Design of Copolymeric Materials

We devise a method for designing materials that will have some desired structural characteristics. We apply it to multiblock copolymers that have two different types of monomers, A and B. We show how to determine what sequence of A's and B's should be synthesised in order to give a particular structure and morphology. %For example in a melt of such %polymers, one may wish to engineer a body-centered %cubic structure. Using this method in conjunction with the theory of microphase separation developed by Leibler, we show it is possible to efficiently search for a desired morphology. The method is quite general and can be extended to design isolated heteropolymers, such as proteins, with desired structural characteristics. We show that by making certain approximations to the exact algorithm, a method recently proposed by Shakhnovich and Gutin is obtained. The problems with this method are discussed and we propose an improved approximate algorithm that is computationally efficient.Comment: 15 pages latex 2.09 and psfig, 1 postscript figure

PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Generating 3D point clouds is challenging yet highly desired. This work presents a novel autoregressive model, PointGrow, which can generate diverse and realistic point cloud samples from scratch or conditioned on semantic contexts. This model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points, allowing inter-point correlations to be well-exploited and 3D shape generative processes to be better interpreted. Since point cloud object shapes are typically encoded by long-range dependencies, we augment our model with dedicated self-attention modules to capture such relations. Extensive evaluations show that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to realism and diversity. Several important applications, such as unsupervised feature learning and shape arithmetic operations, are also demonstrated

Enhancement of Dark Matter Annihilation via Breit-Wigner Resonance

The Breit-Wigner enhancement of the thermally averaged annihilation cross section is shown to provide a large boost factor when the dark matter annihilation process nears a narrow resonance. We explicitly demonstrate the evolution behavior of the Breit-Wigner enhanced as the function of universe temperature for both the physical and unphysical pole cases. It is found that both of the cases can lead an enough large boost factor to explain the recent PAMELA, ATIC and PPB-BETS anomalies. We also calculate the coupling of annihilation process, which is useful for an appropriate model building to give the desired dark matter relic density.Comment: 4 pages, 4 figures, references added, accepted for publication in Physical Review

Explanation and observability of diffraction in time

Diffraction in time (DIT) is a fundamental phenomenon in quantum dynamics due to time-dependent obstacles and slits. It is formally analogous to diffraction of light, and is expected to play an increasing role to design coherent matter wave sources, as in the atom laser, to analyze time-of-flight information and emission from ultrafast pulsed excitations, and in applications of coherent matter waves in integrated atom-optical circuits. We demonstrate that DIT emerges robustly in quantum waves emitted by an exponentially decaying source and provide a simple explanation of the phenomenon, as an interference of two characteristic velocities. This allows for its controllability and optimization.Comment: 4 pages, 6 figure