The Structure Function Relationship of Disordered Networks using Young\u27s Modulus and Floppy Modes

Disordered networks may have the ability to store information that can be retrieved using a Young’s modulus measurement. The effect of the number of floppy modes a network has on the value of this Young’s modulus measurement is unknown. This experiment uses 28 networks consisting of 3D printed edges in a sliding frame to determine how the Young’s modulus of a network is related to the number of floppy modes

Efficient Computation of Multiple Density-Based Clustering Hierarchies

HDBSCAN*, a state-of-the-art density-based hierarchical clustering method, produces a hierarchical organization of clusters in a dataset w.r.t. a parameter mpts. While the performance of HDBSCAN* is robust w.r.t. mpts in the sense that a small change in mpts typically leads to only a small or no change in the clustering structure, choosing a "good" mpts value can be challenging: depending on the data distribution, a high or low value for mpts may be more appropriate, and certain data clusters may reveal themselves at different values of mpts. To explore results for a range of mpts values, however, one has to run HDBSCAN* for each value in the range independently, which is computationally inefficient. In this paper, we propose an efficient approach to compute all HDBSCAN* hierarchies for a range of mpts values by replacing the graph used by HDBSCAN* with a much smaller graph that is guaranteed to contain the required information. An extensive experimental evaluation shows that with our approach one can obtain over one hundred hierarchies for the computational cost equivalent to running HDBSCAN* about 2 times.Comment: A short version of this paper appears at IEEE ICDM 2017. Corrected typos. Revised abstrac

Experimental Analysis of Dynamic Minimum Spanning Tree Algorithms (Extended Abstract)

) Giuseppe Amato Giuseppe Cattaneo y Giuseppe F. Italiano z Abstract We conduct an extensive empirical study on the performance of several algorithms for maintaining the minimum spanning tree of a dynamic graph. In particular, we implemented and tested Frederickson's algorithms, and sparsification on top of Frederickson's algorithms, and compared them to other dynamic algorithms. Moreover, we propose a variant of a dynamic algorithm by Frederickson, which was in our experience always faster than the other implementations derived from the papers. In our experiments, we considered both random and non--random inputs, with non--random inputs trying to enforce bad update patterns on the algorithms. For random inputs, a simple adaptation of a partially dynamic data structure on Kruskal's algorithm was the fastest implementation. For non--random inputs, sparsification yielded the fastest algorithm. In both cases, the performance of our variant of the algorithm of Frederickson was clos..