Assessing uncertainty in extreme events: Applications to risk-based decision making in interdependent infrastructure sectors

Risk-based decision making often relies upon expert probability assessments, particularly in the consequences of disruptive events and when such events are extreme or catastrophic in nature. Naturally, such expert-elicited probability distributions can be fraught with errors, as they describe events which occur very infrequently and for which only sparse data exist. This paper presents a quantitative framework, the extreme event uncertainty sensitivity impact method (EE-USIM), for measuring the sensitivity of extreme event consequences to uncertainties in the parameters of the underlying probability distribution. The EE-USIM is demonstrated with the Inoperability input-output model (IIM), a model with which to evaluate the propagation of inoperability throughout an interdependent set of economic and infrastructure sectors. The EE-USIM also makes use of a two-sided power distribution function generated by expert elicitation of extreme event consequences.     

Long-term perspective on the development of solar energy

We use dynamic optimization methods to analyze the development of solar technologies in light of the increasing scarcity and environmental pollution associated with fossil fuel combustion. Learning from solar R&D efforts accumulates in the form of knowledge to gradually reduce the cost of solar energy, while the scarcity and pollution externalities associated with fossil fuel combustion come into effect through shadow prices that must be included in the effective cost of fossil energy. Accounting for these processes, we characterize the optimal time profiles of fossil and solar energy supply rates and the optimal investment in solar R&D. We find that the optimal rate of fossil energy supply should decrease over time and vanish continuously upon depletion of the fossil fuel reserves, while the optimal supply of solar energy should gradually increase and eventually take over the entire energy demand. The optimal solar R&D investment should initially be set at the highest feasible rate, calling for early engagement in solar R&D programs, long before large scale solar energy production becomes competitive.     

Identification, ranking, and management of risks in a major system acquisition

Risk management is essential to protect the quality of a large-scale engineering effort. It should be a well-defined process that builds on an encompassing and detailed understanding of the purpose, elements, and contexts of the system. It should accommodate qualitative and quantitative information in understanding sources of risk. In an application paper, we use hierarchical holographic modeling (HHM) to identify sources of risk in the acquisition of a large ($1 billion US) software and database system. HHM provides a framework to integrate the perceptions by managers and analysts of what could go wrong in the acquisition. In addition, we filter and prioritize the identified sources of risk based on their likelihoods and potential consequences. Finally, we generate and evaluate alternatives for risk management, focusing on potential impacts to the acquisition schedule.     

The rational-choice theory of neurosis: Unawareness and an integrative therapeutic approach.

In agreement with Freud (1915a, 1915b), patients' unawareness regarding the underlying causes of their behavior seems to be the key to understanding the development and treatment of neurotic disorders. However, Freud's concepts of the unconscious and repression have encountered serious problems that do not allow for their maintenance (see review by Rofé, 2008). An additional issue concerns the fact that none of the available theories can integrate the therapeutic effects of various interventions under a single theoretical framework. Both of these issues are addressed in this article by the rational-choice theory of neurosis (RCTN). RCTN maintains that patients consciously and deliberately adopt neurotic disorders when confronted with intolerable levels of stress. Unawareness is created through sophisticated cognitive processes, by which patients forget their own self-involvement. Subsequently, patients develop a self-deceptive belief that rationalizes the neurotic behavior, thereby preserving unawareness. According to this new theory, all therapies exert their effect either by disrupting patients' ability to preserve unawareness, increasing the cost of the symptom, decreasing the patient's emotional distress, or eliminating the stressor. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Localization of primitives using adaptive projections

An application of an active method in order to compute highly accurate 3D localization of point features from few projections is presented. The angle of projection of the image is controlled by the system and directed to extract 3D information from the environment in a manner leading to accurate location in less computation.This model is relevant for tomographic reconstruction, for feature based stereo and for model based robot registration.     

On the Design of Filters for Gradient-Based Motion Estimation

Gradient based approaches in motion estimation (Optical-Flow) refer to those techniques that estimate the motion of an image sequence based on local derivatives in the image intensity. In order to best evaluate local changes, specific filters are applied to the image sequence. These filters are typically composed of a spatiotemporal pre-smoothing filter followed by discrete derivative ones. The design of these filters plays an important role in the estimation accuracy. This paper proposes a method for such a design. Unlike previous methods that consider these filters as optimized approximations for continuum derivatives, the proposed design procedure defines the optimality directly with respect to the motion estimation goal. One possible result of the suggested scheme is a set of image dependent filters that can be computed prior to the estimation process. An alternative interpretation is the creation of generic filters, capable of treating natural images. Simulations demonstrate the validity of the new design approach.This work was done while the authors where with Hewlett-Packard laboratories, Israel.     

Batch Diffie-Hellman key agreement systems

Batch Diffie-Hellman key agreement schemes are described, motivated, and analyzed for security and efficiency. Work done at Bellcore, Morristown, NJ, U.S.A.     

Set invariance analysis and gain-scheduling control for LPV systems subject to actuator saturation

In this paper, a set invariance analysis and gain scheduling control design approach is proposed for the polytopic linear parameter-varying systems subject to actuator saturation. A set invariance condition is first established. By utilizing this set invariance condition, the design of a time-invariant state feedback law is formulated and solved as an optimization problem with LMI constraints. A gain-scheduling controller is then designed to further improve the closed-loop performance. Numerical examples are presented to demonstrate the effectiveness of the proposed analysis and design method.