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M. Q. Zhang

Publications and source records attributed to M. Q. Zhang.

2 recordsLinked to original sources

Synthetic gauge field enabled realization of bulk- and edge-transported states in an aperiodic acoustic structure

Topologically protected edge states with immunity against various disorders have been implemented in a variety of topological insulators. In this Letter, we reveal that Landau levels in aperiodic acoustic structures can be achieved under different pseudomagnetic fields (PMFs). The produced zero order Landau modes (ZOLMs) could transmit along the channels at the interior or exterior of the inhomogeneous array, which are separately termed as "bulk-transported states" (BTSs) and "edge-transported states" (ETSs). Distinct from conventional valley edge states, the ZOLMs show intriguing self-collimation feature. If a pseudoelectric field (PEF) is further included, the combination of a PMF and PEF can result in the formation of bulk or edge Landau rainbow, where Landau zero modes are distributed at various positions of the bulk or boundary of the sample at different frequencies. The synthetic-gauge-field-controlled topological states can enable fully control of robust transmission, and using the entire footprint of a topological lattice. Our findings not only profoundly advance the current understanding of topological phase matter but also offer new avenues for constructing topological acoustic devices.

cond-mat.mes-hall

Super-paramagnetic clustering of yeast gene expression profiles

High-density DNA arrays, used to monitor gene expression at a genomic scale, have produced vast amounts of information which require the development of efficient computational methods to analyze them. The important first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of a novel clustering algorithm, Super-Paramagnetic Clustering (SPC) to analysis of gene expression profiles that were generated recently during a study of the yeast cell cycle. SPC was used to organize genes into biologically relevant clusters that are suggestive for their co-regulation. Some of the advantages of SPC are its robustness against noise and initialization, a clear signature of cluster formation and splitting, and an unsupervised self-organized determination of the number of clusters at each resolution. Our analysis revealed interesting correlated behavior of several groups of genes which has not been previously identified.

physics.bio-ph