Unpolarized fragmentation function for the pion and kaon via the nonlocal chiral-quark model

In this talk we present our recent studies for the unpolarized fragmentation functions for the pion and kaon, employing the nonlocal chiral quark model, which manifests the nonlocal interaction between the quarks and pseudoscalar mesons, in the light-cone frame. It turns out that the nonlocal interaction produces considerable differences in comparison to typical local-interaction models.Comment: 4 pages, 2 figures, Talk given at the international conference The Fifth Asia-Pacific Conference on Few-Body Systems in Physics 2011 (APFB2011), Seoul, Republic of Korea, 22-26 August 201

Extended nonlocal chiral-quark model for the heavy-light quark systems

In this talk, we report the recent progress on constructing a phenomenological effective model for the heavy-light quark systems, which consist of (u,d,s,c,b) quarks, i.e. extended nonlocal chiral-quark model (ExNLChQM). We compute the heavy-meson weak-decay constants to verify the validity of the model. From the numerical results, it turns out that (f_D, f_B, f_{D_s}, f_{B_s})=(207.54,208.13,262.56,262.39) MeV. These values are in relatively good agreement with experimental data and various theoretical estimations.Comment: 3 pages, 4 figures, Talk given at the 20th International IUPAP Conference on Few-Body Problems in Physics (FB20), 20~25 August 2012, Fukuoka, Japa

ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

Free Energy Approach to the Formation of an Icosahedral Structure during the Freezing of Gold Nanoclusters

The freezing of metal nanoclusters such as gold, silver, and copper exhibits a novel structural evolution. The formation of the icosahedral (Ih) structure is dominant despite its energetic metastability. This important phenomenon, hitherto not understood, is studied by calculating free energies of gold nanoclusters. The structural transition barriers have been determined by using the umbrella sampling technique combined with molecular dynamics simulations. Our calculations show that the formation of Ih gold nanoclusters is attributed to the lower free energy barrier from the liquid to the Ih phases compared to the barrier from the liquid to the face-centered-cubic crystal phases

Beveridge-Nelson Decomposition with Markov Switching

This paper considers Beveridge-Nelson decomposition in a context where the permanent and transitory components both follow a Markov switching process. Our approach incorporates Markov switching into a single source of error state-space framework, allowing business cycle asymmetries and regime switches in the long run multiplier.Beveridge-Nelson decomposition, Markov switching, Single source of error state space models