New Governance and the EU Courts : The Experimentalist Architecture of Judicial Decision-Making

The dissertation examines the role of the EU courts in new governance. New governance has raised unprecedented interest in the EU in recent years. This is manifested in a plethora of instruments and actors at various levels that challenge more traditional forms of command-and-control regulation. New governance and political experimentation more generally is thought to sap the ability of the EU judiciary to monitor and review these experiments. The exclusion of the courts is then seen to add to the legitimacy problem of new governance. The starting point of this dissertation is the observation that the marginalised role of the courts is based on theoretical and empirical assumptions which invite scrutiny. The theoretical framework of the dissertation is deliberative democracy and democratic experimentalism. The analysis of deliberative democracy is sustained by an attempt to apply theoretical concepts to three distinctive examples of governance in the EU. These are the EU Sustainable Development Strategy, the European Chemicals Agency, and the Common Implementation Strategy for the Water Framework Directive. The case studies show numerous disincentives and barriers to judicial review. Among these are questions of the role of courts in shaping governance frameworks, the reviewability of science-based measures, the standing of individuals before the courts, and the justiciability of soft law. The dissertation analyses the conditions of judicial review in each governance environment and proposes improvements. From a more theoretical standpoint it could be said that each case study presents a governance regime which builds on legislation that lays out major (guide)lines but leaves details to be filled out at a later stage. Specification of detailed standards takes place through collaborative networks comprising members from national administrations, NGOs, and the Commission. Viewed this way, deliberative problem-solving is needed to bring people together to clarify, elaborate, and revise largely abstract and general norms in order to resolve concrete and specific problems and to make law applicable and enforceable. The dissertation draws attention to the potential of peer review included there and its profound consequences for judicial accountability structures. It is argued that without this kind of ongoing and dynamic peer review of accountability in governance frameworks, judicial review of new governance is difficult and in some cases impossible. This claim has implications for how we understand the concept of soft law, the role of the courts, participation rights, and the legitimacy of governance measures more generally. The experimentalist architecture of judicial decision-making relies upon a wide variety of actors to provide conditions for legitimate and efficient review.

Information and Decision Technologies

Production scheduling in a flexible manufacturing system (FMS) is a real-time combinatorial optimization problem that has been proved to be NP-complete. Solving this problem needs on-line monitoring of plan execution and requires real-time decision-making in selecting alternative routings, assigning required resources, and rescheduling when failures occur in the system. Expert systems provide a natural framework for solving this kind of NP-complete problems.In this paper an expert system with a novel parallel heuristic approach is implemented for automatic short-term dynamic scheduling of FMS. The principal features of the expert system presented in this paper include easy rescheduling, on-line plan execution, load balancing, an on-line garbage collection process, and the use of advanced knowledge representational schemes. Its effectiveness is demonstrated with two examples.

Interfaces and traps in pentacene field-effect transistor

The equivalent circuit parameters for a pentacene organic field-effect transistor are determined from low frequency impedance measurements in the dark as well as under light illumination. The source-drain channel impedance parameters are obtained from Bode plot analysis and the deviations at low frequency are mainly due to the contact impedance. The charge accumulation at organic semiconductor-metal interface and dielectric-semiconductor interface is monitored from the response to light as an additional parameter to find out the contributions arising from photovoltaic and photoconductive effects. The shift in threshold voltage is due to the accumulation of photogenerated carriers under source-drain electrodes and at dielectric-semiconductor interface, and also this dominates the carrier transport. The charge carrier trapping at various interfaces and in the semiconductor is estimated from the dc and ac impedance measurements under illumination. (c) 2010 American Institute of Physics. doi: 10.1063/1.3517085]

Speedup Macroblock Mode Decision in H.264/SVC Encoding Using Cost-Sensitive Learning

In this paper we present a novel macroblock mode decision algorithm to speedup H.264/SVC Intra frame encoding. We replace the complex mode-decision calculations by a classifier which has been trained specifically to minimize the reduction in RD performance. This results in a significant speedup in encoding. The results show that machine learning has a great potential and can reduce the complexity substantially with negligible impact on quality. The results show that the proposed method reduces encoding time to about 70% in base layer and up to 50% in enhancement layer of the reference implementation with a negligible loss in quality.

