Aqueous Phase C-H Bond Oxidation Reaction of Arylalkanes Catalyzed by a Water-Soluble Cationic Ru(III) Complex [(pymox-Me\u3csub\u3e2\u3c/sub\u3e)\u3csub\u3e2\u3c/sub\u3eRuCl\u3csub\u3e2\u3c/sub\u3e]\u3csup\u3e+\u3c/sup\u3eBF\u3csub\u3e4\u3c/sub\u3e\u3csup\u3e-\u3c/sup\u3e

The cationic complex [(pymox-Me2)RuCl2]+BF4− was found to be a highly effective catalyst for the C−H bond oxidation reaction of arylalkanes in water. For example, the treatment of ethylbenzene (1.0 mmol) with t-BuOOH (3.0 mmol) and 1.0 mol % of the Ru catalyst in water (3 mL) cleanly produced PhCOCH3 at room temperature. Both a large kinetic isotope effect (kH/kD = 14) and a relatively large Hammett value (ρ = −1.1) suggest a solvent-caged oxygen rebounding mechanism via a Ru(IV)-oxo intermediate species

Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM

We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF estimation is then performed within a Bayesian framework, using a variational algorithm to estimate the posterior distribution. The image prior distribution imposes an explicit atomic measure that corresponds to image sparsity. Importantly, the proposed Bayesian deconvolution algorithm does not require hand tuning. Simulation results clearly demonstrate that the semi-blind deconvolution algorithm compares favorably with previous Markov chain Monte Carlo (MCMC) version of myopic sparse reconstruction. It significantly outperforms mismatched non-blind algorithms that rely on the assumption of the perfect knowledge of the PSF. The algorithm is illustrated on real data from magnetic resonance force microscopy (MRFM)

Economic development and poverty reduction in Korea

노트 : This paper is to be presented at the RC 19 annual conference on ‘The Future of Social Citizenship: Politics, Citizenship and Outcome’, co-organized by the Institute for Future Studies and Swedish Institute for Social Research, Stockholm University, 4-6 September 2008

Customer-Supplier Relationships and the Cost of Debt

We examine the relation between the existence of a large customer and the cost of debt financing. We find that credit ratings are lower for firms with a large customer. We also find that yield spreads are higher for firms with a large customer. The results indicate that firms having a large customer have higher cost of debt financing, which is consistent with our capital structure hypothesis

Quantifying the latency benefits of near-edge and in-network FPGA acceleration

Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources