Temporal Hierarchical Clustering

We study hierarchical clusterings of metric spaces that change over time. This is a natural geo- metric primitive for the analysis of dynamic data sets. Specifically, we introduce and study the problem of finding a temporally coherent sequence of hierarchical clusterings from a sequence of unlabeled point sets. We encode the clustering objective by embedding each point set into an ultrametric space, which naturally induces a hierarchical clustering of the set of points. We enforce temporal coherence among the embeddings by finding correspondences between successive pairs of ultrametric spaces which exhibit small distortion in the Gromov-Hausdorff sense. We present both upper and lower bounds on the approximability of the resulting optimization problems

A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT

Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. Results: Acceleration techniques implemented on both levels of the hierarchical algorithm resulted in short, practical runtimes for optimizations. The MOEA improvements were evaluated for example prostate cases with one target and two OARs. The modified MOEA dominated 11.3% of plans using a standard genetic algorithm package. By implementing domination advantage and protocol objectives, small diverse populations of clinically acceptable plans that were only dominated 0.2% by the Pareto front could be generated in a fraction of an hour. Conclusions: Our MOEA produces a diverse Pareto optimal set of plans that meet all dosimetric protocol criteria in a feasible amount of time. It optimizes not only beamlet intensities but also objective function parameters on a patient-specific basis

Simplified Energy Landscape for Modularity Using Total Variation

Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organization of the network. One common quality function used to identify community structure is modularity. In Hu et al. [SIAM J. App. Math., 73(6), 2013], it was shown that modularity optimization is equivalent to minimizing a particular nonconvex total variation (TV) based functional over a discrete domain. They solve this problem, assuming the number of communities is known, using a Merriman, Bence, Osher (MBO) scheme. We show that modularity optimization is equivalent to minimizing a convex TV-based functional over a discrete domain, again, assuming the number of communities is known. Furthermore, we show that modularity has no convex relaxation satisfying certain natural conditions. We therefore, find a manageable non-convex approximation using a Ginzburg Landau functional, which provably converges to the correct energy in the limit of a certain parameter. We then derive an MBO algorithm with fewer hand-tuned parameters than in Hu et al. and which is 7 times faster at solving the associated diffusion equation due to the fact that the underlying discretization is unconditionally stable. Our numerical tests include a hyperspectral video whose associated graph has 2.9x10^7 edges, which is roughly 37 times larger than was handled in the paper of Hu et al.Comment: 25 pages, 3 figures, 3 tables, submitted to SIAM J. App. Mat

Dark Matter annihilation energy output and its effects on the high-z IGM

We study the case of DM self annihilation, in order to assess its importance as an energy injection mechanism, to the IGM in general, and to the medium within particular DM haloes. We consider thermal relic WIMP particles with masses of 10GeV and 1TeV and we analyse in detail the clustering properties of DM in a $\Lambda$CDM cosmology, on all hierarchy levels, from haloes and their mass function, to subhaloes and the DM density profiles within them, considering adiabatic contraction by the presence of a SMBH. We then compute the corresponding energy output, concluding that DM annihilation does not constitute an important feedback mechanism. We also calculate the effects that DM annihilation has on the IGM temperature and ionization fraction, and we find that assuming maximal energy absorption, at z ~ 10, for the case of a 1TeV WIMP, the ionization fraction could be raised to $6 \times 10^{-4}$ and the temperature to 10K, and in the case of a 10GeV WIMP, the IGM temperature could be raised to 200K and the ionization fraction to $8 \times 10^{-3}$. We conclude that DM annihilations cannot be regarded as an alternative reionization scenario. Regarding the detectability of the WIMP through the modifications to the 21 cm differential brightness temperature signal ($\delta$Tb), we conclude that a thermal relic WIMP with mass of 1TeV is not likely to be detected from the global signal alone, except perhaps at the 1-3mK level in the frequency range 30MHz < $\nu$ < 35MHz corresponding to 40 < z < 46. However, a 10GeV mass WIMP may be detectable at the 1-3mK level in the frequency range 55MHz < $\nu$ < 119MHz corresponding to 11 < z < 25, and at the 1-10mK level in the frequency range 30MHz < $\nu$ < 40MHz corresponding to 35 < z < 46.Comment: 23 pages, 12 figures, accepted for publication in MNRA