High Pt hadron-hadron correlations

We propose the formulation of a dihadron fragmentation function in terms of parton matrix elements. Under the collinear factorization approximation and facilitated by the cut-vertex technique, the two hadron inclusive cross section at leading order (LO) in e+ e- annihilation is shown to factorize into a short distance parton cross section and the long distance dihadron fragmentation function. We also derive the DGLAP evolution equation of this function at leading log. The evolution equation for the non-singlet and singlet quark fragmentation function and the gluon fragmentation function are solved numerically with the initial condition taken from event generators. Modifications to the dihadron fragmentation function from higher twist corrections in DIS off nuclei are computed. Results are presented for cases of physical interest.Comment: 7 pages, 8 figures, Latex, Proceedings of Hot Quarks 2004, July 18-24, Taos, New Mexic

Azimuthal asymmetry in transverse energy flow in nuclear collisions at high energies

The azimuthal pattern of transverse energy flow in nuclear collisions at RHIC and LHC energies is considered. We show that the probability distribution of the event-by-event azimuthal disbalance in transverse energy flow is essentially sensitive to the presence of the semihard minijet component.Comment: 6 pages, 2 figure

Gravitational Collapse of Circularly Symmetric Stiff Fluid with Self-Similarity in 2+1 Gravity

Linear perturbations of homothetic self-similar stiff fluid solutions, $S[n]$, with circular symmetry in 2+1 gravity are studied. It is found that, except for those with $n = 1$ and $n = 3$, none of them is stable and all have more than one unstable mode. Hence, {\em none of these solutions can be critical}.Comment: latex file, 1 figure; last version to appear in Prog. Theor. Phy

Input Fast-Forwarding for Better Deep Learning

This paper introduces a new architectural framework, known as input fast-forwarding, that can enhance the performance of deep networks. The main idea is to incorporate a parallel path that sends representations of input values forward to deeper network layers. This scheme is substantially different from "deep supervision" in which the loss layer is re-introduced to earlier layers. The parallel path provided by fast-forwarding enhances the training process in two ways. First, it enables the individual layers to combine higher-level information (from the standard processing path) with lower-level information (from the fast-forward path). Second, this new architecture reduces the problem of vanishing gradients substantially because the fast-forwarding path provides a shorter route for gradient backpropagation. In order to evaluate the utility of the proposed technique, a Fast-Forward Network (FFNet), with 20 convolutional layers along with parallel fast-forward paths, has been created and tested. The paper presents empirical results that demonstrate improved learning capacity of FFNet due to fast-forwarding, as compared to GoogLeNet (with deep supervision) and CaffeNet, which are 4x and 18x larger in size, respectively. All of the source code and deep learning models described in this paper will be made available to the entire research communityComment: Accepted in the 14th International Conference on Image Analysis and Recognition (ICIAR) 2017, Montreal, Canad

Lower dimensional volumes and the Kastler-Kalau-Walze type theorem for Manifolds with Boundary

In this paper, we define lower dimensional volumes of spin manifolds with boundary. We compute the lower dimensional volume ${\rm Vol}^{(2,2)}$ for 5-dimensional and 6-dimensional spin manifolds with boundary and we also get the Kastler-Kalau-Walze type theorem in this case

Model-Independent Measurement of the Primordial Power Spectrum

In inflationary models with minimal amount of gravity waves, the primordial power spectrum of density fluctuations, $A_S^2(k)$, together with the basic cosmological parameters, completely specify the predictions for the cosmic microwave background (CMB) anisotropy and large scale structure. Here we show how we can strongly constrain both $A_S^2(k)$ and the cosmological parameters by combining the data from the Microwave Anisotropy Probe (MAP) and the galaxy redshift survey from the Sloan Digital Sky Survey (SDSS). We allow $A_S^2(k)$ to be a free function, and thus probe features in the primordial power spectrum on all scales. MAP and SDSS have scale-dependent measurement errors that decrease in opposite directions on astrophysically interesting scales; they complement each other and allow the measurement of the primordial power spectrum independent of inflationary models, giving us valuable information on physics in the early Universe, and providing clues to the correct inflationary model.Comment: 4 pages including 4 figures. To appear in "Particle Physics and the Early Universe (COSMO-98)", editor David O. Caldwell (American Institute of Physics