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 & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.

Observations of the Icy Universe

Abstract: Freeze-out of the gas-phase elements onto cold grains in dense interstellar and circumstellar media builds up ice mantles consisting of molecules that are mostly formed in situ (H2O, NH3, CO2, CO, CH3OH, and more). This review summarizes the detected infrared spectroscopic ice features and compares the abundances across Galactic, extragalactic, and Solar System environments. A tremendous amount of information is contained in the ice band profiles. Laboratory experiments play a critical role in the analysis of the observations. Strong evidence is found for distinct ice formation stages, separated by CO freeze-out at high densities. The ice bands have proven to be excellent probes of the thermal history of their environment. The evidence for the long-held idea that processing of ices by energetic photons and cosmic rays produces complex molecules is weak. Recent state-of-the-art observations show promise for much progress in this area with planned infrared facilities.

Dust in the galactic environment

Abstract: Dust in the Galaxy: Our view from within. Abundances and depletions. Extinction and scattering. Polarization and grain alignment. Infrared absorption features. Continuum and line emission. Dust in stellar ejecta. Evolution of dust in the interstellar medium. The early phases of stellar evolution. Towards a unified model for interstellar dust. Appendices. References. Index.

A morphological biosensor for mammalian cells

Abstract: An electrical biosensor is described that can continuously track morphological changes of adherent cells providing quantitative data from both sparse and confluent cultures. The method is capable of detecting vertical motion of cells of the order of 1 nm, much below the resolution of an optical microscope.

Support vector machines: hype or hallelujah?

Abstract: Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. While this overview is not comprehensive, it does provide resources for those interested in further exploring SVMs.