We develop a new systematic procedure for the Regge limit in perturbative QCD to arbitrary logarithmic order. The formalism relies on the IR structure and the gauge symmetry of the theory. We identify leading regions in loop momentum space responsible for the singular structure of the amplitudes and perform power counting to determine the strength of these divergences. Using a factorization pro...

We give a complete classification and present new exotic phenomena of the meromorphic structure of ζ-functions associated to general selfadjoint extensions of Laplace-type operators over conic manifolds. We show that the meromorphic extensions of these ζ-functions have, in general, countably many logarithmic branch cuts on the nonpositive real axis and unusual locations of poles with arbitraril...

Here we will consider examples of conformally flat manifolds that are conformally equivalent to open subsets of the sphere S. For such manifolds we shall introduce a Cauchy kernel and Cauchy integral formula for sections taking values in a spinor bundle and annihilated by a Dirac operator, or generalized Cauchy-Riemann operator. Basic properties of this kernel are examined, in particular we exa...

We discuss how the multi-Regge factorisation of QCD amplitudes can be used in the study of multi-jet processes at colliders. We describe how the next-to-leading logarithmic (NLL) BFKL evolution can be combined with energy and momentum conservation. By recalculating the quark contribution to the next-to-leading logarithmic corrections to the BFKL kernel we can study several properties of the NLL...

Weighted norm estimates and representation formulas are proved for nonhomogeneous singular integrals with no regularity condition on the kernel and only an L logL integrability condition. The representation formulas involve averages over a starshaped set naturally associated with the kernel. The proof of the norm estimates is based on the representation formulas, some new variations of the Hard...

The Linear discriminant analysis (LDA) can be generalized into a nonlinear form ─ kernel LDA (KLDA) expediently by using the kernel functions. But KLDA is often referred to a general eigenvalue problem in singular case. To avoid this complication, this paper proposes an iterative algorithm for the two-class KLDA. The proposed KLDA is used as a nonlinear discriminant classifier, and the experime...

The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this paper, we propose a novel method for learning the kernel matrix based on maximizing a class separability criterion that is similar to those used by linear discriminant analysis (LDA) and kernel Fisher discriminant (KFD)...