Autonomous inference of complex network dynamics from incomplete and noisy data

Multi-model inference of network properties from incomplete data

It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows ...

Hyperspectral Unmixing from Incomplete and Noisy Data

In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting c...

Security Inference from Noisy Data

Security Inference from Noisy Data by Li Zhuang Doctor of Philosophy in Computer Science University of California, Berkeley Professor J. D. Tygar, Chair My thesis is that contemporary information systems allow automatic extraction of security-related information from large amounts of noisy data. Extracting this information is the security inference problem: attackers or defenders extract inform...

Image Reconstruction from Incomplete Data

Incomplete and noisy network data as a percolation process.

We discuss the ramifications of noisy and incomplete observations of network data on the existence of a giant connected component (GCC). The existence of a GCC in a random graph can be described in terms of a percolation process, and building on general results for classes of random graphs with specified degree distributions we derive percolation thresholds above which GCCs exist. We show that ...