Coil-to-globule transition by dissipative particle dynamics simulation

The dynamics of a collapsing polymer under a temperature quench in dilute solution is investigated by dissipative particles dynamics. Hydrodynamic interactions and many-body interaction are preserved naturally by incorporating explicit solvent particles in this approach. Our simulation suggests a four-stage collapse pathway: localized clusters formation, cluster coarsening in situ, coarsening involving global backbone conformation change into a crumpled globule, and compaction of the globule. For all the quench depths and chain lengths used in our study, collapse proceeds without the chain getting trapped in a metastable “sausage” configuration, as reported in some earlier studies. We obtain the time scales for each of the first three stages, as well as its scaling with the quench depths ξ and chain lengths N. The total collapse time scales as τ_c ~ ξ^(−0.46 ± 0.04)N^(0.98 ± 0.09), with the quench depth and degree of polymerization

Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

The theoretical analysis and computer simulation on Parrondo's history dependent games

AbstractThis paper is based on Parrondo's history-dependent game model that has been put forward by P.Arena. Using discrete-time Markov chains and computer simulation, we analyse the parrondo's paradox when games ABC…ABC played periodically and the parameter M=4. And then we find the volume of parameter space for which the paradox takes effect. Meanwhile we simulate the different sequences for mixing games A, B and C by computer and find an interesting phenomenon that when the total time of playing game A,B and C is an even number, the mixing game's payoff dependents on the original capital's parity

“Ask Everyone?” Understanding How Social Q&A Feedback Quality Influences Consumers\u27 Purchase Intentions

Social question & answer (Q&A) feedback is a novel form of electronic word-of-mouth that allows customers to ask questions and share opinions with peer customers. Based on the stimulus-organism-response framework, this paper proposes a model to describe how social Q&A feedback quality affects consumers\u27 willingness to purchase by influencing their perceived risk, perceived usefulness, and use intention. We focused on the social Q&A feature named Ask Everyone in Taobao and collected 153 valid responses through an online survey. Canonical correlation analysis was used to identify the association between feedback characteristics and feedback quality. Then, PLS-SEM was conducted to test the proposed research model. Results show that feedback quality negatively associated with perceived risk, but had a positive impact on perceived usefulness, use intention, and purchase intention. Findings of this research has both theoretical and practical implications for facilitating social Q&A design in e-commerce platforms