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Beam shaping in nanophotonic systems remains a challenge due to the reliance on complex heuristic optimization procedures. In this work, we experimentally demonstrate a novel approach to topological beam shaping using Jackiw-Rebbi states in metasurfaces. By fabricating thin-film dielectric structures with engineered Dirac-mass distributions, we create domain walls that allow precise control over beam profiles. We observe the emergence of Jackiw-Rebbi states and confirm their localized characteristics. Notably, we achieve a flat-top beam profile by carefully tailoring the Dirac mass distribution, highlighting the potential of this method for customized beam shaping. This experimental realization establishes our approach as a new mechanism for beam control, rooted in topological physics, and offers an efficient strategy for nanophotonic design.

We propose a quantum plasmonic sensor using Hong-Ou-Mandel (HOM) interferometry that measures the refractive index of an analyte, embedded in a plasmonic beam splitter composed of a dual-Kretschmann configuration, which serves as a frustrated total internal reflection beamsplitter. The sensing performance of the HOM interferometry, combined with single-photon detectors, is evaluated through Fisher information for estimation of the refractive index of the analyte. This is subsequently compared with the classical benchmark that considers the injection of a coherent state of light into the plasmonic beamsplitter. By varying the wavelength of the single photons and the refractive index of the analyte, we identify a wide range where a 50 % quantum enhancement is achieved and discuss the observed behaviors in comparison with the classical benchmark. We expect this study to provide a useful insight into the advancement of quantum-enhanced sensing technologies, with direct implications for a wide range of nanophotonic beamsplitter structures.

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.