Six Degrees of Separation: Attribution Under the Foreign Sovereign Immunities Act in OBB Personenverkehr AG v. Sachs

The Foreign Sovereign Immunities Act (FSIA) generally prevents foreign sovereigns from falling within the jurisdiction of U.S. courts, subject to exceptions the FSIA lists. This commentary analyzes BB Personenverkehr AG v. Sachs, a case before the Supreme Court on the question of whether the commercial activities exception of the FSIA applies when only one element of a plaintiff\u27s claim is based upon commercial activity occurring in the United States and whether that sale can be attributed to a foreign sovereign. In this case, the plaintiff purchased a rail pass through an online, third-party travel agent. While traveling abroad and using the rail service, the plaintiff suffered severe physical injuries. The plaintiff then sued in U.S. courts for damages resulting from those injuries, arguing that the purchase of the ticket constituted commercial activity and that the ticket was sold by an agent of the rail service. The Author reviews the facts of the case and current law and concludes that the Court should rule in favor of the petitioners and hold that the sale of the ticket does meet the commercial-activity exception of the FSIA but that because a third-party sold the ticket, that action cannot be attributed to the state-owned rail service

Three Approaches to Solve Combinatorial Optimization Problems using Simulated Kalman Filter

Inspired by the estimation capability of Kalman filter, we have recently introduced novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, three extended versions of SKF algorithm, which is termed as Angle Modulated SKF (AMSKF), Distance Evaluated SKF (DESKF), and Binary SKF (BSKF), are proposed. A set of traveling salesman problems is used to evaluate the performance of the proposed algorithms

Three Approaches to Solve Combinatorial Optimization Problems using Simulated Kalman Filter

Inspired by the estimation capability of Kalman filter, we have recently introduced novel estimation-based optimization algorithm called simulated Kalman filter (SKF). Every agent in SKF is regarded as a Kalman filter. Based on the mechanism of Kalman filtering and measurement process, every agent estimates the global minimum/maximum. Measurement, which is required in Kalman filtering, is mathematically modelled and simulated. Agents communicate among them to update and improve the solution during the search process. However, the SKF is only capable to solve continuous numerical optimization problem. In order to solve combinatorial optimization problems, three extended versions of SKF algorithm, which is termed as Angle Modulated SKF (AMSKF), Distance Evaluated SKF (DESKF), and Binary SKF (BSKF), are proposed. A set of traveling salesman problems is used to evaluate the performance of the proposed algorithms