Taking the High Road: Protecting Open Space Along America's Highways

Examines the environmental impacts of road construction and the spiraling of land prices along new roads, and promotes best practices for linking land use and road construction. Includes success stories and recommendations for policymakers

An experiment to detect gravity at sub-mm scale with high-Q mechanical oscillators

Silicon double paddle oscillators are well suited for the detection of weak forces because of their high Q factor (about 10^5 at room temperature). We describe an experiment aimed at the detection of gravitational forces between masses at sub-mm distance using such an oscillator. Gravitational excitation is produced by a rotating aluminium disk with platinum segments. The force sensitivity of this apparatus is about 10 fN at room temperature for 1000 s averaging time at room temperature. The current limitations to detection of the gravitational force are mentioned.Comment: 19 pages, to appear in Proceedings of the Tenth Marcel Grossmann Meeting on General Relativity, edited by M. Novello, S. Perez-Bergliaffa and R. Ruffini, World Scientific. Revision: portable format and revised figure

Real-time dynamics in Quantum Impurity Systems: A Time-dependent Numerical Renormalization Group Approach

We develop a general approach to the nonequilibrium dynamics of quantum impurity systems for arbitrary coupling strength. The numerical renormalization group is used to generate a complete basis set necessary for the correct description of the time evolution. We benchmark our method with the exact analytical solution for the resonant-level model. As a first application, we investigate the equilibration of a quantum dot subject to a sudden change of the gate voltage and external magnetic field. Two distinct relaxation times are identified for the spin and charge dynamics.Comment: 5 pages, 5 figure

Email Similarity Matching and Automatic Reply Generation Using Statistical Topic Modeling and Machine Learning

Responding to email is a time-consuming task that is a requirement for most professions. Many people find themselves answering the same questions over and over, repeatedly replying with answers they have written previously either in whole or in part. In this thesis, the Automatic Mail Reply (AMR) system is implemented to help with repeated email response creation. The system uses past email interactions and, through unsupervised statistical learning, attempts to recover relevant information to give to the user to assist in writing their reply. Three statistical learning models, term frequency-inverse document frequency (tf-idf), Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), are evaluated to find which approach works the best in terms of email document retrieval and similarity matching. AMR is built using the Python programming language in order to take advantage of tools and libraries specifically built for natural language processing and topic modeling. Datasets include the author’s work email and personal email archives, the publicly available 20 Newsgroups dataset, and the recently released email archives of U.S. Secretary Hillary Clinton from the Freedom of Information Act website. In addition to different datasets and statistical modeling approaches, two different system tools, GenSim and SciKit-Learn, are also compared. The outcome of this work is an initial version of the AMR system, which is freely available from the author’s Github page1. The core components of AMR input an email corpus, create a model of that corpus based on unsupervised learning and predict useful replies to new email based on the model. These pieces could be used as a toolkit for many different purposes. Although the best topic modeling approach is not definitively determined, this thesis concludes that using SciKit’s LSA implementation yields the most consistent results (p \u3c 0.05) across the tested databases. These results could be used for future work on developing a more sophisticated product to accomplish a range of machine learning tasks

Self-diffusion of polymers in cartilage as studied by pulsed field gradient NMR

Pulsed field gradient (PFG) nuclear magnetic resonance (NMR) was used to investigate the self-diffusion behaviour of polymers in cartilage. Polyethylene glycol and dextran with different molecular weights and in different concentrations were used as model compounds to mimic the diffusion behaviour of metabolites of cartilage. The polymer self-diffusion depends extremely on the observation time: The short-time self-diffusion coefficients (diffusion time Delta approximately 15 ms) are subjected to a rather non-specific obstruction effect that depends mainly on the molecular weights of the applied polymers as well as on the water content of the cartilage. The observed self-diffusion coefficients decrease with increasing molecular weights of the polymers and with a decreasing water content of the cartilage. In contrast, the long-time self-diffusion coefficients of the polymers in cartilage (diffusion time Delta approximately 600 ms) reflect the structural properties of the tissue. Measurements at different water contents, different molecular weights of the polymers and varying observation times suggest that primarily the collagenous network of cartilage but also the entanglements of the polymer chains themselves are responsible for the observed restricted diffusion. Additionally, anomalous restricted diffusion was shown to occur already in concentrated polymer solutions

The SU(3) Beta Function from Numerical Stochastic Perturbation Theory

The SU(3) beta function is computed from Wilson loops to 20th order numerical stochastic perturbation theory. An attempt is made to include massless fermions, whose contribution is known analytically to 4th order. The question whether the theory admits an infrared stable fixed point is addressed.Comment: 10 pages, 7 figures, version to be published in Physics Letters

Photoemission study of the spin-density wave state in thin films of Cr

Angle-resolved photoemission (PE) was used to characterize the spin-density wave (SDW) state in thin films of Cr grown on W(110). The PE data were analysed using results of local spin density approximation layer-Korringa-Kohn-Rostoker calculations. It is shown that the incommensurate SDW can be monitored and important parameters of SDW-related interactions, such as coupling strength and energy of collective magnetic excitations, can be determined from the dispersion of the renormalized electronic bands close to the Fermi energy. The developed approach can readily be applied to other SDW systems including magnetic multilayer structures.Comment: 4 figure