Pion photoproduction on the nucleon

The $\gamma n \to \pi^- p$ and $\gamma p \to \pi^+ n$ reactions are essential probes of the transition from meson-nucleon degrees of freedom to quark-gluon degrees of freedom in exclusive processes. The cross sections of these processes are also advantageous, for the investigation of oscillatory behavior around the quark counting prediction, since they decrease relatively slower with energy compared with other photon-induced processes. In this talk, we discuss recent results on the $\gamma p \to \pi^+ n$ and $\gamma n \to \pi^{-}p$ processes from Jefferson Lab experiment E94-104. We also discuss a new experiment in which singles $\gamma p \to \pi^+ n$ measurement from hydrogen, and coincidence $\gamma n \to \pi^{-} p$ measurements at the quasifree kinematics from deuterium for center-of-mass energies between 2.3 GeV to 3.4 GeV in fine steps at a center-of-mass angle of $90^\circ$ are planned. The proposed measurement will allow a detailed investigation of the oscillatory scaling behavior in photopion production processes.Comment: 6 pages, 5 figures, Plenary talk presented at the HiX2004 Workshop, July 26-28, Marseille, France. References adde

Neutron Electromagnetic Form Factors

The nucleon electromagnetic form factors have been studied in the past extensively from unpolarized electron scattering experiments. With the development in polarized beam, recoil polarimetry, and polarized target technologies, polarization experiments have provided more precise data on these quantities. In this talk, I review recent experimental progress on this subject.Comment: 7 pages, 3 figures, Plenary talk presented at the 10th International Conference on Meson-Nucleon Physics and the Structure of the Nucleon, August 29 - September 4, 2004, Beijing, Chin

Nonlinear Instability for a Volume-Filling Chemotaxis Model with Logistic Growth

This paper deals with a Neumann boundary value problem for a volume-filling chemotaxis model with logistic growth in a d-dimensional box Td=(0,π)d  (d=1,2,3). It is proved that given any general perturbation of magnitude δ, its nonlinear evolution is dominated by the corresponding linear dynamics along a finite number of fixed fastest growing modes, over a time period of the order ln⁡(1/δ). Each initial perturbation certainly can behave drastically different from another, which gives rise to the richness of patterns

Graph Neural Networks Boosted Personalized Tag Recommendation Algorithm

Personalized tag recommender systems recommend a set of tags for items based on users’ historical behaviors, and play an important role in the collaborative tagging systems. However, traditional personalized tag recommendation methods cannot guarantee that the collaborative signal hidden in the interactions among entities is effectively encoded in the process of learning the representations of entities, resulting in insufficient expressive capacity for characterizing the preferences or attributes of entities. In this paper, we proposed a graph neural networks boosted personalized tag recommendation model, which integrates the graph neural networks into the pairwise interaction tensor factorization model. Specifically, we consider two types of interaction graph (i.e. the user-tag interaction graph and the item-tag interaction graph) that is derived from the tag assignments. For each interaction graph, we exploit the graph neural networks to capture the collaborative signal that is encoded in the interaction graph and integrate the collaborative signal into the learning of representations of entities by transmitting and assembling the representations of entity neighbors along the interaction graphs. In this way, we explicitly capture the collaborative signal, resulting in rich and meaningful representations of entities. Experimental results on real world datasets show that our proposed graph neural networks boosted personalized tag recommendation model outperforms the traditional tag recommendation models