Trees with Convex Faces and Optimal Angles

We consider drawings of trees in which all edges incident to leaves can be extended to infinite rays without crossing, partitioning the plane into infinite convex polygons. Among all such drawings we seek the one maximizing the angular resolution of the drawing. We find linear time algorithms for solving this problem, both for plane trees and for trees without a fixed embedding. In any such drawing, the edge lengths may be set independently of the angles, without crossing; we describe multiple strategies for setting these lengths.Comment: 12 pages, 10 figures. To appear at 14th Int. Symp. Graph Drawing, 200

Design degrees of freedom and mechanisms for complexity

We develop a discrete spectrum of percolation forest fire models characterized by increasing design degrees of freedom (DDOF’s). The DDOF’s are tuned to optimize the yield of trees after a single spark. In the limit of a single DDOF, the model is tuned to the critical density. Additional DDOF’s allow for increasingly refined spatial patterns, associated with the cellular structures seen in highly optimized tolerance (HOT). The spectrum of models provides a clear illustration of the contrast between criticality and HOT, as well as a concrete quantitative example of how a sequence of robustness tradeoffs naturally arises when increasingly complex systems are developed through additional layers of design. Such tradeoffs are familiar in engineering and biology and are a central aspect of the complex systems that can be characterized as HOT

Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks

Effective training of deep neural networks suffers from two main issues. The first is that the parameter spaces of these models exhibit pathological curvature. Recent methods address this problem by using adaptive preconditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to the local geometry of parameter space. A second issue is overfitting, which is typically addressed by early stopping. However, recent work has demonstrated that Bayesian model averaging mitigates this problem. The posterior can be sampled by using Stochastic Gradient Langevin Dynamics (SGLD). However, the rapidly changing curvature renders default SGLD methods inefficient. Here, we propose combining adaptive preconditioners with SGLD. In support of this idea, we give theoretical properties on asymptotic convergence and predictive risk. We also provide empirical results for Logistic Regression, Feedforward Neural Nets, and Convolutional Neural Nets, demonstrating that our preconditioned SGLD method gives state-of-the-art performance on these models.Comment: AAAI 201

A Framework for Assessing Public Private Partnerships

This paper examines in detail Public Private Partnerships (PPPs), discussing their main objectives, implementations and challenges. The possible joint venture between the government and private companies when establishing a PPP is addressed, and an analytical approach to evaluate a PPP measure of success (M) is proposed. Applications of PPP are described, giving special attention to American and European experiences. It concludes by examining future extensions of the analytical Measure of Success of a PPP and what lies ahead for future PPP implementations.Public Private Partnership; Tagus River Bridge, Alameda Corridor; Dulles Gateway; European Experience

Vertex and source determine the block variety of an indecomposable module

AbstractThe block variety VG,b(M) of a finitely generated indecomposable module M over the block algebra of a p-block b of a finite group G, introduced in (J. Algebra 215 (1999) 460), can be computed in terms of a vertex and a source of M. We use this to show that VG,b(M) is connected, and that every closed homogeneous subvariety of the affine variety VG,b defined by block cohomology H*(G,b) (cf. Algebras Rep. Theory 2 (1999) 107) is the variety of a module over the block algebra. This is analogous to the corresponding statements on Carlson's cohomology varieties in (Invent. Math. 77 (1984) 291)