Charm structure functions and gluon shadowing effects with the AdS/CFT model

By means of the UGD function extracted from an AdS/CFT inspired saturation model, the charm and bottom structure functions are studied in fixed-order perturbation theory. It is shown that the theoretical results are in good agreement with the recent HERA data. Then, this UGD function is also used to investigate net-kaon rapidity distribution in Au＋Au collisions at RHIC energies and the theoretical results fit well to the BRAHMS data. In the end of this paper, we give the predicted results for nuclear charm structure function at very small x where the popular shadowing parameterizations are invalid.

Vector meson electroproduction in QCD

Based on the generalized QCD vector meson dominance model, we study the electroproduction of a vector meson off a proton in the QCD inspired eikonalized model. Numerical calculations for the total cross section σ tot and differential cross section dσ/dt are performed for ρ, ω and φ meson electroproduction in this paper. Since gluons interact among themselves （self-interaction）, two gluons can form a glueball with quantum numbers I G , J P C = 0 ＋ , 2 ＋＋ , decay width Γ t ≈ 100 MeV, and mass of m G =2.23 GeV. The three gluons can form a three-gluon colorless bound state with charge conjugation quantum number C = 1, called the Odderon. The mediators of interactions between projectiles （the quark and antiquark pair fluctuated from the virtual photon） and the proton target （a three-quark system） are the tensor glueball and the Odderon. Our calculated results in the tensor glueball and Odderon exchange model fit to the existing data successfully, which evidently shows that our present QCD mechanism is a good description of meson electroproduction off a proton. It should be emphasized that our mechanism is different from the theoretical framework of Block et al. We also believe that the present study and its success are important for the investigation of other vector meson electro- and photoproduction at high energies, as well as for searching for new particles such as tensor glueballs and Odderons, which have been predicted by QCD and the color glass condensate model （CGC）. Therefore, in return, it can test the validity of QCD and the CGC model.

Advances in adaptive nonlinear manifolds and dimensionality reduction

Recent decades have witnessed a much increased demand for advanced,effective and efficient methods and tools for analyzing,understanding and dealing with data of increasingly complex,high dimensionality and large volume.Whether it is in biology,neuroscience,modern medicine and social sciences or in engineering and computer vision,data are being sampled,collected and cumulated in an unprecedented speed.It is no longer a trivial task to analyze huge amounts of high dimensional data.A systematic,automated way of interpreting data and representing them has become a great challenge facing almost all fields and research in this emerging area has flourished.Several lines of research have embarked on this timely challenge and tremendous progresses and advances have been made recently.Traditional and linear methods are being extended or enhanced in order to meet the new challenges.This paper elaborates on these recent advances and discusses various state-of-the-art algorithms proposed from statistics,geometry and adaptive neural networks.The developments mainly follow three lines:multidimensional scaling,eigen-decomposition as well as principal manifolds.Neural approaches and adaptive or incremental methods are also reviewed.In the first line,traditional multidimensional scaling(MDS)has been extended not only to be more adaptive such as neural scale,curvilinear component analysis(CCA)and visualization induced self-organizing map(ViSOM)for online learning,but also to be more local scaling such as Isomap for enhanced flexibility for nonlinear data sets.The second line extends linear principal component analysis(PCA)and has attracted a huge amount of interest and enjoyed flourishing advances with methods like kernel PCA(KPCA),locally linear embedding(LLE)and Laplacian eigenmap.The advantage is obvious:a nonlinear problem is transformed into a linear one and a unique solution can then be sought.The third line starts with the nonlinear principal curve and surface and links up with adaptive neural network approaches such as self-organizing map(SOM)and ViSOM.Many of these frameworks have been further improved and enhanced for incremental learning and mapping function generalization.This paper discusses these recent advances and their connections.Their application issues and implementation matters will also be briefly enlightened and commented on.