Laser spectroscopic characterization of the nuclear-clock isomer 229mTh.

The isotope 229Th is the only nucleus known to possess an excited state 229mTh in the energy range of a few electronvolts-a transition energy typical for electrons in the valence shell of atoms, but about four orders of magnitude lower than typical nuclear excitation energies. Of the many applications that have been proposed for this nuclear system, which is accessible by optical methods, the most promising is a highly precise nuclear clock that outperforms existing atomic timekeepers. Here we present the laser spectroscopic investigation of the hyperfine structure of the doubly charged 229mTh ion and the determination of the fundamental nuclear properties of the isomer, namely, its magnetic dipole and electric quadrupole moments, as well as its nuclear charge radius. Following the recent direct detection of this long-sought isomer, we provide detailed insight into its nuclear structure and present a method for its non-destructive optical detection.

Reconstructing Evolving Tree Structures in Time Lapse Sequences by Enforcing Time-Consistency.

We propose a novel approach to reconstructing curvilinear tree structures evolving over time, such as road networks in 2D aerial images or neural structures in 3D microscopy stacks acquired in vivo. To enforce temporal consistency, we simultaneously process all images in a sequence, as opposed to reconstructing structures of interest in each image independently. We formulate the problem as a Quadratic Mixed Integer Program and demonstrate the additional robustness that comes from using all available visual clues at once, instead of working frame by frame. Furthermore, when the linear structures undergo local changes over time, our approach automatically detects them.

Reconstructing Curvilinear Networks Using Path Classifiers and Integer Programming.

We propose a novel approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discriminatively-trained classifiers that are both robust and generic enough to be applied to very different imaging modalities. We then present an Integer Programming approach to finding the optimal subset of paths, subject to structural and topological constraints that eliminate implausible solutions. Unlike earlier approaches that assume a tree topology for the networks, ours explicitly models the fact that the networks may contain loops, and can reconstruct both cyclic and acyclic ones. We demonstrate the effectiveness of our approach on a variety of challenging datasets including aerial images of road networks and micrographs of neural arbors, and show that it outperforms state-of-the-art techniques.