Fast performance estimation of block codes

Importance sampling is used in this paper to address the classical yet important problem of performance estimation of block codes. Simulation distributions that comprise discreteand continuous-mixture probability densities are motivated and used for this application. These mixtures are employed in concert with the so-called g-method, which is a conditional importance sampling technique that more effectively exploits knowledge of underlying input distributions. For performance estimation, the emphasis is on bit by bit maximum a-posteriori probability decoding, but message passing algorithms for certain codes have also been investigated. Considered here are single parity check codes, multidimensional product codes, and briefly, low-density parity-check codes. Several error rate results are presented for these various codes, together with performances of the simulation techniques

Thermal rectification and negative differential thermal resistance in lattices with mass gradient

We study thermal properties of one dimensional(1D) harmonic and anharmonic lattices with mass gradient. It is found that the temperature gradient can be built up in the 1D harmonic lattice with mass gradient due to the existence of gradons. The heat flow is asymmetric in the anharmonic lattices with mass gradient. Moreover, in a certain temperature region the {\it negative differential thermal resistance} is observed. Possible applications in constructing thermal rectifier and thermal transistor by using the graded material are discussed.Comment: 4 pages 5 eps figs. Accepted for pub. in Phys. Rev. B Rap. Com

Modulation of Thermal Conductivity in Kinked Silicon Nanowires: Phonon Interchanging and Pinching Effects

We perform molecular dynamics simulations to investigate the reduction of the thermal conductivity by kinks in silicon nanowires. The reduction percentage can be as high as 70% at room temperature. The temperature dependence of the reduction is also calculated. By calculating phonon polarization vectors, two mechanisms are found to be responsible for the reduced thermal conductivity: (1) the interchanging effect between the longitudinal and transverse phonon modes and (2) the pinching effect, i.e a new type of localization, for the twisting and transverse phonon modes in the kinked silicon nanowires. Our work demonstrates that the phonon interchanging and pinching effects, induced by kinking, are brand new and effective ways in modulating heat transfer in nanowires, which enables the kinked silicon nanowires to be a promising candidate for thermoelectric materials.Comment: Nano. Lett. accepted (2013

MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201