We present a computational model to predict users' spatio-temporal visual attention on WIMP-style (windows, icons, menus, pointer) graphical user interfaces. Like existing models of bottom-up visual attention in computer vision, our model does not require any eye tracking equipment. Instead, it predicts attention solely using information available to the interface, specifically users' mouse and keyboard input as well as the UI components they interact with. To study our model in a principled way, we further introduce a method to synthesize user interface layouts that are functionally equivalent to real-world interfaces, such as from Gmail, Facebook, or GitHub. We first quantitatively analyze attention allocation and its correlation with user input and UI components using ground-truth gaze, mouse, and keyboard data of 18 participants performing a text editing task. We then show that our model predicts attention maps more accurately than state-of-the-art methods. Our results underline the significant potential of spatio-temporal attention modeling for user interface evaluation, optimization, or even simulation.

The lasso problem, least squares with a ℓ1regularization penalty, has been very successful as a tool for obtaining sparse representations of data in terms of given dictionary. It is known, but not widely appreciated, that the lasso problem need not have a unique solution. Sufficient conditions which ensure uniqueness of the solution are known but necessary and sufficient conditions have been elusive. We present three structural results on the lasso problem. First, we show that when the dictionary has more columns than rows, it is always possible to ensure that the dictionary has full row rank. Next we show that the feasible set for the dual lasso problem is bounded if and only if the dictionary has full row rank. Lastly, we give necessary and sufficient conditions for the uniqueness of a lasso solution.

We consider the problem of detecting stimulus driven changes in brain functional connectivity. Estimating functional connectivity from fMRI data sampled over a small time period is difficult - there is simply not enough data to permit reliable estimates. We investigate the use of a sparse Gaussian graphical model regularized by a graph learned from data sampled over a longer time period. We establish a framework to identify the changes in brain connectivity driven by short-term stimuli. Results of experiments on both synthetic and real fMRI data illustrate the attributes of our methods as well as the difficulty of the problem.

We propose a new multichannel image denoising algorithm. To exploit important inter-channel dependencies, we first use dynamic programming to learn an explicit dyadic tree representation of the common structure of the channels. Based on this dyadic tree, optimal Haar wavelet thresholding is then applied to denoise the image. In addition to the original channels, the algorithm can employ multiple derived channels to improve tree learning. Experimental results confirm that the approach improves multichannel image denoising performance both in PSNR and in edge preservation.