65,895 research outputs found
Chiral-Odd and Spin-Dependent Quark Fragmentation Functions and their Applications
We define a number of quark fragmentation functions for spin-0, -1/2 and -1
hadrons, and classify them according to their twist, spin and chirality. As an
example of their applications, we use them to analyze semi-inclusive
deep-inelastic scattering on a transversely polarized nucleon.Comment: 19 pages in Plain TeX, MIT CTP #221
Conformal Symmetry and Pion Form Factor: Soft and Hard Contributions
We discuss a constraint of conformal symmetry in the analysis of the pion
form factor. The usual power-law behavior of the form factor obtained in the
perturbative QCD analysis can also be attained by taking negligible quark
masses in the nonperturbative quark model analysis, confirming the recent
AdS/CFT correspondence. We analyze the transition from soft to hard
contributions in the pion form factor considering a momentum-dependent
dynamical quark mass from a nonnegligible constituent quark mass at low
momentum region to a negligible current quark mass at high momentum region. We
find a correlation between the shape of nonperturbative quark distribution
amplitude and the amount of soft and hard contributions to the pion form
factor.Comment: 7 pages, 6 figures, extensively revised, to appear in Phys. Rev.
Relatively hyperbolic groups, rapid decay algebras, and a generalization of the Bass conjecture
By deploying dense subalgebras of we generalize the Bass
conjecture in terms of Connes' cyclic homology theory. In particular, we
propose a stronger version of the -Bass Conjecture. We prove that
hyperbolic groups relative to finitely many subgroups, each of which posses the
polynomial conjugacy-bound property and nilpotent periodicity property, satisfy
the -Stronger-Bass Conjecture. Moreover, we determine the
conjugacy-bound for relatively hyperbolic groups and compute the cyclic
cohomology of the -algebra of any discrete group.Comment: 32 pages, 2 figures; added an appendix also by C. Ogl
Generalizing smoothness constraints from discrete samples
We study how certain smoothness constraints, for example, piecewise continuity, can be generalized from a discrete set of analog-valued data, by modifying the error backpropagation, learning algorithm. Numerical simulations demonstrate that by imposing two heuristic objectives — (1) reducing the number of hidden units, and (2) minimizing the magnitudes of the weights in the network — during the learning process, one obtains a network with a response function that smoothly interpolates between the training data
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