Yale University

About: Yale University is a education organization based out in New Haven, Connecticut, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 89824 authors who have published 220665 publications receiving 12834776 citations. The organization is also known as: Yale & Collegiate School.

Clinical practice. Preventing falls in elderly persons.

Abstract: A 79-year-old woman with a history of congestive heart failure, arthritis, depression, and difficulty sleeping presents for a follow-up visit. She takes several prescription medications, including an antidepressant, a diuretic, an angiotensin-converting–enzyme inhibitor, and a beta-blocker, as well as over-the-counter sleep and allergy medications. Her chronic conditions appear to be stable. Her daughter reports that the patient has fallen twice during the past six months. What can be done to prevent future falls?

Unidirectional invisibility induced by PT-symmetric periodic structures.

Abstract: Parity-time (PT) symmetric periodic structures, near the spontaneous PT-symmetry breaking point, can act as unidirectional invisible media. In this regime, the reflection from one end is diminished while it is enhanced from the other. Furthermore, the transmission coefficient and phase are indistinguishable from those expected in the absence of a grating. The phenomenon is robust even in the presence of Kerr nonlinearities, and it can also effectively suppress optical bistabilities.

Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps

Abstract: We provide a framework for structural multiscale geometric organization of graphs and subsets of R(n). We use diffusion semigroups to generate multiscale geometries in order to organize and represent complex structures. We show that appropriately selected eigenfunctions or scaling functions of Markov matrices, which describe local transitions, lead to macroscopic descriptions at different scales. The process of iterating or diffusing the Markov matrix is seen as a generalization of some aspects of the Newtonian paradigm, in which local infinitesimal transitions of a system lead to global macroscopic descriptions by integration. We provide a unified view of ideas from data analysis, machine learning, and numerical analysis.