Charm Lifetimes and Mixing

A review of the latest results on charm lifetimes and D-mixing is presented. The e+e- collider experiments are now able to measure charm lifetimes quite precisely, however comparisons with the latest results from fixed-target experiments show that possible systematic effects could be evident. The new D-mixing results from the B-factories have changed the picture that is emerging. Although the new world averaged value of y_CP is now consistent with zero, there is still a very interesting and favoured scenario if the strong phase difference between the Doubly-Cabibbo-suppressed and the Cabibbo-flavoured D0 -> Kpi decay is large.Comment: Presented at the 9th International Symposium on Heavy Flavors, Caltech, Pasadena, 10-13 Sept. 2001. To appear in proceeding

Light pseudoscalar eta and H->eta eta decay in the simplest little Higgs mode

The SU(3) simplest little Higgs model in its original framework without the so-called mu term inevitably involves a massless pseudoscalar boson eta, which is problematic for b-physics and cosmological axion limit. With the mu term introduced by hand, the eta boson acquires mass m_eta ~ mu, which can be lighter than half the Higgs boson mass in a large portion of the parameter space. In addition, the introduced mu term generates sizable coupling of H-eta-eta. The Higgs boson can dominantly decay into a pair of eta's especially when mH below the WW threshold. Another new decay channel of H->Z+eta can be dominant or compatible with H -> WW for mH above the Z+eta threshold. We show that the LEP bound on the Higgs boson mass is loosened to some extent due to this new H->eta eta decay channel as well as the reduced coupling of H-Z-Z. The Higgs boson mass bound falls to about 110 GeV for f=3-4 TeV. Since the eta boson decays mainly into a bb pair, H-> eta eta -> 4b and H-> Z eta -> Z bb open up other interesting search channels in the pursuit of the Higgs boson in the future experiments. We discuss on these issues.Comment: major modification considering the simplest little Higgs model with the mu ter

B−>πlνB -> \pi l \nu Form Factors Calculated on the Light-Front

A consistent treatment of $B\rightarrow \pi l \nu$ decay is given on the light-front. The $B$ to $\pi$ transition form factors are calculated in the entire physical range of momentum transfer for the first time. The valence-quark contribution is obtained using relativistic light-front wave functions. Higher quark-antiquark Fock-state of the $B$-meson bound state is represented effectively by the $|B^*\pi\rangle$ configuration, and its effect is calculated in the chiral perturbation theory. Wave function renormalization is taken into account consistently. The $|B^*\pi\rangle$ contribution dominates near the zero-recoil point ($q^2\simeq 25$ GeV$^2$), and decreases rapidly as the recoil momentum increases. We find that the calculated form factor $f_+(q^2)$ follows approximately a dipole $q^2$-dependence in the entire range of momentum transfer.Comment: Revtex, 19 pages, 9 figure

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation