost savings will be modest

Communications Regulation in the Age of Digital Convergence : Legal and Economic Perspectives

This book brings together contributions of a distinguished panel of regulators as well as lawyers and economists from both academia and industry to present their insights on the digital convergence phenomenon in the telecommunications industry. The contributions cover a great deal of the relevant topics in communications regulation, such as technological and network neutrality, distribution of the digital dividend, and incentives for investment and innovation

Synthesis, Interdiction, and Protection of Layered Networks

This research developed the foundation, theory, and framework for a set of analysis techniques to assist decision makers in analyzing questions regarding the synthesis, interdiction, and protection of infrastructure networks. This includes extension of traditional network interdiction to directly model nodal interdiction; new techniques to identify potential targets in social networks based on extensions of shortest path network interdiction; extension of traditional network interdiction to include layered network formulations; and develops models/techniques to design robust layered networks while considering trade-offs with cost. These approaches identify the maximum protection/disruption possible across layered networks with limited resources, find the most robust layered network design possible given the budget limitations while ensuring that the demands are met, include traditional social network analysis, and incorporate new techniques to model the interdiction of nodes and edges throughout the formulations. In addition, the importance and effects of multiple optimal solutions for these (and similar) models is investigated. All the models developed are demonstrated on notional examples and were tested on a range of sample problem sets

Mixed-integer Programming Methods for Modeling and Optimization of Cascading Processes in Complex Networked Systems

Dynamics and growth of many natural and man-made systems can be represented by large-scale complex networks. Entity interactions and community interconnections within complex networks increase the level of difficulty for the investigation on structural network properties such as robustness, vulnerability and resilience. In this dissertation, we develop methodologies based on mixed-integer programming techniques to solve challenging optimization problems that model cascading processes in complex networked systems. In particular, we seek to provide decision making recommendations for problems related to different types of cascading processes in networks commonly considered in a variety of applications: interdependent infrastructure networks and social networks. In the first part, we propose a novel optimization model to enhance the resilience against cascading failure by mitigation and restoration in interdependent networks. We derive a polynomial class of valid inequalities from the cascading constraints and reformulate the substructure that describes capacity restriction to guarantee integral solutions. The computational experiments illustrate that our strengthened formulation outperforms the default setting of a commercial solver on all tested instances. Next, we study the least cost influence maximization problem that arises in social network analytics. We investigate the polyhedral properties of a substructure that is a relaxation of the mixed 0-1 knapsack polyhedron. We give three exponential class of facet-defining inequalities from this substructure and an exact polynomial time separation algorithm for the inequalities. In addition, we propose another new class of strong valid inequalities that dominates the cycle elimination constraints. Through the computational experiments, we demonstrate that a delayed cut generation algorithm that exploits these inequalities is very effective to solve the problem under different settings of network size, density and connectivity

Transportation Implications of Coal

This report describes the direct economic relationship between the coal and railroad industries in Appalachia. It finds that between 2015 and 2016, changing electric generation strategies—including accelerated coal-powered plant retirements—combined with a downturn in coal demand contributed to losses of nearly 2,000 full-time jobs and $150 million in income across Appalachia’s railroad sector