Spatially-distributed coverage optimization and control with limited-range interactions

This paper presents coordination algorithms for groups of mobile agents performing deployment and coverage tasks. As an important modeling constraint, we assume that each mobile agent has a limited sensing/communication radius. Based on the geometry of Voronoi partitions and proximity graphs, we analyze a class of aggregate objective functions and propose coverage algorithms in continuous and discrete time. These algorithms have convergence guarantees and are spatially distributed with respect to appropriate proximity graphs. Numerical simulations illustrate the results.Comment: 31 pages, some figures left out because of size limits. Complete preprint version available at http://motion.csl.uiuc.ed

The influence of weather regimes on European renewable energy production and demand

The growing share of variable renewable energy increases the meteorological sensitivity of power systems. This study investigates if large-scale weather regimes capture the influence of meteorological variability on the European energy sector. For each weather regime, the associated changes to wintertime -mean and extreme- wind and solar power production, temperature-driven energy demand and energy shortfall (residual load) are explored. Days with a blocked circulation pattern, i.e. the Scandinavian Blocking and NAO negative regimes, on average have lower than normal renewable power production, higher than normal energy demand and therefore, higher than normal energy shortfall. These average effects hide large variability of energy parameters within each weather regime. Though the risk of extreme high energy shortfall events increases in the two blocked regimes (by a factor of 2.0 and 1.5, respectively), it is shown that such events occur in all regimes. Extreme high energy shortfall events are the result of rare circulation types and smaller-scale features, rather than extreme magnitudes of common large-scale circulation types. In fact, these events resemble each other more strongly than their respective weather regime mean pattern. For (sub-)seasonal forecasting applications weather regimes may be of use for the energy sector. At shorter lead times or for more detailed system analyses, their ineffectiveness at characterising extreme events limits their potential

Developments in Algorithmic Management from an IR-perspective : Germany

The INCODING project studies dynamics in the (co)governance of Algorithmic Management and Artificial Intelligence-techniques from a Comparative Industrial Relations-perspective. By identifying the main challenges for workers and their representatives, it aims to explore how to contribute to Inclusive and Transparent Algorithmic Management

Emerging organizational architecture of algorithmic management and the institutional context of weak collective voice : Hungary

The INCODING project studies dynamics in the (co)governance of Algorithmic Management and Artificial Intelligence-techniques from a Comparative Industrial Relations-perspective. By identifying the main challenges for workers and their representatives, it aims to explore how to contribute to Inclusive and Transparent Algorithmic Management

Computing Tropical Linear Spaces

We define and study the cyclic Bergman fan of a matroid M, which is a simplicial polyhedral fan supported on the tropical linear space T(M) of M and is amenable to computational purposes. It slightly refines the nested set structure on T(M), and its rays are in bijection with flats of M which are either cyclic flats or singletons. We give a fast algorithm for calculating it, making some computational applications of tropical geometry now viable. Our C++ implementation, called TropLi, and a tool for computing vertices of Newton polytopes of A-discriminants, are both available online.Comment: 15 pages, 2 figures. Added a few examples and made some minor correction

Developments in algorithmic management from an IR-perspective : Denmark

The INCODING project studies dynamics in the (co)governance of Algorithmic Management and Artificial Intelligence-techniques from a Comparative Industrial Relations-perspective. By identifying the main challenges for workers and their representatives, it aims to explore how to contribute to Inclusive and Transparent Algorithmic Management. The present stock tacking reports provide an insight on the latest developments in this field in Denmar

Developments in algorithmic management from an IR-perspective : Spain

The INCODING project studies dynamics in the (co)governance of Algorithmic Management and Artificial Intelligence-techniques from a Comparative Industrial Relations-perspective. By identifying the main challenges for workers and their representatives, it aims to explore how to contribute to Inclusive and Transparent Algorithmic Management

The importance of weather and climate to energy systems: a workshop on next generation challenges in energy-climate modelling

Over 80 international participants, representing weather, climate, and energy systems research, joined two 4-hour remote sessions to highlight and prioritize ongoing and future challenges in energy-climate modelling. The workshop had two primary goals: to build a deeper engagement across the “energy” and “climate” research communities, and to identify and begin to address the scientific challenges associated with modelling climate risk in energy systems

Give more data, awareness and control to individual citizens, and they will help COVID-19 containment.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates-if and when they want and for specific aims-with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society