Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method

This paper presents a computational approach to evaluate the resilience of electricity Distribution Networks (DNs) to cyber-physical failures. In our model, we consider an attacker who targets multiple DN components to maximize the loss of the DN operator. We consider two types of operator response: (i) Coordinated emergency response; (ii) Uncoordinated autonomous disconnects, which may lead to cascading failures. To evaluate resilience under response (i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is computationally challenging due to mixed-integer variables in the inner problem and non-convex constraints. Our solution approach is based on the Generalized Benders Decomposition method, which achieves a reasonable tradeoff between computational time and solution accuracy. Our approach involves modifying the Benders cut based on structural insights on power flow over radial DNs. We evaluate DN resilience under response (ii) by sequentially computing autonomous component disconnects due to operating bound violations resulting from the initial attack and the potential cascading failures. Our approach helps estimate the gain in resilience under response (i), relative to (ii)

Spatial Exporters

In this paper, we provide evidence that expanding firms tend to serve new markets which are geographically close and culturally related to their prior export destinations. We quantify the impact of this spatial pattern using a Chinese firm-level data set. To ensure an exogenous set of potential new destinations (25 EU countries, US and Canada) and an exogenous timing of entry, we focus on firms that benefited from the abrupt end of the textile quota restrictions in 2005. Controlling for firmproduct and destination specific effects and ac- counting for possible multiple new export destinations we show that the probability to export to a country increases by 15 to 38 percent for each prior export destination with a geographical or cultural link with this country.export destination choice, spatial correlation, firm-level customs data, MFA/ATC quotaremoval

Attention and Anticipation in Fast Visual-Inertial Navigation

We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.Comment: 20 pages, 7 figures, 2 table

AIL Theory and the Ailing Phillips Curve: A Contract Based Approach to Aggregate Supply

This paper presents empirical evidence from U.S. data of a structurally stable aggregate supply relationship between real and nominal rates of interest and the rate of unemployment. The paper reviews theories of contracts that are based on the twin assumptions of asymmetric information and limited collateral and it argues that these theories (referred to as A.I.L. theories) provide a strong theoretical foundation for a contract-based theory of aggregate supply. It is suggested that the original Phillips curve estimates should be reinterpreted in the light of A.I.L. theories which represent alternatives to the Phelps-Friedman interpretation of the Phillips relationship.