1,243 research outputs found
Pion photoproduction on the nucleon
The and 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 and processes from Jefferson Lab experiment E94-104. We also discuss a
new experiment in which singles measurement from
hydrogen, and coincidence 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 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
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