We investigate the power of Katm-style datattow networks, with processes that may exhibit indeterminate behavior. Our main result is a theorem about networks of "monotone" processes, which shows: (1) that the input/output relation of such a network is a total and monotone relation; and (2) every relation that is total, monotone, and continuous in a certain sense, is the input/output relation of...

Conditional simulation of ergodic and stationary Gaussian random fields using successive residuals is a new approach used to overcome the size limitations of the LU decomposition algorithm as well as provide fast updating of existing simulated realizations with new data. This paper discusses two different implementations of this approach. The implementations differ in the use of the new informa...

We introduce a design strategy for neural network macro-architecture based on selfsimilarity. Repeated application of a single expansion rule generates an extremely deep network whose structural layout is precisely a truncated fractal. Such a network contains interacting subpaths of different lengths, but does not include any pass-through connections: every internal signal is transformed by a f...

PURPOSE The exact definition of pathologic complete response (pCR) and its prognostic impact on survival in intrinsic breast cancer subtypes is uncertain. METHODS Tumor response at surgery and its association with long-term outcome of 6,377 patients with primary breast cancer receiving neoadjuvant anthracycline-taxane-based chemotherapy in seven randomized trials were analyzed. RESULTS Dise...

BACKGROUND: The practice of using disinfectants to safeguard against the acute effects of waterborne disease are well established and regulated in the United States. New regulations designed to minimize the risks associated with chronic exposure to disinfection by-products (DBPs), however, are changing the current regulatory framework. Changes in disinfection practices meant to comply with thes...

The lasso, introduced by Robert Tibshirani in 1996, has become one of the most popular techniques for estimating Gaussian linear regression models. An important reason for this popularity is that the lasso can simultaneously estimate all regression parameters as well as select important variables, yielding accurate regression models that are highly interpretable. This paper derives an efficient...