Corrections to Chiral Dynamics of Heavy Hadrons: SU(3) Symmetry Breaking, (with some minor corrections)

In previous publications we have analyzed the strong and electromagnetic decays of heavy mesons and heavy baryons in a formalism which incorporates heavy-quark and chiral symmetries. There are two possible symmetry-breaking effects on the chiral dynamics of heavy hadrons: the finite-mass effects from light quarks and the $1/ m_Q$ corrections from heavy quarks. In the present paper, chiral-symmetry-breaking effects are studied and applications to various strong and radiative decays of heavy hadrons are illustrated. SU(3) violations induced by chiral loops in the radiative decays of charmed mesons and charmed baryons are compared with those predicted by the constituent quark model. In particular, available data for $D^*$ decays favor values of the parameters in chiral perturbation theory which give predictions for $D^*$ decays close to the quark model results except for the $D^{*+}_s$. Implications are discussed.Comment: PHYZZX, 56 pages and 8 figures (available upon request), CLNS 93/1189, IP-ASTP-01-9

Accelerated Training for Massive Classification via Dynamic Class Selection

Massive classification, a classification task defined over a vast number of classes (hundreds of thousands or even millions), has become an essential part of many real-world systems, such as face recognition. Existing methods, including the deep networks that achieved remarkable success in recent years, were mostly devised for problems with a moderate number of classes. They would meet with substantial difficulties, e.g. excessive memory demand and computational cost, when applied to massive problems. We present a new method to tackle this problem. This method can efficiently and accurately identify a small number of "active classes" for each mini-batch, based on a set of dynamic class hierarchies constructed on the fly. We also develop an adaptive allocation scheme thereon, which leads to a better tradeoff between performance and cost. On several large-scale benchmarks, our method significantly reduces the training cost and memory demand, while maintaining competitive performance.Comment: 8 pages, 6 figures, AAAI 201

Person Re-identification with Correspondence Structure Learning

This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach

The study of neutron spectra in water bath from Pb target irradiated by 250MeV/u protons

The spallation neutrons were produced by the irradiation of Pb with 250 MeV protons. The Pb target was surrounded by water which was used to slow down the emitted neutrons. The moderated neutrons in the water bath were measured by using the resonance detectors of Au, Mn and In with Cd cover. According to the measured activities of the foils, the neutron flux at different resonance energy were deduced and the epithermal neutron spectra were proposed. Corresponding results calculated with the Monte Carlo code MCNPX were compared with the experimental data to check the validity of the code.Comment: 6 pages,9 figure