Hsieh Fushing

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The advent of high-throughput technologies and the concurrent advances in information sciences have led to an explosion in size and complexity of the data sets collected in biological sciences. The biggest challenge today is to assimilate this wealth of information into a conceptual framework that will help us decipher biological functions. A large and(More)
We propose a new method inspired from statistical mechanics for extracting geometric information from undirected binary networks and generating random networks that conform to this geometry. In this method an undirected binary network is perceived as a thermodynamic system with a collection of permuted adjacency matrices as its states. The task of(More)
Social stability in group-living animals is an emergent property which arises from the interaction amongst multiple behavioral networks. However, pinpointing when a social group is at risk of collapse is difficult. We used a joint network modeling approach to examine the interdependencies between two behavioral networks, aggression and status signaling,(More)
We develop a three-step computing approach to explore a hierarchical ranking network for a society of captive rhesus macaques. The computed network is sufficiently informative to address the question: Is the ranking network for a rhesus macaque society more like a kingdom or a corporation? Our computations are based on a three-step approach. These steps are(More)
Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus(More)
We demonstrate that the geometry of a data cloud is computable on multiple scales without prior knowledge about its structure. We show that the concepts of "time" and "temperature" are beneficial for constructing a hierarchical geometry based on local information provided by a similarity measure. We design two devices for construction of this hierarchy.(More)
We introduce a between-ness-based distance metric to extract local and global information for each pair of nodes (or "vertices" used interchangeably) located in a binary network. Since this distance then superimposes a weighted graph upon such a binary network, a multiscale clustering mechanism, called data cloud geometry, is applicable to discover(More)
We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with(More)
In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society.(More)
We address two largely overlooked, fundamental issues in computing a ranking hierarchy within a society: which information in the network is relevant, and what effect chance has on the hierarchy. To properly account for uncertainty from limited data, we construct a random field in a matrix form having entry-wise posterior Beta distributions based on a graph(More)