Network Model Selection Using Task-Focused Minimum Description Length

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

A Compressed Sensing Algorithm for Sparse-View Pinhole Single Photon Emission Computed Tomography

Single Photon Emission Computed Tomography (SPECT) systems are being developed with multiple cameras and without gantry rotation to provide rapid dynamic acquisitions. However, the resulting data is angularly undersampled, due to the limited number of views. We propose a novel reconstruction algorithm for sparse-view SPECT based on Compressed Sensing (CS) theory. The algorithm models Poisson noise by modifying the Iterative Hard Thresholding algorithm to minimize the Kullback-Leibler (KL) distance by gradient descent. Because the underlying objects of SPECT images are expected to be smooth, a discrete wavelet transform (DWT) using an orthogonal spline wavelet kernel is used as the sparsifying transform. Preliminary feasibility of the algorithm was tested on simulated data of a phantom consisting of two Gaussian distributions. Single-pinhole projection data with Poisson noise were simulated at 128, 60, 15, 10, and 5 views over 360 degrees. Image quality was assessed using the coefficient of variation and the relative contrast between the two objects in the phantom. Overall, the results demonstrate preliminary feasibility of the proposed CS algorithm for sparse-view SPECT imaging

Generalized Miller Formulae

We derive the spectral dependence of the non-linear susceptibility of any order, generalizing the common form of Sellmeier equations. This dependence is fully defined by the knowledge of the linear dispersion of the medium. This finding generalizes the Miller formula to any order of non-linearity. In the frequency-degenerate case, it yields the spectral dependence of non-linear refractive indices of arbitrary order.Comment: 12 pages, 1 figure (4 panels

A sputtering derived atomic oxygen source for studying fast atom reactions

A technique for the generation of fast atomic oxygen was developed. These atoms are created by ion beam sputtering from metal oxide surfaces. Mass resolved ion beams at energies up to 60 KeV are produced for this purpose using a 150 cm isotope separator. Studies have shown that particles sputtered with 40 KeV Ar(+) on Ta2O5 were dominantly neutral and exclusively atomic. The atomic oxygen also resided exclusively in its 3P ground state. The translational energy distribution for these atoms peaked at ca 7 eV (the metal-oxygen bond energy). Additional measurements on V2O5 yielded a bimodal distribution with the lower energy peak at ca 5 eV coinciding reasonably well with the metal-oxygen bond energy. The 7 eV source was used to investigate fast oxygen atom reactions with the 2-butene stereoisomers. Relative excitation functions for H-abstraction and pi-bond reaction were measured with trans-2-butene. The abstraction channel, although of minor relative importance at thermal energy, becomes comparable to the addition channel at 0.9 eV and dominates the high-energy regime. Structural effects on the specific channels were also found to be important at high energy