Power and multistakeholderism in internet global governance. Towards a synergetic theoretical framework

With the advancement of multistakeholder collaboration as a governance principle in theglobal Internet Governance, how to investigate the political process in a ‘shared power’environment emerges as a challenging methodological issue. In this paper, a synergetic theoretical approach is proposed to the study of Internet governance political process, which focuses on the concept of power, and crosses the boundaries of three academic fields, namely, Political Philosophy, Political Science and International Relations, and Organization Studies. This approach aggregates, in a descending analytical manner, concepts intrinsically linked to the contemporary shifting governance paradigm (i.e. governmentality, global governance, global public-policy networks, shared power, multistakeholder collaboration). In addition, such an approach brings the collaborative process into focus (rather than the decisions it leads to) by accentuating the productive potential of a collaboration based on the ‘shared power’ formula. Each of those theoretical reflections on shifting power relations provides building elements for a synergetic theoretical framework that can be, and has been, applied to the investigation of the emergent Internet governance regime. As a result, stakeholder alliances can be mapped, instances of power dynamics can be discerned, and some longitudinal tangible and intangible outcomes of the multistakeholder collaboration can be envisioned

Radial distribution of the inner magnetosphere plasma pressure using low-altitude satellite data during geomagnetic storm: the March 1-8, 1982 Event

Plasma pressure distribution in the inner magnetosphere is one of the key parameters for understanding the main magnetospheric processes including geomagnetic storms and substorms. However, the pressure profiles obtained from in-situ particle measurements by the high-altitude satellites do not allow tracking the pressure variations related to the storms and substorms, because a time interval needed to do this generally exceeds the characteristic times of them. On contrary, fast movement of low-altitude satellites makes it possible to retrieve quasi-instantaneous profiles of plasma pressure along the satellite trajectory, using the fluxes of precipitating particles. For this study, we used the Aureol-3 satellite data for plasma pressure estimation, and the IGRF, Tsyganenko 2001 and Tsyganenko 2004 storm time geomagnetic field models for the pressure mapping into the equatorial plane. It was found that during quiet geomagnetic condition the radial pressure profiles obtained coincide with the profiles, obtained previously from the high-altitude measurements. On the contrary, it was found that during geomagnetic storm the plasma pressure profiles became sharper; the position of the maximum of plasma pressure corresponds to expected one for given Dst minimum; the maximum value of inner magnetosphere static pressure correlates with the solar wind dynamic pressure. Increase in the plasma pressure profiles indicates the possibility to consider the interchange instability as one of important factors for the development of the main phase of geomagnetic storm.Comment: Accepted in Advances in Space Researc

The neutron 'thunder' accompanying large extensive air showers

The bulk of neutrons which appear with long delays in neutron monitors nearby the EAS core (~'neutron thunder'~) are produced by high energy EAS hadrons hitting the monitors. This conclusion raises an important problem of the interaction of EAS with the ground, the stuff of the detectors and their environment. Such interaction can give an additional contribution to the signal in the EAS detectors at {\em km}-long distances from the large EAS core after a few $\mu s$ behind the EAS front.Comment: 4 pages, 2 figures, to appear in Proc. of 14th Int. Symp. on Very High Energy Cosmic Ray Interactions, Weihai, China, 15-22 August 2006, to be published in Nucl. Phys. B (Proc.Suppl.), 200

A TIME-SERIES ANALYSIS OF THE BEEF SUPPLY RESPONSE IN RUSSIA: IMPLICATIONS FOR AGRICULTURAL SECTOR DEVELOPMENT POLICIES

This study analyses the substantial decline in livestock sector in Russia during the last twenty years. The observed decline could be explained by a range of factors, which are supported in this paper through a review of past research results as well as time series data related to the livestock sector. The study concludes with implications and recommendations for agricultural sector development policies.Russia, beef livestock decline, prices of beef, Agricultural and Food Policy, Livestock Production/Industries,

Deep Kernels for Optimizing Locomotion Controllers

Sample efficiency is important when optimizing parameters of locomotion controllers, since hardware experiments are time consuming and expensive. Bayesian Optimization, a sample-efficient optimization framework, has recently been widely applied to address this problem, but further improvements in sample efficiency are needed for practical applicability to real-world robots and high-dimensional controllers. To address this, prior work has proposed using domain expertise for constructing custom distance metrics for locomotion. In this work we show how to learn such a distance metric automatically. We use a neural network to learn an informed distance metric from data obtained in high-fidelity simulations. We conduct experiments on two different controllers and robot architectures. First, we demonstrate improvement in sample efficiency when optimizing a 5-dimensional controller on the ATRIAS robot hardware. We then conduct simulation experiments to optimize a 16-dimensional controller for a 7-link robot model and obtain significant improvements even when optimizing in perturbed environments. This demonstrates that our approach is able to enhance sample efficiency for two different controllers, hence is a fitting candidate for further experiments on hardware in the future.Comment: (Rika Antonova and Akshara Rai contributed equally

Sample Efficient Optimization for Learning Controllers for Bipedal Locomotion

Learning policies for bipedal locomotion can be difficult, as experiments are expensive and simulation does not usually transfer well to hardware. To counter this, we need al- gorithms that are sample efficient and inherently safe. Bayesian Optimization is a powerful sample-efficient tool for optimizing non-convex black-box functions. However, its performance can degrade in higher dimensions. We develop a distance metric for bipedal locomotion that enhances the sample-efficiency of Bayesian Optimization and use it to train a 16 dimensional neuromuscular model for planar walking. This distance metric reflects some basic gait features of healthy walking and helps us quickly eliminate a majority of unstable controllers. With our approach we can learn policies for walking in less than 100 trials for a range of challenging settings. In simulation, we show results on two different costs and on various terrains including rough ground and ramps, sloping upwards and downwards. We also perturb our models with unknown inertial disturbances analogous with differences between simulation and hardware. These results are promising, as they indicate that this method can potentially be used to learn control policies on hardware.Comment: To appear in International Conference on Humanoid Robots (Humanoids '2016), IEEE-RAS. (Rika Antonova and Akshara Rai contributed equally