Steven Smith

Chemical exchange effects in the NMR spectra of rotating solids

We present a theoretical analysis based on Floquet theory describing the effect of intermediate rate exchange on line shapes in magic angle sample spinning (MASS) spectra. As a test case, 13C spectra were obtained of dimethyl sulfone (DMS) from 25–55 °C. DMS exhibits an axially symmetric powder pattern at 25 °C which becomes asymmetric due to 180° flips about the twofold axis bisecting the CH3–S–CH3 angle. As the temperature is increased, the principal effect on the MASS spectrum is a pronounced broadening of both the center and sidebands. Calculated spectra fit the experimental spectra well and provide information on the rate constants and the activation energy for the exchange process. These results extend the line shape methods successfully used to study molecular motion in static samples to include high resolution MASS.

Static graph challenge: Subgraph isomorphism

The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges to move these communities forward. The proposed Subgraph Isomorphism Graph Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a graph challenge that is reflective of many real-world graph analytics processing systems. The Subgraph Isomorphism Graph Challenge is a holistic specification with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. Subgraph isomorphism is amenable to both vertex-centric implementations and array-based implementations (e.g., using the Graph-BLAS.org standard). The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed graph challenge is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance have been measured. Specifications, data, and software are publicly available at GraphChallenge.org.

Streaming graph challenge: Stochastic block partition

An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.

Determination of Chromophore Structure and Photochemistry in Bacteriorhodopsin with Resonance Raman, NMR, and Chemical Analogues

Bacteriorhodopsin (BR) is an intrinsic membrane protein that transduces light-energy into a trans-membrane protonmotive force. To elucidate the mechanism of this important process, it is necessary to determine the structure of the retinal chromophore in BR and its intermediates and to characterize important chromophore-protein interactions. This paper will review recent physical chemical and bio-organic approaches that we have developed to study chromophore structure and environment in bacteriorhodopsin. A model for the molecular mechanism of proton-pumping based on inversion of the unprotonated Schiff base nitrogen is presented.