Rensselaer Polytechnic Institute

About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Population. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Social consensus through the influence of committed minorities.

Abstract: We show how the prevailing majority opinion in a population can be rapidly reversed by a small fraction p of randomly distributed committed agents who consistently proselytize the opposing opinion and are immune to influence. Specifically, we show that when the committed fraction grows beyond a critical value p(c) ≈ 10%, there is a dramatic decrease in the time T(c) taken for the entire population to adopt the committed opinion. In particular, for complete graphs we show that when p p(c), T(c) ~ ln N. We conclude with simulation results for Erdős-Renyi random graphs and scale-free networks which show qualitatively similar behavior.

Self-heating–induced healing of lithium dendrites

Abstract: Lithium (Li) metal electrodes are not deployable in rechargeable batteries because electrochemical plating and stripping invariably leads to growth of dendrites that reduce coulombic efficiency and eventually short the battery. It is generally accepted that the dendrite problem is exacerbated at high current densities. Here, we report a regime for dendrite evolution in which the reverse is true. In our experiments, we found that when the plating and stripping current density is raised above ~9 milliamperes per square centimeter, there is substantial self-heating of the dendrites, which triggers extensive surface migration of Li. This surface diffusion heals the dendrites and smoothens the Li metal surface. We show that repeated doses of high-current-density healing treatment enables the safe cycling of Li-sulfur batteries with high coulombic efficiency.

Fast approximation of matrix coherence and statistical leverage

Abstract: The statistical leverage scores of a matrix A are the squared row-norms of the matrix containing its (top) left singular vectors and the coherence is the largest leverage score These quantities are of interest in recently-popular problems such as matrix completion and Nystrom-based low-rank matrix approximation as well as in large-scale statistical data analysis applications more generally; moreover, they are of interest since they define the key structural nonuniformity that must be dealt with in developing fast randomized matrix algorithms Our main result is a randomized algorithm that takes as input an arbitrary n × d matrix A, with n ≫ d, and that returns as output relative-error approximations to all n of the statistical leverage scores The proposed algorithm runs (under assumptions on the precise values of n and d) in O(nd logn) time, as opposed to the O(nd2) time required by the naive algorithm that involves computing an orthogonal basis for the range of A Our analysis may be viewed in terms of computing a relative-error approximation to an underconstrained least-squares approximation problem, or, relatedly, it may be viewed as an application of Johnson-Lindenstrauss type ideas Several practically-important extensions of our basic result are also described, including the approximation of so-called cross-leverage scores, the extension of these ideas to matrices with n ≈ d, and the extension to streaming environments

Expanding Entrepreneurial Cognition's Toolbox: Potential Contributions from the Field of Cognitive Science

Abstract: Several measures and methods developed by the field of cognitive science may prove useful to researchers investigating various aspects of entrepreneurial cognition. These techniques include ones th...