An actor-critic algorithm with function approximation for discounted cost constrained Markov decision processes

We develop in this article the first actor-critic reinforcement learning algorithm with function approximation for a problem of control under multiple inequality constraints. We consider the infinite horizon discounted cost framework in which both the objective and the constraint functions are suitable expected policy-dependent discounted sums of certain sample path functions. We apply the Lagrange multiplier method to handle the inequality constraints. Our algorithm makes use of multi-timescale stochastic approximation and incorporates a temporal difference (TD) critic and an actor that makes a gradient search in the space of policy parameters using efficient simultaneous perturbation stochastic approximation (SPSA) gradient estimates. We prove the asymptotic almost sure convergence of our algorithm to a locally optimal policy. (C) 2010 Elsevier B.V. All rights reserved.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Multi-Agent Rendezvous Algorithm with Rectilinear Decision Domain

The aim of this paper is to develop a computationally efficient decentralized rendezvous algorithm for a group of autonomous agents. The algorithm generalizes the notion of sensor domain and decision domain of agents to enable implementation of simple computational algorithms. Specifically, the algorithm proposed in this paper uses a rectilinear decision domain (RDD) as against the circular decision domain assumed in earlier work. Because of this, the computational complexity of the algorithm reduces considerably and, when compared to the standard Ando's algorithm available in the literature, the RDD algorithm shows very significant improvement in convergence time performance. Analytical results to prove convergence and supporting simulation results are presented in the paper.

Positional Consensus of Multi-Agent Systems Using Linear Programming Based Decentralized Control with Rectilinear Decision Domain

In this paper we develop a Linear Programming (LP) based decentralized algorithm for a group of multiple autonomous agents to achieve positional consensus. Each agent is capable of exchanging information about its position and orientation with other agents within their sensing region. The method is computationally feasible and easy to implement. Analytical results are presented. The effectiveness of the approach is illustrated with simulation results.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

Video coding mode decision as a classification problem

In this paper, we show that it is possible to reduce the complexity of Intra MB coding in H.264/AVC based on a novel chance constrained classifier. Using the pairs of simple mean-variances values, our technique is able to reduce the complexity of Intra MB coding process with a negligible loss in PSNR. We present an alternate approach to address the classification problem which is equivalent to machine learning. Implementation results show that the proposed method reduces encoding time to about 20% of the reference implementation with average loss of 0.05 dB in PSNR.

Risk-sensitive optimal control for Markov decision processes with monotone cost

The existence of an optimal feedback law is established for the risk-sensitive optimal control problem with denumerable state space. The main assumptions imposed are irreducibility and a near monotonicity condition on the one-step cost function. A solution can be found constructively using either value iteration or policy iteration under suitable conditions on initial feedback law.

Use of climate information for farm-level decision making: rainfed groundnut in southern India

The production of rainfed crops in semi-arid tropics exhibits large variation in response to the variation in seasonal rainfall. There are several farm-level decisions such as the choice of cropping pattern, whether to invest in fertilizers, pesticides etc., the choice of the period for planting, plant population density etc. for which the appropriate choice (associated with maximum production or minimum risk) depends upon the nature of the rainfall variability or the prediction for a specific year. In this paper, we have addressed the problem of identifying the appropriate strategies for cultivation of rainfed groundnut in the Anantapur region in a semi-arid part of the Indian peninsula. The approach developed involves participatory research with active collaboration with farmers, so that the problems with perceived need are addressed with the modern tools and data sets available. Given the large spatial variation of climate and soil, the appropriate strategies are necessarily location specific. With the approach adopted, it is possible to tap the detailed location specific knowledge of the complex rainfed ecosystem and gain an insight into the variety of options of land use and management practices available to each category of stakeholders. We believe such a participatory approach is essential for identifying strategies that have a favourable cost-benefit ratio over the region considered and hence are associated with a high chance of acceptance by the stakeholders. (C) 2002 Elsevier Science Ltd. All rights reserved.

Role of Management Decision Variables in Determining Technology Progress and Technical Efficiency in Indian Electronic Industry

This paper analyses the influence of management on Technical Efficiency Change (TEC) and Technological Progress (TP) in the communication equipment and consumer electronics sub-sectors of Indian hardware electronics industry. Each sub-sector comprises 13 sample firms for two time periods.The primary objective is to determine the relative contribution of TP and TEC to TFP Growth (TFPG) and to establish the influence of firm specific operational management decision variables on these two components. The study finds that both the sub-sectors have strived and achieved steady TP but not TEC in the period of economic liberalisation to cope with the intensifying competition. The management decisions with respect to asset and profit utilization, vertical integration, among others, improved TP and TE in the sub-sectors. However, R&D investments and technology imports proved costly for TFP indicating inadequate efforts and/or poor resource utilisation by the management. Management was found to be complacent in terms of improving or developing their own technology as indicated by their higher dependence on import of raw materials and no influence of R&D on TP.