Optical antennas enable efficient coupling between propagating light and bonding electromagnetic waves like surface plasmon polaritons (SPPs). Under the illumination of inhomogeneous optical fields, propagating SPPs mediated by multimode antennas could be spatially asymmetric and the asymmetry strongly depends on the position of the antennas relative to the illumination field. Here we develop such asymmetric excitation of SPPs through illuminating a pair of slot antennas with the (1,0) mode Hermite-Gaussian beam. The physical scenario of the interaction between the illumination optical field and the paired slot antennas are elaborated by full-wave electromagnetic simulations. We also carry out experiments to monitor the asymmetric SPPs propagation with a back-focal plane imaging technique. By retrieving the asymmetric intensity ratio of the SPP pattern in the back-focal plane image, lateral displacement of the antennas down to angstrom level is demonstrated.

An important feature of human learning is the ability to continuously accept new information and unify it with existing knowledge, a process that proceeds largely automatically and without catastrophic side-effects. A generally intelligent machine (AGI) should be able to learn a wide range of tasks in a variety of environments. Knowledge acquisition in partially-known and dynamic task-environments cannot happen all-at-once, and AGI-aspiring systems must thus be capable of cumulative learning: efficiently making use of existing knowledge while learning new things, increasing the scope of ability and knowledge incrementally—without catastrophic forgetting or damaging existing skills. Many aspects of such learning have been addressed in artificial intelligence (AI) research, but relatively few examples of cumulative learning have been demonstrated to date and no generally accepted explicit definition exists of this category of learning. Here we provide a general definition of cumulative learning and describe how it relates to other concepts frequently used in the AI literature.

In isogeometric shape optimization, the use of the search direction directly predicted from the discrete shape gradient makes the optimization history strongly dependent on the discretization. This discretization-dependency can affect the convergence and may lead the optimization process into a sub-optimal solution. The source of this discretization-dependency is traced to the lack of consistency with the local steepest descent search direction in the continuous formulation. In the present contribution, this inconsistency is analyzed using the shape variation equations and subsequently illustrated with a volume minimization problem. It is found that the inconsistency originates from the NURBS discretization which induces a discrete quadratic norm to represent the continuous Euclidean norm. To fix this inconsistency, three normalization approaches are proposed to obtain a discretization-independent normalized descent search direction. The discretization-independence of the proposed approaches is verified with a benchmark problem. The superiority of the proposed search direction and its suitability for numerical implementation is illustrated with examples of shape optimization for mechanical and thermal problems. Although the present work focuses on a NURBS-based discretization usually used in conjunction with isogeometric analysis, the proposed methodology may also be applied to alleviate the “mesh-dependency” in (traditional) Finite Element-based shape optimization.