Cone-manifolds and the density conjecture

We give an expository account of our proof that each cusp-free hyperbolic 3-manifold M with finitely generated fundamental group and incompressible ends is an algebraic limit of geometrically finite hyperbolic 3-manifolds.Comment: 19 Pages, 2 figures; to appear, proceedings of the Warwick Conference: Kleinian Groups and Hyperbolic 3-Manfiolds, September 200

The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences

Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions. A key application for learning structures from data is to automatically discover knowledge. In practice, structure learning algorithms focused on "knowledge discovery" present a limitation: they use a coarse-grained representation of the structure. As a result, this representation cannot describe context-specific independences. Very recently, an algorithm called CSPC was designed to overcome this limitation, but it has a high computational complexity. This work tries to mitigate this downside presenting CSGS, an algorithm that uses the Grow-Shrink strategy for reducing unnecessary computations. On an empirical evaluation, the structures learned by CSGS achieve competitive accuracies and lower computational complexity with respect to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

Modeling design rework in a product development process

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program, Massachusetts Institute of Technology, 2000.Includes bibliographical references (p. 37-38).Managing the product development process is of vital concern to corporations. A critical aspect of product development that negatively impacts program cost and timing is rework. Unfortunately, in large organizations with successive development cycles, the product, process and organizational complexity preclude simple solutions. Even given sufficient data, many organizations do not understand what constitutes good and bad performance relative to rework. Through research at General Motors Truck Product Group, a model was developed that forecasts expected total rework. The model assumes rework is a function of: 1) The product portfolio and timing; 2) The complexity of each product program; 3) The pattern of rework over time for product programs; 4) The "lifecycle age" of each product program. The model has four potential uses: A) To aid in portfolio/project planning; B) To provide a rework performance baseline for management; C) To evaluate initiatives with regards to their impact on design rework; D) To identify leverage targets for management attention and improvement.by Matthew F. Bromberg.S.M.M.B.A

ARIES-AT Magnet Systems

This report presents a conceptual design of the magnet systems for an advanced tokamak fusion reactor (ARIES-AT). The main focus of the paper is to anticipate and extrapolate the current state-of-the-art in high temperature superconductors and coil design, and apply them to an advanced commercial fusion reactor concept. The current design point is described and supported with a preliminary structural analysis and a discussion of the merits, performance, and economics of high temperature vs. low temperature superconductors in an advanced fusion tokamak reactor design

Failed Gamma-Ray Bursts: Thermal UV/Soft X-ray Emission Accompanied by Peculiar Afterglows

We show that the photospheres of "failed" Gamma-Ray Bursts (GRBs), whose bulk Lorentz factors are much lower than 100, can be outside of internal shocks. The resulting radiation from the photospheres is thermal and bright in UV/Soft X-ray band. The photospheric emission lasts for about one thousand seconds with luminosity about several times 10^46 erg/s. These events can be observed by current and future satellites. It is also shown that the afterglows of failed GRBs are peculiar at the early stage, which makes it possible to distinguish failed GRBs from ordinary GRBs and beaming-induced orphan afterglows.Comment: 19 pages, 7 figures, accepted for publication in the Astrophysical Journa